Abstract

Cognitive control involves the flexible allocation of mental resources during goal-directed behavior and comprises three correlated but distinct domains—inhibition, shifting, and working memory. The work of Don Stuss and others has demonstrated that frontal and parietal cortices are crucial to cognitive control, particularly in normal aging, which is characterized by reduced control mechanisms. However, the structure–function relationships specific to each domain and subsequent impact on performance are not well understood. In the current study, we examined both age and individual differences in functional activity associated with core domains of cognitive control in relation to fronto-parietal structure and task performance. Participants (n = 140, aged 20–86 years) completed three fMRI tasks: go/no-go (inhibition), task switching (shifting), and n-back (working memory), in addition to structural and diffusion imaging. All three tasks engaged a common set of fronto-parietal regions; however, the contributions of age, brain structure, and task performance to functional activity were unique to each domain. Aging was associated with differences in functional activity for all tasks, largely in regions outside common fronto-parietal control regions. Shifting and inhibition showed greater contributions of structure to overall decreases in brain activity, suggesting that more intact fronto-parietal structure may serve as a scaffold for efficient functional response. Working memory showed no contribution of structure to functional activity but had strong effects of age and task performance. Together, these results provide a comprehensive and novel examination of the joint contributions of aging, performance, and brain structure to functional activity across multiple domains of cognitive control.

INTRODUCTION

Cognitive control refers to the cognitive mechanisms that modulate and oversee the flexible allocation of mental resources during goal-directed behavior (Miyake & Friedman, 2012; Miyake et al., 2000) and is an essential component of day-to-day functioning (Craik & Bialystok, 2006). Behavioral work has generally identified three correlated, yet distinct, domains comprising cognitive control: (1) inhibition, which describes an internally generated act of controlled suppression of a predominant response (Mostofsky & Simmonds, 2008); (2) shifting, which involves switching between task sets or rules and requires the disengagement of an irrelevant task set and subsequent active engagement of a relevant task set (Monsell, 2003); and (3) working memory, which involves updating and monitoring stored information through active manipulation (Jonides & Smith, 1997). The degree to which these domains are completely distinct rather than a unitary construct of control remains unclear from behavioral evidence (Miyake & Friedman, 2012; Miyake et al., 2000), prompting researchers to further examine cognitive control and its subdomains at the neural level.

Neuropsychological work has identified the frontal lobes as crucial to successful cognitive control across all domains (e.g., Duncan & Owen, 2000), and the work of Donald Stuss was instrumental in defining the roles of the different frontal areas in various types of control (Stuss, 2011; Stuss & Knight, 2002). Indeed, his contributions to the field inspired the senior author of this article to move beyond memory and develop an interest in cognitive control and how it changes over the adult lifespan. The work of Stuss and others has shown that lesions to pFC can result in failures to ignore distracting information (Alexander, Stuss, Picton, Shallice, & Gillingham, 2007; Stuss, Floden, Alexander, Levine, & Katz, 2001), flexibly adjust to changing task demands (Troyer, Moscovitch, Winocur, Alexander, & Stuss, 1998; Owen et al., 1993), and manage, maintain, and update task goals (Owen, Downes, Sahakian, Polkey, & Robbins, 1990; see Stuss & Levine, 2002, for a review). Although neuropsychological research has largely focused on the role of pFC in cognitive control, evidence from functional neuroimaging has revealed a broader set of regions in prefrontal and posterior parietal cortices that are active across a wide range of control tasks (Niendam et al., 2012). Specifically, a meta-analysis of fMRI tasks that assessed inhibition, shifting/flexibility, and working memory found common activity in dorsolateral pFC, cingulate regions, and inferior and superior parietal lobes, suggesting that these fronto-parietal regions comprise a superordinate cognitive control network that may subserve many different components of control (Niendam et al., 2012). The few studies that have compared functional activity for two domains (McNab et al., 2008; Sylvester et al., 2003) or across all three domains (Lemire-Rodger et al., 2019; Derrfuss, Brass, & von Cramon, 2004) within the same sample provide some additional evidence for domain-specific functional activity both within and outside fronto-parietal regions. However, these previous studies included relatively small sample sizes (11–22 participants) consisting of only healthy younger adults; therefore, it is still unclear how functional activity for each domain might be expressed when cognitive control processes are reduced, as is found in aging.

Normal aging is characterized by reduced performance on tasks that require a high degree of regulatory processing (Salthouse, Atkinson, & Berish, 2003), and subsequently, cognitive control is reduced in older age across all domains (Maldonado, Orr, Goen, & Bernard, 2020). Researchers have proposed that these cognitive control alterations can be attributed to age-related differences in brain structure and function underlying cognitive control (Braver & Barch, 2002). Older age is associated with degradation of white matter microstructure and thinning of the cortical ribbon, with frontal and association cortices (including parietal regions) showing the greatest vulnerability to aging (Hoagey, Rieck, Rodrigue, & Kennedy, 2019; Raz & Rodrigue, 2006). Furthermore, frontal and parietal white matter integrity has been selectively linked to performance on tasks of working memory, switching, and inhibition in aging (Ziegler et al., 2010; Kennedy & Raz, 2009; Colcombe, Kramer, Erickson, & Scalf, 2005). Likewise, greater frontal thickness in older adults has been associated with better performance on tasks of executive control (Westlye, Grydeland, Walhovd, & Fjell, 2011; Fjell et al., 2006), although some work suggests that white matter measures show stronger contributions to age-related differences in cognitive control than gray matter thickness (Ziegler et al., 2010; Colcombe et al., 2005).

In addition to brain structure accounting for behavioral differences in aging, older adults also show alterations in functional activity underlying each domain of cognitive control (Spreng, Shoemaker, & Turner, 2017; Spreng, Wojtowicz, & Grady, 2010). Compared to younger adults, older adults generally show overrecruitment of prefrontal regions, and this is evident across inhibition (Colcombe et al., 2005; Nielson, Langenecker, & Garavan, 2002), shifting (Zhu, Johnson, Kim, & Gold, 2015; DiGirolamo et al., 2001), and working memory (Cappell, Gmeindl, & Reuter-Lorenz, 2010; Schneider-Garces et al., 2010; Reuter-Lorenz et al., 2000; see Spreng et al., 2017, for a meta-analysis of age effects on all three domains) tasks. Older age has also been associated with decreased engagement of frontal and posterior parietal regions in response to increasing working memory load, and this is coupled with failure to suppress task-irrelevant activity in regions largely outside fronto-parietal cortex, including “default mode” regions (Kennedy, Boylan, Rieck, Foster, & Rodrigue, 2017; Kaup, Drummond, & Eyler, 2014; Prakash, Heo, Voss, Patterson, & Kramer, 2012). Similarly, as demands increase during a switching task, older adults show less engagement of frontal, parietal, and subcortical regions relative to younger adults (Gazes, Rakitin, Habeck, Steffener, & Stern, 2012). Direct comparisons (via meta-analysis) of the age effects on functional activity during working memory and inhibition show distinct effects for each domain—working memory age differences were largely found in inferior parietal, insula, supplementary motor, and frontal eye fields, whereas inhibition age differences were found in inferior occipital and superior frontal regions, with some overlap between the two domains in middle/inferior frontal gyrus (Turner & Spreng, 2012).

Together, these findings suggest that, although aging may be accompanied by some common alterations in functional activity across cognitive control domains (e.g., increased frontal recruitment), each domain also exhibits dissociable and distinct effects of aging, both within and outside the fronto-parietal control network. Furthermore, both gray and white matter structures, particularly in fronto-parietal cortex, contribute to individual and age differences in cognitive control performance. However, to date, studies examining the contributions of structural integrity to brain function underlying cognitive control have focused on only one domain (inhibition: Forstmann et al., 2008; Colcombe et al., 2005; shifting: Hakun, Zhu, Brown, Johnson, & Gold, 2015; Zhu et al., 2015; Madden et al., 2010; working memory: Burzynska et al., 2013), making it difficult to understand how structure–function relationships might manifest in aging across cognitive control processes.

Therefore, the overarching goal of the current study was to provide a comprehensive examination of functional activity associated with each domain of cognitive control in the context of normal aging to better understand the common and unique brain mechanisms underlying inhibition, shifting, and working memory. We also examined how fronto-parietal structure (both gray and white matter) and behavioral performance contributed to individual differences in functional activity beyond the effects of normal aging. We utilized an adult lifespan approach, including healthy adults aged 20–86 years, to examine the joint relationships among age, cognitive performance, brain structure, and three fMRI tasks of cognitive control: go/no-go (inhibition), letter judgment switching (shifting), and n-back (working memory). We first examined functional activity common to and unique to each fMRI task for the entire sample. Then, using a data-driven multivariate approach, we explored age–brain, behavior–brain, and brain structure–function relationships for each domain. In line with prior work, we hypothesized that we would find robust fronto-parietal activity across all three domains of control; however, we expected aging and individual differences in brain structure and task performance to show unique contributions to functional patterns associated with the separate domains.

METHODS

Participants

One hundred fifty-eight adults, aged 20–86 years, were recruited from the greater Toronto area and completed two testing sessions (cognitive testing via the National Institutes of Health (NIH) Cognitive Toolbox and MRI testing) scheduled approximately 1 week apart. Of the participants who completed both sessions, 18 were excluded for the following reasons: Four participants did not undergo diffusion tensor imaging (because of time constraints), one participant's high-resolution anatomical scan failed adequate FreeSurfer reconstruction, and 13 participants had poor (i.e., below-chance) performance on one or more of the fMRI tasks (four go/no-go, eight n-back, and one n-back and task switch), resulting in a final sample of 140 participants for the current study. Participants were screened to be healthy (i.e., free from any major psychiatric, or neurological, conditions; no history of head trauma), cognitively normal (Mini-Mental State Exam > 26), right-handed, fluent English speakers, with normal or corrected-to-normal vision (at least 20/30), and if necessary, vision was corrected using MRI-compatible lenses during scanning. Participants in the final sample ranged in age from 20 to 86 years (mean age = 48.32 years, SD = 18.42), with 61.43% female and 16.86 mean years of education (see Table 1 for demographics broken down by decade). Sex did not differ by age group (χ2 = 1.52, p = .912), nor did education differ with age (r = −.04, p = .64). The unadjusted cognition composites generated from the NIH Cognitive Toolbox (see Weintraub et al., 2013) showed that fluid cognition decreased with age (r = −.72, p < .001), whereas crystallized intelligence increased (r = .27, p < .001), in line with expected cognitive aging findings (Park et al., 2002).

Table 1.

Sample Demographics: Mean (Standard Deviation)

Age Range (Years)nMean AgeYears of Education% FemaleFluid Cognition NIH CompositeCrystallized Cognition NIH Composite
20–29 29 23.48 (2.54) 16.56 (2.18) 56.0 125.7 (13.8) 121.9 (8.4) 
30–39 29 33.11 (3.32) 17.41 (2.98) 55.6 117.3 (10.8) 122.7 (9.6) 
40–49 22 43.55 (3.04) 16.86 (2.49) 63.6 111.1 (11.4) 131.6 (14.9) 
50–59 22 55.35 (2.50) 16.85 (3.88) 70.0 104.4 (7.1) 125.6 (11.4) 
60–69 26 64.30 (3.05) 17.33 (3.51) 60.9 99.8 (7.3) 134.1 (13.9) 
70–86 31 75.70 (4.58) 16.09 (2.76) 65.2 94.3 (6.0) 130.7 (13.1) 
20–86 (full sample) 140 48.33 (18.52) 16.86 (2.97) 61.4 109.3 (14.6) 127.6 (12.7) 
Age Range (Years)nMean AgeYears of Education% FemaleFluid Cognition NIH CompositeCrystallized Cognition NIH Composite
20–29 29 23.48 (2.54) 16.56 (2.18) 56.0 125.7 (13.8) 121.9 (8.4) 
30–39 29 33.11 (3.32) 17.41 (2.98) 55.6 117.3 (10.8) 122.7 (9.6) 
40–49 22 43.55 (3.04) 16.86 (2.49) 63.6 111.1 (11.4) 131.6 (14.9) 
50–59 22 55.35 (2.50) 16.85 (3.88) 70.0 104.4 (7.1) 125.6 (11.4) 
60–69 26 64.30 (3.05) 17.33 (3.51) 60.9 99.8 (7.3) 134.1 (13.9) 
70–86 31 75.70 (4.58) 16.09 (2.76) 65.2 94.3 (6.0) 130.7 (13.1) 
20–86 (full sample) 140 48.33 (18.52) 16.86 (2.97) 61.4 109.3 (14.6) 127.6 (12.7) 

Data were analyzed with age as a continuous variable, but demographic and cognitive variables are presented by decade. NIH = National Institutes of Health.

MRI Session and Acquisition

All participants were scanned on the same Siemens Trio 3-T magnet at Baycrest Health Sciences. The MRI session (2 hr in total) began with a 30-min mock-scanning session in which participants completed practice for each of the three fMRI tasks in a demagnetized MRI simulator. Afterward, participants underwent a 1.5-hr MRI scan that included T2-weighted fluid-attenuated inversion recovery, 10-min BOLD resting state, T1-weighted anatomical imaging, three BOLD fMRI tasks (detailed below), diffusion-weighted imaging, and, if time permitted, arterial spin labeling. The BOLD fMRI tasks each measured a different domain of cognitive control (inhibition, shifting, and working memory), and the order of the three fMRI tasks was randomized across participants.

The current study included participants with complete and high-quality data for the T1-weighted high-resolution anatomical scan (to examine cortical thickness), the diffusion tensor scan (to examine white matter microstructure), and each of the three BOLD fMRI tasks (to examine functional activity during cognitive control). High-resolution anatomical scans were acquired with a T1-weighted magnetization prepared rapid gradient echo sequence in which 160 axial slices were collected with the following parameters: repetition time (TR) = 2000 msec, echo time (TE) = 2.63 msec, field of view (FOV) = 256 mm, acquisition matrix = 192 × 256 × 160, and 1-mm3 isotropic voxels. Diffusion-weighted imaging was acquired with the following parameters: TR = 7900 msec, TE = 84 msec, FOV = 242 mm, and 68 slices per volume; 60 directions were acquired at b = 1000 s/mm2 plus two b0 non-diffusion-weighted images. BOLD fMRI data were collected using an EPI sequence with 40 axial slices acquired parallel to the AC–PC with the following parameters: TR = 2000 msec, TE = 27 msec, flip angle = 70°, FOV = 192 mm, acquisition matrix = 64 × 64 × 40, 3-mm3 isotropic voxels (with a 0.5-mm gap). Two hundred sixteen volumes were collected for the go/no-go task; 223 volumes for task switching; and 266 volumes for the n-back task.

High-Resolution Anatomical Image Processing

FreeSurfer Version 5.3.0 (Fischl & Dale, 2000; Dale, Fischl, & Sereno, 1999; RRID:SCR_001847) was used to segment gray and white matter of high-resolution T1-weighted volumes and to map each participant's structural anatomy to a labeled atlas (Desikan et al., 2006). This allowed for the isolation and quantification of tissue properties, such as cortical thickness, in anatomical ROIs, in this case frontal and parietal cortices. Trained researchers visually inspected the automated cortical segmentation and conducted manual editing (i.e., removal of dura or addition of control points) when necessary.

Mean cortical thickness was extracted from 12 bilateral frontal and parietal parcels from the Desikan–Killiany atlas (Desikan et al., 2006): caudal and rostral middle frontal, lateral and medial orbitofrontal, superior frontal, pars opercularis, pars orbitalis, pars triangularis, inferior parietal, superior parietal, angular gyrus, and precuneus. Because we were interested in the age-independent contributions of thickness to functional activity (and fronto-parietal thickness was strongly correlated with age: r = −.59, p < .001), age was regressed out of all thickness values for each parcel. The residualized thickness values for all 24 parcels were then submitted to a principal component analysis (PCA), and factor scores from the first component were used as an age-independent composite measure of frontal and parietal thickness. Finally, age-residualized thickness values in the left and right pericalcarine sulcus were averaged to serve as a control gray matter region of no interest.

Diffusion Tensor Imaging Processing

Diffusion imaging data were reconstructed and preprocessed using the TRActs Constrained by UnderLying Anatomy (TRACULA) toolbox available in FreeSurfer Version 5.3.0 (Yendiki et al., 2011; RRID:SCR_013152). The cortical and subcortical labels generated from FreeSurfer were combined with the diffusion data in TRACULA to designate start and end points for global probabilistic fiber tracking using a ball-and-stick model of diffusion via the bedpostx function (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007, Behrens et al., 2003). For each participant, TRACULA reconstructed 18 major white matter pathways (including commissural, projection, and association fibers) in native diffusion space. All pathways were visually inspected to ensure anatomical accuracy.

For the current study, mean fractional anisotropy (FA; a proxy measure for white matter microstructure) weighted by probability distribution was extracted for six major white matter pathways of interest: forceps major, forceps minor, bilateral cingulate bundle, bilateral uncinate fasciculus, and bilateral superior longitudinal fasciculus (parietal and temporal bundles). These pathways were chosen because they connect and underlie frontal and parietal cortices. As with the thickness measures, white matter microstructure showed a strong effect of age (r = −.37, p < .001). Therefore, age was regressed out of the mean FA value for each of the 12 white matter bundles before submitting them to a PCA. Factor scores from the first component were used as an age-independent composite measure of frontal and parietal white matter microstructure. Age-residualized mean FA in the left and right corticospinal tracts was averaged to serve as a control white matter region of no interest.

fMRI Cognitive Control Task Design

In-scanner, participants completed one run each of the three tasks designed to look at different domains of cognitive control. For all tasks, stimuli were white letters presented in the middle of a dark gray background, and participants made their responses on an MRI-compatible button box using the index and middle fingers of their right hand (corresponding finger response was randomized and counterbalanced across participants).

Go/No-go

To measure inhibition, a go/no-go paradigm was used in which participants were presented with a series of letters and told to respond (i.e., go) when they saw the letter “X” and not respond (i.e., no-go) for all other letters (Figure 1A). Letters were presented for 400 msec with a mean ISI of 1200 msec (jittered between 900 and 1500 msec), during which a fixation cross was presented. The task had two blocks (with the order randomized across participants): an “inhibition” block in which there were more go trials than no-go trials (120 go, 40 no-go) and a shorter “initiation” block in which there were more no-go trials than go trials (20 go, 60 no-go). In total, the go/no-go task lasted 6 min 26 sec. The current study was primarily concerned with functional activity during inhibitory processing; therefore, only the inhibition block was analyzed.

Figure 1.

fMRI Tasks. (A) Inhibition was measured using a go/no-go paradigm in which participants withheld responses to infrequent, non-x letters. The performance measure of interest was a speed–accuracy trade-off computation that showed decreases with aging. (B) Shifting was measured using a local switching task in which participants switched between making judgments if the letter was lower/upper case or vowel/consonant. Performance was measured by switch cost calculated as the difference between RT for switching and repeated trials and showed no differences with age. (C) Working memory was measured using an n-back paradigm with 0-, 1-, and 2-back loads. Mean accuracy (Mean Acc) on 1- and 2-back trials decreased with age.

Figure 1.

fMRI Tasks. (A) Inhibition was measured using a go/no-go paradigm in which participants withheld responses to infrequent, non-x letters. The performance measure of interest was a speed–accuracy trade-off computation that showed decreases with aging. (B) Shifting was measured using a local switching task in which participants switched between making judgments if the letter was lower/upper case or vowel/consonant. Performance was measured by switch cost calculated as the difference between RT for switching and repeated trials and showed no differences with age. (C) Working memory was measured using an n-back paradigm with 0-, 1-, and 2-back loads. Mean accuracy (Mean Acc) on 1- and 2-back trials decreased with age.

Overall performance on the inhibition block of the go/no-go task was high, with accuracy near ceiling (go trials: mean accuracy = 98.6%, SD = 0.05; no-go trials: mean accuracy = 95.0%, SD = 0.06; see Table 2 for accuracy broken down by decade). Given the high performance and low variability in accuracy, we chose to use a behavioral measure that takes into account response speed and accuracy that were more normally distributed (Shapiro–Wilk test: W = .985, p = .118). Specifically, this measure of speed–accuracy trade-off that was computed as accuracy of no-go responses divided by the median RT to go responses multiplied by 100 (Seli, 2016). The go/no-go speed–accuracy measure showed a significant decrease with age (r = −.34, p < .001). The primary contrast of functional activity was no-go trials minus go trials.

Table 2.

fMRI Task Accuracy by Decade

Age Range (Years)InhibitionShiftingWorking Memory
No-goGoSwitchRepeat0-Back1-Back2-Back
20–29 93.6 (4.5) 98.3 (4.4) 93.2 (4.6) 96.2 (6.1) 93.5 (5.3) 93.5 (5.2) 85.4 (8.8) 
30–39 95.1 (5.9) 97.7 (6.4) 94 (7.0) 96.3 (6.2) 94.5 (8.6) 92.9 (5.3) 80.5 (12.0) 
40–49 96.9 (2.8) 98.7 (5.0) 94.8 (7.6) 95.3 (7.6) 96.9 (3.4) 91.2 (10.4) 82.7 (11.2) 
50–59 95.8 (3.8) 99.3 (1.2) 93.7 (4.8) 96.2 (5.5) 97.0 (3.3) 93.4 (3.4) 80.9 (10.7) 
60–69 92.8 (8.8) 98.0 (8.3) 91.3 (9.9) 93.3 (9) 96.6 (3.2) 90.5 (9.7) 80.2 (9.6) 
70–86 95.5 (5.4) 99.8 (0.4) 94.3 (6.6) 95.4 (4.1) 95.9 (6.4) 90.7 (6.4) 80.1 (7.1) 
Age Range (Years)InhibitionShiftingWorking Memory
No-goGoSwitchRepeat0-Back1-Back2-Back
20–29 93.6 (4.5) 98.3 (4.4) 93.2 (4.6) 96.2 (6.1) 93.5 (5.3) 93.5 (5.2) 85.4 (8.8) 
30–39 95.1 (5.9) 97.7 (6.4) 94 (7.0) 96.3 (6.2) 94.5 (8.6) 92.9 (5.3) 80.5 (12.0) 
40–49 96.9 (2.8) 98.7 (5.0) 94.8 (7.6) 95.3 (7.6) 96.9 (3.4) 91.2 (10.4) 82.7 (11.2) 
50–59 95.8 (3.8) 99.3 (1.2) 93.7 (4.8) 96.2 (5.5) 97.0 (3.3) 93.4 (3.4) 80.9 (10.7) 
60–69 92.8 (8.8) 98.0 (8.3) 91.3 (9.9) 93.3 (9) 96.6 (3.2) 90.5 (9.7) 80.2 (9.6) 
70–86 95.5 (5.4) 99.8 (0.4) 94.3 (6.6) 95.4 (4.1) 95.9 (6.4) 90.7 (6.4) 80.1 (7.1) 

Mean accuracy (and standard deviation) for each trial type of the three fMRI tasks has been broken down by decade.

Task Switch

To measure shifting, a local-switching paradigm was used in which participants saw a letter in the center of the screen and one of two cues above the letter to categorize the letter as either uppercase/lowercase or consonant/vowel (Figure 1B). The cues and letters were also presented in two different colors (e.g., blue and green) to differentiate between the two kinds of judgments and facilitate performance. There were 60 total trials (50% uppercase/lower case judgments; 50% vowel/consonant judgments) organized such that half of the trials involved switching between judgments (i.e., vowel/consonant and then uppercase/lowercase) and half of the trials repeated the same judgment (i.e., vowel/consonant and then vowel/consonant). Letters were presented for 2000 msec with a mean ISI of 4500 msec (jittered between 1500 and 7500 msec), during which a fixation cross was presented. In total, task switch lasted 7 min 26 sec.

Task-switching accuracy was near ceiling for both repeat (mean accuracy = 95.4%, SD = 0.07) and switching (mean accuracy = 94.5%, SD = 0.07) trials (see Table 2 for accuracy broken down by decade). Therefore, the primary behavioral measure we chose was RT switch cost, which was computed as the difference in median RT for trials that required switching versus trials that repeated the same letter judgment. RT switch cost showed no significant association with age (r = −.03, p = .730), but the vast majority of participants (i.e., 88.6%) exhibited a switch cost effect, as evident by slower RTs to switching (positive switch cost values). The primary shifting contrast of functional activity was switch trials minus repeat trials.

n-Back

To measure working memory, an n-back paradigm was utilized with 0-, 1-, and 2-back loads in which participants saw a series of letters and had to respond if the letter was a “target” or “nontarget” (Figure 1C). For 0-back, the targets were “X.” For 1-back, the targets were letters that matched the previously presented letter. For 2-back, the targets were letters that matched the letter presented two positions back. The task was organized into three blocks (one for each load level), the order of which was randomized across participants, and each block (0-back, 1-back, and 2-back) had 90 total trials (30 target and 60 nontarget). Letters were presented for 500 msec with a mean ISI of 1200 msec (jittered between 900 and 1500 msec), during which a fixation cross was presented. The total time for the n-back task was 8 min 52 sec.

Mean accuracy on the n-back task decreased with increasing working memory load: 0-back accuracy = 94.7% (SD = 0.09), 1-back accuracy = 92.1% (SD = 0.07), and 2-back accuracy = 81.7% (SD = 0.10), as determined by a within-participant ANOVA, F(2, 278) = 156.6, p <. 001 (Table 2). The primary behavioral measure of interest was mean accuracy for 1- and 2-back working memory loads, and this measure showed a weak decrease with older age (r = −.17, p = .047). The primary contrast of functional activity was 1- and 2-back trials minus 0-back trials.

fMRI Preprocessing

Functional data for each task were preprocessed with a mix of AFNI (RRID:SCR_005927) functions and Octave (RRID:SCR_014398) and MATLAB (RRID:SCR_001622) scripts using the Optimizing of Preprocessing Pipelines for NeuroImaging software package (an overview and more details of the preprocessing pipeline can be found in Churchill, Raamana, Spring, & Strother, 2017). For the current study, the following steps were conducted: (1) rigid-body alignment of the time series to correct for movement via 3dvolreg in AFNI, (2) removal and interpolation of outlier volumes using Octave scripts (see Campbell, Grigg, Saverino, Churchill, & Grady, 2013), (3) correction for physiological noise via 3dretroicor in AFNI, (4) slice timing correction via 3dTshift in AFNI, (5) spatial smoothing with a 6-mm smoothing kernel via 3dmerge in AFNI, (6) temporal detrending, (7) motion parameter regression (see Churchill et al., 2017), (8) regression of signal in tissue of no interest (white matter, vessels, and cerebrospinal fluid), and, finally, (9) warping to Montreal Neurological Institute space and resampling to 4-mm3 isotropic voxels.

At the individual participant level, functional data from each task underwent first-level processing in SPM12 (Wellcome Department of Cognitive Neurology; RRID:SCR_007037) to model the BOLD response (using a canonical hemodynamic response function) associated with each experimental condition. The resulting beta values were contrasted for each task to generate one spatial map of functional activity per task. Specifically, for the go/no-go task, BOLD response was contrasted for no-go versus go trials during the inhibition block. For task switching, BOLD response was contrasted for switch versus repeat trials. Finally, for the n-back task, BOLD response was modeled for individual trials within the 0-, 1-, and 2-back blocks, and the primary contrast was for 1- and 2-back trials versus 0-back trials.

Statistical Analyses

Univariate fMRI Group-Level Analyses

Group-level one-sample t tests were conducted in SPM12 separately for each domain to characterize whole-brain functional activity associated with the contrast of interest for each fMRI task. A whole-brain FWE-corrected p value < .05 and a minimum cluster extent (k) of 15 voxels were used to determine significance. The resulting group-level maps were thresholded and overlaid to illustrate regions that showed significant activity across all three tasks.

To further determine functional activity common to all three tasks, as well as activity specific to each task, a within-participant ANOVA (modeling the primary functional contrast from each task as a within-participant factor) was also conducted to statistically test for the common functional activity associated with overall cognitive control. Within this model, functional activity associated with each domain was also contrasted against the other domains in a pairwise manner to identify regions with domain-specific activity. As in the prior univariate SPM analyses, the within-participant ANOVA used a whole-brain FWE-corrected p < .05 with a minimum cluster extent of 15 voxels.

Multivariate Analyses

The primary goal of the current study was to examine how different patterns of brain activity during each domain related to age, performance on the task, and brain structure. To do this, we utilized a multivariate technique called partial least squares (PLS) using the PLS toolbox available in MATLAB (www.rotman-baycrest.on.ca/pls/; McIntosh & Lobaugh, 2004) and ran one PLS model that included all three domains so that we could identify common as well as unique patterns across the three control domains. PLS was used to calculate the covariance between the beta maps from the primary whole-brain functional contrast of interest (e.g., no-go vs. go trials for inhibition) and our other variables of interest: age, task performance, fronto-parietal cortical thickness, and fronto-parietal white matter FA. The task performance metrics for inhibition, shifting, and working memory were speed–accuracy trade-off, RT switch cost, and 1- and 2-back accuracy averaged, respectively. Because task performance and fronto-parietal brain structure measures are generally highly correlated with age, age was removed from these measures before the PLS analysis (see earlier sections for descriptions on how this was computed) to examine their age-independent contributions to functional activity.

In PLS, the covariance matrix undergoes the singular value decomposition, resulting in orthogonal latent variables (LVs; similar to PCA) that explain the covariance between the measures. Significant LVs were identified via permutation (1000 resamples) of the singular values. Each PLS analysis also resulted in a loading (or salience) per voxel (the right singular vectors resulting from the singular value decomposition), which indicated the contribution of activity in that voxel to each LV. Voxel saliences were used to calculate “brain scores” for each participant by multiplying each voxel's salience by the original activity in that voxel and summing over all brain voxels for each participant. Brain scores described the degree to which each participant expressed the activation pattern of a particular LV (see McIntosh & Lobaugh, 2004). The correlations between participant brain scores and age, task performance, and structural measures were calculated to determine how each measure contributed to the resulting pattern of brain activity. Significant correlations were identified via 1000 bootstrap resamples (with replacement) of the correlation values to compute 95% confidence intervals (CIs) around the original correlation values. Correlation 95% CIs that did not cross zero were considered to be the significant relationships associated with the LV.

Significant voxels contributing to each LV were determined via bootstrap resampling of voxel saliences to compute a t-like statistic called the bootstrap ratio (BSR; McIntosh & Mišić, 2013; Krishnan, Williams, McIntosh, & Abdi, 2011; McIntosh & Lobaugh, 2004). BSRs were computed as the ratio of a voxel's salience to an estimate of standard error generated through the resampling procedure (1000 resamples). In the current analysis, a BSR cutoff of 3 (analogous to p ≈ .0013) and a cluster minimum of 15 were used to determine which regions significantly contributed to each LV.

RESULTS

Univariate Group-Level Results

One-sample t tests were conducted separately for each task to examine the overall pattern of activity associated with each task. For the go/no-go task, greater activity to no-go trials was evident in bilateral parietal, bilateral lateral occipital, left inferior frontal, and anterior cingulate, whereas greater activity for go trials was evident in left motor and right cerebellar regions (Figure 2A, Table 3A). For task switching, greater activity was evident for switch relative to repeated trials in bilateral parietal, bilateral insula, left inferior frontal, anterior cingulate, and calcarine sulcus. There were no regions with greater activity for repeated relative to switching trials (Figure 2B, Table 3B). Finally, for n-back, there was greater activity for 1- and 2-back trials relative to 0-back trials in bilateral superior parietal, bilateral middle frontal, bilateral insula, and anterior cingulate. There was greater activity for 0-back trials in medial frontal and posterior cingulate (Figure 2C, Table 3C). An additional contrast of working memory load levels found one cluster in medial superior frontal cortex (peak: x = −1, y = 29, z = 51) with greater activity for 2-back relative to 1-back trials (not illustrated), suggesting that difference between the two working memory loads was minimal in general cognitive control regions. Finally, the significant voxels from each group-level analysis were overlaid to illustrate the common regions activated across each domain (Figure 2D). This illustration showed that, across all three tasks, there was significant functional activity in bilateral parietal, left inferior frontal, and ACC.

Figure 2.

Group-level contrasts of interest for each task. One-sample t tests conducted across the entire sample show functional activity associated with the primary contrast of interest for each fMRI task (pFWE < .05, k > 15): (A) inhibition, (B) shifting, and (C) working memory. Axial slices are 10 mm apart. (D) The common significant voxels across all three tasks (e.g., conjunction effect). 0b = 0-back; 1b = 1-back; 2b = 2-back.

Figure 2.

Group-level contrasts of interest for each task. One-sample t tests conducted across the entire sample show functional activity associated with the primary contrast of interest for each fMRI task (pFWE < .05, k > 15): (A) inhibition, (B) shifting, and (C) working memory. Axial slices are 10 mm apart. (D) The common significant voxels across all three tasks (e.g., conjunction effect). 0b = 0-back; 1b = 1-back; 2b = 2-back.

Table 3.

Group-Level Contrasts of Interest for Each Task

Cluster LabelxyzCluster Sizet Value
A. Inhibition: no-go > go 
 LH lateral occipital/inferior temporal −49 −72 −6 268 9.68 
 RH lateral occipital/inferior temporal 44 −64 −14 192 9.26 
 LH superior lateral occipital/superior parietal −29 −68 51 226 8.64 
 RH superior lateral occipital/superior parietal 28 −64 43 98 7.32 
 LH inferior frontal −49 21 27 68 6.38 
 RH anterior cingulate 21 35 21 6.10 
  
Inhibition: go > no-go 
 RH cerebellum lobule VI 20 −56 −22 99 11.03 
 LH precentral gyrus −41 −16 59 142 8.82 
 LH parietal operculum −57 −24 15 15 5.81 
  
B. Switching: switch > repeat 
 LH lateral occipital/superior parietal −21 −72 51 603 7.87 
 RH lateral occipital/superior parietal 20 −68 55 67 7.02 
 RH superior parietal 36 −44 43 18 6.54 
 LH frontal orbitofrontal/insula −41 21 −6 29 6.42 
 LH anterior cingulate −9 13 47 69 6.35 
 LH temporo-occipital cortex −45 −60 −14 49 6.31 
 LH inferior frontal −53 33 15 16 6.17 
 RH insula 40 21 −2 15 6.00 
 RH calcarine −76 15 20 5.52 
  
C. Working memory: 1- and 2-back > 0-back 
 RH middle/superior frontal 28 13 55 104 8.58 
 LH superior parietal −33 −52 47 232 7.93 
 LH superior medial frontal −1 33 39 93 7.83 
 LH inferior oribitofrontal/insula −33 29 −2 29 7.81 
 RH lateral occipital/superior parietal 32 −60 47 199 7.45 
 RH insula 36 25 −2 45 7.25 
 LH middle frontal −29 59 251 6.99 
 RH middle/inferior frontal 36 35 73 6.79 
  
Working memory: 0-back > 1- and 2-back 
 LH medial frontal −5 57 −14 87 6.82 
 LH posterior cingulate −5 −48 27 31 6.73 
Cluster LabelxyzCluster Sizet Value
A. Inhibition: no-go > go 
 LH lateral occipital/inferior temporal −49 −72 −6 268 9.68 
 RH lateral occipital/inferior temporal 44 −64 −14 192 9.26 
 LH superior lateral occipital/superior parietal −29 −68 51 226 8.64 
 RH superior lateral occipital/superior parietal 28 −64 43 98 7.32 
 LH inferior frontal −49 21 27 68 6.38 
 RH anterior cingulate 21 35 21 6.10 
  
Inhibition: go > no-go 
 RH cerebellum lobule VI 20 −56 −22 99 11.03 
 LH precentral gyrus −41 −16 59 142 8.82 
 LH parietal operculum −57 −24 15 15 5.81 
  
B. Switching: switch > repeat 
 LH lateral occipital/superior parietal −21 −72 51 603 7.87 
 RH lateral occipital/superior parietal 20 −68 55 67 7.02 
 RH superior parietal 36 −44 43 18 6.54 
 LH frontal orbitofrontal/insula −41 21 −6 29 6.42 
 LH anterior cingulate −9 13 47 69 6.35 
 LH temporo-occipital cortex −45 −60 −14 49 6.31 
 LH inferior frontal −53 33 15 16 6.17 
 RH insula 40 21 −2 15 6.00 
 RH calcarine −76 15 20 5.52 
  
C. Working memory: 1- and 2-back > 0-back 
 RH middle/superior frontal 28 13 55 104 8.58 
 LH superior parietal −33 −52 47 232 7.93 
 LH superior medial frontal −1 33 39 93 7.83 
 LH inferior oribitofrontal/insula −33 29 −2 29 7.81 
 RH lateral occipital/superior parietal 32 −60 47 199 7.45 
 RH insula 36 25 −2 45 7.25 
 LH middle frontal −29 59 251 6.99 
 RH middle/inferior frontal 36 35 73 6.79 
  
Working memory: 0-back > 1- and 2-back 
 LH medial frontal −5 57 −14 87 6.82 
 LH posterior cingulate −5 −48 27 31 6.73 

Coordinates indicate the peak voxel in each cluster, pFWE < .05, k ≥ 15. LH = left hemisphere; RH = right hemisphere.

Next, a within-participant ANOVA (using each task contrast as a repeated within-participant measure) was conducted to test statistically for cortical regions commonly activated across all tasks, as well as which regions may show greater activity to a specific task. The main effect of all tasks indicated common activity in bilateral parietal, bilateral superior and middle frontal, bilateral insula, left precentral, and anterior cingulate (Figure 3A, Table 4A). Directly contrasting each domain found that both inhibition (Figure 3B, Table 4B) and shifting (Figure 3C, Table 4C) showed greater activity in medial pFC and posterior cingulate compared to working memory. There were no regions in which inhibition showed greater activity than shifting.1 Compared to inhibition, shifting showed greater activity in right inferior frontal, left precentral, left angular, and right cerebellar cortex (Figure 3D, Table 4D). Finally, compared to inhibition (Figure 3E, Table 4E) and shifting (Figure 3F, Table 4F), working memory showed greater activity in areas largely overlapping with the common fronto-parietal regions; however, there also was a unique right inferior frontal cluster (axial slice: z = 30 in Figure 3E and F) illustrating working-memory-specific activity outside the common control network. Additional comparisons directly contrasting each domain to the other two (i.e., inhibition > shifting + working memory) resulted in similar patterns of activity, with the exception of shifting > inhibition and working memory, which only showed differences in medial frontal and left precentral gyri.

Figure 3.

Within-participant ANOVA. (A) A within-participant ANOVA of all three domains found bilateral frontal, parietal, insula, and anterior cingulate were consistently activated across all three tasks (pFWE < .05, k > 15). Contrasting specific domains found that both inhibition (B) and shifting (C) had greater activity in medial prefrontal and posterior cingulate compared to working memory. (D) Compared to inhibition, shifting showed greater activity in right inferior frontal, left precentral, left angular, and right cerebellar cortex. Working memory showed greater activity in frontal, parietal, anterior cingulate, and insula regions compared to inhibition (E) and shifting (F).

Figure 3.

Within-participant ANOVA. (A) A within-participant ANOVA of all three domains found bilateral frontal, parietal, insula, and anterior cingulate were consistently activated across all three tasks (pFWE < .05, k > 15). Contrasting specific domains found that both inhibition (B) and shifting (C) had greater activity in medial prefrontal and posterior cingulate compared to working memory. (D) Compared to inhibition, shifting showed greater activity in right inferior frontal, left precentral, left angular, and right cerebellar cortex. Working memory showed greater activity in frontal, parietal, anterior cingulate, and insula regions compared to inhibition (E) and shifting (F).

Table 4.

Shared and Unique Cognitive Control Activity across All Three Tasks

Cluster LabelxyzCluster SizeF Value
A. SPM within-subject ANOVA: main effect of task 
 LH superior/inferior parietal −21 −68 51 132 133.56 
 RH middle/superior frontal 28 13 55 39 116.38 
 LH insula/inferior orbitofrontal −33 29 −2 15 110.20 
 LH precentral/postcentral −41 −16 59 63 104.94 
 LH medial frontal/anterior cingulate −1 33 39 35 95.42 
 RH superior/inferior parietal 20 −68 59 97 93.06 
 RH insula 36 25 −2 19 88.30 
 LH middle frontal −33 59 74 86.44 
  
B. Inhibition > Working Memory 
 LH ventral medial frontal −1 57 −18 92 6.32 
 LH posterior cingulate −5 −48 27 22 6.13 
  
C. Shifting > Working Memory 
 LH medial frontal −5 57 −18 109 6.61 
 LH posterior cingulate −5 −48 27 20 5.81 
  
D. Shifting > Inhibition 
 LH precentral −41 −16 59 343 10.57 
 RH cerebellum Lobule VI 20 −56 −22 117 9.10 
 LH supramarginal −61 −24 19 33 7.15 
 RH inferior frontal 52 13 11 18 6.20 
  
E. Working Memory > Inhibition 
 RH middle/superior frontal 28 13 55 96 8.45 
 LH insula −33 29 −2 26 7.73 
 LH superior/inferior parietal −21 −68 51 130 7.2o 
 LH medial frontal −1 33 39 66 7.18 
 RH insula 36 25 −2 44 6.87 
 RH superior/inferior parietal 44 −44 51 160 6.71 
 LH inferior/middle frontal −45 37 27 187 6.63 
 RH inferior frontal 36 35 57 6.54 
  
F. Working Memory > Shifting 
 RH middle/superior frontal 28 13 55 73 7.91 
 LH insula −33 29 −2 16 7.00 
 LH medial frontal −1 33 39 42 6.66 
 LH inferior frontal −37 13 31 77 6.55 
 RH superior parietal 20 −72 59 120 6.48 
 LH superior parietal −21 −68 51 90 6.34 
 RH inferior frontal 36 35 33 6.05 
 LH middle frontal −33 59 48 6.00 
Cluster LabelxyzCluster SizeF Value
A. SPM within-subject ANOVA: main effect of task 
 LH superior/inferior parietal −21 −68 51 132 133.56 
 RH middle/superior frontal 28 13 55 39 116.38 
 LH insula/inferior orbitofrontal −33 29 −2 15 110.20 
 LH precentral/postcentral −41 −16 59 63 104.94 
 LH medial frontal/anterior cingulate −1 33 39 35 95.42 
 RH superior/inferior parietal 20 −68 59 97 93.06 
 RH insula 36 25 −2 19 88.30 
 LH middle frontal −33 59 74 86.44 
  
B. Inhibition > Working Memory 
 LH ventral medial frontal −1 57 −18 92 6.32 
 LH posterior cingulate −5 −48 27 22 6.13 
  
C. Shifting > Working Memory 
 LH medial frontal −5 57 −18 109 6.61 
 LH posterior cingulate −5 −48 27 20 5.81 
  
D. Shifting > Inhibition 
 LH precentral −41 −16 59 343 10.57 
 RH cerebellum Lobule VI 20 −56 −22 117 9.10 
 LH supramarginal −61 −24 19 33 7.15 
 RH inferior frontal 52 13 11 18 6.20 
  
E. Working Memory > Inhibition 
 RH middle/superior frontal 28 13 55 96 8.45 
 LH insula −33 29 −2 26 7.73 
 LH superior/inferior parietal −21 −68 51 130 7.2o 
 LH medial frontal −1 33 39 66 7.18 
 RH insula 36 25 −2 44 6.87 
 RH superior/inferior parietal 44 −44 51 160 6.71 
 LH inferior/middle frontal −45 37 27 187 6.63 
 RH inferior frontal 36 35 57 6.54 
  
F. Working Memory > Shifting 
 RH middle/superior frontal 28 13 55 73 7.91 
 LH insula −33 29 −2 16 7.00 
 LH medial frontal −1 33 39 42 6.66 
 LH inferior frontal −37 13 31 77 6.55 
 RH superior parietal 20 −72 59 120 6.48 
 LH superior parietal −21 −68 51 90 6.34 
 RH inferior frontal 36 35 33 6.05 
 LH middle frontal −33 59 48 6.00 

Coordinates indicate the peak voxel in each cluster, pFWE < .05, k ≥ 15. No significant clusters for Inhibition > Shifting at this significance threshold.

Multivariate Results

After establishing group-level functional activity associated with each task, we next took a multivariate approach using PLS to examine how age, task performance, and gray and white matter structure contributed to individual differences in functional activity on each domain. Because of the strong age effects on task performance, gray matter thickness, and white matter FA, age was regressed out of the behavioral and structural measures before the PLS (see Methods for more details). The PLS resulted in two significant LVs.

LV 1

The first LV (p < .001) explained 22.3% covariance between functional activity and measures of age, task performance, and brain structure. The pattern identified by this LV was specific to the inhibition and shifting domains (working memory had no contribution to this LV; see Figure 4A). The regions identified by this LV included bilateral angular gyrus, bilateral middle frontal gyri, medial superior frontal cortex, and precuneus (Figure 5, Table 5A).

Figure 4.

PLS Results: brain score correlations (Corr). The PLS resulted in two significant latent variables that characterized the multivariate correlations between brain activity and age, performance, and brain structure measures (thickness and FA). The first latent variable was specific to inhibition and shifting domains. The second latent variable characterized brain correlations for all three domains. Error bars illustrate bootstrapped 95% CIs.

Figure 4.

PLS Results: brain score correlations (Corr). The PLS resulted in two significant latent variables that characterized the multivariate correlations between brain activity and age, performance, and brain structure measures (thickness and FA). The first latent variable was specific to inhibition and shifting domains. The second latent variable characterized brain correlations for all three domains. Error bars illustrate bootstrapped 95% CIs.

Figure 5.

PLS Results for LV 1. PLS revealed a pattern of activation on the first latent variable that correlated with age, behavioral, and structural measures for inhibition and shifting. Brain maps illustrate BSR for significant voxels (BSR > |3|). Scatterplots illustrate the correlation between individual difference measures and the brain score for each task, which represents the degree to which a participant expresses that pattern of activation. Axial slices are 10 mm apart. GNG = go/no-go; TSW = task switching.

Figure 5.

PLS Results for LV 1. PLS revealed a pattern of activation on the first latent variable that correlated with age, behavioral, and structural measures for inhibition and shifting. Brain maps illustrate BSR for significant voxels (BSR > |3|). Scatterplots illustrate the correlation between individual difference measures and the brain score for each task, which represents the degree to which a participant expresses that pattern of activation. Axial slices are 10 mm apart. GNG = go/no-go; TSW = task switching.

Table 5.

Behavioral PLS Results Examining Functional Activity with Age, Performance, and Structure

Cluster LabelxyzCluster SizeBSR
A. LV 1: inhibition (Beh, WM) and shifting (age, GM) 
 Positive clusters 
  Frontal lobe 
   RH inferior orbital frontal 44 40 −16 102 5.36 
   RH middle orbital frontal 28 48 −8 23 4.79 
   LH middle frontal −28 16 48 88 4.81 
   RH superior frontal 28 40 52 156 4.88 
   LH superior frontal −16 64 90 4.19 
   LH superior frontal −20 64 28 17 4.16 
   LH superior frontal −16 −8 80 45 4.08 
   RH insula 44 12 −8 20 4.26 
  
  Parietal lobe 
   LH angular −52 −64 28 132 5.30 
   RH supramarginal/angular 56 −48 28 152 4.33 
   RH precentral 24 −20 60 37 4.97 
   LH precentral −52 −8 40 27 4.08 
   LH precuneus −12 −64 36 89 4.95 
  
  Cingulate cortex 
   Middle cingulate −12 44 23 5.16 
   Posterior cingulate −4 −40 36 98 4.58 
  
  Temporal/occipital 
   LH middle/superior temporal −60 −12 −8 28 4.43 
   RH superior temporal 60 −24 12 52 4.38 
   LH temporal pole −52 31 4.00 
   RH hippocampus 28 −8 −12 20 4.29 
   LH hippocampus −20 −12 −16 64 4.28 
   LH lingual/fusiform −16 −84 −12 84 5.14 
  
B. LV 2: inhibition (age, Beh), shifting (age, GM), and working memory (age, Beh) 
 Positive clusters 
   Medial frontal/anterior cingulate 52 43 5.90 
   Posterior cingulate −12 −44 36 17 4.75 
   RH angular/superior occipital 48 −64 24 57 4.15 
   RH middle temporal 64 −32 −8 21 4.10 
  
 Negative clusters 
   LH superior frontal −24 64 53 −4.78 
   RH supplementary motor 12 12 52 16 −4.30 
   RH precentral 28 44 24 −4.15 
Cluster LabelxyzCluster SizeBSR
A. LV 1: inhibition (Beh, WM) and shifting (age, GM) 
 Positive clusters 
  Frontal lobe 
   RH inferior orbital frontal 44 40 −16 102 5.36 
   RH middle orbital frontal 28 48 −8 23 4.79 
   LH middle frontal −28 16 48 88 4.81 
   RH superior frontal 28 40 52 156 4.88 
   LH superior frontal −16 64 90 4.19 
   LH superior frontal −20 64 28 17 4.16 
   LH superior frontal −16 −8 80 45 4.08 
   RH insula 44 12 −8 20 4.26 
  
  Parietal lobe 
   LH angular −52 −64 28 132 5.30 
   RH supramarginal/angular 56 −48 28 152 4.33 
   RH precentral 24 −20 60 37 4.97 
   LH precentral −52 −8 40 27 4.08 
   LH precuneus −12 −64 36 89 4.95 
  
  Cingulate cortex 
   Middle cingulate −12 44 23 5.16 
   Posterior cingulate −4 −40 36 98 4.58 
  
  Temporal/occipital 
   LH middle/superior temporal −60 −12 −8 28 4.43 
   RH superior temporal 60 −24 12 52 4.38 
   LH temporal pole −52 31 4.00 
   RH hippocampus 28 −8 −12 20 4.29 
   LH hippocampus −20 −12 −16 64 4.28 
   LH lingual/fusiform −16 −84 −12 84 5.14 
  
B. LV 2: inhibition (age, Beh), shifting (age, GM), and working memory (age, Beh) 
 Positive clusters 
   Medial frontal/anterior cingulate 52 43 5.90 
   Posterior cingulate −12 −44 36 17 4.75 
   RH angular/superior occipital 48 −64 24 57 4.15 
   RH middle temporal 64 −32 −8 21 4.10 
  
 Negative clusters 
   LH superior frontal −24 64 53 −4.78 
   RH supplementary motor 12 12 52 16 −4.30 
   RH precentral 28 44 24 −4.15 

Beh = behavioral performance; GM = gray matter thickness; WM = white matter FA.

For inhibition, increased activity during no-go trials in these regions was associated with better performance (i.e., a higher speed–accuracy trade-off; Figures 4A and 5) independent of age. Decreased activity during no-go trials was associated with greater fronto-parietal white matter microstructure (after accounting for age). A post hoc examination found that brain scores associated with inhibition did not correlate with FA in a control white matter pathway (corticospinal tract: r = −.13, p = .12), suggesting this relationship was somewhat specific to fronto-parietal white matter. For LV 1, functional activity during inhibition was not associated with age or cortical thickness as evident by 95% CIs that crossed zero (Figure 4A).

For shifting, increased activity to switch trials in these regions was associated with older age (Figures 4A and 5). Decreased activity during switching was associated with greater fronto-parietal cortical thickness after controlling for age. A post hoc correlation of the shifting brain scores with thickness in a pericalcarine control region was not significant (r = −.06, p = .43), suggesting that the function–thickness correlation identified in the PLS was somewhat specific to fronto-parietal cortex. For LV 1, functional activity during shifting was not associated with RT switch cost or white matter FA. To summarize this LV, increased activity in these fronto-parietal regions during inhibition and switching showed a similar relation to reduced brain structural measures but also had specific relations to age (switching) and performance (inhibition); no relation between activity during working memory and the variables of interest was seen on this LV.

LV 2

The second LV (p = .033) explained 10.7% covariance between functional activity and measures of age, task performance, and brain structure, and relations were found with all three domains (Figure 4B). The second LV identified a pattern of functional activity in anterior and posterior cingulate, right angular, and right middle temporal gyrus (hot clusters in Figure 6) that was differentiated from activity in superior frontal, supplementary motor, and premotor gyrus (cool clusters; Figure 6, Table 5B). For no-go trials during the inhibition task, increased activity in hot regions was associated with younger age, and increased activity in cool regions was associated with older age (Figures 4B and 6). In addition, more activity in hot regions was associated with better speed–accuracy trade-off after accounting for age. The LV 2 pattern of functional activity during inhibition was not significantly related to fronto-parietal cortical thickness or fronto-parietal white matter FA.

Figure 6.

PLS Results for LV 2. PLS revealed a pattern of activation on the second latent variable that correlated with age, behavioral, and structural measures for each task. Brain maps illustrate BSR for significant voxels (BSR > |3|). Scatterplots illustrate the correlation between individual difference measures and the brain score for each task, which represents the degree to which a participant expresses that pattern of activation. Axial slices are 10 mm apart. GNG = go/no-go; TSW = task switching; NBK = n-back.

Figure 6.

PLS Results for LV 2. PLS revealed a pattern of activation on the second latent variable that correlated with age, behavioral, and structural measures for each task. Brain maps illustrate BSR for significant voxels (BSR > |3|). Scatterplots illustrate the correlation between individual difference measures and the brain score for each task, which represents the degree to which a participant expresses that pattern of activation. Axial slices are 10 mm apart. GNG = go/no-go; TSW = task switching; NBK = n-back.

Similar to LV 1, for shifting, the second LV described relationships among functional activity, age, and fronto-parietal thickness (Figures 4B and 6). Specifically, during switch trials, increased activity in hot regions was associated with older ages and greater fronto-parietal thickness, whereas increased activity in cool regions was associated with younger ages and lower thickness values. A post hoc correlation of shifting brain scores with thickness in the control pericalcarine region was not significant (r = .04, p = .63). For LV 2, functional activity during shifting was not associated with RT switch cost or white matter FA.

Finally, for working memory, increased activity for 1- and 2-back working memory loads in hot regions was associated with older age and better task performance after accounting for age (Figures 4B and 6). In contrast, more activity in cool regions was associated with younger age and worse performance. For LV 2, functional activity during working memory did not significantly correlate with gray matter thickness or white matter microstructure. To summarize LV 2, greater activity in the hot regions during the shifting and working memory tasks was associated with older age, and increased activity in these areas also was related to better performance in the inhibition and working memory tasks. Other effects, such as a relation between increased activity in hot regions during shifting and greater cortical thickness in fronto-parietal regions, were specific to only one control domain.

DISCUSSION

The goal of the current study was twofold: (1) to examine functional correlates common and unique to the three core domains of cognitive control—inhibition, shifting, and working memory—in a large adult lifespan sample and (2) to examine relations of age, brain structure, and performance with functional brain activity across domains. We report that, although inhibition, shifting, and working memory generally engage a common set of fronto-parietal cognitive control regions, there are unique contributions of age, behavior, and brain structure to patterns of functional activity that differ by cognitive control domain. Interestingly, the multivariate patterns of functional activity associated with age, behavior, and structure primarily included functional regions outside the fronto-parietal network identified at the group level. Together, these results suggest that individual difference measures, like age, task performance, and gray and white matter structure, exert a stronger effect on functional activity in regions beyond those primarily underlying common cognitive control processes. Group-level differences in functional activity by task domain, as well as individual differences in the contributions of age, task performance, and brain structure to functional activity, are discussed in turn.

Functional Activity Common and Unique to Tasks of Cognitive Control

In line with previous work suggesting that different cognitive control processes engage a superordinate fronto-parietal network, the current study found that, across all participants, our three fMRI tasks activated a common set of regions that included bilateral superior and middle frontal, bilateral superior and inferior parietal, bilateral insula, anterior cingulate, precuneus, and left precentral gyrus. A prior meta-analysis examining the conjunction of functional activity for inhibition, shifting (termed “flexibility”), and working memory identified an identical set of regions as the current work, illustrating that the common effect of cognitive control for these three domains is robust, regardless of specific fMRI task or stimuli (Niendam et al., 2012). Furthermore, the few previous within-participant neuroimaging experiments that included all three domains report consistent activation of fronto-parietal cortex across domains (Lemire-Rodger et al., 2019; Derrfuss et al., 2004), with some additional domain-common activation in regions not found in the current study nor in the Niendam et al. (2012) meta-analysis (i.e., thalamus: Derrfuss et al., 2004; right middle/inferior temporal gyrus: Lemire-Rodger et al., 2019).

When examining the individual cognitive control domains at the group level, we found that each domain had unique functional activity associated with it relative to the other two domains. Specifically, for inhibition, we report greater activity relative to working memory in posterior cingulate and superior and ventral medial frontal regions, which overlaps with the midline default mode network (Greicius, Krasnow, Reiss, & Menon, 2003). Our findings also correspond with prior work directly comparing functional activity for inhibition and working memory, which also finds greater activity during inhibition in ventral medial frontal regions (Collette et al., 2005). Greater midline default activity during inhibition could reflect relative differences in the difficulty of our chosen fMRI tasks (i.e., go/no-go mean accuracy of 98.2% vs. 1- and 2-back mean accuracy of 86.0%), as medial default regions have been found to show greater activity during easy task conditions that is suppressed as task demands and difficulty increase (Rieck, Rodrigue, Boylan, & Kennedy, 2017; Leech, Kamourieh, Beckmann, & Sharp, 2011; Persson, Lustig, Nelson, & Reuter-Lorenz, 2007). Contrary to prior studies that report the right inferior frontal gyrus as a core region of inhibitory processing (Lemire-Rodger et al., 2019; Aron, Robbins, & Poldrack, 2004), we did not find unique inhibitory activity in this region that surpassed the common effect across domains, suggesting that the right inferior frontal gyrus may subserve broader cognitive control processes that are only evident when examining multiple domains of control together. Similar to our findings, a previous study contrasting functional activity during inhibition and working memory (across multiple tasks) also failed to isolate inhibition-specific activity in right inferior frontal gyrus that surpassed activity common to both domains (McNab et al., 2008). We also report that directly contrasting inhibition and shifting revealed no regions of the brain in which inhibition showed greater activity than shifting (although relaxing the significance threshold did reveal bilateral clusters in occipital cortex). Although prior researchers have reported unique inhibition activity relative to shifting (Collette et al., 2007), they examined differences in functional activity for each domain between different groups of participants; therefore, their finding of inhibition-specific activity could reflect interindividual differences in how each domain is represented, whereas our work offers a unique within-participant contrast of these two domains.

For shifting, we observed relatively greater activity in ventromedial prefrontal and posterior cingulate regions compared to working memory, similar to inhibition. The shifting-specific functional activity in ventromedial pFC may also reflect decision-making processes rather than default mode activity alone. Prior work demonstrates that ventromedial pFC is crucial when there are heavy demands on response selection and decision-making, particularly when there are changing task rules (as is the case in a switching paradigm; Fellows & Farah, 2005). Compared to inhibition, shifting showed greater activity in angular gyrus, inferior frontal, precentral cortex, and cerebellum. The angular gyrus is an important hub for multimodal integration (Seghier, 2013), and both inferior frontal and angular gyri play a key role in retrieving semantic and prior knowledge (Bellana, Ladyka-Wojcik, Lahan, Moscovitch, & Grady, 2019; Seghier, 2013; Roskies, Fiez, Balota, Raichle, & Petersen, 2001). Therefore, this pattern of shifting-specific activity may reflect coordination of semantic knowledge (e.g., “Is this letter a vowel?”) with subsequent motor response in precentral and cerebellar regions. To note, Lemire-Rodger et al. (2019) also report widespread shifting-specific activity in superior parietal, medial, and lateral pFC, including regions similar to those we report, but their task design did not specifically allow for a comparison of switching versus repeated trials, making it difficult to directly compare our findings.

Finally, compared to the inhibition and shifting tasks, working memory showed relatively greater activity in the widespread set of commonly activated bilateral frontal, parietal, anterior cingulate, and insula regions, in addition to a unique region in right inferior frontal gyrus. Greater activation in fronto-parietal regions is consistent with previous fMRI studies that also utilize an n-back paradigm (see Owen, McMillan, Laird, & Bullmore, 2005, for a review). Furthermore, this finding of relatively greater fronto-parietal activity is in line with other direct comparisons of working memory to shifting and inhibition within the same sample, including a similar working-memory-specific region of right inferior frontal gyrus (Collette et al., 2005). Overall increased fronto-parietal activity in addition to the unique recruitment of an additional prefrontal region could reflect modulations in activity in response to relative increases in task difficulty (Cappell et al., 2010) and working memory load (Kennedy et al., 2017). Our finding is somewhat at odds with recent work that found no working-memory-specific patterns of functional activity using a data-driven approach (Lemire-Rodger et al., 2019), leading researchers to conclude that, when cognitive demands are similar across domains, working memory may not have unique contributions beyond superordinate cognitive control processes. The fronto-parietal regions in which we found working-memory-specific increases largely overlapped with our common cognitive control regions (plus a unique right prefrontal region)—therefore, in addition to subserving general cognitive control, these fronto-parietal regions show enhanced engagement during working memory, suggesting that updating and maintaining processes may be involved in many aspects of cognitive control, generally in line with previous conclusions (Lemire-Rodger et al., 2019; Spreng et al., 2017).

Overall, our findings generally support evidence for a hybrid account of cognitive control in which a unified set of fronto-parietal regions subserve processes common to all three domains in addition to domain-specific activity both within and outside core cognitive control regions. Specifically, working memory may more broadly engage fronto-parietal regions and recruit additional frontal regions, especially when task demands are increased, whereas inhibition and shifting show unique activity in default (inhibition and shifting), motor (shifting), semantic (shifting), and decision-making (shifting) regions. However, it is important to note that our comparisons of cognitive control domains may have been limited by having each domain as a separate fMRI task (compared to other studies that have attempted to parse the different domain processes from a single task; see Lemire-Rodger et al., 2019). Specifically, our working memory and inhibition tasks may have required additional sustained cognitive demands (e.g., to remember response rules throughout the task), whereas for shifting, task response rules were presented with every trial (as required by the local-switching task design). Future work examining multiple cognitive domains should consider implementing a mixed block/event-related to better address the role of sustained versus phasic task demands (see Dosenbach et al., 2007).

Age Effects on Functional Activity during Cognitive Control

By utilizing a lifespan approach, we were able to examine age as a continuous variable, allowing us to capture interindividual differences (including often understudied middle-aged adults) rather than relying on extreme age group comparisons. When comparing analyses across tasks, age was found to be a strong contributor to individual differences in patterns of functional activity during cognitive control; however, the current study shows that age does not have a singular effect on functional activity across domains, in line with recent meta-analyses (Spreng et al., 2017; Turner & Spreng, 2012). For all domains of cognitive control, the effects of aging on functional activity were evident in regions outside the common fronto-parietal regions engaged by each task, suggesting that the overall functional processes supporting cognitive control are intact within our sample, and aging exerts a stronger effect on other functional systems that might interact with cognitive control.

We found that older age was associated with decreased activity in largely default-type regions during response inhibition (e.g., right angular gyrus, posterior cingulate, and medial orbitofrontal/anterior cingulate) and increased activity in supplementary motor, premotor, and superior frontal regions (LV 2). Our finding of decreased default-type activity is at odds with prior work that generally finds that older age is associated with increased activity in default mode regions during task engagement relative to no task (i.e., a fixation condition; Persson et al., 2007). However, in the current study, inhibitory processing (i.e., withholding a “no-go” response during a frequent “go” task) was operationalized as a functional activity for infrequent “no-go” trials relative to predominant “go” trials. Therefore, this age-related decrease in activity during response inhibition could reflect a relative increase in activity for the predominant “go” task condition, indicating an age-related failure to suppress task-irrelevant default regions for most of the task. During no-go trials, older adults also showed increased activity in supplementary and premotor areas (LV 2), regions involved in response initiation (Niendam et al., 2012), which may reflect a maladptive functional response for trials that require participants to inhibit prepotent response mechanisms. This interpretation is further supported by the pattern of increased activity in these regions being associated with worse speed–accuracy performance (after controlling for age).

During shifting, older adults showed increased activity in a widespread set of regions largely overlapping with sensory/motor regions (precentral gyrus, superior temporal), salience regions (dorsal anterior cingulate, insula), and default network regions (angular gyrus, hippocampus, posterior cingulate, and medial orbitofrontal/anterior cingulate) for trials that required switching letter decisions (LVs 1 and 2). Age-related increases in default-type activity for more difficult trials (i.e., switch trials) are in line with previous reports that older adults may fail to suppress less-relevant regions during cognitively demanding tasks (Persson et al., 2007). Furthermore, prior work examining local task-switching costs in aging has also reported overrecruitment of frontal regions (Spreng et al., 2017; Zhu et al., 2015; Colcombe et al., 2005), including insula and anterior cingulate regions. Given that we see no behavioral age differences in switch cost (i.e., the difference in RT for switch relative to repeat trials), it is possible that this widespread increased activity could represent compensatory mechanisms that enable older adults to maintain similar response costs when having to switch judgment types (Cabeza et al., 2018). Furthermore, prior work examining functional connectivity finds that, in older adults, the salience network, particularly dorsal portions, modulates interactions between fronto-parietal and default networks (Chand, Wu, Hajjar, & Qiu, 2017). Therefore, the increased activity in dorsal anterior cingulate, insula, and supplementary motor regions during switching might reflect enhanced engagement of attention and initiation mechanisms associated with the dorsal salience network (Touroutoglou, Hollenbeck, Dickerson, & Feldman Barrett, 2012) that, in turn, modulates functional activity in fronto-parietal control regions to similar levels as younger adults, thus allowing for similar levels of performance in older age.

Finally, during working memory, older age was associated with increased activity for 1- and 2-back trials in default-type regions (anterior/posterior cingulate, angular, middle temporal; LV 2). As with shifting, prior work examining working memory has also found greater default activity during higher working memory loads in older age (Kennedy et al., 2017), suggesting that older adults fail to suppress task-irrelevant activity during more effortful processing. On the other hand, during working memory, older age was associated with decreased activity in supplementary, superior frontal, and premotor regions, which are regions that show consistent activation during tasks that require initiation of complex behaviors, another subdomain of executive functioning (Niendam et al., 2012). Furthermore, supplementary motor and premotor regions are thought to be important for the internal rehearsal and maintenance of information (Smith, Jonides, Marshuetz, & Koeppe, 1998). Given our finding that younger adults activate these regions at higher working memory loads (1- and 2-back) whereas older adults activate these regions when the load is lowest (0-back), this finding could reflect failure of older adults to engage these additional regions involved in initiation and internal rehearsal as working memory demands increase.

Brain–Behavior Relationships in Cognitive Control

Independent of age, performance on the inhibition and working memory tasks was supported by individual differences in functional activity. Although prior work has generally found that performance on cognitive control tasks is related to activity in fronto-parietal cortex (Grady, Rieck, Nichol, Rodrigue, & Kennedy, 2021; Kennedy et al., 2017), the current study reports that go/no-go and n-back task performance was associated with activity outside fronto-parietal control regions. This finding is likely because of the way in which we tested for brain–behavior relations. That is, because we examined the joint relationships among age, task performance, and brain structure within a single multivariate model, the patterns of brain activity identified will reflect this joint effect, rather than the effect of each variable examined independently, as prior work has done.

For inhibition (LVs 1 and 2; hot regions), faster speed–accuracy tradeoff was associated with greater activity in largely widespread default (posterior/anterior cingulate, angular, gyrus, hippocampus, medial orbitofrontal), sensory/motor (precentral gyrus, superior temporal), and salience (dorsal anterior cingulate, insula) regions for infrequent no-go trials and decreased activity during the frequent go trials. This would suggest that those participants who dampen default activity during the predominant go condition (resulting in relatively greater activity during the no-go condition) are able to more quickly and accurately respond to rapid go trials, while maintaining the ability to withhold a response when No-go trials occur. A previous study using a similar go/no-go paradigm found that increased default activity preceded trials in which participants made errors, in addition to self-reports of mind wandering (i.e., being “off-task”; Stawarczyk, Majerus, Maquet, & D'Argembeau, 2011; Christoff, Gordon, Smallwood, Smith, & Schooler, 2009), further suggesting that increased default activity for most of the task is maladaptive to performance. Similarly, those participants who increased activity in salience and sensory/motor regions during the less frequent no-go trials (which required inhibitory processing) also had better speed–accuracy measures. Given that the dorsal salience network is believed to modulate interactions between fronto-parietal control and default networks (Chand et al., 2017; Touroutoglou et al., 2012), this association could indicate that task performance is supported by increased activity in regions that aid in switching cognitive resources. Furthermore, worse performance on the go/no-go task was associated with increased activity in regions involved in response initiation (e.g., supplementary motor, premotor; LV 2, cool regions), suggesting that those participants who dampened response initiation-related activity were better at inhibiting a prepotent motor response.

For working memory, better 1- and 2-back accuracy corresponded to decreased activity in supplementary motor, premotor, and superior frontal regions and increased activity in default-type regions (angular, medial frontal, posterior cingulate, angular, and middle temporal) for 1- and 2-back relative to 0-back trials (LV 2). None of these regions was evident at the group level as being involved in the current working memory task; therefore, activation in these regions may represent individual differences in working memory strategies. As previously mentioned, increased activation in superior frontal, supplementary motor, and premotor gyrus may represent initiation of internal rehearsal processes (Niendam et al., 2012; Smith et al., 1998). Therefore, those individuals who perform worse overall may have lower working memory capacity and therefore need to engage in additional rehearsal strategies to perform the task, whereas high performers do not need such strategies; however, further work explicitly testing functional activity underlying strategy differences in working memory is needed.

On the other hand, increased default-mode or task-negative activity during n-back has generally been associated with worse task performance (Kennedy et al., 2017), contrary to what we report. However, there is also evidence that angular default regions in particular may mediate functional activity in the default mode regions as task complexity increases (Hearne, Cocchi, Zalesky, & Mattingley, 2015), and researchers also have reported that increased connectivity between angular gyrus and posterior cingulate regions supports n-back performance (Vatansever, Manktelow, Sahakian, Menon, & Stamatakis, 2017). Furthermore, increased default activity during preparatory stages has also been associated with better subsequent performance (Koshino, Minamoto, Yaoi, Osaka, & Osaka, 2014) and “in the zone” periods where task performance is high and attentional resources are available for internal processes (Kucyi, Esterman, Riley, & Valera, 2016; Esterman, Noonan, Rosenberg, & Degutis, 2013). Although increased mean activity does not necessarily reflect network dynamics, our finding of increased default activity could indicate individual differences in performance supported by greater engagement of default-mediated internal preparation processes.

Structure–Function Relationships in Cognitive Control

Inhibition and shifting showed greater contributions of structure to overall decreases in brain activity that was selective to fronto-parietal structural measures. Specifically, greater fronto-parietal FA corresponded to patterns of decreased activity specific to inhibitory processing (i.e., withholding a “no-go” response during a frequent “go” task) in bilateral middle/superior frontal, medial frontal, bilateral angular gyrus, precuneus, and medial/lateral temporal regions (LV 1). For shifting, greater fronto-parietal gray matter thickness corresponded with decreased activity for trials that required switching letter judgment types (i.e., switching from a “lower”/“uppercase” to a “vowel”/“consonant” judgment) in similar regions (LV 1), including additional supplementary motor and premotor regions (LV 2; cool regions). For shifting, there was also a positive relationship between activity in anterior/posterior cingulate and right angular gyrus (LV 2; hot regions) and fronto-parietal thickness. These structure–function patterns for inhibition and shifting in largely default and sensory–motor brain regions did not overlap with the group-level univariate analysis of activity associated with each domain, suggesting that these patterns of activity identified by the multivariate analysis were outside regions specific to inhibitory or shifting processes.

Our finding of decreased activation associated with greater structure suggests that intact fronto-parietal gray and white matter microstructure may allow for the efficient use of functional resources, resulting in decreased activity in functional regions not fundamental to the task. Prior work examining the association between white matter integrity and functional activity during cognitive control has found similar results. Specifically, global white matter integrity has been associated with decreased activity during working memory beyond the effects of aging (Burzynska et al., 2013). Furthermore, white matter integrity in specific frontal pathways has been associated with decreased activity during a shifting task (Zhu et al., 2015) and a flanker inhibition task (Colcombe et al., 2005), in addition to a reduced range of functional activity in response to increased task demands (Webb, Hoagey, Rodrigue, & Kennedy, 2020). This supports the idea that white matter, particularly in frontal regions, might serve as a scaffold to enable more efficient functional response during cognitive control regardless of age.

Few studies have examined the relationship between cortical thickness and functional activity during cognitive control in adults, with most focusing on childhood samples (Peters, Van Duijvenvoorde, Koolschijn, & Crone, 2016; Squeglia et al., 2013). One prior study utilizing a go/no-go paradigm examined local structure–function relationships and found greater thickness in anterior cingulate corresponded to greater functional activity in that region (Hegarty et al., 2012), somewhat in line with our finding of increased fronto-parietal thickness being associated with greater anterior cingulate activity during switching. Hegarty et al. (2012) also demonstrated several regions as having negative structure–function correlations (e.g., occipital, temporal, precentral), which correspond to those functional regions that we found to have a negative association for our switching task. Together, these findings suggest that, in general, greater cortical thickness might facilitate decreased activity in functional regions not specific to cognitive control processes (with some exceptions, like anterior cingulate), similar to our finding of decreased activity associated with higher FA values during inhibition; however, more work is needed to better understand the specific contributions of gray matter thickness to different functional processes underlying cognitive control.

Finally, we found no structural contributions to patterns of functional activity (beyond the effect of aging) during working memory, unlike prior studies utilizing a similar n-back paradigm in a healthy aging sample (Burzynska et al., 2013). Our finding suggests that, for the current study, aging processes and individual differences in performance may have a stronger influence on the neural correlates of working memory than individual differences in structural integrity alone.

Summary and Conclusions

The current study provides a novel examination of the joint contributions of aging, behavioral performance, and gray and white matter structure to functional activity associated with three correlated, but distinct, core domains of cognitive control: inhibition, shifting, and working memory. Although we found that the three cognitive control domains engaged a common set of fronto-parietal regions, each domain showed unique and differing contributions of aging, performance, and fronto-parietal structure to functional activity in regions beyond core fronto-parietal control regions. This suggests that individual difference factors (like aging) might be sensitive to neural activity underlying lower-level cognitive mechanisms (i.e., initiation, attention, the “default mode”) that might, in turn, interact with higher level cognitive control processes reliant on fronto-parietal regions. This work provides a comprehensive overview of the complex interactions among age, cognitive performance, brain structure, and brain function. In addition to furthering and extending the seminal work of Don Stuss on the role of the frontal lobes in cognitive control, our findings emphasize the connected nature of fronto-parietal contributions to control functions in general and lay the groundwork for future research to explore more specific hypotheses for each cognitive control domain.

Acknowledgments

This work was supported by a Canadian Institutes of Health Research Foundation Grant (MOP143311 awarded to C. L. G.). We thank Elizabeth Howard, Daniel Nichol, and Brennan DeSouza for help with data collection and curation.

Reprint requests should be sent to Cheryl Grady, Rotman Research Institute at Baycrest, 3560 Bathurst Street, Toronto, ON, Canada M6A2E1, or via e-mail: cgrady@research.baycrest.org.

Funding Information

Cheryl L. Grady, Canadian Institutes of Health Research (http://dx.doi.org/10.13039/501100000024), grant number: MOP143311.

Diversity in Citation Practices

A retrospective analysis of the citations in every article published in this journal from 2010 to 2020 has revealed a persistent pattern of gender imbalance: Although the proportions of authorship teams (categorized by estimated gender identification of first author/last author) publishing in the Journal of Cognitive Neuroscience (JoCN) during this period were M(an)/M = .408, W(oman)/M = .335, M/W = .108, and W/W = .149, the comparable proportions for the articles that these authorship teams cited were M/M = .579, W/M = .243, M/W = .102, and W/W = .076 (Fulvio et al., JoCN, 33:1, pp. 3–7). Consequently, JoCN encourages all authors to consider gender balance explicitly when selecting which articles to cite and gives them the opportunity to report their article's gender citation balance. The authors of this article report its proportions of citations by gender category to be M/M = .441, W/M = .147, M/W = .221, and W/W = .191.

Note

1. 

Relaxing the p threshold to p < .001, uncorrected, did reveal bilateral clusters of activity in lateral occipital cortex (left: x = −57, y = −68, z = −22; right: x = 48, y = −72, z = −2) for the inhibition > shifting contrast.

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