Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder

Fu, Zening; Sui, Jing; Turner, Jessica A.; Du, Yuhui; Assaf, Michal; Pearlson, Godfrey D.; Calhoun, Vince D.

doi:10.1002/hbm.25205

Cited by 36 publications

(30 citation statements)

References 82 publications

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“…Reducing the brain regions by almost 80% helps in identifying the important regions for the classification of SZ. The regions selected by our model, such as cerebellum, temporal lobe, caudate, SMA, etc., have been linked to the disease by multiple previous studies, hence reassuring the correctness of our model [27][28][29][30]. We see an immediate benefit of using GNNs to study functional connectivity and our BrainGNN model specifically.…”

Section: Discussionsupporting

confidence: 64%

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Mahmood

Calhoun

et al. 2021

Algorithms

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Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of functional magnetic resonance imaging (fMRI) correlation matrix. However, most of the work with the FC depends on the way the connectivity is computed, and it further depends on the manual post-hoc analysis of the FC matrices. In this work, we propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. It simultaneously applies a graphical neural network to this learned graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate that the model’s state-of-the-art classification performance on a schizophrenia fMRI dataset and demonstrate how introspection leads to disorder relevant findings. The graphs that are learned by the model exhibit strong class discrimination and the sparse subset of relevant regions are consistent with the schizophrenia literature.

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Section: Discussionsupporting

confidence: 64%

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Mahmood

Calhoun

et al. 2021

Algorithms

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“…In this study, a set of robust network priors were used to extracted comparable components across subjects from the OASIS dataset. The network priors were extracted via the NeuroMark pipeline ( Du et al, 2020 ; Fu et al, 2020 , 2021 ). This framework performed group ICA with model order as 100 on two healthy controls datasets, human connectome project (HCP 2 , 823 subjects after the subject selection) and genomics superstruct project (GSP 3 , 1005 subjects after the subject selection) for creating the network priors.…”

Section: Methodsmentioning

confidence: 99%

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Sendi

Zendehrouh

Miller

et al. 2021

Front. Neural Circuits

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BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

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“…During the recent decade, increasing literature has shown that the functional connectivity is highly dynamic even during the resting-state and the dynamic functional connectivity are neuronally original (Hutchison et al, 2013;Allen et al, 2014). It is believed that capturing the functional connectivity on a more detailed temporal scale will provide valuable information underlying the brain mechanisms and disease pathophysiology (Calhoun et al, 2014;Zalesky et al, 2014;Abrol et al, 2017;Fu et al, 2020). In this work, by using a k-mean clustering method, three dFNC states were identified reoccurring across windows and individuals.…”

Section: Altered Features Of Dynamic Fnc Brain Statesmentioning

confidence: 99%

“…Neuromark is a robust ICA-based analysis pipeline that can capture corresponding functional network features while retaining more single-subject variability (Du et al, 2020). This pipeline has been successfully applied to multiple studies and identified a wide range of connectivity abnormalities in numerous brain diseases (Fu et al, 2019a(Fu et al, , 2020Li et al, 2020;Tu et al, 2020). In this pipeline, group ICA was first performed on two large healthy control datasets to construct spatial network (component) priors.…”

Section: Schematic Diagram Of Neuromark Pipelinementioning

confidence: 99%

Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis

Iraji

Sui

et al. 2021

Front. Neurosci.

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Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called “Neuromark” was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.

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Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder

Cited by 36 publications

References 82 publications

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis

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