Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients
© 2022. The Author(s), under exclusive licence to American College of Neuropsychopharmacology..
Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
|---|---|
| Enthalten in: |
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology - 48(2023), 4 vom: 01. März, Seite 633-641 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Meiling [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Biomarkers |
|---|
| Anmerkungen: |
Date Completed 23.02.2023 Date Revised 02.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1038/s41386-022-01500-4 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM349160511 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM349160511 | ||
| 003 | DE-627 | ||
| 005 | 20240302232005.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41386-022-01500-4 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1314.xml |
| 035 | |a (DE-627)NLM349160511 | ||
| 035 | |a (NLM)36402836 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Meiling |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients |
| 264 | 1 | |c 2023 | |
| 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 23.02.2023 | ||
| 500 | |a Date Revised 02.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. The Author(s), under exclusive licence to American College of Neuropsychopharmacology. | ||
| 520 | |a Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 700 | 1 | |a Dahmani, Louisa |e verfasserin |4 aut | |
| 700 | 1 | |a Hubbard, Catherine S |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Yongbo |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Meiyun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Danhong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hesheng |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology |d 1988 |g 48(2023), 4 vom: 01. März, Seite 633-641 |w (DE-627)NLM012626821 |x 1740-634X |7 nnns |
| 773 | 1 | 8 | |g volume:48 |g year:2023 |g number:4 |g day:01 |g month:03 |g pages:633-641 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41386-022-01500-4 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 48 |j 2023 |e 4 |b 01 |c 03 |h 633-641 | ||