scholarly journals Extraction and Analysis of Dynamic Functional Connectome Patterns in Migraine Sufferers: A Resting-State fMRI Study

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weifang Nie ◽  
Weiming Zeng ◽  
Jiajun Yang ◽  
Yuhu Shi ◽  
Le Zhao ◽  
...  
Keyword(s):  
Resting State ◽  
Occipital Lobe ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Neural Mechanisms ◽  
Imaging Data ◽  
Physical And Mental Health ◽  
Brain Areas ◽  
Functional Connectome ◽  
Network Metrics

Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs—including 1 that was specific and 16 that were general—based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients.

scholarly journals Genetic and Environmental Influence on the Human Functional Connectome

2019 ◽  
Vol 30 (4) ◽  
pp. 2099-2113
Author(s):  
Andrew E Reineberg ◽  
Alexander S Hatoum ◽  
John K Hewitt ◽  
Marie T Banich ◽  
Naomi P Friedman
Keyword(s):  
Resting State ◽  
Environmental Influence ◽  
Brain Regions ◽  
Genetic Influence ◽  
Genetic Influences ◽  
Imaging Data ◽  
Functional Connections ◽  
Functional Connectome ◽  
Intermediate Phenotypes ◽  
Shared Environmental Influence

Abstract Detailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyzed resting-state functional magnetic resonance imaging data from two adult twin samples (Nos = 446 and 371) to quantify genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35 000 functional connections). Nonshared environmental influence was high across the whole connectome. Approximately 14–22% of connections had nominally significant genetic influence in each sample, 4.6% were significant in both samples, and 1–2% had heritability estimates greater than 30%. Evidence of shared environmental influence was weak. Genetic influences on connections were distinct from genetic influences on a global summary measure of the connectome, network-based estimates of connectivity, and movement during the resting-state scan, as revealed by a novel connectome-wide bivariate genetic modeling procedure. The brain’s genetic organization is diverse and not as one would expect based solely on structure evident in nongenetically informative data or lower resolution data. As follow-up, we make novel classifications of functional connections and examine highly localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.

scholarly journals Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI

2019 ◽  
Author(s):  
Matthew F. Singh ◽  
Todd S. Braver ◽  
Michael W. Cole ◽  
ShiNung Ching
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Nonlinear Dynamical Systems ◽  
Brain Activity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Data Driven ◽  
Imaging Data ◽  
Individual Level

AbstractA key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

scholarly journals A computational model of shared fine-scale structure in the human connectome

2017 ◽  
Author(s):  
J. Swaroop Guntupalli ◽  
Ma Feilong ◽  
James V. Haxby
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Scale Structure ◽  
High Dimensional ◽  
Fine Scale ◽  
Coarse Scale ◽  
Brain Areas ◽  
Shared Structure ◽  
Analysis And Study

AbstractVariation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.Author SummaryResting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse-scale connectivity among brain regions, ignoring fine-scale structure within those regions. We developed a high-dimensional common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for substantially more shared variance of connectivity compared to previous models. Our model opens new territory — shared fine-scale structure, a dominant but mostly unexplored component of the human connectome — for analysis and study.

Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

2021 ◽  
pp. 1-29
Author(s):  
Kangyu Jin ◽  
Zhe Shen ◽  
Guoxun Feng ◽  
Zhiyong Zhao ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Resting State ◽  
Parietal Lobe ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Effects of long-term cocaine self-administration on brain resting-state functional connectivity in nonhuman primates

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephen J. Kohut ◽  
Dionyssios Mintzopoulos ◽  
Brian D. Kangas ◽  
Hannah Shields ◽  
Kelly Brown ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Resting State Functional Connectivity ◽  
Self Administration ◽  
Chronic Cocaine

AbstractLong-term cocaine use is associated with a variety of neural and behavioral deficits that impact daily function. This study was conducted to examine the effects of chronic cocaine self-administration on resting-state functional connectivity of the dorsal anterior cingulate (dACC) and putamen—two brain regions involved in cognitive function and motoric behavior—identified in a whole brain analysis. Six adult male squirrel monkeys self-administered cocaine (0.32 mg/kg/inj) over 140 sessions. Six additional monkeys that had not received any drug treatment for ~1.5 years served as drug-free controls. Resting-state fMRI imaging sessions at 9.4 Tesla were conducted under isoflurane anesthesia. Functional connectivity maps were derived using seed regions placed in the left dACC or putamen. Results show that cocaine maintained robust self-administration with an average total intake of 367 mg/kg (range: 299–424 mg/kg). In the cocaine group, functional connectivity between the dACC seed and regions primarily involved in motoric behavior was weaker, whereas connectivity between the dACC seed and areas implicated in reward and cognitive processing was stronger. In the putamen seed, weaker widespread connectivity was found between the putamen and other motor regions as well as with prefrontal areas that regulate higher-order executive function; stronger connectivity was found with reward-related regions. dACC connectivity was associated with total cocaine intake. These data indicate that functional connectivity between regions involved in motor, reward, and cognitive processing differed between subjects with recent histories of cocaine self-administration and controls; in dACC, connectivity appears to be related to cumulative cocaine dosage during chronic exposure.

scholarly journals Resting-state neural network disturbances that underpin the emergence of emotional symptoms in adolescent girls: resting-state fMRI study

2019 ◽  
Vol 215 (3) ◽  
pp. 545-551 ◽  
Author(s):  
Gin S. Malhi ◽  
Pritha Das ◽  
Tim Outhred ◽  
Richard A. Bryant ◽  
Vince Calhoun
Keyword(s):  
Emotional Disorders ◽  
Adolescent Girls ◽  
Resting State ◽  
Early Adulthood ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Imaging Data ◽  
Emotional Symptoms ◽  
Fmri Study ◽  
The Right

BackgroundSubsyndromal emotional symptoms in adolescence may represent precursors for full-blown emotional disorders in early adulthood. Understanding the neurobiological mechanisms that drive this development is essential for prevention.AimsSelf-referential processing and emotion regulation are remodelled substantively during adolescence, therefore this study examined integration of key neural networks involved in these processes.MethodAt baseline, clinical and resting-state functional magnetic resonance imaging data were collected for 88 adolescent girls (mean age 15 years), and 71 of these girls underwent repeat clinical assessment after 2 years. These 71 girls were then partitioned into two groups depending on the presence (ES+) or absence (ES−) of emotional symptoms, and differences in dynamic functional network connectivity were determined and correlated with clinical variables.ResultsThe two groups displayed a differential pattern of functional connectivity involving the left lateral prefrontal network (LPFN). Specifically, in the ES+ group this network displayed positive coupling with the right LPFN but negative coupling with the default mode network, and the inverse of this pattern was found in the ES− group. Furthermore, the coupling strengths between left and right LPFN at the irst time point predicted follow-up depression and state anxiety scores.ConclusionsOur findings suggest that in adolescent girls, emotional symptoms may emerge as a result of impaired integration between networks involved in self-referential information processing and approach-avoidance behaviours. These impairments can compromise the pursuit of important goals and have an impact on emotion processing and finally may lead to the development of emotional disorders, such as anxiety and depression in adulthood.Declaration of interestNone.

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

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