BETA: Resting-state fMRI Biotypes for tDCS Efficacy in Anxiety among Older Adults at Risk for Alzheimer's Disease

Skylar Stolte, Junfu Cheng, Chintan Acharya, Lin Gu, Andrew O'Shea, Aprinda Indahlastari, Adam J. Woods, Ruogu Fang

Keywords: Anxiety, tDCS, ADRD, functional MRI, biotypes

TL;DR: BETA uses fMRI data to identify anxiety subtypes in ADRD patients, predicting who will benefit from tDCS therapy.

Abstract: Anxiety is usually gauged by self‑report, yet a single symptom level can reflect disparate neural circuitry. In Alzheimer’s disease and related dementias (ADRD) this heterogeneity becomes a barrier to effective neuromodulation: some patients may benefit from transcranial direct‑current stimulation (tDCS), while others may not. To overcome this obstacle, we introduced BETA (Biotypes for tDCS Efficacy in Anxiety), a data‑driven pipeline that uses resting‑state fMRI functional connectivity to derive anxiety subtypes that are intrinsically linked to tDCS response. A transformer‑based variational autoencoder compresses high‑dimensional connectivity into a 50‑dimensional latent embedding that emphasizes networks implicated in cognitive aging and anxiety. A deep‑embedded clustering loss, regularized by a clinically informed term that pulls together individuals who exhibit similar post‑tDCS anxiety change, yields four distinct subtypes. Across all subtypes, disrupted coupling between sensory‑processing and higher‑order cognitive regions emerges as a common hallmark. Crucially, one cluster is resistant to frontal‑lobe tDCS, whereas two clusters demonstrate significant anxiety reduction following stimulation. The responsive subtypes are defined by strengthened connectivity between the lateral occipital cortex—superior division (sLOC) and medial frontal cortex (MedFC), and between sLOC and the intracalcarine cortex (ICC). BETA demonstrates that fMRI‑based subtyping can directly identify which patients are likely to benefit from tDCS, providing a concrete roadmap for precision psychiatry in ADRD and facilitating tailored therapeutic strategies for anxiety.

Primary Subject Area: Application: Neuroimaging

Secondary Subject Area: Unsupervised Learning and Representation Learning

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LLM Policy: Yes

Submission Number: 108