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Socioeconomic disadvantage during childhood is associated with a myriad of negative adult outcomes. One mechanism through which disadvantage undermines positive outcomes may be by disrupting the development of self-control. The goal of the present study was to examine pathways from three key indicators of socioeconomic disadvantage – low family income, low maternal education, and neighborhood poverty – to neural and behavioral measures of response inhibition. We utilized data from a representative cohort of 215 twins (ages 7–18 years, 70% male) oversampled for exposure to disadvantage, who participated in the Michigan Twins Neurogenetics Study (MTwiNS), a study within the Michigan State University Twin Registry (MSUTR). Our child-friendly Go/No-Go task activated the bilateral inferior frontal gyrus (IFG), and activation during this task predicted behavioral inhibition performance, extending prior work on adults to youth. Critically, we also found that neighborhood poverty, assessed via geocoding, but not family income or maternal education, was associated with IFG activation, a finding that we replicated in an independent sample of disadvantaged youth. Further, we found that neighborhood poverty predicted response inhibition performance via its effect on IFG activation. These results provide the first mechanistic evidence that disadvantaged contexts may undermine self-control via their effect on the brain. The broader neighborhood, beyond familial contexts, may be critically important for this association, suggesting that contexts beyond the home have profound effects on the developing brain and behaviors critical for future health, wealth, and wellbeing. •The mechanisms linking socioeconomic disadvantage to poor life outcomes are unclear.•We found neighborhood poverty to be correlated with behavioral response inhibition.•Neighborhood poverty, not income or education, uniquely predicted IFG activity.•IFG activity mediated neighborhood poverty-response inhibition associations.•Where youth live may be critically important to their brain development.

Autism spectrum disorders (ASD) impact social functioning and communication, and individuals with these disorders often have restrictive and repetitive behaviors. Accumulating data indicate that ASD is associated with alterations of neural circuitry. Functional MRI (FMRI) studies have focused on connectivity in the context of psychological tasks. However, even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic or resting connectivity. Notably, the default network, which includes the posterior cingulate cortex, retro-splenial, lateral parietal cortex/angular gyrus, medial prefrontal cortex, superior frontal gyrus, temporal lobe, and parahippocampal gyrus, is strongly active when there is no task. Altered intrinsic connectivity within the default network may underlie offline processing that may actuate ASD impairments. Using FMRI, we sought to evaluate intrinsic connectivity within the default network in ASD. Relative to controls, the ASD group showed weaker connectivity between the posterior cingulate cortex and superior frontal gyrus and stronger connectivity between the posterior cingulate cortex and both the right temporal lobe and right parahippocampal gyrus. Moreover, poorer social functioning in the ASD group was correlated with weaker connectivity between the posterior cingulate cortex and the superior frontal gyrus. In addition, more severe restricted and repetitive behaviors in ASD were correlated with stronger connectivity between the posterior cingulate cortex and right parahippocampal gyrus. These findings indicate that ASD subjects show altered intrinsic connectivity within the default network, and connectivity between these structures is associated with specific ASD symptoms.

Brain activity typically increases with increasing working memory (WM) load, regardless of age, before reaching an apparent ceiling. However, older adults exhibit greater brain activity and reach ceiling at lower loads than younger adults, possibly reflecting compensation at lower loads and dysfunction at higher loads. We hypothesized that WM training would bolster neural efficiency, such that the activation peak would shift towards higher memory loads after training. Pre-training, older adults showed greater recruitment of the WM network than younger adults across all loads, with decline at the highest load. Ten days of adaptive training on a verbal WM task improved performance and led to greater brain responsiveness at higher loads for both groups. For older adults the activation peak shifted rightward towards higher loads. Finally, training increased task-related functional connectivity in older adults, both within the WM network and between this task-positive network and the task-negative/default-mode network. These results provide new evidence for functional plasticity with training in older adults and identify a potential signature of improvement at the neural level. •Brain activity increases with working memory load before reaching an apparent ceiling.•Older adults show greater brain activity and reach ceiling faster than younger adults.•Training increases the range of brain activity across memory loads regardless of age.•Training shifts the activation peak rightward toward higher loads in older adults.•Results identify a potential signature of improvement at the neural level.

Normal aging is associated with declines in sensorimotor function. Previous studies have linked age-related behavioral declines to decreases in neural differentiation (i.e., dedifferentiation), including decreases in the distinctiveness of neural activation patterns and in the segregation of large-scale neural networks at rest. However, no studies to date have explored the relationship between these two neural measures and whether they explain the same aspects of behavior. To investigate these issues, we collected a battery of sensorimotor behavioral measures in older and younger adults and estimated (a) the distinctiveness of neural representations in sensorimotor cortex and (b) sensorimotor network segregation in the same participants. Consistent with prior findings, sensorimotor representations were less distinct and sensorimotor resting state networks were less segregated in older compared to younger adults. We also found that participants with the most distinct sensorimotor representations exhibited the most segregated sensorimotor networks. However, only sensorimotor network segregation was associated with individual differences in sensorimotor performance, particularly in older adults. These novel findings link network segregation to neural distinctiveness, but also suggest that network segregation may play a larger role in maintaining sensorimotor performance with age.

Healthy aging is marked by declines in a variety of cognitive and motor abilities. A better understanding of the aging brain may aid in elucidating the neural substrates of these behavioral effects. Investigations of resting state functional brain connectivity have provided insights into pathology, and to some degree, healthy aging. Given the role of the cerebellum in both motor and cognitive behaviors, as well as its known volumetric declines with age, investigating cerebellar networks may shed light on the neural bases of age-related functional declines. We mapped the resting state networks of the lobules of the right hemisphere and the vermis of the cerebellum in a group of healthy older adults and compared them to those of young adults. We report disrupted cortico-cerebellar resting state network connectivity in older adults. These results remain even when controlling for cerebellar volume, signal-to-noise ratio, and signal-to-fluctuation noise ratio. Specifically, there was consistent disruption of cerebellar connectivity with both the striatum and the medial temporal lobe. Associations between connectivity strength and both sensorimotor and cognitive task performances indicate that cerebellar engagement with the default mode network and striatal pathways is associated with better performance for older adults. These results extend our understanding of the resting state networks of the aging brain to include cortico-cerebellar networks, and indicate that age differences in network connectivity strength are important for behavior. •We investigated age differences in resting state cortico-cerebellar networks.•Older adults showed widespread decreases in cortico-cerebellar connectivity.•Cerebellar connectivity with the basal ganglia and medial temporal lobe is impacted.•Cerebellar connectivity relates to motor and cognitive behavior in older adults.

The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into \"motor\" and \"non-motor\" regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on \"resting-state\" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

The ability to distinguish between high and low levels of task engagement in the real world is important for detecting and preventing performance decrements during safety-critical operational tasks. We therefore investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neuroimaging technique, can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task. A group of participants performed the multi-source interference task (MSIT) while we recorded brain activity with fNIRS from two brain regions. One was a key region of the \"task-positive\" network, which is associated with relatively high levels of task engagement. The second was a key region of the \"task-negative\" network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, we were able to predict above chance levels whether participants were engaged in performing the MSIT or resting. We were also able to replicate prior findings from functional magnetic resonance imaging (fMRI) indicating that activity in task-positive and task-negative regions is negatively correlated during task performance. Finally, data from a companion fMRI study verified our assumptions about the sources of brain activity in the fNIRS experiment and established an upper bound on classification accuracy in our task. Together, our findings suggest that fNIRS could prove quite useful for monitoring cognitive state in real-world settings.

Aging is typically associated with declines in sensorimotor performance. Previous studies have linked some age-related behavioral declines to reductions in network segregation. For example, compared to young adults, older adults typically exhibit weaker functional connectivity within the same functional network but stronger functional connectivity between different networks. Based on previous animal studies, we hypothesized that such reductions of network segregation are linked to age-related reductions in the brain's major inhibitory transmitter, gamma aminobutyric acid (GABA). To investigate this hypothesis, we conducted graph theoretical analyses of resting state functional MRI data to measure sensorimotor network segregation in both young and old adults. We also used magnetic resonance spectroscopy to measure GABA levels in the sensorimotor cortex and collected a battery of sensorimotor behavioral measures. We report four main findings. First, relative to young adults, old adults exhibit both less segregated sensorimotor brain networks and reduced sensorimotor GABA levels. Second, less segregated networks are associated with lower GABA levels. Third, less segregated networks and lower GABA levels are associated with worse sensorimotor performance. Fourth, network segregation mediates the relationship between GABA and performance. These findings link age-related differences in network segregation to age-related differences in GABA levels and sensorimotor performance. More broadly, they suggest a neurochemical substrate of age-related dedifferentiation at the level of large-scale brain networks. •Older age is associated with reduced segregation in the sensorimotor network and reduced GABA levels in sensorimotor cortex.•Reduced segregation in the sensorimotor network is associated with lower sensorimotor GABA levels.•Individual differences in sensorimotor GABA levels correlate with sensorimotor performance.•This relationship between sensorimotor GABA and sensorimotor behavior is mediated by sensorimotor network segregation.

There is a fundamental gap in understanding how brain structural and functional network connectivity are interrelated, how they change with age, and how such changes contribute to older adults' sensorimotor deficits. Recent neuroimaging approaches including resting state functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) have been used to assess brain functional (fcMRI) and structural (DTI) network connectivity, allowing for more integrative assessments of distributed neural systems than in the past. Declines in corpus callosum size and microstructure with advancing age have been well documented, but their contributions to age deficits in unimanual and bimanual function are not well defined. Our recent work implicates age-related declines in callosal size and integrity as a key contributor to unimanual and bimanual control deficits. Moreover, our data provide evidence for a fundamental shift in the balance of excitatory and inhibitory interhemispheric processes that occurs with age, resulting in age differences in the relationship between functional and structural network connectivity. Training studies suggest that the balance of interhemispheric interactions can be shifted with experience, making this a viable target for future interventions.