Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders

Woo-Sung Kim; Da-Woon Heo; Junyeong Maeng; Jie Shen; Uyanga Tsogt; Soyolsaikhan Odkhuu; Xuefeng Zhang; Sahar Cheraghi; Sung-Wan Kim; Byung-Joo Ham; Fatima Zahra Rami; Jing Sui; Chae Yeong Kang; Heung-Il Suk; Young-Chul Chung

doi:10.1093/schbul/sbad167

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders

Schizophr Bull. 2023 Dec 12:sbad167. doi: 10.1093/schbul/sbad167. Online ahead of print.

Authors

Woo-Sung Kim^{1

2}, Da-Woon Heo³, Junyeong Maeng³, Jie Shen^{1

2

4}, Uyanga Tsogt^{1

2}, Soyolsaikhan Odkhuu^{1

2}, Xuefeng Zhang², Sahar Cheraghi^{1

2}, Sung-Wan Kim⁵, Byung-Joo Ham⁶, Fatima Zahra Rami^{1

2}, Jing Sui^{7

8}, Chae Yeong Kang², Heung-Il Suk^{3

9}, Young-Chul Chung^{1

10

2}

Affiliations

¹ Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea.
² Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
³ Department of Artificial Intelligence, Korea University, Seoul, Korea.
⁴ Department of Psychiatry, Yanbian University, Medical School, Yanji, China.
⁵ Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea.
⁶ Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
⁷ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
⁸ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
⁹ Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
¹⁰ Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea.

PMID: 38085061
DOI: 10.1093/schbul/sbad167

Abstract

Background and hypothesis: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI).

Study design: We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated.

Study results: The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs.

Conclusions: Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.

Keywords: brain age; schizophrenia; simple fully convolutional network-based regression; structural magnetic resonance imaging.

Grants and funding

HL19C0015/Ministry of Health and Welfare