Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2
Objective: The B-SNIP consortium validated neurobiologically defined psychosis Biotypes (BT1, BT2, BT3) using cognitive and psychophysiological measures. B-SNIP’s biomarker panel is not practical for most settings. Previously, B-SNIP developed an efficient classifier of Biotypes using only clinical...
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| Format: | Article |
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Elsevier
2025-06-01
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| Series: | Biomarkers in Neuropsychiatry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666144624000352 |
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| author | Brett A. Clementz Ishanu Chattopadhyay S. Kristian Hill Jennifer E. McDowell Sarah K. Keedy David A. Parker Rebekah L. Trotti Elena I. Ivleva Matcheri S. Keshavan Elliot S. Gershon Godfrey D. Pearlson Carol A. Tamminga Robert D. Gibbons |
| author_facet | Brett A. Clementz Ishanu Chattopadhyay S. Kristian Hill Jennifer E. McDowell Sarah K. Keedy David A. Parker Rebekah L. Trotti Elena I. Ivleva Matcheri S. Keshavan Elliot S. Gershon Godfrey D. Pearlson Carol A. Tamminga Robert D. Gibbons |
| author_sort | Brett A. Clementz |
| collection | DOAJ |
| description | Objective: The B-SNIP consortium validated neurobiologically defined psychosis Biotypes (BT1, BT2, BT3) using cognitive and psychophysiological measures. B-SNIP’s biomarker panel is not practical for most settings. Previously, B-SNIP developed an efficient classifier of Biotypes using only clinical assessments (called ADEPT-CLIN) with acceptable accuracy (∼.81). Adding cognitive performance may improve ADEPT’s performance. Method: Clinical assessments from ADEPT-CLIN plus 18 cognitive measures from 1907 individuals with a B-SNIP psychosis Biotype were used to create an additional diagnostic algorithm called ADEPT-COG. Extremely randomized trees were used to create this low burden classifier. Results: Total Biotype classification accuracy peaked at 94.6 % with 65 items. A reduced set of 18 items showed 90.5 % accuracy. Only 9–10 items achieved a one-vs-all (e.g., BT1 or not) accuracy of ∼.95, considerably better than using clinical assessments alone. The top discriminators of psychosis Biotypes were antisaccade proportion correct, BACS total, symbol coding, antisaccade correct response latency, verbal memory, digit sequencing, stop signal reaction times, stop signal proportion correct, Tower of London, and WRAT Reading. Except for antisaccade proportion correct and Tower of London, there was no overlap of the top discriminating items for B-SNIP Biotypes and DSM psychosis categories. Conclusions: This low-burden algorithm using clinical and cognitive measures achieved high classification accuracy and can support Biotype-specific etiological and treatment investigations in clinical and research environments. It may be especially useful for clinical trials. |
| format | Article |
| id | doaj-art-b2d8a506c7ab43c38687d206bccc1775 |
| institution | Kabale University |
| issn | 2666-1446 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomarkers in Neuropsychiatry |
| spelling | doaj-art-b2d8a506c7ab43c38687d206bccc17752025-01-09T06:14:36ZengElsevierBiomarkers in Neuropsychiatry2666-14462025-06-0112100117Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2Brett A. Clementz0Ishanu Chattopadhyay1S. Kristian Hill2Jennifer E. McDowell3Sarah K. Keedy4David A. Parker5Rebekah L. Trotti6Elena I. Ivleva7Matcheri S. Keshavan8Elliot S. Gershon9Godfrey D. Pearlson10Carol A. Tamminga11Robert D. Gibbons12Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, United States; Corresponding author.Department of Medicine, Section of Hospital Medicine, University of Chicago, Chicago IL, United StatesDepartment of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United StatesDepartments of Psychology and Neuroscience, Owens Institute for Behavioral Research, University of Georgia, Athens GA, United StatesDepartment of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United StatesDepartment of Human Genetics, Emory University School of Medicine, Atlanta VA Medical Center, Atlanta GA, United StatesDepartment of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, United StatesDepartment of Psychiatry, UT Southwestern Medical Center, Dallas TX, United StatesDepartment of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, United StatesDepartments of Psychiatry and Human Genetics, University of Chicago, United StatesDepartments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven CT, and Olin NeuroPsychiatry Research Center, Institute of Living, Hartford, CT, United StatesDepartment of Psychiatry, UT Southwestern Medical Center, Dallas TX, United StatesCenter for Health Statistics, Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, IL, United StatesObjective: The B-SNIP consortium validated neurobiologically defined psychosis Biotypes (BT1, BT2, BT3) using cognitive and psychophysiological measures. B-SNIP’s biomarker panel is not practical for most settings. Previously, B-SNIP developed an efficient classifier of Biotypes using only clinical assessments (called ADEPT-CLIN) with acceptable accuracy (∼.81). Adding cognitive performance may improve ADEPT’s performance. Method: Clinical assessments from ADEPT-CLIN plus 18 cognitive measures from 1907 individuals with a B-SNIP psychosis Biotype were used to create an additional diagnostic algorithm called ADEPT-COG. Extremely randomized trees were used to create this low burden classifier. Results: Total Biotype classification accuracy peaked at 94.6 % with 65 items. A reduced set of 18 items showed 90.5 % accuracy. Only 9–10 items achieved a one-vs-all (e.g., BT1 or not) accuracy of ∼.95, considerably better than using clinical assessments alone. The top discriminators of psychosis Biotypes were antisaccade proportion correct, BACS total, symbol coding, antisaccade correct response latency, verbal memory, digit sequencing, stop signal reaction times, stop signal proportion correct, Tower of London, and WRAT Reading. Except for antisaccade proportion correct and Tower of London, there was no overlap of the top discriminating items for B-SNIP Biotypes and DSM psychosis categories. Conclusions: This low-burden algorithm using clinical and cognitive measures achieved high classification accuracy and can support Biotype-specific etiological and treatment investigations in clinical and research environments. It may be especially useful for clinical trials.http://www.sciencedirect.com/science/article/pii/S2666144624000352DiagnosisPsychosisBiotypesDSMCognition |
| spellingShingle | Brett A. Clementz Ishanu Chattopadhyay S. Kristian Hill Jennifer E. McDowell Sarah K. Keedy David A. Parker Rebekah L. Trotti Elena I. Ivleva Matcheri S. Keshavan Elliot S. Gershon Godfrey D. Pearlson Carol A. Tamminga Robert D. Gibbons Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2 Biomarkers in Neuropsychiatry Diagnosis Psychosis Biotypes DSM Cognition |
| title | Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2 |
| title_full | Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2 |
| title_fullStr | Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2 |
| title_full_unstemmed | Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2 |
| title_short | Cognitive performance and differentiation of B-SNIP psychosis Biotypes: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - 2 |
| title_sort | cognitive performance and differentiation of b snip psychosis biotypes algorithmic diagnostics for efficient prescription of treatments adept 2 |
| topic | Diagnosis Psychosis Biotypes DSM Cognition |
| url | http://www.sciencedirect.com/science/article/pii/S2666144624000352 |
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