Evidence map›Paper›PMID 22927673›Full record

ArticleSchizophrenia bulletin2013

Diagnostic specificity of neurophysiological endophenotypes in schizophrenia and bipolar disorder.

Jason K Johannesen, Brian F O'Donnell, Anantha Shekhar, John H McGrew, William P Hetrick

Open access · bronzeAbstract readValidation Study
In one paragraph

Article in Schizophrenia bulletin, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

23 citing papers in PubMed, 60 citations in OpenAlex.

  1. Article
  2. How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024
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  15. Genetic correlates of the development of theta event related oscillations in adolescents and young adults.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2017
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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

5 authors at 3 institutions in 1 country.

Jason K JohannesenTo whom correspondence should be addressed; VA Connecticut Health Care System, Psychology Service 116-B, 950 Campbell Ave, West Haven, CT 06516, US; tel: 203-932-5711 ext. 2224, fax: 203-937-4735. Jason.Johannesen@yale.edu.
Brian F O'Donnell
Anantha Shekhar
John H McGrew
William P Hetrick
Indiana University – Purdue University Indianapolis · USLarue Carter Hospital · USVA Connecticut Healthcare System · US

Funding

Cerebellar timing dysfunction in schizophreniaR01MH074983 · NIMH · TRUSTEES OF INDIANA UNIVERSITY · PI HETRICK, WILLIAM P · 2006 to 2016
$3.6M
Neurobehavioral correlates of bipolar disorderR21MH071876 · NIMH · TRUSTEES OF INDIANA UNIVERSITY · PI O'DONNELL, BRIAN FRANCIS · 2004 to 2006
$564k
ERPs in schizophrenia and alcohol dependenceR03MH066149 · NIMH · INDIANA UNIVERSITY BLOOMINGTON · PI HETRICK, WILLIAM P · 2003 to 2003
$75k
NIMH NIH HHS R01 MH074983NIMH NIH HHS R01 MH074983-01NIMH NIH HHS R03 MH066149-01A1NIMH NIH HHS R21MH071876-01
6 · The paper itself

Abstract

backgroundThe utility of an endophenotype depends on its ability to reduce complex disorders into stable, genetically linked phenotypes. P50 and P300 event-related potential (ERP) measures are endophenotype candidates for schizophrenia; however, their abnormalities are broadly observed across neuropsychiatric disorders. This study examined the diagnostic efficiency of P50 and P300 in schizophrenia as compared with healthy and bipolar disorder samples. Supplemental ERP measures and a multivariate classification approach were evaluated as methods to improve specificity.

methodsDiagnostic classification was first modeled in schizophrenia (SZ = 50) and healthy normal (HN = 50) samples using hierarchical logistic regression with predictors blocked by 4 levels of analysis: (1) P50 suppression, P300 amplitude, and P300 latency; (2) N100 amplitude; (3) evoked spectral power; and (4) P50 and P300 hemispheric asymmetry. The optimal model was cross-validated in a holdout sample (SZ = 34, HN = 31) and tested against a bipolar (BP = 50) sample.

resultsP50 and P300 endophenotypes classified SZ from HN with 71% accuracy (sensitivity = .70, specificity = .72) but did not differentiate SZ from BP above chance level. N100 and spectral power measures improved classification accuracy of SZ vs HN to 79% (sensitivity = .78, specificity = .80) and SZ vs BP to 72% (sensitivity = .74, specificity = .70). Cross validation analyses supported the stability of these models.

conclusionsAlthough traditional P50 and P300 measures failed to differentiate schizophrenia from bipolar participants, N100 and evoked spectral power measures added unique variance to classification models and improved accuracy to nearly the same level achieved in comparison of schizophrenia to healthy individuals.

Indexed as

EndophenotypesAdultBipolar DisorderBrainElectroencephalographyEvent-Related Potentials, P300Evoked PotentialsEvoked Potentials, AuditoryFemaleFunctional LateralityHumansMaleMiddle AgedModels, NeurologicalNeuropsychological TestsPsychiatric Status Rating Scalesevent-related potentialgamma frequencyN100P300P50

Identifiers

PMID22927673
PMCPMC3796068
OpenAlexW2156577834

What OpenQuestion holds

Textmetadata
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.