Gray Matter Volume Abnormalities in Schizophrenia: Comparisons Between P-Value and Effect Size Inference Frameworks Using Mega- and Meta-Analyses.

Background And Hypothesis: Identifying generalizable brain imaging markers from large multi-center datasets remains challenging due to varying statistical aggregation approaches and p-hacking with increasing big data. We hypothesized that effect size (ES) inference surpasses P-value-based inference in reliably identifying core brain damage of schizophrenia, regardless of whether Mega- or Meta-analyses are used.

Study Design: We examined voxel-wise inter-group differences in gray matter volume (GMV) based on individual data from 976 schizophrenia patients and 801 healthy controls across 16 datasets, along with published coordinates data from 103 studies involving 5151 patients and 5438 controls, using Mega-analysis (Mega), Image-Based Meta-analysis (IBMA), and Coordinate-Based Meta-analysis (CBMA) under P-value and ES inference frameworks, respectively.

Deciphering the Heterogeneity of Schizophrenia: A Multimodal and Multivariate Neuroimaging Framework for Unveiling Brain-Symptom Relationships and Underlying Subtypes.

Background And Hypothesis: Schizophrenia manifests large heterogeneities in either symptoms or brain abnormalities. However, the neurobiological basis of symptomatic diversity remains poorly understood. We hypothesized that schizophrenia's diverse symptoms arise from the interplay of structural and functional alterations across multiple brain regions, rather than isolated abnormalities in a single area.

Highly Efficient Methods with a Generalized Linear Mixed Model for the Quantitative Trait Locus Mapping of Resistance to Columnaris Disease in Rainbow Trout ().

Linear mixed models (LMMs) are commonly used in genome-wide association studies (GWASs) to evaluate population structures and relatedness. However, LMMs have been shown to be ineffective in controlling false positive errors for the analysis of resistance to Columnaris disease in Rainbow Trout. To solve this problem, we conducted a series of studies using generalized linear mixed-model association software such as GMMAT (v1.

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies.

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images.

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia.

Objective: Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia.

Methods: To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers.

Individualized Texture Similarity Network in Schizophrenia.

Background: Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia.

Methods: The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant.

The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer's disease.

Objective: To investigate the relationship between changes in cerebral blood flow (CBF) and gray matter (GM) microstructure in Alzheimer's disease (AD) and mild cognitive impairment (MCI).

Methods: A recruited cohort of 23 AD patients, 40 MCI patients, and 37 normal controls (NCs) underwent diffusional kurtosis imaging (DKI) for microstructure evaluation and pseudo-continuous arterial spin labeling (pCASL) for CBF assessment. We investigated the differences in diffusion- and perfusion-related parameters across the three groups, including CBF, mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA).