Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity.

Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.

The intracortical myelin content of impulsive choices: results from T1- and T2-weighted MRI myelin mapping.

Delay discounting (DD) refers to a phenomenon that humans tend to choose small-sooner over large-later rewards during intertemporal choices. Steep discounting of delayed outcome is related to a variety of maladaptive behaviors and is considered as a transdiagnostic process across psychiatric disorders. Previous studies have investigated the association between brain structure (e.g. gray matter volume) and DD; however, it is unclear whether the intracortical myelin (ICM) influences DD. Here, based on a sample of 951 healthy young adults drawn from the Human Connectome Project, we examined the relationship between ICM, which was measured by the contrast of T1w and T2w images, and DD and further tested whether the identified associations were mediated by the regional homogeneity (ReHo) of brain spontaneous activity. Vertex-wise regression analyses revealed that steeper DD was significantly associated with lower ICM in the left temporoparietal junction (TPJ) and right middle-posterior cingulate cortex. Region-of-interest analysis revealed that the ReHo values in the left TPJ partially mediated the association of its myelin content with DD. Our findings provide the first evidence that cortical myelination is linked with individual differences in decision impulsivity and suggest that the myelin content affects cognitive performances partially through altered local brain synchrony.

Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children.

Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children's BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children's food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.

Risk of insomnia during COVID-19: effects of depression and brain functional connectivity.

Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (ß = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes.

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia.

BACKGROUND: Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. METHODS: We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). RESULTS: We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). CONCLUSIONS: The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Connectome-based prediction of eating disorder-associated symptomatology.

BACKGROUND: Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS: CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS: The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS: These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Vascular feature as a modulator of the aging brain.

The cerebral functional reorganization and declined cognitive function of aging might associate with altered vascular features. Here, we explored the altered cerebral hierarchical functional network of 2 conditions (task-free and naturalistic stimuli) in older adults and its relationship with vascular features (systemic microvascular and perfusion features, measured by magnetic resonance imaging) and behavior. Using cerebral gradient analysis, we found that compressive gradient of resting-state mainly located on the primary sensory-motor system and transmodal regions in aging, and further compress in these regions under the continuous naturalistic stimuli. Combining cerebral functional gradient, vascular features, and cognitive performance, the more compressive gradient in the resting-state, the worse vascular state, the lower cognitive function in older adults. Further modulation analysis demonstrated that both vascular features can regulate the relationship between gradient scores in the insula and behavior. Interestingly, systemic microvascular oxygenation also can modulate the relationship between cerebral gradient and cerebral perfusion. Furthermore, the less alteration of the compressive gradient with naturalistic stimuli came with lower cognitive function. Our findings demonstrated that the altered cerebral hierarchical functional structure in aging was linked with changed vascular features and behavior, offering a new framework for studying the physiological mechanism of functional connectivity in aging.

[Research progress on the effects of childhood obstructive sleep apnea syndrome on cognition and brain functions].

Obstructive sleep apnea syndrome (OSAS), a prevalent sleep disorder in children, is characterized by recurring upper airway obstruction during sleep. OSAS in children can cause intermittent hypoxia and sleep fragmentation, ultimately affect brain development and further lead to cognitive impairment if lack of timely effective intervention. In recent years, magnetic resonance imaging (MRI) and electroencephalogram (EEG) have been employed to investigate brain structure and function abnormalities in children with OSAS. Previous studies have indicated that children with OSAS showed extensive gray and white matter damage, abnormal brain function in regions such as the frontal lobe and hippocampus, as well as a significant decline in general cognitive function and executive function. However, the existing studies mainly focused on the regional activity, and the mechanism of pediatric OSAS affecting brain networks remains unknown. Moreover, it's unclear whether the alterations in brain structure and function are associated with their cognitive impairment. In this review article, we proposed two future research directions: 1) future studies should utilize the multimodal neuroimaging techniques to reveal the alterations of brain networks organization underlying pediatric OSAS; 2) further investigation is necessary to explore the relationship between brain network alteration and cognitive dysfunction in children with OSAS. With these efforts, it will be promising to identify the neuroimaging biomarkers for monitoring the brain development of children with OSAS as well as aiding its clinical diagnosis, and ultimately develop more effective strategies for intervention, diagnosis, and treatment.

Oxytocin Modulates the Intrinsic Dynamics Between Attention-Related Large-Scale Networks.

Attention and salience processing have been linked to the intrinsic between- and within-network dynamics of large-scale networks engaged in internal (default network [DN]) and external attention allocation (dorsal attention network [DAN] and salience network [SN]). The central oxytocin (OXT) system appears ideally organized to modulate widely distributed neural systems and to regulate the switch between internal attention and salient stimuli in the environment. The current randomized placebo (PLC)-controlled between-subject pharmacological resting-state fMRI study in N = 187 (OXT, n = 94; PLC, n = 93; single-dose intranasal administration) healthy male and female participants employed an independent component analysis approach to determine the modulatory effects of OXT on the within- and between-network dynamics of the DAN-SN-DN triple network system. OXT increased the functional integration between subsystems within SN and DN and increased functional segregation of the DN with both attentional control networks (SN and DAN). Whereas no sex differences were observed, OXT effects on the DN-SN interaction were modulated by autistic traits. Together, the findings suggest that OXT may facilitate efficient attention allocation by modulating the intrinsic functional dynamics between DN components and large-scale networks involved in external attentional demands (SN and DAN).

Reconfiguration of dynamic large-scale brain network functional connectivity in generalized tonic-clonic seizures.

An increasing number of studies in patients with generalized tonic-clonic seizures (GTCS) have reported the alteration of functional connectivity (FC) in many brain networks. However, little is known about the underlying temporal variability of FC in large-scale brain functional networks in patients. Recently, dynamic FC could provide novel insight into the physiological mechanisms in the brain. Here, we recruited 63 GTCS and 65 age- and sex-matched healthy controls. Dynamic FC approaches were used to evaluate alterations in the temporal variability of FC in patients at the region- and network-levels. In addition, two kinds of brain templates (>102 and > 103 regions) and two kinds of temporal variability FC approaches were adopted to verify the stability of the results. Patients showed increased FC variability in regions of the default mode network (DMN), ventral attention network (VAN) and motor-related areas. The DAN, VAN, and DMN illustrated enhanced FC variability at the within-network level. In addition, increased FC variabilities between networks were found between the DMN and cognition-related networks, including the VAN, dorsal attention network and frontal-parietal network in GTCS. Meanwhile, the alterations in FC variability were relatively consistent across different methods and templates. Therefore, the consistent alteration of FC variability would reflect a dynamic restructuring of the large-scale brain networks in patients with GTCS. Overly frequent information communication among cognition-related networks, especially in the DMN, might play a role in the epileptic activity and/or cognitive dysfunction in patients.

Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia.

Schizophrenia is thought as a self-disorder with dysfunctional brain connectivity. This self-disorder is often attributed to high-order cognitive impairment. Yet due to the frequent report of sensorial and perceptual deficits, it has been hypothesized that self-disorder in schizophrenia is dysfunctional communication between sensory and cognitive processes. To further verify this assumption, the present study comprehensively examined dynamic reconfigurations of resting-state functional connectivity (rsFC) in schizophrenia at voxel level, region level, and network levels (102 patients vs. 124 controls). We found patients who show consistently increased rsFC variability in sensory and perceptual system, including visual network, sensorimotor network, attention network, and thalamus at all the three levels. However, decreased variability in high-order networks, such as default mode network and frontal-parietal network were only consistently observed at region and network levels. Taken together, these findings highlighted the rudimentary role of elevated instability of information communication in sensory and perceptual system and attenuated whole-brain integration of high-order network in schizophrenia, which provided novel neural evidence to support the hypothesis of disrupted perceptual and cognitive function in schizophrenia. The foci of effects also highlighted that targeting perceptual deficits can be regarded as the key to enhance our understanding of pathophysiology in schizophrenia and promote new treatment intervention.

Trait-based food-cravings are encoded by regional homogeneity in the parahippocampal gyrus.

Food cravings can reflect an intense trait-like emotional-motivational desire to eat palatable food, often resulting in the failure of weight loss efforts. Studies have linked trait-based food-cravings to increased risk of overeating. However, little is known about resting-state neural mechanisms that underlie food cravings. We investigated this issue using resting-state functional magnetic resonance imaging (fMRI) to test the extent to which spontaneous neural activity occurs in regions implicated in emotional memory and reward motivation associated with food cravings. Spontaneous regional activity patterns correlating to food cravings were assessed among 65 young healthy women using regional homogeneity analysis to assess temporal synchronization of spontaneous activity. Analyses indicated that women with higher scores on the Food Cravings Questionnaire displayed increased local functional homogeneity in brain regions involved in emotional memory and visual attention processing (i.e., parahippocampal gyrus and fusiform gyrus) but not reward. In view of parahippocampal gyrus involvement in hedonic learning and incentive memory encoding, this study suggests that trait-based food cravings are encoded by emotional memory circuits.

Impulse control and restrained eating among young women: Evidence for compensatory cortical activation during a chocolate-specific delayed discounting task.

Theory and associated research indicate that people with elevated restrained eating (RE) scores have higher risk for binge eating, future bulimic symptom onset and weight gain. Previous imaging studies have suggested hyper-responsive reward brain area activation in response to food cues contributes to this risk but little is known about associated neural impulse control mechanisms, especially when considering links between depleted cognitive resources related to unsuccessful RE. Towards illuminating this issue, we used a chocolate-specific delayed discounting (DD) task to investigate relations between RE scores, behavior impulsivity, and corresponding neural impulse control correlates in a functional magnetic resonance imaging (fMRI) study of 27 young women. Specifically, participants were required to choose between more immediate, smaller versus delayed, larger hypothetical chocolate rewards following initial consumption of a chocolate. As predicted, RE scores were correlated positively with behavior impulse control levels. More critically, higher RE scores were associated with stronger activation in impulse control region, the dorsal-lateral prefrontal cortex (DLPFC) during the completion of difficult decision trials reflecting higher cognitive demands and resource depletion relative to easy decision trials. Exploratory analyses revealed a positive correlation between RE scores and activity in a reward system hub, the right striatum. Moreover, a positive correlation between left DLPFC and striatum activation was posited to reflect, in part, impulse control region compensation in response to stronger reward signal among women with RE elevations. Findings suggested impulse control lapses may contribute to difficulties in maintaining RE, particularly when cognitive demands are high.

Altered morphometric similarity networks in insomnia disorder.

Previous studies on structural covariance network (SCN) suggested that patients with insomnia disorder (ID) show abnormal structural connectivity, primarily affecting the somatomotor network (SMN) and default mode network (DMN). However, evaluating a single structural index in SCN can only reveal direct covariance relationship between two brain regions, failing to uncover synergistic changes in multiple structural features. To cover this research gap, the present study utilized novel morphometric similarity networks (MSN) to examine the morphometric similarity between cortical areas in terms of multiple sMRI parameters measured at each area. With seven T1-weighted imaging morphometric features from the Desikan-Killiany atlas, individual MSN was constructed for patients with ID (N = 87) and healthy control groups (HCs, N = 84). Two-sample t-test revealed differences in MSN between patients with ID and HCs. Correlation analyses examined associations between MSNs and sleep quality, insomnia symptom severity, and depressive symptoms severity in patients with ID. The right paracentral lobule (PCL) exhibited decreased morphometric similarity in patients with ID compared to HCs, mainly manifested by its de-differentiation (meaning loss of distinctiveness) with the SMN, DMN, and ventral attention network (VAN), as well as its decoupling with the visual network (VN). Greater PCL-based de-differentiation correlated with less severe insomnia and fewer depressive symptoms in the patients group. Additionally, patients with less depressive symptoms showed greater PCL de-differentiation from the SMN. As an important pilot step in revealing the underlying morphometric similarity alterations in insomnia disorder, the present study identified the right PCL as a hub region that is de-differentiated with other high-order networks. Our study also revealed that MSN has an important potential to capture clinical significance related to insomnia disorder.

Electrophysiological Characteristics of Inhibitive Control for Adults with Different Physiological or Psychological Obesity.

Individuals exhibiting high scores on the fatness subscale of the negative-physical-self scale (NPSS-F) are characterized by heightened preoccupation with body fat accompanied by negative body image perceptions, often leading to excessive dieting behaviors. This demographic constitutes a considerable segment of the populace in China, even among those who are not obese. Nonetheless, scant empirical inquiries have delved into the behavioral and neurophysiological profiles of individuals possessing a healthy body mass index (BMI) alongside elevated NPSS-F scores. This study employed an experimental paradigm integrating go/no-go and one-back tasks to assess inhibitory control and working memory capacities concerning food-related stimuli across three adult cohorts: those with normal weight and low NPSS-F scores, those with normal weight and high NPSS-F scores, and individuals classified as obese. Experimental stimuli comprised high- and low-caloric-food pictures with concurrent electroencephalogram (EEG) and photoplethysmogram (PPG) recordings. Individuals characterized by high NPSS-F scores and normal weight exhibited distinctive electrophysiological responses compared to the other two cohorts, evident in event-related potential (ERP) components, theta and alpha band oscillations, and heart rate variability (HRV) patterns. In essence, the findings underscore alterations in electrophysiological reactivity among individuals possessing high NPSS-F scores and a healthy BMI in the context of food-related stimuli, underscoring the necessity for increased attention to this demographic alongside individuals affected by obesity.

Elevated BMI impacts brain-state dynamics within the sensorimotor-to-transmodal hierarchy.

OBJECTIVE: Overweight and obesity, as commonly indicated by a higher BMI, are associated with functional alterations in the brain, which may potentially result in cognitive decline and emotional illness. However, the manner in which these detrimental impacts manifest in the brain's dynamic characteristics remains largely unknown. METHODS: Based on two independent resting-state functional magnetic resonance imaging data sets (Behavioral-Brain Research Project of Chinese Personality, n = 1923; Human Connectome Project, n = 998), the current study employed a Hidden Markov model to identify the spatiotemporal features of brain activity states. Subsequently, the study examined the changes in brain-state dynamics and the corresponding functional outcomes that arise with an increase in BMI. RESULTS: Elevated BMI tends to shift the brain's activity states toward a greater emphasis on a specific set of states, i.e., the metastate, that are relevant to the joint activities of sensorimotor systems, making it harder to transfer to the metastate of transmodal systems. These findings were reconfirmed in a longitudinal sample (Behavioral-Brain Research Project of Chinese Personality, n = 34) that exhibited a significant increase in BMI at follow-up. Importantly, the alternation of brain-state dynamics specifically mediated the relationships between BMI and adverse functional outcomes, including cognitive decline and symptoms of mental illness. CONCLUSIONS: The altered brain-state dynamics within the sensorimotor-to-transmodal hierarchy provide new insights into obesity-related brain dysfunctions and mental health issues.

Volume of the Dentate Gyrus/CA4 Hippocampal subfield mediates the interplay between sleep quality and depressive symptoms.

Background: Emerging evidence increasingly suggests that poor sleep quality is associated with depressive symptoms. The hippocampus might play a crucial role in the interplay between sleep disturbance and depressive symptomatology, e.g., hippocampal atrophy is typically seen in both insomnia disorder and depression. Thus, examining the role of hippocampal volume in the interplay between poor sleep quality and depressive symptoms in large healthy populations is vital. Methods: We investigated the association between self-reported sleep quality, depressive symptoms, and hippocampal total and subfields' volumes in 1603 healthy young adults from the Behavioral Brain Research Project. Mediation analysis explored the mediating role of hippocampal volumes between sleep quality and depressive symptoms. Results: Self-reported sleep quality and depressive symptoms were positively correlated. In addition, it negatively related to three hippocampal subfields but not total hippocampal volume. In particular, hippocampal subfield DG and CA4 volumes mediated the interrelationship between poor sleep quality and depressive symptoms. Conclusions: Our findings improved the current understanding of the relationship between sleep disturbance, depressive symptomatology, and hippocampal subfields in healthy populations. Considering the crucial role of DG in hippocampal neurogenesis, our results suggest that poor sleep quality may contribute to depression through a reduction of DG volume leading to impaired neurogenesis which is crucial for the regulation of mood.

Multimodal Covariance Network Reflects Individual Cognitive Flexibility.

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.

Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning.

Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functional networks, we newly iterated a framework of contrastive variational autoencoders (cVAEs) applied in the contrasts among three groups, to disentangle the latent resting-state network patterns specified for the SCZ and R-SCZ. We demonstrated that the comparison in reconstructed resting-state networks among SCZ, R-SCZ, and healthy controls (HC) revealed network distortions of the inner-frontal hypoconnectivity and frontal-occipital hyperconnectivity, while the original ones illustrated no differences. And only the classification by adopting the reconstructed network metrics achieved satisfying performances, as the highest accuracy of 96.80% ± 2.87%, along with the precision of 95.05% ± 4.28%, recall of 98.18% ± 3.83%, and F1-score of 96.51% ± 2.83%, was obtained. These findings consistently verified the validity of the newly proposed framework for the contrasts among the three groups and provided related resting-state network evidence for illustrating the pathological mechanism underlying the brain deficits in SCZ, as well as facilitating the diagnosis of SCZ.