Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial

Abstract

Introduction

One major challenge in developing personalised repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses exhibited high inter-individual variations. Brain morphometry might contribute to these variations. This study sought to determine whether individual’s brain morphometry could predict the rTMS responders and remitters.

Methods

This was a secondary analysis of data from a randomised clinical trial that included fifty-five patients over the age of 60 with both comorbid depression and neurocognitive disorder. Based on magnetic resonance imaging scans, estimated brain age was calculated with morphometric features using a support vector machine. Brain-predicted age difference (brain-PAD) was computed as the difference between brain age and chronological age.

Results

The rTMS responders and remitters had younger brain age. Every additional year of brain-PAD decreased the odds of relieving depressive symptoms by ∼25.7% in responders (Odd ratio [OR] = 0.743, p = .045) and by ∼39.5% in remitters (OR = 0.605, p = .022) in active rTMS group. Using brain-PAD score as a feature, responder-nonresponder classification accuracies of 85% (3^rd week) and 84% (12^th week), respectively were achieved.

Conclusion

In elderly patients, younger brain age appears to be associated with better treatment responses to active rTMS. Pre-treatment brain age models informed by morphometry might be used as an indicator to stratify suitable patients for rTMS treatment.

Trial registration

ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191

Keywords:

• rTMS
• MRI
• brain age
• neurocognitive disorder
• prediction
• cortical features
• depression
• neuroplasticity

Acknowledgements

The authors are appreciated the great help from the research staff at the Chan Wei Wei Therapeutic Physical Mental Exercise Centre at the Chinese University of Hong Kong. We gratefully acknowledge the study investigators and the dedication of all the participants and their families. Meanwhile, we are grateful to the research teams and the participants of the Cam-CAN project for sharing the data with the world.

Ethics approval

Ethics approval was obtained from the Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee (The Joint CUHK-NTEC CREC) before the commencement of this study (CRE-2015.127-T). Participants and their caregivers were acknowledged about the aims and logistics of this clinical trial before making a decision for informed consent.

Authors contributions

HL conceived and designed this analysis. LS helped to conduct the MRI scanning. CWL, STC and DPM helped to recruit the participants. JL helped to calculate the brain age matrices. HL developed the hypotheses, analysed the MRI data, conducted the rTMS treatment and wrote this manuscript. All authors contributed, edited, and approved the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Code availability

The source code and pre-trained models of brain age metrics have been uploaded to our GitHub page (https://github.com/hannabrainscience/Brain-age-prediction).

Data availability statement

Raw and processed imaging data under the General Data Protection Regulations (GDPR) may be available for research collaboration purpose upon reasonable request to the corresponding authors (Hanna Lu and Linda Chiu Wa Lam) and will require the completion of a data processing agreement.

Additional information

Funding

This work was supported by the Hong Kong Research Grant Council (RGC) - General Research Fund (GRF) (Grant No.14111115) and the Direct Grants for Research the Chinese University of Hong Kong (Grant No: 4054569, 4054645).