Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform.

Area of Science:

• Neuroimaging
• Biomarkers
• Mental Health Research

Background:

• Major depressive disorder (MDD) is a prevalent global condition characterized by persistent depression.
• Resting-state functional magnetic resonance imaging (rs-fMRI) offers non-invasive diagnostic potential for MDD.
• Standard fMRI signals may exhibit non-linearity and non-stationarity, posing analytical challenges.

Purpose of the Study:

• To apply the Hilbert-Huang transform (HHT) to rs-fMRI data for identifying brain abnormalities in MDD patients.
• To investigate the utility of HHT-derived features as potential neuroimaging biomarkers for depression.

Main Methods:

• Utilized rs-fMRI data from 35 MDD patients and 37 healthy controls.
• Applied the Hilbert-Huang transform (HHT) to extract Hilbert-weighted mean frequency from rs-fMRI signals.
• Employed multivariate receiver operating characteristic (ROC) analysis for region identification and performance evaluation.

Main Results:

• Significant differences in Hilbert-weighted mean frequency were observed between MDD patients and controls.
• Abnormalities were primarily localized in the right hippocampus, right parahippocampal gyrus, left amygdala, and bilateral caudate nucleus.
• These identified regions demonstrated high sensitivity and specificity in distinguishing between the groups.

Conclusions:

• HHT analysis of rs-fMRI signals reveals distinct patterns in specific brain regions in MDD.
• The identified brain areas and HHT-derived metrics show promise as neuroimaging biomarkers for MDD.
• This approach aids in understanding the pathophysiological underpinnings of depression.