Tohoku University · Neuroscience
Professor Kai Wu's research lab specializes in computational neuroscience and brain network analysis, focusing on the topological organization of structural and functional brain networks across the lifespan. The lab investigates how brain network properties—such as small-world organization, modularity, and nodal efficiency—evolve with age, and how they are influenced by biological factors like sex and IQ, as well as clinical conditions such as schizophrenia. Using advanced neuroimaging techniques (e.g., MRI, fMRI) combined with graph theory and machine learning, the lab explores both typical brain development and neuropsychiatric disorders with a strong emphasis on individual-level prediction and network-level mechanisms. The research also extends to psychological and social factors, such as parental education anxiety and family functioning, in relation to adolescent mental health.
Figures are computed from collected data and may differ slightly.
Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then inv
The aim of this study was to examine structural brain networks using regional gray matter volume, as well as to investigate changes in small-world and modular organization with normal aging. We constructed structural brain networks composed of 90 regions in young, middle, and old age groups. We randomly selected 350 healthy subjects for each group from a Japanese magnetic resonance image database. Structural brain networks in three age groups showed economical small-world properties, providing h
Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients
This study aimed to explore the effect of parents' education anxiety on children's academic burnout, and the mediation effect of parental burnout and the moderating effect of family function. A total of 259 paired parents and children from two middle schools in central China participated in the survey. The questionnaire was conducted using the Educational Anxiety Scale, Parental Burnout Scale, Adolescent Student Burnout Inventory, and Family APGAR Index. Our results indicated that parental educa
Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network amo
The aim of this study was to investigate age-related changes in the topological organization of structural brain networks by applying a longitudinal design over 6 years. Structural brain networks were derived from measurements of regional gray matter volume and were constructed in age-specific groups from baseline and follow-up scans. The structural brain networks showed economical small-world properties, providing high global and local efficiency for parallel information processing at low conne
Comorbidity with anxiety disorder is a relatively common occurrence in major depressive disorder. However, the unique and shared neuroanatomical characteristics of depression and anxiety disorders have not been fully identified. The aim of this study was to identify gray matter abnormalities and their clinical correlates in depressive patients with and without anxiety disorders. We applied voxel-based morphometry and region-of-interest analyses of gray matter volume (GMV) in normal controls (NC
Our results suggest that levodopa significantly changes the motor and cognitive networks of the cortico-striatal pathways. This knowledge will lead clinicians to survey a broader range of symptoms in determining optimal therapy.
Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feat
Abstract Neural biointerfacing, enabling direct communication between neural systems and external devices, holds great promises for applications in brain machine interfaces, neural prosthetics, and neuromodulation. However, current neural electronics made of conventional rigid materials are challenged by their inherent mechanical mismatch with the neural tissues. Hydrogel bioelectronics, with mechanical properties compatible with the neural tissues, represent an alternative to these limitations
In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (R
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