Increased functional interaction within frontoparietal network during working memory task in major depressive disorder

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Deep learning methods are increasingly being applied to raw electroencephalogram (EEG) data. However, if these models are to be used in clinical or research contexts, methods to explain them must be developed, and if these models are to be used in research contexts, methods for combining explanations across large numbers of models must be developed to counteract the inherent randomness of existing training approaches. Model visualization-based explainability methods for EEG involve structuring a model architecture such that its extracted features can be characterized and have the potential to offer highly useful insights into the patterns that they uncover. Nevertheless, model visualization-based explainability methods have been underexplored within the context of multichannel EEG, and methods to combine their explanations across folds have not yet been developed. In this study, we present two novel convolutional neural network-based architectures and apply them for automated major depressive disorder diagnosis. Our models obtain slightly lower classification performance than a baseline architecture. However, across 50 training folds, they find that individuals with MDD exhibit higher β power, potentially higher δ power, and higher brain-wide correlation that is most strongly represented within the right hemisphere. This study provides multiple key insights into MDD and represents a significant step forward for the domain of explainable deep learning applied to raw EEG. We hope that it will inspire future efforts that will eventually enable the development of explainable EEG deep learning models that can contribute both to clinical care and novel medical research discoveries.

Alterations of functional brain activity and connectivity in female nurses working on long-term shift

AIM

To investigate the alterations of functional brain activity and connectivity in female nurses working on long-term shifts and explore their correlations with work-related psychological traits.

DESIGN

An exploratory cross-sectional study.

METHODS

Thirty-five female nurses working on long-term shifts (shift nurses) and 35 female nurses working on fixed days (fixed nurses) were enrolled. After assessing the work-related psychological traits, including burnout, perceived stress, anxiety, and depression of nurses, the fractional amplitude of low-frequency fluctuations (fALFF) and region of interest (ROI)-based functional connectivity (FC) analyses were performed to investigate the differences of brain spontaneous activity and functional connectivity between these two groups of nurses. Thereafter, correlations between the functional brain parameters (fALFF and FC) and clinical metrics were investigated among the shift nurses.

RESULTS

Compared to fixed nurses, shift nurses had higher burnout, perceived stress and depression scores, lower fALFF in the right dorsolateral prefrontal cortex (dlPFC), left and right superior parietal lobule (SPL), bilateral anterior cingulate cortex (ACC), and higher fALFF in the right superior/middle temporal gyrus, as well as decreased FC between the right dlPFC (the selected ROI) and bilateral ACC, left and right inferior frontal/orbitofrontal gyrus (IFG/IOFG), right SPL, and left middle occipital gyrus (voxel-level p < 0.001, cluster level p < 0.05, GRF correction). Correlation analyses demonstrated that the fALFF value of the right dlPFC was significantly correlated with the burnout and anxiety scores, the FC value of the right dlPFC-right SPL was correlated with the perceived stress and burnout scores, the FC value of the right dlPFC-right IFG/IOFG was correlated with the burnout score in shift nurses (p < 0.05).

CONCLUSION

Shift nurses had work-related altered functional activity and connectivity in the right frontoparietal network, which provided objective and visualised evidence to clarify the hazards of long-term shift work on female nurses.

PATIENT OR PUBLIC CONTRIBUTION

Seventy nurses participated deeply as subjects in this study. These findings are expected to draw managers' attention to the harmful influences of shift work on nurses.

Elevated VCAM-1 levels in peripheral blood are associated with brain structural and functional alterations in major depressive disorder

BACKGROUND

Vascular cell adhesion molecule-1 (VCAM-1) is a well-known biomarker of endothelial activation. This study aimed to determine whether changes in peripheral VCAM-1 levels occurred in major depressive disorder (MDD) patients and explored immune-brain interactions based on neuroimaging.

METHODS

This study included 165 subjects (80 healthy controls [HCs] and 85 MDD patients). Of them, 133 underwent magnetic resonance imaging. VCAM-1 was measured using a commercially available Enzyme-Linked Immunosorbent Assay kit following the manufacturer's instructions. The gray matter volume (GMV) and surface-based functional connectivity (FC) were calculated based on Schaefer parcellation 400 parcels.

RESULTS

Compared with the HCs, MDD patients exhibited significantly higher level of VCAM-1. The correlation analysis showed that VCAM-1 had a significant negative correlation with GMV of the right medial frontal cortex (MFC) and postcentral (PostCG). The mediation analyses showed that VCAM-1 mediated the association between group and GMV of PostCG and the FC of left ventral prefrontal cortex (vPFC) with right inferior parietal lobe (IPL).

CONCLUSIONS

This study showed that a high level of VCAM-1 was associated to the decreased GMV in the right MFC and PostCG, and mediated the FC of the left vPFC with right IPL. These findings suggested that VCAM-1 might contribute to the etiology of MDD by influencing brain structure and function.

LIMITATIONS

The cross-sectional design makes it difficult to determine the causal relationship and dynamic effect among VCAM-1, brain structure/function features, and depressive symptoms.

Resting-state cerebral blood flow and functional connectivity abnormalities in depressed patients with childhood maltreatment: Potential biomarkers of vulnerability?

AIM

Childhood maltreatment (CM) is an important risk factor for major depressive disorder (MDD). This study aimed to explore the specific effect of CM on cerebral blood flow (CBF) and brain functional connectivity (FC) in MDD patients.

METHODS

A total of 150 subjects were collected including 55 MDD patients with CM, 34 MDD patients without CM, 19 healthy controls (HC) with CM, and 42 HC without CM. All subjects completed MRI scans and neuropsychological tests. Two-way analysis of covariance was used to detect the main and interactive effects of disease and CM on CBF and FC across subjects. Then, partial correlation analyses were conducted to explore the behavioral significance of altered CBF and FC in MDD patients. Finally, a support vector classifier model was applied to differentiate MDD patients.

RESULTS

MDD patients represented increased CBF in bilateral temporal lobe and decreased CBF in right visual cortex. Importantly, significant depression-by-CM interactive effects on CBF were primarily located in the frontoparietal regions, including orbitofrontal cortex (OFC), lateral prefrontal cortex (PFC), and parietal cortex. Moreover, significant FC abnormalities were seen in OFC-PFC and frontoparietal-visual cortex. Notably, the abnormal CBF and FC were significantly associated with behavioral performance. Finally, a combination of altered CBF and FC behaved with a satisfactory classification ability to differentiate MDD patients.

CONCLUSIONS

These results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.

Functional connectivity of the left inferior parietal lobule mediates the impact of anxiety and depression symptoms on sleep quality in healthy adults

Individuals with anxiety and depression symptoms are vulnerable to sleep disturbances. The current study aimed to explore the shared neuro-mechanisms underlying the effect of anxiety and depression symptoms on sleep quality. We recruited a cohort of 92 healthy adults who underwent functional magnetic resonance imaging scanning. We measured anxiety and depression symptoms using the Zung Self-rating Anxiety/Depression Scales and sleep quality using the Pittsburgh Sleep Quality Index. Independent component analysis was used to explore the functional connectivity (FC) of brain networks. Whole-brain linear regression analysis showed that poor sleep quality was associated with increased FC in the left inferior parietal lobule (IPL) within the anterior default mode network. Next, we extracted the covariance of anxiety and depression symptoms using principal component analysis to represent participants' emotional features. Mediation analysis revealed that the intra-network FC of the left IPL mediated the association between the covariance of anxiety and depression symptoms and sleep quality. To conclude, the FC of the left IPL may be a potential neural substrate in the association between the covariance of anxiety and depression symptoms and poor sleep quality, and may serve as a potential intervention target for the treatment of sleep disturbance in the future.

Dual-functional network regulation underlies the central executive system in working memory

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON→FPN, CON→DMN, CON→visual areas, FPN→visual areas, and phonological areas→FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON→FPN, CON→DMN, visual areas→CON, and visual areas→FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Working memory function in patients with major depression disorder: A narrative review

Working memory (WM) deficits are recognized as serious cognitive impairment in patients with major depressive disorder (MDD). This review aims to clarify the effects of impaired WM function in patients with MDD and explore non-invasive and effective treatments that can be adopted in clinical practice. This review (1) synthesizes extant literature examining brain function and brain areas in terms of WM in individuals with depression, (2) utilizes the outcomes of the studies presented in this review to discuss the effects of impaired WM function on cognitive processing in individuals with depression, (3) integrates the treatments explored in current studies and (4) provides some suggestions for future research. We found that (1) central executive (CE) components affect the processing of WM, and this might be one of the factors influencing cognitive biases, as it is implicated in repetitive negative thinking and rumination; (2) the left dorsal anterior cingulate cortex (dACC), the left dorsolateral prefrontal cortex (DLPFC) and the regions of the default mode network (DMN) play a vital role in CE functioning; and (3) psychotherapy, cognitive training, exercise and physical therapy can be used as complementary treatments for MDD.

Repetitive Anodal TDCS to the Frontal Cortex Increases the P300 during Working Memory Processing

Transcranial direct current stimulation (TDCS) is a technique with which neuronal activity, and therefore potentially behavior, is modulated by applying weak electrical currents to the scalp. Application of TDCS to enhance working memory (WM) has shown promising but also contradictory results, and little emphasis has been placed on repeated stimulation protocols, in which effects are expected to be increased. We aimed to characterize potential behavioral and electrophysiological changes induced by TDCS during WM training and evaluate whether repetitive anodal TDCS has a greater modulatory impact on the processes underpinning WM than single-session stimulation. We examined the effects of single-session and repetitive anodal TDCS to the dorsolateral prefrontal cortex (DLPFC), targeting the frontal-parietal network, during a WM task in 20 healthy participants. TDCS had no significant impact on behavioral measures, including reaction time and accuracy. Analyzing the electrophysiological response, the P300 amplitude significantly increased following repetitive anodal TDCS, however, positively correlating with task performance. P300 changes were identified over the parietal cortex, which is known to engage with the frontal cortex during WM processing. These findings support the hypothesis that repetitive anodal TDCS modulates electrophysiological processes underlying WM.