Salat, David H.

Introduction Neurological post-acute sequelae of COVID-19 (neuroPASC) are associated with persistent cognitive dysfunction and quality-of-life decline. We aimed to identify clinical, behavioral and sociodemographic factors associated with neuroPASC symptom burden two years after COVID-19 among individuals without prior neurological disease. Methods In this prospective, observational study, individuals with neuroPASC ( n = 102) and controls without symptomatic COVID-19 ( n = 74), all without prior neurological, psychiatric, or post-viral conditions, were enrolled between February 2022 and June 2024 across five academic sites. An unsupervised algorithm identified clusters with differing self-reported neurological symptom burden within the neuroPASC group. Functional differences between clusters were evaluated using quality-of-life, neurological and cognitive evaluations. Demographics, behavioral history, comorbidities, and blood biomarkers were compared across clusters and controls. Multivariable logistic regression assessed predictors of neuroPASC severity, including demographics, body-mass-index, Charlson Comorbidity Index, Framingham Risk Score, pre-existing endocrine/metabolic and/or gastrointestinal/hepatobiliary conditions, COVID-19 vaccination prior to infection, hospitalization during acute infection, and cumulative alcohol use. Results Two clusters emerged based on neurological symptom burden, labeled “high-burden” and “low-burden” neuroPASC, reflecting differences in the number and frequency of symptoms. Both clusters had deficits in quality-of-life and cognitive function compared to controls, with greater impairment in high-burden than low-burden neuroPASC. The clusters did not differ by sex, education, tobacco and cannabis use, blood pressure, body-mass-index, HbA1C, days since infection, hospitalization during COVID-19, pre-COVID vaccination rate, antibody-positivity, inflammation, and neurodegeneration biomarkers. The high-burden cluster was older and exhibited higher comorbidity burden and greater cumulative alcohol use compared with the low-burden cluster and controls. Pre-existing endocrine/metabolic and gastrointestinal/hepatobiliary conditions were more common in high-burden (63%) than in low-burden neuroPASC (35%). After adjusting for clinical and demographic factors, these pre-existing conditions remained the only independent predictor of severity, conferring a 3.5-fold increase in the odds of high-burden versus low-burden neuroPASC. Discussion Older age, higher comorbidity burden, greater cumulative alcohol use, and endocrine/metabolic and gastrointestinal conditions, rather than acute COVID-19 severity, were observed in the high-burden neuroPASC cluster. After multivariable adjustment, only pre-existing endocrine/metabolic and/or gastrointestinal/hepatobiliary conditions remained independently associated with high-burden neuroPASC, conferring a 3.5-fold increase in odds and highlighting the need for targeted post-infection monitoring in at-risk patients.

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.

AbstractBackgroundApproximately 10% of patients with acute SARS‐CoV‐2 infection present with persistent symptoms recognized as the long‐COVID. Neurological and cognitive symptoms are prevalent in long‐COVID, requiring a deeper understanding of the biological basis of this condition for potential therapeutic interventions. Cerebrovascular complications are observed during acute infection, underscoring the importance of understanding cerebrovascular outcomes. Our objective was to investigate white matter hyperintensities (WMH) as a potential indicator of microvascular disease associated with long‐COVID in the brainMethodParticipants with long‐COVID (n=71; hospitalized, n=9, non‐hospitalized, n=62 during the acute infection) and controls (n=50) who did not have a reported infection were recruited from the community at five US sites that participated in the COVID‐Brain‐Advanced‐Imaging‐Network (COVID‐BRAIN) project. To be included, participants with long‐COVID had to experience neurological or cognitive sequelae within 6 months after a confirmed infection and continued to show at least one neurological symptom. Clinical evaluation and MRI were performed an average of 20±10 months after the acute infection. WMH volumes were measured from the 3D T2‐weighted FLAIR MRIs using a semi‐automated approach for the WMH segmentation. A linear regression model predicting log transformed WMH volume was used to investigate the association of age, groups (hospitalized long‐COVID, non‐hospitalized long‐COVID, controls), and the interaction of these two predictors along with an adjustment for total intracranial volume. Additionally, predicted regression lines have been plotted on the untransformed WMH scale.ResultWe observed a greater age‐dependent increase in WMH volumes in the hospitalized patients with long‐COVID compared to the control group (group*age interaction, p=0.005). We did not observe this difference in slopes in the non‐hospitalized patients with long‐COVID compared to the controls. Figure shows the slopes of WMH volume as a percentage of the total intracranial volume by age in the three groups.ConclusionOur preliminary observation in this ongoing long‐Covid study suggests that hospitalization during acute COVID is associated with the age‐dependent increase in microvascular injury in the white matter. Older adults with acute infection that require hospitalization are more susceptible to cerebrovascular adverse outcomes than controls or those who do not require hospitalization. Impact of such microvascular changes on cognitive outcomes and persistence of post‐COVID symptoms is yet to be studied.

AbstractBackgroundApproximately 10% of patients with acute SARS‐CoV‐2 infection present with persistent symptoms recognized as the long‐COVID. Neurological and cognitive symptoms are prevalent in long‐COVID, requiring a deeper understanding of the biological basis of this condition for potential therapeutic interventions. Cerebrovascular complications are observed during acute infection, underscoring the importance of understanding cerebrovascular outcomes. Our objective was to investigate white matter hyperintensities (WMH) as a potential indicator of microvascular disease associated with long‐COVID in the brainMethodParticipants with long‐COVID (n = 71; hospitalized,n = 9,non‐hospitalized,n = 62 during the acute infection) and controls(n = 50) who did not have a reported infection were recruited from the community at five US sites that participated in the COVID‐Brain‐Advanced‐Imaging‐Network (COVID‐BRAIN) project. To be included, participants with long‐COVID had to experience neurological or cognitive sequelae within 6 months after a confirmed infection and continued to show at least one neurological symptom. Clinical evaluation and MRI were performed an average of 20±10 months after the acute infection. WMH volumes were measured from the 3D T2‐weighted FLAIR MRIs using a semi‐automated approach for the WMH segmentation. A linear regression model predicting log transformed WMH volume was used to investigate the association of age, groups (hospitalized long‐COVID, non‐hospitalized long‐COVID, controls), and the interaction of these two predictors along with an adjustment for total intracranial volume. Additionally, predicted regression lines have been plotted on the untransformed WMH scale.ResultWe observed a greater age‐dependent increase in WMH volumes in the hospitalized patients with long‐COVID compared to the control group (group*age interaction, p = 0.005). We did not observe this difference in slopes in the non‐hospitalized patients with long‐COVID compared to the controls. Figure shows the slopes of WMH volume as a percentage of the total intracranial volume by age in the three groups.ConclusionOur preliminary observation in this ongoing long‐Covid study suggests that hospitalization during acute COVID is associated with the age‐dependent increase in microvascular injury in the white matter. Older adults with acute infection that require hospitalization are more susceptible to cerebrovascular adverse outcomes than controls or those who do not require hospitalization. Impact of such microvascular changes on cognitive outcomes and persistence of post‐COVID symptoms is yet to be studied.

Abstract Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

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.