Evidence of Neuroplasticity Due to Acupuncture: An fNIRS Study

Abstract

Background: Mild cognitive impairment (MCI) is a precursor to the critical disease known as Alzheimer’s. It is imperative to develop a proper treatment for this neurological disease in the aging society. This study investigates the effects of acupuncture therapy (AT) on MCI patients.Methods: Eleven healthy individuals and eleven MCI patients were recruited for this study. Oxy- and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy. Before the AT, working-memory experiments were conducted for healthy control (HC) and MCI groups (MCI-0), followed by 24 sessions of AT for the MCI group. The AT sessions were initially carried out for six weeks (two sessions per week), after which experiments were performed again on the MCI group (MCI-1). This was followed by another set of AT sessions that also lasted for six weeks, after which the experiments were repeated again on the MCI group (MCI-2). Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.Results: The highest classification accuracies obtained using binary connectivity maps were 85.7% HC vs. MCI-0, 69.5% HC vs. MCI-1, and 61.69% HC vs. MCI-2. The classification accuracies using the temporal features mean (i.e., mean(5:28 s)) and maximum (i.e., max(5:28 s)) values were 60.6% HC vs. MCI-0, 56.9% HC vs. MCI-1, and 56.4% HC vs. MCI-2. Conclusions: The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture. This was reflected by a reduction in the classification accuracy after the therapy, indicating that the patients’ brain response improved and became comparable to those of healthy subjects. Similar trend was reflected in the classification using image feature. These results indicate that acupuncture can be used for the treatment of MCI patients.Trial registration: Clinical research and information service (CRIS), KCT 0002451, Registered 05 September 2017, https://cris.nih.go.kr/cris/en/

An Isolated CNN Architecture for Classification of Finger-Tapping Tasks Using Initial Dip Images: A Functional Near-Infrared Spectroscopy Study

“…Deep learning has also demonstrated its effectiveness in fNIRS-based diagnosis in patients [29,[36][37][38]. In study [39], the activation t-maps and channel correlation maps were used to diagnose patients' mild cognitive impairment (MCI).…”