scholarly journals A sub+ cortical fMRI-based surface parcellation

2019 ◽  
Author(s):  
John D. Lewis ◽  
Gleb Bezgin ◽  
Vladimir S. Fonov ◽  
D. Louis Collins ◽  
Alan C. Evans
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Life Span ◽  
Brain Volume ◽  
Subcortical Structures ◽  
Common Space ◽  
Cortical Parcellation ◽  
Connectivity Patterns ◽  
Deep Gray Matter ◽  
Volumetric Registration

AbstractBoth the cortex and the subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with mapping results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface-based approaches fare substantially better, but have thus far been used only for cortical parcellation. We extend this surface-based approach to include also the subcortical deep gray-matter structures. Using the life-span data from the Enhanced Nathan Klein Institute - Rockland Sample, comprised of data from 590 individuals from 6 to 85 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that our extended functional parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels. We also show that our subcortical parcels align with known subnuclei. Further, we show that this parcellation is appropriate for use with data from other modalities; we generate cortical and subcortical white/gray contrast measures for this same dataset, and draw on the fact that areal differences are evident in the relation of white/gray contrast to age, to sex, to brain volume, and to interactions of these terms; we show that our extended functional parcellation provides an improved fit to the complexity of the life-span changes in the white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels. We provide our extended functional parcellation for the use of the neuroimaging community.

Preserved individual differences in functional connectivity patterns under dexmedetomidine-induced sedation

2019 ◽  
Vol 707 ◽  
pp. 134289
Author(s):  
Haiyang Liu ◽  
Minyu Jian ◽  
Shu Liu ◽  
Ang Li ◽  
Shaowu Li ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Connectivity Patterns

Functional connectivity patterns reflect individual differences in conflict adaptation

2015 ◽  
Vol 70 ◽  
pp. 177-184 ◽  
Author(s):  
Xiangpeng Wang ◽  
Ting Wang ◽  
Zhencai Chen ◽  
Glenn Hitchman ◽  
Yijun Liu ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Conflict Adaptation ◽  
Connectivity Patterns

scholarly journals Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

2020 ◽  
Vol 32 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Emily W. Avery ◽  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Abigail S. Greene ◽  
Siyuan Gao ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Fluid Intelligence ◽  
Memory Performance ◽  
Data Set ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Intelligence Models

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

scholarly journals Hippocampal volume and navigational ability: The map(ping) is not to scale.

2020 ◽  
Author(s):  
Steven Marc Weisberg ◽  
Arne Ekstrom
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Structure Function ◽  
Skill Acquisition ◽  
Hippocampal Volume ◽  
Cognitive Skill ◽  
Neural Basis ◽  
Critical Question ◽  
Connectivity Patterns ◽  
Cognitive Skill Acquisition

A critical question regards the neural basis of complex cognitive skill acquisition. One extensively studied skill is navigation, with evidence suggesting that humans vary widely in navigation abilities. Yet, data supporting the neural underpinning of these individual differences are mixed. Some evidence suggests robust structure-function relations between hippocampal volume and navigation ability, whereas other experiments show no such correlation. We focus on several possibilities for these discrepancies: 1) volumetric hippocampal changes are relevant only at the extreme ranges of navigational abilities; 2) hippocampal volume correlates across individuals but only for specific measures of navigation skill; 3) hippocampal volume itself does not correlate with navigation skill acquisition; connectivity patterns are more relevant. To explore this third possibility, we present a model emphasizing functional connectivity changes, particularly to extra-hippocampal structures. This class of models arises from the premise that navigation is dynamic and that good navigators flexibly solve spatial challenges. These models pave the way for research on other skills and provide more precise predictions for the neural basis of skill acquisition.

scholarly journals Functional connectivity predicts changes in attention observed across minutes, days, and months

2020 ◽  
Vol 117 (7) ◽  
pp. 3797-3807 ◽  
Author(s):  
Monica D. Rosenberg ◽  
Dustin Scheinost ◽  
Abigail S. Greene ◽  
Emily W. Avery ◽  
Young Hye Kwon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Deep Sedation ◽  
Brain Connectivity ◽  
Attentional State ◽  
Single Person ◽  
Connectivity Patterns ◽  
State Changes ◽  
Attention Function

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

scholarly journals Functional connectivity predicts changes in attention over minutes, days, and months

2019 ◽  
Author(s):  
Monica D. Rosenberg ◽  
Dustin Scheinost ◽  
Abigail S. Greene ◽  
Emily W. Avery ◽  
Young Hye Kwon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Deep Sedation ◽  
Brain Connectivity ◽  
Attentional State ◽  
Single Person ◽  
Connectivity Patterns ◽  
State Changes ◽  
Attention Function

AbstractThe ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention in single individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attention changes across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

Individual differences in depression are associated with abnormal function of the limbic system in multiple sclerosis patients

2015 ◽  
Vol 22 (8) ◽  
pp. 1094-1105 ◽  
Author(s):  
Roberta Riccelli ◽  
Luca Passamonti ◽  
Antonio Cerasa ◽  
Salvatore Nigro ◽  
Salvatore Maria Cavalli ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Prefrontal Cortex ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Limbic System ◽  
Emotional Processing ◽  
Dorsolateral Prefrontal Cortex ◽  
Connectivity Patterns ◽  
Dorsolateral Prefrontal ◽  
Multiple Sclerosis Patients

Background: Depression is common in patients with multiple sclerosis (MS), although the brain mechanisms of this psychiatric condition in MS are poorly understood. Specifically, it remains to be determined whether depression in MS is related to altered activity and functional connectivity patterns within limbic circuits. Methods: Seventy-seven MS patients with variable levels of depression (as assessed via the Beck Depression Inventory) underwent functional magnetic resonance imaging while performing an emotional processing task. To conduct the functional connectivity analyses, the bilateral amygdala and hippocampus, two areas critically involved in the pathophysiology of depression, were chosen as ‘seed’ regions. Multiple regression models were used to assess how depression in MS patients was correlated with the activity and functional connectivity patterns within the limbic system. Results: Depression scores in MS patients were negatively correlated: (1) with the activity in the subgenual cingulate cortex; (2) with the functional connectivity between the hippocampus and orbitofrontal cortex as well as the dorsolateral prefrontal cortex, and (3) with the functional connectivity between the amygdala and dorsolateral prefrontal cortex. Conclusions: Our study showed that individual differences in depression in MS patients were significantly associated with altered regional activity and functional connectivity patterns within the limbic system.

scholarly journals Strengthened Corticosubcortical Functional Connectivity during Muscle Fatigue

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiguo Jiang ◽  
Xiao-Feng Wang ◽  
Guang H. Yue
Keyword(s):  
Motor Control ◽  
Functional Connectivity ◽  
Quantile Regression ◽  
Muscle Fatigue ◽  
Motor Performance ◽  
Primary Motor Cortex ◽  
Motor Task ◽  
Voluntary Contraction ◽  
Control Network ◽  
Subcortical Structures

The present study examined functional connectivity (FC) between functional MRI (fMRI) signals of the primary motor cortex (M1) and each of the three subcortical neural structures, cerebellum (CB), basal ganglia (BG), and thalamus (TL), during muscle fatigue using the quantile regression technique. Understanding activation relation between the subcortical structures and the M1 during prolonged motor performance should help delineate how central motor control network modulates acute perturbations at peripheral sensorimotor system such as muscle fatigue. Ten healthy subjects participated in the study and completed a 20-minute intermittent handgrip motor task at 50% of their maximal voluntary contraction (MVC) level. Quantile regression analyses were carried out to compare the FC between the contralateral (left) M1 and CB, BG, and TL in the minimal (beginning 100 s) versus significant (ending 100 s) fatigue stages. Widespread, statistically significant increases in FC were found in bilateral BG, CB, and TL with the left M1 during significant versus minimal fatigue stages. Our results imply that these subcortical nuclei are critical components in the motor control network and actively involved in modulating voluntary muscle fatigue, possibly, by working together with the M1 to strengthen the descending central command to prolong the motor performance.

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