Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders
Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved..
BACKGROUND: Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD.
METHODS: Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses.
RESULTS: Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets.
CONCLUSIONS: Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
|---|---|
| Enthalten in: |
Biological psychiatry. Cognitive neuroscience and neuroimaging - 9(2024), 3 vom: 03. März, Seite 326-336 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhu, Tan [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Alcohol use disorder |
|---|
| Anmerkungen: |
Date Completed 11.03.2024 Date Revised 29.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.bpsc.2023.08.010 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM361939159 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM361939159 | ||
| 003 | DE-627 | ||
| 005 | 20240330000435.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.bpsc.2023.08.010 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1355.xml |
| 035 | |a (DE-627)NLM361939159 | ||
| 035 | |a (NLM)37696489 | ||
| 035 | |a (PII)S2451-9022(23)00222-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhu, Tan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 11.03.2024 | ||
| 500 | |a Date Revised 29.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD | ||
| 520 | |a METHODS: Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses | ||
| 520 | |a RESULTS: Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets | ||
| 520 | |a CONCLUSIONS: Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Alcohol use disorder | |
| 650 | 4 | |a Biotyping | |
| 650 | 4 | |a Genetic association analysis | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Nicotine use disorder | |
| 650 | 4 | |a Resting-state functional magnetic resonance imaging (MRI) | |
| 700 | 1 | |a Wang, Wuyi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Kranzler, Henry R |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chiang-Shan R |e verfasserin |4 aut | |
| 700 | 1 | |a Bi, Jinbo |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biological psychiatry. Cognitive neuroscience and neuroimaging |d 2016 |g 9(2024), 3 vom: 03. März, Seite 326-336 |w (DE-627)NLM255946007 |x 2451-9030 |7 nnns |
| 773 | 1 | 8 | |g volume:9 |g year:2024 |g number:3 |g day:03 |g month:03 |g pages:326-336 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.bpsc.2023.08.010 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 9 |j 2024 |e 3 |b 03 |c 03 |h 326-336 | ||