Machine Learning-Based Alexithymia Assessment Using Resting-State Default Mode Network Functional Connectivity
Kei Suzuki, Midori Sugaya
Sensors September 12, 2025 DOI: 10.3390/s25175515 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Topics | Default mode network |
| Keywords | Alexithymia Source localization Electroencephalogram Machine learning |
| Key findings | A machine-learning model using resting-state EEG functional connectivity achieved a maximum ROC-AUC of 0.70 for classifying low versus high alexithymia severity, with theta and gamma band connectivity in the Left Hippocampus being most informative. |
Abstract
Alexithymia is regarded as one of the risk factors for several prevalent mental disorders, and there is a growing need for convenient and objective methods to assess alexithymia. Therefore, this study proposes a method for constructing models to assess alexithymia using machine learning and electroencephalogram (EEG) signals. The explanatory variables for the models were functional connectivity calculated from resting-state EEG data, reflecting the default mode network (DMN). The functional connectivity was computed for each frequency band in brain regions estimated by source localization. The objective variable was defined as either low or high alexithymia severity. Explainable artificial intelligence (XAI) was used to analyze which features the models relied on for their assessments. The results indicated that the classification model suggested effective assessment depending on the threshold used to define low and high alexithymia. The maximum receiver operating characteristic area under the curve (ROC-AUC) score was 0.70. Furthermore, analysis of the classification model indicated that functional connectivity in the theta and gamma frequency bands, and specifically in the Left Hippocampus, was effective for alexithymia assessment. This study demonstrates the potential applicability of EEG signals and machine learning in alexithymia assessment.
Comparable studies
Other observational and cohort studies on default mode network, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| A default mode of brain function Normal adult human brain | 2001 | Observational study | |
| Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network Healthy subjects | 2008 | Observational cohort | |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis Healthy subjects | 2008 | Observational study |