Abstract:Diagnosing and monitoring recovery of patients with mild traumatic brain injury (mTBI) is challenging because of the lack of objective, quantitative measures. Diagnosis is based on description of injuries often not witnessed, subtle neurocognitive symptoms, and neuropsychological testing. Since working memory (WM) is at the center of cognitive functions impaired in mTBI, this study was designed to define objective quantitative electroencephalographic (qEEG) measures of WM processing that may correlate with cog… Show more
“…The brain cognitive challenge, or N-back WM test (N = 0, 2 that reflect the load conditions of the task), was administered using E-prime software (Psychology Software Tools, Inc., Sharpsburg PA) on a Dell Precision T5610 with a 20" screen. Procedures were described previously (Arakaki et al, 2018(Arakaki et al, , 2019. Participants were comfortably seated before a computer screen and were instructed, practiced for 2-3 min, and were then tested for 0-back, then for 2-back.…”
Section: Methodsmentioning
confidence: 99%
“…We performed group comparisons on participant baseline characteristics using two-sided t-tests or Fisher's exact test. For each participant, we averaged the total gamma power from all sensors and the gamma power from each sensor for each of the following 6 regions (Lianyang et al, 2016;Arakaki et al, 2018): frontal or F (Fz, F3, F4), central or C (Cz, C3, C4), parietal or P (Pz, P3, P4), left temporal or LT (F7, T3, T5), right temporal or RT (F8, T4, T6), and occipital or O (O1, O2) (demonstrated in the results section). We compared gamma power between two groups (PAT, NAT).…”
Section: Methodsmentioning
confidence: 99%
“…Online EEG data were collected during resting or the WM challenge as previously described (Arakaki et al, 2018). We placed a 21-sensor, dry electrode system (Quasar Wearable Sensing, DSI-24, San Diego, CA) approximately at locations at the international 10-20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2, M1, and M2).…”
Section: Eeg Recordingsmentioning
confidence: 99%
“…Preprocessing steps included epoching, filtering, re-referencing, large artifact removal, and time-frequency analysis. Preprocessing and time-frequency (TF) analyses were as previously described (Arakaki et al, 2018). Briefly, epochs were filtered between 30 and 80 Hz.…”
Section: Behavioral and Eeg Data Processingmentioning
Rochart et al. WM Gamma in CH-PATs findings encourage further investigations in combining cognitive challenges and qEEG in developing neurophysiology-based markers for identifying individuals in the prodromal stage, to help improving our understanding of AD pathophysiology and the contributions of low-and high-frequency gamma oscillations in cognitive functions.
“…The brain cognitive challenge, or N-back WM test (N = 0, 2 that reflect the load conditions of the task), was administered using E-prime software (Psychology Software Tools, Inc., Sharpsburg PA) on a Dell Precision T5610 with a 20" screen. Procedures were described previously (Arakaki et al, 2018(Arakaki et al, , 2019. Participants were comfortably seated before a computer screen and were instructed, practiced for 2-3 min, and were then tested for 0-back, then for 2-back.…”
Section: Methodsmentioning
confidence: 99%
“…We performed group comparisons on participant baseline characteristics using two-sided t-tests or Fisher's exact test. For each participant, we averaged the total gamma power from all sensors and the gamma power from each sensor for each of the following 6 regions (Lianyang et al, 2016;Arakaki et al, 2018): frontal or F (Fz, F3, F4), central or C (Cz, C3, C4), parietal or P (Pz, P3, P4), left temporal or LT (F7, T3, T5), right temporal or RT (F8, T4, T6), and occipital or O (O1, O2) (demonstrated in the results section). We compared gamma power between two groups (PAT, NAT).…”
Section: Methodsmentioning
confidence: 99%
“…Online EEG data were collected during resting or the WM challenge as previously described (Arakaki et al, 2018). We placed a 21-sensor, dry electrode system (Quasar Wearable Sensing, DSI-24, San Diego, CA) approximately at locations at the international 10-20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2, M1, and M2).…”
Section: Eeg Recordingsmentioning
confidence: 99%
“…Preprocessing steps included epoching, filtering, re-referencing, large artifact removal, and time-frequency analysis. Preprocessing and time-frequency (TF) analyses were as previously described (Arakaki et al, 2018). Briefly, epochs were filtered between 30 and 80 Hz.…”
Section: Behavioral and Eeg Data Processingmentioning
Rochart et al. WM Gamma in CH-PATs findings encourage further investigations in combining cognitive challenges and qEEG in developing neurophysiology-based markers for identifying individuals in the prodromal stage, to help improving our understanding of AD pathophysiology and the contributions of low-and high-frequency gamma oscillations in cognitive functions.
“…This enables us in concluding that the positive correlation of affect and approach/ withdrawal measures with frontal asymmetry score α α â lpha lpha (ln( ) ln( )) Right L eft is the resultant of left hemispherical neuronal activity and vice versa. However, recently, many neuro-vascular studies [48][49][50][51] have observed alpha-BOLD synchronization wherein the alpha power correlates positively with neural activation during task engagement. Hence, there is a need to fully understand the neurovascular coupling and neural underpinning associated with frontal EEG asymmetry 5 and how alpha-BOLD synchronization or desynchronization during resting-state associates with affect and approach/withdrawal behavior.…”
The role of resting frontal alpha-asymmetry in explaining neural-mechanisms of affect and approach/ withdrawal behavior is still debatable. the present study explores the ability of the quasi-stable resting EEG asymmetry information and the associated neurovascular synchronization/desynchronization in bringing more insight into the understanding of neural-mechanisms of affect and approach/withdrawal behavior. For this purpose, a novel frontal alpha-asymmetry based on microstates, that assess quasi-stable EEG scalp topography information, is proposed and compared against standard frontalasymmetry. Both proposed and standard frontal alpha-asymmetries were estimated from thirty-nine healthy volunteers resting-EEG simultaneously acquired with resting-fMRI. Further, neurovascular mechanisms of these asymmetry measures were estimated through EEG-informed fMRI. Subsequently, the Hemodynamic Lateralization index (HLi) of the neural-underpinnings of both asymmetry measures was assessed. Finally, the robust correlation of both asymmetry-measures and their HLI's with PANAS, BIS/BAS was carried out. The standard resting frontal-asymmetry and its HLI yielded no significant correlation with any psychological-measures. However, the microstate resting frontal-asymmetry correlated significantly with negative affect and its neural underpinning's HLI significantly correlated with Positive/Negative affect and BIS/BAS measures. Finally, alpha-BOLD desynchronization was observed in neural-underpinning whose HLI correlated significantly with negative affect and BIS. Hence, the proposed resting microstate-frontal asymmetry better assesses the neural-mechanisms of affect, approach/withdrawal behavior.
Background
Alzheimerâs disease (AD) studies suggested the need to detect preâsymptomatic stage, when cognitive challenges reveal changes of alpha power (brain hyperactivity). Heart rate (HR) regulates brain oxygen supply, is regulated by the brain (eg. hippocampus and amygdala), and correlated with resting state alpha power. We aimed to compare alpha power, HR, and hippocampal and amygdala volume between preâsymptomatic AD and normal, aging individuals.
Method
We employed quantitative electroencephalography (qEEG) to monitor brain activity during resting and during Stroop interference testing. Cognitively healthy (CH) study participants (demographically matched) were recruited from the local community, consisting of two subgroups based on cerebrospinal fluid (CSF) proteins: with normal amyloid/tau ratio (CHâNAT, n=20) or pathological amyloid/tau ratio (CHâPAT, equals preâsymptomatic AD, n=21). Cognition was assessed using Montreal Cognitive Assessment (MoCA) and MiniâMental State Examinationâ7 (MMSEâ7). Participants were presented a series of colored words and asked to respond to each word for the color of the ink, including low load (congruent trials, when color matches the word) and high load (incongruent trials, when color does not match the word). Comparisons between two groups include: alpha desynchronization and alpha spectral entropy (SE), as well as the relationships of alpha desynchronization, HR after Stroop testing, and hippocampal and amygdala volumes (1.5T MRI, demographically balanced subgroups).
Result
No alpha differences were found during the resting state. Occipital alpha desynchronization of CHâPATs was more negative than in that of CHâNATs (p=0.024) during the congruent trials (Figure 1), indicating hyperactivity during low load. CHâPATs had higher alpha SE during congruent trials (p=0.042 frontal, p=0.039 occipital), and lower frontal SE change from congruent to incongruent trials (p=0.012) (Figure 2), supporting reduced functional reserve. Alpha desynchronization positively correlated with HR in CHâPATs but not CHâNATs; alpha desynchronization correlated with hippocampal or amygdala differently between CHâNATs and CHâPATs (Figure 4). Multiple spectral frequencies revealed correlations in MMSEâ7 & MoCA that differed between CHâNATs and CHâPATs.
Conclusion
These results suggest that hyperâexcitability during low load challenge and limited brain reserve is manifest with increasing interference load in preâsymptomatic AD. We also find brainâheart coupling is altered in preâsymptomatic AD.
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