Effective Connectivity of Right Amygdala Subregions Predicts Symptom Improvement Following 12-Week Pharmacological Therapy in Major Depressive Disorder

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.

Effects of escitalopram therapy on effective connectivity among core brain networks in major depressive disorder

BACKGROUND

Patients with major depressive disorder (MDD) have abnormal functional interaction among large-scale brain networks, indicated by aberrant effective connectivity of the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). However, it remains unclear whether antidepressants can normalize the altered effective connectivity in MDD.

METHODS

In this study, we collected resting-state functional magnetic resonance imaging data from 46 unmedicated patients with MDD at baseline and after 12 weeks of escitalopram treatment. We also collected data from 58 healthy controls (HCs) at the same time point with the same interval. Using spectral dynamic causal modeling and parametric empirical Bayes, we examined group differences, time effect and their interaction on the casual interactions among the regions of interest in the three networks.

RESULTS

Compared with HCs, patients with MDD showed increased positive (excitatory) connections within the DMN, decreased positive connections within the SN and DAN, decreased absolute value of negative (inhibitory) connectivity from the SN and DAN to the DMN, and decreased positive connections between the DAN and the SN. Furthermore, we found that six connections related to the DAN showed decreased group differences in effective connectivity between MDD and HCs during follow-up compared to the baseline.

CONCLUSIONS

Our findings suggest that escitalopram therapy can partly improve the disrupted effective connectivity among high-order brain functional networks in MDD. These findings deepened our understanding of the neural basis of antidepressants' effect on brain function in patients with MDD.

Structural and functional improvement of amygdala sub-regions in postpartum depression after acupuncture

Objective

This study aimed to analyze the changes in structure and function in amygdala sub-regions in patients with postpartum depression (PPD) before and after acupuncture.

Methods

A total of 52 patients with PPD (All-PPD group) were included in this trial, 22 of which completed 8 weeks of acupuncture treatment (Acu-PPD group). An age-matched control group of 24 healthy postpartum women (HPW) from the hospital and community were also included. Results from the 17-Hamilton Depression Scale (17-HAMD) and the Edinburgh Postnatal Depression Scale (EPDS) were evaluated, and resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed at baseline and after the acupuncture treatment. Sub-regions of the amygdala were used as seed regions to measure gray matter volume (GMV) and analyzed for resting-state functional connectivity (RSFC) values separately. Finally, correlation analyses were performed on all patients with PPD to evaluate association values between the clinical scale scores, GMV, and RSFC values, while controlling for age and education. Pearson's correlation analyses were conducted to investigate the relevance between GMV and RSFC values of brain regions that differed before and after acupuncture treatment and clinical scale scores in Acu-PPD patients.

Results

The HAMD scores for Acu-PPD were reduced after acupuncture treatment (P < 0.05), suggesting the positive effects of acupuncture on depression symptoms. Structurally, the All-PPD group showed significantly decreased GMV in the left lateral part of the amygdala (lAMG.L) and the right lateral part of the amygdala (lAMG.R) compared to the HPW group (P < 0.05). In addition, the GMV of lAMG.R was marginally increased in the Acu-PPD group after acupuncture (P < 0.05). Functionally, the Acu-PPD group showed a significantly enhanced RSFC between the left medial part of the amygdala (mAMG.L) and the left vermis_6, an increased RSFC between the right medial part of the amygdala (mAMG.R) and left vermis_6, and an increased RSFC between the lAMG.R and left cerebelum_crus1 (P < 0.05). Moreover, correlation studies revealed that the GMV in the lAMG.R was significantly related to the EPDS scores in the All-PPD group (P < 0.05).

Conclusion

Our findings demonstrated that the structure of amygdala sub-regions is impaired in patients with PPD. Acupuncture may improve depressive symptoms in patients with PPD, and the mechanism may be attributed to changes in the amygdala sub-region structure and the functional connections of brain areas linked to the processing of negative emotions. The fMRI-based technique can provide comprehensive neuroimaging evidence to visualize the central mechanism of action of acupuncture in PPD.

Aberrant Brain Functional Connectivity Strength and Effective Connectivity in Patients with Type 2 Diabetes Mellitus

Alterations of brain functional connectivity in patients with type 2 diabetes mellitus (T2DM) have been reported by resting-state functional magnetic resonance imaging studies, but the underlying precise neuropathological mechanism remains unclear. This study is aimed at investigating the implicit alterations of functional connections in T2DM by integrating functional connectivity strength (FCS) and Granger causality analysis (GCA) and further exploring their associations with clinical characteristics. Sixty T2DM patients and thirty-three sex-, age-, and education-matched healthy controls (HC) were recruited. Global FCS analysis of resting-state functional magnetic resonance imaging was performed to explore seed regions with significant differences between the two groups; then, GCA was applied to detect directional effective connectivity (EC) between the seeds and other brain regions. Correlations of EC with clinical variables were further explored in T2DM patients. Compared with HC, T2DM patients showed lower FCS in the bilateral fusiform gyrus, right superior frontal gyrus (SFG), and right postcentral gyrus, but higher FCS in the right supplementary motor area (SMA). Moreover, altered directional EC was found between the left fusiform gyrus and bilateral lingual gyrus and right medial frontal gyrus (MFG), as well as between the right SFG and bilateral frontal regions. In addition, triglyceride, insulin, and plasma glucose levels were correlated with the abnormal EC of the left fusiform, while disease duration and cognitive function were associated with the abnormal EC of the right SFG in T2DM patients. These results suggest that T2DM patients show aberrant brain function connectivity strength and effective connectivity which is associated with the diabetes-related metabolic characteristics, disease duration, and cognitive function, providing further insights into the complex neural basis of diabetes.