Evidence map›Paper›PMID 28876500›Full record

ArticleHuman brain mapping2017

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Rachel N Pläschke, Edna C Cieslik, Veronika I Müller, Felix Hoffstaedter, Anna Plachti, Deepthi P Varikuti, Mareike Goosses, Anne Latz, Svenja Caspers, Christiane Jockwitz and 12 more

Erratum issuedOpen access · greenAbstract read
In one paragraph

Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 10% 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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.

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  17. Age differences in predicting working memory performance from network-based functional connectivity.Cortex; a journal devoted to the study of the nervous system and behavior · 2020
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors at 6 institutions in 1 country.

Rachel N PläschkeInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.ORCID 0000-0003-0505-2531
Edna C CieslikInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Veronika I MüllerInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Felix HoffstaedterInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Anna PlachtiInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany.
Deepthi P VarikutiInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Mareike GoossesInstitute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany.
Anne LatzInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Svenja CaspersInstitute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany.
Christiane JockwitzInstitute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany.
Susanne MoebusCenter for Urban Epidemiology, University of Duisburg-Essen, Essen, Germany.
Oliver GruberSection for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany.
Claudia R EickhoffInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany.
Kathrin ReetzJARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.
Julia HellerJARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.
Martin SüdmeyerInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Christian MathysDepartment of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Julian CaspersInstitute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany.
Christian GrefkesDepartment of Neurology, University Hospital Cologne, Cologne, Germany.
Tobias KalenscherComparative Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Robert LangnerInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Simon B EickhoffInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Heinrich Heine University Düsseldorf · DEJülich Aachen Research Alliance · DEHeidelberg University · DERWTH Aachen University · DEUniversity Hospital Cologne · DEUniversity of Duisburg-Essen · DE

Funding

Meta-anaylsis in human brain mappingR01MH074457 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON · 2006 to 2024
$10.5M
Meta-analysis in human brain mappingR56MH074457 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON · 2020 to 2020
$543k
NIMH NIH HHS R01 MH074457NIMH NIH HHS R56 MH074457
6 · The paper itself

Abstract

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Indexed as

AdultAgedAgingBrainBrain MappingHumansMagnetic Resonance ImagingMental ProcessesMeta-Analysis as TopicMiddle AgedNeural PathwaysNeuropsychological TestsParkinson DiseaseRestSchizophreniaSupport Vector Machinebrain networksfunctional connectivitymachine learningnormal agingParkinson's diseaseresting-state fMRIschizophreniasupport vector machine

Identifiers

PMID28876500
PMCPMC5931403
OpenAlexW2752448864

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.