Reversing Neural Slowing in Aging Brains with Neurofeedback-Based Working Memory Training
Poster No:
2111
Submission Type:
Abstract Submission
Authors:
Yang JIANG1, Ziming liu2, Sylvia Cerel-Suhl3, Xiaopeng Zhao2
Institutions:
1University of Kentucky, Lexington, KY, 2University of Tennessee, Knoxville, TN, 3Lexington VAMC, Lexington, KY
First Author:
Co-Author(s):
Introduction:
Brain oscillations have emerged as key biomarkers of pathophysiological changes in brain aging and cognitive health, offering a noninvasive method for detecting early dysfunctions associated with mild cognitive impairment (MCI) risk. EEG-based neurofeedback (NF), a brain-training technique that operates in real-time, holds significant potential for enhancing memory function and mitigating mild cognitive decline (Jiang et al., 2024; 2022; 2017). However, effectively and noninvasively modifying complex, cognitively relevant brainwave patterns through NF remains a significant challenge. While NF targeting brain oscillations has shown promise in treating various neurological conditions, its application in reversing age-related cognitive decline is still underexplored. We hypothesize that rewarding optimal brainwave patterns of memory retrieval can enhance cognitive efficiency, promote more youthful brain, and counteract neural slowing, particularly in frequency power (Fig. 1). If post-training resting EEG demonstrates increased alpha oscillations and reduced delta waves, this would support the efficacy of our NF approach in fostering a more youthful and efficient brain function.
·Figure 1
Methods:
We developed a personalized NF training protocol designed to enhance brain health in older adults. Using predictive brainwave algorithms, the NF protocol was tailored to reward brainwave patterns associated with successful working memory retrieval. A "reward" was signaled by images turning to color based on personalized brainwave thresholds, while a "no-reward" condition left the images in black-and-white. Specifically, during the Sham phase, rewards and no-rewards were displayed randomly, whereas in the NF phase, rewards were contingent on participants achieving the targeted brainwave in real time.
Four older adults were recruited from the Lexington VA Medical Center for this study. The experimental protocols were approved by the IRB of both the VAMC and the University of Kentucky. All participants provided written informed consent prior to participation. One participant withdrew midway due to travel constraints and surgery, leaving three male participants (aged 63, 77, and 79 years) who completed up to 16 weeks of the clinical protocol. Resting EEG (eyes-closed) was recorded before and after each session to evaluate effectiveness of NF, which serve as biomarkers of brain aging and neurodegeneration.
Four older adults were recruited from the Lexington VA Medical Center for this study. The experimental protocols were approved by the IRB of both the VAMC and the University of Kentucky. All participants provided written informed consent prior to participation. One participant withdrew midway due to travel constraints and surgery, leaving three male participants (aged 63, 77, and 79 years) who completed up to 16 weeks of the clinical protocol. Resting EEG (eyes-closed) was recorded before and after each session to evaluate effectiveness of NF, which serve as biomarkers of brain aging and neurodegeneration.
Results:
Performance:
To evaluate participants' behavioral changes across sessions, two primary metrics were analyzed: response accuracy (correct vs. incorrect) and reaction times. Assessments were conducted at three consistent time points. A one-way ANOVA was used to determine the impact of training sessions on reaction time. The analysis revealed no significant differences in either accuracy or reaction times across sessions.
NF Training Effects:
Compared to the Baseline and Sham sessions, the NF training sessions significantly enhanced intrinsic brain oscillations in the alpha band within the occipital region. Additionally, a notable decrease in delta band activity was observed in the left parieto-occipital region (P7 electrode). These changes align with previous findings identifying similar oscillatory patterns as biomarkers of cognitive improvement (Borhani et al., 2021). As illustrated in Fig. 2, a 79-year-old male participant exhibited shifts in alpha and delta frequencies toward a more youthful brain profile within just 10 days.
To evaluate participants' behavioral changes across sessions, two primary metrics were analyzed: response accuracy (correct vs. incorrect) and reaction times. Assessments were conducted at three consistent time points. A one-way ANOVA was used to determine the impact of training sessions on reaction time. The analysis revealed no significant differences in either accuracy or reaction times across sessions.
NF Training Effects:
Compared to the Baseline and Sham sessions, the NF training sessions significantly enhanced intrinsic brain oscillations in the alpha band within the occipital region. Additionally, a notable decrease in delta band activity was observed in the left parieto-occipital region (P7 electrode). These changes align with previous findings identifying similar oscillatory patterns as biomarkers of cognitive improvement (Borhani et al., 2021). As illustrated in Fig. 2, a 79-year-old male participant exhibited shifts in alpha and delta frequencies toward a more youthful brain profile within just 10 days.
·Figure 2
Conclusions:
Post-training results revealed an increase in alpha oscillations and a decrease in delta waves, demonstrating the potential efficacy of our approach in reversing the "neural slowing" associated with age-related brain changes. This proof-of-concept study highlights a promising trend in EEG-based neurofeedback, indicating its ability to promote more youthful brain activity and improvements in memory and cognitive function to varying degrees.
Brain Stimulation:
Non-invasive Electrical/tDCS/tACS/tRNS 2
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Learning and Memory:
Working Memory
Lifespan Development:
Aging
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
Aging
ELECTROPHYSIOLOGY
1|2Indicates the priority used for review
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Provide references using APA citation style.
Jiang, Y. (2022). Sharpening working memory with real-time electrophysiological brain signals: Which neurofeedback paradigms work? Frontiers in Aging Neuroscience, 14, 780817. https://doi.org/10.3389/fnagi.2022.780817
Jiang, Y. (2024). Parallel electrophysiological abnormalities due to COVID-19 infection and to Alzheimer's disease and related dementia. Alzheimer’s & Dementia, 20(10), 7296–7319. https://doi.org/10.1002/alz.14089
Jiang, Y. (2017). Tuning up the old brain with new tricks: Attention training via neurofeedback. Frontiers in Aging Neuroscience, 9, 52. https://doi.org/10.3389/fnagi.2017.00052
Jiang, Y. (2021). Memory-related frontal brainwaves predict transition to mild cognitive impairment in healthy older individuals five years before diagnosis. Journal of Alzheimer’s Disease, 79(2), 531–541.