AbstractBackgroundAlzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia, but differentiating between them can be challenging due to overlapping symptoms [1]. Quantitative electroencephalography (EEG) is emerging as a promising tool to identify potential biosignatures that can distinguish AD and FTD [2]–[5]. Prior EEG research has revealed slowing of the posterior dominant rhythm (PDR) in both AD and FTD patients compared to controls, reflecting underlying neurodegeneration. Our study aimed to compare quantitative EEG measures during resting‐state and attention tasks between bvFTD, PPAFTD, AD patients, and controls to determine if distinctive EEG/ERP profiles exist that can aid differential diagnosis.MethodsParticipants included individuals with behavioral‐variant Fronto‐temporal dementia (bvFTD, n=8, ages 42‐78), Primary Progressive Aphasia Frontotemporal dementia (PPAFTD, n=6, ages 53‐75), Alzheimer’s dementia (AD, n=20, ages 58‐79), and controls with normal cognition (HC, n=14, ages 47‐78). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). A subset of participants also completed a 3‐Choice‐vigilance‐task (3CVT) ERP attention task. Power spectral densities (PSD) during resting‐state and ERP waveforms elicited by the ERP task were computed and compared between groups.ResultsbvFTD on average exhibited a slowing of the posterior‐dominant rhythm (PDR) similar to the slowing observed in the AD group. However, unlike the AD group that showed a significant reduction in the power of the PDR (alpha power), both FTD groups had alpha power similar to HC. In the ERP attention task, FTD groups exhibited a delayed and reduced late positive potential (LPP) compared to controls.ConclusionsWe identified distinctive EEG biosignatures associated with Frontotemporal Dementia (FTD) and Alzheimer's Disease (AD). Quantitative analysis of the posterior dominant rhythm (PDR) revealed that both AD and FTD patients exhibited slowing of the PDR frequency. However, the power of PDR was significantly reduced in the AD while relatively preserved in FTD. FTD subtypes may also demonstrate distinct EEG abnormalities such as more pronounced PDR slowing in bvFTD/ more delayed event‐related potential (ERP) components in PPAFTD. Additional research is still needed to validate the reliability and specificity of these EEG biosignatures in differentiating FTD subtypes from each other and from AD.

AbstractBackgroundQuantitative EEG measures can be used as biosignatures of disease conditions. As such, the effect of interventions/treatments can be studied by longitudinal analysis of changes in these measures. The consistency of these measures can be assessed by test‐retest reliability scores such as intra‐class correlation coefficient (ICC) that depends on intra‐ and inter‐subject variability. The magnitude of an effect can be described by a normalized effect size (i.e. normalizing the effect with respect to the sample variability). However, the inherent variability of EEG and its effect on interpretation of effect size is less explored. The aim of this work is to investigate inter‐ and intra‐subject variability of PSD‐based resting‐state EEG measures and their effect on analysis of change scores.MethodsWe collected 20‐channel longitudinal EEG data (initial visit and 1‐year follow‐up) from healthy volunteers (n=61, ages 40‐82). After artifact decontamination, we computed power spectral density (PSD) at 1‐40 Hz frequency bins. At each frequency, we computed inter‐ and intra‐subject variability of PSD as well as ICC. We simulated a hypothetical effect by adding a constant value to the 2nd visit’s data equivalent to half the standard deviation of the sample. We computed the normalized effect size (Cohen’s d) using both the pooled variance as well the variability in the change score. We compared these effect sizes for different frequencies.ResultsOverall, absolute PSD exhibited high test‐retest reliability in the frequencies 2‐20 Hz (ICC>0.7) with 7‐9 Hz having the highest ICC (ICC>0.94). Inter‐subject variability was highest at 8Hz, and intra‐subject variability was lowest at 13‐15 Hz frequencies. For frequencies between 5 – 10 Hz (fast‐Theta/slow‐Alpha bands), the difference between the two types of normalized effect size ranged between 0.3 to 0.6.ConclusionsInter‐ and intra‐subject variability of PSD‐based EEG measures depend on frequency. As such, changes in PSD due to potential interventions should be interpreted with respect to the inherent variability of PSDs. In longitudinal analysis of paired data, normalized effect size should be reported using both population variability (inter‐subject) and change‐score variability (intra‐subject).

Cognitive decline in Alzheimer's disease is associated with electroencephalographic (EEG) biosignatures even at early stages of mild cognitive impairment (MCI). The aim of this work is to provide a unified measure of cognitive decline by aggregating biosignatures from multiple EEG modalities and to evaluate repeatability of the composite measure at an individual level. These modalities included resting state EEG (eyes-closed) and two event-related potential (ERP) tasks on visual memory and attention. We compared individuals with MCI (n = 38) to age-matched healthy controls HC (n = 44). In resting state EEG, the MCI group exhibited higher power in Theta (3-7Hz) and lower power in Beta (13-20Hz) frequency bands. In both ERP tasks, the MCI group exhibited reduced ERP late positive potential (LPP), delayed ERP early component latency, slower reaction time, and decreased response accuracy. Cluster-based permutation analysis revealed significant clusters of difference between the MCI and HC groups in the frequency-channel and time-channel spaces. Cluster-based measures and performance measures (12 biosignatures in total) were selected as predictors of MCI. We trained a support vector machine (SVM) classifier achieving AUC = 0.89, accuracy = 77% in cross-validation using all data. Split-data validation resulted in (AUC = 0.87, accuracy = 76%) and (AUC = 0.75, accuracy = 70%) on testing data at baseline and follow-up visits, respectively. Classification scores at baseline and follow-up visits were correlated (r = 0.72, p<0.001, ICC = 0.84), supporting test-retest reliability of EEG biosignature. These results support the utility of EEG/ERP for prognostic testing, repeated assessments, and tracking potential treatment outcomes in the limited duration of clinical trials.