https://orcid.org/0000-0001-9275-3111
Figures & data
Table 1 Demographic and Baseline Clinical Characteristics of Participants and Clinical Effects of Real and Sham Acupuncture Treatment in Patients with Atopic Dermatitis
Table 2 Functional Regions of Interest Employed in Long Short-Term Memory Models for AD Patients
Figure 1 The Long Short-Term Memory analysis procedure. The resting-state fMRI data (Atopic Dermatitis [AD] n=41, healthy controls n=40) was pre-processed using FSL to remove artifacts, correct motion, smooth spatially, and functional data were registered to standard space. Blood oxygen level-dependent signals of each time point (n=115) were extracted for 38 pre-defined regions of interest (ROIs) of Power’s functional atlas. We identified two Long Short-Term Memory (LSTM) models; a model to identify mild-moderate AD patients and healthy controls and another model to identify high and low responders to acupuncture treatment among AD patients. The signals were input into a dual-layer LSTM network designed to capture temporal dependencies within the fMRI data. Each layer is composed of multiple LSTM cells that process data from the corresponding time points, allowing the network to retain information over time. The final layer’s output is forwarded through a fully connected layer followed by a sigmoid activation function to generate the classification result. We tested models’ prediction performance of each ROI based on the classification accuracy and area under receiver operating characteristic curve, assessed from a 4-fold cross-validation test. A t-test against a chance level (0.5) was conducted for the accuracy, computed by bootstrapping (n=100). The significance threshold was set at p < 0.05.
![Figure 1 The Long Short-Term Memory analysis procedure. The resting-state fMRI data (Atopic Dermatitis [AD] n=41, healthy controls n=40) was pre-processed using FSL to remove artifacts, correct motion, smooth spatially, and functional data were registered to standard space. Blood oxygen level-dependent signals of each time point (n=115) were extracted for 38 pre-defined regions of interest (ROIs) of Power’s functional atlas. We identified two Long Short-Term Memory (LSTM) models; a model to identify mild-moderate AD patients and healthy controls and another model to identify high and low responders to acupuncture treatment among AD patients. The signals were input into a dual-layer LSTM network designed to capture temporal dependencies within the fMRI data. Each layer is composed of multiple LSTM cells that process data from the corresponding time points, allowing the network to retain information over time. The final layer’s output is forwarded through a fully connected layer followed by a sigmoid activation function to generate the classification result. We tested models’ prediction performance of each ROI based on the classification accuracy and area under receiver operating characteristic curve, assessed from a 4-fold cross-validation test. A t-test against a chance level (0.5) was conducted for the accuracy, computed by bootstrapping (n=100). The significance threshold was set at p < 0.05.](/cms/asset/34e1cce3-3803-4f92-9afa-f75262e8bb90/djaa_a_12302082_f0001_c.jpg)
Figure 2 Neural biomarkers classification accuracies achieved by Long Short-Term Memory Models. We observed that patients with Atopic Dermatitis (AD) were accurately distinguished, and the outcomes of their acupuncture treatment were solely determined by the signals from resting-state functional magnetic resonance imaging (rs-fMRI) and long short-term memory models. (A) We identified distinct temporal features in the left supplementary motor area (SMA; mean accuracy 0.85, mean area under receiver operating characteristic curve [AUC] 0.85), right SMA (0.78, 0.78), right middle/posterior cingulate cortex (0.79–0.82, 0.79–0.81), left superior/middle/medial frontal gyri including the dorsolateral prefrontal cortex (0.71–0.81, 0.79–0.81), right superior/middle frontal gyrus (0.75–0.78, 0.75–0.79), left precentral gyrus (0.80, 0.80), right temporal pole (0.77, 0.77), left fusiform gyrus (0.74, 0.74), and right precuneus (0.71, 0.70) between patients with AD and healthy participants. These differences contributed to the classification of AD patients from healthy controls. (B) Using rs-fMRI signals obtained even before the acupuncture treatment, it was revealed that the right lingual-parahippocampal-fusiform gyrus (0.90, 0.89), right SMA (0.87, 0.87), left fusiform gyrus (0.90, 0.89), right superior middle frontal gyrus (0.84, 0.83), left superior/middle frontal gyrus (0.81–0.82, 0.80–0.84), left posterior cingulate cortex and precuneus (0.79, 0.68) have temporal characteristics that can distinguish high and low responders to acupuncture treatment in AD patients. The brain regions identified for diagnosing and predicting treatment responses may provide a basis for further research on the neural mechanisms of AD and the exploration of innovative treatment modalities in the future.
![Figure 2 Neural biomarkers classification accuracies achieved by Long Short-Term Memory Models. We observed that patients with Atopic Dermatitis (AD) were accurately distinguished, and the outcomes of their acupuncture treatment were solely determined by the signals from resting-state functional magnetic resonance imaging (rs-fMRI) and long short-term memory models. (A) We identified distinct temporal features in the left supplementary motor area (SMA; mean accuracy 0.85, mean area under receiver operating characteristic curve [AUC] 0.85), right SMA (0.78, 0.78), right middle/posterior cingulate cortex (0.79–0.82, 0.79–0.81), left superior/middle/medial frontal gyri including the dorsolateral prefrontal cortex (0.71–0.81, 0.79–0.81), right superior/middle frontal gyrus (0.75–0.78, 0.75–0.79), left precentral gyrus (0.80, 0.80), right temporal pole (0.77, 0.77), left fusiform gyrus (0.74, 0.74), and right precuneus (0.71, 0.70) between patients with AD and healthy participants. These differences contributed to the classification of AD patients from healthy controls. (B) Using rs-fMRI signals obtained even before the acupuncture treatment, it was revealed that the right lingual-parahippocampal-fusiform gyrus (0.90, 0.89), right SMA (0.87, 0.87), left fusiform gyrus (0.90, 0.89), right superior middle frontal gyrus (0.84, 0.83), left superior/middle frontal gyrus (0.81–0.82, 0.80–0.84), left posterior cingulate cortex and precuneus (0.79, 0.68) have temporal characteristics that can distinguish high and low responders to acupuncture treatment in AD patients. The brain regions identified for diagnosing and predicting treatment responses may provide a basis for further research on the neural mechanisms of AD and the exploration of innovative treatment modalities in the future.](/cms/asset/76eb99b6-d7e3-491a-b04e-216e620c3629/djaa_a_12302082_f0002_c.jpg)