Precuneus responds to a wide range of cognitive processes. Here, we examined how the patterns of resting state connectivity may define functional subregions in the precuneus. Using a K-means ...algorithm to cluster the whole-brain “correlograms” of the precuneus in 225 adult individuals, we corroborated the dorsal-anterior, dorsal-posterior, and ventral subregions, each involved in spatially guided behaviors, mental imagery, and episodic memory as well as self-related processing, with the ventral precuneus being part of the default mode network, as described extensively in earlier work. Furthermore, we showed that the lateral/medial volumes of dorsal anterior and dorsal posterior precuneus are each connected with areas of motor execution/attention and motor/visual imagery, respectively. Compared to the ventral precuneus, the dorsal precuneus showed greater connectivity with occipital and posterior parietal cortices, but less connectivity with the medial superior frontal and orbitofrontal gyri, anterior cingulate cortex as well as the parahippocampus. Compared to dorsal-posterior and ventral precuneus, the dorsal-anterior precuneus showed greater connectivity with the somatomotor cortex, as well as the insula, supramarginal, Heschl's, and superior temporal gyri, but less connectivity with the angular gyrus. Compared to ventral and dorsal-anterior precuneus, dorsal-posterior precuneus showed greater connectivity with the middle frontal gyrus. Notably, the precuneus as a whole has negative connectivity with the amygdala and the lateral and inferior orbital frontal gyri. Finally, men and women differed in the connectivity of precuneus. Men and women each showed greater connectivity with the dorsal precuneus in the cuneus and medial thalamus, respectively. Women also showed greater connectivity with ventral precuneus in the hippocampus/parahippocampus, middle/anterior cingulate gyrus, and middle occipital gyrus, compared to men. Taken together, these new findings may provide a useful platform upon which to further investigate sex-specific functional neuroanatomy of the precuneus and to elucidate the pathology of many neurological illnesses.
► Precuneus contains dorsal anterior, dorsal posterior, and ventral subregions. ► Precuneus is negatively connected with the amygdala and orbitofrontal cortex. ► Men and women show different patterns of precuneus connectivity.
Inhibitory control or the ability to refrain from incorrect responses is a critical executive function known to diminish during aging. Imaging studies have elucidated cerebral changes that may ...underlie the age-related deficits. However, it remains unclear whether the structural and functional changes occur in the same brain regions and whether reduced gray matter volumes (GMV) mediate decreased activation during inhibition. Here, in a sample of 149 participants, we addressed the issues using structural and functional magnetic resonance imaging. Individual's response inhibition was evaluated by the stop signal reaction time (SSRT) in a stop signal task. The results showed that age was associated with prolonged SSRT across participants. Many cortical and subcortical regions demonstrated age-related reduction in GMV and activation to response inhibition. Additionally, age-related diminution in inhibitory control, as indexed by the SSRT, was associated with both shared and distinct morphometric and functional changes. Voxel-based morphometry demonstrated age-related reduction in GMV in the right dorsolateral prefrontal cortex and caudate head as well as bilateral insula, in association with prolonged SSRT. In a contrast of stop success versus go success trials, age was associated with lower activation in the medial and inferior frontal cortex and inferior parietal cortex. Further, reduction in GMV mediated age-related differences in activations only of the medial prefrontal cortex, providing limited evidence for structure function association. Thus, the decline in inhibitory control, as evidenced in the stop signal task, manifest with both shared and distinct structural and functional processes during aging.
The right inferior frontal cortex (rIFC) and the right anterior insula (rAI) have been implicated consistently in inhibitory control, but their differential roles are poorly understood. Here we use ...multiple quantitative techniques to dissociate the functional organization and roles of the rAI and rIFC. We first conducted a meta-analysis of 70 published inhibitory control studies to generate a commonly activated right fronto-opercular cortex volume of interest (VOI). We then segmented this VOI using two types of features: (1) intrinsic brain activity; and (2) stop-signal task-evoked hemodynamic response profiles. In both cases, segmentation algorithms identified two stable and distinct clusters encompassing the rAI and rIFC. The rAI and rIFC clusters exhibited several distinct functional characteristics. First, the rAI showed stronger intrinsic and task-evoked functional connectivity with the anterior cingulate cortex, whereas the rIFC had stronger intrinsic and task-evoked functional connectivity with dorsomedial prefrontal and lateral fronto-parietal cortices. Second, the rAI showed greater activation than the rIFC during Unsuccessful, but not Successful, Stop trials, and multivoxel response profiles in the rAI, but not the rIFC, accurately differentiated between Successful and Unsuccessful Stop trials. Third, activation in the rIFC, but not rAI, predicted individual differences in inhibitory control abilities. Crucially, these findings were replicated in two independent cohorts of human participants. Together, our findings provide novel quantitative evidence for the dissociable roles of the rAI and rIFC in inhibitory control. We suggest that the rAI is particularly important for detecting behaviorally salient events, whereas the rIFC is more involved in implementing inhibitory control.
Alpha rhythm (8 to 12 Hz) observed in EEG over human posterior cortex is prominent during eyes‐closed (EC) resting and attenuates during eyes‐open (EO) resting. Research shows that the degree of ...EC‐to‐EO alpha blocking or alpha desynchronization, termed alpha reactivity here, is a neural marker of cognitive health. We tested the role of acetylcholine in EC‐to‐EO alpha reactivity by applying a multimodal neuroimaging approach to a cohort of young adults and a cohort of older adults. In the young cohort, simultaneous EEG‐fMRI was recorded from twenty‐one young adults during both EO and EC resting. In the older cohort, functional MRI was recorded from forty older adults during EO and EC resting, along with FLAIR and diffusion MRI. For a subset of twenty older adults, EEG was recorded during EO and EC resting in a separate session. In both young and older adults, functional connectivity between the basal nucleus of Meynert (BNM), the major source of cortical acetylcholine, and the visual cortex increased from EC to EO, and this connectivity increase was positively associated with alpha reactivity; namely, the stronger the BNM‐visual cortex functional connectivity increase from EC to EO, the larger the EC‐to‐EO alpha desynchronization. In older adults, lesions of the fiber tracts linking BNM and visual cortex quantified by leukoaraiosis volume, associated with reduced alpha reactivity. These findings support a role of acetylcholine and particularly cholinergic pathways in mediating EC‐to‐EO alpha reactivity and suggest that impaired alpha reactivity could serve as a marker of the integrity of the cholinergic system.
Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that ...static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10–60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.
•Temporal variability in functional connectivity predicts attention task performance.•Dynamic functional connectivity can be measured during task performance or rest.•Models generalized across 3 completely independent studies.•Higher functional connectivity variability generally predicts worse attention.
A hallmark of cognitive control is the ability to rein in impulsive responses. Previously, we used a Bayesian model to describe trial-by-trial likelihood of the stop signal or p(Stop) and related ...regional activations to p(Stop) to response slowing in a stop signal task. Here, we characterized the regional processes of conflict anticipation in association with intersubject variation in impulse control in 114 young adults. We computed the stop signal reaction time (SSRT) and a measure of motor urgency, indexed by the reaction time (RT) difference between go and stop error trials or "GoRT - SERT," where GoRT is the go trial RT and SERT is the stop error RT. Motor urgency and SSRT were positively correlated across subjects. A linear regression identified regional activations to p(Stop), each in correlation with SSRT and motor urgency. We hypothesized that shared neural activities mediate the correlation between motor urgency and SSRT in proactive control of impulsivity. Activation of the ventromedial prefrontal cortex, posterior cingulate cortex and right superior frontal gyrus (SFG) during conflict anticipation correlated negatively with the SSRT. Activation of the right SFG also correlated negatively with GoRT - SERT. Therefore, activation of the right SFG was associated with more efficient response inhibition and less motor urgency. A mediation analysis showed that right SFG activation to conflict anticipation mediates the correlation between SSRT and motor urgency bidirectionally. The current results highlight a specific role of the right SFG in translating conflict anticipation to the control of impulsive response, which is consistent with earlier studies suggesting its function in action restraint.
Individuals vary in impulse control. However, the neural bases underlying individual variation in proactive control of impulsive responses remain unknown. Here, in a large sample of young adults, we showed that activation of the right superior frontal gyrus (SFG) during conflict anticipation is positively correlated with the capacity of inhibitory control and negatively with motor urgency in the stop signal task. Importantly, activity of the right SFG mediates the counteracting processes of inhibitory control and motor urgency across subjects. The results support a unique role of the right SFG in individual variation in cognitive control.
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn ...et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square PLS regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.
•Functional connectivity can predict individual differences in attention.•We compared different connectivity measures and feature selection algorithms.•Four different data sets permitted both internal and external validation.•For rest data, PLS regression models were numerically better than linear regression.•Pearson’s correlation, accordance, and discordance did not meaningfully differ.
The dopaminergic motive system is compromised in cocaine addiction. Abundant research has examined the roles of the dopaminergic midbrain and ventral striatum (VS) in cue‐induced craving and habitual ...drug consumption. Interconnected with the dopaminergic circuits, the hypothalamus is widely implicated in motivated behavior, including food and drug seeking. However, very few studies have investigated how the hypothalamus responds to drug cues and whether hypothalamic responses are related to clinical features such as craving and addiction severity. Here, in 23 cocaine‐dependent individuals (CD) exposed to cocaine vs neutral cues during functional magnetic resonance imaging (fMRI), we examined regional responses using established routines. At a corrected threshold, CD demonstrated increased activation to cocaine vs neutral cues in bilateral visual cortex, inferior parietal and middle frontal gyri, and the hypothalamus. The extent of hypothalamus but not other regional response was correlated with craving and cocaine addiction severity, each as assessed by the Cocaine Craving Questionnaire (CCQ) and Cocaine Selective Severity Assessment (CSSA). In contrast, subjective “acute” craving as elicited by cocaine cues during fMRI involved deactivation of bilateral orbitofrontal cortex (OFC) and angular gyri (AG), and the OFC and AG responses were not related to CCQ or CSSA score. These findings distinguished tonic craving as a critical factor in capturing cocaine addiction severity and substantiated a role of the hypothalamus in motivational dysfunction in cocaine addiction.
Cocaine‐addicted individuals demonstrated increased activation to cocaine versus neutral cues in bilateral visual cortex, inferior parietal, and middle frontal gyri as well as the hypothalamus. The extent of hypothalamus but not other regional response was correlated with craving and cocaine addiction severity, each as assessed by the Cocaine Craving Questionnaire (CCQ) and Cocaine Selective Severity Assessment (CSSA). These findings substantiate a role of the hypothalamus in cue‐induced tonic craving and addiction severity in cocaine dependence.
Cognitive control plays an important role in goal-directed behavior, but dynamic brain mechanisms underlying it are poorly understood. Here, using multisite fMRI data from over 100 participants, we ...investigate causal interactions in three cognitive control tasks within a core Frontal-Cingulate-Parietal network. We found significant causal influences from anterior insula (AI) to dorsal anterior cingulate cortex (dACC) in all three tasks. The AI exhibited greater net causal outflow than any other node in the network. Importantly, a similar pattern of causal interactions was uncovered by two different computational methods for causal analysis. Furthermore, the strength of causal interaction from AI to dACC was greater on high, compared with low, cognitive control trials and was significantly correlated with individual differences in cognitive control abilities. These results emphasize the importance of the AI in cognitive control and highlight its role as a causal hub in the Frontal-Cingulate-Parietal network. Our results further suggest that causal signaling between the AI and dACC plays a fundamental role in implementing cognitive control and are consistent with a two-stage cognitive control model in which the AI first detects events requiring greater access to cognitive control resources and then signals the dACC to execute load-specific cognitive control processes.
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