Evidence map›Paper›PMID 25379435›Full record

ArticleNeuroImage. Clinical2014

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach.

Ian C Gould, Alana M Shepherd, Kristin R Laurens, Murray J Cairns, Vaughan J Carr, Melissa J Green

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 9 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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  14. Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ian C GouldSchizophrenia Research Institute, Darlinghurst, NSW, Australia ; School of Psychiatry, University of New South Wales, Australia.
Alana M ShepherdSchizophrenia Research Institute, Darlinghurst, NSW, Australia ; School of Psychiatry, University of New South Wales, Australia.
Kristin R LaurensSchizophrenia Research Institute, Darlinghurst, NSW, Australia ; School of Psychiatry, University of New South Wales, Australia ; Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK.
Murray J CairnsSchizophrenia Research Institute, Darlinghurst, NSW, Australia ; School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia.
Vaughan J CarrSchizophrenia Research Institute, Darlinghurst, NSW, Australia ; School of Psychiatry, University of New South Wales, Australia.
Melissa J GreenSchizophrenia Research Institute, Darlinghurst, NSW, Australia ; School of Psychiatry, University of New South Wales, Australia ; Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia ; Neuroscience Research Australia, Randwick, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia - 'cognitive deficit' and 'cognitively spared' - in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features.

Indexed as

Schizophrenic PsychologyAdultBrainCognitionFemaleGray MatterHumansMaleMiddle AgedMultivariate AnalysisSchizophreniaSupport Vector MachineWhite MatterCognitionCognitive deficitMagnetic resonance imagingSex differencesSubtypesVoxel based morphometry

Identifiers

PMID25379435
PMCPMC4215428

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.