Prefrontal–bed nucleus of the stria terminalis physiological and neuropsychological biomarkers predict therapeutic outcomes in depression

L Linbin Wang Y Yingying Zhang Y Yuhan Wang Q Qiong Ding L Luling Dai K Kejia Hu K Kuanghao Ye X Xin Lv X Xiaoxiao Zhang (iHuman Institute ShanghaiTech University) A Alekhya Mandali L Luis Manssuer S Saurabh Sonkusare Y Yijie Zhao P Peng Huang X Xian Qiu Y Yixin Pan Y Yijie Lai D Dianyou Li W Wei Liu S Shikun Zhan B Bomin Sun V Valerie Voon

Abstract

Abstract Therapeutic options for refractory depression are urgently needed. We conducted a deep brain stimulation (DBS) randomized controlled trial of the bed nucleus of the stria terminalis (BNST), an extended amygdala structure, and nucleus accumbens (NAc) in 26 refractory depression patients to assess treatment efficacy and predictors of response. BNST-NAc DBS had a 50% depression response rate and 35% remission rate in the open-label phase. We identified an objective intracranial physiological biomarker using acute and chronic intracranial recordings, machine learning, and an integrated framework combining electrophysiology, neuroimaging, and behavior: lower BNST theta and prefrontal-BNST coherence with top-down connectivity predicted better depression outcomes and quality-of-life after chronic stimulation at 3, 6 and 12 months, confirmed across eyes -open and -closed states and machine learning. We identified a physiology-guided connectivity network involving dorsal anterior cingulate and lateral inferior frontal cortex tracts. These biomarkers, linked to negative emotional bias and anxiety, highlight the efficacy of BNST-NAc DBS for refractory depression and has potential broader clinical implications. ClinicalTrials.gov registration: NCT04530942.

Article Details

Volume / Issue Vol. 16, Issue 1
Published November 18, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (22)

L

Linbin Wang

Y

Yingying Zhang

Y

Yuhan Wang

Q

Qiong Ding

L

Luling Dai

K

Kejia Hu

K

Kuanghao Ye

X

Xin Lv

X

Xiaoxiao Zhang

iHuman Institute ShanghaiTech University

A

Alekhya Mandali

L

Luis Manssuer

S

Saurabh Sonkusare

Y

Yijie Zhao

P

Peng Huang

X

Xian Qiu

Y

Yixin Pan

Y

Yijie Lai

D

Dianyou Li

W

Wei Liu

S

Shikun Zhan

B

Bomin Sun

V

Valerie Voon