Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia

Jiang, Yuchao; Wang, Jijun; Zhou, Enpeng; Palaniyappan, Lena; Luo, Cheng; Ji, Gong-Jun; Yang, Jie; Wang, Yingchan; Zhang, Yuyanan; Huang, Chu-Chung; Tsai, Shih‐Jen; Chang, Xiao; Xie, Chao; Zhang, Wei; Lv, JinChao; Chen, Di; Shen, Chun; Wu, Xinran; Zhang, Bei; Kuang, Nanyu; Sun, Yun-Jun; Kang, Jujiao; Zhang, Jie; Huang, Hong; He, Hong; Duan, Mingjun; Tang, Yingying; Zhang, Tianhong; Li, Chunbo; Yu, Xin; Si, Tianmei; Yue, Weihua; Liu, Zhening; Cui, Long‐Biao; Wang, Kai; Cheng, Jingliang; Lin, Ching-Po; Yao, Dezhong; Cheng, Wei; Feng, Jianfeng

doi:10.1038/s44220-023-00024-0

Cited by 27 publications

(40 citation statements)

References 48 publications

(55 reference statements)

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“…Conversely, ‘trajectory’ 2 exhibited an ‘early subcortical-predominant loss’ biotype where volume loss began in the hippocampus, spread to the amygdala and parahippocampus, and then extended to the accumbens and caudate before affecting the cerebral cortex ( Fig.1d ). The two ‘trajectories’ were highly consistent with our previous findings in a predominantly Chinese schizophrenia cohort [22]. The phenotypic subtypes, based on the different pathophysiological ‘trajectories’, are thus replicated in a large cross-geography sample, confirming the presence of two different neuropathological pathways with different anatomical origins in schizophrenia [22].…”

Section: Resultssupporting

confidence: 87%

“…The two ‘trajectories’ were highly consistent with our previous findings in a predominantly Chinese schizophrenia cohort [22]. The phenotypic subtypes, based on the different pathophysiological ‘trajectories’, are thus replicated in a large cross-geography sample, confirming the presence of two different neuropathological pathways with different anatomical origins in schizophrenia [22].…”

Section: Resultssupporting

confidence: 87%

“…The Broca’s area/fronto-insular cortex and hippocampus are identified separately in subtype 1 and subtype 2 as the first regions to show gray matter deficits. This is consistent with our prior finding based on individuals with schizophrenia primarily collected from the Chinese population [22]. Furthermore, the current study replicates the same two primary regions in a medication-naÏve and a first-episode cohort, suggesting that these neuropathological changes are a reflection of the disease process, rather than medication effects.…”

Section: Discussionsupporting

confidence: 92%

“…SuStaIn uses a large number of cross-sectional observations, derived from single time-point MRI scans, to identify clusters (subtypes) of individuals with common trajectory of disease progression (i.e., the sequence of MRI abnormalities across different brain regions) in brain disorders [20-22]. By applying SuStaIn to MRI data from individuals with schizophrenia, primarily collected from the Chinese population, we found that the progression of gray matter loss in schizophrenia can be better characterized through two distinct phenotypes: one characterized by a cortical-predominant progression, originating in the Broca’s area/fronto-insular cortex, and another marked by a subcortical-predominant progression, starting in the hippocampus [22]. Such brain-based taxonomies may reflect neurostructural subtypes with shared pathophysiological foundations, with relevance for neurobiological classification [22].…”

Section: Introductionmentioning

confidence: 99%

“…By applying SuStaIn to MRI data from individuals with schizophrenia, primarily collected from the Chinese population, we found that the progression of gray matter loss in schizophrenia can be better characterized through two distinct phenotypes: one characterized by a cortical-predominant progression, originating in the Broca’s area/fronto-insular cortex, and another marked by a subcortical-predominant progression, starting in the hippocampus [22]. Such brain-based taxonomies may reflect neurostructural subtypes with shared pathophysiological foundations, with relevance for neurobiological classification [22]. However, the generalisability of the two neurostructural subtypes to diverse populations outside of China, and external validation of the subgrouping is required before applying this knowledge to stratify clinical trials.…”

Section: Introductionmentioning

confidence: 99%

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Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Jiang,

Luo,

Wang

et al. 2023

Preprint

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Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

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Section: Resultssupporting

confidence: 87%

Section: Resultssupporting

confidence: 87%

Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Jiang,

Luo,

Wang

et al. 2023

Preprint

Self Cite

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Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms

Ding,

Li,

Xie

et al. 2024

Advanced Science

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To elucidate the brain‐wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information‐resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time‐series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally‐specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole‐brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean‐field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.

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Biomarkers of Schizophrenia

Salgado,

Cruz,

Mantovani

et al. 2023

Biomarkers in Neuropsychiatry

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Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia

Cited by 27 publications

References 48 publications

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms

Biomarkers of Schizophrenia

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