Quantifying consciousness through intrinsic probability density function.

Area of Science:

• Neuroscience
• Quantitative Biology
• Signal Processing

Background:

• Consciousness is complex and challenging to quantify using traditional methods.
• Understanding neural dynamics underlying different conscious states is crucial.
• Existing methods may not fully capture subtle variations in neural activity.

Purpose of the Study:

• To propose and validate the intrinsic probability density function (iPDF) as a quantitative measure for evaluating inter-cortical interactions in conscious states.
• To assess the utility of iPDF in differentiating various physiological and pathological brain conditions.
• To establish a robust framework for the assessment of consciousness.

Main Methods:

• Empirical Mode Decomposition (EMD) to extract intrinsic mode functions (IMFs) from electroencephalogram (EEG) signals.
• Generation of scale-dependent probability density functions for successive partial sums of IMFs.
• Testing iPDF analysis across general anesthesia, sleep stages, sensory conditions, and in dementia patients versus healthy controls.

Main Results:

• Super-Gaussian iPDF patterns characterize active neural interactions during wakefulness and REM sleep.
• Near-Gaussian iPDF profiles are associated with reduced neural interactions in anesthesia and deep sleep.
• A classification model using iPDF features achieved ~87% accuracy in distinguishing dementia patients from healthy subjects.

Conclusions:

• Consciousness emerges from complex, scale-dependent neural processes.
• The iPDF provides a robust quantitative framework for assessing consciousness.
• iPDF analysis shows potential as a biomarker for clinical screening, particularly for neurodegenerative diseases.