Tien-Wen Lee

Autism spectrum disorder (ASD) is one of the most prevalent developmental disorders. This study utilized 3‐Tesla resting‐state fMRI data analysed with the functional parcellation algorithm MOSI (modular analysis and similarity measurement) to investigate cortical functional organization in ASD. Sixty individuals with ASD and sixty healthy controls were selected from the Autism Brain Imaging Data Exchange (ABIDE), with no significant differences in age and gender distribution. The MOSI‐derived metrics were compared using independent two‐sample t‐tests. The findings revealed a significant reduction in the functional volume of the left frontal lobe, a region critical for language, cognitive, and social processing. This reduction appears to be accompanied by compensatory expansion in other brain regions, suggesting a reallocation of neural resources that may contribute to ASD heterogeneity. These results support the notion of left frontal lobe developmental deviation (LFDD) as a parsimonious neural mechanism underlying key ASD features. The accountability of LFDD in various cognitive, symptomatic and behavioural characteristics of ASD is briefly discussed, along with its implications for male predominance and evolutionary relevance. Overall, the study provides a novel brain‐based perspective on ASD, moving beyond traditional psychological models to offer a neurobiological explanation for its defining characteristics and possible underlying developmental origins.

The general linear model (GLM) has been widely used in research, where the error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and nonbiological noise. Using the expectation and maximization (EM) scheme can distinguish biological variance from noise, termed EMSEV (EM for separating variances). The performance of EMSEV was evaluated by varying noise levels, dimensions of the design matrix, and covariance structures of the target variables. The deviation between EMSEV outputs and the predefined distribution parameters increased with noise level. With a proper initial guess, when the noise magnitude and the variance of the target variables were similar, there were deviations of 3% and 10%–16% in the estimated mean and covariance of the target variables, respectively, along with a 1.7% deviation in noise estimation. EMSEV appears promising for distinguishing signal variance from noise in biological systems. The potential applications and implications in biological science and statistical inference are discussed.

Background: Previous resting-state functional MRI (rsfMRI) studies have identified a robust inverse relationship between nodal strength and amplitude of low-frequency fluctuations (ALFF) across cortical modules. This study examined whether this negative relationship exists within modules and further explored age-related effects on these associations. Methods: Using MOSI (modularity analysis and similarity measurements), rsfMRI data from three public datasets spanning different age cohorts were analyzed. Correlations between ALFF and nodal strength between modules or voxel concordance within modules were calculated. Geometric mean p-values assessed robustness at the individual level. Results: Confirming prior findings, a significant inverse correlation between nodal strength and ALFF was observed at the between-module level (geometric p-values 10 ⁻⁴ to 10 ⁻⁵ ). Within-module negative associations were nonsignificant in younger cohorts at individual level (mean ages 10 and 21) but became significant in the older cohort (mean age 33). The magnitude of this negative association increased with age, consistent with maturation of local inhibitory network mechanisms. Conclusions: The findings support MOSI as a valid FP method for cortical network construction, with within-module inhibitory effects strengthening in adulthood. The age-dependent modulation reveals network maturation at the modular level, with implications for neurodevelopmental and neuropsychiatric conditions.

Background: This study empirically tests a theoretical model of ADHD that identifies aberrant frontal-to-subcortical projections—particularly those involving the ventral tegmental area and, consequently, the nucleus accumbens (NAc)—as a core neurobiological vulnerability. The model predicts reduced frontal influence on subcortical structures and group-by-direction interaction effects, especially at the NAc. Methods: Structural and resting-state fMRI data from sixty individuals with ADHD and sixty healthy controls were selected from the ADHD-200 dataset. The cortex was parcellated using MOSI (Modular Analysis and Similarity Measurements). The mean amplitude of low-frequency fluctuations (ALFF) and the mean time series were extracted for each frontal node. Subcortical nuclei—including the caudate, putamen, globus pallidus, NAc, and thalamus—were included in the analyses, with each voxel treated as a neural node. Correlation analyses were performed to examine the relationship between nodal power (ALFF) and nodal degree. Directional metrics were defined as frontal-to-subcortical (F2S) and subcortical-to-frontal (S2F) correlations between nodal strength and nodal power. Group differences and interactions were tested using t-tests and linear mixed-effects (LME) models. Results: Correlation and LME analyses revealed a consistent bimodal pattern, with positive F2S and negative S2F associations across all subcortical nuclei, suggesting a directional excitation–inhibition balance. Reduced F2S slopes in ADHD and a significant group-by-direction interaction in the NAc (p = 0.009) supported model predictions. Conclusions: The proposed developmental model of ADHD was empirically supported.

Autism spectrum disorder (ASD) is one of the most prevalent developmental disorders. This study utilized 3-Tesla resting-state fMRI data analyzed with the functional parcellation algorithm MOSI (Modularity Analysis and Similarity Measurements) to investigate cortical functional organization in ASD. Sixty individuals with ASD and sixty healthy controls were recruited, with no significant differences in age and gender distribution. The MOSI-derived metrics were compared using independent two-sample t-tests. The findings revealed a significant reduction in the functional volume of the left frontal lobe, a region critical for language and social processing. This reduction appears to be accompanied by compensatory expansion in other brain regions, suggesting a reallocation of neural resources that may contribute to ASD heterogeneity. These results support the notion of left frontal lobe developmental deviation (LFDD) as a parsimonious neural mechanism underlying core ASD features. The accountability of LFDD in various cognitive, symptomatic, and behavioral characteristics of ASD is briefly discussed, along with its implications for male predominance and evolutionary relevance. Overall, these findings provide a novel brain science perspective that moves beyond traditional psychological frameworks in explaining major psychiatric disorders. Lay summary This study reveals that individuals with autism spectrum disorder (ASD) exhibit a significant reduction in the functional volume of the left frontal lobe, a brain region crucial for language and social skills. This alteration may lead to compensatory changes in other brain areas, contributing to the diversity of ASD traits.

Major depressive disorder (MDD) is associated with hypoactivity in the frontoparietal (FP) system and hyperactivity in the limbic (LM) system. The widely accepted limbic-cortical dysregulation model has recently been extended by the concept of imbalanced reciprocal suppression between these 2 systems. This study investigates the refined theoretical framework. Neuroimaging datasets from 60 MDD and 60 healthy controls were obtained from the Canadian Biomarker Integration Network in Depression database, including structural magnetic resonance imaging (MRI) and resting-state functional MRI (rsfMRI). The cerebral cortex was parcellated using the modular analysis and similarity measurements (MOSI) technique. For each node, the average amplitude of low-frequency fluctuation (avgALFF) and nodal strength were calculated. Correlation analyses were conducted to establish an adjacency matrix and assess the relationship between nodal power and strength. The results indicated that the LM system in MDD displayed higher partition numbers and avgALFF (P < 0.005). A significant negative correlation between nodal strength and power was replicated (P < 1E-10), suggesting that greater functional input enhances regional neural suppression. Notably, MDD participants exhibited a higher negative correlation between FP nodal power and LM-FP connectivity (stronger suppression) but a lower negative correlation between LM nodal power and FP-LM connectivity (weaker suppression). These findings support the theory of abnormal cortical signal organization and reciprocal suppression in MDD.

Objectives Bipolar disorder (BPD) is a major and complex psychiatric condition, clinically characterized by euthymic, depressive, and manic phases. Through the concept of “dialectic neuroscience,” the ventral tegmental area (VTA) has been proposed as a potential key hub in BPD. This study utilized resting-state functional magnetic resonance imaging (rsfMRI) to investigate functional connectivity (FC) changes seeded by the VTA in BPD. Methods MRI data from 98 participants in a publicly available dataset were analyzed (49 stable BPD patients and 49 healthy controls). Using the dopamine receptor D1 distribution template from JuSpace, the representative voxel of the VTA in the midbrain was identified based on the highest correlation with the D1 template. This voxel served as the seed for FC map construction. The FC maps were Fisher-transformed and compared statistically using an independent two-sample t-test. Results Significant deviations in BPD were observed in brain regions that were highly consistent with the projections of the VTA, including the ventral and medial prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex, and nucleus accumbens. Conclusions The FC analysis provided empirical support for the VTA theory in BPD. The role of VTA abnormalities in contributing to the manifestation of opposing polarities within a single disorder was discussed, offering insights into the potential physiological mechanisms and genesis of BPD. The framework of dialectic neuroscience may provide a valuable approach for developing guiding brain theories in psychiatric disorders.