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.

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 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 Propofol modulates functional connectivity signatures of sustained attention

2021 ◽  
Author(s):  
Taylor A Chamberlain ◽  
Monica D. Rosenberg
Keyword(s):  
Individual Differences ◽  
Cognitive Function ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Fluid Intelligence ◽  
Pharmacological Intervention ◽  
Attentional State ◽  
Attention Networks ◽  
Functional Connections ◽  
Functional Brain Networks

Sustained attention is a critical cognitive function reflected in an individuals whole-brain pattern of fMRI functional connectivity. However sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively specific to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function.

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.

scholarly journals Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

2020 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Organization ◽  
Functional Brain Network

Abstract Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

scholarly journals Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Emiliano Santarnecchi ◽  
Chiara Del Bianco ◽  
Isabella Sicilia ◽  
Davide Momi ◽  
Giorgio Di Lorenzo ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Default Mode Network ◽  
Age Of Onset ◽  
Brain Connectivity ◽  
Disruptive Effect ◽  
Default Mode ◽  
Connectivity Patterns ◽  
The Impact

Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients.

scholarly journals How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
J. Toppi ◽  
F. De Vico Fallani ◽  
G. Vecchiato ◽  
A. G. Maglione ◽  
F. Cincotti ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Adjacency Matrix ◽  
Brain Connectivity ◽  
Small World ◽  
Connectivity Pattern ◽  
Edge Density ◽  
Statistical Validation ◽  
Common Procedure ◽  
Connectivity Patterns ◽  
Definition Of

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

scholarly journals Sex-Related Neurocircuitry Supporting Camouflaging in Adults with Autism: Female Protection Insights

2021 ◽  
Author(s):  
Melissa Walsh ◽  
Broc Pagni ◽  
Leanna Monahan ◽  
Shanna Delaney ◽  
Christopher J Smith ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Emotion Recognition ◽  
Brain Connectivity ◽  
Data Driven ◽  
Sex Bias ◽  
Adults With Autism ◽  
Connectivity Patterns ◽  
Females With Autism

Background: The male preponderance in autism led to the hypothesis that aspects of female biology are protective against autism. Females with autism report engaging in more compensatory behaviors (i.e., camouflaging) to overcome autism-related social differences, which may be a downstream result of protective pathways. No studies have examined sex-related brain pathways supporting camouflaging in females with autism, despite its potential to inform mechanisms underlying the sex bias in autism. Methods: This study included 45 non-intellectually-disabled adults with autism (male/female: 21/24) and 40 neurotypical adults (male/female: 19/21) ages 18-71. We used group multivariate voxel pattern analysis to conduct a data-driven, connectome-wide characterization of "sex-atypical" (sex-by-diagnosis) and "sex-typical" (sex) brain functional connectivity features linked to camouflaging, and validated findings in females with autism multi-modally via structural connectometry. Exploratory associations with cognitive control, memory, emotion recognition, and depression/anxiety examined the adaptive nature of functional connectivity patterns supporting camouflaging in females with autism. Results: We found 1) "sex-atypical" functional connectivity patterns predicting camouflaging in the hypothalamus and precuneus and 2) "sex-typical" patterns in the anterior cingulate and right anterior parahippocampus. Higher hypothalamic functional connectivity with a limbic reward cluster was the strongest predictor of camouflaging in females with autism (a "sex-atypical" pattern), and also predicted better cognitive control/emotion recognition. Structural connectometry validated functional connectivity results with consistent brain pathways/effect patterns implicated across multi-modal findings in females with autism. Conclusion: This data-driven, connectome-wide characterization of "sex-atypical" and "sex-typical" brain connectivity features supporting compensatory social behavior in autism suggests hormones may play a role in the autism sex bias. Furthermore, both "male-typical" and "female-typical" brain connectivity patterns are implicated in camouflaging in females with autism in circuits associated with reward, emotion, and memory processing. "Sex-atypical" results are consistent with the fetal steroidogenic hypothesis, which would result in masculinized brain features in females with autism. However, female genetics/biology may contribute to "female-typical" patterns implicated in camouflaging.

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

scholarly journals Individual Variation in Brain Network Topography is Linked to Schizophrenia Symptomatology

2019 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Brain Organization

AbstractBackgroundResting state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia negative symptom severity and network connectivity are actually due to individual differences in network spatial organization.Methods44 participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole brain functional connectivity correlates with negative symptom severity at the individual voxel level.ResultsBrain connectivity to a region of the right dorso-lateral pre-frontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of two networks: the default mode network (DMN) and the task positive network (TPN). Both networks demonstrate strong (r∼0.49) and significant (p<0.001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN.ConclusionPreviously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (e.g. TMS).

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