Exploring the Interactions Between Neurophysiology and Cognitive and Behavioral Changes Induced by a Non-pharmacological Treatment: A Network Approach.

Víctor Rodríguez-González,Yoshihito Shigihara,Roberto Hornero,Hideyuki Hoshi,Jesús Poza,Carlos Gómez

doi:10.3389/fnagi.2021.696174

Abstract

Dementia due to Alzheimer’s disease (AD) is a neurological syndrome which has an increasing impact on society, provoking behavioral, cognitive, and functional impairments. AD lacks an effective pharmacological intervention; thereby, non-pharmacological treatments (NPTs) play an important role, as they have been proven to ameliorate AD symptoms. Nevertheless, results associated with NPTs are patient-dependent, and new tools are needed to predict their outcome and to improve their effectiveness. In the present study, 19 patients with AD underwent an NPT for 83.1 ± 38.9 days (mean ± standard deviation). The NPT was a personalized intervention with physical, cognitive, and memory stimulation. The magnetoencephalographic activity was recorded at the beginning and at the end of the NPT to evaluate the neurophysiological state of each patient. Additionally, the cognitive (assessed by means of the Mini-Mental State Examination, MMSE) and behavioral (assessed in terms of the Dementia Behavior Disturbance Scale, DBD-13) status were collected before and after the NPT. We analyzed the interactions between cognitive, behavioral, and neurophysiological data by generating diverse association networks, able to intuitively characterize the relationships between variables of a different nature. Our results suggest that the NPT remarkably changed the structure of the association network, reinforcing the interactions between the DBD-13 and the neurophysiological parameters. We also found that the changes in cognition and behavior are related to the changes in spectral-based neurophysiological parameters. Furthermore, our results support the idea that MEG-derived parameters can predict NPT outcome; specifically, a lesser degree of AD neurophysiological alterations (i.e., neural oscillatory slowing, decreased variety of spectral components, and increased neural signal regularity) predicts a better NPT prognosis. This study provides deeper insights into the relationships between neurophysiology and both, cognitive and behavioral status, proving the potential of network-based methodology as a tool to further understand the complex interactions elicited by NPTs.

Highlights

Dementia is a neurological syndrome that induces cognitive, behavioral, and functional alterations (Cummings, 2003)
The effectiveness of the non-pharmacological treatments (NPTs) was assessed by comparing the Mini-Mental State Examination (MMSE) and DBD-13 before and after conducting the NPT
While Alzheimer’s disease (AD) provokes a shift to lower frequencies and a reduced Spectral entropy (SE) (Poza et al, 2008b; Dauwels et al, 2011; Bruña et al, 2012), we have found that a power spectral density (PSDn) skewed towards higher frequencies (observed in the correlations MMSE-Relative power (RP)(Delta), MMSE-RP(Beta 1), MMSE-Median frequency (MF), and DBD-13-RP(Beta 1)) and with a richer variety of frequency components predicts a better outcome of the therapy

Summary

Introduction

Dementia is a neurological syndrome that induces cognitive, behavioral, and functional alterations (Cummings, 2003). NPTs include a wide variety of strategies, ranging from physical training to cognitive stimulation, through psychological therapy (Dyer et al, 2018) As pharmacological therapies, they are not able to repair or stop the neuronal death caused by AD, but they are beneficial to patients with the disease (Dyer et al, 2018; Alzheimer’s Association, 2019). NPTs are recommended as first-line managers to cope with behavioral and psychological symptoms of dementia, as they do not have adverse effects (Dyer et al, 2018) Their effectiveness has been shown to be patient-dependent (Kurz et al, 2011; Maki et al, 2018; Alzheimer’s Association, 2019). Being able to a-priori predict NPT outcome is a problem of paramount importance, since it would lead to personalized treatments and, to increased treatment efficiency

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Results

Conclusion