Meghdadi, Amir H.

Abstract Background Healthy aging and cognitive decline in Alzheimer's Disease (AD) and its precursor, Mild Cognitive Impairment (MCI), are linked to changes in resting‐state EEG power spectral density (PSD). Theta band (3‐7 Hz) power decreases with normal aging but increases with cognitive decline. However, frequency content does not always indicate true oscillatory activity. Oscillations reflect power concentration at a specific frequency, appearing as peaks in the frequency domain and visible rhythms in the time domain. Rhythmicity analysis quantifies oscillation persistence (percentage of time observed) independently of power. This study examined Theta power and Theta rhythms in healthy controls (HC) versus individuals with MCI/AD. Method Participants included 32 AD, 88 MCI, and 115 and age‐ matched HC, who completed 5‐minutes of resting‐state eyes‐closed EEG at their baseline visit. PSD was computed for one‐second epochs. Relative Theta power was defined as the proportion of total power in the Theta band, averaging across epochs. Theta rhythm duration was defined as the percentage of epochs with Theta oscillations. Baseline differences and longitudinal changes were analyzed for a subset of participants: 28 MCI and 5 AD with at least one follow‐up visit, and 25 HC with at least two follow‐ups. Result At baseline, the MCI/AD group exhibited significantly higher relative Theta power ( p <0.01, df=233, ES=0.35 at T6) and a greater Theta rhythm duration ( p <0.01, df=231, ES=0.41 at T6) compared to HC. Longitudinally, HC exhibited a significant reduction in Theta power by the second follow‐up ( p <0.05, df=24, ES=0.51 at Cz), with no significant changes in Theta rhythms duration. In contrast, MCI/AD showed a significant increase in Theta rhythm duration (and not power) at first follow‐up visit ( p < 0.05, df =32, ES=0.30 at T3). Conclusion Theta power and Theta rhythm duration are both linked to cognitive decline in MCI and AD but differ from healthy aging patterns. While Theta power reflects age‐related changes, Theta rhythms duration may serve as a specific biomarker for disease progression in MCI and AD. These findings support the value of Rhythmicity analysis in EEG‐based biomarkers of cognitive decline.

AbstractBackgroundEEG slowing, a biomarker of cognitive decline in Alzheimer’s disease (AD), has been reported in wake or sleep EEG [1,2,3]. However, the relation between wake and sleep EEG and their comparative sensitivity to early stage of cognitive decline is less understood. In this study we compared sleep and wake EEG biomarkers using a common set of endpoints.MethodsParticipants included individuals with mild cognitive impairment (MCI, n = 30, ages 53‐88), Alzheimer’s disease dementia (ADem, n = 15, ages 60‐84), and controls with normal cognition (HC, n = 55, ages 50‐84). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). Sleep EEG recordings from AF7‐AF8 were collected in‐home and staged using Sleep ProfilerTM. Power spectral densities (PSD) and the derived Theta‐Alpha ratios were computed for wake EEG sites (counterpart bipolar montage), with the wake FP1‐FP2 compared by stage to the sleep EEG at AF7‐AF8. Significant differences in Theta‐Alpha ratios between MCI or ADem vs. HC groups were identified using independent t‐test (p<0.05) with the normalized effect size measured using Hedges‐g (E.S.).ResultsCorrelation coefficients between wake Alpha, Theta power and non‐REM sleep were: r = 0.83, 0.75 for stage N1, r = 0.71, 0.66 for stage N2, and r = 0.55, 0.50 for stage N3. Based on the frontal Theta‐Alpha ratios, the ADem group showed significant EEG slowing during both wake (E.S. = 0.73) and sleep (E.S. = 1.65, 1.31, 0.61, 1.71 for N1, N2, N3, and REM) as compared to the HC, while MCI group only exhibited EEG slowing during sleep (E.S. = 0.56, 0.77, 0.51 for N1, N2 and REM). Temporal (T5‐T6) wake EEG manifested significant slowing in both ADem (E.S. = 1.21) and MCI (E.S. = 0.56) groups. Compared to the HC, ADem patients exhibited significantly less sleep time (E.S. = 0.91, p = 0.002) and less stage REM (E.S. = 1.0, p = 0.001).ConclusionsCognitive decline in the AD spectrum is associated with common signatures in both sleep and wake EEG. However, the magnitude of these effects and their spatial distribution may be different in sleep versus wakefulness. These findings support a multi modal approach to neurophysiological assessment using both sleep and wakefulness data.

AbstractBackgroundEvent‐related‐potentials (ERPs) during cognitive tasks are sensitive measures of cognitive decline [1]‐[2] and could quantitatively measure the trajectory of disease progression or response to clinical interventions. In this longitudinal study, we aimed to compare ERPs between individuals with mild cognitive impairment (MCI) and controls, and to demonstrate the test‐retest reliability of ERP measures to assess their potential utility for future observational studies and treatment trials.MethodsParticipants included individuals with mild cognitive impairment (MCI, n=31) and healthy controls stratified by age: HC2 (n=46, 40‐60 yrs.), HC3 (n=56, 60‐90 yrs.). ERPs during attention, image recognition, and verbal memory tasks were recorded using a wireless EEG to determine amplitudes and latencies of the P200 and late positive potential (LPP) ERP components in response to target (memorized) and non‐target (novel) stimuli. Differences between MCI and HC3 groups were characterized at the initial visit, and longitudinal changes in ERP components were determined at 1‐year follow‐up for both groups. Reliability of ERP measures was demonstrated by computing intraclass correlation coefficient (ICC) measures of test‐retest variability in all healthy controls (HC2 and HC3) from initial visit to 1‐year follow‐up.ResultsAt the initial visit, the MCI group demonstrated reduced LPP amplitude (Hedges‐g=0.8, p=10‐3) and delayed P200‐latency (Hedges‐g=0.89, p=10‐4) when compared to HC3. Reliability in healthy controls was high for LPP (0.85 and 0.75 for target and non‐target stimuli, respectively) and moderate for P200‐latency (0.67 and 0.53 for target and non‐target stimuli, respectively), without longitudinal changes after 1‐year follow‐up. Individuals with MCI demonstrated increased P200‐latency (Hedges‐g= 1.05, p=0.02, df=8).ConclusionsIndividuals with MCI demonstrated quantifiable memory ERP deficits at baseline with detectable change in the P200 component at 1‐year follow‐up, demonstrating that ERPs are sensitive measures for quantifying mild cognitive impairment. Test‐retest reliability of these measures was good or moderate after 1‐year, supporting their feasibility for longitudinal monitoring of patients during disease progression or quantifying the cognitive impact of potential interventions.

AbstractBackgroundResting state electroencephalography (rsEEG) is a neurophysiologic Alzheimer disease (AD) and alpha‐synucleinopathy (dementia with Lewy body (DLB) and Parkinson disease (PDD)) biomarker [1]–[5]. Background slowing (increased low frequency power, dominant rhythm (DR) slowing) is frequent in AD and DLB, more so in DLB. We sought to identify distinguishing rsEEG signatures between DLB and AD.MethodParticipants included dementia patients [AD (n=26), DLB (n=12), PD (PDD; n=5)] and controls (HC; n=56). 20‐channel rsEEG was collected in eyes‐closed/eyes‐open conditions (5‐min each). Power spectral densities and coherences were measured using established methods [6]. Individual rsEEG power (delta, 1‐3 Hz; theta, 3‐7 Hz; alpha, 8‐13 Hz), theta/alpha ratio (TAR), dominant rhythm (DR), and DR frequency domain prominence were measured and averaged across groups, and effect sizes were determined and compared using Hedges g.ResultAD, DLB, and PDD groups exhibited increased low frequency (delta‐theta, 1‐5 Hz) power and TAR compared to controls (p<4x10‐4), with higher effect sizes in the DLB and PDD groups compared to AD. DR frequency was also decreased in all patient groups (DLB>PDD>AD) compared to controls (p=3x10‐9, p=3x10‐5, p=4x10‐4).AD alpha power and DR frequency domain prominence were reduced compared to controls and PDD/DLB groups (p<0.003). Both AD patients and controls had comparable posterior power distribution predominance of DR, while DLB power of DR was more anteriorly distributed. Compared to controls, delta coherence was higher in AD, but lower in DLB. Delta power was positively correlated with cognitive fluctuation (CAF) in DLB (r(8)=+0.67, p=0.03), while negatively correlated with MMSE in AD (r(24)=‐0.64, p=10‐5).ConclusionDLB and AD had both common and distinct rsEEG signatures. Both had DR slowing and background slowing that was more severe in DLB than AD. Distinctive DLB findings included reduced posterior DR spatial dominance and decreased delta coherence. Conversely, distinct AD features were increased delta coherence, decreased alpha power, and decreased DR frequency prominence. Finally, the DLB and PDD groups had a similar magnitude of DR slowing, consistent with posteriorly dominant functional impairment in synucleinopathy reflected by corresponding regional rsEEG slowing, although future longitudinal prospective sequential rsEEG analyses are necessary to confirm these findings.