Resting-state functional connectivity in normal brain aging

Neuroscience and Biobehavioral Reviews 37 (2013) 384–400

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Neuroscience and Biobehavioral Reviews journal homepage: www.elsevier.com/locate/neubiorev

Review

Resting-state functional connectivity in normal brain aging Luiz Kobuti Ferreira a,b,∗ , Geraldo F. Busatto a,b,1 a b

Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, SP, Brazil Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, SP, Brazil

a r t i c l e

i n f o

Article history: Received 2 October 2012 Received in revised form 17 December 2012 Accepted 8 January 2013 Keywords: Aging Elderly Functional magnetic resonance imaging fMRI Resting state Functional connectivity Cognition Memory Attention

a b s t r a c t The world is aging and, as the elderly population increases, age-related cognitive decline emerges as a major concern. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), allow the investigation of the neural bases of age-related cognitive changes in vivo. Typically, fMRI studies map brain activity while subjects perform cognitive tasks, but such paradigms are often difficult to implement on a wider basis. Resting-state fMRI (rs-fMRI) has emerged as an important alternative modality of fMRI data acquisition, during which no specific task is required. Due to such simplicity and the reliability of rsfMRI data, this modality presents increased feasibility and potential for clinical application in the future. With rs-fMRI, fluctuations in regional brain activity can be detected across separate brain regions and the patterns of intercorrelation between the functioning of these regions are measured, affording quantitative indices of resting-state functional connectivity (RSFC). This review article summarizes the results of recent rs-fMRI studies that have documented a variety of aging-related RSFC changes in the human brain, discusses the neurophysiological hypotheses proposed to interpret such findings, and provides an overview of the future, highly promising perspectives in this field. © 2013 Elsevier Ltd. All rights reserved.

Contents 1. 2. 3.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic principles and current applications of resting-state fMRI (rs-fMRI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age-related decreases in resting-stated functional connectivity (RSFC) as assessed in rs-fMRI studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Focus on the DMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Diminished functional connectivity in other brain systems: salience, motor and visual networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Decreases in RSFC are not simply the reflection of brain atrophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Age and RSFC decline: concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Findings of aging-related increased RSFC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RSFC: friend or foe? Results from rs-fMRI studies investigating correlations between brain connectivity and behavioral measures . . . . . . . . . . . Causal hypotheses for age-related functional connectivity changes in healthy populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Reduced white matter integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Dopaminergic deficits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Amyloid-␤ deposition as a potential cause of RSFC abnormalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1. Amyloid-␤ and functional connectivity: bidirectional causality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current limitations of RSFC studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1. Sample recruitment issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2. Neuroimaging acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3. Clinical translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4. Indexing: “functional connectivity” as an essential keyword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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∗ Corresponding author at: Centro de Medicina Nuclear, 3◦ andar, LIM-21, Rua Dr. Ovídio Pires de Campos, s/n, Postal code 05403-010, São Paulo, SP, Brazil. Tel.: +55 11 2661 8132; fax: +55 11 26618193. E-mail addresses: [email protected] (L.K. Ferreira), [email protected] (G.F. Busatto). 1 Centro de Medicina Nuclear, 3◦ andar, LIM-21, Rua Dr. Ovídio Pires de Campos, s/n, Postal code 05403-010, São Paulo, SP, Brazil. Tel.: +55 11 2661 8132; fax: +55 11 26618193. 0149-7634/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neubiorev.2013.01.017

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Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction Population aging has been pervasive and enduring over the past decades (United Nations, 2002). Such demographic change is leading to an unprecedented increase in the prevalence of disorders characterized by prominent cognitive deficits such as Alzheimer’s disease (the most common cause of dementia), as well as other neurodegenerative disorders. Normal aging itself is also associated with cognitive decline, affecting most often the domains of attention, memory and executive functioning (Hedden and Gabrieli, 2004; Whalley et al., 2004). Age-related cognitive decline has an impact on quality of life (Abrahamsom et al., 2012) and life satisfaction (St John and Montgomery, 2010). The impact of such decline is expected to increase in the next years due to the rise of life expectancy. Because cognitive health is not simply the absence of disease and cognitive functioning is essential for overall well-being (CDC, 2011), promoting healthy brain aging has been increasingly emphasized (Williams and Kemper, 2010). While great efforts have been dedicated to unravel the pathophysiology of Alzheimer’s disease (Huang and Mucke, 2012), much less attention has been given to the underpinnings of cognitive deficits that may emerge during non-pathological aging. Though a number of hypotheses have been proposed to explain the cognitive decline of healthy elderly individuals, we are still unable to prevent such deficits and their neural basis is yet to be fully clarified (Craik and Rose, 2012; Whalley et al., 2004). Advancing knowledge about the aging brain and age-related cognitive decline in healthy humans may provide important clues to deepen our understanding about the neural basis of neurodegenerative diseases. Contemporary functional neuroimaging techniques provide excellent opportunities for investigating the aging human brain in vivo (Grady, 2008). Functional magnetic resonance imaging (fMRI) has been the most appealing of such methods, allowing investigators to non-invasively map neural activity changes while subjects perform motor, sensory, cognitive or emotion-provoking tasks (Huettel et al., 2009). This methodology has been applied in several studies of older adults (Spreng et al., 2010), although the interpretation of findings has been often difficult due to the considerable degree of inter-individual variations in task compliance and/or performance during fMRI scanning (Huettel et al., 2009). Nowadays, it is also possible to study human brain functioning using fMRI when subjects are not performing any particular task, and this is known as resting-state fMRI (rs-fMRI). Fluctuations in regional brain activity can be detected across separate brain regions during rest, and the patterns of intercorrelation between the functioning of these regions is measured, affording quantitative indices of resting-state functional connectivity (RSFC) (Biswal et al., 1995). Abnormal patterns of RSFC have been recently investigated across a variety of neuropsychiatric disorders, and there is growing evidence that such abnormalities may potentially provide valid and reliable biomarkers of brain diseases (Broyd et al., 2009; Greicius, 2008). Recently, there has also been a sharp rise in the attention given to brain imaging studies investigating the influence of aging on RSFC (Mevel et al., 2011). Using the search tool of the Web of Knowledge database (isiwebofknowledge.com July 2012), a total of 854 articles are retrievable containing the keywords fMRI, “functional connectivity” and “resting state”. Almost a quarter of those studies were published in the biennium 2008–2009, while half appeared

in 2010–2011. Adding the words “aging OR elderly” to the search, a total of 151 articles emerges; 29% of those were published in the first semester of 2012 and 48% were published in 2010–2011, while the papers from all previous years represented only 23%. This clearly indicates that fMRI research on RSFC has gained momentum in recent years, with a significant proportion of studies dedicated to the investigation of elderly populations (Fig. 1). Capitalizing on such growing body of rs-fMRI studies, the present article aims to: describe the basic principles of rs-fMRI methods; review recent studies that have documented a variety of aging-related RSFC changes in the human brain; discuss the neurophysiological hypotheses proposed to interpret such findings; and, finally, provide an overview of the future perspectives in this field. 2. Basic principles and current applications of resting-state fMRI (rs-fMRI) Magnetic resonance imaging (MRI) is nowadays the technology most often used to acquire tridimensional images of biological tissues in vivo, and fMRI is a modality of this technology that allows the investigation of brain activity. This is based on the principle that local changes in neuronal activity are associated with changes in oxygenated and deoxygenated hemoglobin concentrations, given that there is an increase in the regional inflow of oxygenated blood provided by the vascular system in regions of increased neuronal activity. Because oxygenated and deoxygenated hemoglobin present different magnetic properties, activity-related changes in the proportion between oxygenated and deoxygenated hemoglobin can be detected by MRI. This is referred to as the blood oxygenation level-dependent (BOLD) effect, which allows inferences to be made about neuronal activity. By using fast-acquiring MRI methods (the most popular being echo-planar imaging), it is possible to acquire images of the whole brain in just 2 or 3 s with reasonable spatial resolution (∼2 mm) (Huettel et al., 2009), thus displaying subtle BOLD signal changes over time. Typically, subjects are asked to perform specific tasks during fMRI data acquisitions, so that researchers can examine the brain activity changes induced when subjects are performing the tests. More recently, resting-state fMRI (rs-fMRI) protocols have been developed to investigate brain functioning in human subjects while not engaged in any specific task. In rs-fMRI studies, differently from task-related fMRI protocols, subjects are asked to rest quietly for several minutes while brain images are acquired. The rationale behind this approach is that the brain, rather than being idle during “rest”, displays instead vigorous and persistent functional activity (Buckner et al., 2008). Such activity may be detected as spontaneous low-frequency (<0.1 Hz) BOLD signal fluctuations in rs-fMRI studies (Van Dijk et al., 2010). Interregional correlations of these fluctuations can be estimated, and these quantitative estimates provide measures of functional connectivity (which is defined as the temporal correlation between neurophysiological measurements obtained in different brain areas) (Friston et al., 1993). In rs-fMRI studies, these estimates are referred to as resting-state functional connectivity (RSFC). Increasing evidence suggests that coherent intrinsic brain activity is an important feature of healthy brain functioning (Fox and Raichle, 2007; van den Heuvel et al., 2009). The coherent activity of functionally related brain areas can be captured as temporally correlated fluctuations in BOLD signal during resting-stated fMRI acquisitions, as shown in the initial rs-fMRI studies conducted by

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Fig. 1. Relative number of published articles retrieved using the keywords fMRI, “functional connectivity”, “resting state” and “aging OR elderly” (Web of Knowledge, Thomson Reuters, July 2012). The vertical axis represents the number of articles published in the selected years divided by the total number of articles retrieved when using the keywords in the search function of the Web of Knowledge database (isiwebofknowledge.com). fMRI, functional magnetic resonance imaging; FC, functional connectivity; RS, resting state.

Biswal and colleagues in the 1990s (Biswal et al., 1995; Biswal, 2012). This concept is illustrated in Fig. 2. Typical rs-fMRI acquisition protocols require the subject to lie still during 5–8 min. Subjects may be asked to keep their eyes closed or opened, or to stare at a fixation cross (Van Dijk et al., 2010). Using computational methods, the imaging data acquired can be subsequently analyzed using several different approaches, among which the most common are: (1) seed-based analysis, when the BOLD signal time course of a predefined region of interest is tested for significant correlations with the signal detected in other

Fig. 2. Single-subject time courses of brain activity fluctuations in the medial prefrontal cortex, posterior cingulate cortex and bilateral occipital cortex, and their intercorrelations. Data from a 6-min long resting-state fMRI acquisition session from a single subject were analyzed after standard preprocessing steps (cardiorespiratory noise reduction, motion correction, slice timing correction, spatial normalization and band-pass temporal filtering at 0.01 ∼ 0.08 Hz). Spherical seeds (radius of 4 mm) were used to extract the temporal time courses from the bilateral occipital cortex, mPFC and PCC. Pearson’s correlation coefficients (r) were calculated. There was a very high correlation between the left and right occipital cortices (r = 0.87), a moderate correlation between the PCC and mPFC (r = 0.48), and no significant correlation between the PCC and the occipital cortex (r = 0.03). mPFC, medial prefrontal cortex; PCC, posterior cingulate cortex.

regions of the brain; (2) independent component analysis (ICA), which is designed to identify patterns of connectivity across the brain without the need to define an a priori region of interest; this technique is aimed at identifying the signals unique to each brain network, allowing the determination of the spatial distribution of functionally discrete networks (Fig. 3); and (3) graph theory methods, which employ mathematical tools to describe the networks’ properties (such as its major hubs) and to provide measures of local and global connectivity (for methodological reviews, see Fox and Raichle, 2007; Margulies et al., 2010; van den Heuvel and Hulshoff Pol, 2010 and controversies in Cole et al., 2010a). With any of those three approaches, it is possible to characterize functional brain networks and also to perform quantitative between-group comparisons – for instance between a group of patients and a demographically matched healthy control group, or between samples of healthy elderly subjects and young adults. Finally, in addition to the analysis methods described above, there has been recent interest in the use of pattern classification methods that allow inferences to be made at an individual level. Using machine learning approaches (such as support vector machine), an algorithm is developed using a training imaging dataset (e.g. patients versus controls) and then the algorithm is tested for its accuracy in determining from which group a new subject comes from. By providing characterization of brain imaging patterns at the individual level, such approach has a high clinical translation potential (Orru et al., 2012). Since rs-fMRI is non-invasive and does not require the subject to perform cognitive tasks during image acquisition, its use is substantially simpler than other functional neuroimaging methods. Using rs-fMRI, it is possible to detect multiple brain networks presenting consistent intercorrelations of low-frequency activity, including: the primary sensorimotor network; the primary visual and extrastriate visual networks; frontoparietal attention networks; and the default-mode network (DMN), which is directly associated with episodic memory retrieval, self-referential processes, social cognition and mind wandering (see Section 3.1 “Focus on the DMN” below) (De Luca et al., 2006; van den Heuvel and Hulshoff Pol, 2010). Functional connectivity abnormalities in one or more of those networks may be found in subjects with neuropsychiatric disorders relative to healthy controls, and show significant correlations with the degree of behavioral changes and cognitive deficits

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Fig. 3. Default mode network from a healthy subject obtained with independent component analysis. Data from a 6-min long resting-state fMRI acquisition session of a healthy adult underwent standard preprocessing steps (motion correction, slice timing correction, spatial smoothing and high-pass temporal filtering). Subsequently, independent component analysis, (ICA) was performed using the software MELODIC (FMRIB Software Library; http://www.fmrib.ox.ac.uk/analysis/research/melodic/; Beckmann and Smith, 2004), and the independent components (i.e. spatially independent patterns of brain activity) were inspected. The figure displays a component compatible with the default-mode network (DMN), which is relevant for memory and self-referential processing and includes the medial prefrontal cortex, precuneus/posterior cingulate cortex and inferior parietal lobule.

in these patients (Broyd et al., 2009; Greicius, 2008). Indeed, it has been possible to apply the rs-fMRI approach to study a wide range of neurological and psychiatric disorders to date, such as Alzheimer’s disease (Damoiseaux et al., 2012), dementia with Lewy bodies (Galvin et al., 2011), frontotemporal dementia (Whitwell et al., 2011), epilepsy (Luo et al., 2012), Parkinson’s disease (Kwak et al., 2010), stroke (Park et al., 2011), depression (Sheline et al., 2010b), schizophrenia (Cole et al., 2010b), obsessive-compulsive disorder (Fitzgerald et al., 2011), attention-deficit/hyperactivity disorder (Fair et al., 2010) and Tourette’s disorder (Church et al., 2009), among others. Across a broad spectrum of populations, rs-fMRI studies have been shown to afford reliable and replicable results (Buckner et al., 2009; Meindl et al., 2010; Shehzad et al., 2009; Zuo et al., 2010a). Specifically in cognitively healthy elderly samples, such reliability has been supported by a recent test-retest study which evaluated different methods for data processing, including seed-based, ICA and graph theoretical approaches (Guo et al., 2012). Given the great feasibility of acquiring rs-fMRI data, RSFC abnormalities associated with the above disorders are considered candidate disease biomarkers to be used in clinical settings in the future, both for diagnostic and treatment monitoring applications (Chou et al., 2012; Koch et al., 2012; Van Dijk and Sperling, 2011). Another advantage of rs-fMRI is that independent datasets can be pooled together for aggregate analysis in large-sized samples; accordingly, a recent rs-fMRI study of 1093 healthy adults has shown a high degree of concordance among 24 centers (Biswal et al., 2010). This has facilitated international collaboration between different research groups and large-scale data-sharing, such as the 1000 Functional Connectomes Project (http://fcon 1000.projects.nitrc.org/). It should be noted that functional connectivity studies of healthy aging have been performed not only using rs-fMRI but also using task-based fMRI approaches. However, this review article concentrates specifically on rs-fMRI studies, based on the reliability and superior feasibility of fMRI data acquired at rest, as well as the potential clinical applicability of this methodology. Moreover, because the performance of cognitive and emotional tests during fMRI data acquisition modulates functional connectivity (Albert et al., 2009; Harrison et al., 2008), age effects on functional connectivity during tasks are bound to be much more varied and complex to interpret. Furthermore, functional connectivity patterns during cognitive or emotion-provoking paradigms may be task-specific, and therefore, data from task-based fMRI investigations have limited comparability across studies. 3. Age-related decreases in resting-stated functional connectivity (RSFC) as assessed in rs-fMRI studies Age is a significant determinant of inter-individual variability in RSFC (Allen et al., 2011; Biswal et al., 2010; Whitfield-Gabrieli and

Ford, 2012), and findings regarding age-related decreases in RSFC usually attract considerable attention (Grady, 2012). Such findings have been found to be topographically heterogeneous, similarly to the non-uniform profile of cognitive decline associated with normal aging (which affects most intensely the domains of attention, memory and executive functioning) (Buckner, 2004). As discussed below, age-related decrements in RSFC preferentially affect the DMN and the dorsal attention network; these networks, not surprisingly, are thought to be critically implicated in attention, memory and executive functions (van den Heuvel and Hulshoff Pol, 2010). 3.1. Focus on the DMN Overall, the DMN has been the most investigated resting-state network in fMRI studies to date. The DMN comprises a set of heteromodal cortical regions consistently found to be active at rest, including the medial prefrontal cortex, the inferior parietal lobule, the hippocampus and the posterior cingulate cortex/retrosplenial cortex/precuneus (a set of components also referred to as the posteromedial cortex) (Buckner et al., 2008; Raichle et al., 2001). The DMN has been the main focus of much of the aging-related fMRI research to date, for two main reasons. First, the DMN includes two key areas implicated in Alzheimer’s disease, namely the posterior cingulate cortex and the hippocampus (Ferreira and Busatto, 2011; Mevel et al., 2011). Moreover, amyloid deposition (a hallmark of Alzheimer’s disease, as discussed in Section 6.3. “Amyloid-␤ deposition as a potential cause of RSFC abnormalities” below) is seen initially and most prominently in brain regions encompassed in the DMN, such as the hippocampus and the posterior cingulate cortex (Buckner et al., 2008), and such deposition may be found even in subsets of asymptomatic or minimally cognitively impaired elderly adults (Sperling et al., 2009). Most of the rs-fMRI studies that reported aging-related decrements in RSFC to date have focused on the DMN (Achard and Bullmore, 2007; Batouli et al., 2009; Bluhm et al., 2008; Damoiseaux et al., 2008; Esposito et al., 2008; Koch et al., 2010; Tomasi and Volkow, 2012; Wang et al., 2010). In general, these studies indicate that aging is associated with decreased connectivity within this system (Hafkemeijer et al., 2012; Mevel et al., 2011; Prvulovic et al., 2011). Though less common, there have also been studies showing both positive and negative associations between RSFC and age (Jones et al., 2011). This is possibly due to methodological differences across separate studies, especially regarding variations in the techniques used for data analysis in each investigation (Jones et al., 2011). Moreover, one study to date reported absent effects of aging on RSFC in the DMN (Westlye et al., 2011), but the sample of this investigation included only elderly and middle-aged adults (with no inclusion of a group of young adults). The non-significant results obtained in this study may reflect the dynamics of age-related

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alterations in brain connectivity: RSFC patterns in middle-aged adults may resemble much more those of elderly individuals than young adults (Zuo et al., 2010b); thus, studies that compare elderly subjects against middle-aged adults may be underpowered to detect more subtle age-related connectivity differences, especially regarding the DMN (Onoda et al., 2012). Damoiseaux et al. (2008) applied the ICA approach to characterize the DMN and other networks in a set of rs-fMRI data of young adults and elderly individuals, comparing these two groups in regard to their patterns of resting-state brain activity. Elderly subjects were found to present decreased activity in the DMN relative to the young group, and there was also a significant inverse correlation between current age and levels of activity within this network in the elderly group (Damoiseaux et al., 2008). Interestingly, no significant findings were identified when between-group differences were investigated in other brain networks (such as those implicated in visual processing, working memory, motor function, auditory processing and executive functioning). This suggests a preferential susceptibility of the DMN to the aging effects on RSFC. Patterns of age-related decreased connectivity strength regarding the medial prefrontal cortex and the posterior cingulate cortex have also been demonstrated by Batouli et al. (2009) using the ICA approach to compare young and older adults, as well as by Wu et al. (2011) in a seed-based rs-fMRI study. Similar results were identified in one other rs-fMRI investigation that also applied the ICA approach, with resulting DMN maps statistically weaker in elderly subjects relative to young adults (Esposito et al., 2008). Moreover, negative correlations between age and RSFC were identified either when the overall DMN was inspected, or when analyses were restricted to the anterior cingulate and left medial prefrontal cortices. The connectivity of the anterior cingulate cortex presented the strongest negative correlation with age in this study; conversely, the linear correlations between other DMN regions and age failed to reach statistical significance (Esposito et al., 2008). These findings underscore the fact that, although the DMN is characteristically vulnerable to aging effects, such influence is not homogenous across the separate components within this network. In a sample restricted to young and middle-aged healthy adults, Bluhm et al. (2008) investigated age-related changes in DMN applying both the ICA approach and a region of interest (ROI)-based analysis method (using a seed in the posterior cingulate cortex). They documented significant negative correlations between age and RSFC in the posterior cingulate cortex/precuneus using both analysis methods, as well as in the left medial prefrontal cortex only when the ROI-based method was used (Bluhm et al., 2008). A possible explanation for the different results emerging from the ROI and ICA-based analyses performed in this study may be the greater vulnerability of ROI-based methods to non-neural effects on functional brain connectivity (i.e. cardiorespiratory noise influencing fMRI data) when compared to ICA (Cole et al., 2010a). Such possibility, however, is yet to be directly tested before one can draw conclusions about the causes of such different results. DMN differences between young and middle-aged adults were also reported in other recent investigations, being taken as suggestive that decreases in RSFC begin during middle age (Evers et al., 2012); this is in line with evidence that cognitive abilities start to decline at middle age in humans (Hedden and Gabrieli, 2004). The data-driven ICA approach was also applied in one other rsfMRI study by Koch et al. (2010), in which older adults were found to present diminished RSFC in the posterior cingulate cortex and, to a lesser degree, in the anterior cingulate cortex, compared with young adults. This further corroborates the concept that the DMN is preferentially affected during aging (Koch et al., 2010). However, it is relevant to note that these authors also performed volume of interest analysis using DMN regions as seeds, and could not

find between-group statistical significant differences with the latter methodology. This highlights that the age-related RSFC decline in the DMN is subtle, and that different methods of data processing can have a great impact on the results of such investigations. In this study, a volume-of-interest approach was used to estimate RSFC between pairs of regions included within the DMN, while the ICA identified the whole DMN for each subject, thus affording distinct RSFC estimates. Therefore, the between-group differences resulting from the ICA analysis referred to whole-network characteristics, whereas the volume-of-interest method provided estimates of RSFC between specific pairs of brain regions (Koch et al., 2010). Besides from the ICA and seed-based studies cited above, graph theory approaches have also been used to study age effects on RSFC. The notion of age-related disconnectivity within the DMN has been corroborated by a study that applied graph theoretical techniques to characterize the modular organization of human brain functional networks at rest (Meunier et al., 2009). The authors compared young adults and elderly individuals and found that the latter subjects presented fewer connections between posterior cortical and frontal regions, which is in keeping with observations from other resting-state fMRI studies that applied either ICA analysis methods (Jones et al., 2011) or seed-based methods (Li et al., 2009). In line with these findings, a linear effect of age associated with resting functional disconnectivity between medial frontal and parietal regions has been recently described in a sample of adults including individuals with a wide age range (19–80 years) (Mevel et al., 2013). Moreover, a large number of task-related fMRI studies documented a consistent association between aging and anterior–posterior cortical disconnection (Andrews-Hanna et al., 2007; Bollinger et al., 2011; Campbell et al., 2012; Grady et al., 2012, 2010; Kalkstein et al., 2011; Sambataro et al., 2010; Voss et al., 2010a). In a sample of 913 healthy subjects from the above-mentioned 1000 Functional Connectomes Project, one other research group applied a different approach – functional connectivity density analysis – to estimate long- and short-range RSFC. This method involves the computation of correlations between the BOLD time series of a given voxel and other voxels in the brain and, then, counting the number of functional connections regarding each brain voxel (Tomasi and Volkow, 2012). The authors demonstrated an age-related decline in RSFC within the DMN (posterior cingulate cortex, precuneus and ventral prefrontal cortex), especially regarding long-range connections. Additionally, they found that age-related disconnectivity occurs also in the dorsal attention network, a set of brain regions comprising the prefrontal, anterior cingulate and posterior parietal cortices (Tomasi and Volkow, 2012). Moreover, this study indicated that long-range connections are more prone to age effects than short-range connections. The authors speculated that “longer fibers may be more vulnerable to degeneration than shorter ones [. . .]. However, a limitation on our interpretation [. . .] is the fact that long-distance functional connectivity also relies on polysynaptic circuits, which may not necessarily entail longer fibers” (Tomasi and Volkow, 2012). The association between aging and deficient long-range connections has also been reported in a study that used indices derived from graph theory to measure RSFC (Toussaint et al., 2011), and also from a support vector machine study aimed at classifying young versus older subjects using rs-fMRI data (Meier et al., 2012).

3.2. Diminished functional connectivity in other brain systems: salience, motor and visual networks Age-related RSFC deficits have been reported not only in the DMN but also in several other brain regions such as the salience and motor networks (Allen et al., 2011; Onoda et al., 2012).

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A very recent study found that age was negatively correlated with RSFC between the anterior cingulate cortex and bilateral insula (Onoda et al., 2012). These regions have been related to a salience network, which is supposed to participate in the integration of sensory data from multiple modalities and in decision-making processes (Seeley et al., 2007). Based on their findings regarding this system, the authors stressed the importance of not restricting the studies of RSFC to the DMN (Onoda et al., 2012). Changes in the motor network of elderly individuals were the focus of a study based on graph theory (Wu et al., 2007a). The authors found that elderly subjects presented decreased RSFC in left premotor area and right cingulate motor cortex (anterior cingulate cortex). Moreover, there were significant negative correlations between the degree of RSFC in these regions and the reaction time in a motor task (the greater the measures of RSFC, the faster the motor response). The authors suggested that normal aging disrupts the functioning of the motor network: at rest, the motor areas involved in preparation and planning of movements might not be at an optimally ready condition – as evidenced by the decreased RSFC – thus resulting in longer reaction times (Wu et al., 2007a). The same group also performed additional investigations using regional homogeneity analysis. This technique measures the similarity of the time series of a given voxel to those of its nearest neighbors, assuming that within a functional cluster, the BOLD signal variation of each voxel is synchronous with that of its neighbors (Wu et al., 2007b). The authors focused on motor-related regions and found age-related decreases in regional homogeneity bilaterally at the supplementary motor area, primary motor cortex, premotor area, thalamus, putamen, globus pallidus and cerebellum (Wu et al., 2007b). On the other hand, subsequent investigations on RSFC patterns of the motor cortex in a seed-based study showed mixed results, with older adults being found to display both increases and decreases in RSFC (Langan et al., 2010; Tomasi and Volkow, 2012). However, results from these studies are not directly comparable to each other due to substantial methodological differences. For instance, while Wu et al. (2007b) applied the regional homogeneity method, Langan et al. (2010) investigated the motor cortex using a seed-based approach and Tomasi and Volkow (2012) applied functional connectivity density analysis to assess long- and short-range estimates of RSFC. Differences in RSFC between young and older adults have also been reported in a study focused on the cost-efficiency of brain networks at rest (Achard and Bullmore, 2007). In this article, Achard and Bullmore (2007) used wavelet-based connectivity analysis methods and a novel metrics of network efficiency. They reported global deleterious effects of older age on functional networks in the brain, with such decreased network performance being localized to the middle frontal and inferior temporal gyri; these findings were discussed as suggestive that older people present a “topological marginalisation of medial temporal and frontal regions”. Finally, subtly decreased RSFC has also been reported in the visual cortex of elderly subjects in one single study (Yan et al., 2011), with no replication of such finding as yet in the literature. 3.3. Decreases in RSFC are not simply the reflection of brain atrophy Older subjects consistently display gray matter atrophy in several foci throughout the brain, and this may have a significant impact on patterns of decreased brain activity as detected in functional neuroimaging investigations (Curiati et al., 2011; Kalpouzos et al., 2012). Therefore, controlling for gray matter volume can increase the reliability of the results of rs-fMRI studies (He et al., 2007; Sheppard et al., 2011). Several rs-fMRI studies have addressed this issue, and decreased RSFC in the DMN of elderly subjects has been found to persist after correction for gray

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matter volume, thus showing that decreased resting-state activity of older subjects cannot be simply explained by locally decreased gray matter volume (Damoiseaux et al., 2008; Onoda et al., 2012). 3.4. Age and RSFC decline: concluding remarks As shown above, several lines of rs-fMRI evidence indicate that significant RSFC changes occur during the normal aging process. The most consistent of such changes is a pattern of decreased functional connectivity in components of the DMN, the most investigated higher-order brain network to date. This is consistent with the “last-in-first-out” hypothesis according to which late-maturing brain regions (such as heteromodal association cortices) are more vulnerable to the deleterious effects of aging (Grieve et al., 2005; Kalpouzos et al., 2009; Terribilli et al., 2011). There is also evidence suggesting aging-related RSFC decline in attention networks, motor networks and other systems, but additional studies are needed in order to further substantiate these findings. 4. Findings of aging-related increased RSFC Although RSFC decreases are the most common pattern reported in rs-fMRI studies of the elderly population, findings of increased functional connectivity related to aging have also been described (Biswal et al., 2010). For instance, a recent rs-fMRI study that used a graph theory analysis approach showed increased functional interaction within frontal and parietal networks (both involved in attention), evidenced by a higher degree of intra-network connectivity in elderly individuals compared to young controls (Toussaint et al., 2011). As aging effects in the brain preferentially affect longrange connections, one possible interpretation of these findings is that hubs which are relatively more isolated could reach a status of increased local connectivity (Meunier et al., 2009). Although age-related RSFC decreases have been reported in motor and subcortical networks (as discussed above) (Allen et al., 2011; Wu et al., 2007a), increased functional connectivity in these brain regions have also been frequently found (Allen et al., 2011; Biswal et al., 2010). In a study that included rs-fMRI data of 913 healthy subjects from the ‘1000 Functional Connectomes Project’ repository (Tomasi and Volkow, 2012), aging-related increases in both long- and short-range functional connectivity were detected in the somatosensory and motor cortices, cerebellum and brainstem. Further evidence linking aging and increased RSFC in the motor network came from a recent support vector machine classifier study (Meier et al., 2012). Based on resting-state fMRI data from healthy younger and older adults, the classifier was 84% accurate in distinguishing between these two groups. Interestingly, the sensorimotor regions provided the greatest contribution to the accuracy of the classifier – with the elderly group presenting stronger connections involving these brain regions (Meier et al., 2012). Also, an ICA-based rs-fMRI study of a group of healthy individuals ranging from 17 to 58 years of age reported increased RSFC in the left superior frontal gyrus and thalamus in those with greater ages (Bluhm et al., 2008). These findings provide further evidence suggesting that age-related RSFC changes in the motor and subcortical networks are different from those that occur in higher-order systems such as the DMN and the dorsal attention network (Tomasi and Volkow, 2012). Zuo et al. (2010b) studied the homotopic RSFC (i.e. the degree of synchrony in functional activity between interhemispheric corresponding regions during rest) and showed that global homotopic RSFC decreased over the course of childhood and adolescence. This is expected due to the increasing hemispheric specialization for cognitive functions that occurs as part of maturation processes in

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the brain. However, homotopic RSFC increased later in life (from middle adulthood onwards), characterizing a U-shaped curve with an inflection point occurring at the age of 53 years (Zuo et al., 2010b). These authors also found that the age of inflection of the Ushape curves varied across separate brain regions following a highto low-level order of neural development, as follows: heteromodal cortex (at 49 years), limbic regions (at 50 years), unimodal cortex (at 53 years), paralimbic cortex (at 55 years), primary somatosensory (at 56 years) and subcortical nuclei (at 58 years). Therefore, the findings regarding age-related increases in homotopic RSFC support the notion that those brain regions that develop during later stages of brain maturing are the first to present age-related degeneration (Grieve et al., 2005; Kalpouzos et al., 2009; Terribilli et al., 2011). Age-related increases in homotopic RSFC may also be taken to support the hypothesis that aging is associated with an attenuation of the known functional hemispheric asymmetry (related to a corresponding loss of lateralization of cognitive functions), possibly due to aging-related dedifferentiation processes or compensatory functional brain responses (Cabeza, 2002). The fact that both decreases and increases in RSFC may be found in elderly individuals suggests that the interpretation of age-related RSFC changes is not straightforward (Tomasi and Volkow, 2012). The true nature and implications of the age-related increases in RSFC described herein are not yet clear. There are several possible interpretations for such findings, including: compensatory responses to decreased functioning of other, critical brain networks, as mentioned above; aging-related developmental changes in the architecture of brain networks; or a reflection of brain degenerative effects due to aging, such as gray and white matter changes, neurotransmitter decline, or even non-neural factors such as changes in cerebrovascular reactivity and changes in cardiorespiratory rhythms. These interpretations highlight the fact that greater connectivity does not necessarily imply better task performance (see next section on results from connectivity–behavior fMRI studies). Age-related increases in functional connectivity are intriguing and certainly warrant further investigation.

5. RSFC: friend or foe? Results from rs-fMRI studies investigating correlations between brain connectivity and behavioral measures In addition to comparing RSFC patterns between younger and older adults and testing for associations between age and RSFC patterns, a number of rs-fMRI studies have also addressed the possible relationship between RSFC and performance in neuropsychological tasks carried out off the fMRI scanning environment. In non-elderly adults, studies of this kind have reported significant positive correlations between RSFC and task performance in several different neuropsychological domains, including: working memory performance and RSFC between the anterior and the posterior cingulate cortices (Hampson et al., 2006); performance on the Trail Making Test (a task that assesses working memory and resistance to distraction) and RSFC within executive control networks (Seeley et al., 2007); creativity and RSFC involving the medial prefrontal cortex (Takeuchi et al., 2012); and reading competence and RSFC between the left precentral gyrus and other motor cortical regions, as well as between Broca’s and Wernicke’s areas (Koyama et al., 2011). More recently, fluency in recalling autobiographical events was found to be positively correlated with the degree of RSFC between the posterior cingulate gyrus and the lateral and medial temporal cortices in a sample comprised of young, middle-aged and older normal adults (Mevel et al., 2013). The authors of these studies often suggest that greater RSFC reflects the integrity of higher-order brain networks, a requirement of effective cognitive abilities. These investigations correlating brain

connectivity with behavioral measures have further attested the biological relevance of RSFC, and provide a useful means to investigate associations between stable patterns of brain functioning and variance in relevant behaviors (Fox et al., 2007). In addition to the above findings, results regarding connectivity–behavior correlations specifically in elderly life have also been reported. The above-mentioned Trail Making Test is a neuropsychological test of attention, executive functioning and processing speed in which elderly individuals perform worse than younger subjects (Damoiseaux et al., 2008). Performance in this test was negatively correlated with RSFC in the anterior DMN in a group of elderly individuals, but not in young subjects (Damoiseaux et al., 2008). In one other rs-fMRI study of a group of elderly subjects, RSFC between the hippocampus and the posterior cingulate cortex was found to be positively correlated with performance on a memory task (Wang et al., 2010). The authors noted that worse-performing elderly individuals presented very low positive correlation values between the hippocampus and the posterior cingulate cortex. Interestingly, there were no such patterns when performance in non-memory tasks was tested in the same individuals, suggesting a specificity of the posterior cingulate cortex–hippocampal connectivity to memory functioning. These results provide additional support to the notion that the DMN is vulnerable to aging effects. They also reinforce the importance of this network to memory performance, as the posterior cingulate cortex is a major hub of the DMN and the hippocampus is considered a component of this network (Buckner et al., 2008). One other recent rs-fMRI study found that performance in the recall of word lists was positively associated with RSFC between two components of the DMN: the medial prefrontal cortex and the left inferior parietal cortex (He et al., 2012). Taken together, such findings further indicate that specific fluctuations in intrinsic brain activity may have a critical impact on memory performance, possibly providing a potential biomarker of failing integrity of memory systems. Recently, scores on the Frontal Assessment Battery (a series of tasks commonly used to test frontal lobe functioning), added to scores on an intelligence test, were positively correlated with RSFC between the dorsal anterior cingulate cortex and the insula – components of a network supposed to be related to motivation and attention (Onoda et al., 2012). The authors included both young and older adults in their sample (age range 36–86; n = 73), and correlation findings remained significant after correction for gray matter atrophy. Contrary to the above findings, one recent rs-fMRI study has described significant negative correlations between cognitive performance and RSFC in a healthy elderly group using a grammar learning task (Antonenko et al., 2012). This investigation illustrates the importance of taking into account both the method of analysis and the behavioral measures employed off the scanning session when interpreting the results of such studies. The analysis of RSFC was seed-based and focused on the bilateral Brodmann area 44/45 (including Broca’s area). Connectivity between the left and right seeds showed a negative correlation with grammar learning performance. The authors suggested that such task depends on a highly lateralized degree of brain functioning; thus, “greater functional correlation between bilateral prefrontal areas might be explained by a lack of inhibition between functionally connected prefrontal brain regions” and in this specific task, “reduced interhemispheric coupling might be beneficial for performance” (Antonenko et al., 2012). Further evidence showing that decreased RSFC may be associated with better performance in elderly subjects was provided by one other rs-fMRI study that correlated resting connectivity in subcortical structures and verbal memory performance; the authors found negative correlations between memory scores and RSFC within the thalamus and basal ganglia (Ystad et al., 2010).

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Although the above results seem rather contradictory at first, they may be judged as actually complementary after closer examination. In order to fully grasp the meaning of connectivity–behavior relationships, readers should always have in mind two critical questions: “where in the brain?” (i.e. which structures were included in the analysis) and “which behavior was evaluated?” (and how). Thus greater RSFC represents more efficient brain networks in a number of conditions, but not invariably. An overly simplistic view – “the more, the better” – is not supported by current evidence and may lead to misinterpretation of findings. Actually, recent rsfMRI findings support a view that not only functional connectivity but also functional segregation play relevant roles fostering efficient behavior. 6. Causal hypotheses for age-related functional connectivity changes in healthy populations A number of hypotheses have been formulated to explain age-related functional connectivity deficits associated with cognitive decline in healthy elderlies. Three potential causal factors have been most often implicated, including: loss of white matter integrity, dopaminergic deficits, and amyloid deposition. Taken together, these three aspects may be responsible for the emergence of cognitive decline during aging. In the following section, we review findings related to each of these phenomena and their possible relationship with functional connectivity deficits in the aging brain. 6.1. Reduced white matter integrity Convergent evidence supports the notion that RSFC reflects the underlying architecture of anatomical connectivity (van den Heuvel et al., 2009), and that it is positively correlated with indices of structural connectivity (Damoiseaux and Greicius, 2009). Patterns of in vivo structural brain connectivity can be nowadays investigated using the magnetic resonance imaging modality named as diffusion tensor imaging (DTI). This technique is based on the principle that water diffusion measures are sensitive to the integrity of tissue microstructure being therefore usable to study neural fiber tracts (Beaulieu, 2002). The DTI technique has been used to demonstrate that cognitively healthy elderlies present decreased white matter integrity, in direct proportion to the degree of cognitive decline (Sullivan and Pfefferbaum, 2006; Sullivan et al., 2010; Voineskos et al., 2012; Zahr et al., 2009). It is noteworthy that variations in white matter integrity are especially correlated with abilities of information processing and executive functioning, two aspects of cognition that are consistently reported to decline during healthy aging (Madden et al., 2012, 2009). Based on these findings, it has been suggested that age-related cognitive decline “arises from functional disruption in the coordination of large-scale brain systems that support cognition” (Andrews-Hanna et al., 2007; O’Sullivan et al., 2001). This idea is supported by studies that have shown a negative correlation between DTI indexes of white matter integrity and patterns of functional connectivity within specific brain networks (as assessed either with task- and rs-fMRI acquisition protocols), not only in non-elderly adults (Andrews-Hanna et al., 2007; Damoiseaux and Greicius, 2009) but also in older populations (Chen et al., 2009; Davis et al., 2012; Teipel et al., 2010). In the study by Teipel et al. (2010), these results remained significant after controlling for age, thus indicating the existence of an specific association between structural and functional brain disconnectivity that is not completely explained by the effect of age on these variables (Teipel et al., 2010). Regions where the structural–functional associations were shown in the elderly are those included in the DMN (connected by

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neural fibers between its component regions) (Teipel et al., 2010), as well as the prefrontal cortices (bilaterally connected by the genu of the corpus callosum) (Chen et al., 2009; Davis et al., 2012). White matter hyperintensities are a very common MRI finding in the elderly population, both in clinically normal and cognitively impaired older adults, and are considered a biomarker of white matter injury (Soderlund et al., 2003). An MRI study that combined measures of functional connectivity and volume of white matter hyperintensities showed that subjects with severe white matter lesions presented reduced functional connectivity in a circuitry related to the dorsolateral prefrontal cortex (Mayda et al., 2011). On the other hand, one study could not find associations between RSFC within the DMN and volume of white matter hyperintensities (He et al., 2012). It should be noted that although white matter hyperintensities are thought to reflect cerebrovascular-related brain injury (DeCarli et al., 2005), quantification of these lesions is considered a less specific index of diminished structural white matter integrity than DTI estimates (Gouw et al., 2008). Moreover, DTI estimates have been shown to be a more sensitive biomarker of white matter integrity loss than measures of white matter lesions (Vernooij et al., 2008). In conclusion, an increasing amount of evidence supports the disconnection hypothesis of the healthy aging brain (O’Sullivan et al., 2001), according to which there are detrimental changes in the functional integration of brain networks in elderly individuals due to a progressive loss of white matter integrity. Thus loss of structural white matter connectivity may lead to decreased functional connectivity, reflecting less effective neural systems. Finally, it has been suggested that structural disconnectivity may lead not only to decreases but also increases in functional connectivity (Meunier et al., 2009). For instance, it has been shown that age is associated with segregation between major hubs of a fronto-parietal network and also to an increase in local connectivity within these hubs (Meunier et al., 2009). We can hypothesize that, when structural and functional connectivity between two regions become compromised, brain plasticity may lead to increases in connectivity between other regions as a possible compensatory mechanism. On the other hand, another possibility is to understand age-related disconnectivity in light of the dedifferentiation process that is thought to occur in the elderly brain (Cabeza, 2002). Support for this hypothesis was provided by the finding that homotopic brain regions become more synchronous in the elderly (Zuo et al., 2010b), as we discussed before. A recent study that found both decreased structural connectivity and increased functional connectivity in patients with multiple sclerosis – a disease that affects the white matter tracts – also discussed this issue (Hawelleka et al., 2011). The authors suggested that, when, increased functional connectivity is accompanied by structural disconnectivity it may: (1) represent less-differentiated patterns of neural activity, (2) determine reduced cognitive efficiency and (3) be a manifestation of anatomical disconnectivity leading to loss of functional diversity among brain networks (Hawelleka et al., 2011). However, information is still scarce and future studies should investigate the mechanisms underpinning age-related increases in functional connectivity. 6.2. Dopaminergic deficits It is well established that aging is associated with declines in dopaminergic neurotransmission as demonstrated by studies measuring dopamine D2 receptor density, transporter availability and dopamine concentration in the brain (Hedden and Gabrieli, 2004). Dopaminergic deficits have been associated with worse cognitive performance as shown in studies using experimental models in animals, as well as in pharmacological investigations evaluating effects of dopaminergic blockade in young adults, and in neuroimaging

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studies focused on elderly populations (Backman et al., 2010, 2006; Goh, 2011; Park and Reuter-Lorenz, 2009). Moreover, studies that combined task-fMRI and molecular imaging using positron emission tomography (PET) have shown that age-related changes in dopaminergic activity may contribute to alterations in BOLD response during cognitive processing (Backman et al., 2010). The relationship between patterns of functional connectivity at rest and indices of dopaminergic transmission has also been shown by a number of independent rs-fMRI studies (Diaconescu et al., 2010; Honey et al., 2003; Krajcovicova et al., 2012; Nagano-Saito et al., 2008; Wallace et al., 2011). One study enrolled healthy young adults who performed an fMRI-adapted version of the Wisconsin Card Sorting Task before and after acute dopamine depletion (ingestion of amino acid drinks) in a randomized, double-blind design. The authors found that dopamine depletion caused impairments in frontostriatal functional connectivity suggesting that corticostriatal connectivity is under great influence of dopaminergic transmission (Nagano-Saito et al., 2008). An interesting study investigated both the effects of aging and pharmacological dopaminergic blockade using sulpiride on functional connectivity of brain networks (Achard and Bullmore, 2007). Using a placebo-controlled crossover design the authors found that D2 receptor blockade caused a decrease in brain network efficiency. They noticed that a similar decline in network performance was also found when comparing elderly with young adults but the effects of age were even more extensive. Thus, they concluded that “attenuated dopamine transmission may contribute to age-related impairments in functional network efficiency, but it seems likely that additional mechanisms must be involved” (Achard and Bullmore, 2007). Further evidence was provided by a more recent study that replicated the finding of decreased estimates of caudate D1 receptor density in the elderly and also showed a positive association between these molecular imaging estimates and functional connectivity in a working memory network that includes the dorsolateral prefrontal cortex (Rieckmann et al., 2011). Aging is associated with increased striatal uptake of the radiotracer 6-[18 F]fluoro-l-mtyrosine (FMT) – a substrate of a dopamine-synthesizing enzyme (aromatic amino acid decarboxylase) – and this finding has been considered an evidence for non-optimal dopamine processing (Braskie et al., 2008). A recent study found that caudate FMT uptake was increased in the elderly and showed that functional connectivity in the caudate was negatively correlated with caudate FMT signal (Klostermann et al., 2012). This represents additional evidence linking dopaminergic dysfunction and decreased functional connectivity in the elderly. Finally, a genetic study focused on a dopamine-related gene, DARPP-32, provided further evidence between dopamine and connectivity (Meyer-Lindenberg et al., 2007). In conclusion, it seems very likely that age-related dopaminergic dysfunction plays an important role in determining functional connectivity disturbances. These studies suggest that age-related changes in dopaminergic transmission contribute to age-related reductions in functional connectivity and that the impact of dopamine transmission is possibly greater within frontostriatal circuits. 6.3. Amyloid-ˇ deposition as a potential cause of RSFC abnormalities Amyloid-␤ is the product of sequential processes of proteolytic cleavage of the amyloid precursor protein by the enzymes ␤- and ␥-secretases (Evin and Weidemann, 2002). This proteolytic process occurs in the normal brain and it is suggested that an imbalance between production and clearance of amyloid-␤ may lead

to the pathological cascade of Alzheimer’s disease (Querfurth and LaFerla, 2010). Amyloid-␤ oligomers are synaptotoxic and neurotoxic (Bu, 2009; Selkoe, 2008) and their deposition in extracellular brain tissue is considered a hallmark of Alzheimer’s disease (Hardy and Selkoe, 2002). However, studies have shown that a significant proportion of healthy older adults may present substantial amyloid burden in brain tissue (Aizenstein et al., 2008), and there is evidence that such amyloid accumulation in non-demented individuals may be associated with cognitive deficits (Rentz et al., 2011; Rodrigue et al., 2012; Villemagne et al., 2008b). Finally, increased amyloid-␤ levels cause abnormal synaptic activity and lead to large-scale network instabilities (for a review see Palop and Mucke, 2010). In recent years, the development of PET techniques for molecular imaging has allowed investigations of patterns of amyloid deposition in the living human brain, using intravenously injected, radiolabeled ligands that bind specifically to amyloid aggregates (Rabinovici and Jagust, 2009). This technology is being increasingly used in multimodal neuroimaging research, for instance in studies that have shown direct associations between amyloid burden and: gray matter atrophy as assessed with morphometric MRI in cognitively healthy older adults (Becker et al., 2011; Jack et al., 2009; Oh et al., 2011); aberrant task-induced brain activation as assessed with fMRI in healthy elderlies (Sperling et al., 2009; Vannini et al., 2012); and abnormal fMRI findings in patients with Alzheimer’s disease (Sperling et al., 2010). Recently, this PET imaging modality has also been applied to investigate associations between amyloid burden in the brain and abnormal changes in RSFC. Hedden et al. (2009) used the firstly developed and most often employed amyloid PET ligand – Pittsburgh Compound B – and also acquired rs-fMRI data from clinically normal elderlies. Their analyses were controlled for age and brain atrophy, and they found that those individuals with highest amyloid burden presented significant RSFC reductions within the DMN (Hedden et al., 2009). The authors noted that there was no significant association between the degree of amyloid deposition in the brain and neuropsychological test performance, and they suggested that amyloid accumulation may lead to brain functional changes (possibly compensatory) but not necessarily to significant cognitive impairment at such stage (Hedden et al., 2009). Subsequently, other groups replicated the finding of a significant association between amyloid deposition and RSFC decrements within the DMN in healthy elderlies, especially regarding the precuneus and posterior cingulate cortex (Drzezga et al., 2011; Mormino et al., 2011; Sheline et al., 2010c). One of these studies also reported RSFC increases in a subsystem of the DMN, involving the prefrontal cortex and temporal cortex (Mormino et al., 2011). The authors interpreted this finding as suggestive that brain networks may respond differently to amyloid accumulation: connectivity patterns between medial parietal regions (precuneus), the posterior cingulate cortex and the medial temporal cortex would decrease, while connectivity between the dorsal prefrontal cortex and the lateral temporal cortex would increase (Mormino et al., 2011). Whether such connectivity increment represents compensatory activity, excitotoxicity or other phenomenon, is still a matter of debate. The finding that RSFC may be abnormal in clinically normal elderly individuals harboring amyloid burden has also fostered hope that such rs-fMRI patterns may prove to be clinically useful as a biomarker for preclinical Alzheimer’s disease in the future (Sperling et al., 2011; Vemuri et al., 2012). It should be noted that, although amyloid deposition may have a role in causing RSFC changes, it has been shown that functional disconnectivity within the DMN also occurs in elderly subjects with no signs of amyloid brain deposition as assessed with PET imaging (Andrews-Hanna et al., 2007).

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6.3.1. Amyloid-ˇ and functional connectivity: bidirectional causality? As discussed above, amyloid deposition has been proposed to be critical to the emergence of brain connectivity disturbances. On the other hand, it has also been suggested that high functional connectivity between specific brain regions may play a role in amyloid pathogenesis (Jagust and Mormino, 2011). The deposition of amyloid aggregates occurs initially and preferentially in certain brain regions such as the hippocampus and the posterior cingulate cortex and researchers have noted the striking spatial correlation between active brain regions during rest and amyloid deposition (Bero et al., 2012; Buckner et al., 2005). Insights regarding the determinants of such anatomical preference have been recently attained. Interestingly, they point to a role of DMN activity in Alzheimer’s disease pathogenesis. Using an Alzheimer’s disease model in mice, it has been shown that neuronal activity is associated with higher amyloid-␤ production and causes an increase in interstitial amyloid-␤ levels, leading to subsequent amyloid-␤ aggregation (Bero et al., 2011). Such findings have been interpreted as suggesting that “regional differences in basal neuronal activity levels govern region-specific amyloid deposition through long-term regulation of steady-state interstitial fluid amyloid-␤ concentration” (Bero et al., 2011). Accordingly, other studies have shown that synaptic activity regulates amyloid␤ levels in mice (Cirrito et al., 2005). It is interesting to note that, in normal conditions, the brain regions comprising the DMN present intense metabolic activity especially when the subject is not engaged in any goal-directed activity (Buckner et al., 2008; Raichle et al., 2001; Vlassenko et al., 2010). Because amyloid deposition is most prominent in two regions of the DMN – namely the posterior cingulate cortex and the hippocampal formation – a possible topographic relationship between high neuronal activity throughout life within the DMN and amyloid deposition has been suggested (Bero et al., 2011; Buckner et al., 2005). There is evidence that amyloid-␤ aggregates can seed amyloidosis (Eisele et al., 2010). Thus, the early amyloid deposition in the hippocampus and posterior cingulate cortex – possibly modulated by activity within the DMN – could kickstart subsequent amyloid aggregation in adjacent brain regions and this could evolve to widespread cerebral amyloidosis (Bero et al., 2011). This hypothesis is in accordance with the sequential topographic distribution of cerebral amyloidosis, as well characterized by both post-mortem and neuroimaging studies (Braak et al., 1999; Villemagne et al., 2008a). Additional evidence linking DMN activity to early Alzheimer’s disease pathogenesis has also been provided by rs-fMRI investigations showing that the APOE4 allele – the best characterized genetic risk factor for non-familial Alzheimer’s disease – is related to increased coactivation within the DMN in healthy young adults (Filippini et al., 2009). Moreover, the APOE4 allele has been associated with higher DMN RSFC in healthy middle-aged and older adults (Westlye et al., 2011). Still, these findings should be balanced against mixed results from other fMRI studies that showed that the relationship between APOE4 and RSFC is not homogenous throughout the DMN (Fleisher et al., 2009; Sheline et al., 2010a). Overall, the above findings have led a number of authors to suggest that the relationship between amyloid-␤ and functional connectivity in the brain is bidirectional: regions of higher RSFC are more prone to amyloid deposition, and amyloid accumulation leads to changes in functional connectivity (Bero et al., 2012). In summary, increasing evidence suggests that white matter integrity, dopaminergic dysfunction and amyloid deposition all contribute to functional connectivity changes in the elderly brain (Fig. 4). As noted recently by He et al. (2012), although significant advances in this field have been achieved, we are still unable

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Fig. 4. Causal hypotheses for the functional connectivity changes associated with non-pathological brain aging. Age-related changes in RSFC are thought to reflect declines in structural connectivity (white matter integrity), dopaminergic system dysfunction, and/or amyloid deposition in the brain. The latter phenomenon is possibly mediated by high levels of brain activity throughout the lifespan. RSFC, resting-state functional connectivity.

to conclude “whether functional connectivity reduction is simply a downstream result of injury to the underlying neuronal architecture, a functional consequence of neuropathology on cognition independent of structural injury to neurons, a background factor that helps to buffer the brain against the effects of neuronal injury, or some combination of all these factors” (He et al., 2012). There are high hopes that ongoing and future research will help to unravel these issues. 7. Current limitations of RSFC studies There are several limitations concerning the investigation of the aging brain using fMRI techniques (D’Esposito et al., 2003) and also related to the analysis of rs-fMRI data (Cole et al., 2010a). Such limitations are discussed in detail in the following paragraphs. RSFC relies on measures of BOLD signal, which is actually an indirect estimate of neural activity. Such estimate depends on neurovascular coupling – the process whereby neural activity influences the hemodynamic properties of the surrounding vasculature (Margulies et al., 2010). Aging is supposed to change cerebrovascular dynamics (for instance, due to reduced vascular reactivity, atherosclerosis and tortuosity of vessels), so that differences between age groups in BOLD signal may – at least in part – reflect non-neural alterations (for a review see D’Esposito et al., 2003). Therefore, results from fMRI studies that compared young and older adults should be interpreted in light of this limitation. Another issue refers to noise reduction. It is well known that BOLD time courses may include non-neural sources of noise, such as those caused by cardiorespiratory processes (van Buuren et al., 2009). Such noise is especially problematic in functional connectivity studies because it leads to spurious correlations (Birn et al., 2006). Therefore, rs-fMRI studies usually add precautionary steps to prevent the occurrence of such false positive findings. These steps include the acquisition of cardiorespiratory data during fMRI acquisitions (e.g. electrocardiogram and abdominal band), and the use of such physiological data during the preprocessing pipeline of images in order to minimize the cardiorespiratory noise in the BOLD signal (van Buuren et al., 2009). Alternatively, investigators may remove sources of spurious covariance using estimates of whole-brain average signal (“global signal”) or signals from the cerebrospinal fluid and white matter as these are supposed to reflect physiological noise and are characterized by fluctuations unlikely to be related to neuronal activity (Birn et al., 2006; Fox et al., 2005). It should be noted that the use of methods for physiological noise correction may have a significant impact on the final results of rs-fMRI studies (Chang and Glover, 2009).

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During resting-state fMRI acquisitions, subjects remain in a state of unconstrained cognitive processing. It is therefore possible that different individuals may present different covert behavior (e.g. thinking, imagining, and feelings), and this is generally not controlled for in rs-fMRI studies. It is known that functional connectivity varies depending on the patterns of cognitive engagement during fMRI data acquisiton (Albert et al., 2009; Harrison et al., 2008). If covert behavior during rs-fMRI acquisitions is consistently different between groups, then RSFC findings may be affected by such differences in behavior (Doucet et al., 2012). Therefore, in general, it is not possible to exclude the possibility that differences in spontaneous thoughts exist between older and younger subjects and this may complicate the interpretation of rs-fMRI results. However, evidence against this possibility was recently reported: using a semi-structured interview to evaluate participants’ subjective experience during rs-fMRI data acquisition sessions, a study found no effect of age on inner experience during scanning in a sample of 69 adults (age range 19–80 years) (Mevel et al., 2013). Although much has already been published in this field (see “Introduction”), we still do not know the precise physiological basis and behavioral significance of RSFC. Functional connectivity may be a marker of effective coordinated information processing but its biological significance is still a matter of debate (Fingelkurts et al., 2005). During standard preprocessing steps of image analysis, rs-fMRI studies in general use software to correct for head motion. However, even after such corrections, head motion effects may be misinterpreted as neuronal effects in functional connectivity analyses (Van Dijk et al., 2012). This issue is important in the field of aging, since older subjects – as well as children – tend to move more substantially inside the scanner than young adults (Van Dijk et al., 2012). A recent investigation addressing this issue demonstrated not only a significant correlation between motion and age but also between RSFC and motion, underscoring the importance of minimizing motion during image acquisition and implementing methods to correct for movement (Mowinckel et al., 2012). An additional concern regards to how subjects should keep their eyes (open or closed) during image acquisition. In most rs-fMRI studies, pre-scanning instructions are given to subjects regarding whether they should keep their eyes opened at rest, closed or opened looking at a fixation cross. Although all these conditions are generally regarded as “rest”, it is known that RSFC can be affected by these different “visual tasks” (Van Dijk et al., 2010). Therefore, differences in pre-scanning instructions may represent another important source of between-study noise. Moreover, people frequently fall asleep while lying still in the scanner, and this may affect functional connectivity (Fukunaga et al., 2008; Horovitz et al., 2008; Tagliazucchi et al., 2012). Although frequent, the “try not to fall asleep” instruction can be difficult to follow, especially when subjects lie with eyes closed. When debriefing the participant after scanning, asking whether he/she fell asleep is an alternative (e.g. Damoiseaux et al., 2008). However, people may fall asleep without realizing it. If groups of subject present different proneness to sleep, part of between-group differences in RSFC can be due to sleep versus wakefulness differences, thus confounding the results. Therefore, if falling asleep is to be avoided, asking subjects to keep their eyes opened (with or without use of fixation cross) should be preferred. As discussed earlier in this article, a common feature of functional imaging studies involves the off-scanning acquisition of behavioral and/or cognitive test measurements in order to allow investigations of the relationship between behavioral performance and RSFC. A possible problem is that if the behavioral testing is performed just before the rs-fMRI session, then RSFC could theoretically be modulated by the testing session (e.g. subjects may keep recalling items from a memory task) (Wang et al., 2010). In

fact, it has been shown that RSFC changes can be affected by sustained cognitive engagement prior to rs-fMRI scanning (Evers et al., 2012; Grigg and Grady, 2010). This has led some authors to state that rs-fMRI acquisitions “occurring immediately following some other experimental manipulation cannot be described as occurring during true, stimulus-unguided rest” (Cole et al., 2010a). Unfortunately, clear methodological descriptions regarding whether behavioral tests were performed before or after rs-fMRI data acquisition are not often provided (Antonenko et al., 2012; Damoiseaux et al., 2008; He et al., 2012; Seeley et al., 2007; Takeuchi et al., 2012; Westlye et al., 2011; Ystad et al., 2010). Finally and most importantly, behavioral evaluations whose scores will be correlated with rs-fMRI data should be performed after imaging data acquisition, in order to ensure that the latter is not influenced by systematic inter-subject differences in prior cognitive engagement (Whitfield-Gabrieli and Ford, 2012). The current review has focused on functional connectivity – the study of temporal correlations of BOLD signal time courses between different brain regions. This approach does not allow inferences to be made about how one brain region influences another. Conversely, analyses of effective connectivity address the directionality of influence between brain regions (Margulies et al., 2010), thus providing additional information regarding causal interactions between brain regions. Such approach has been applied in a modest number of studies of the elderly population to date (Addis et al., 2010; Waring et al., 2013) and these are beyond the scope of the present article. As discussed earlier in this review, there are several different methods for rs-fMRI data analysis, and it is clear that the choice of image processing techniques may have an impact on the results obtained. Therefore, at least part of the discrepancy between findings of different rs-fMRI studies is due to methodological heterogeneity, as well as to the fact that most of these analysis techniques are still under development (Mevel et al., 2011). For instance, a number of authors have applied more than one method for analyzing the same datasets and found that distinct processing pipelines and analytical approaches may afford different results regarding RSFC (e.g. Bluhm et al., 2008; Chang and Glover, 2009; Koch et al., 2010; van Buuren et al., 2009).

8. Future perspectives 8.1. Sample recruitment issues The samples in most of the rs-fMRI investigations discussed in this review have been relatively modest in size. Studies with larger samples shall provide more reliable results, as the inter-subject variability of data in connectivity investigations tends to decrease with increments in sample size (Biswal et al., 2010). Biswal et al. (2010) have recently suggested that rs-fMRI studies with small samples (<50 subjects) have a substantial risk of producing false negative results, recommending that sample sizes in such studies should exceed 100 participants. The majority of the rs-fMRI studies reviewed herein compared groups of elderly subjects with young adults. It is known that a substantial proportion of the cognitively healthy elderly population presents significant brain amyloid load (Rowe et al., 2010). Therefore, it is probable that the overall elderly population presents a combination of subjects with amyloid-positive and amyloidnegative brain patterns. As discussed earlier in this review, amyloid deposition disrupts RSFC (Drzezga et al., 2011; Hedden et al., 2009; Mormino et al., 2011; Sheline et al., 2010c); therefore, healthy samples that are not tested for amyloid brain deposition are likely to include heterogeneous subgroups regarding RSFC patterns, thus decreasing power to detect relevant findings. In the near future,

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a more widespread use of amyloid mapping methods with PET and other molecular imaging advances should allow rs-fMRI investigations of cognitively preserved subjects classified according to their amyloid-free or amyloid-positive status. The rs-fMRI studies included in this review have been based on cross-sectional between-group comparisons. When studying aging, investigators aim at including groups of different ages but with the same distribution of other characteristics such as gender and education, in order to accurately identify changes caused by aging. However, groups that are matched for such demography characteristics may still present differences across generations that may confound the results of neuroimaging investigations (e.g. cultural and environmental differences) (Morgenstern, 1995). Longitudinal studies provide more accurate estimates of age effects on brain structure and functioning, since data from the same individual can be compared at different time points. Longitudinal rs-fMRI studies of healthy aging are eagerly awaited, since we are far from fully understanding the dynamic changes (increases and decreases) in brain RSFC that occur in humans as they age (Damoiseaux, 2012). Additionally, longitudinal rs-fMRI studies may help to predict the cognitive prognosis of healthy aging individuals based on rs-fMRI data, similarly to what has been achieved in recent studies of MCI and Alzheimer’s disease (Prvulovic et al., 2011). 8.2. Neuroimaging acquisition Although important insights about brain functioning have emerged from recent rs-fMRI investigations, a deeper understanding about the aging brain is likely to arise from studies that integrate information from different aspects of brain imaging. For instance, rs-fMRI data can be combined with other neuroimaging modalities such as task-fMRI (Cole et al., 2010a), DTI methods to investigate structural brain connectivity (Greicius et al., 2009; Luk et al., 2011; Ystad et al., 2011) and concurrent EEG-fMRI measurements (Ritter and Villringer, 2006). Finally, the use of rs-fMRI in combination with multiple other imaging biomarkers may allow testing of the value of combined imaging results as surrogates of clinical endpoints in trials of therapies targeting the preclinical stages of Alzheimer’s disease (Sperling, 2011). Due to fMRI constraints in regard to spatial resolution, scanning times and need to reduce scanner noise, the parameters for image acquisition in rs-fMRI studies are often set not to allow for complete whole-brain coverage, often leaving the cerebellum out. However, inclusion of the cerebellum in fMRI acquisitions would be desirable, as this brain structure is nowadays known to be relevant for a variety of cognitive processes (Strick et al., 2009). Findings implicating the cerebellum in RSFC have been reported in rs-fMRI studies of healthy young adults (Grigg and Grady, 2010), patients with mild cognitive impairment (Bai et al., 2011) and subjects with geriatric depression (Wang et al., 2012), as well as in studies investigating brain developmental variations throughout the life span (Zuo et al., 2010b). 8.3. Clinical translation A number of studies have provided indication of the accuracy of rs-fMRI data to help in the diagnosis of Alzheimer’s disease and mild cognitive impairment (for a review see Prvulovic et al., 2011). A very interesting application would be to use the rs-fMRI approach in preclinical stages of cognitive decline, given that functional brain abnormalities are thought to precede clinical impairment by many years (Jack et al., 2010). Sample selection problems are a critical issue in current Alzheimer’s disease trials, and it would be interesting to test RSFC data as a potential predictor of prognosis in the preclinical phase of Alzheimer’s disease (Sperling et al., 2011). If RSFC measurements prove to be a valuable tool to select those

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individuals who are more likely to develop cognitive decline, then rs-fMRI examinations could be employed in clinical trials of disease modifying therapies, allowing the selection of more homogeneous samples and reducing the required number of participants. In trials aimed at modifying brain functioning in elderly individuals, it is interesting to include an fMRI acquisition in order to test whether changes in functional connectivity can be detected in association with the intervention (Prvulovic et al., 2011). In patients with mild cognitive impairment, for instance, task-related fMRI changes have been used to map the effects of therapeutic interventions on the brain (for a review see Simon et al., 2012). Because cognitive decline occurs many years after the initial triggering of the neuropathological processes that characterize Alzheimer’s disease (Jack et al., 2010), it seems plausible to use fMRI to investigate the effects of preventive interventions in older adults without cognitive impairment. For instance, Voss et al. (2010a,b) performed a randomized trial of aerobic exercise versus placebo in older adults and investigated patterns of correlation between aerobic fitness, functional brain connectivity and cognitive functioning. They acquired fMRI data in a group of old age participants and young adults during passive visual stimulation; in this study, moderate exercise was associated with increased functional connectivity in brain regions sensitive to age-related disruption in connectivity. The findings of this study indicated that the profile of functional brain connectivity in elderly subjects after the exercise intervention approximates more closely the pattern of functional connectivity detected in young adults (Voss et al., 2010b). One other recent study acquired rs-fMRI data to measure the brain effects of a comprehensive training program designed to stimulate different cognitive, motor, and sensorial domains in healthy elderly subjects (Pieramico et al., 2012). The program lasted 6 months and after this period the exposed group presented significant changes in the DMN and in the dorsal attention network, which are both crucial to memory and attention (Pieramico et al., 2012). Variables that indirectly affect connectivity can also be investigated in rs-fMRI studies, especially cardiovascular risk factors such as hypertension and neurovascular changes. Cardiovascular riskrelated brain changes are potentially modifiable and represent an opportunity for prevention in the field of cognitive aging (Alves et al., 2010). One recent rs-fMRI study has shown that RSFC between the medial prefrontal cortex and the hippocampus is decreased in adult women with higher fasting insulin levels, a marker of insulin resistance (which is a risk factor for metabolic syndrome and vascular disease) (Kenna et al., 2013). In another center, rsfMRI was acquired from patients with diabetes; RSFC in attention and language networks was decreased when compared to controls, being lowest in patients with microvascular complications (as determined by the presence of proliferative retinopathy, a marker of microangiopathy) (van Duinkerken et al., 2012). These crosssectional studies indicate that RSFC may be useful as an early indicator of abnormalities in brain functioning that could be due to cardiovascular risk factors. Delirium – an acute confusional state – is a common lifethreatening syndrome in older hospitalized patients (Inouye, 2006). Though many risk factors have been identified, it is still difficult to predict who will develop delirium after a hospital admission or a surgical procedure (Noimark, 2009). A recent rs-fMRI study described RSFC abnormalities during episodes of delirium, and also identified that connectivity within the posterior components of the DMN is associated with faster recovery from delirium (Choi et al., 2012). Since RSFC has been considered a biomarker of the integrity of cerebral networks and brain health status, it seems that testing the prognostic value of rs-fMRI data in elderly subjects at risk for delirium is a logical next research step towards the development of algorithms aimed at affording more precise prognoses in these cases.

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8.4. Indexing: “functional connectivity” as an essential keyword Articles reporting temporal correlations between neurophysiological signals from different brain regions should consider including “functional connectivity” as a keyword for indexing purposes. Online databases are pivotal tools for neuroscience investigators, and the accuracy of such databases depends on the terms employed for indexing documents. If the term “functional connectivity” is not included in the fields used by informationretrieval systems, papers lacking this keyword will not be retrieved, and relevant articles may be missed. For instance, when entering “functional connectivity” in PubMed (http://www.ncbi.nlm. nih.gov/pubmed/) or Web of Science (isiwebofknowledge.com), one cannot find the study by Andrews-Hanna et al. (2007) which is very relevant to the field (cited 202 times according to Thomson Reuters – Web of Knowledge, as of September 2012). Future functional connectivity studies should include this term in their title, abstract or keyword for indexing purposes. The use of this term – when appropriate (Fingelkurts et al., 2005) – shall also facilitate communication between researchers. 9. Conclusion Aging is associated with cognitive decline, even in those individuals without a pathological condition such as Alzheimer’s disease. As the world population ages, understanding the neurophysiological basis of such decline comes to the top of the scientific agenda. Functional connectivity analysis of rs-fMRI data has been shown to be a feasible and reliable approach to study the human brain in vivo and is drawing increasing interest from the neuroimaging community. The rs-fMRI studies reviewed in this paper provide strong indication that RSFC provides relevant information regarding the aging effects on brain functioning and cognition. The most consistent age-related change is decreased RSFC in the DMN, which is crucial for memory. Another convergent finding is that the dorsal attention network is also affected by the aging process. Such deficits are thought to mediate cognitive declines in memory and attention. An increasing body of evidence points towards age-related changes in white matter integrity, dopaminergic transmission and amyloid-␤ deposition as potential causes of RSFC modifications in the elderly population. Our review underscores the importance that should be given to methodological issues such as data acquisition, preprocessing steps and data processing approaches in rs-fMRI studies of aging. It is clear that methodological differences across separate studies underlie much of the uncertainty in the field to date. Major advances in data analysis techniques have been achieved and much work is still in progress. It is expected that, in the next few years, current technology will be optimized and new methods will be developed. Besides providing methodological improvements and neurophysiological insights about the effects of aging on the brain, future studies shall also test the utility of rs-fMRI in clinical trials. The relative simplicity of rs-fMRI acquisition allows this methodology to be easily implemented in multicentric studies, and applied in a broad spectrum of populations. Including such measure of brain functioning might provide relevant information that could be used as biomarkers for sample selection, prognosis and/or efficacy estimates in trials aimed at modifying the course of cognitive decline in elderly individuals. Acknowledgments We thank Fabricio R S Pereira for assistance in preprocessing data used in Fig. 2.

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