scholarly journals Neural Correlates of Positive and Negative Valence System Dysfunction in Adolescents Revealed by Data-Driven Parcellation and Resting-State Network Modeling

2020 ◽  
Author(s):  
Vilma Gabbay ◽  
Qi Liu ◽  
Samuel J. DeWitt ◽  
Lushna M. Mehra ◽  
Carmen M. Alonso ◽  
...  
Keyword(s):  
Resting State ◽  
Clinical Status ◽  
Data Driven ◽  
Eigenvector Centrality ◽  
Whole Brain ◽  
Illness Onset ◽  
Negative Valence ◽  
Resting State Network ◽  
Wide Range ◽  
Positive And Negative Valence

AbstractObjectiveAdolescence is a period of rapid brain development when symptoms of mood, anxiety, and other disorders often first emerge, suggesting disruptions in maturing reward circuitry may play a role in mental illness onset. Here, we characterized associations between resting-state network properties and psychiatric symptomatology in medication-free adolescents with a wide range of symptom severity.MethodsAdolescents (age 12-20) with mood and/or anxiety symptoms (n=68) and healthy controls (n=19) completed diagnostic interviews, depression/anhedonia/anxiety questionnaires, and 3T resting-state fMRI (10min/2.3mm/TR=1s). Data were preprocessed (HCP Pipelines), aligned (MSMAll), and parcellated into 750 nodes encompassing the entire cortex/subcortex (Cole-Anticevic Brain-wide Network Partition). Weighted graph theoretical metrics (Strength Centrality=CStr; Eigenvector Centrality=CEig; Local Efficiency=ELoc) were estimated within Whole Brain and task-derived Reward Anticipation/Attainment/Prediction Error networks. Associations with clinical status and symptoms were assessed non-parametrically (two-tailed pFWE<0.05).ResultsRelative to controls, clinical adolescents had increased ventral striatum CEig within the Reward Attainment network. Across subjects, depression correlated with subgenual cingulate CStr and ELoc, anhedonia correlated with ventromedial prefrontal CStr and lateral amygdala ELoc, and anxiety negatively correlated with parietal operculum CEig and medial amygdala ELoc within the Whole Brain network.ConclusionsUsing a data-driven analysis approach, high-quality parcellation, and clinically diverse adolescent cohort, we found that symptoms within positive and negative valence system constructs differentially associated with resting-state network abnormalities: depression and anhedonia, as well as clinical status, involved greater influence and communication efficiency in prefrontal and limbic reward areas, whereas anxiety was linked to reduced influence/efficiency in amygdala and cortical regions involved in stimulus monitoring.

scholarly journals Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity

2019 ◽  
Vol 92 (1101) ◽  
pp. 20180886 ◽  
Author(s):  
Christian Rubbert ◽  
Christian Mathys ◽  
Christiane Jockwitz ◽  
Christian J Hartmann ◽  
Simon B Eickhoff ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Resting State ◽  
Good Accuracy ◽  
Network Connectivity ◽  
High Sensitivity ◽  
Data Driven ◽  
Healthy Controls ◽  
Whole Brain ◽  
Model Based

Objective: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson’s disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). Methods: Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. Results: Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 – 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 – 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 – 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. Conclusion: A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. Advances in knowledge: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson’s disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

scholarly journals Regression dynamic causal modeling for resting-state fMRI

2020 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Computationally Efficient ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Wide Range

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

scholarly journals Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency

2016 ◽  
Vol 47 (4) ◽  
pp. 585-596 ◽  
Author(s):  
K. Baek ◽  
L. S. Morris ◽  
P. Kundu ◽  
V. Voon
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Data Driven ◽  
Local Network ◽  
Healthy Controls ◽  
Network Efficiency ◽  
Whole Brain ◽  
Network Properties ◽  
Obese Subjects

BackgroundThe efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED.MethodMulti-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%–25%. We also verified our findings using a separate parcellation, the Harvard–Oxford atlas parcellated into 470 regions.ResultsObese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis.ConclusionsUsing this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

scholarly journals Resolution limit-free community detection reveals unique patterns of resting-state network connectivity in posttraumatic stress disorder: A PGC-ENIGMA PTSD Consortium investigation

2021 ◽  
Author(s):  
Marisa C. Ross ◽  
Josh M. Cisler ◽  
Saskia B.J. Koch ◽  
Miranda Olff ◽  
Dick Veltman J. Veltman ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Community Detection ◽  
Resting State ◽  
Stress Disorder ◽  
Network Connectivity ◽  
Resolution Limit ◽  
Whole Brain ◽  
Default Mode ◽  
Resting State Network ◽  
Connectivity Strength

Posttraumatic stress disorder (PTSD) is a complex psychiatric condition that has generated much attention in the neuroimaging literature. A neurocircuitry model supporting fronto-limbic dysfunction as a major player in facilitating clinical symptoms of PTSD is well-characterized; however, recent literature suggests that network-based approaches may provide additional insight into neural dysfunction in PTSD. Our analysis uses resting-state neuroimaging scans of 1063 adults from the PGC-ENIGMA PTSD Consortium to investigate a network-based model of functional connectivity in PTSD. With a novel, resolution limit-free community detection approach, 16 communities corresponding to functionally meaningful networks were detected with high quality. After group-level community detection, participants were classified into three groups (PTSD, n=418, trauma-exposed controls without PTSD, n=434, and non-trauma exposed healthy controls, n=211). Individual network connectivity metrics were calculated, including whole-brain, default mode network, and central executive network participation coefficient and connectivity strength. Linear mixed effects models revealed group differences in the whole-brain, default mode, and central executive network participation coefficient and connectivity strength such that individuals with PTSD demonstrated overall greater values. We also described sex differences such that males demonstrate greater whole-brain participation coefficient vs. females and females demonstrate greater default mode network connectivity strength vs. males. Our results suggest that PTSD in adults is associated with reduced specialization and enhanced inter-module communication throughout the brain network, which may contribute to inefficient information processing and poor emotional regulation. This study presents a novel use of resolution limit-free community detection in a large PTSD sample, revealing robust differences in resting-state network topology.

scholarly journals EEG and fMRI connectomes are reliably related: a simultaneous EEG-fMRI study from 1.5T to 7T

2020 ◽  
Author(s):  
Jonathan Wirsich ◽  
João Jorge ◽  
Giannina R Iannotti ◽  
Elhum A Shamshiri ◽  
Frédéric Grouiller ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Function ◽  
High Field ◽  
Brain Activity ◽  
Consistent Estimate ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Diverse Range ◽  
Wide Range

AbstractBoth electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite their differences in probing brain activity, both electrophysiology and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage).Here, we investigated the reliability of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reliably correlates across different datasets and that the crossmodal correlation between EEG and fMRI connectivity of r≈0.3 can be reliably extracted in low and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and withing intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship.This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects being organized into reliable ICNs across different timescales. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. Alterations of this coupling could be explored as a potential clinical marker of pathological brain function.

scholarly journals Data-driven parcellation and graph theory analyses to study adolescent mood and anxiety symptoms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Benjamin A. Ely ◽  
Qi Liu ◽  
Samuel J. DeWitt ◽  
Lushna M. Mehra ◽  
Carmen M. Alonso ◽  
...  
Keyword(s):  
Graph Theory ◽  
Resting State ◽  
Ventral Striatum ◽  
Psychiatric Symptoms ◽  
Clinical Status ◽  
Anterior Cingulate ◽  
Data Driven ◽  
High Quality ◽  
Human Connectome Project ◽  
Psychiatric Conditions

AbstractAdolescence is a period of rapid brain development when psychiatric symptoms often first emerge. Studying adolescents may therefore facilitate the identification of neural alterations early in the course of psychiatric conditions. Here, we sought to utilize new, high-quality brain parcellations and data-driven graph theory approaches to characterize associations between resting-state networks and the severity of depression, anxiety, and anhedonia symptoms—salient features across psychiatric conditions. As reward circuitry matures considerably during adolescence, we examined both Whole Brain and three task-derived reward networks. Subjects were 87 psychotropic-medication-free adolescents (age = 12–20) with diverse psychiatric conditions (n = 68) and healthy controls (n = 19). All completed diagnostic interviews, dimensional clinical assessments, and 3T resting-state fMRI (10 min/2.3 mm/TR = 1 s). Following high-quality Human Connectome Project-style preprocessing, multimodal surface matching (MSMAll) alignment, and parcellation via the Cole-Anticevic Brain-wide Network Partition, weighted graph theoretical metrics (Strength Centrality = CStr; Eigenvector Centrality = CEig; Local Efficiency = ELoc) were estimated within each network. Associations with symptom severity and clinical status were assessed non-parametrically (two-tailed pFWE < 0.05). Across subjects, depression scores correlated with ventral striatum CStr within the Reward Attainment network, while anticipatory anhedonia correlated with CStr and ELoc in the subgenual anterior cingulate, dorsal anterior cingulate, orbitofrontal cortex, caudate, and ventral striatum across multiple networks. Group differences and associations with anxiety were not detected. Using detailed functional and clinical measures, we found that adolescent depression and anhedonia involve increased influence and communication efficiency in prefrontal and limbic reward areas. Resting-state network properties thus reflect positive valence system anomalies related to discrete reward sub-systems and processing phases early in the course of illness.

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain
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