Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia

Shalaila S. Haas, Ruiyang Ge, Nicole Sanford, Amirhossein Modabbernia, Abraham Reichenberg, Heather C. Whalley, René S. Kahn, Sophia Frangou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms. Methods: Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16–35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16–37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters. Results: HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE. Discussion: Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

Original languageEnglish
Article number913470
JournalFrontiers in Psychiatry
Volume13
DOIs
StatePublished - 22 Jun 2022

Keywords

  • brainAGE
  • clustering
  • cognitive subtypes
  • early psychosis
  • machine learning

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