Neural Biomarkers for Identifying Atopic Dermatitis and Assessing Acupuncture Treatment Response Using Resting-State fMRI

Abstract

Purpose

Only a few studies have focused on the brain mechanisms underlying the itch processing in AD patients, and a neural biomarker has never been studied in AD patients. We aimed to develop a deep learning model-based neural signature which can extract the relevant temporal dynamics, discriminate between AD and healthy control (HC), and between AD patients who responded well to acupuncture treatment and those who did not.

Patients and Methods

We recruited 41 AD patients (22 male, age mean ± SD: 24.34 ± 5.29) and 40 HCs (20 male, age mean ± SD: 26.4 ± 5.32), and measured resting-state functional MRI signals. After preprocessing, 38 functional regions of interest were applied to the functional MRI signals. A long short-term memory (LSTM) was used to extract the relevant temporal dynamics for classification and train the prediction model. Bootstrapping and 4-fold cross-validation were used to examine the significance of the models.

Results

For the identification of AD patients and HC, we found that the supplementary motor area (SMA), posterior cingulate cortex (PCC), temporal pole, precuneus, and dorsolateral prefrontal cortex showed significantly greater prediction accuracy than the chance level. For the identification of high and low responder to acupuncture treatment, we found that the lingual-parahippocampal-fusiform gyrus, SMA, frontal gyrus, PCC and precuneus, paracentral lobule, and primary motor and somatosensory cortex showed significantly greater prediction accuracy than the chance level.

Conclusion

We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.

Keywords:

• Atopic Dermatitis
• deep learning
• functional MRI
• biomarkers
• personalized medicine

Data Sharing Statement

All data presented in the manuscript, including fMRI and clinical data devoid of identifiable participant information, may be shared solely for research purposes. Requests from other parties will be directed to the corresponding authors for consideration.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A4A1018598, NRF-2021R1F1A1055814, RS-2023-00279315). The present research has been conducted by the Research Grant of Kwangwoon University in 2023.