Publications

Abstract

Schizophrenia is a common and complex mental disorder with neuroimaging alterations. Recent neuroanatomical pattern recognition studies attempted to distinguish individuals with schizophrenia by structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI). 1, 2 Applications of cutting-edge machine learning approaches in structural neuroimaging studies have revealed potential pathways to classification of schizophrenia based on regional gray matter volume (GMV) or density or cortical thickness. 3–5 Additionally, cortical folding may have high discriminatory value in correctly identifying symptom severity in schizophrenia. 6 Regional GMV and cortical thickness have also been combined in attempts to differentiate individuals with schizophrenia from healthy controls (HCs). 7 Applications of machine learning algorithms to diffusion imaging data analysis to predict individuals with first-episode schizophrenia (FES) have achieved encouraging accuracy. 8–10 White matter (WM) abnormalities in schizophrenia as estimated by DTI appear to be present in the early stage of the disorder, most likely reflecting the developmental stage of the sample of interest.

Abstract

Identifying data-driven biotypes of major depressive disorder (MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and 118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive evaluation. Hierarchical clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with MDD. Clinical and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1 (25.9% of patient sample), widespread white-matter disruption; subgroup 2 (43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate; and subgroup 3 (31.0% of patient sample), possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory; subgroup 2, dysfunction in delayed memory; and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize type-specific treatment approaches.

Abstract

Creativity is crucial to the progression of human civilization and has led to important scientific discoveries. Especially, individuals are more likely to have scientific discoveries if they possess certain personality traits of creativity (trait creativity), including imagination, curiosity, challenge and risk-taking. This study used voxel-based morphometry to identify the brain regions underlying individual differences in trait creativity, as measured by the Williams creativity aptitude test, in a large sample (n = 246). We found that creative individuals had higher gray matter volume in the right posterior middle temporal gyrus (pMTG), which might be related to semantic processing during novelty seeking (e.g. novel association, conceptual integration and metaphor understanding). More importantly, although basic personality factors such as openness to experience, extroversion, conscientiousness and agreeableness (as measured by the NEO Personality Inventory) all contributed to trait creativity, only openness to experience mediated the association between the right pMTG volume and trait creativity. Taken together, our results suggest that the basic personality trait of openness might play an important role in shaping an individual’s trait creativity.

Abstract

Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics.

Abstract

Face-selective regions (FSRs) are among the most widely studied functional regions in the human brain. However, individual variability of the FSRs has not been well quantified. Here we use functional magnetic resonance imaging (fMRI) to localize the FSRs and quantify their spatial and functional variabilities in 202 healthy adults. The occipital face area (OFA), posterior and anterior fusiform face areas (pFFA and aFFA), posterior continuation of the superior temporal sulcus (pcSTS), and posterior and anterior STS (pSTS and aSTS) were delineated for each individual with a semi-automated procedure. A probabilistic atlas was constructed to characterize their interindividual variability, revealing that the FSRs were highly variable in location and extent across subjects. The variability of FSRs was further quantified on both functional (i.e., face selectivity) and spatial (i.e., volume, location of peak activation, and anatomical location) features. Considerable interindividual variability and rightward asymmetry were found in all FSRs on these features. Taken together, our work presents the first effort to characterize comprehensively the variability of FSRs in a large sample of healthy subjects, and invites future work on the origin of the variability and its relation to individual differences in behavioral performance. Moreover, the probabilistic functional atlas will provide an adequate spatial reference for mapping the face network.