Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives
Abstract
:1. Introduction
- Novel Investigation: This study is the first to investigate the impact of excluding the aperiodic component of resting-state EEG in different brain regions and in both electrode- and source-level EEGs for individuals with MDD vs. HC.
- Improved Classification: By utilizing periodic and aperiodic components separately, we aim to enhance the accuracy of distinguishing between MDD and HC individuals.
- Exploratory Analysis: We calculate the correlation between EEG features and the BDI and its subscales, providing additional insights into the neural correlates of depression.
2. Materials and Methods
2.1. Participants
2.2. EEG Data Recording and Processing
2.3. Statistical Analysis
- Combination by frequency band: we paired features from the same frequency band to form the feature sets.
- Combination by analysis type: we paired features from the same analysis type to form the feature sets.
- Dual-component combination: we combined features from different components of our analysis (periodic + aperiodic) to form the feature sets.
3. Results
3.1. EEG Power Comparisons in Different Brain Regions
3.2. Classification
3.3. Correlation Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACC | Anterior cingulate cortex |
ANOVA | One-way analysis of variance |
AUC | Area Under the Curve |
BDI | Beck Depression Inventory |
BDNF | Brain-derived neurotrophic factor |
DUI | Duration of untreated illness |
EEG | Electroencephalography |
eLORETA | exact Low-Resolution Electric Tomography |
ES | Effect size |
GABA | Gamma-aminobutyric acid |
HAPPE | Harvard Automated Preprocessing Pipeline |
HC | Healthy control |
LOSO | Leave-One-Subject-Out |
MARA | Multiple Artifact Rejection Algorithm |
MEG | Magnetoencephalography |
MDD | Major depressive disorder |
MNE | Minimum norm estimation |
PSD | Power spectral density |
ROIs | Regions of Interest |
sLORETA | standardized Low-Resolution Electric Tomography |
SSRIs | Selective Serotonin Reuptake Inhibitors |
TAI | Trait Anxiety Inventory |
W-ICA | Wavelet-enhanced independent component analysis |
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HC (n = 74) | MDD (n = 40) | F/(df) | p-Value | |
---|---|---|---|---|
Age in years (SD) | 18.98 (1.21) * | 18.70 (1.15) | 1.47 (1, 111) | 0.22 |
Gender (male/female) | 35/39 | 10/30 | 5.40 (1) | 0.02 |
BDI | 1.71 (1.65) | 22.27 (4.69) | 1.15e3 (1, 112) | <0.001 |
BDI_Anh | 0.16 (0.46) | 4 (1.58) | 3.75e2 (1, 112) | <0.001 |
BDI_Mel | 0.83 (0.90) | 6.42 (1.72) | 5.16e2 (1, 112) | <0.001 |
TAI | 31.12 (5.49) | 56.15 (6.81) | 4.53e2 (1, 112) | <0.001 |
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Zandbagleh, A.; Sanei, S.; Azami, H. Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives. Sensors 2024, 24, 6103. https://doi.org/10.3390/s24186103
Zandbagleh A, Sanei S, Azami H. Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives. Sensors. 2024; 24(18):6103. https://doi.org/10.3390/s24186103
Chicago/Turabian StyleZandbagleh, Ahmad, Saeid Sanei, and Hamed Azami. 2024. "Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives" Sensors 24, no. 18: 6103. https://doi.org/10.3390/s24186103