Abstract
With the aging process, brain functions, such as attention, memory, and cognitive functions, degrade over time. In a super-aging society, the alteration of neural activity owing to aging is considered crucial for interventions for the prevention of brain dysfunction. The complexity of temporal neural fluctuations with temporal scale dependency plays an important role in optimal brain information processing, such as perception and thinking. Complexity analysis is a useful approach for detecting cortical alteration in healthy individuals, as well as in pathological conditions, such as senile psychiatric disorders, resulting in changes in neural activity interactions among a wide range of brain regions. Multi-fractal (MF) and multi-scale entropy (MSE) analyses are known methods for capturing the complexity of temporal scale dependency of neural activity in the brain. MF and MSE analyses exhibit high accuracy in detecting changes in neural activity and are superior with regard to complexity detection when compared with other methods. In addition to complex temporal fluctuations, functional connectivity reflects the integration of information of brain processes in each region, described as mutual interactions of neural activity among brain regions. Thus, we hypothesized that the complementary relationship between functional connectivity and complexity could improve the ability to detect the alteration of spatiotemporal patterns observed on electroencephalography (EEG) with respect to aging. To prove this hypothesis, this study investigated the relationship between the complexity of neural activity and functional connectivity in aging based on EEG findings. Concretely, MF and MSE analyses were performed to evaluate the temporal complexity profiles, and phase lag index analyses assessing the unique profile of functional connectivity were performed based on the EEGs conducted for young and older participants. Subsequently, these profiles were combined through machine learning. We found that the complementary relationship between complexity and functional connectivity improves the classification accuracy among aging participants. Thus, the outcome of this study could be beneficial in formulating interventions for the prevention of age-related brain dysfunction.
Highlights
Complex temporal variability within brain activity plays an important role in perceptual and overall mind and behavioral processes and is known to be a mechanism for stochastic resonance and facilitation
Recent studies have shown that the profile of temporal complexity for EEG signals can be utilized for classifying EEG for Alzheimer’s disease (AD), and the combination of complexity profiles obtained via MF and Multi-scale entropy (MSE) enhances the accuracy of AD identification based on their complementary relationship (Zorick et al, 2020; Ando et al, 2021)
area under the ROC curve (AUC) values were averaged among 20 trials to choose tested and evaluated data set in 5-fold cross-validation and their standard deviations (SD) were derived
Summary
Complex temporal variability within brain activity plays an important role in perceptual and overall mind and behavioral processes and is known to be a mechanism for stochastic resonance and facilitation (as reviewed in McDonnell and Ward, 2011; Garrett et al, 2013; Takahashi, 2013; Yang and Tsai, 2013; Nobukawa and Nishimura, 2020). Multi-scale entropy (MSE) and multi-fractal (MF) analyses are widely utilized as an effective evaluation method for complexity with temporal scale dependency (as reviewed in Takahashi, 2013; Yang and Tsai, 2013). These methods that focus on the temporal scale dependency of complexity can capture alterations in brain activity within a variety of psychiatric disorders (Yang and Tsai, 2013). Recent studies have shown that the profile of temporal complexity for EEG signals can be utilized for classifying EEG for AD, and the combination of complexity profiles obtained via MF and MSE enhances the accuracy of AD identification based on their complementary relationship (Zorick et al, 2020; Ando et al, 2021). Approaches for combining complexity profiles could open new avenues for the identification and characterization of the complex patterns of neural activity regarding cognitive alteration in psychiatric disorders
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
