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Sleep spindles: a physiological marker of age-related changes in gray matter in brain regions supporting motor skill memory consolidation
Accepted Manuscript Sleep spindles: A physiological marker of age-related changes in grey matter in brain regions supporting motor skill memory consolidation S. Fogel, C. Vien, A. Karni, H. Benali, J. Carrier, J. Doyon PII:
S0197-4580(16)30249-4
DOI:
10.1016/j.neurobiolaging.2016.10.009
Reference:
NBA 9748
To appear in:
Neurobiology of Aging
Received Date: 16 March 2016 Revised Date:
8 September 2016
Accepted Date: 3 October 2016
Please cite this article as: Fogel, S, Vien, C., Karni, A., Benali, H., Carrier, J, Doyon, J, Sleep spindles: A physiological marker of age-related changes in grey matter in brain regions supporting motor skill memory consolidation, Neurobiology of Aging (2016), doi: 10.1016/j.neurobiolaging.2016.10.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Sleep spindles: A physiological marker of age-related changes in grey matter in brain
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regions supporting motor skill memory consolidation
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Fogel S,a,b,c Vien, C.,a,b Karni, A.,d Benali, H.,a,e Carrier Ja,b,f & Doyon Ja,b a
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Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; bDepartment of Psychology, University of Montreal, Montreal, Canada; cSchool of Psychology, University of Ottawa, Ottawa, Canada; dLaboratory for Human Brain & Learning, Sagol Department of Neurobiology & the E.J. Safra Brain Research Center, University of Haifa, Haifa, Israel; eFunctional Neuroimaging Laboratory, INSERM, France; fCentre d’études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada.
Email: [email protected] Phone: 1-514-340-2800 x 3284 Fax: 1-514-340-3530
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Corresponding Author: Julien Doyon, Ph.D. Professor, Department of Psychology, University of Montreal Director, Functional Neuroimaging Unit Centre de Recherche, Institut Universitaire de Gériatrie de Montréal 4545 Chemin Queen Mary Montreal, Quebec, Canada, H3W 1W5
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ACCEPTED MANUSCRIPT ABSTRACT
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Sleep is necessary for the optimal consolidation of procedural learning, and in particular, for
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motor sequential skills. Motor sequence learning (MSL) remains intact with age, but sleep-
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dependent consolidation is impaired, suggesting that memory deficits for procedural skills are
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specifically impacted by age-related changes in sleep. Age-related changes in spindles may
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be responsible for impaired MSL consolidation, but the morphological basis for this deficit is
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unknown. Here we found that grey matter in the hippocampus and cerebellum was positively
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correlated with both sleep spindles and offline improvements in performance in young
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participants, but not in older participants. These results suggest that age-related changes in
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grey matter in the hippocampus relate to spindles, and may underlie age-related deficits in
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sleep-related motor sequence memory consolidation. In this way, spindles can serve as a
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biological marker for structural brain changes and the related memory deficits in older adults.
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41 KEYWORDS
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Sleep, spindle, age, memory, learning, consolidation, motor sequence learning, motor skills,
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procedural memory, hippocampus, cerebellum
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1. INTRODUCTION Alongside a healthy diet and regular exercise, sleep is one of the pillars of good
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physical and mental health, and has important links to memory and cognition (e.g., see
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(Rasch and Born, 2013), for a recent comprehensive review). However, as we age, the
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quantity and quality of sleep are markedly reduced (Bixler et al., 1984; Buysse et al., 1992;
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Carrier et al., 2011, 2001; Crowley et al., 2002; Darchia et al., 2003; Feinberg et al., 1967;
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Floyd et al., 2007; Fogel et al., 2012; Gaudreau et al., 2001; Landolt and Borbely, 2001;
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Martin et al., 2012; Ohayon et al., 2004; Peters et al., 2014; Redline et al., 2004; Zepelin and
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McDonald, 1987), and recent studies suggest that the age-related changes in sleep may have
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an impact on sleep-dependent memory processes (Fogel et al., 2014; Mander et al., 2015,
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2013a, 2013b; Pace-Schott and Spencer, 2011; Spencer et al., 2007; Wilson et al., 2012). In
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healthy young adults, non-rapid eye movement stage 2 (N2) sleep typically occupies about
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45% to 55% of total sleep time (Carskadon and Dement, 2011). However, with age, the total
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amount of N2 is reduced (Martin et al., 2012; Ohayon et al., 2004; Ramanand et al., 2010).
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One of the main characteristic features of N2 sleep is the presence of sleep spindle (Iber et
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al., 2007). Sleep spindles are brief bursts (<1 sec to ~3 sec) of oscillatory activity within the
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sigma band, with a frequency of ~12-16 Hz and with a waxing and waning amplitude that is
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discrete from the ongoing EEG (Ferrarelli et al., 2010; Fogel et al., 2007; Landolt et al., 1996;
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Silber et al., 2007; Zeitlhofer et al., 1997). One of the most pronounced age-related changes
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in N2 sleep is the reduction of sleep spindles marked by reduced duration, amplitude and
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density (Bowersox et al., 1985; Cajochen et al., 2006; Carrier et al., 2001; Crowley et al.,
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2002; Feinberg, 1974; Guazzelli et al., 1986; Huupponen et al., 2002; Knoblauch et al., 2005;
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Landolt et al., 1996; Mander et al., 2013a; Martin et al., 2012; Nicolas et al., 2001; Peters et
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al., 2014; Principe and Smith, 1982). However, the age-related changes in brain structure that
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underlie age-related reduction in spindles, and their functional consequences, remain to be
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fully elucidated. Animal studies of the neural circuitry that supports the generation of sleep spindles
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have shown that spindles originate from rhythmic depolarization of thalamocortical neurons
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(Steriade, 2006), modulated by GABAergic pathways in the reticular nucleus of the thalamus
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(Bazhenov et al., 2000, 1999). However, the brain structures involved in the generation and
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regulation of spindles remains a topic of debate (Timofeev and Chauvette, 2013), as recent
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evidence suggests that spindles are initiated cortically and that the neocortex contributes to
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the termination of spindle events (Bonjean et al., 2011; Timofeev, 2001). In addition to the
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thalamus, human functional neuroimaging studies employing simultaneous EEG-fMRI, have
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revealed that brain regions involved in sensory processing are activated time-locked to sleep
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spindles, including the anterior cingulate, insula and superior temporal gyri (Schabus et al.,
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2007). Moreover, fast spindles have been associated with increased activity in sensorimotor
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areas, the mesial frontal cortex and the hippocampus (Schabus et al., 2007) – the latter
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findings being consistent with the putative role of fast spindles in declarative memory
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processing. To further investigate the structural correlates of sleep spindles, Saletin et al
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(Saletin et al., 2013) found that inter-individual differences in grey matter volume of the insula
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and auditory cortex were related to slow spindles, whereas, grey matter volume in the
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hippocampus was associated with fast spindles. Thus, the latter findings suggest that slow
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and fast spindles serve a dissociable role for sleep maintenance and possibly memory
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processing, respectively.
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Indeed, spindles are thought to serve a protective function by preventing potentially
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arousing external stimuli such as loud noises that disrupt sleep (Cote et al., 2000; Dang-Vu et
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al., 2010; Pivik et al., 1999; Schabus et al., 2012), although see (Crowley et al., 2004) for data
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to suggest otherwise. In addition to their function for sleep maintenance via the gating of
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intrusive sensory information, however, a rich literature now exists supporting the role of 4
ACCEPTED MANUSCRIPT spindles for procedural memory including motor skills, reasoning and rule-learning (Albouy et
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al., 2013a; Barakat et al., 2012, 2011; Fogel and Smith, 2011, 2006; Fogel et al., 2015, 2014;
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Lafortune et al., 2014; Milner et al., 2006; Nielsen et al., 2014; Nishida and Walker, 2007;
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Peters et al., 2007; Ramanathan et al., 2015; Smith and Smith, 2003; Tamaki et al., 2009,
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2008; Wamsley et al., 2013, 2012; Wilhelm et al., 2012). Sleep is thought to enhance the
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memory consolidation process via the interplay between the hippocampus and striatum
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(Albouy et al., 2015, 2013a, 2013b, 2013c, 2008; Schabus et al., 2007) by strengthening
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newly formed, labile memory traces for lasting long-term neocortical storage. Animal studies
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have shown that during the spindle event, there is a large influx of intracellular calcium, which
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is necessary to set in motion long-term potentiation (LTP) of synapses. In vivo (Bergmann et
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al., 2008; Rosanova and Ulrich, 2005; Steriade, 2005; Werk et al., 2005) and in vitro
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(Rosanova and Ulrich, 2005) studies have also shown that spaced and repeated stimulation
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in the spindle oscillatory frequency range is necessary for cortical LTP to take place in the
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neocortex. Thus, spindles are an ideal physiological candidate for neocortical memory
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consolidation via memory trace reactivation.
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Recent human neuroimaging studies have shown that sleep spindle amplitude was
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correlated with overnight consolidation in motor sequence skill (Barakat et al., 2012, 2011)
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and that overnight changes in activation of brain regions including the striatum and motor
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cortical regions, recruited during motor sequence learning (MSL) (Debas et al., 2010) were
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correlated with post-training spindle amplitude (Barakat et al., 2012). EEG-fMRI studies have
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also identified activations in the striatum (Caporro et al., 2011; Tyvaert et al., 2008) and
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hippocampus (Schabus et al., 2007) associated with spindle events, hence suggesting that
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spindles are involved in the activation of structures important for sleep-dependent motor skill
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memory consolidation. Taken together, this literature suggests that spindles play an active
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role in sleep-dependent memory consolidation via related interaction between striatal,
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hippocampal and cortical memory systems, and may ultimately result in strengthening of
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memory traces. The functional consequences of age-related changes in sleep on memory
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consolidation are beginning to be better appreciated. Age-related changes in sleep have been
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shown to have a negative impact on overnight memory consolidation of some procedural
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skills (Brown et al., 2009; Spencer et al., 2007), and spindles, in particular, have been related
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to this deficit (Fogel et al., 2014; Peters et al., 2008). Age-related characteristics of spindles,
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including duration, amplitude, and density, decrease with age (Carrier et al., 2001; Feinberg,
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1974; Martin et al., 2012; Peters et al., 2014). Taken together, these studies suggest that an
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age-related reduction in spindle activity may help explain age-related, sleep-dependent
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deficits in memory consolidation. In support of this notion, a recent functional neuroimaging
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study by our group has shown that reduced spindles in the elderly are associated with a
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reduction of the spindle-related, overnight changes in cerebral activity in structures important
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for skill learning (e.g., putamen and cerebellum in young, but only the cerebellum in older
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individuals) (Fogel et al., 2014). Thus, a spindle-related, sleep-dependent increase of activity
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in structures such as the putamen and cerebellum appears to be crucial for off-line
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improvements, for which older individuals do not derive the same benefit due to an age-
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related reduction of sleep spindles. However, it is not known if changes in brain structure that
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accompany normal, healthy aging may underlie this deficit, and whether this is related to an
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age-related reduction of sleep spindles.
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In terms of brain structures, adults who learn to play a musical instrument from a young
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age have been found to have increased grey matter in brain regions that support motor skill
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learning (e.g., hippocampus, putamen motor cortex, somatosensory cortex, superior parietal
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cortex and the cerebellum)(Gaser and Schlaug, 2003; Groussard et al., 2014; Vaquero et al.,
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2016). However, changes in grey matter have been observed over much shorter time periods. 6
ACCEPTED MANUSCRIPT Motor sequence learning is associated with grey matter changes in the cerebellum, motor
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cortex, premotor and dorsolateral prefrontal cortex (Gryga et al., 2012), and the earliest
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detectable signs of grey matter changes with motor skill training (over 3 days) have been
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observed in the striatum (Hamzei et al., 2012). These structural changes in the striatum were
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related to grey matter changes in motor cortical regions at later stages of motor skill
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performance, with an increase in functional coupling between motor and striatal regions over
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the same time period. However, it is not known if age-related changes in brain structures
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relate to age-related changes in sleep and the resulting memory deficits. Here, we
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investigated the age-related differences in grey matter (GM) assessed by voxel-based
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morphometry that were correlated with both sleep spindles and sleep-dependent changes in
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motor skills performance.
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We hypothesized that GM in structures that support MSL consolidation (e.g.,
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cerebellum, striatum, hippocampus, motor cortical regions), would be correlated with both
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offline changes in MSL performance and spindles in young participants. Yet in older
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participants, the results of a recent functional neuroimaging study from our group suggests
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that the correlation between brain activation during practice of a motor sequence task and
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sleep spindles deteriorates with age (Fogel et al., 2014). Accordingly, if structural deficits
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underlie (or are linked to) the functional deficits observed with age, then a similar reduction in
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the magnitude of the relationship between spindles and grey matter should be expected.
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Thus this study aimed to ascertain if structural brain changes in grey matter were associated
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with sleep spindles and memory deficits in older adults. The latter findings are important as
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they may lead to novel insights into the neurological basis for age-related memory deficits,
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and highlight the importance of good sleep quality later in life, and perhaps even novel
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treatments or interventions to improve sleep in elderly populations in order to derive a benefit
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to memory and related mitigate cognitive impairment.
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2. METHODS
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2.1. Participants All participants provided informed written consent before entering the study. Ethical
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and scientific approval was obtained from the Ethics Committee at the “Institut de Geriatrie de
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Montreal”, Montreal, Quebec, Canada. In total, 22 young participants and 34 older
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participants were recruited to participate in the study that met the inclusion and exclusion
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criteria (see section 2.1.1). The results presented here were part of a larger series of studies
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investigating both the functional and structural basis of age-related changes of sleep on
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memory consolidation (Fogel et al., 2014; Vien et al., n.d.).
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2.1.1. Inclusion and Exclusion Criteria.
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Eligibility was determined at the start of the experiment through both a telephone pre-
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screening interview and a battery of questionnaires. To be included in the study, participants
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had to be right-handed (scored > 40 on the Edinburgh Handedness Inventory (Oldfield,
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1971)), non-smokers, prescription medication-free, and with a normal body mass index (≤ 25).
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Also, they must not have been diagnosed with a neurological, psychological or psychiatric
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condition, nor with a sleep disorder. "Extreme morning" or "extreme evening" types were also
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excluded (defined as scoring > 70 "moderate morning" or < 30 "moderate evening",
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respectively on the Horne-Ostberg Morningness-Eveningness Scale (Horne and Ostberg,
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1976)). By contrast, participants who were not formally trained on a musical instrument, did
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not work night-shifts and who had not taken a trans-meridian trip ≤ 3 months before the study
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were included in the study. Participants scoring ≤ 8 on both the Beck Depression Scale (Beck
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et al., 1974) and the Beck Anxiety Scale (Beck et al., 1988) were also included, as did older
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participants who scored ≥ 24 on the Mini Mental State Examination (Cockrell and Folstein,
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1988; Tombaugh and McIntyre, 1992). Participants were instructed to abstain from alcohol,
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a bed-time between 10:00 PM and 1:00 AM and wake-time between 07:00 AM and 10:00 AM,
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was required to be included. Compliance with sleep-wake instructions was verified with
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actigraphy measured from the left wrist, and with a sleep journal (using a modified version of
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the National Sleep Foundation "Sleep Diary" http://sleepfoundation.org).
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2.1.2. Polysomnographic and Additional Screening.
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If participants met the above criteria, a 90-min acclimatization nap was conducted at
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1:00 PM, three days before the experimental sessions. To ensure that participants were able
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to sleep in the laboratory environment during the daytime, participants had to achieve at least
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5 minutes of consolidated NREM sleep during this nap opportunity to be included in the study.
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An overnight polysomnographic (PSG) screening night was used in older participants to
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exclude individuals who exhibited signs of sleep apnea (e.g., apnea-hypopnea index (AHI) >
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5) and periodic limb movements (PLM; e.g., PLM index > 10). This acclimatization and
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screening procedure provided participants extensive opportunity to become accustomed to
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sleeping in the laboratory environment, and thus to minimize the impact on sleep quality
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during the experimental testing sessions. As a result, 19 older participants were excluded in
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total; 10 due to an elevated AHI or PLM index, 2 for sleeping < 5 min during the
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acclimatization nap, and 2 for MRI safety concerns. Five voluntarily withdrew before
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completing the study. In addition, 7 young participants were excluded; 6 voluntarily withdrew
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and 1 subject was excluded due to a structural abnormality that was uncovered upon
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examination of their structural T1-weighted scan. The final participant pool that completed the
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study was comprised of 15 young (Y) participants (8 female) between 19 and 30 years of age
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(M = 23.9, SD = 3.5) and 15 older (O) participants (9 female) between 55 and 69 years (M =
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62.2, SD = 3.8). However, an additional 2 participants were excluded from data analyses in
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the group of young participants. One participant reportedly suffered from severe sleepiness in
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ACCEPTED MANUSCRIPT the last five blocks of training and succumbed to extreme tiredness during practice, thus
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producing lapses in performance during the training session. A second subject performed
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poorly on the MSL task (as this participant was outside the 99% confidence interval for all 14
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blocks) and did not comply well with the instructions given throughout the experiment. The
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remaining 13 young participants (6 female, M = 24.1, SD = 3.5 years old) were included in the
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data analyses.
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2.2. Overall Experimental Design
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Participants arrived at the laboratory for 10:30 AM. The Stanford Sleepiness Scale
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(SSS) (MacLean et al., 1992) and Psychomotor Vigilance Task (PVT) (Dinges and Powell,
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1985) were then administered to assess both subjective and objective sleepiness,
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respectively, before MSL practice. At 11:00 AM, the MSL task was performed with alternating
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periods of rest, in a block design, at the same time functional blood oxygen level dependent
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(BOLD) imaging data were acquired (see (Fogel et al., 2014) for corresponding behavioural
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and functional neuroimaging results in these participants). After being provided a light lunch,
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participants were given a 90-min nap interval commencing at 1:00 PM. The MSL retest
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session was administered at 4:00 PM, that is 1.5 h after the end of the 90-min experimental
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interval to reduce possible effects of sleep inertia.
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2.3. Finger Sequence Task
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2.3.1. Task Instructions.
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An adapted version of the finger sequence task developed by Karni (Karni et al., 1995)
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was used to assess MSL in young and older participants across an interval of sleep (i.e., a
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90-minute daytime nap opportunity). An MR-compatible response box (model HH-1x4-L;
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Current Designs, Inc., Philadelphia PA) with a row of four equidistant push buttons was used.
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To familiarize each subject with the task, the experimenter first demonstrated the use of the
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keypad and explained the on-screen appearance of the task and instructions. Participants 10
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participants were asked to “slowly and accurately practice the 5-item sequence "4-1-3-2-4",
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where 1 stands for the index finger and 4 for the little finger of the left hand” until they were
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capable of performing the sequence three times in a row, without error. This ensured
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comprehension of instructions, memorization of the sequence, and that the sequence could
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be performed reliably prior to the start of the scanning session.
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2.3.2. Training and Retest Practice Sessions.
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During the training and retest sessions, the exact same sequence of finger movements
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was to be performed, but participants were explicitly instructed "to perform the sequence by
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pressing the buttons as quickly as possible, while making as few errors as possible". The
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beginning of each practice block was indicated by the appearance of a green cross in the
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center of the screen. After 60 key presses, the green cross turned red for 15 seconds,
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indicating that the subject should stop practicing and rest. There were 14 practice blocks in
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both training and retest sessions. If participants realized that they had made an error, they
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were instructed to continue practicing at the start of the 5-item sequence. This ensured that
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errors were isolated events within the train of 60 key presses and encouraged participants to
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continue practicing the task despite making an error. To minimize age-related differences in
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reaction time, the task was un-cued, (i.e., self-initiated). Performance was measured by key
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press speed (defined as the average correct inter-key-press interval). All participants included
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in the data analysis achieved an accuracy > 83.3%, equivalent to 10/12 correct sequences
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per block. Offline changes in performance across the nap interval were calculated as the
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mean performance of the first 4 blocks of retest minus the mean of the last 4 blocks of
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training. This measure of consolidation was then used in the behavioral analyses, and as a
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covariate of interest in the MRI analyses.
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2.4. PSG Recording and Analysis
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and Oz) using a Brain Products GmbH (Gilching, Germany) 16-channel, V-Amp16 system
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(high pass filter = 0.3 Hz, low pass filter = 70 Hz). Signals were digitalized at 500 samples/s.
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During the overnight polysomnographic screening in older participants, a nasal/oral thermistor
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and respiratory effort belt recordings were used to screen for sleep apnea. Leg
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electromyographic (EMG) recordings were used to screen participants for periodic leg
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movements.
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Sleep scoring (Table 1) was done visually in 20-s epochs of EEG (high pass filter = 0.5
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Hz, low pass filter = 35 Hz) from central and occipital derivations (C3, C4, and Oz) re-
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referenced to average mastoids (A1 and A2). Electrooculographic (EOG; high pass filter = 0.5
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Hz, low pass filter = 35 Hz) activity was recorded from the lateral outer canthus of the eyes.
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Bipolar submental EMG (high pass filter = 10 Hz) was also recorded. Sleep stages were
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scored by an expert PSG technologist, following standards of the American Academy of
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Sleep Medicine [Iber, 2007] using the "fMRI Artifact rejection and Sleep Scoring Toolbox
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(FASST)" (ver. 0.302; http://www.montefiore.ulg.ac.be/~phillips/FASST.html) for Matlab
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(Mathworks, Natick, MA; (Leclercq et al., 2011)). Movement continuously exceeding 100 µV
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for > 100 ms during sleep was automatically detected and identified as bad data. This was
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later confirmed visually and this data was excluded from further analyses.
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Sleep spindles were detected from Fz, Cz, and Pz in NREM sleep using a an
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established (Albouy et al, 2014; Fogel et al, 2014) and validated method (Ray et al., 2015)
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implemented in Brain Products (Brain Products GmbH, Gilching, Germany) Analyzer software
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(Version 2.1). For details of this method more recently implemented in Matlab, see Ray et al
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(Ray et al., 2015). Given that previous studies have suggested that age-related differences
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vary systematically along the midline (Martin et al., 2012), recordings from Fz, Cz, and Pz
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were used to derive the number of spindles during NREM sleep amplitude (µV2), duration (s), 12
ACCEPTED MANUSCRIPT and peak frequency (Hz). Slow and fast spindles have a known topography (De Gennaro and
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Ferrara, 2003; De Gennaro et al., 2005, 2000; Dehghani et al., 2011; Doran, 2003;
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Huupponen et al., 2002; Jobert et al., 1992; Werth et al., 1997a, 1997b; Zeitlhofer et al.,
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1997) whereby slow spindles predominate anterior regions and fast spindles predominate
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posterior regions. A frequency range for total bandwidth spindles of 11-17Hz was employed
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so as to encompass spindles defined as 12-16Hz (Ferrarelli et al., 2010; Fogel et al., 2014,
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2007; Landolt et al., 1996; Zeitlhofer et al., 1997). Slow (11-14 Hz) spindles, and fast (14-17
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Hz) spindles were categorized depending on their peak frequency in the same way as a
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previously published study on the relationship between age-related changes in sleep spindles
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and functional brain activation (Fogel et al., 2014) and also based on a thorough validation of
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this detection procedure (Ray et al., 2015). This procedure ensures that slow and fast
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spindles are orthogonal to one another at any given derivation. Thus, spindles from the
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derivations for which they predominate (e.g., slow spindles from Fz and fast spindles from Pz)
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were included in the structural imaging regression analyses (Table 2).
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2.5. Brain Imaging Data Acquisition and Analysis
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2.5.1. MRI Sequence Parameters.
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A 3.0T TIM TRIO MRI system (Siemens, Germany) with a 12-channel head coil was
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used. A 3D MPRAGE sequence (TR = 2300 ms, TE = 2.98 ms, TI = 900 ms, FA = 9°, 176
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slices, FoV = 256 x 256 mm2, matrix size = 256 x 256 x 176, voxel size = 1 x 1 x 1 mm3) was
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used to obtain high resolution T1-weighted anatomical images for all participants used for
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subsequent VBM analyses.
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2.5.2. Preprocessing.
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Structural
data was
analyzed
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FSL
(Douaud
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(but
see
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http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM for a detailed description of analysis procedures
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employed here) using an optimized VBM protocol (Good et al., 2001a; Smith et al., 2004) 13
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before being registered to the MNI 152 standard space using non-linear registration
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(Andersson et al., 2007). Second, a study-specific grey matter (GM) template was created
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with the FSL-VBM protocol. All brain-extracted images were segmented into GM, WM and
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CSF. Then, GM images were affine-registered to the GM ICBM-152 template, concatenated
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and averaged. Next, the GM images were then re-registered to this first-pass "affine" GM
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template using non-linear registration, concatenated into a 4D image, averaged and flipped
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along the x-axis. Both mirror images were averaged to create the final symmetric, study-
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specific "non-linear" GM template based on an equal number of participants per group at
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2x2x2mm3 resolution in standard space. Third, all native grey matter images were non-
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linearly registered to the study-specific template generated in the previous step, and
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modulated to correct for local expansion (or contraction) due to the non-linear component of
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the spatial transformation (see Good et al., 2001). The modulated grey matter images were
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then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm.
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2.5.3. Statistical Analysis.
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To investigate age-related differences in GM, voxelwise general linear modeling (GLM)
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was applied using threshold-free cluster enhancement (TFCE) permutation-based non-
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parametric testing using 10,000 permutations (Nichols and Holmes, 2002; Winkler et al.,
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2014), for: 1) young, 2) older, and 3) young – older participants. This procedure generated
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maps of t-statistics and maps of corresponding uncorrected p-values and Family-Wise Error
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(FWE) corrected p-value maps.
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Total intracranial volume (TIV) was means-centered and included as a variable of non-
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interest in the models. TIV was obtained using FSL (Jenkinson et al., 2012) by linearly
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aligning each subject brain extracted image to the MNI152 space, computing the inverse of
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the determinant of the affine matrix ((Sargolzaei et al., 2015); see ENIGMA protocol: 14
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extracted using an automated brain extraction tool (BET) (Smith, 2002) and segmentation
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using FAST (Zhang et al., 2001). Offline changes in MSL performance and each spindle
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parameter (density, amplitude, duration) were entered as mean-centered covariates of
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interest into separate GLMs to investigate brain regions where GM correlated to a different
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extent in young and older subjects with offline changes in MSL performance and sleep
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spindles.
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In addition, to co-localize differences in grey matter in young and older participants
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which correlated with both offline changes in performance and sleep spindle characteristics,
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the conjunction of performance-related GM t-maps and spindle-related GM t-maps was taken
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as the minimum t-statistic using the global null hypothesis (Friston et al., 2005). The resulting
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conjunction maps identify brain regions where GM was correlated to a different extent in
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young and older (and young vs. older) participants with both offline changes in performance
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and spindles, over: 1) the performance-correlated statistical parametric maps (SPM) in young
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and older participants with 2) each spindle-correlated map (e.g., density, amplitude, duration)
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in young and older participants (e.g., young, older and young vs. older). The conjunction
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maps were thresholded so that results were significant at a combined p-level of p < 0.001
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(uncorrected). Clusters that were significant at p < 0.05 (FWE corrected) were also indicated
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in the results. Note that a significant conjunction does not necessarily mean all the contrasts
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were individually significant (i.e., a conjunction of significance). Instead it indicates that the
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effects were consistently high across all conditions included in the conjunction, such that they
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were jointly statistically significant (Friston et al., 2005). Note that the minimum t-values do not
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have the usual Student’s t-distribution and small minimum t-values can be highly significant.
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3.1. MSL Behavioral Results Holm-Bonferonni corrected independent t-tests revealed that young participants had
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significantly higher offline improvements in performance than older participants (t(26) = 3.87,
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p = 0.001; Figure 1). Similarly, one sample t-tests showed that young participants offline
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performance improvements were significantly different from zero (t(12) = 4.32, p = 0.004),
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while the older participants performance did not significantly change (t(14) = -1.47, p = 0.163).
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A previous report by our group indicated that offline performance improvements were
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observed in young participants only, and only after a period of sleep, but not after a wake
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period (Fogel et al., 2014), hence suggesting that sleep supported offline consolidation of
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MSL in young, but not older participants.
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Figure 1. Time between key presses (A) decreased over the course of the 14 blocks in the training session at the same rate for young and older participants. Young participants, but not 16
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older participants performance was then enhanced following a nap during the retest session, as indicated by (B) significant offline improvements in performance from the last 4 blocks of the training session to the first four blocks of the retest session. Note: Young participants performance in panel A scaled along the right-hand y-axis to illustrate similarity between young and older participants in MSL.
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Both subjective and objective levels of sleepiness were measured before the MSL
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training and retest sessions. A main effect of session revealed that subjective (F(1,26) = 8.47,
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p = 0.007) and objective (F(1,26) = 8.63, p = 0.007) sleepiness was reduced following the
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nap. There was no main effect of age group, nor a session by age group interaction for the
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objective or subjective sleepiness scores (all p > 0.05). The latter findings suggest that sleep
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during a daytime nap reduced sleepiness consistently across young and older participants,
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but that sleepiness or vigilance alone did not affect MSL performance differentially across
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groups, or explain group differences in MSL performance.
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3.3. Sleep Architecture
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3.2. Subjective and Objective Sleepiness
As expected, young and older participants differed with respect to the sleep
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characteristics measured during the 90-min daytime nap (Table 1). Older participants spent
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significantly more time awake (t(26) = 3.31, p = 0.004). This was reflected by an increase in
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wake time after sleep onset (t(26) = 2.77, p = 0.012), a decrease in total sleep time (t(26) =
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3.23, p = 0.004) and a reduced sleep efficiency (t(26) = 3.06, p = 0.006). Older participants
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had significantly less NREM sleep (t(26) = 2.76, p = 0.012) than young participants, the latter
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being most likely driven by the significant reduction of slow wave sleep (SWS) as a percent of
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total sleep time (t(26) = 2.09, p = 0.047). By contrast, N1 and REM sleep did not significantly
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differ between young and older participants, hence indicating that sleep deficits in older
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individuals were predominantly a result of changes in NREM sleep. The difference between
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young and older participants for REM sleep did approach statistical significance (p < 0.10).
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However, it should be noted that this was largely due to a floor effect, whereby 73% of older
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this unusual distribution, we also conducted non-parametric tests for REM sleep. However,
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this revealed the same pattern of results, which did only approach statistical significance (U =
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55.5, p > 0.05). Although this non-significant age difference is somewhat difficult to interpret, it
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likely represents age-related difficulty in the initiation of REM sleep in a daytime nap, and not
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necessarily REM duration per se. Due to the highly variable duration of N2 and an insufficient
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quantity of SWS across participants in a daytime nap, N2 and SWS were collapsed into a
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total measure of NREM sleep for spindle detection and analyses.
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3.4. Sleep Spindles
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In addition to age-related differences in sleep architecture, older participants had
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significantly shorter sleep spindle duration measured during NREM sleep at Fz (t(26) = 3.70,
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p = 0.003), Cz (t(26) = 3.55, p = 0.002) and Pz(t(26) = 2.29, p = 0.030). In addition, their
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spindles were significantly slower at Fz (t(26) = 3.33, p = 0.009; see Table 2). However, sleep
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spindle density and amplitude were not significantly different between young and older
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participants.
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3.5. Spindle and MSL Behavioral Correlations
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We conducted Pearson bivariate correlation tests to ascertain if sleep spindles were
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linked to MSL performance improvements in young and older participants. These analyses
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revealed that there were no significant correlations between spindles (density, amplitude and
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duration) and offline performance improvements for the total spindles bandwidth at Cz, or
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slow spindles at Fz for young and older participants (all p > 0.05). However, for fast spindles
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at Pz, spindle density (r(11) = 0.60, p = 0.030), amplitude (r(11) = 0.614, p = 0.026) and
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duration (r(11) = 0.69, p = 0.009) were significantly correlated with offline changes in MSL
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performance in young participants. By contrast, spindle duration was negatively correlated
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with offline performance changes in older participants (r(13) = -0.67, p = 0.006) , whereby
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larger spindle duration was associated with reduced offline changes in performance.
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3.6. Structural MRI Results 3.6.1. GM differences in young vs. older participants:
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As expected, greater GM was observed in young compared to older participants in a
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widespread (n.b., the size of the significant striatal-cortical cluster in Table 3A) set of brain
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regions bilaterally including the somatosensory, sensorimotor, premotor, frontal, dorsolateral,
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medial prefrontal, cingulate, supplementary motor area, parietal, stratum and hippocampus
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(Figure 2C).
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in young and older participants:
GM in young and older subjects correlated to a different extent with offline changes in
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performance in the cerebellum (lobules Crus1, IV, IX; Table 3B). In addition, GM in young
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and older subjects correlated to a different extent with total spindle duration in the cerebellum,
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the hippocampus as well as the cingulate and parietal cortices (Table 3B). Slow spindle
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amplitude and duration also correlated to a different extent in young and older adults with GM
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in the supplementary motor area, and for fast spindle duration in the cerebellum (Table 3B).
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A conjunction analysis was used to identify regions where GM was correlated to a
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different extent in young vs. older adults with both offline changes in performance and spindle
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parameters (density, duration and amplitude) for slow, fast and total bandwidth spindles. In
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young, but not older participants, GM in the hippocampus (bilaterally) and cerebellum were
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positively correlated with both offline changes in performance and total spindle duration
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(Table 3C; Figure 2). Furthermore, GM correlated with spindle duration to a different extent
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when directly comparing young vs. older participants in these same regions (Table 3B).
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Figure 2. Upper: Significant clusters of grey matter (GM) positively correlated with offline changes in performance in conjunction with Cz spindle duration in the hippocampus and cerebellum for young (red) participants (see Table 3C): GM in the hippocampus was (A) positively correlated with offline changes in performance and (B) spindle duration in young (filled circles) but not older (open circles) participants, which was (C) also higher on average for young as compared to older participants (see Table 3A). This difference in GM between young and older subjects correlated with both spindle duration and offline changes in MSL performance in the hippocampus and cerebellum (Table 3B). Results significant at p<0.001, uncorrected. Note: Only significant clusters and scatterplots shown for Cz spindles duration, as these clusters overlapped completely with significant regions for other spindle parameters (amplitude, density) for either fast or slow spindles (see Table 3 for details). X, Y Z coordinates given in voxel coordinates, displayed in radiological orientation.
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Over the course of normal, healthy aging, sleep quantity and quality is reduced (Bixler
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et al., 1984; Buysse et al., 1992; Carrier et al., 2011, 2001; Crowley et al., 2002; Darchia et
481
al., 2003; Feinberg et al., 1967; Floyd et al., 2007; Fogel et al., 2012; Gaudreau et al., 2001;
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Landolt and Borbely, 2001; Martin et al., 2012; Ohayon et al., 2004; Peters et al., 2014;
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Redline et al., 2004; Zepelin and McDonald, 1987). The functional consequences of this for
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4. DISCUSSION
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ACCEPTED MANUSCRIPT learning and memory are just starting to become clear. Specifically, recent studies have
485
shown that for simple, explicitly known motor sequences, older adults are able to learn at a
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normal rate. Yet their ability to consolidate the memory trace compared to younger individuals
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is impaired (Fogel et al., 2014; Mander et al., 2015, 2013a, 2013b; Pace-Schott and Spencer,
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2011; Spencer et al., 2007; Wilson et al., 2012). Interestingly, the preservation of MSL during
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training in older adults appears to be specific to explicitly known sequences. By contrast,
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implicit probabilistic motor sequence learning is impaired with age (Howard and Howard,
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2013; Simon et al., 2011), and a growing body of literature suggests that sleep may not be
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involved in the enhancement of implicit motor skills, even in younger adults (Nemeth et al.,
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2010). These results suggest that while explicit MSL is unaffected by age, age has an impact
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on the offline consolidation/stabilization of the newly learned explicit motor skill. In other
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words, older individuals do not derive the same benefit as younger individuals from sleep for
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the consolidation of newly learned explicit motor skills.
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In terms of brain functional activation related to MSL and sleep-dependent
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consolidation, recent work from our group, in the same set of participants reported here, has
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revealed that a spindle-related reduction of activity in the putamen, cerebellum, parietal and
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temporal cortex may underlie this age-related, sleep-dependent memory deficit (Fogel et al.,
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2014). Consistent with this finding, others have demonstrated that grey matter is also reduced
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with normal aging, with the most pronounced changes in the thalamus, hippocampus,
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striatum, frontal cortex, motor cortical regions and cingulate cortex (Fjell and Walhovd, 2010;
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Good et al., 2001b; Gunning-Dixon et al., 1998; Raz et al., 2015, 2005). Here we investigated
505
if the changes in brain structure related to spindles and offline motor sequence memory
506
consolidation may help to explain why older adults do not derive the same benefit from sleep
507
as younger adults for the consolidation of motor skills. More specifically, we explored whether
508
age-related reductions of grey matter integrity relate to spindle characteristics (e.g., density,
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ACCEPTED MANUSCRIPT amplitude, duration) and overnight, sleep-dependent offline improvements in motor skill
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performance. We found that a widespread network of brain regions had reduced GM in older
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compared to young participants, including motor cortical regions, the hippocampus, striatum,
512
cingulate, dorsolateral, medial prefrontal, temporal, parietal, frontal regions of the neocortex.
513
Interestingly, only age-related differences in GM in the cerebellum were correlated with offline
514
changes in MSL performance following a daytime nap. By contrast, sleep spindles correlated
515
with age-related changes in GM in a well-known set of brain regions which support motor
516
sequence memory consolidation (Doyon and Benali, 2005; Doyon and Ungerleider, 2002;
517
Doyon et al., 2003) including the hippocampus, parietal, cerebellum and supplementary motor
518
area. However, when both offline changes in MSL performance and sleep spindles were
519
considered together, grey matter in the hippocampus and cerebellum was correlated to a
520
greater extent in young vs. older subjects with both sleep spindles and sleep-related changes
521
in performance. Thus, suggesting that sleep spindles may serve as markers for age-related
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changes in GM in the hippocampus and cerebellum, regions which support sleep-related
523
memory consolidation for motor skills. These results are in line with previous work showing
524
that sleep-dependent consolidation of skills are related to increased functional activation of
525
the cortico-striatal-hippocampal and cortico-cerebellar network in young healthy adults
526
(Albouy et al., 2015; Debas et al., 2010; Fogel et al., 2014; Walker et al., 2005). This is also
527
consistent with our recent work in the same set of participants as the current study, showing
528
that sleep had a differential effect on functional brain activity from the MSL training session to
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the retest session (retest-training) in the hippocampus and cerebellum between young and
530
older adults (Fogel et al., 2014). More specifically, young adults that slept and older adults
531
that were awake had increased activations, whereas young adults that were awake and older
532
adults that slept had decreased BOLD activity in the hippocampus, cerebellum and cortical
533
regions recruited during MSL (e.g., parietal and frontal cortex). This pattern of activity
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hippocampal activity supports memory consolidation in young but not older adults. The
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current study extends these results, providing the first evidence that structural grey matter
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differences in regions correlated with sleep spindles may underlie age-related motor skill
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memory consolidation deficits. Moreover, our results suggest that age-related GM atrophy in
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regions such as the cerebellum and hippocampus, in particular (Kennedy and Raz, 2005; Raz
540
et al., 2015) may underlie spindle-related motor sequence memory consolidation deficits.
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Although still controversial, age-related changes in grey matter may affect spindle
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generating mechanisms that depend on synchronization of the neocortex (Bonjean et al.,
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2012; Contreras and Steriade, 1996; Timofeev and Chauvette, 2013). Decreased spindle size
544
and frequency may be the result of cortical cell loss associated with the aging process. It has
545
been suggested that age-related loss of neurons may lead to a reduction of large-scale
546
synchronization, thereby affecting the size and frequency of sleep spindles (Crowley et al.,
547
2002; Nicolas et al., 2001). Consistent with this suggestion and a previous study by our group
548
(Martin et al., 2012), it was observed that sleep spindle duration was reduced in older
549
participants for spindles compared to younger participants, although unlike the current study,
550
no age-difference was previously observed for spindle frequency. However, similar to Landolt
551
et al. (Landolt and Borbely, 2001; Landolt et al., 1996) slow spindles were slower at frontal
552
regions in older participants with no change in faster frequencies. It should be noted that the
553
method of spindle detection and characterization used in the present study (Ray et al., 2015)
554
does not employ a minimum duration criteria, as we have demonstrated that the majority of
555
spindles are below the minimum criteria conventionally used to visually identify spindles.
556
Despite this, no spindles below 0.2 sec were identified. However, this is an important
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methodological consideration, since not including spindles <0.5sec, may bias or
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underestimate age-related changes in spindles. Thus the results of the current study, suggest
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that with age, the relationship between grey matter and spindles is negatively impacted, and
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related to a reduction in the sleep-related benefit to memory consolidation. Consistent with previous reports (Fogel et al., 2014; Pace-Schott and Spencer, 2011;
562
Spencer et al., 2007), and despite the fact that performance in older participants was slower
563
overall as compared to younger participants, older participants exhibited the same pattern of
564
increasing performance over the course of the training session as the young participants. This
565
suggests that despite slower motor execution, explicit motor sequence learning remains
566
largely intact with age except under greater task complexity and implicit MSL (Howard and
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Howard, 2013; King et al., 2013; Rieckmann et al., 2010; Simon et al., 2011). However,
568
following a retention interval filled with sleep, older individuals do not derive the same benefit
569
as younger participants who experienced offline consolidation/stabilization (Fogel et al., 2014;
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Pace-Schott and Spencer, 2011; Spencer et al., 2007).
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In conclusion, our results suggest that age-related changes in sleep spindles are
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differentially related to grey matter in young and older individuals in brain regions that support
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sleep-dependent memory consolidation for motor skills. In this way, sleep spindles can serve
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as a biological marker for structural brain changes in grey matter and their associated
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memory deficits in older adults.
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5. ACKNOWLEDGEMENTS
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The authors would like to acknowledge the technical support of Vo An Nguyen, Laura Ray,
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Andre Cyr, Carollyn Hurst, Frederic Jeay, and Amel Bouyoucef. This research was funded by
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a Canadian Institutes of Health research (CIHR) grant to author JD and fellowship support to
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author SF from the Natural Sciences and Engineering Research Council of Canada (NSERC),
582
the Fonds de Recherche du Quebec (FRSQ).
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11.51 60.98 15.41 76.39 12.10
2.36 2.86 3.10 2.74 2.61
20.14 67.40 7.01 74.41 5.45
4.15 3.33 2.61 3.96 2.73
-1.08 -1.44 2.09 0.41 1.75
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Stage 1 % TST Stage 2 % TST SWS % TST NREM % TST REM % TST
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Table 1. Mean (and standard error; SE) sleep parameters recorded during the 90-minute daytime nap retention interval in both young and older participants. Young Older M SE M SE t p Wake (min) 19.85 3.44 47.09 7.47 -3.31 0.004 * Stage 1 (min) 7.87 1.36 6.18 0.76 1.28 0.270 Stage 2 (min) 43.31 2.28 31.02 5.22 2.16 0.044 * SWS (min) 11.45 2.41 4.60 1.75 2.35 0.027 * NREM (min) 54.77 3.13 35.62 6.20 2.76 0.012 * REM (min) 9.44 2.10 4.20 2.18 1.72 0.098 Total sleep time (min) 72.08 3.56 46.00 7.26 3.23 0.004 * 0.084 0.162 0.047 0.684 0.093
*
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Sleep onset latency (min) 11.92 1.91 18.40 5.26 -1.16 0.263 Wake after sleep onset (min) 10.87 2.69 31.13 6.79 -2.77 0.012 * Total recording time (min) 94.87 0.74 93.76 0.73 1.06 0.297 Sleep efficiency (%) 76.05 3.85 49.31 7.83 3.06 0.006 * Note: * indicates p < 0.05, df = 26, corrected for heterogeneity of variance where appropriate. Abbreviations: Minutes of sleep (min), Total sleep time (TST), rapid eye movement sleep (REM).
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Table 2. Number, density (#/min NREM), duration (sec) and amplitude (uV ) and frequency (Hz) of NREM sleep spindles at Fz (slow: 11-14Hz), Cz (total: 11-17Hz) and Pz (fast: 14-17Hz) during the 90min daytime nap retention interval in young and older participants. Young Older M SE M SE t p Number Fz (slow) 193.38 17.27 149.73 26.61 1.99 0.394 Cz (total) 251.54 21.31 186.13 31.73 1.65 0.336 Pz (fast) 99.85 15.86 70.73 15.59 1.85 0.219 Density (#/min) Fz (slow) 3.45 0.22 4.07 0.14 -3.44 0.099 Cz (total) 4.51 0.25 5.21 0.15 -2.39 0.144 Pz (fast) 1.78 0.26 1.94 0.21 -0.68 0.651 Duration (sec) Fz (slow) * 0.50 0.02 0.38 0.02 5.19 0.005 Cz (total) * 0.48 0.03 0.34 0.02 3.55 0.004 Pz (fast) * 0.47 0.02 0.40 0.02 3.09 0.045 Amplitude (uV2) Fz (slow) 62.88 8.06 41.68 5.02 3.24 0.132 Cz (total) 90.18 12.32 67.24 8.12 1.50 0.298 Pz (fast) 67.78 11.07 74.39 11.70 -0.58 0.696 Frequency (Hz) Fz (slow) * 12.50 0.04 12.23 0.04 6.57 0.000 Cz (total) 13.36 0.07 13.29 0.10 0.55 0.585 Pz (fast) 14.69 0.04 14.83 0.07 -2.72 0.177 Note: * indicates p < 0.05, two-tailed. df = 26, corrected for homogeneity of variance, where appropriate and Holm-Bonferroni corrected for multiple comparisons for the number of sites.
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B) YOUNG vs. OLDER: GM CORRELATED WITH MSL & SPINDLES MSL Right Left
MSL MSL
Cerebellum (IV) Cerebellum (IX,X)
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Spindles Left Left Left Left Right Right Left
Total spindle duration Total spindle duration Total spindle duration Total spindle duration Slow spindle amplitude Slow spindle duration Fast spindle duration
Cerebellum (IV) Hippocampus Cingulate cortex Parietal Supplementary motor area Supplementary motor area Cerebellum (IV)
Cerebellum (IX) Cerebellum (IIX) Hippocampus
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Table 3. Brain regions with (A) significantly greater GM in young vs. older participants, (B) GM correlated with offline changes (MSL) in performance in conjunction with spindles in young vs. older, (C) young and (D) older participants. X, Y Z coordinates given in voxel coordinates, in radiological orientation. L/R Covariates of interest Brain Region Voxels t p x y z A) YOUNG vs. OLDER Bilateral None Striatal-Cortical-Hippocampal 43200 6.98 0.0002* 46 82 47
0.002 0.002
35 53
39 47
25 15
439 439 245 11 22 23 225
6.13 5.40 5.22 4.63 4.30 4.7 4.69
0.001 0.001 0.001 0.001 0.001 0.001 0.001
61 65 52 66 33 33 59
47 52 43 35 66 66 29
21 27 55 64 66 67 26
42 9 4
2.16 2.1 2.17
0.018 0.029 0.030
53 28 61
44 40 50
12 24 28
918 10 90 16
2.68 2.16 2.17 1.94
0.006 0.009 0.002 0.017
62 53 61 62
34 44 50 52
20 12 28 28
MSL & spindle conjunction: Left MSL & total spindle duration Right MSL & total spindle duration Left MSL & total spindle duration
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C) YOUNG: GM CORRELATED WITH MSL & SPINDLES
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MSL & spindle conjunction: Bilateral MSL & total spindle duration Bilateral MSL & total spindle duration Left MSL & total spindle duration Bilateral MSL & fast spindle density
Cerebellum (crus I, IV) Cerebellum (IX) Hippocampus Hippocampus
No significant voxels
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D) OLDER: GM CORRELATED WITH MSL & SPINDLES
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Results from total spindles from Cz, slow spindles from Fz and fast spindles from Pz presented. Note: * indicates FWE-corrected p-value significant at p < 0.05. Coordinates for side where effect was maximal reported when bilateral.
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ACCEPTED MANUSCRIPT HIGHLIGHTS Older individuals had intact motor sequence learning but impaired offline gains
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Grey matter related to spindles and offline gains in young vs. older individuals
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Spindles may serve as a biological marker for age-related memory deficits
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1. All authors disclose that: (a) There are no actual or potential conflicts of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the work submitted that could inappropriately influence (bias) their work. (b)The author's institutions have no contracts relating to this research through which it or any other organization may stand to gain financially now or in the future. (c) No other agreements of authors or their institutions that could be seen as involving a financial interest in this work. 2. This research was funded by a Canadian Institutes of Health research (CIHR) grant to author JD and fellowship support to author SF from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds de Recherche du Quebec (FRSQ).
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3. The results reported in the manuscript being submitted have not been previously published, have not been submitted elsewhere and will not be submitted elsewhere while under consideration at Neurobiology of Aging.
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4. Ethical and scientific approval was obtained from the Ethics Committee at the “Institut de Geriatrie de Montreal”, Montreal, Quebec, Canada.
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5. All authors have had the opportunity to review the contents of the manuscript being submitted, approve of its contents and validate the accuracy of the data.
S0197-4580(16)30249-4
DOI:
10.1016/j.neurobiolaging.2016.10.009
Reference:
NBA 9748
To appear in:
Neurobiology of Aging
Received Date: 16 March 2016 Revised Date:
8 September 2016
Accepted Date: 3 October 2016
Please cite this article as: Fogel, S, Vien, C., Karni, A., Benali, H., Carrier, J, Doyon, J, Sleep spindles: A physiological marker of age-related changes in grey matter in brain regions supporting motor skill memory consolidation, Neurobiology of Aging (2016), doi: 10.1016/j.neurobiolaging.2016.10.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Sleep spindles: A physiological marker of age-related changes in grey matter in brain
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regions supporting motor skill memory consolidation
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Fogel S,a,b,c Vien, C.,a,b Karni, A.,d Benali, H.,a,e Carrier Ja,b,f & Doyon Ja,b a
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Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; bDepartment of Psychology, University of Montreal, Montreal, Canada; cSchool of Psychology, University of Ottawa, Ottawa, Canada; dLaboratory for Human Brain & Learning, Sagol Department of Neurobiology & the E.J. Safra Brain Research Center, University of Haifa, Haifa, Israel; eFunctional Neuroimaging Laboratory, INSERM, France; fCentre d’études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada.
Email: [email protected] Phone: 1-514-340-2800 x 3284 Fax: 1-514-340-3530
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Corresponding Author: Julien Doyon, Ph.D. Professor, Department of Psychology, University of Montreal Director, Functional Neuroimaging Unit Centre de Recherche, Institut Universitaire de Gériatrie de Montréal 4545 Chemin Queen Mary Montreal, Quebec, Canada, H3W 1W5
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Sleep is necessary for the optimal consolidation of procedural learning, and in particular, for
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motor sequential skills. Motor sequence learning (MSL) remains intact with age, but sleep-
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dependent consolidation is impaired, suggesting that memory deficits for procedural skills are
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specifically impacted by age-related changes in sleep. Age-related changes in spindles may
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be responsible for impaired MSL consolidation, but the morphological basis for this deficit is
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unknown. Here we found that grey matter in the hippocampus and cerebellum was positively
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correlated with both sleep spindles and offline improvements in performance in young
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participants, but not in older participants. These results suggest that age-related changes in
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grey matter in the hippocampus relate to spindles, and may underlie age-related deficits in
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sleep-related motor sequence memory consolidation. In this way, spindles can serve as a
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biological marker for structural brain changes and the related memory deficits in older adults.
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41 KEYWORDS
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Sleep, spindle, age, memory, learning, consolidation, motor sequence learning, motor skills,
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procedural memory, hippocampus, cerebellum
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1. INTRODUCTION Alongside a healthy diet and regular exercise, sleep is one of the pillars of good
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physical and mental health, and has important links to memory and cognition (e.g., see
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(Rasch and Born, 2013), for a recent comprehensive review). However, as we age, the
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quantity and quality of sleep are markedly reduced (Bixler et al., 1984; Buysse et al., 1992;
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Carrier et al., 2011, 2001; Crowley et al., 2002; Darchia et al., 2003; Feinberg et al., 1967;
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Floyd et al., 2007; Fogel et al., 2012; Gaudreau et al., 2001; Landolt and Borbely, 2001;
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Martin et al., 2012; Ohayon et al., 2004; Peters et al., 2014; Redline et al., 2004; Zepelin and
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McDonald, 1987), and recent studies suggest that the age-related changes in sleep may have
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an impact on sleep-dependent memory processes (Fogel et al., 2014; Mander et al., 2015,
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2013a, 2013b; Pace-Schott and Spencer, 2011; Spencer et al., 2007; Wilson et al., 2012). In
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healthy young adults, non-rapid eye movement stage 2 (N2) sleep typically occupies about
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45% to 55% of total sleep time (Carskadon and Dement, 2011). However, with age, the total
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amount of N2 is reduced (Martin et al., 2012; Ohayon et al., 2004; Ramanand et al., 2010).
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One of the main characteristic features of N2 sleep is the presence of sleep spindle (Iber et
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al., 2007). Sleep spindles are brief bursts (<1 sec to ~3 sec) of oscillatory activity within the
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sigma band, with a frequency of ~12-16 Hz and with a waxing and waning amplitude that is
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discrete from the ongoing EEG (Ferrarelli et al., 2010; Fogel et al., 2007; Landolt et al., 1996;
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Silber et al., 2007; Zeitlhofer et al., 1997). One of the most pronounced age-related changes
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in N2 sleep is the reduction of sleep spindles marked by reduced duration, amplitude and
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density (Bowersox et al., 1985; Cajochen et al., 2006; Carrier et al., 2001; Crowley et al.,
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2002; Feinberg, 1974; Guazzelli et al., 1986; Huupponen et al., 2002; Knoblauch et al., 2005;
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Landolt et al., 1996; Mander et al., 2013a; Martin et al., 2012; Nicolas et al., 2001; Peters et
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al., 2014; Principe and Smith, 1982). However, the age-related changes in brain structure that
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underlie age-related reduction in spindles, and their functional consequences, remain to be
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fully elucidated. Animal studies of the neural circuitry that supports the generation of sleep spindles
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have shown that spindles originate from rhythmic depolarization of thalamocortical neurons
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(Steriade, 2006), modulated by GABAergic pathways in the reticular nucleus of the thalamus
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(Bazhenov et al., 2000, 1999). However, the brain structures involved in the generation and
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regulation of spindles remains a topic of debate (Timofeev and Chauvette, 2013), as recent
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evidence suggests that spindles are initiated cortically and that the neocortex contributes to
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the termination of spindle events (Bonjean et al., 2011; Timofeev, 2001). In addition to the
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thalamus, human functional neuroimaging studies employing simultaneous EEG-fMRI, have
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revealed that brain regions involved in sensory processing are activated time-locked to sleep
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spindles, including the anterior cingulate, insula and superior temporal gyri (Schabus et al.,
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2007). Moreover, fast spindles have been associated with increased activity in sensorimotor
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areas, the mesial frontal cortex and the hippocampus (Schabus et al., 2007) – the latter
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findings being consistent with the putative role of fast spindles in declarative memory
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processing. To further investigate the structural correlates of sleep spindles, Saletin et al
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(Saletin et al., 2013) found that inter-individual differences in grey matter volume of the insula
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and auditory cortex were related to slow spindles, whereas, grey matter volume in the
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hippocampus was associated with fast spindles. Thus, the latter findings suggest that slow
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and fast spindles serve a dissociable role for sleep maintenance and possibly memory
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processing, respectively.
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Indeed, spindles are thought to serve a protective function by preventing potentially
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arousing external stimuli such as loud noises that disrupt sleep (Cote et al., 2000; Dang-Vu et
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al., 2010; Pivik et al., 1999; Schabus et al., 2012), although see (Crowley et al., 2004) for data
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to suggest otherwise. In addition to their function for sleep maintenance via the gating of
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intrusive sensory information, however, a rich literature now exists supporting the role of 4
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al., 2013a; Barakat et al., 2012, 2011; Fogel and Smith, 2011, 2006; Fogel et al., 2015, 2014;
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Lafortune et al., 2014; Milner et al., 2006; Nielsen et al., 2014; Nishida and Walker, 2007;
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Peters et al., 2007; Ramanathan et al., 2015; Smith and Smith, 2003; Tamaki et al., 2009,
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2008; Wamsley et al., 2013, 2012; Wilhelm et al., 2012). Sleep is thought to enhance the
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memory consolidation process via the interplay between the hippocampus and striatum
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(Albouy et al., 2015, 2013a, 2013b, 2013c, 2008; Schabus et al., 2007) by strengthening
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newly formed, labile memory traces for lasting long-term neocortical storage. Animal studies
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have shown that during the spindle event, there is a large influx of intracellular calcium, which
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is necessary to set in motion long-term potentiation (LTP) of synapses. In vivo (Bergmann et
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al., 2008; Rosanova and Ulrich, 2005; Steriade, 2005; Werk et al., 2005) and in vitro
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(Rosanova and Ulrich, 2005) studies have also shown that spaced and repeated stimulation
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in the spindle oscillatory frequency range is necessary for cortical LTP to take place in the
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neocortex. Thus, spindles are an ideal physiological candidate for neocortical memory
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consolidation via memory trace reactivation.
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Recent human neuroimaging studies have shown that sleep spindle amplitude was
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correlated with overnight consolidation in motor sequence skill (Barakat et al., 2012, 2011)
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and that overnight changes in activation of brain regions including the striatum and motor
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cortical regions, recruited during motor sequence learning (MSL) (Debas et al., 2010) were
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correlated with post-training spindle amplitude (Barakat et al., 2012). EEG-fMRI studies have
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also identified activations in the striatum (Caporro et al., 2011; Tyvaert et al., 2008) and
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hippocampus (Schabus et al., 2007) associated with spindle events, hence suggesting that
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spindles are involved in the activation of structures important for sleep-dependent motor skill
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memory consolidation. Taken together, this literature suggests that spindles play an active
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role in sleep-dependent memory consolidation via related interaction between striatal,
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hippocampal and cortical memory systems, and may ultimately result in strengthening of
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memory traces. The functional consequences of age-related changes in sleep on memory
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consolidation are beginning to be better appreciated. Age-related changes in sleep have been
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shown to have a negative impact on overnight memory consolidation of some procedural
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skills (Brown et al., 2009; Spencer et al., 2007), and spindles, in particular, have been related
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to this deficit (Fogel et al., 2014; Peters et al., 2008). Age-related characteristics of spindles,
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including duration, amplitude, and density, decrease with age (Carrier et al., 2001; Feinberg,
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1974; Martin et al., 2012; Peters et al., 2014). Taken together, these studies suggest that an
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age-related reduction in spindle activity may help explain age-related, sleep-dependent
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deficits in memory consolidation. In support of this notion, a recent functional neuroimaging
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study by our group has shown that reduced spindles in the elderly are associated with a
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reduction of the spindle-related, overnight changes in cerebral activity in structures important
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for skill learning (e.g., putamen and cerebellum in young, but only the cerebellum in older
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individuals) (Fogel et al., 2014). Thus, a spindle-related, sleep-dependent increase of activity
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in structures such as the putamen and cerebellum appears to be crucial for off-line
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improvements, for which older individuals do not derive the same benefit due to an age-
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related reduction of sleep spindles. However, it is not known if changes in brain structure that
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accompany normal, healthy aging may underlie this deficit, and whether this is related to an
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age-related reduction of sleep spindles.
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In terms of brain structures, adults who learn to play a musical instrument from a young
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age have been found to have increased grey matter in brain regions that support motor skill
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learning (e.g., hippocampus, putamen motor cortex, somatosensory cortex, superior parietal
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cortex and the cerebellum)(Gaser and Schlaug, 2003; Groussard et al., 2014; Vaquero et al.,
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2016). However, changes in grey matter have been observed over much shorter time periods. 6
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cortex, premotor and dorsolateral prefrontal cortex (Gryga et al., 2012), and the earliest
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detectable signs of grey matter changes with motor skill training (over 3 days) have been
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observed in the striatum (Hamzei et al., 2012). These structural changes in the striatum were
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related to grey matter changes in motor cortical regions at later stages of motor skill
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performance, with an increase in functional coupling between motor and striatal regions over
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the same time period. However, it is not known if age-related changes in brain structures
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relate to age-related changes in sleep and the resulting memory deficits. Here, we
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investigated the age-related differences in grey matter (GM) assessed by voxel-based
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morphometry that were correlated with both sleep spindles and sleep-dependent changes in
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motor skills performance.
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We hypothesized that GM in structures that support MSL consolidation (e.g.,
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cerebellum, striatum, hippocampus, motor cortical regions), would be correlated with both
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offline changes in MSL performance and spindles in young participants. Yet in older
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participants, the results of a recent functional neuroimaging study from our group suggests
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that the correlation between brain activation during practice of a motor sequence task and
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sleep spindles deteriorates with age (Fogel et al., 2014). Accordingly, if structural deficits
164
underlie (or are linked to) the functional deficits observed with age, then a similar reduction in
165
the magnitude of the relationship between spindles and grey matter should be expected.
166
Thus this study aimed to ascertain if structural brain changes in grey matter were associated
167
with sleep spindles and memory deficits in older adults. The latter findings are important as
168
they may lead to novel insights into the neurological basis for age-related memory deficits,
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and highlight the importance of good sleep quality later in life, and perhaps even novel
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treatments or interventions to improve sleep in elderly populations in order to derive a benefit
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to memory and related mitigate cognitive impairment.
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2. METHODS
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2.1. Participants All participants provided informed written consent before entering the study. Ethical
176
and scientific approval was obtained from the Ethics Committee at the “Institut de Geriatrie de
177
Montreal”, Montreal, Quebec, Canada. In total, 22 young participants and 34 older
178
participants were recruited to participate in the study that met the inclusion and exclusion
179
criteria (see section 2.1.1). The results presented here were part of a larger series of studies
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investigating both the functional and structural basis of age-related changes of sleep on
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memory consolidation (Fogel et al., 2014; Vien et al., n.d.).
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2.1.1. Inclusion and Exclusion Criteria.
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Eligibility was determined at the start of the experiment through both a telephone pre-
184
screening interview and a battery of questionnaires. To be included in the study, participants
185
had to be right-handed (scored > 40 on the Edinburgh Handedness Inventory (Oldfield,
186
1971)), non-smokers, prescription medication-free, and with a normal body mass index (≤ 25).
187
Also, they must not have been diagnosed with a neurological, psychological or psychiatric
188
condition, nor with a sleep disorder. "Extreme morning" or "extreme evening" types were also
189
excluded (defined as scoring > 70 "moderate morning" or < 30 "moderate evening",
190
respectively on the Horne-Ostberg Morningness-Eveningness Scale (Horne and Ostberg,
191
1976)). By contrast, participants who were not formally trained on a musical instrument, did
192
not work night-shifts and who had not taken a trans-meridian trip ≤ 3 months before the study
193
were included in the study. Participants scoring ≤ 8 on both the Beck Depression Scale (Beck
194
et al., 1974) and the Beck Anxiety Scale (Beck et al., 1988) were also included, as did older
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participants who scored ≥ 24 on the Mini Mental State Examination (Cockrell and Folstein,
196
1988; Tombaugh and McIntyre, 1992). Participants were instructed to abstain from alcohol,
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a bed-time between 10:00 PM and 1:00 AM and wake-time between 07:00 AM and 10:00 AM,
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was required to be included. Compliance with sleep-wake instructions was verified with
200
actigraphy measured from the left wrist, and with a sleep journal (using a modified version of
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the National Sleep Foundation "Sleep Diary" http://sleepfoundation.org).
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2.1.2. Polysomnographic and Additional Screening.
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If participants met the above criteria, a 90-min acclimatization nap was conducted at
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1:00 PM, three days before the experimental sessions. To ensure that participants were able
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to sleep in the laboratory environment during the daytime, participants had to achieve at least
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5 minutes of consolidated NREM sleep during this nap opportunity to be included in the study.
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An overnight polysomnographic (PSG) screening night was used in older participants to
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exclude individuals who exhibited signs of sleep apnea (e.g., apnea-hypopnea index (AHI) >
209
5) and periodic limb movements (PLM; e.g., PLM index > 10). This acclimatization and
210
screening procedure provided participants extensive opportunity to become accustomed to
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sleeping in the laboratory environment, and thus to minimize the impact on sleep quality
212
during the experimental testing sessions. As a result, 19 older participants were excluded in
213
total; 10 due to an elevated AHI or PLM index, 2 for sleeping < 5 min during the
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acclimatization nap, and 2 for MRI safety concerns. Five voluntarily withdrew before
215
completing the study. In addition, 7 young participants were excluded; 6 voluntarily withdrew
216
and 1 subject was excluded due to a structural abnormality that was uncovered upon
217
examination of their structural T1-weighted scan. The final participant pool that completed the
218
study was comprised of 15 young (Y) participants (8 female) between 19 and 30 years of age
219
(M = 23.9, SD = 3.5) and 15 older (O) participants (9 female) between 55 and 69 years (M =
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62.2, SD = 3.8). However, an additional 2 participants were excluded from data analyses in
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the group of young participants. One participant reportedly suffered from severe sleepiness in
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ACCEPTED MANUSCRIPT the last five blocks of training and succumbed to extreme tiredness during practice, thus
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producing lapses in performance during the training session. A second subject performed
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poorly on the MSL task (as this participant was outside the 99% confidence interval for all 14
225
blocks) and did not comply well with the instructions given throughout the experiment. The
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remaining 13 young participants (6 female, M = 24.1, SD = 3.5 years old) were included in the
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data analyses.
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2.2. Overall Experimental Design
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Participants arrived at the laboratory for 10:30 AM. The Stanford Sleepiness Scale
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(SSS) (MacLean et al., 1992) and Psychomotor Vigilance Task (PVT) (Dinges and Powell,
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1985) were then administered to assess both subjective and objective sleepiness,
232
respectively, before MSL practice. At 11:00 AM, the MSL task was performed with alternating
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periods of rest, in a block design, at the same time functional blood oxygen level dependent
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(BOLD) imaging data were acquired (see (Fogel et al., 2014) for corresponding behavioural
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and functional neuroimaging results in these participants). After being provided a light lunch,
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participants were given a 90-min nap interval commencing at 1:00 PM. The MSL retest
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session was administered at 4:00 PM, that is 1.5 h after the end of the 90-min experimental
238
interval to reduce possible effects of sleep inertia.
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2.3. Finger Sequence Task
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2.3.1. Task Instructions.
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An adapted version of the finger sequence task developed by Karni (Karni et al., 1995)
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was used to assess MSL in young and older participants across an interval of sleep (i.e., a
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90-minute daytime nap opportunity). An MR-compatible response box (model HH-1x4-L;
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Current Designs, Inc., Philadelphia PA) with a row of four equidistant push buttons was used.
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To familiarize each subject with the task, the experimenter first demonstrated the use of the
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keypad and explained the on-screen appearance of the task and instructions. Participants 10
ACCEPTED MANUSCRIPT were then positioned supine in the MR scanner. Before the start of the training session, all
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participants were asked to “slowly and accurately practice the 5-item sequence "4-1-3-2-4",
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where 1 stands for the index finger and 4 for the little finger of the left hand” until they were
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capable of performing the sequence three times in a row, without error. This ensured
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comprehension of instructions, memorization of the sequence, and that the sequence could
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be performed reliably prior to the start of the scanning session.
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2.3.2. Training and Retest Practice Sessions.
254
During the training and retest sessions, the exact same sequence of finger movements
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was to be performed, but participants were explicitly instructed "to perform the sequence by
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pressing the buttons as quickly as possible, while making as few errors as possible". The
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beginning of each practice block was indicated by the appearance of a green cross in the
258
center of the screen. After 60 key presses, the green cross turned red for 15 seconds,
259
indicating that the subject should stop practicing and rest. There were 14 practice blocks in
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both training and retest sessions. If participants realized that they had made an error, they
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were instructed to continue practicing at the start of the 5-item sequence. This ensured that
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errors were isolated events within the train of 60 key presses and encouraged participants to
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continue practicing the task despite making an error. To minimize age-related differences in
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reaction time, the task was un-cued, (i.e., self-initiated). Performance was measured by key
265
press speed (defined as the average correct inter-key-press interval). All participants included
266
in the data analysis achieved an accuracy > 83.3%, equivalent to 10/12 correct sequences
267
per block. Offline changes in performance across the nap interval were calculated as the
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mean performance of the first 4 blocks of retest minus the mean of the last 4 blocks of
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training. This measure of consolidation was then used in the behavioral analyses, and as a
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covariate of interest in the MRI analyses.
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2.4. PSG Recording and Analysis
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and Oz) using a Brain Products GmbH (Gilching, Germany) 16-channel, V-Amp16 system
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(high pass filter = 0.3 Hz, low pass filter = 70 Hz). Signals were digitalized at 500 samples/s.
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During the overnight polysomnographic screening in older participants, a nasal/oral thermistor
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and respiratory effort belt recordings were used to screen for sleep apnea. Leg
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electromyographic (EMG) recordings were used to screen participants for periodic leg
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movements.
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Sleep scoring (Table 1) was done visually in 20-s epochs of EEG (high pass filter = 0.5
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Hz, low pass filter = 35 Hz) from central and occipital derivations (C3, C4, and Oz) re-
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referenced to average mastoids (A1 and A2). Electrooculographic (EOG; high pass filter = 0.5
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Hz, low pass filter = 35 Hz) activity was recorded from the lateral outer canthus of the eyes.
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Bipolar submental EMG (high pass filter = 10 Hz) was also recorded. Sleep stages were
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scored by an expert PSG technologist, following standards of the American Academy of
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Sleep Medicine [Iber, 2007] using the "fMRI Artifact rejection and Sleep Scoring Toolbox
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(FASST)" (ver. 0.302; http://www.montefiore.ulg.ac.be/~phillips/FASST.html) for Matlab
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(Mathworks, Natick, MA; (Leclercq et al., 2011)). Movement continuously exceeding 100 µV
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for > 100 ms during sleep was automatically detected and identified as bad data. This was
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later confirmed visually and this data was excluded from further analyses.
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Sleep spindles were detected from Fz, Cz, and Pz in NREM sleep using a an
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established (Albouy et al, 2014; Fogel et al, 2014) and validated method (Ray et al., 2015)
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implemented in Brain Products (Brain Products GmbH, Gilching, Germany) Analyzer software
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(Version 2.1). For details of this method more recently implemented in Matlab, see Ray et al
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(Ray et al., 2015). Given that previous studies have suggested that age-related differences
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vary systematically along the midline (Martin et al., 2012), recordings from Fz, Cz, and Pz
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were used to derive the number of spindles during NREM sleep amplitude (µV2), duration (s), 12
ACCEPTED MANUSCRIPT and peak frequency (Hz). Slow and fast spindles have a known topography (De Gennaro and
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Ferrara, 2003; De Gennaro et al., 2005, 2000; Dehghani et al., 2011; Doran, 2003;
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Huupponen et al., 2002; Jobert et al., 1992; Werth et al., 1997a, 1997b; Zeitlhofer et al.,
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1997) whereby slow spindles predominate anterior regions and fast spindles predominate
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posterior regions. A frequency range for total bandwidth spindles of 11-17Hz was employed
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so as to encompass spindles defined as 12-16Hz (Ferrarelli et al., 2010; Fogel et al., 2014,
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2007; Landolt et al., 1996; Zeitlhofer et al., 1997). Slow (11-14 Hz) spindles, and fast (14-17
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Hz) spindles were categorized depending on their peak frequency in the same way as a
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previously published study on the relationship between age-related changes in sleep spindles
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and functional brain activation (Fogel et al., 2014) and also based on a thorough validation of
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this detection procedure (Ray et al., 2015). This procedure ensures that slow and fast
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spindles are orthogonal to one another at any given derivation. Thus, spindles from the
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derivations for which they predominate (e.g., slow spindles from Fz and fast spindles from Pz)
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were included in the structural imaging regression analyses (Table 2).
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2.5. Brain Imaging Data Acquisition and Analysis
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2.5.1. MRI Sequence Parameters.
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A 3.0T TIM TRIO MRI system (Siemens, Germany) with a 12-channel head coil was
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used. A 3D MPRAGE sequence (TR = 2300 ms, TE = 2.98 ms, TI = 900 ms, FA = 9°, 176
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slices, FoV = 256 x 256 mm2, matrix size = 256 x 256 x 176, voxel size = 1 x 1 x 1 mm3) was
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used to obtain high resolution T1-weighted anatomical images for all participants used for
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subsequent VBM analyses.
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2.5.2. Preprocessing.
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Structural
data was
analyzed
with
FSL
(Douaud
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2007)
(but
see
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http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM for a detailed description of analysis procedures
321
employed here) using an optimized VBM protocol (Good et al., 2001a; Smith et al., 2004) 13
ACCEPTED MANUSCRIPT described below. First, structural images were brain-extracted and grey matter-segmented
323
before being registered to the MNI 152 standard space using non-linear registration
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(Andersson et al., 2007). Second, a study-specific grey matter (GM) template was created
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with the FSL-VBM protocol. All brain-extracted images were segmented into GM, WM and
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CSF. Then, GM images were affine-registered to the GM ICBM-152 template, concatenated
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and averaged. Next, the GM images were then re-registered to this first-pass "affine" GM
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template using non-linear registration, concatenated into a 4D image, averaged and flipped
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along the x-axis. Both mirror images were averaged to create the final symmetric, study-
330
specific "non-linear" GM template based on an equal number of participants per group at
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2x2x2mm3 resolution in standard space. Third, all native grey matter images were non-
332
linearly registered to the study-specific template generated in the previous step, and
333
modulated to correct for local expansion (or contraction) due to the non-linear component of
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the spatial transformation (see Good et al., 2001). The modulated grey matter images were
335
then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm.
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2.5.3. Statistical Analysis.
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To investigate age-related differences in GM, voxelwise general linear modeling (GLM)
338
was applied using threshold-free cluster enhancement (TFCE) permutation-based non-
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parametric testing using 10,000 permutations (Nichols and Holmes, 2002; Winkler et al.,
340
2014), for: 1) young, 2) older, and 3) young – older participants. This procedure generated
341
maps of t-statistics and maps of corresponding uncorrected p-values and Family-Wise Error
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(FWE) corrected p-value maps.
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Total intracranial volume (TIV) was means-centered and included as a variable of non-
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interest in the models. TIV was obtained using FSL (Jenkinson et al., 2012) by linearly
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aligning each subject brain extracted image to the MNI152 space, computing the inverse of
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the determinant of the affine matrix ((Sargolzaei et al., 2015); see ENIGMA protocol: 14
ACCEPTED MANUSCRIPT http://enigma.ini.usc.edu) and taking the sum of the gray matter and white matter partitions
348
extracted using an automated brain extraction tool (BET) (Smith, 2002) and segmentation
349
using FAST (Zhang et al., 2001). Offline changes in MSL performance and each spindle
350
parameter (density, amplitude, duration) were entered as mean-centered covariates of
351
interest into separate GLMs to investigate brain regions where GM correlated to a different
352
extent in young and older subjects with offline changes in MSL performance and sleep
353
spindles.
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In addition, to co-localize differences in grey matter in young and older participants
355
which correlated with both offline changes in performance and sleep spindle characteristics,
356
the conjunction of performance-related GM t-maps and spindle-related GM t-maps was taken
357
as the minimum t-statistic using the global null hypothesis (Friston et al., 2005). The resulting
358
conjunction maps identify brain regions where GM was correlated to a different extent in
359
young and older (and young vs. older) participants with both offline changes in performance
360
and spindles, over: 1) the performance-correlated statistical parametric maps (SPM) in young
361
and older participants with 2) each spindle-correlated map (e.g., density, amplitude, duration)
362
in young and older participants (e.g., young, older and young vs. older). The conjunction
363
maps were thresholded so that results were significant at a combined p-level of p < 0.001
364
(uncorrected). Clusters that were significant at p < 0.05 (FWE corrected) were also indicated
365
in the results. Note that a significant conjunction does not necessarily mean all the contrasts
366
were individually significant (i.e., a conjunction of significance). Instead it indicates that the
367
effects were consistently high across all conditions included in the conjunction, such that they
368
were jointly statistically significant (Friston et al., 2005). Note that the minimum t-values do not
369
have the usual Student’s t-distribution and small minimum t-values can be highly significant.
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3.1. MSL Behavioral Results Holm-Bonferonni corrected independent t-tests revealed that young participants had
374
significantly higher offline improvements in performance than older participants (t(26) = 3.87,
375
p = 0.001; Figure 1). Similarly, one sample t-tests showed that young participants offline
376
performance improvements were significantly different from zero (t(12) = 4.32, p = 0.004),
377
while the older participants performance did not significantly change (t(14) = -1.47, p = 0.163).
378
A previous report by our group indicated that offline performance improvements were
379
observed in young participants only, and only after a period of sleep, but not after a wake
380
period (Fogel et al., 2014), hence suggesting that sleep supported offline consolidation of
381
MSL in young, but not older participants.
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Figure 1. Time between key presses (A) decreased over the course of the 14 blocks in the training session at the same rate for young and older participants. Young participants, but not 16
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older participants performance was then enhanced following a nap during the retest session, as indicated by (B) significant offline improvements in performance from the last 4 blocks of the training session to the first four blocks of the retest session. Note: Young participants performance in panel A scaled along the right-hand y-axis to illustrate similarity between young and older participants in MSL.
393
Both subjective and objective levels of sleepiness were measured before the MSL
394
training and retest sessions. A main effect of session revealed that subjective (F(1,26) = 8.47,
395
p = 0.007) and objective (F(1,26) = 8.63, p = 0.007) sleepiness was reduced following the
396
nap. There was no main effect of age group, nor a session by age group interaction for the
397
objective or subjective sleepiness scores (all p > 0.05). The latter findings suggest that sleep
398
during a daytime nap reduced sleepiness consistently across young and older participants,
399
but that sleepiness or vigilance alone did not affect MSL performance differentially across
400
groups, or explain group differences in MSL performance.
401
3.3. Sleep Architecture
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As expected, young and older participants differed with respect to the sleep
403
characteristics measured during the 90-min daytime nap (Table 1). Older participants spent
404
significantly more time awake (t(26) = 3.31, p = 0.004). This was reflected by an increase in
405
wake time after sleep onset (t(26) = 2.77, p = 0.012), a decrease in total sleep time (t(26) =
406
3.23, p = 0.004) and a reduced sleep efficiency (t(26) = 3.06, p = 0.006). Older participants
407
had significantly less NREM sleep (t(26) = 2.76, p = 0.012) than young participants, the latter
408
being most likely driven by the significant reduction of slow wave sleep (SWS) as a percent of
409
total sleep time (t(26) = 2.09, p = 0.047). By contrast, N1 and REM sleep did not significantly
410
differ between young and older participants, hence indicating that sleep deficits in older
411
individuals were predominantly a result of changes in NREM sleep. The difference between
412
young and older participants for REM sleep did approach statistical significance (p < 0.10).
413
However, it should be noted that this was largely due to a floor effect, whereby 73% of older
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ACCEPTED MANUSCRIPT participants had no REM sleep at all, as compared to only 23% of young participants. Given
415
this unusual distribution, we also conducted non-parametric tests for REM sleep. However,
416
this revealed the same pattern of results, which did only approach statistical significance (U =
417
55.5, p > 0.05). Although this non-significant age difference is somewhat difficult to interpret, it
418
likely represents age-related difficulty in the initiation of REM sleep in a daytime nap, and not
419
necessarily REM duration per se. Due to the highly variable duration of N2 and an insufficient
420
quantity of SWS across participants in a daytime nap, N2 and SWS were collapsed into a
421
total measure of NREM sleep for spindle detection and analyses.
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3.4. Sleep Spindles
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In addition to age-related differences in sleep architecture, older participants had
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significantly shorter sleep spindle duration measured during NREM sleep at Fz (t(26) = 3.70,
425
p = 0.003), Cz (t(26) = 3.55, p = 0.002) and Pz(t(26) = 2.29, p = 0.030). In addition, their
426
spindles were significantly slower at Fz (t(26) = 3.33, p = 0.009; see Table 2). However, sleep
427
spindle density and amplitude were not significantly different between young and older
428
participants.
429
3.5. Spindle and MSL Behavioral Correlations
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We conducted Pearson bivariate correlation tests to ascertain if sleep spindles were
431
linked to MSL performance improvements in young and older participants. These analyses
432
revealed that there were no significant correlations between spindles (density, amplitude and
433
duration) and offline performance improvements for the total spindles bandwidth at Cz, or
434
slow spindles at Fz for young and older participants (all p > 0.05). However, for fast spindles
435
at Pz, spindle density (r(11) = 0.60, p = 0.030), amplitude (r(11) = 0.614, p = 0.026) and
436
duration (r(11) = 0.69, p = 0.009) were significantly correlated with offline changes in MSL
437
performance in young participants. By contrast, spindle duration was negatively correlated
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with offline performance changes in older participants (r(13) = -0.67, p = 0.006) , whereby
439
larger spindle duration was associated with reduced offline changes in performance.
440
3.6. Structural MRI Results 3.6.1. GM differences in young vs. older participants:
442
As expected, greater GM was observed in young compared to older participants in a
443
widespread (n.b., the size of the significant striatal-cortical cluster in Table 3A) set of brain
444
regions bilaterally including the somatosensory, sensorimotor, premotor, frontal, dorsolateral,
445
medial prefrontal, cingulate, supplementary motor area, parietal, stratum and hippocampus
446
(Figure 2C).
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in young and older participants:
GM in young and older subjects correlated to a different extent with offline changes in
450
performance in the cerebellum (lobules Crus1, IV, IX; Table 3B). In addition, GM in young
451
and older subjects correlated to a different extent with total spindle duration in the cerebellum,
452
the hippocampus as well as the cingulate and parietal cortices (Table 3B). Slow spindle
453
amplitude and duration also correlated to a different extent in young and older adults with GM
454
in the supplementary motor area, and for fast spindle duration in the cerebellum (Table 3B).
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A conjunction analysis was used to identify regions where GM was correlated to a
456
different extent in young vs. older adults with both offline changes in performance and spindle
457
parameters (density, duration and amplitude) for slow, fast and total bandwidth spindles. In
458
young, but not older participants, GM in the hippocampus (bilaterally) and cerebellum were
459
positively correlated with both offline changes in performance and total spindle duration
460
(Table 3C; Figure 2). Furthermore, GM correlated with spindle duration to a different extent
461
when directly comparing young vs. older participants in these same regions (Table 3B).
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Figure 2. Upper: Significant clusters of grey matter (GM) positively correlated with offline changes in performance in conjunction with Cz spindle duration in the hippocampus and cerebellum for young (red) participants (see Table 3C): GM in the hippocampus was (A) positively correlated with offline changes in performance and (B) spindle duration in young (filled circles) but not older (open circles) participants, which was (C) also higher on average for young as compared to older participants (see Table 3A). This difference in GM between young and older subjects correlated with both spindle duration and offline changes in MSL performance in the hippocampus and cerebellum (Table 3B). Results significant at p<0.001, uncorrected. Note: Only significant clusters and scatterplots shown for Cz spindles duration, as these clusters overlapped completely with significant regions for other spindle parameters (amplitude, density) for either fast or slow spindles (see Table 3 for details). X, Y Z coordinates given in voxel coordinates, displayed in radiological orientation.
479
Over the course of normal, healthy aging, sleep quantity and quality is reduced (Bixler
480
et al., 1984; Buysse et al., 1992; Carrier et al., 2011, 2001; Crowley et al., 2002; Darchia et
481
al., 2003; Feinberg et al., 1967; Floyd et al., 2007; Fogel et al., 2012; Gaudreau et al., 2001;
482
Landolt and Borbely, 2001; Martin et al., 2012; Ohayon et al., 2004; Peters et al., 2014;
483
Redline et al., 2004; Zepelin and McDonald, 1987). The functional consequences of this for
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4. DISCUSSION
20
ACCEPTED MANUSCRIPT learning and memory are just starting to become clear. Specifically, recent studies have
485
shown that for simple, explicitly known motor sequences, older adults are able to learn at a
486
normal rate. Yet their ability to consolidate the memory trace compared to younger individuals
487
is impaired (Fogel et al., 2014; Mander et al., 2015, 2013a, 2013b; Pace-Schott and Spencer,
488
2011; Spencer et al., 2007; Wilson et al., 2012). Interestingly, the preservation of MSL during
489
training in older adults appears to be specific to explicitly known sequences. By contrast,
490
implicit probabilistic motor sequence learning is impaired with age (Howard and Howard,
491
2013; Simon et al., 2011), and a growing body of literature suggests that sleep may not be
492
involved in the enhancement of implicit motor skills, even in younger adults (Nemeth et al.,
493
2010). These results suggest that while explicit MSL is unaffected by age, age has an impact
494
on the offline consolidation/stabilization of the newly learned explicit motor skill. In other
495
words, older individuals do not derive the same benefit as younger individuals from sleep for
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the consolidation of newly learned explicit motor skills.
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In terms of brain functional activation related to MSL and sleep-dependent
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consolidation, recent work from our group, in the same set of participants reported here, has
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revealed that a spindle-related reduction of activity in the putamen, cerebellum, parietal and
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temporal cortex may underlie this age-related, sleep-dependent memory deficit (Fogel et al.,
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2014). Consistent with this finding, others have demonstrated that grey matter is also reduced
502
with normal aging, with the most pronounced changes in the thalamus, hippocampus,
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striatum, frontal cortex, motor cortical regions and cingulate cortex (Fjell and Walhovd, 2010;
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Good et al., 2001b; Gunning-Dixon et al., 1998; Raz et al., 2015, 2005). Here we investigated
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if the changes in brain structure related to spindles and offline motor sequence memory
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consolidation may help to explain why older adults do not derive the same benefit from sleep
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as younger adults for the consolidation of motor skills. More specifically, we explored whether
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age-related reductions of grey matter integrity relate to spindle characteristics (e.g., density,
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ACCEPTED MANUSCRIPT amplitude, duration) and overnight, sleep-dependent offline improvements in motor skill
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performance. We found that a widespread network of brain regions had reduced GM in older
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compared to young participants, including motor cortical regions, the hippocampus, striatum,
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cingulate, dorsolateral, medial prefrontal, temporal, parietal, frontal regions of the neocortex.
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Interestingly, only age-related differences in GM in the cerebellum were correlated with offline
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changes in MSL performance following a daytime nap. By contrast, sleep spindles correlated
515
with age-related changes in GM in a well-known set of brain regions which support motor
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sequence memory consolidation (Doyon and Benali, 2005; Doyon and Ungerleider, 2002;
517
Doyon et al., 2003) including the hippocampus, parietal, cerebellum and supplementary motor
518
area. However, when both offline changes in MSL performance and sleep spindles were
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considered together, grey matter in the hippocampus and cerebellum was correlated to a
520
greater extent in young vs. older subjects with both sleep spindles and sleep-related changes
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in performance. Thus, suggesting that sleep spindles may serve as markers for age-related
522
changes in GM in the hippocampus and cerebellum, regions which support sleep-related
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memory consolidation for motor skills. These results are in line with previous work showing
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that sleep-dependent consolidation of skills are related to increased functional activation of
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the cortico-striatal-hippocampal and cortico-cerebellar network in young healthy adults
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(Albouy et al., 2015; Debas et al., 2010; Fogel et al., 2014; Walker et al., 2005). This is also
527
consistent with our recent work in the same set of participants as the current study, showing
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that sleep had a differential effect on functional brain activity from the MSL training session to
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the retest session (retest-training) in the hippocampus and cerebellum between young and
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older adults (Fogel et al., 2014). More specifically, young adults that slept and older adults
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that were awake had increased activations, whereas young adults that were awake and older
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adults that slept had decreased BOLD activity in the hippocampus, cerebellum and cortical
533
regions recruited during MSL (e.g., parietal and frontal cortex). This pattern of activity
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ACCEPTED MANUSCRIPT paralleled the offline consolidation of MSL performance, thus suggesting that increased
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hippocampal activity supports memory consolidation in young but not older adults. The
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current study extends these results, providing the first evidence that structural grey matter
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differences in regions correlated with sleep spindles may underlie age-related motor skill
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memory consolidation deficits. Moreover, our results suggest that age-related GM atrophy in
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regions such as the cerebellum and hippocampus, in particular (Kennedy and Raz, 2005; Raz
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et al., 2015) may underlie spindle-related motor sequence memory consolidation deficits.
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Although still controversial, age-related changes in grey matter may affect spindle
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generating mechanisms that depend on synchronization of the neocortex (Bonjean et al.,
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2012; Contreras and Steriade, 1996; Timofeev and Chauvette, 2013). Decreased spindle size
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and frequency may be the result of cortical cell loss associated with the aging process. It has
545
been suggested that age-related loss of neurons may lead to a reduction of large-scale
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synchronization, thereby affecting the size and frequency of sleep spindles (Crowley et al.,
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2002; Nicolas et al., 2001). Consistent with this suggestion and a previous study by our group
548
(Martin et al., 2012), it was observed that sleep spindle duration was reduced in older
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participants for spindles compared to younger participants, although unlike the current study,
550
no age-difference was previously observed for spindle frequency. However, similar to Landolt
551
et al. (Landolt and Borbely, 2001; Landolt et al., 1996) slow spindles were slower at frontal
552
regions in older participants with no change in faster frequencies. It should be noted that the
553
method of spindle detection and characterization used in the present study (Ray et al., 2015)
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does not employ a minimum duration criteria, as we have demonstrated that the majority of
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spindles are below the minimum criteria conventionally used to visually identify spindles.
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Despite this, no spindles below 0.2 sec were identified. However, this is an important
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methodological consideration, since not including spindles <0.5sec, may bias or
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underestimate age-related changes in spindles. Thus the results of the current study, suggest
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that with age, the relationship between grey matter and spindles is negatively impacted, and
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related to a reduction in the sleep-related benefit to memory consolidation. Consistent with previous reports (Fogel et al., 2014; Pace-Schott and Spencer, 2011;
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Spencer et al., 2007), and despite the fact that performance in older participants was slower
563
overall as compared to younger participants, older participants exhibited the same pattern of
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increasing performance over the course of the training session as the young participants. This
565
suggests that despite slower motor execution, explicit motor sequence learning remains
566
largely intact with age except under greater task complexity and implicit MSL (Howard and
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Howard, 2013; King et al., 2013; Rieckmann et al., 2010; Simon et al., 2011). However,
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following a retention interval filled with sleep, older individuals do not derive the same benefit
569
as younger participants who experienced offline consolidation/stabilization (Fogel et al., 2014;
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Pace-Schott and Spencer, 2011; Spencer et al., 2007).
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In conclusion, our results suggest that age-related changes in sleep spindles are
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differentially related to grey matter in young and older individuals in brain regions that support
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sleep-dependent memory consolidation for motor skills. In this way, sleep spindles can serve
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as a biological marker for structural brain changes in grey matter and their associated
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memory deficits in older adults.
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5. ACKNOWLEDGEMENTS
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The authors would like to acknowledge the technical support of Vo An Nguyen, Laura Ray,
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Andre Cyr, Carollyn Hurst, Frederic Jeay, and Amel Bouyoucef. This research was funded by
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a Canadian Institutes of Health research (CIHR) grant to author JD and fellowship support to
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author SF from the Natural Sciences and Engineering Research Council of Canada (NSERC),
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the Fonds de Recherche du Quebec (FRSQ).
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11.51 60.98 15.41 76.39 12.10
2.36 2.86 3.10 2.74 2.61
20.14 67.40 7.01 74.41 5.45
4.15 3.33 2.61 3.96 2.73
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Stage 1 % TST Stage 2 % TST SWS % TST NREM % TST REM % TST
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Table 1. Mean (and standard error; SE) sleep parameters recorded during the 90-minute daytime nap retention interval in both young and older participants. Young Older M SE M SE t p Wake (min) 19.85 3.44 47.09 7.47 -3.31 0.004 * Stage 1 (min) 7.87 1.36 6.18 0.76 1.28 0.270 Stage 2 (min) 43.31 2.28 31.02 5.22 2.16 0.044 * SWS (min) 11.45 2.41 4.60 1.75 2.35 0.027 * NREM (min) 54.77 3.13 35.62 6.20 2.76 0.012 * REM (min) 9.44 2.10 4.20 2.18 1.72 0.098 Total sleep time (min) 72.08 3.56 46.00 7.26 3.23 0.004 * 0.084 0.162 0.047 0.684 0.093
*
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Sleep onset latency (min) 11.92 1.91 18.40 5.26 -1.16 0.263 Wake after sleep onset (min) 10.87 2.69 31.13 6.79 -2.77 0.012 * Total recording time (min) 94.87 0.74 93.76 0.73 1.06 0.297 Sleep efficiency (%) 76.05 3.85 49.31 7.83 3.06 0.006 * Note: * indicates p < 0.05, df = 26, corrected for heterogeneity of variance where appropriate. Abbreviations: Minutes of sleep (min), Total sleep time (TST), rapid eye movement sleep (REM).
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Table 2. Number, density (#/min NREM), duration (sec) and amplitude (uV ) and frequency (Hz) of NREM sleep spindles at Fz (slow: 11-14Hz), Cz (total: 11-17Hz) and Pz (fast: 14-17Hz) during the 90min daytime nap retention interval in young and older participants. Young Older M SE M SE t p Number Fz (slow) 193.38 17.27 149.73 26.61 1.99 0.394 Cz (total) 251.54 21.31 186.13 31.73 1.65 0.336 Pz (fast) 99.85 15.86 70.73 15.59 1.85 0.219 Density (#/min) Fz (slow) 3.45 0.22 4.07 0.14 -3.44 0.099 Cz (total) 4.51 0.25 5.21 0.15 -2.39 0.144 Pz (fast) 1.78 0.26 1.94 0.21 -0.68 0.651 Duration (sec) Fz (slow) * 0.50 0.02 0.38 0.02 5.19 0.005 Cz (total) * 0.48 0.03 0.34 0.02 3.55 0.004 Pz (fast) * 0.47 0.02 0.40 0.02 3.09 0.045 Amplitude (uV2) Fz (slow) 62.88 8.06 41.68 5.02 3.24 0.132 Cz (total) 90.18 12.32 67.24 8.12 1.50 0.298 Pz (fast) 67.78 11.07 74.39 11.70 -0.58 0.696 Frequency (Hz) Fz (slow) * 12.50 0.04 12.23 0.04 6.57 0.000 Cz (total) 13.36 0.07 13.29 0.10 0.55 0.585 Pz (fast) 14.69 0.04 14.83 0.07 -2.72 0.177 Note: * indicates p < 0.05, two-tailed. df = 26, corrected for homogeneity of variance, where appropriate and Holm-Bonferroni corrected for multiple comparisons for the number of sites.
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B) YOUNG vs. OLDER: GM CORRELATED WITH MSL & SPINDLES MSL Right Left
MSL MSL
Cerebellum (IV) Cerebellum (IX,X)
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Spindles Left Left Left Left Right Right Left
Total spindle duration Total spindle duration Total spindle duration Total spindle duration Slow spindle amplitude Slow spindle duration Fast spindle duration
Cerebellum (IV) Hippocampus Cingulate cortex Parietal Supplementary motor area Supplementary motor area Cerebellum (IV)
Cerebellum (IX) Cerebellum (IIX) Hippocampus
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Table 3. Brain regions with (A) significantly greater GM in young vs. older participants, (B) GM correlated with offline changes (MSL) in performance in conjunction with spindles in young vs. older, (C) young and (D) older participants. X, Y Z coordinates given in voxel coordinates, in radiological orientation. L/R Covariates of interest Brain Region Voxels t p x y z A) YOUNG vs. OLDER Bilateral None Striatal-Cortical-Hippocampal 43200 6.98 0.0002* 46 82 47
0.002 0.002
35 53
39 47
25 15
439 439 245 11 22 23 225
6.13 5.40 5.22 4.63 4.30 4.7 4.69
0.001 0.001 0.001 0.001 0.001 0.001 0.001
61 65 52 66 33 33 59
47 52 43 35 66 66 29
21 27 55 64 66 67 26
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2.16 2.1 2.17
0.018 0.029 0.030
53 28 61
44 40 50
12 24 28
918 10 90 16
2.68 2.16 2.17 1.94
0.006 0.009 0.002 0.017
62 53 61 62
34 44 50 52
20 12 28 28
MSL & spindle conjunction: Left MSL & total spindle duration Right MSL & total spindle duration Left MSL & total spindle duration
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C) YOUNG: GM CORRELATED WITH MSL & SPINDLES
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MSL & spindle conjunction: Bilateral MSL & total spindle duration Bilateral MSL & total spindle duration Left MSL & total spindle duration Bilateral MSL & fast spindle density
Cerebellum (crus I, IV) Cerebellum (IX) Hippocampus Hippocampus
No significant voxels
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Results from total spindles from Cz, slow spindles from Fz and fast spindles from Pz presented. Note: * indicates FWE-corrected p-value significant at p < 0.05. Coordinates for side where effect was maximal reported when bilateral.
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Grey matter related to spindles and offline gains in young vs. older individuals
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Spindles may serve as a biological marker for age-related memory deficits
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1. All authors disclose that: (a) There are no actual or potential conflicts of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the work submitted that could inappropriately influence (bias) their work. (b)The author's institutions have no contracts relating to this research through which it or any other organization may stand to gain financially now or in the future. (c) No other agreements of authors or their institutions that could be seen as involving a financial interest in this work. 2. This research was funded by a Canadian Institutes of Health research (CIHR) grant to author JD and fellowship support to author SF from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds de Recherche du Quebec (FRSQ).
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3. The results reported in the manuscript being submitted have not been previously published, have not been submitted elsewhere and will not be submitted elsewhere while under consideration at Neurobiology of Aging.
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4. Ethical and scientific approval was obtained from the Ethics Committee at the “Institut de Geriatrie de Montreal”, Montreal, Quebec, Canada.
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5. All authors have had the opportunity to review the contents of the manuscript being submitted, approve of its contents and validate the accuracy of the data.