Disentangling pain and fatigue in chronic fatigue syndrome: a resting state connectivity study before and after cognitive behavioral therapy.

BACKGROUND: Fatigue is a central feature of myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), but many ME/CFS patients also report comorbid pain symptoms. It remains unclear whether these symptoms are related to similar or dissociable brain networks. This study used resting-state fMRI to disentangle networks associated with fatigue and pain symptoms in ME/CFS patients, and to link changes in those networks to clinical improvements following cognitive behavioral therapy (CBT). METHODS: Relationships between pain and fatigue symptoms and cortico-cortical connectivity were assessed within ME/CFS patients at baseline (N = 72) and after CBT (N = 33) and waiting list (WL, N = 18) and compared to healthy controls (HC, N = 29). The analyses focused on four networks previously associated with pain and/or fatigue, i.e. the fronto-parietal network (FPN), premotor network (PMN), somatomotor network (SMN), and default mode network (DMN). RESULTS: At baseline, variation in pain and fatigue symptoms related to partially dissociable brain networks. Fatigue was associated with higher SMN-PMN connectivity and lower SMN-DMN connectivity. Pain was associated with lower PMN-DMN connectivity. CBT improved SMN-DMN connectivity, compared to WL. Larger clinical improvements were associated with larger increases in frontal SMN-DMN connectivity. No CBT effects were observed for PMN-DMN or SMN-PMN connectivity. CONCLUSIONS: These results provide insight into the dissociable neural mechanisms underlying fatigue and pain symptoms in ME/CFS and how they are affected by CBT in successfully treated patients. Further investigation of how and in whom behavioral and biomedical treatments affect these networks is warranted to improve and individualize existing or new treatments for ME/CFS.

Lifespan differences in visual short-term memory load-modulated functional connectivity.

Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge center for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N = 101, age 23-86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated functional connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention and visual networks during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Whole-brain analyses for the relation between connectivity and behavior were non-significant. Our results give additional support to the sensory recruitment model of working memory. We also demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their neural resources at the lowest load and therefore less able to further increase connectivity with increasing task demands.

The role of the arousal system in age-related differences in cortical functional network architecture.

A common finding in the aging literature is that of the brain's decreased within- and increased between-network functional connectivity. However, it remains unclear what is causing this shift in network organization with age. Given the essential role of the ascending arousal system (ARAS) in cortical activation and previous findings of disrupted ARAS functioning with age, it is possible that age differences in ARAS functioning contribute to disrupted cortical connectivity. We test this possibility here using resting state fMRI data from over 500 individuals across the lifespan from the Cambridge Center for Aging and Neuroscience (Cam-CAN) population-based cohort. Our results show that ARAS-cortical connectivity declines with age and, consistent with our expectations, significantly mediates some age-related differences in connectivity within and between association networks (specifically, within the default mode and between the default mode and salience networks). Additionally, connectivity between the ARAS and association networks predicted cognitive performance across several tasks over and above the effects of age and connectivity within the cortical networks themselves. These findings suggest that age differences in cortical connectivity may be driven, at least in part, by altered arousal signals from the brainstem and that ARAS-cortical connectivity relates to cognitive performance with age.

Detecting neural state transitions underlying event segmentation.

Segmenting perceptual experience into meaningful events is a key cognitive process that helps us make sense of what is happening around us in the moment, as well as helping us recall past events. Nevertheless, little is known about the underlying neural mechanisms of the event segmentation process. Recent work has suggested that event segmentation can be linked to regional changes in neural activity patterns. Accurate methods for identifying such activity changes are important to allow further investigation of the neural basis of event segmentation and its link to the temporal processing hierarchy of the brain. In this study, we introduce a new set of elegant and simple methods to study these mechanisms. We introduce a method for identifying the boundaries between neural states in a brain area and a complementary one for identifying the number of neural states. Furthermore, we present the results of a comprehensive set of simulations and analyses of empirical fMRI data to provide guidelines for reliable estimation of neural states and show that our proposed methods outperform the current state-of-the-art in the literature. This methodological innovation will allow researchers to make headway in investigating the neural basis of event segmentation and information processing during naturalistic stimulation.

When functional blurring becomes deleterious: Reduced system segregation is associated with less white matter integrity and cognitive decline in aging.

Healthy aging is accompanied by progressive decline in cognitive performance and concomitant changes in brain structure and functional architecture. Age-accompanied alterations in brain function have been characterized on a network level as weaker functional connections within brain networks along with stronger interactions between networks. This phenomenon has been described as age-related differences in functional network segregation. It has been suggested that functional networks related to associative processes are particularly sensitive to age-related deterioration in segregation, possibly related to cognitive decline in aging. However, there have been only a few longitudinal studies with inconclusive results. Here, we used a large longitudinal sample of 284 participants between 25 to 80 years of age at baseline, with cognitive and neuroimaging data collected at up to three time points over a 10-year period. We investigated age-related changes in functional segregation among two large-scale systems comprising associative and sensorimotor-related resting-state networks. We found that functional segregation of associative systems declines in aging with exacerbated deterioration from the late fifties. Changes in associative segregation were positively associated with changes in global cognitive ability, suggesting that decreased segregation has negative consequences for domain-general cognitive functions. Age-related changes in system segregation were partly accounted for by changes in white matter integrity, but white matter integrity only weakly influenced the association between segregation and cognition. Together, these novel findings suggest a cascade where reduced white-matter integrity leads to less distinctive functional systems which in turn contributes to cognitive decline in aging.

Cortical beta-band power modulates with uncertainty in effector selection during motor planning.

Although beta-band activity during motor planning is known to be modulated by uncertainty about where to act, less is known about its modulations to uncertainty about how to act. To investigate this issue, we recorded oscillatory brain activity with EEG while human participants (n = 17) performed a hand choice reaching task. The reaching hand was either predetermined or of participants' choice, and the target was close to one of the two hands or at about equal distance from both. To measure neural activity in a motion artifact-free time window, the location of the upcoming target was cued 1,000-1,500 ms before the presentation of the target, whereby the cue was valid in 50% of trials. As evidence for motor planning during the cuing phase, behavioral observations showed that the cue affected later hand choice. Furthermore, reaction times were longer in the choice trials than in the predetermined trials, supporting the notion of a competitive process for hand selection. Modulations of beta-band power over central cortical regions, but not alpha-band or theta-band power, were in line with these observations. During the cuing period, reaches in predetermined trials were preceded by larger decreases in beta-band power than reaches in choice trials. Cue direction did not affect reaction times or beta-band power, which may be due to the cue being invalid in 50% of trials, retaining effector uncertainty during motor planning. Our findings suggest that effector uncertainty modulates beta-band power during motor planning.NEW & NOTEWORTHY Although reach-related beta-band power in central cortical areas is known to modulate with the number of potential targets, here we show, using a cuing paradigm, that the power in this frequency band, but not in the alpha or theta band, is also modulated by the uncertainty of which hand to use. This finding supports the notion that multiple possible effector-specific actions can be specified in parallel up to the level of motor preparation.

Improving the sensitivity of cluster-based statistics for functional magnetic resonance imaging data.

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster-based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false-alarm rate, (b) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.

Consequence of stroke for feature recall and binding in visual working memory.

Visual memory for objects involves the integration, or binding, of individual features into a coherent representation. We used a novel approach to assess feature binding, using a delayed-reproduction task in combination with computational modeling and lesion analysis. We assessed stroke patients and neurotypical controls on a visual working memory task in which spatial arrays of colored disks were presented. After a brief delay, participants either had to report the color of one disk cued by its location or the location of one disk cued by its color. Our results demonstrate that, in the controls, report imprecision and swap errors (non-target reports) can be explained by a single source of variability. Stroke patients showed an overall decrease in memory precision for both color and location, with only limited evidence for deviations from the predicted relationship between report precision and swap errors. These deviations were primarily deficits in reporting items rather than selecting items based on the cue. Atlas-based lesion-symptom mapping showed that selection and reporting deficits, precision in reporting color, and precision in reporting location were associated with different lesion profiles. Deficits in binding are associated with lesions in the left somatosensory cortex, deficits in the precision of reporting color with bilateral fronto-parietal regions, and no anatomical substrates were identified for precision in reporting location. Our results converge with previous reports that working memory representations are widely distributed in the brain and can be found across sensory, parietal, temporal, and prefrontal cortices. Stroke patients demonstrate mostly subtle impairments in visual working memory, perhaps because representations from different areas in the brain can partly compensate for impaired encoding in lesioned areas. These findings contribute to understanding of the relation between memorizing features and their bound representations.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT: Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18-88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability.

Robust Resilience of the Frontotemporal Syntax System to Aging.

UNLABELLED: Brain function is thought to become less specialized with age. However, this view is largely based on findings of increased activation during tasks that fail to separate task-related processes (e.g., attention, decision making) from the cognitive process under examination. Here we take a systems-level approach to separate processes specific to language comprehension from those related to general task demands and to examine age differences in functional connectivity both within and between those systems. A large population-based sample (N = 111; 22-87 years) from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) was scanned using functional MRI during two versions of an experiment: a natural listening version in which participants simply listened to spoken sentences and an explicit task version in which they rated the acceptability of the same sentences. Independent components analysis across the combined data from both versions showed that although task-free language comprehension activates only the auditory and frontotemporal (FTN) syntax networks, performing a simple task with the same sentences recruits several additional networks. Remarkably, functionality of the critical FTN is maintained across age groups, showing no difference in within-network connectivity or responsivity to syntactic processing demands despite gray matter loss and reduced connectivity to task-related networks. We found no evidence for reduced specialization or compensation with age. Overt task performance was maintained across the lifespan and performance in older, but not younger, adults related to crystallized knowledge, suggesting that decreased between-network connectivity may be compensated for by older adults' richer knowledge base. SIGNIFICANCE STATEMENT: Understanding spoken language requires the rapid integration of information at many different levels of analysis. Given the complexity and speed of this process, it is remarkably well preserved with age. Although previous work claims that this preserved functionality is due to compensatory activation of regions outside the frontotemporal language network, we use a novel systems-level approach to show that these "compensatory" activations simply reflect age differences in response to experimental task demands. Natural, task-free language comprehension solely recruits auditory and frontotemporal networks, the latter of which is similarly responsive to language-processing demands across the lifespan. These findings challenge the conventional approach to neurocognitive aging by showing that the neural underpinnings of a given cognitive function depend on how you test it.

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging.

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre-processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting-state fMRI from 214 adults aged 18-88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between-participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high-pass filtering, instead of band-pass filtering, produced stronger and more reliable age-effects. Head motion was correlated with gray-matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125-4156, 2017. © 2017 Wiley Periodicals, Inc.

State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State.

Resting-state functional connectivity, as measured by functional magnetic resonance imaging (fMRI), is often treated as a trait, used, for example, to draw inferences about individual differences in cognitive function, or differences between healthy or diseased populations. However, functional connectivity can also depend on the individual's mental state. In the present study, we examined the relative contribution of state and trait components in shaping an individual's functional architecture. We used fMRI data from a large, population-based human sample (N = 587, age 18-88 years), as part of the Cambridge Centre for Aging and Neuroscience (Cam-CAN), which were collected in three mental states: resting, performing a sensorimotor task, and watching a movie. Whereas previous studies have shown commonalities across mental states in the average functional connectivity across individuals, we focused on the effects of states on the pattern of individual differences in functional connectivity. We found that state effects were as important as trait effects in shaping individual functional connectivity patterns, each explaining an approximately equal amount of variance. This was true when we looked at aging, as one specific dimension of individual differences, as well as when we looked at generic aspects of individual variation. These results show that individual differences in functional connectivity consist of state-dependent aspects, as well as more stable, trait-like characteristics. Studying individual differences in functional connectivity across a wider range of mental states will therefore provide a more complete picture of the mechanisms underlying factors such as cognitive ability, aging, and disease. SIGNIFICANCE STATEMENT: The brain's functional architecture is remarkably similar across different individuals and across different mental states, which is why many studies use functional connectivity as a trait measure. Despite these trait-like aspects, functional connectivity varies over time and with changes in cognitive state. We measured connectivity in three different states to quantify the size of the trait-like component of functional connectivity, compared with the state-dependent component. Our results show that studying individual differences within one state (such as resting) uncovers only part of the relevant individual differences in brain function, and that the study of functional connectivity under multiple mental states is essential to disentangle connectivity differences that are transient versus those that represent more stable, trait-like characteristics of an individual.

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation.

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on "distance correlation"; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data.

The effects of hippocampal lesions on MRI measures of structural and functional connectivity.

Focal lesions can affect connectivity between distal brain regions (connectional diaschisis) and impact the graph-theoretic properties of major brain networks (connectomic diaschisis). Given its unique anatomy and diverse range of functions, the hippocampus has been claimed to be a critical "hub" in brain networks. We investigated the effects of hippocampal lesions on structural and functional connectivity in six patients with amnesia, using a range of magnetic resonance imaging (MRI) analyses. Neuropsychological assessment revealed marked episodic memory impairment and generally intact performance across other cognitive domains. The hippocampus was the only brain structure exhibiting reduced grey-matter volume that was consistent across patients, and the fornix was the only major white-matter tract to show altered structural connectivity according to both diffusion metrics. Nonetheless, functional MRI revealed both increases and decreases in functional connectivity. Analysis at the level of regions within the default-mode network revealed reduced functional connectivity, including between nonhippocampal regions (connectional diaschisis). Analysis at the level of functional networks revealed reduced connectivity between thalamic and precuneus networks, but increased connectivity between the default-mode network and frontal executive network. The overall functional connectome showed evidence of increased functional segregation in patients (connectomic diaschisis). Together, these results point to dynamic reorganization following hippocampal lesions, with both decreased and increased functional connectivity involving limbic-diencephalic structures and larger-scale networks. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.

A Brain-Wide Study of Age-Related Changes in Functional Connectivity.

Aging affects functional connectivity between brain areas, however, a complete picture of how aging affects integration of information within and between functional networks is missing. We used complex network measures, derived from a brain-wide graph, to provide a comprehensive overview of age-related changes in functional connectivity. Functional connectivity in young and older participants was assessed during resting-state fMRI. The results show that aging has a large impact, not only on connectivity within functional networks but also on connectivity between the different functional networks in the brain. Brain networks in the elderly showed decreased modularity (less distinct functional networks) and decreased local efficiency. Connectivity decreased with age within networks supporting higher level cognitive functions, that is, within the default mode, cingulo-opercular and fronto-parietal control networks. Conversely, no changes in connectivity within the somatomotor and visual networks, networks implicated in primary information processing, were observed. Connectivity between these networks even increased with age. A brain-wide analysis approach of functional connectivity in the aging brain thus seems fundamental in understanding how age affects integration of information.

Brain mechanisms underlying the effects of aging on different aspects of selective attention.

The ability to suppress irrelevant information declines with age, while the ability to enhance relevant information remains largely intact. We examined mechanisms behind this dissociation in an fMRI study, using a selective attention task in which relevant and irrelevant information appeared simultaneously. Slowing of response times due to distraction by irrelevant targets was larger in older than younger participants. Increased distraction was related to larger increases in activity and connectivity in areas of the dorsal attention network, indicating a more pronounced (re-)orientation of attention. The decreases in accuracy in target compared to nontarget trials were smaller in older compared to younger participants. In older adults we found increased recruitment of areas in the fronto-parietal control network (FPCN) during target detection. Moreover, older adults showed increased connectivity between the FPCN, supporting cognitive control, and somatomotor areas implicated in response selection and execution. This connectivity increase was related to improved target detection, suggesting that older adults engage additional cognitive control, which might enable the observed intact performance in detecting and responding to target stimuli.

Reduced specificity of functional connectivity in the aging brain during task performance.

The importance of studying connectivity in the aging brain is increasingly recognized. Recent studies have shown that connectivity within the default mode network is reduced with age and have demonstrated a clear relation of these changes with cognitive functioning. However, research on age-related changes in other functional networks is sparse and mainly focused on prespecified functional networks. Using functional magnetic resonance imaging, we investigated age-related changes in functional connectivity during a visual oddball task in a range of functional networks. It was found that compared with young participants, elderly showed a decrease in connectivity between areas belonging to the same functional network. This was found in the default mode network and the somatomotor network. Moreover, in all identified networks, elderly showed increased connectivity between areas within these networks and areas belonging to different functional networks. Decreased connectivity within functional networks was related to poorer cognitive functioning in elderly. The results were interpreted as a decrease in the specificity of functional networks in older participants.

Flexible connectivity in the aging brain revealed by task modulations.

Recent studies have shown that aging has a large impact on connectivity within and between functional networks. An open question is whether elderly still have the flexibility to adapt functional network connectivity (FNC) to the demands of the task at hand. To study this, we collected fMRI data in younger and older participants during resting state, a selective attention (SA) task and an n-back working memory task with varying levels of difficulty. Spatial independent component (IC) analysis was used to identify functional networks over all participants and all conditions. Dual regression was used to obtain participant and task specific time-courses per IC. Subsequently, functional connectivity was computed between all ICs in each of the tasks. Based on these functional connectivity matrices, a scaled version of the eigenvector centrality (SEC) was used to measure the total influence of each IC in the complete graph of ICs. The results demonstrated that elderly remain able to adapt FNC to task demands. However, there was an age-related shift in the impetus for FNC change. Older participants showed the maximal change in SEC patterns between resting state and the SA task. Young participants, showed the largest shift in SEC patterns between the less demanding SA task and the more demanding 2-back task. Our results suggest that increased FNC changes from resting state to low demanding tasks in elderly reflect recruitment of additional resources, compared with young adults. The lack of change between the low and high demanding tasks suggests that elderly reach a resource ceiling.

Compensation through increased functional connectivity: neural correlates of inhibition in old and young.

With increasing age, people experience more difficulties with suppressing irrelevant information, which may have a major impact on cognitive functioning. The extent of decline of inhibitory functions with age is highly variable between individuals. In this study, we used ERPs and phase locking analyses to investigate neural correlates of this variability in inhibition between individuals. Older and younger participants performed a selective attention task in which relevant and irrelevant information was presented simultaneously. The participants were split into high and low performers based on their level of inhibition inefficiency, that is, the slowing of RTs induced by information that participants were instructed to ignore. P1 peak amplitudes were larger in low performers than in high performers, indicating that low performers were less able to suppress the processing of irrelevant stimuli. Phase locking analyses were used as a measure of functional connectivity. Efficient inhibition in both age groups was related to the increased functional connectivity in the alpha band between frontal and occipito-parietal ROIs in the prestimulus interval. In addition, increased power in the alpha band in occipito-parietal ROIs was related to better inhibition both before and after stimulus onset. Phase locking in the upper beta band before and during stimulus presentation between frontal and occipito-parietal ROIs was related to a better performance in older participants only, suggesting that this is an active compensation mechanism employed to maintain adequate performance. In addition, increased top-down modulation and increased power in the alpha band appears to be a general mechanism facilitating inhibition in both age groups.

Bridging the big (data) gap: levels of control in small- and large-scale cognitive neuroscience research.

Recently, cognitive neuroscience has experienced unprecedented growth in the availability of large-scale datasets. These developments hold great methodological and theoretical promise: they allow increased statistical power, the use of nonparametric and generative models, the examination of individual differences, and more. Nevertheless, unlike most 'traditional' cognitive neuroscience research, which uses controlled experimental designs, large-scale projects often collect neuroimaging data not directly related to a particular task (e.g., resting state). This creates a gap between small- and large-scale studies that is not solely due to differences in sample size. Measures obtained with large-scale studies might tap into different neurocognitive mechanisms and thus show little overlap with the mechanisms probed by small-scale studies. In this opinion article, we aim to address this gap and its potential implications for the interpretation of research findings in cognitive neuroscience.