How attention modulates encoding of dynamic stimuli in older adults

Accepted Manuscript Title: How Attention Modulates Encoding of Dynamic Stimuli in Older Adults Authors: Noga Oren, Irit Shapira-Lichter, Yulia Lerner, Talma Hendler, Nir Giladi, Elissa L. Ash PII: DOI: Reference:

S0166-4328(17)31695-9 https://doi.org/10.1016/j.bbr.2018.02.031 BBR 11306

To appear in:

Behavioural Brain Research

Received date: Revised date: Accepted date:

18-10-2017 20-2-2018 22-2-2018

Please cite this article as: Oren N, Shapira-Lichter I, Lerner Y, Hendler T, Giladi N, Ash EL, How Attention Modulates Encoding of Dynamic Stimuli in Older Adults, Behavioural Brain Research (2010), https://doi.org/10.1016/j.bbr.2018.02.031 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: How Attention Modulates Encoding of Dynamic Stimuli in Older Adults Authors: Noga Oren 1,2*, Irit Shapira-Lichter 3,4, Yulia Lerner 1,2,3, Talma Hendler 1,2,5,6

, Nir Giladi 1,3,5, and Elissa L Ash 1,3,7

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Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

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Affiliation:

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Tel Aviv Center for Brain Functions, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

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Functional MRI Center, The Cognitive Neurology Clinic and the Neurology Department, Beilinson hospital, Rabin Medical Center, Israel Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

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School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel

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*Corresponding author.

author: Noga Oren, E-mail: [email protected]

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*Corresponding

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Center for Memory and Attention Disorders, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

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Short title: Attention modulation of encoding at old-age

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Highlights 

In older adults, inter-SC increased with attentional loading in frontal regions

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Inter-SC decreased with attentional loading in widespread visual regions

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Greater inter-SC in the dPCC at high attentional load predicted poorer memory

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The dPCC modulates the interplay between attention and memory across the

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lifespan

Abstract

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Aging is marked by memory decline that is exacerbated with attentional loading.

Achieving a complex understanding of older adults' neural functions when encoding information in conditions of high and low attentional load is a necessary step toward

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understanding this phenomenon. Furthermore, the information gained may be used to

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devise strategies aimed to prevent age-related decline in these processes. To address this issue, a group of older adults underwent fMRI scanning while encoding short

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movies under two levels of attentional loading. High attentional load was associated

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with increased inter-subject correlation (inter-SC) in only a subset of prefrontal regions that were previously identified in younger adults. It was also associated with

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lower inter-SC in task-relevant visual regions, suggesting that as load increased, visual processing became less synchronized across participants. Critically, while we have shown that inter-SC in the dorsal posterior cingulate cortex (dPCC) was

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increased for younger adults at high load, older adults did not generally show this effect. However, those older adults who did display this pattern also displayed a

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'younger-like' behavioral profile. These results point to a pivotal role of the dPCC in the interplay between attention and memory across the lifespan.

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Key words: aging, episodic memory, intersubject correlation, divided attention, fMRI

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Introduction Episodic memory declines with age [Grady, 2012]. This decline is more pronounced with attentional loading, as induced by divided attention (i.e., dual task) [Craik et al., 2010]. Moreover, tasks which require substantial executive control are associated with greater dual task impairments in older adults [Riby et al., 2004]. One of the reasons for load-related memory decline with age is diminished top-down

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processing [Gazzaley, 2013], leading to distraction from and impaired inhibition of task-irrelevant input during encoding [Healey et al., 2008]. Previous studies have shown reduced neural modulation in older adults in response to dual task, as

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compared to a single task [Fernandes et al., 2006; Hartley et al., 2011]. However, the neural basis of age-related memory decline with attentional loading is still largely unknown. The current study aims to shed light on this matter in order to advance our understanding of age-related memory deficits. Specifically, we focused on encoding

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information to episodic memory, and examined which neural functions changed when

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encoding was conducted in conditions of high vs. low attentional load in older adults.

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Dividing attention during encoding can be used to examine how encoding changes as a function of attentional loading, as the concurrent presentation of a

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secondary distracting task and an encoding task leads to competition for limited attention resources and requires allocation of attention between the two tasks [Chun

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and Turk-Browne, 2007]. If one of the tasks becomes harder, it pulls attention away from the other task, and performance is impaired [Naci et al., 2014]. Orchestrating the

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concurrent processes and allocating attention is done by frontal and parietal regions [Duncan, 2013; Duncan and Owen, 2000], known to be part of the frontoparietal

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control network (FPN), which includes lateral frontal and parietal regions and the superior medial frontal cortex [Vincent et al., 2008]. Indeed, divided attention during encoding activates the left vlPFC [Johnson and Zatorre, 2006; Kensinger et al., 2003; Uncapher and Rugg, 2005], thought to support cognitive control over memory [Badre

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and Wagner, 2007; Wagner, 2002] and allocation of attentional resources to two streams of incoming information [Uncapher and Rugg, 2008]. Unlike younger adults, older adults show diminished ability to modulate neural activity of frontal and parietal regions in a dual task as compared to a single task [Fernandes et al., 2006; Hartley et al., 2011]. Hence, for older adults, these regions are anticipated to show further

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decrease in neural modulation when comparing low to high attentional loading during encoding. A powerful tool to tap into neural function is examining the synchronization of neural activity across participants in response to complex dynamic stimuli [Naci et al., 2014]. Synchronization is quantified as inter-subject correlation (inter-SC) [Hasson et al., 2004; Hasson et al., 2010], which is a measure of how much activity in a given region of one participant is correlated (i.e., similar) to the activity of the same region

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in other participants. The degree to which neural activity of one participant can be

predicted from the neural activity of another participant (or a group) is an index of

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how similar the cognitive processing of the former is to the latter [Naci et al., 2014]. Hence higher inter-SC indicates greater similarity across participants and reflects similar cognitive experience, while lower inter-SC indicates lower similarity and

greater idiosyncratic processing of each individual. Since the neural activity is evoked

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by and locked to the stimulus, inter-SC also represents how much a region engages

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with- and follows the stimulus. Therefore higher inter-SC level in a given region

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indicates that the region is more attuned to the stimulus [van Kesteren et al., 2010], while lower inter-SC level reflects disengagement from the stimulus [Campbell et al.,

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2015]. The advantage of inter-SC over other methods is that it can infer common cognitive processing without recourse to behavior [Naci et al., 2014] and it is

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sensitive to the neural fluctuations and the moment-to-moment dynamics that occur in response to naturalistic, long dynamic stimuli encountered in real world situations,

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such as movies [Ames et al., 2015].

Using inter-SC, many studies have showed that in young adults, the same neural circuits process new incoming information and also retain representations of

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past events [Hasson et al., 2015]. Additionally, brain regions differ in the amount of information they can sustain [Hasson et al., 2008; Lerner et al., 2011]: primary sensory regions assimilate the incoming information with representation of the

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previous milliseconds or seconds, while higher order regions integrate current information with events that unfolded seconds or minutes earlier. The topographic organization of this hierarchical processing is preserved with aging [YogevSeligmann et al., 2016]. However, the level of synchronization is reduced in multiple regions, including the left ventrolateral prefrontal cortex (vlPFC), which is part of the FPN [Vincent et al., 2008], and also posterior cingulate cortex (PCC), which is part to the default mode network (DMN) [Buckner et al., 2008; Raichle et al., 2001]. The 4

reduced synchronization is associated with lower attentional control functions [Campbell et al., 2015]. These findings are in line with evidence regarding diminished ability to maintain top-down control in older adults [Gazzaley, 2013], which in turn influences the function of visual regions [Gazzaley et al., 2005]. We recently showed that in younger adults, attentional loading induced by dual task modulated the level of synchronization in FPN regions [Oren et al., 2016] that are known to host executive control [Duncan, 2013; Duncan and Owen, 2000;

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Hampshire and Owen, 2006; Naci et al., 2014] and sustain information over longer timescales [Hasson et al., 2015; Lerner et al., 2011]. Different levels of attentional

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loading during encoding of movies were introduced by a simultaneous secondary task which had two difficulty levels (low and high) [Oren et al., 2016]. With high

attentional loading, the frontal areas of the FPN displayed higher inter-SC as well as increased activation levels, and were more functionally connected with each other.

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The inter-SC patterns of the FPN regions indicated that they tracked both the movie

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and secondary task more closely as load increased. In contrast, the dorsal PCC

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(dPCC) had increased inter-SC but was more deactivated and was anti-correlated to the FPN at high load, as expected from a node of the DMN [Buckner et al., 2008;

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Raichle et al., 2001]. Moreover, increased inter-SC in the dPCC during the high load condition predicted greater reduction of memory in that condition, implying that the

[Oren et al., 2016].

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dPCC was more attuned to the secondary task at the expense of encoding the movie

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Age hinders the ability to efficiently modulate the activity level of the frontal regions of the FPN [Nagel et al., 2011] and the PCC [Persson et al., 2007] in the face of load. Consequently, we hypothesized that older adults will also display less

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modulation of inter-SC, reflecting not just overall diminished top-down regulation in the face of challenge [Campbell et al., 2015], but also decreased ability to track two streams of information simultaneously [Oren et al., 2016]. Of special interest here was

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the modulation of inter-SC in the dPCC, which predicted memory strength in our previous study with younger adults. Older adults also demonstrate reduced modulation in functional connectivity (FC) within the FPN regions, and between the FPN the PCC [Nagel et al., 2011]. Here, we sought to replicate this finding while employing dynamic stimuli, and to examine whether disengagement of the FPN from interfering processes that occur in the dPCC [Oren et al., 2016] is related to memory level. 5

Together, the current study examined neural function related to encoding in conditions of high and low attentional load at older age. We focused on inter-SC of frontal and parietal regions of the FPN and dPCC of the DMN. To this end we tested older adults, using the same aforementioned paradigm with movies and attentional loading and the same analytic procedures as in our previous study with younger

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adults.

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Material and methods Participants Twenty-eight healthy older adults (age range: 65-79 y; mean age (SD): 71.8 y (4.6); mean education (SD): 17.1 y (3.05); 16 females) participated in the study. All participants were right-handed, as indicated by the Hebrew translation of the Edinburgh Handedness Inventory [Oldfield, 1971]. All participants were native or

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fluent Hebrew speakers; none had history of neurological or psychiatric problems; all reported normal or corrected to normal vision; none reported significant cognitive

decline during a screening interview; all had intact cognitive function, as indicated by

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a comprehensive neuropsychological assessment (see full details below and in [Oren

et al., 2017a]); and none were diagnosed as suffering from mild cognitive impairment or dementia using established criteria [American Psychiatric Association, 2013; Petersen, 2004]. The experimental procedures were approved by the Tel Aviv

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Sourasky Medical Center Institutional Review Board and all participants provided

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written informed consent.

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Neuropsychological assessment

Participants completed a comprehensive neuropsychological assessment in a

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separate meeting prior to the fMRI scan, which included the following tests (all in Hebrew): Montreal cognitive assessment (MoCA) [Nasreddine et al., 2005], Rey-

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auditory verbal learning test (RAVLT) (Vakil and Blachstein, 1997), logical memory from Wechsler memory scale, Rey-Osterrieth complex figure [Lezak et al., 2004],

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phonemic and semantic verbal fluency tests [Kavé and Knafo-Noam, 2015], digit span and general knowledge tests form the Wechsler adult intelligence scale (WAIS)

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and trail-making test parts A and B [TMT; Lezak et al., 2004]. All tests except the MoCA were scored using norms for age. For the MoCA test, the raw scores were used since there are no formal norms for age [Oren et al., 2015]. The assessment confirmed the intact cognitive status of the participants. Specifically, the average MoCA score

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was 26.5 (1.5), and in all the other tests the average score was around the mean (for full results of neuropsychological assessment see [Oren et al., 2017a]). fMRI paradigm and experimental design The fMRI task examined episodic memory and consisted of 24 consecutive cycles, each comprised of 3 phases – encoding, distraction and recognition (Figure 1; a detailed description of the stimuli and paradigm may be found in [Oren et al.,

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2016]). In brief, a 21 s movie was played during the encoding phase. The movies in the task were unfamiliar, non-emotional, silent clips edited from feature length films. The original films were not silent but sound was omitted during editing. The 9 s distraction phase followed immediately and consisted of 3 mathematical questions meant to diminish rehearsal and recency effects [Baddeley, 2003]. The mathematical questions were comprised of a simple equation (e.g. “56+7=?”) at the top of the screen, with the correct answer and a foil in 2 separate rectangles. The recognition

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phase lasted 15 s and consisted of 3 recognition questions, in which the question

“What did the last movie show?” was presented at the top of the screen with 2 picture

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choices presented below. The correct answer was a picture frame taken from the short movie that immediately preceded the question. The foil was a frame taken from the original feature length film presenting the same characters and setting, but from a segment that did not appear in either of the two short movies viewed. A fixation cross

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of interchangeable length of 9 or 12 s was presented between the distraction and

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recognition phases, and between recognition and the next sequence. Each cycle of

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encoding, distraction and recognition lasted between 63 s to 69 s and presented different movies and questions. The paradigm was divided into 3 runs of 582 s each,

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and each run consisted of 8 cycles. A run started with a fixation cross for 24 s and an additional single movie to habituate the neural response to the visual stimuli and to

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accustom the participants to the task. This movie was discarded from the analyses.

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Participants responded to the tasks using a hand-held response box.

Figure 1. The experimental paradigm A cycle of the experimental paradigm composed of 3 phases – encoding, distraction and recognition. Attention load was modified via an easy or hard secondary linguistic task performed during encoding.

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Two variables were manipulated during the encoding phase: attentional load and context. To manipulate attentional load, a distracting linguistic task was performed during encoding. Participants had to indicate whether a string of letters was a word or pseudo-word. This task was either easy (low load) or hard (high load), depending on the stimuli that were used. The exact same task was used in the low and high load conditions, but judgment regarding the stimuli was either easy or hard, respectively. Easy stimuli were composed of common well-known words and of

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pseudo-words created by very distinct mistakes. Hard stimuli were composed of rare

words and pseudo-words created by replacing similar letters, thus making the mistake

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harder to detect. Task difficulty levels were characterized by higher accuracy and

shorter RT for the easy compared to the hard distracting task, as confirmed previously in a separate pretest with younger adults and described in full in [Oren et al., 2016]. Three words were presented during each movie: 2 words and 1 pseudo-word or vice

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versa. The words appeared one at a time for 2 s each as subtitles below the movie.

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Importantly, each word appeared 0.5 s before the frame that served as a target in the

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recognition questions. Consequently, for each movie the words were presented at the same exact time for all participants, regardless of the experimental condition. To

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manipulate context, pairs of related movies were edited from the same feature length film portraying the same characters in a similar setting performing a similar action.

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Different pairs of movies were edited from different films. However, the current study focuses solely on attentional load (see details below).

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Participants responded to the tasks using a hand-held response box. They were instructed to perform to the best of their ability on all aspects of the task, and underwent a training session prior to performing the task in the magnet to reduce

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learning effects (described in full in [Oren et al., 2016]). MRI acquisition

MRI scans were performed on a 3.0 Tesla MRI scanner (GE Signa EXCITE,

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Milwaukee, WI, USA) using an eight channel head coil. Blood-oxygen-dependentlevel (BOLD) functional MRI was acquired with T2*-weighted imaging: repetition time (TR) = 3000 ms; echo time (TE) = 35 ms; flip angle (FA) = 90°; field of view (FOV) = 200 mm; matrix size = 96×96; 39 axial slices of 3 mm thickness, 0 gap. A high-resolution anatomical T1-weighted fast spoiled gradient echo imaging was acquired: FOV = 256 mm; matrix = 256×256; TR = 9.2 ms; TE = 3.5 ms; axial slices of 1 mm thickness, no gap. This anatomical scan was used for surface reconstruction. 9

To minimize head movements, participants’ heads were stabilized with foam padding. Stimuli were controlled using PsychoPy software [Peirce, 2007; Peirce, 2008] and presented via an LCD projector to a tilted (45°) mirror positioned over participants' foreheads. A MR-compatible response box was used to collect responses. Data analysis The aim of the current study was to portray how the modulation of memory by attention occurs in older adults. To this end we used the same procedure recently

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employed in our study with younger adults [Oren et al., 2016]. All analyses examined

the difference between low and high attentional load, averaged across the first and the

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second movies (see below). Behavioral analysis

There were 3 behavioral tasks: linguistic (performed during the encoding phase), mathematical (performed during the distraction phase) and mnemonic

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(performed during the recognition phase). In each task, accuracy level and mean RT

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were measured. The accuracy level was calculated as the proportion of correct

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responses. Questions with RT shorter than 0.1 s were discarded from analysis. Incorrect questions were discarded from the RT analysis. Additionally, 3 recognition

details see [Oren et al., 2016]).

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questions were discarded from analyses due to overall poor accuracy level (for further

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The analyses commenced with validation of the distracting and attentional load manipulations. Analysis of the distracting mathematical task aimed to verify that

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the participants actually performed the task above chance level. To this end, accuracy level across all the experimental conditions was evaluated. The linguistic task was analyzed in order to confirm that it had indeed two difficulty levels. This was done

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using paired-sample t-tests comparing low versus high attentional loads, once for accuracy level and once for RT. The recognition task was analyzed using a 2-way repeated measure analysis of

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variance (ANOVA) with context (first/second movie) and attentional load (low/high) as within-subject variables. There was neither an effect for context nor any interaction between context and attentional load, for either accuracy level or RT (p>0.05 for all). Therefore, the first and the second movies were collapsed together in all subsequent analyses.

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fMRI analysis As elaborated above, in the current study only the effect of attentional load was analyzed by comparing the low and high attentional load conditions. fMRI data analysis was confined to the signal recorded during the encoding phase. Data were analyzed using the BrainVoyager QX software [Formisano et al., 2006; Goebel et al., 2006] and an in-house code implemented in MATLAB. Pre-processing: Pre-processing of the functional scans included cropping of

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the first 6 TRs in each run to allow the hemodynamic responses to reach steady-state,

3D motion and slice scan time correction, linear trend removal and high-pass filtering

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(1 cycle per run). Runs in which head movements exceeded 3 mm were discarded from the analyses (see Supplementary Information for mean square and mean

maximum head movements). Accordingly, for 3 older adults, a single run was

removed and 5 other older adults were discarded from all neural analyses. Hence,

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fMRI analyses were conducted on 23 older adults. Spatial smoothing was applied

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using a Gaussian spatial filter (6 mm full-width at half-maximum value). The

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functional images were superimposed on 2D anatomical images and incorporated into the 3D datasets through trilinear interpolation. The complete functional dataset was

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transformed into Talairach space [Talairach and Tournoux, 1988]. See Supplementary

SC in older adults.

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Information for examination of the effect of additional pre-processing steps on inter-

Inter-SC analysis: In order to identify regions which changed their inter-SC

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level in response to the encoded movie as a function of attentional load, we used the same procedure implemented in our previous study with younger adults [Oren et al., 2016], which included the following steps. First, for every participant and movie, a

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Pearson correlation map was constructed by calculating, on a voxel-by-voxel basis (in Talairach space), the correlation between the BOLD signal of that participant and the averaged BOLD signal of all the other participants sharing the same experimental

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condition (e.g., between participant k and all the other participants who saw the same movie as a low load first movie). The averaged BOLD signal was calculated across participants (4 to 5 participants, depending on the movie. Notably, in our group of older adults, in 4 out of the 48 calculations the average signal could be calculated across 3 participants only, therefore these movies were discarded from the inter-SC analysis). The correlation coefficients were transformed using a Fisher transformation. Once the correlation maps were constructed, they were averaged 11

across conditions. Specifically, the 12 correlation maps of each experimental condition (low load/high load) were averaged together, yielding one correlation map per condition for each participant. Second, voxels in which the correlation coefficient was significantly higher than zero were identified. For this step, in every voxel, a one sample t-test was used cross all the 23 participants to determine whether the correlation was different from zero. This was done once for the low and once for the high attentional load

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conditions. Controlling for multiple comparisons across the whole brain was done

using a false discovery rate (FDR) method [Benjamini and Hochberg, 1995], with a

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threshold of p<0.05. The next stage of analysis was conducted only on voxels whose average correlation coefficient was significantly larger than zero in at least one condition.

Finally, to find voxels in which correlation changed as a function of

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attentional load, the correlation during the low and high attentional load conditions

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was compared across all the 23 participants in every voxel, using a paired-sample ttest. Controlling for multiple comparisons was done using FDR method, with a

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threshold of p<0.05 and minimum cluster size of 50×33 voxels.

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Characterizing signal level via activation analysis: Regions may have similar inter-SC but different activation levels [Oren et al., 2016]. Hence, to fully characterize

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the contribution of regions which demonstrated significant changes in inter-SC patterns to data processing and encoding, a standard activation analysis was also

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employed. This information was further used to identify regions which are part of the FPN or DMN and defining them as regions of interest (ROIs; see below). Specifically, activity level in each region was examined using the following steps.

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First, for each participant, the time course of the encoding phase was extracted, averaged across all the voxels in a region, incorporating a hemodynamic delay of 2 TRs (6 s). This time course was used to calculate the region's percent signal change

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(%SC) from baseline in the low and high attentional load conditions. The periods of fixation cross served as baseline. Second, a one-sample t-test across participants was used to determine whether the activation level in either the low or high attentional load condition was significantly different than zero. Finally, in regions where average activation was significantly larger than zero in at least one condition, the difference between the low and high attentional load was determined using a paired-sample ttest. 12

Defining regions of interest: In line with our research hypotheses which focus on the FPN and DMN regions, ROIs were defined functionally for subsequent analyses based on their inter-SC, activation profiles, and location. First, we defined regions which demonstrated significant load-related modulation in inter-SC (defined via the inter-SC analysis), and their location and activation profiles (defined via the activation analysis) matched FPN or DMN regions. The FPN includes lateral frontal and parietal regions and the superior medial frontal cortex and is more activated as

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load increases [Vincent et al., 2008]. In contrast, the DMN includes several midline and temporal regions and is less activated as load increases [Buckner et al., 2008;

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Raichle et al., 2001]. Given the distinct location and activation profile of each

network, in the current study, we identified regions that fulfilled either the criteria for FPN (%SC>baseline and high load>low load) or the criteria for DMN (%SChigh load) [Oren et al., 2016]. In addition, we defined the dPCC based

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on the data of the younger adults, since in our previous study it emerged as a pivotal

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analyses were conducted solely on these ROIs.

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region that predicted memory strength in young adults [Oren et al., 2016]. Subsequent

Functional connectivity analysis: We examined whether older adults retained

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the load dependent modulation of FC within the FPN regions and between them and the dPCC, which was reported in younger adults [Oren et al., 2016]. The analysis was

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conducted as follows for every possible pair of ROIs. First, for a given participant, the time course of each movie was extracted from a pair of ROIs, and a Pearson

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correlation was calculated between the two time courses. This resulted with 24 correlation coefficients for each participant, one for each movie. All were Fishertransformed. Second, all the correlations corresponding to the low attentional load

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condition were averaged together, and the same was done for the high attentional load condition. Third, across all participants, a one-sample t-test was used to determine whether the correlation in either the low or the high attentional load condition was

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significantly different than zero. The fourth step was conducted only in cases where at least one condition was significantly different from zero and consisted of a pairedsample t-test to assess the difference between the low and high attentional load conditions. Controlling for multiple comparisons was done using the Bonferroni method, taking into account the total number of paired comparisons. Functional significance of inter-SC patterns: In younger adults, only the interSC of the dPCC predicted accuracy level in the recognition task [Oren et al., 2016]. 13

Hence, the final stage of analysis aimed to examine whether this relationship was maintained in older adults, and whether the FPN regions or other neural measures can also predict memory level in older adults. To this end, a step-wise regression analysis was used. The dependent variable was accuracy level in the recognition task. The predicting variables were the inter-SC patterns of each one of the ROIs. Accuracy level and inter-SC of each ROI were modeled as the difference between low and high attentional load conditions (high load – low load; to be termed index). Additional

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models were calculated with the predicting variables being either the ROI activation indices or the FC indices.

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Comparing inter-SC levels of older and younger adults: Analyses thus far

gave a comprehensive description of the older group. However, in order to provide a more detailed description of inter-SC in aging, a quantitative comparison of older to younger adults was also conducted. To this end we used data from a sample of

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younger adults who performed the same paradigm (n=24; mean age (SD) 29 (3.7);

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mean education (SD) 15.37 (2.24); 8 females; The fMRI data of two participants were

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discarded since they completed a version of the fMRI task which had shorter fixation periods; for full details see [Oren et al., 2016]). The data of the younger adults were

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analyzed using exactly the same procedure as the older adults. Please note that since the data of the younger cohort was already published ([Oren et al., 2016]), limited

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comparisons between the age groups were done here. A comparison of inter-SC in older and younger adults was done on two independent sets of ROIs. The first set was

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composed of the ROIs of the older adults, which were defined in a previous stage (see description above). In each ROI, the time courses of the older adults and of the younger adults were extracted and the inter-SC was calculated for each participant

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within his/her age-group. The correlation coefficients were transformed using a Fisher transformation. For each condition (low load/high load) and in every ROI, a onesample t-test across participants was used to test whether any of the correlations was

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significantly higher than zero. This stage was essential since it prevented comparison of two non-significant correlations, which may itself be significant but bears no substantial meaning. The next stage of analysis was conducted if the averaged correlation across participants was significantly higher than zero in either the low or high load. Next, older and younger adults' correlation coefficients were compared using a 2-way mixed ANOVA with group (older/younger) as a between-subjects variable and load (low/high) as a within-subject variable. Interactions were examined 14

post-hoc using the Tukey method. To confirm that our results were not affected by the method of ROIs selection (which was biased in favor of older adults), a second set of ROIs were used, namely the ROIs previously defined in the younger group: left vlPFC, right vlPFC, mSFG and dPCC [Oren et al., 2016]. Notably, correlations were significantly higher than zero in all ROIs (taken from younger and from older adults), conditions and groups (p<0.05 for all). Hence, comparing inter-SC levels of the

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groups was done for all ROIs.

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Results Behavioral results First, we validated the efficiency of the mathematical and linguistic tasks. For the mathematical task, the accuracy level was above chance (mean (SD): 75.33% (13.25)). This indicates that participants were engaged in the task (distracted from the movies), and confirms that performance in the recognition task was not based on

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active working memory. Examination of the linguistic task verified a significant difference between the low and high attentional load conditions, thus validating the

attentional load manipulation. The analysis revealed a significantly higher accuracy

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level (easy: 96.14% (3.72); hard: 81.52% (10.94); t(27)=6.42, p<0.001) and a shorter RT (easy: 0.99s (0.08); hard: 1.25s (0.12); t(27)=-18.21, p<0.001) for the easy compared to the hard linguistic questions.

We previously showed that in the current task the ability to recognize stimuli

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from the encoded movies was impaired at high as compared to low attentional load in

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younger adults [Oren et al., 2016]. We also showed that overall, older adults

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displayed lower memory levels than younger adults in this task [Oren et al., 2017b]. Here we examined how load influenced memory level in older adults. This was done

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by comparing performance in the mnemonic task (i.e., recognition questions) as a function of attentional load (low load vs. high load, which was manipulated during the

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encoding phase) (for quantitative comparison of performance in the recognition task of older and younger adults see Supplementary Information). The low and high

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attentional load conditions did not significantly differ in terms of accuracy level of the recognition questions (low load: 66.2% (8.9); high load: 61.5% (12.11); F(1,27)=2.07,

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p=0.16), or in RT (low load: 2.1s (0.36); high load: 2.07s (0.34); F(1,27)=0.2, p=0.6). Despite the fact that there were two levels of attentional loads, as indicated by performance of the older adults in the linguistic task, and the fact that this attentional loading manipulation was previously shown to influence memory level in younger

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adults, older adults did not display changes in memory level as a function of load. The significant neural results as well as the relationship between behavioral and neural measures, all described below, suggest that this may have been due to a floor effect. And indeed, in the current task, performance at 61% accuracy is just above chance level, as determined by a binomial distribution (p=0.045). That is, if the older group performance was lower than 61% it would have been at chance level.

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fMRI results Inter-SC analysis: Correlation maps of the encoding phase for low and high attentional load conditions were created (Figure S1) and compared using a paired ttest in order to depict regions which changed their response synchronization as a function of attentional load (Table 1; Figure 2). Higher synchronization in the high compared to the low attentional load was observed in left frontal and parietal regions,

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which were previously attributed to the FPN [Vincent et al., 2008]. Specifically, responses were more synchronized in the high load condition in left vlPFC,

dorsolateral prefrontal cortex (dlPFC), mSFG, medial prefrontal cortex (mPFC),

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orbitofrontal cortex and superior parietal lobule (SPL; Figure 2, red). In contrast,

lower synchronization in the high compared to the low attentional load was observed in more posterior regions bilaterally, including occipital and temporal visual regions, middle insula and pre/postcentral gyrus, bilaterally (Figure 2, blue).

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In contrast to our previous finding in young adults [Oren et al., 2016], inter-

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SC in the dPCC was not significantly modulated in response to load in the older

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cohort. Yet given that it was an a-priori ROI, it was defined here based on the data of

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the younger adults and used for subsequent analyses (Figure 2, brown outline).

Figure 2. Inter-SC as a function of attentional load across the lifespan Whole brain t map depicting changes in inter-SC level as a function of attentional load (red and blue). Inter-SC increased during high attentional load in left frontal and parietal regions (red) and decreased in bilateral visual regions and insula (blue). The

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dPCC (brown outline) was an a-priori ROI based on our previous findings with younger adults [Oren et al., 2016]. Other results of the younger adults in the same task are also presented (brown and azure outline; adapted with permission from [Oren et al., 2016]) in order to allow an integrative view of inter-SC as a function of load across the lifespan (see Discussion). All in all, in the high attentional load condition, the older adults showed less increased and more decreased synchronization across the brain, compared to the young group. Older adults n=23, p<0.05, FDR corrected,

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cluster size>50x33. Abbreviations: R: right; L: left; A: anterior; P: posterior; vlPFC: ventrolateral prefrontal cortex; Mid.: middle; LO: lateral occipital cortex; SPL:

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superior parietal lobule; mSFG: medial superior frontal gyrus; mPFC: medial

prefrontal cortex; dPCC: dorsal posterior cingulate cortex; inter-SC: intersubject

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correlation.

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Characterizing signal level via activation analysis: Activation levels were calculated in regions which emerged from the inter-SC analysis as well as in the dPCC. If their activation and location fitted either FPN [Vincent et al., 2008] or DMN [Buckner et al., 2008; Raichle et al., 2001], they were labeled accordingly. In all regions except the right posterior insula and right orbitofrontal cortex, signal level

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was significantly different from zero, indicating that the average amplitude during the conditions was different than the amplitude during fixation (Table 1). The left vlPFC, dlPFC, SPL and visual cortex had %SC higher than baseline and at high load they

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were more activated. Hence, the left vlPFC, dlPFC and SPL were labeled as FPN

regions, and they showed increased inter-SC and activation at high load (Figure 3 A). The only region that was deactivated and also had greater deactivation in the high compared to the low load condition was the dPCC (t(22)=2.73, p=0.012), which was

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expected given that it is a node of the DMN. Though the dPCC in older adults did not

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demonstrate changes in inter-SC with the manipulation, dPCC activation was

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modulated in a similar fashion to that observed previously in younger adults [Oren et al., 2016]. The other regions that demonstrated significant inter-SC patterns did not

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show significant change in activation level between the conditions.

Figure 3. Activation and functional connectivity in regions of interest of older adults

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(A) Bar graphs showing activity levels in all ROIs of older adults. Complementing the inter-SC by activation measures allowed the characterization of both the synchronization of signal across participants and the level of activity. The frontal and parietal regions were more synchronized across participants and more activated at high attentional load. Inter-SC of the dPCC did not change as a function of load, but the dPCC was more deactivated at high load. (B) Functional connectivity between the ROIs. Only the FC between the left vlPFC and dlPFC increased at high attentional

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load. Note that although statistical analyses were conducted on Fisher-transformed

correlations, in (B) the results are presented in the original correlation, for the reader's

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convenience. Error bars represent standard errors. Abbreviations: L: left; R: right; l: low load; h: high load; vlPFC: ventrolateral prefrontal cortex; dlPFC: dorsolateral prefrontal cortex; SPL: superior parietal lobule; dPCC: dorsal posterior cingulate

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cortex; %SC: percent signal change from baseline; *: p<0.05, Bonferroni corrected.

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Defining regions of interest: In line with our hypotheses and based on both

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activation and location, subsequent analyses focused only on the FPN regions (left vlPFC, dlPFC and SPL), and the dPCC (defined based on the data of the younger

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adults), which was the only representative of the DMN identified in the current study.

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Functional connectivity analysis: Functional connectivity was used to assess the relationship among the FPN regions and between the FPN regions and the dPCC,

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and how these relationships changed as a function of attentional load. Results showed that correlations between all FPN pairs were positive, yet only the FC between the left vlPFC and dlPFC increased with high attentional load, while the other connections

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(i.e., left vlPFC-SPL, left dlPFC-SPL) showed only marginal change between the conditions (Figure 3 B; Table 2). Regarding FC between the FPN and the dPCC, the left vlPFC and dPCC were functionally connected only during the low load condition,

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but the difference in FC of these regions in the low and high load conditions was not significant. The dlPFC and SPL were not functionally connected to the dPCC at either low or high attentional load.

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Functional significance of inter-SC patterns: To clarify the functional significance of the load-dependent changes in the ROIs, we examined their ability to predict accuracy level of the mnemonic task. This was done using a step-wise multiple regression analysis. In the first model, there were four potential predicting variables, one for each inter-SC index of each ROI (left vlPFC, dlPFC, SPL and dPCC). This model contained only the inter-SC index of the dPCC, which explained

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26.1% of the variance (R2=0.261, Adj-R2=0.226, F(1,21)=7.42, p=0.013; β=-0.511; Figure 4). This means that individuals who had higher inter-SC in the high load

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condition (compared to low load) also had lower memory level in the high load condition (compared to low load). This result expand our previous findings in

younger adults [Oren et al., 2016] to older age. It further indicates that although at the group level inter-SC in the dPCC was not modulated by load, inter-individual

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differences in this measure were related to memory strength, hence corroborating the

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functional significance of both the region (dPCC) and the measure (inter-SC). In the

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second model, the activation indices of the ROIs were the predicting variables, yet it was non-significant. In the third model, the FC indices were the predicting variables,

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and it contained only the left vlPFC-dlPFC FC index, which explained 18% of the variance (R2=0.18, Adj-R2=0.14, F(1,21)=4.59, p=0.04; β=0.42). Remarkably, when

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both the inter-SC and FC indices were included in a single model, only the dPCC inter-SC index emerged as predicting variable, suggesting there was a considerable

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index.

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overlap between the variance that is explained by the inter-SC index and the FC

Figure 4. Predicting accuracy level via inter-SC in the dPCC

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A scatter plot of the regression model that predicts interference to memory using inter-SC of the dPCC. The significant negative regression coefficient indicates that older adults who had increased inter-SC in the dPCC during the high attentional load condition also had greater memory impairment in that condition. *: p<0.05. Note that although statistical analyses were conducted on the Fisher-transformed correlation, the results are presented in the original correlation, for the reader's convenience.

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Comparing inter-SC levels of older and younger adults: We were interested to understand how the modulation of inter-SC by attentional load changes with aging, as

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a function of load (see Supplementary Information for examination of the effect of head movements on age-related differences in inter-SC). Therefore, a quantitative

comparison was conducted between the inter-SCs of older and younger adults in the ROIs of the older group: left vlPFC, dlPFC and SPL. In all ROIs there was the

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expected load main effect, indicating that across both groups inter-SCs were higher in

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the high as compared to the low load condition (left vlPFC: F(1,43)=55.43; p<0.0001;

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left dlPFC: F(1,43)=30.38; p<0.0001; left SPL: F(1,43)=10.99; p=0.0018; Figure 5 A). Additionally, the left SPL demonstrated a group main effect, as inter-SCs were higher

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for older than younger adults (F(1,43)=17.85; p=0.0001). The left SPL had a significant group by load interaction (left SPL: F(1,43)=6.84; p=0.012). Post-hoc analyses revealed

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that the interaction in the left SPL was derived from a load effect in older but not younger adults (older: p=0.0007; younger: p=0.96); and in the high load condition,

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older adults had higher inter-SC as compared to younger adults (p=0.0017). Since the current set of ROIs was biased towards older adults, we repeated the analyses using the ROIs previously defined for younger adults [Oren et al., 2016]. Although such a

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selection biased the responses toward younger adults, similar results were obtained. Specifically, in all ROIs defined from the younger adults, there was the expected load main effect of increased inter-SCs in the high load condition (left vlPFC:

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F(1,43)=66.04; p<0.0001; right vlPFC: F(1,43)=35; p<0.0001; mSFG: F(1,43)=39.559; p<0.0001; dPCC: F(1,43)=11.22; p=0.0016; Figure 5 B). The right vlPFC and dPCC also had a significant group by load interaction (right vlPFC: F(1,43)=8.12; p=0.006; dPCC: F(1,43)=13.71; p=0.0006). Post-hoc analyses revealed that in the right vlPFC and dPCC there was a load effect in younger but not older adults (right vlPFC: younger: p=0.0001; older: p=0.14; dPCC: younger: p=0.0002; older: p=0.99).

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Additionally, in the dPCC in the high load condition, older adults had lower inter-SC

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as compared to younger adults (p=0.045).

Figure 5. Inter-SC in regions of interest of older and younger adults

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(A) Bar graphs representing the inter-SC levels of older and younger adults, in ROIs of older adults. (B) Bar graphs representing the inter-SC levels of older and younger

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adults, in ROIs of younger adults. Inter-SC levels in all ROIs, conditions and groups were significantly higher than zero. Load effect, manifested as higher inter-SC in the

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high load as compared to the low load, was evident in all ROIs. Nevertheless, in a few ROIs there was a group by load interaction, manifested as a load effect only for older

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adults (left SPL, see A) or only for younger adults (right vlPFC and dPCC, see B). Note that although statistical analyses were conducted on Fisher-transformed correlations, the results are presented in the original correlation, for the reader's convenience. In all graphs error bars represent standard errors. Abbreviations: L: left;

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R: right; vlPFC: ventrolateral prefrontal cortex; dlPFC: dorsolateral prefrontal cortex; SPL: superior parietal lobule; mSFG: medial superior frontal gyrus; dPCC: dorsal posterior cingulate cortex; inter-SC: inter-subject correlation; *: p<0.05.

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Discussion We recently demonstrated how attention modulates the interplay between processing and memory of dynamic stimuli in younger adults [Oren et al., 2016]. Here, using the same paradigm and analytic procedure with older adults, we were able to explore how this interplay manifested with older age. Three main findings emerged. The first was the functional significance of the dPCC to the interface

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between memory and attention, which together with a similar finding in the young group [Oren et al., 2016], pointed to the significance of the dPCC in this role across the lifespan. The second finding was an altered distribution of load-related inter-SC

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patterns with age. The third finding was reduction in the modulation of FC as a function of load within the FPN and between the FPN and the dPCC with age. The older adults demonstrated load-related modulation of inter-SC in

widespread cortical areas, including left vlPFC, dlPFC, mSFG and visual regions

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(Figure 2, red and blue). These results were interpreted in a broader context of the

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inter-SC patterns of younger adults [Oren et al., 2016], by superimposing the maps of

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the two groups (Figure 2; Figure 5), which gave rise to several distinct effects. First, in response to high load, both groups had increased inter-SC in the left vlPFC, dlPFC

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and mSFG (Figure 2, overlap), and the increase of inter-SC from low to high load was similar across the lifespan (Figure 5). These regions are known to accumulate

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information across long timescales [Hasson et al., 2015; Lerner et al., 2011], are a part of the FPN [Vincent et al., 2008] and are important for cognitive control [Duncan,

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2013; Duncan and Owen, 2000; Hampshire and Owen, 2006; Naci et al., 2014]. The left vlPFC in particular is a pivotal region for dealing with divided attention

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[Uncapher and Rugg, 2008]. Higher frontal inter-SC was probably due to greater usage of executive functions in the hard condition. The frontal executive functions were possibly recruited as the attentional load of the secondary task increased, since tracking both the movie and the task was more demanding [Oren et al., 2016].

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Alternatively, the secondary task deviated attention away from the movie [Naci et al., 2014]. Since this deviation was more extreme in the hard condition it required greater frontal executive control to deal with it. Regardless of the exact cognitive operation, preservation of frontal inter-SC response at older age indicates that some degree of top-down frontal control was maintained [Campbell et al., 2015; Gazzaley, 2013]. The inter-SC modulation in the left vlPFC, dlPFC and mSFG was accompanied by a

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failure of the older adults to modulate the right vlPFC (Figure 2; Figure 5), caudate and most of the mPFC, compared to younger adults. Though such a null result must be interpreted with great caution, it may reflect age-related deterioration in top-down control mechanisms [Campbell et al., 2015]. Additionally, older adults demonstrated lower inter-SC levels in the high attentional load condition in bilateral visual regions and insula (Figure 2, blue). This effect was not evident in the younger adults. Taken together, these findings may reflect two co-occurring processes. The first was

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depletion of some of the frontal top-down control mechanisms with aging when

facing attentional burden [Campbell et al., 2015], manifested as a reduced ability to

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modulate the synchronization level of frontal cognitive control regions. The second

was an increase in idiosyncratic processing of the stimuli, manifested as lower interSC in visual regions in the high as compared to the low attentional load condition. Another notable effect was increased inter-SC during the high attentional load in the

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left SPL in the older group, unlike the younger group (Figure 2, red; Figure 5). This

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region is also a part of the FPN [Vincent et al., 2008] and is known to subserve

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attention [Sestieri et al., 2010]. Hence, its unique emergence in older age may reflect an attempt to compensate for the reduction in the function of the prefrontal control

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mechanisms [Grady, 2012]. Interestingly, the younger group did not show increased inter-SC of the left SPL at high attentional load, which may support the above

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conclusion, or may simply reflect differential neural processing with age (Figure 5). Finally, the inter-SC level in the dPCC was modulated by load in the younger

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but not older adults (Figure 2, brown outline; Figure 5). This finding is especially surprising given the pivotal role the dPCC had in predicting memory strength in younger adults [Oren et al., 2016]. The dPCC presented a mirror image of the left

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SPL, in which the null result of the older group was due to insufficient increase at high load (Figure 5). Interestingly though, in the older adults, like younger adults, the neural and behavioral measures were related, and inter-SC in the dPCC predicted

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memory strength (Figure 4). That a larger increase in inter-SC in the high load predicted a reduced memory level in that condition implies that as load increased, the dPCC tracked the secondary task at the expense of encoding the movie [Oren et al., 2016]. Hence the dPCC is not just involved in multiple cognitive functions [Leech and Sharp, 2014] including episodic memory [Shapira-Lichter et al., 2013; Svoboda et al., 2006; Vann et al., 2009], but it also can change its focus of processing in a flexible manner in both younger and older adults. 25

Unlike previous results in younger adults [Oren et al., 2016], in older adults attentional load did not affect FC level between the frontal and parietal regions (Figure 3 B). It is possible that the older adults already reached a ceiling in their ability to adjust the frontal control regions during the low load condition [Geerligs et al., 2013]. According to this interpretation, the reduced FC modulation is an additional sign of age-related changes in frontal control mechanisms [Gazzaley, 2013]. This finding contributes to the growing evidence regarding diminished and

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altered FC patterns in aging [Campbell et al., 2015].

The current study has several limitations. First, indications of differences

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between older and younger adults in their abilities to modulate inter-SC as a function of attentional load were not shown at the whole brain level. It should be noted,

however, that a whole brain analysis that pools together the BOLD signal of older and younger adults may primarily demonstrate regions important for younger adults and

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will bias against functionality with older age. This is due to lower inter-SC [Campbell

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et al., 2015] and reduced activity modulation [Reuter-Lorenz and Cappell, 2008] at

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older age. In the current study we overcame such limitations by using both qualitative comparisons (Figure 2) and quantitative ROI analysis using ROIs defined based on

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data of each group separately (Figure 5). Therefore, our ability to show how attention modulates memory in older age differently than in younger age, is limited in scope.

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Future studies should perform a direct comparison between the age groups at the whole brain level. Another limitation is that in the current setting it was not possible

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to separate and distinguish between the effects the movies had on the inter-SC from those of the linguistic stimuli. Therefore, as mentioned above, we can interpret the changes in inter-SC in frontal regions in at least two ways. Future studies that will use

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longer movies could distinguish between the effects and determine which of the interpretations is more representative. Finally, a behavioral effect for older adults was not seen in the recognition task, despite the clear effect demonstrated in the linguistic

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task and the effect seen in younger adults previously in the same recognition task [Oren et al., 2016]. While it is possible that the altered neural dynamics in older adults stemmed from a reduced sensitivity to the attentional load manipulation, the robust and widespread modulations of inter-SC as a function of load (Figure 2, red and blue; Figure 5) indicated that the older adults did differentiate between low and high loads, and thus contradict this possibility. Moreover, the relationship between the inter-SC of the dPCC and memory strength (Figure 4) showed that older adults with neural 26

responses similar to younger adults also had behavioral patterns that were similar to younger adults. The data thus support an interpersonal variability of older adults to modulate neural dynamics in response to load, that is differentially affected by aging

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on the individual level.

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Funding This work was supported by the academic grant "Adopt a Researcher" program at the Tel Aviv Sourasky Medical Center to E. L. A. N. O. received the Israeli National Network of Excellence in Neuroscience (NNE) Studentship Award sponsored by Teva Pharmaceutical Industries Ltd.

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The authors report no conflict of interests.

Acknowledgements

The authors wish to thank Prof. Naama Friedmann from the Tel Aviv University for

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her help with the linguistic task and to Shahar Jamshy for his help with programming the experiment.

This work was performed in partial fulfillment of the requirements of a Ph.D. degree

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of Noga Oren, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

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Tables Table 1. Inter-SC results. Regions demonstrating change in inter-SC level as a function of attentional loading. Activation results in each region are also reported. n=23, p < 0.05, FDR corrected, cluster size>50 × 33 voxels. Abbreviations: Inter-SC: inter-subject correlation; L: left; R: right; Low: low attentional load; High: high attentional load; %SC: percent signal change from baseline (during the task); Base: baseline; ~=: conditions are not significantly different.

X

Y

t(21)

Z

Inter-SC effect: Low>High 41123

9.29

R Posterior Insula 51

1

1

1988

5.84

0.000007

%SC~=Base

R Precentral Gyrus

30

-16

43

2595

0.000001

High~=Low, %SC>Base

L Middle Insula

-32

-4

19

3622

7.24

<0.000001

High~=Low, %SC>Base

L Postcentral Gyrus

-54

-28

16

1726

7.34

<0.000001

High~=Low, %SC>Base

6.52

A

M

<0.000001

U

1

ED

54

High>Low, %SC>Base

-70

N

Bilateral Visual Cortex

p-value

Activation effect

SC R

Region

Number of voxels

IP T

Talairach coordinates

Inter-SC effect: Low
53

-2

1661

-9.12

<0.000001

Medial Superior Frontal Gyrus

-6

11

46

8412

-6.72

0.000001

High~=Low, %SC>Base

L Medial Prefrontal Cortex

-18

51

4

1387

-6.37

0.000002

High~=Low, %SC>Base

L Ventrolateral Prefrontal Cortex

-42

29

19

11168

-7.08

<0.000001

High>Low, %SC>Base

L Dorsolateral Prefrontal Cortex

-48

-4

52

2243

-6.82

0.000001

High>Low, %SC>Base

L Superior Parietal Lobule

-45

-49

40

3345

-7.02

<0.000001

High>Low, %SC>Base

A

CC E

PT

R Orbitofrontal Cortex

34

%SC~=Base

p-value

L vlPFC0.53 R vlPFC

0.61

-4.01

0.003

L vlPFC0.62 L SPL

0.69

-2.88

0.053

L dlPFC0.54 L SPL

0.62

-2.8

0.06

L vlPFC0.15 dPCC

0.08

1.64

0.7

L dlPFC0.03 dPCC

-0.04

1.52

L SPLdPCC

0.05

0.98

A

CC E

PT

ED

M

0.09

35

IP T

t(22)

A

U

low load, r (23)

SC R

high load, r(23)

N

Table 2. Functional connectivity between pairs ROIs. For each pair of ROIs, functional connectivity was examined using a paired-sample t-test. Correction for multiple comparisons was done using the Bonfferonni method (i.e., multiplying the pvalue by 6).

0.8 0.9