Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification

Temporal and spatial variability of large-scale dynamic brain networks in ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant impairments in social-cognitive functioning. Prior studies have identified abnormal brain functional connectivity (FC) patterns in individuals with ASD, which are associated with core symptoms and serve as potential biomarkers for diagnosis. However, the patterns of temporal and spatial variability in dynamic functional connectivity networks (dFCNs) in ASD and their relationship with ASD behaviors remain underexplored. This study uses fuzzy entropy to analyze the temporal variability and spatial variability of dFCNs, aiming to reveal distinctive FC patterns in ASD and identify new biomarkers. We conducted a comparative analysis between ASD and healthy controls (HCs), examining the association with clinical symptoms. Our findings indicate increased FC temporal variability in sensorimotor, subcortical, and cerebellar networks in ASD compared to HCs. Additionally, increased spatial variability was observed primarily in visual, limbic, subcortical, and cerebellar networks. Notably, these variability patterns correlated with symptom severity in ASD. Utilizing these spatiotemporal variability features, we developed multi-site classification models that achieved high accuracy (81.25%) in identifying ASD. These results provide novel insights into the neural mechanisms and clinical characteristics of ASD, suggesting that integrated spatiotemporal dFCN features may enhance diagnostic accuracy.

Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Classification and Lateralization Analysis

Available evidence suggests that dynamic functional connectivity can capture time-varying abnormalities in brain activity in resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural advantage in uncovering mechanisms of abnormal brain activity in schizophrenia (SZ) patients. Hence, an advanced dynamic brain network analysis model called the temporal brain category graph convolutional network (Temporal-BCGCN) was employed. Firstly, a unique dynamic brain network analysis module, DSF-BrainNet, was designed to construct dynamic synchronization features. Subsequently, a revolutionary graph convolution method, TemporalConv, was proposed based on the synchronous temporal properties of features. Finally, the first modular test tool for abnormal hemispherical lateralization in deep learning based on rs-fMRI data, named CategoryPool, was proposed. This study was validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% average accuracies, respectively, outperforming the baseline model and other state-of-the-art methods. The ablation results also demonstrate the advantages of TemporalConv over the traditional edge feature graph convolution approach and the improvement of CategoryPool over the classical graph pooling approach. Interestingly, this study showed that the lower-order perceptual system and higher-order network regions in the left hemisphere are more severely dysfunctional than in the right hemisphere in SZ, reaffirmings the importance of the left medial superior frontal gyrus in SZ. Our code was available at: https://github.com/swfen/Temporal-BCGCN.

Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia

BACKGROUND

Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.

METHODS

We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows.

RESULTS

SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data.

COMPARISON WITH EXISTING METHODS

Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs.

CONCLUSIONS

This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.

Overcoming the translational crisis of contemporary psychiatry - converging phenomenological and spatiotemporal psychopathology

Despite all neurobiological/neurocomputational progress in psychiatric research, recent authors speak about a 'crisis of contemporary psychiatry'. Some argue that we do not yet know the computational mechanisms underlying the psychopathological symptoms ('crisis of mechanism') while others diagnose a neglect of subjectivity, namely first-person experience ('crisis of subjectivity'). In this perspective, we propose that Phenomenological Psychopathology, due to its focus on first-person experience of space and time, is in an ideal position to address the crisis of subjectivity and, if extended to the brain's spatiotemporal topographic-dynamic structure as key focus of Spatiotemporal Psychopathology, the crisis of mechanism. We demonstrate how the first-person experiences of space and time differ between schizophrenia, mood disorders and anxiety disorders allowing for their differential-diagnosis - this addresses the crisis of subjectivity. Presupposing space and time as shared features of brain, experience, and symptoms as their "common currency", the structure of abnormal space and time experience may also serve as template for the structure of the brain's spatiotemporal neuro-computational mechanisms - this may address the crisis of mechanism. Preliminary scientific evidence in our examples of schizophrenia, bipolar disorder, anxiety disorder, and depression support such clinically relevant spatiotemporal determination of both first-person experience (crisis of subjectivity) and the brain's neuro-computational structure (crisis of mechanism). In conclusion, converging Phenomenological Psychopathology with Spatiotemporal Psychopathology might help to overcome the translational crisis in psychiatry by delineating more fine-grained neuro computational and -phenomenal mechanisms; this offers novel candidate biomarkers for diagnosis and therapy including both pharmacological and non-pharmacological treatment.

Multi-Graph Attention Networks With Bilinear Convolution for Diagnosis of Schizophrenia

The explorations of brain functional connectivity (FC) network using resting-state functional magnetic resonance imaging (rs-fMRI) can provide crucial insights into discriminative analysis of neuropsychiatric disorders such as schizophrenia (SZ). Graph attention network (GAT), which could capture the local stationary on the network topology and aggregate the features of neighboring nodes, has advantages in learning the feature representation of brain regions. However, GAT only can obtain the node-level features that reflect local information, ignoring the spatial information within the connectivity-based features that proved to be important for SZ diagnosis. In addition, existing graph learning techniques usually rely on a single graph topology to represent neighborhood information, and only consider a single correlation measure for connectivity features. Comprehensive analysis of multiple graph topologies and multiple measures of FC can leverage their complementary information that may contribute to identifying patients. In this paper, we propose a multi-graph attention network (MGAT) with bilinear convolution (BC) neural network framework for SZ diagnosis and functional connectivity analysis. Besides multiple correlation measures to construct connectivity networks from different perspectives, we further propose two different graph construction methods to capture both the low- and high-level graph topologies, respectively. Especially, the MGAT module is developed to learn multiple node interaction features on each graph topology, and the BC module is utilized to learn the spatial connectivity features of the brain network for disease prediction. Importantly, the rationality and advantages of our proposed method can be validated by the experiments on SZ identification. Therefore, we speculate that this framework may also be potentially used as a diagnostic tool for other neuropsychiatric disorders.