PMID- 32648913
OWN - NLM
STAT- MEDLINE
DCOM- 20211025
LR  - 20211025
IS  - 1745-1701 (Electronic)
IS  - 0586-7614 (Print)
IS  - 0586-7614 (Linking)
VI  - 46
IP  - 6
DP  - 2020 Dec 1
TI  - Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience.
PG  - 1346-1352
LID - 10.1093/schbul/sbaa091 [doi]
AB  - Research suggests that early identification and intervention with individuals at 
      clinical high risk (CHR) for psychosis may be able to improve the course of 
      illness. The first generation of studies suggested that the identification of CHR 
      through the use of specialized interviews evaluating attenuated psychosis 
      symptoms is a promising strategy for exploring mechanisms associated with illness 
      progression, etiology, and identifying new treatment targets. The next generation 
      of research on psychosis risk must address two major limitations: (1) interview 
      methods have limited specificity, as recent estimates indicate that only 15%-30% 
      of individuals identified as CHR convert to psychosis and (2) the expertise 
      needed to make CHR diagnosis is only accessible in a handful of academic centers. 
      Here, we introduce a new approach to CHR assessment that has the potential to 
      increase accessibility and positive predictive value. Recent advances in clinical 
      and computational cognitive neuroscience have generated new behavioral measures 
      that assay the cognitive mechanisms and neural systems that underlie the 
      positive, negative, and disorganization symptoms that are characteristic of 
      psychotic disorders. We hypothesize that measures tied to symptom generation will 
      lead to enhanced sensitivity and specificity relative to interview methods and 
      the cognitive intermediate phenotype measures that have been studied to date that 
      are typically indicators of trait vulnerability and, therefore, have a high false 
      positive rate for conversion to psychosis. These new behavioral measures have the 
      potential to be implemented on the internet and at minimal expense, thereby 
      increasing accessibility of assessments.
CI  - (c) The Author(s) 2020. Published by Oxford University Press on behalf of the 
      Maryland Psychiatric Research Center.
FAU - Gold, James M
AU  - Gold JM
AD  - Department of Psychiatry and Maryland Psychiatric Research Center, University of 
      Maryland School of Medicine, Baltimore, MD.
FAU - Corlett, Philip R
AU  - Corlett PR
AD  - Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
FAU - Strauss, Gregory P
AU  - Strauss GP
AD  - Department of Psychology, University of Georgia, Athens, GA.
FAU - Schiffman, Jason
AU  - Schiffman J
AD  - University of Maryland, Baltimore, MD.
FAU - Ellman, Lauren M
AU  - Ellman LM
AD  - Department of Psychology, Temple University, Philadelphia, PA.
FAU - Walker, Elaine F
AU  - Walker EF
AD  - Department of Psychology, Emory University, Atlanta, GA.
FAU - Powers, Albert R
AU  - Powers AR
AD  - Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
FAU - Woods, Scott W
AU  - Woods SW
AD  - Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
FAU - Waltz, James A
AU  - Waltz JA
AD  - Department of Psychiatry and Maryland Psychiatric Research Center, University of 
      Maryland School of Medicine, Baltimore, MD.
FAU - Silverstein, Steven M
AU  - Silverstein SM
AD  - Departments of Psychiatry, Neuroscience, and Ophthalmology, University of 
      Rochester Medical Center, Rochester, NY.
FAU - Mittal, Vijay A
AU  - Mittal VA
AD  - Departments of Psychology, Psychiatry, Medical Social Sciences, Institutes for 
      Policy Research (IPR) and Innovations in Developmental Sciences (DevSci), 
      Evanston and Chicago, IL.
LA  - eng
GR  - R01 MH115031/MH/NIMH NIH HHS/United States
GR  - UL1 TR001863/TR/NCATS NIH HHS/United States
GR  - R01 MH112612/MH/NIMH NIH HHS/United States
GR  - R01 MH120091/MH/NIMH NIH HHS/United States
GR  - R01 MH120092/MH/NIMH NIH HHS/United States
GR  - R01 MH120089/MH/NIMH NIH HHS/United States
GR  - R21 MH119438/MH/NIMH NIH HHS/United States
GR  - R01 MH112613/MH/NIMH NIH HHS/United States
GR  - R01 MH120088/MH/NIMH NIH HHS/United States
GR  - U01 MH081988/MH/NIMH NIH HHS/United States
GR  - R01 MH112545/MH/NIMH NIH HHS/United States
GR  - R01 MH120090/MH/NIMH NIH HHS/United States
GR  - R01 MH116039/MH/NIMH NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - Schizophr Bull
JT  - Schizophrenia bulletin
JID - 0236760
SB  - IM
MH  - *Cognitive Neuroscience/methods/standards
MH  - Disease Progression
MH  - Humans
MH  - Prodromal Symptoms
MH  - Prognosis
MH  - Psychotic Disorders/*diagnosis/physiopathology
MH  - Risk Assessment
MH  - Schizophrenia/*diagnosis/physiopathology
PMC - PMC7707066
OTO - NOTNLM
OT  - clinical high risk
OT  - conversion
OT  - schizophrenia prodrome
EDAT- 2020/07/11 06:00
MHDA- 2021/10/26 06:00
PMCR- 2020/07/10
CRDT- 2020/07/11 06:00
PHST- 2020/07/11 06:00 [pubmed]
PHST- 2021/10/26 06:00 [medline]
PHST- 2020/07/11 06:00 [entrez]
PHST- 2020/07/10 00:00 [pmc-release]
AID - 5869747 [pii]
AID - sbaa091 [pii]
AID - 10.1093/schbul/sbaa091 [doi]
PST - ppublish
SO  - Schizophr Bull. 2020 Dec 1;46(6):1346-1352. doi: 10.1093/schbul/sbaa091.