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

Cao, Wanyi; Liao, Haiyan; Cai, Sainan; Peng, Wanrong; Liu, Zhaoxia; Zheng, Kaili; Liu, Jinyu; Zhong, Mingtian; Tan, Chongqing; Yi, Jinyao

doi:10.1002/hbm.25611

Cited by 14 publications

(8 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This could explain why M1 primarily identified right hemisphere and central electrodes as important to model performance. It also fits with previous studies that have identified the right hemisphere as particularly overactive in MDD [78], as having increased regional homogeneity [74], as having increased frontal (M2) and fronto-parietal (M3) β-band coherence [79], and as having increased frontoparietal connectivity (M3) related to reduced working memory [80].…”

Section: Resultssupporting

confidence: 89%

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Ellis,

Lapera Sancho,

Miller

et al. 2024

Preprint

View full text Add to dashboard Cite

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 beta power, potentially higher delta 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.

show abstract

Section: Resultssupporting

confidence: 89%

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Ellis,

Lapera Sancho,

Miller

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Significant depression-by-CM interactive effects on CBF were observed in frontoparietal areas. 30,31 The OFC intimately involves negative thoughts and depressive emotions, regarded as a key target for MDD intervention. 32,33 Previous research reported the effects of OFC on memory conducing to rumination.…”

Section: Discussionmentioning

confidence: 99%

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

Liu,

Fan,

et al. 2023

Psychiatry Clin Neurosci

View full text Add to dashboard Cite

AimChildhood 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.MethodsA 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 employed 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.ResultsMDD 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 classific ability to differentiate MDD patients.ConclusionsThese results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.This article is protected by copyright. All rights reserved.

show abstract

“…In addition, the DLPFC is involved in emotion regulation in MDD (Nejati et al, 2022), which means that more frequent rumination and a lack of reappraisal are possibly correlated with the DLPFC. Furthermore, Cao et al (2021) suggested that MDD patients had abnormal connectivity between the right inferior parietal lobule and the right DLPFC. Therefore, overactivity in the PFC of MDD patients occurs in order to compensate for a lack of deactivation in regions of the default mode network (DMN) (Wang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

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

Chen

Wang

2022

Clin Psychology and Psychoth

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 14 publications

References 72 publications

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

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

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

Contact Info

Product

Resources

About