Photobiomodulation and QEEG in assessment and treatment of Mild Cognitive Impairment: A Case study´
Poster No:
52
Submission Type:
Abstract Submission
Authors:
Joseph Wallach1, Nerida Saunders2, Seungwan Kang3
Institutions:
1Maccabi Health Services, Modiin, Israel, 2iMediSync Australasia, TWEED HEADS, Australia, Australia, 3iMediSync Inc., Seoul, Korea, Republic of
First Author:
Co-Author(s):
Introduction:
This is a single case study presenting the treatment of an individual with complaints of word-finding difficulty, short-term memory loss, balance issues, gait alterations, and sleep disturbance. Prior research has demonstrated methods of using QEEG, with high precision, reproducibility, and objective results, for accurate identification of Mild Cognitive Impairment (MCI) (Burcu et al.,2021). Easy and convenient methods of screening for MCI may enable early intervention, and allow for treatments that slow down, or reverse, the progression of MCI. This may enable better overall disease management. A fully automatic procedure that could significantly distinguish Control (C) subjects from those experiencing MCI, via resting-state EEG signals, has previously been developed (Ding et al., 2022). A prior case study suggested that Photobiomodulation (PBM) may improve desired brain activity, with positive impact for elderly individuals with memory and thinking disorders (Vrankic et al., 2022). This study examined the use of a dry QEEG for measurement and identification of EEG findings associated with the complaints consistent with MCI. The study also evaluated the use of PBM for treatment of the presenting complaints. Finally, the study monitored changes in the individuals QEEG over the course of the treatment, at treatment completion, and at four week post treatment follow-up using the same dry QEEG system.
Methods:
Participant: 76 year-old Caucasian male, presenting with complaints of word-finding difficulties, short-term memory disturbance, balance issues, alteration in gait, and sleep disturbance.
Apparatus: The iMediSync Wave system is a wireless helmet device for dry QEEG measurement. Its utilizes cloud based iSyncBrain software that accesses big data-driven artificial intelligence (AI) for artifact identification and cleaning, data analysis, and uses a sex-classified, and age-segregated, EEG normative database. The software generates three automated preliminary reports on QEEG analysis, heart variability (HRV), and the MCI probability. The helmet also includes nineteen LEDs that emit 850 nm (near infrared) wavelengths, that allow for PBM treatment, based upon the results of the QEEG.
Procedure: Baseline assessment is followed by initial QEEG report and preliminary PBM treatment-plan development and implementation. After two weeks a follow-up QEEG was completed, and the subject provided a subjective report of his experience. Adjustments to the treatment plan were made based on these tests. Additional adjustments to treatment plan were made at week five and week eight follow-ups, based on additional self-reports and QEEGs. A fifth and sixth QEEG will be completed along with self-report at weeks eleven and fifteen. At present, the treatment and the data collection are ongoing, though preliminary results based upon the probability of MCI risk were examined.
Apparatus: The iMediSync Wave system is a wireless helmet device for dry QEEG measurement. Its utilizes cloud based iSyncBrain software that accesses big data-driven artificial intelligence (AI) for artifact identification and cleaning, data analysis, and uses a sex-classified, and age-segregated, EEG normative database. The software generates three automated preliminary reports on QEEG analysis, heart variability (HRV), and the MCI probability. The helmet also includes nineteen LEDs that emit 850 nm (near infrared) wavelengths, that allow for PBM treatment, based upon the results of the QEEG.
Procedure: Baseline assessment is followed by initial QEEG report and preliminary PBM treatment-plan development and implementation. After two weeks a follow-up QEEG was completed, and the subject provided a subjective report of his experience. Adjustments to the treatment plan were made based on these tests. Additional adjustments to treatment plan were made at week five and week eight follow-ups, based on additional self-reports and QEEGs. A fifth and sixth QEEG will be completed along with self-report at weeks eleven and fifteen. At present, the treatment and the data collection are ongoing, though preliminary results based upon the probability of MCI risk were examined.
Results:
A preliminary paired t-test was completed utilizing the probability of an MCI, based on the QEEG at baseline and at the third assessment. Results of the paired t-test indicated that there is a non-significant difference between 64.8 (M = 166.9 ,SD = 10.6) and 61.7 (M = 168 ,SD = 14.4), t(4) = 0.4, p = .703.
·MCI Risk Probability
Conclusions:
Though nonsignificant, the results appear to show a trend of reduced risk of MCI from baseline over the course of intervention. Additionally, subject's qualitative self-report suggested improvement in cognitive and physical complaints.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Language:
Speech Production
Lifespan Development:
Aging
Motor Behavior:
Motor Planning and Execution
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
Aging
Electroencephaolography (EEG)
Language
Sleep
Treatment
Other - Photobiomodulation (PBM)
1|2Indicates the priority used for review
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Montazeri, K., Farhadi, M., Fekrazad, R., Chaibakhsh, S., Mahmoudian, S. (2022). Photobiomodulation therapy in mood disorders: a systematic review. Lasers in Medical Science, 37, 3343–3351 (2022). https://doi.org/10.1007/s10103-022-03641-w
Vrankic, M., Vlahinić, S., Šverko, Z., & Markovinović, I. (2022). EEG-Validated Photobiomodulation Treatment of Dementia—Case Study. Sensors, 22(19), 7555. https://doi.org/10.3390/s22197555