Neurobiological Commonalities and Distinctions Among Three Major Psychiatric Diagnostic Categories: A Structural MRI Study

Chang, Miao; Womer, Fay Y.; Edmiston, E. Kale; Bai, Chujie; Zhou, Qian; Jiang, Xiaowei; Wei, Shengnan; Wei, Yange; Ye, Yuting; Huang, Haiyan; He, Yong; Xu, Ke; Tang, Yanqing; Wang, Fei

doi:10.1093/schbul/sbx028

Cited by 84 publications

(57 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[9][10][11] Abnormal oligodendrocyte function and demyelination have been associated with alterations in white matter integrity in the 3 disorders. 7,12,13 Moreover, MRI studies have found commonalities in alterations in white matter integrity among schizophrenia, bipolar disorder and MDD, [14][15][16][17][18][19] with more prominent alterations in schizophrenia and bipolar disorder than in MDD. 20 Increasing attention has been focused on white matter net work alterations in schizophrenia, bipolar disorder and MDD.…”

Section: Introductionmentioning

confidence: 99%

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

Wang

Gong

Zhong

et al. 2020

jpn

Self Cite

View full text Add to dashboard Cite

Background: White matter network alterations have increasingly been implicated in major depressive disorder, bipolar disorder and schizophrenia. The aim of this study was to identify shared and distinct white matter network alterations among the 3 disorders. Methods: We used analysis of covariance, with age and gender as covariates, to investigate white matter network alterations in 123 patients with schizophrenia, 123 with bipolar disorder, 124 with major depressive disorder and 209 healthy controls. Results: We found significant group differences in global network efficiency (F = 3.386, p = 0.018), nodal efficiency (F = 8.015, p < 0.001 corrected for false discovery rate [FDR]) and nodal degree (F = 5.971, p FDR < 0.001) in the left middle occipital gyrus, as well as nodal efficiency (F = 6.930, p FDR < 0.001) and nodal degree (F = 5.884, p FDR < 0.001) in the left postcentral gyrus. We found no significant alterations in patients with major depressive disorder. Post hoc analyses revealed that compared with healthy controls, patients in the schizophrenia and bipolar disorder groups showed decreased global network efficiency, nodal efficiency and nodal degree in the left middle occipital gyrus. Furthermore, patients in the schizophrenia group showed decreased nodal efficiency and nodal degree in the left postcentral gyrus compared with healthy controls. Limitations: Our findings could have been confounded in part by treatment differences. Conclusion: Our findings implicate graded white matter network alterations across the 3 disorders, enhancing our understanding of shared and distinct pathophysiological mechanisms across diagnoses and providing vital insights into neuroimaging-based methods for diagnosis and research. to Fei Wang). The authors thank Dr.

show abstract

Section: Introductionmentioning

confidence: 99%

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

Wang

Gong

Zhong

et al. 2020

jpn

Self Cite

View full text Add to dashboard Cite

show abstract

“…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][4][5] Additionally, cortical folding may have high discriminatory value in correctly identifying symptom severity in schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

Classification of First-Episode Schizophrenia Using Multimodal Brain Features: A Combined Structural and Diffusion Imaging Study

Liang

Zhang

et al. 2018

Schizophrenia Bulletin

View full text Add to dashboard Cite

Recent neuroanatomical pattern recognition studies have shown some promises for developing an objective neuroimaging-based classification related to schizophrenia. This study explored the feasibility of reliably identifying schizophrenia using single and multimodal multivariate neuroimaging features. Multiple brain measures including regional gray matter (GM) volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) were extracted using fully automated procedures. We used Gradient Boosting Decision Tree to identify the most frequently selected features of each set of neuroanatomical metric and fused multimodal measures. The current classification model was trained and validated based on 98 patients with first-episode schizophrenia (FES) and 106 matched healthy controls (HCs). The classification model was trained and tested in an independent dataset of 54 patients with FES and 48 HCs using imaging data acquired on a different magnetic resonance imaging scanner. Using the most frequently selected features from fused structural and diffusion tensor imaging metrics, a classification accuracy of 75.05% was achieved, which was higher than accuracy derived from a single imaging metric. Most prominent discriminative features included cortical thickness of left transverse temporal gyrus and right parahippocampal gyrus, the FA of left corticospinal tract and right external capsule. In the independent cohort, average accuracy was 76.54%, derived from combined features selected from cortical thickness, gyrification, FA, and MD. These features characterized by GM abnormalities and white matter disruptions have discriminative power with respect to the underlying pathological changes in the brain of individuals having schizophrenia. Our results further highlight the potential advantage of multimodal data fusion for identifying schizophrenia.

show abstract

“…While previous studies have addressed similar questions , our work is the first to use multivariate methods to assess the relationship between volumetric patterns and symptom severity and is also the first to investigate this relationship across unipolar and bipolar depression.…”

Section: Introductionmentioning

confidence: 99%

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders

Wise

Marwood

Perkins

et al. 2018

Acta Psychiatr Scand

View full text Add to dashboard Cite

show abstract

Neurobiological Commonalities and Distinctions Among Three Major Psychiatric Diagnostic Categories: A Structural MRI Study

Abstract: Our findings of common alterations in SZ, BD, and MDD support the presence of core neurobiological disruptions in these disorders and suggest that neural structural distinctions between these disorders may be less prominent than initially postulated, particularly between SZ and BD.

Cited by 84 publications

References 45 publications

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

Classification of First-Episode Schizophrenia Using Multimodal Brain Features: A Combined Structural and Diffusion Imaging Study

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders

Contact Info

Product

Resources

About