2020
DOI: 10.1093/brain/awaa025
|View full text |Cite
|
Sign up to set email alerts
|

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning

Abstract: Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evalu… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

11
167
0
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
9
1

Relationship

3
7

Authors

Journals

citations
Cited by 174 publications
(205 citation statements)
references
References 61 publications
11
167
0
1
Order By: Relevance
“…In a recent study, Lutz et al have found no large neurobiological differences between paranoid and nonparanoid schizophrenia [ 48 ]. In line with the previous study, our results indicated that subtypes of schizophrenia characterized by clinical phenomenology might have difficulty in resolving neurobiological heterogeneity in schizophrenia due to overlapping symptomatology and longitudinal instability [ 48 ], while machine learning may stand a chance of investigating neurobiological heterogeneity in schizophrenia [ 49 ].…”
Section: Discussionsupporting
confidence: 88%
“…In a recent study, Lutz et al have found no large neurobiological differences between paranoid and nonparanoid schizophrenia [ 48 ]. In line with the previous study, our results indicated that subtypes of schizophrenia characterized by clinical phenomenology might have difficulty in resolving neurobiological heterogeneity in schizophrenia due to overlapping symptomatology and longitudinal instability [ 48 ], while machine learning may stand a chance of investigating neurobiological heterogeneity in schizophrenia [ 49 ].…”
Section: Discussionsupporting
confidence: 88%
“…There is emerging skepticism as to whether this is indeed the case. For example, a recent attempt to parse schizophrenia into subgroups defined by their neuroanatomy identified two subtypes of patients; these subtypes differed in the extent of the volumetric reductions along a continuum of severity and were associated with differences in IQ rather than any aspect of disease expression 44 . Additionally, studies that identified cognitive subtypes in either disorder have typically found that such subtypes were on a continuum of severity from non-impaired to having global deficits 30 .…”
Section: Discussionmentioning
confidence: 99%
“…Thus the use of additional subcategories or using dimensional information (as suggested in the NIMH RDoC approach)instead of a categorical approach may provide a more meaningful representation of relabeled subjects and underlying relation between them. More detailed mood disorder diagnoses will lead to more effective treatments [38] Cleansed data showed many more significant voxels than did the original data. In addition, the DSM-IV cleansed data showed more significant voxels than the Biotype cleansed data.…”
Section: Angular Gyrusmentioning
confidence: 96%