Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study

Harikumar Pallathadka; Zhanna R.  Gardanova; Ahmed Read Al-Tameemi; Aiman Mohammed  Baqir Al-Dhalimy; Eftikhaar Hasan Kadhum; Ahmed Huseen Redhee

doi:10.18502/ijps.v19i3.15808

Harikumar Pallathadka Manipur International University, Imphal, Manipur, India.
Zhanna R. Gardanova Pirogov Russian National Research Medical University, Moscow, Russia.
Ahmed Read Al-Tameemi Department of Pharmacy, AL-Nisour University College, Baghdad, Iraq.
Aiman Mohammed Baqir Al-Dhalimy Department of Nursing, Altoosi University College, Najaf, Iraq.
Eftikhaar Hasan Kadhum College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq.
Ahmed Huseen Redhee Medical Laboratory Technique College, the Islamic University, Najaf, Iraq.

DOI: https://doi.org/10.18502/ijps.v19i3.15808

Keywords: Cognition; Complexity Analysis; Dementi; Diagnosis; Electroencephalography

Abstract

Objective: Dementia is a broad term referring to a decline in problem-solving abilities, language skills, memory, and other cognitive functions to a degree that it significantly disrupts everyday activities. The underlying cause of dementia is the impairment or loss of nerve cells and their connections within the brain. The particular symptoms experienced are contingent upon specific regions of the brain affected by this damage. In this research, we aimed to investigate the nonlinear dynamics of the mixed demented brain compared to healthy subjects using electroencephalogram (EEG) analysis.

Method: For this purpose, EEG was recorded from 66 patients with mixed dementia and 65 healthy subjects during rest. After signal preprocessing, sample entropy and Katz fractal dimension analyses were applied to the preprocessed EEG data. Analysis of variance with repeated measures was utilized to compare the nonlinear dynamics of brain activity between dementia and healthy states and partial correlation analysis was employed to explore the relationship between EEG complexity measures and cognitive and neuropsychiatric symptoms of patients.

Results: Based on repeated measures ANOVA, there was a significant main effect between groups for both Katz fractal dimension (F = 4.10, P = 0.01) and sample entropy (F = 4.81, P = 0.009) measures. Post hoc comparisons revealed that EEG complexity was significantly reduced in dementia mainly in the occipitoparietal and temporal areas (P < 0.05). MMSE scores were positively correlated with EEG complexity measures, while NPI scores were negatively correlated with EEG complexity measures, mainly in the occipitoparietal and temporal areas (P < 0.05). Moreover, using a KNN classifier, all significant complexity measures yielded the best classification performance with an accuracy of 98.05%, sensitivity of 97.03% and specificity of 99.16% in detecting dementia.

Conclusion: This study demonstrated a unique dynamic system within the brain impacted by dementia that results in more predictable patterns of cortical activity mainly in the occipitoparietal and temporal areas. These abnormal patterns were associated with patients' cognitive capacity and neuropsychiatric symptoms.