Evidence map›Paper›PMID 25601228›Full record

SynthesisNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2015

Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.

Joseph Kambeitz, Lana Kambeitz-Ilankovic, Stefan Leucht, Stephen Wood, Christos Davatzikos, Berend Malchow, Peter Falkai, Nikolaos Koutsouleris

Open access · bronzeAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
104citing papers in PubMed, 11 pooled it
12.5field-weighted citation impact, top 1% of its field
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

104 citing papers in PubMed, 11 syntheses or guidelines pooled it, 210 citations in OpenAlex.

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44 more citing papers are in PubMed but not listed here.

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

8 authors at 4 institutions in 3 countries.

Joseph Kambeitz1] Department of Psychiatry, Ludwig-Maximilians University Munich, Munich, Germany [2] King's College London, Psychosis Clinical Academic Group, Institute of Psychiatry, London, UK.
Lana Kambeitz-IlankovicDepartment of Psychiatry, Ludwig-Maximilians University Munich, Munich, Germany.
Stefan LeuchtDepartment of Psychiatry, Technical University Munich, Munich, Germany.
Stephen WoodSchool of Psychology, University of Birmingham, Birmingham, UK.
Christos DavatzikosSection of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Pennsylvania, USA.
Berend MalchowDepartment of Psychiatry, Ludwig-Maximilians University Munich, Munich, Germany.
Peter FalkaiDepartment of Psychiatry, Ludwig-Maximilians University Munich, Munich, Germany.
Nikolaos KoutsoulerisDepartment of Psychiatry, Ludwig-Maximilians University Munich, Munich, Germany.
Ludwig-Maximilians-Universität München · DETechnical University of Munich · DEUniversity of Birmingham · GBUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7-83.5%) and a specificity of 80.3% (95% CI: 76.9-83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9-88.2%) and similar specificity (76.9%, 95% CI: 71.3-81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9-80.4%, specificity of 79.0%, 95% CI: 74.6-82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity.

Indexed as

NeuroimagingBiomarkersBrainDatabases, BibliographicHumansRecognition, PsychologySchizophreniaBiomarkers

Identifiers

PMID25601228
PMCPMC4915258
OpenAlexW2055683310

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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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.