Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you

This paper develops a model using resting-state fMRI to identify functional connectivity patterns in schizophrenia, finding a robust cerebellothalamic disruption signature.

The paper integrates transcriptomic, molecular imaging, and neuroimaging data to show that cellular and molecular architecture aligns with intrinsic connectivity networks and functional network connectivity, mediating cognitive processes.

The paper develops population-specific brain charts for Chinese individuals, revealing distinct neurodevelopmental patterns compared to Western populations and showing better performance in predicting brain phenotypes.

This study identifies a structural connectivity pattern shared between physical activity and depressive mood using UK Biobank data, linking it to neurotransmitter receptors and pathways.

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

This paper introduces pmg-jICA, a method that fuses structural and functional brain data to identify structure-function links in Alzheimer's disease, revealing disrupted brain regions and network connectivity.

This paper uses ml-jICA to analyze multimodal MRI data from Alzheimer's patients and controls, finding significant function/structure coupling in brain regions like the default mode network and hippocampus.

This paper introduces a multi-scale spatially constrained ICA approach to analyze fMRI data, revealing consistent group differences and predictive patterns in schizophrenia.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

The paper uses a kernel SVM algorithm with neuromark ICA to predict treatment response in bipolar and major depressive disorders with up to 89% accuracy.

This paper introduces multi-model order ICA to evaluate brain functional network connectivity across multiple spatial scales and finds that FNC between model orders 25 and 50 best predicts schizophrenia vs. healthy controls.

This study uses resting-state fMRI to analyze dynamic functional connectivity in major depressive disorder patients before and after ECT, finding that specific network states predict treatment response.

This paper reviews machine learning approaches for predicting cognition and behavior from neuroimaging data, highlighting challenges and future directions in the field.

The paper identifies and replicates expression and methylation modules in schizophrenia patients and healthy controls, showing their association with astrocyte and oligodendrocyte functions and genetic risk.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.

This study compares GIG-ICA and STR for identifying brain functional networks in schizophrenia, finding GIG-ICA more sensitive to group differences and better at classification.

This study identifies multimodal neural patterns related to cognitive control using two HCP cohorts and mCCA+jICA, finding consistent components across imaging modalities.