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Record W3080233045 · doi:10.3389/fpsyt.2020.00833

Evidence of Slow Neural Processing, Developmental Differences and Sensitivity to Cannabis Effects in a Sample at Clinical High Risk for Psychosis From the NAPLS Consortium Assessed With the Human Startle Paradigm

2020· article· en· W3080233045 on OpenAlexaff
Kristin S. Cadenhead, Erica Duncan, Jean Addington, Carrie E. Bearden, Tyrone D. Cannon, Barbara A. Cornblatt, Daniel H. Mathalon, Thomas H. McGlashan, Diana O. Perkins, Larry J. Seidman, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods, Peter Bauchman, Ayşenil Belger, Ricardo E. Carrión, Franc C. L. Donkers, Jason Johannesen, Gregory A. Light, Margaret Niznikiewicz, Jason Nunag, Brian J. Roach

Bibliographic record

VenueFrontiers in Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsPsychosisPrepulse inhibitionProdromeCannabisPsychologySchizophrenia (object-oriented programming)PsychopathologyPsychiatryPopulationDiseaseClinical psychologyNeurocognitiveYoung adultMoro reflexBiomarkerMedicineInternal medicineCognitionDevelopmental psychologyNeuroscienceReflexBiology

Abstract

fetched live from OpenAlex

ABSTRACT: Biomarkers are important in the study of the prodromal period of psychosis because they can help to identify individuals at greatest risk for future psychotic illness and provide insights into disease mechanism underlying neurodevelopmental abnormalities. The biomarker abnormalities can then be targeted with treatment, with an aim toward prevention or mitigation of disease. The human startle paradigm has been used in translational studies of psychopathology including psychotic illness to assess preattentive information processing for over 50 years. In one of the largest studies to date in clinical high risk (CHR) for psychosis participants, we aimed to evaluate startle indices as biomarkers of risk along with the role of age, sex, treatment, and substance use in this population of high risk individuals. METHODS: Startle response reactivity, latency, and prepulse inhibition (PPI) were assessed in 543 CHR and 218 Normal Comparison (NC) participants between the ages of 12 and 35. RESULTS: At 1 year follow-up, 58 CHR participants had converted to psychosis. CHR and NC groups did not differ across any of the startle measures but those CHR participants who later converted to psychosis had significantly slower startle latency than did those who did not convert to psychosis, and this effect was driven by female CHR participants. PPI was significantly associated with age in the CHR, but not the NC, participants with the greatest positive age correlations present in those CHR participants who later converted to psychosis, consistent with a prior report. Finally, there was a significant group by cannabis use interaction due to greater PPI in cannabis users and opposite PPI group effects in users (CHR>NC) and non-users (NC>CHR). DISCUSSION: This is the first study to demonstrate a relationship of startle response latency to psychotic conversion in a CHR population. PPI is an important biomarker that may be sensitive to the neurodevelopmental abnormalities thought to be present in psychosis prone individuals and the effects of cannabis. The significant correlations with age in this sample as well as the finding of greater PPI in CHR cannabis users replicate findings from another large sample of CHR participants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.328
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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