Jinnan Gong

Investigating abnormal brain network characteristics in schizophrenia can improve our understanding of disease mechanisms and help identify potential intervention targets. Graph learning techniques can capture high-dimensional features of large-scale brain networks and offer an inherent advantage for integrating multimodal data. To better integrate multimodal data and accurately localize network abnormalities associated with the disorder, this study proposes a channel-based graph neural network (C-GNN) model. First, node embedding of brain regions was constructed to capture structural connectivity patterns. Second, a branched attention module was introduced to adaptively identify important brain regions through channel attention. Finally, a graph feature-constraint module was developed to extract salient features by computing difference scores across feature channels. The C-GNN model achieved an accuracy of 84.37% in classifying individuals with schizophrenia. Interpretability analysis revealed key abnormal brain regions (e.g. orbital cortex, temporal fusiform cortex, lingual gyrus) and multimodal metrics (such as cortical thickness and ReHo) that contributed substantially to the classification. These findings offer insights into the underlying neural alterations in schizophrenia and may inform the development of targeted intervention strategies.

Previous studies have demonstrated that the thalamus is involved in multiple functional circuits in participants with schizophrenia. However, less is known about the thalamocortical circuit in the rare subtype of early-onset schizophrenia. A total of 110 participants with early-onset schizophrenia (47 antipsychotic–naive patients) and 70 matched healthy controls were recruited and underwent resting-state functional and diffusion-weighted magnetic resonance imaging scans. A data-driven parcellation method that combined the high spatial resolution of diffusion magnetic resonance imaging and the high sensitivity of functional magnetic resonance imaging was used to divide the thalamus. Next, the functional connectivity between each thalamic subdivision and the cortex/cerebellum was investigated. Compared to healthy controls, individuals with early-onset schizophrenia exhibited hypoconnectivity between subdivisions of the thalamus and the frontoparietal network, visual network, ventral attention network, somatomotor network and cerebellum, and hyperconnectivity between subdivisions of thalamus and the parahippocampal and temporal gyrus, which were included in limbic network. The functional connectivity between the right posterior cingulate cortex and 1 subdivision of the thalamus (region of interest 1) was positively correlated with the general psychopathology scale score. This study showed that the specific thalamocortical dysconnection in individuals with early-onset schizophrenia involves the prefrontal, auditory and visual cortices, and cerebellum. This study identified thalamocortical connectivity as a potential biomarker and treatment target for early-onset schizophrenia.

Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node–node and edge–edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.

Background: Internet addiction (IA), recognized as a behavioral addiction, is emerging as a global public health problem. Acupuncture has been demonstrated to be effective in alleviating IA; however, the mechanism is not yet clear. To fill this knowledge gap, our study aimed to investigate the modulatory effects of acupuncture on the functional interactions among the addiction‐related networks in adolescents with IA.Methods: Thirty individuals with IA and thirty age‐ and sex‐matched healthy control subjects (HCs) were recruited. Subjects with IA were given a 40‐day acupuncture treatment, and resting‐state functional magnetic resonance imaging (fMRI) data were collected before and after acupuncture sessions. HCs received no treatment and underwent one fMRI scan after enrollment. The intergroup differences in functional connectivity (FC) among the subcortical nucleus (SN) and fronto‐parietal network (FPN) were compared between HCs and subjects with IA at baseline. Then, the intragroup FC differences between the pre‐ and post‐treatment were analyzed in the IA group. A multiple linear regression model was further employed to fit the FC changes to symptom relief in the IA group.Results: In comparison to HCs, subjects with IA exhibited significantly heightened FC within and between the SN and FPN at baseline. After 40 days of acupuncture treatment, the FC within the FPN and between the SN and FPN were significantly decreased in individuals with IA. Symptom improvement in subjects with IA was well fitted by the decrease in FC between the left midbrain and ventral prefrontal cortex and between the left thalamus and ventral anterior prefrontal cortex.Conclusion: These findings confirmed the modulatory effects of acupuncture on the aberrant functional interactions among the SN and FPN, which may partly reflect the neurophysiological mechanism of acupuncture for IA.

With the rapid development of earth observation by optical remote sensing satellites, there is an urgent requirement of remote sensing data with high-resolution in environmental monitoring, urban planning and other applications. The resolution of remote sensing images (RSIs) significantly depends on the aperture size of space imaging system. However, limited by manufacturing level and carrying capacity, the traditional optical materials and reflection/refraction imaging principle have encountered a bottleneck in the manufacturing of space imaging system with ultra-large aperture and lightweight. Consequently, it is necessary to develop brand new space optical imaging systems. In this paper, we summarize the imaging mechanism and development history of three ultra-large aperture imaging technologies, including synthetic aperture imaging technology, diffractive membrane imaging technology and rotating synthetic aperture imaging technology, and analyze the challenges existing in their application.

The aberrant thalamocortical pathways of epilepsy have been detected recently, while its underlying effects on epilepsy are still not well understood. Exploring pathoglytic changes in two important thalamocortical pathways, that is, the basal ganglia (BG)‐thalamocortical and the cerebellum‐thalamocortical pathways, in people with idiopathic generalized epilepsy (IGE), could deepen our understanding on the pathological mechanism of this disease. These two pathways were reconstructed and investigated in this study by combining diffusion and functional MRI. Both pathways showed connectivity changes with the perception and cognition systems in patients. Consistent functional connectivity (FC) changes were observed mainly in perception regions, revealing the aberrant integration of sensorimotor and visual information in IGE. The pathway‐specific FC alterations in high‐order regions give neuroimaging evidence of the neural mechanisms of cognitive impairment and epileptic activities in IGE. Abnormal functional and structural integration of cerebellum, basal ganglia and thalamus could result in an imbalance of inhibition and excitability in brain systems of IGE. This study located the regulated cortical regions of BG and cerebellum which been affected in IGE, established possible links between the neuroimaging findings and epileptic symptoms, and enriched the understanding of the regulatory effects of BG and cerebellum on epilepsy.

Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age² on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.