Investigating the association between depression and cerebral haemodynamics—A systematic review and meta-analysis

Chithiramohan, Tamara; Parekh, Jvalant Nayan; Kronenberg, Golo; Haunton, Victoria J.; Minhas, Jatinder S.; Panerai, Ronney B.; Robinson, Thompson G.; Divall, Pip; Subramaniam, Hari; Mukaetova‐Ladinska, Elizabeta B.; Beishon, Lucy

doi:10.1016/j.jad.2021.11.037

Cited by 10 publications

(8 citation statements)

References 115 publications

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“…In this framework, neuroimaging meta-analysis emerges as a potent approach to synthesizing the multitude of results from published imaging literature in an unbiased manner, with the advantages of enlarging sample size, increasing power, and separating the consistent findings from those occurring by chance (Muller et al, 2018). Although there have been prior neuroimaging meta-analyses examining CBF abnormalities in MDD (Chen et al, 2015; Chithiramohan et al, 2022; Hamilton et al, 2012; Li et al, 2017; Wang & Yang, 2022), a rapidly increasing number of recent publications in the field along with continuing improvements in meta-analytic methods (Albajes-Eizagirre, Solanes, Vieta, & Radua, 2019b) have allowed us to attempt a more thorough characterization of CBF changes in MDD.…”

Section: Introductionmentioning

confidence: 99%

Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: a combined neuroimaging and transcriptome study

et al. 2023

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Background Extensive research has shown abnormal cerebral blood flow (CBF) in patients with major depressive disorder (MDD) that is a heritable disease. The objective of this study was to investigate the genetic mechanisms of CBF abnormalities in MDD. Methods To achieve a more thorough characterization of CBF changes in MDD, we performed a comprehensive neuroimaging meta-analysis of previous literature as well as examined group CBF differences in an independent sample of 133 MDD patients and 133 controls. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial association analyses were conducted to identify genes whose expression correlated with CBF changes in MDD, followed by a set of gene functional feature analyses. Results We found increased CBF in the reward circuitry and default-mode network and decreased CBF in the visual system in MDD patients. Moreover, these CBF changes were spatially associated with expression of 1532 genes, which were enriched for important molecular functions, biological processes, and cellular components of the cerebral cortex as well as several common mental disorders. Concurrently, these genes were specifically expressed in the brain tissue, in immune cells and neurons, and during nearly all developmental stages. Regarding behavioral relevance, these genes were associated with domains involving emotion and sensation. In addition, these genes could construct a protein-protein interaction network supported by 60 putative hub genes with functional significance. Conclusions Our findings suggest a cerebral perfusion redistribution in MDD, which may be a consequence of complex interactions of a wide range of genes with diverse functional features.

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Section: Introductionmentioning

confidence: 99%

Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: a combined neuroimaging and transcriptome study

et al. 2023

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“…Specifically, rCBF is lower in patients with mild depression compared to the normal control group, whereas it is higher in patients with moderate to severe depression compared to the rCBF in most cortical regions of the normal control group. 45 , 46 The changes in rCBF can lead to hemodynamic responses, which can be measured using an fNIRS device, making it an important tool for studying depression. However, the fNIRS device is sensitive to motion artifacts and noise, posing a challenge in filter out noise while retaining depression-related information.…”

Section: Discussionmentioning

confidence: 99%

Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches

Huang,

Liu,

Yang

et al. 2024

Neurophoton.

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. Significance Early diagnosis of depression is crucial for effective treatment. Our study utilizes functional near-infrared spectroscopy (fNIRS) and machine learning to accurately classify mild and severe depression, providing an objective auxiliary diagnostic tool for mental health workers. Aim Develop prediction models to distinguish between severe and mild depression using fNIRS data. Approach We collected the fNIRS data from 140 subjects and applied a complete ensemble empirical mode decomposition with an adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove noise during the verbal fluency task. The temporal features (TF) and correlation features (CF) from 18 prefrontal lobe channels of subjects were extracted as predictors. Using recursive feature elimination with cross-validation, we identified optimal TF or CF and examined their role in distinguishing between severe and mild depression. Machine learning algorithms were used for classification. Results The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than using either TF or CF alone. Among the prediction models, the SVM-based model demonstrates excellent performance in nested cross-validation, achieving an accuracy rate of 92.8%. Conclusions The proposed model can effectively distinguish mild depression from severe depression.

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“…Patients with depression have signi cantly reduced CBF, particularly in the prefrontal region, and rCBF differs among those with varying degrees of depression. Speci cally, patients with mild depression have signi cantly lower rCBF than the normal control group, while those with moderate to severe depression have signi cantly higher rCBF than most cortical regions [35,36]. FNIRS is an important tool for investigating CBF in depressed patients, but it is sensitive to jitter and artefact noise.…”

Section: Discussionmentioning

confidence: 99%

Functional Near-Infrared Spectroscopy-based Diagnosis Support System for Distinguishing between Mild and Severe Depression Using Machine Learning Approaches

Huang

Liu

Yang

et al. 2023

Preprint

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Due to the fast-paced lifestyle, individuals may experience varying degrees of depression attributed to stress, academic pursuits, and other causative factors. Hemodynamics and functional connectivity (correlation between channels) of the prefrontal lobe have been identified as crucial factors in assessing the severity of depression. As a non-invasive technique for monitoring cerebral blood flow, functional near-infrared spectroscopy (fNIRS) shows promising potential as a tool for objective auxiliary diagnosis of depression.This study aimed to develop prediction models for distinguishing patients with severe depression from those with mild depression based on the dataset collected by fNIRS.We collected the fNIRS data from 140 subjects, and used a complete ensemble empirical mode decomposition with adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove the jitter and artefact noise generated during the verbal fluency task (VFT). The temporal and correlation features of 18 channels in the prefrontal lobe of the subjects were extracted as predictors. We screened out the optimal temporal features (TF) or correlation features (CF) using the RFECV, and investigated their role in distinguishing severe and mild depression, respectively. The fusion of TF and CF, as the input of the prediction model, yielded higher classification accuracy than using TF or CF alone as the prediction factor. Among the prediction models, the SVM-based predictive model performed well in nested cross-validation, with an accuracy rate of 92.8%.The proposed model effectively distinguishes mild depression from severe depression, provides an objective diagnostic method for mental health workers, and is significant in treating patients with depression.

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Investigating the association between depression and cerebral haemodynamics—A systematic review and meta-analysis

Cited by 10 publications

References 115 publications

Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: a combined neuroimaging and transcriptome study

Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: a combined neuroimaging and transcriptome study

Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches

Functional Near-Infrared Spectroscopy-based Diagnosis Support System for Distinguishing between Mild and Severe Depression Using Machine Learning Approaches

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