Cognitively Normal Individuals Research Articles

Prefrontal intra-individual ERP variability and its asymmetry: exploring its biomarker potential in mild cognitive impairment

BackgroundThe worldwide trend of demographic aging highlights the progress made in healthcare, albeit with health challenges like Alzheimer’s Disease (AD), prevalent in individuals aged 65 and above. Its early detection at the mild cognitive impairment (MCI) stage is crucial. Event-related potentials (ERPs) obtained by averaging EEG segments responded to repeated events are vital for cognitive impairment research. Consequently, examining intra-trial ERP variability is vital for comprehending fluctuations within psychophysiological processes of interest. This study aimed to investigate cognitive deficiencies and instability in MCI using ERP variability and its asymmetry from a prefrontal two-channel EEG device.MethodsIn this study, ERP variability for both target and non-target responses was examined using the response variance curve (RVC) in a sample comprising 481 participants with MCI and 1,043 age-matched healthy individuals. The participants engaged in auditory selective attention tasks. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The research employed various statistical methods, including independent t-tests, and univariate and multiple logistic regression analyses. These analyses were conducted to investigate group differences and explore the relationships between neuropsychological test results, ERP variability and its asymmetry measures, and the prevalence of MCI.ResultsOur results showed that patients with MCI exhibited unstable cognitive processing, characterized by increased ERP variability compared to cognitively normal (CN) adults. Multiple logistic regression analyses confirmed the association between ERP variability in the target and non-target responses with MCI prevalence, independent of demographic and neuropsychological factors.DiscussionThe unstable cognitive processing in the MCI group compared to the CN individuals implies abnormal neurological changes and reduced and (or) unstable attentional maintenance during cognitive processing. Consequently, utilizing ERP variability measures from a portable EEG device could serve as a valuable addition to the conventional ERP measures of latency and amplitude. This approach holds significant promise for identifying mild cognitive deficits and neural alterations in individuals with MCI.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Language uncovers visuospatial dysfunction in posterior cortical atrophy: a natural language processing approach.

Posterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease. We compared the language samples of 25 patients with PCA with age-matched cognitively normal (CN) individuals across two distinct tasks: a visually-dependent picture description and a visually-independent job description task. We extracted word frequency, word utterance latency, and spatial relational words for this comparison. We then conducted an in-depth analysis of the language used in the picture description task to identify specific linguistic indicators that reflect the visuospatial processing deficits of PCA. Patients with PCA showed significant language deficits in the visually-dependent task, characterized by higher word frequency, prolonged utterance latency, and fewer spatial relational words, but not in the visually-independent task. An in-depth analysis of the picture description task further showed that PCA patients struggled to identify certain visual elements as well as the overall theme of the picture. A predictive model based on these language features distinguished PCA patients from CN individuals with high classification accuracy. The findings indicate that language is a sensitive behavioral construct to detect visuospatial processing abnormalities of PCA. These insights offer theoretical and clinical avenues for understanding and managing PCA, underscoring language as a crucial marker for the visuospatial deficits of this atypical variant of Alzheimer's disease.

Amyloid β‐specific T cell response is enhanced in individuals with mild cognitive impairment

AbstractBackgroundNeuroinflammation is a key process in initiating and propagating Alzheimer’s disease (AD). Even though it is widely known that the deposit of amyloid plaques and CSF levels of amyloid distinguishes patients with AD or mild cognitive impairment (MCI) from cognitively normal (CN) individuals, little is known about the role of amyloid‐specific immune response in cognitive decline.MethodUsing a polyfunctionality assay typically used for detecting virus‐specific T cell responses, we tested participants from the Epidemiology of Mild Cognitive Impairment in Taiwan study (EMCIT) and the Taiwan Precision Medicine Initiative of Cognitive impairment and dementia (TPMIC) study to compare the amyloid‐specific T cell responses between CN and MCI individuals. The abilities of T cell response parameters and plasma p‐Tau181 to distinguish MCI from CN were tested.ResultResults from both cohorts showed an enhanced amyloid‐specific T‐cell response in individuals with MCI. In the EMCIT cohort, the individual’s amyloid‐specific CD4+ response frequency of total CD4+ cells was significantly larger in MCI (n = 69, 0.93%) than in CN (n = 69, 0.51%, p &lt; 0.001). CD4+ T cell response discriminated MCI versus CN (area under curve [AUC], 0.72 [0.64‐0.81]) with significantly higher accuracy than p‐Tau181 (AUC: 0.59 [0.5‐0.69], p &lt; 0.01). In the TPMIC cohort, both CD4+ and CD8+ response frequencies were higher in MCI individuals (n = 21, CD4: 1.2%, CD8: 2.02%) than in CN (n = 30, CD4: 0.14%, CD8:0.27%; both p &lt; 0.001). CD4+ T cell response frequency and CD8+ response frequency also outperform p‐Tau181 in their discriminative accuracy of MCI versus NC (CD4+ AUC, 0.97, [0.94‐1.01]; CD8+ AUC, 0.96, [0.92‐1.01]; p‐Tau181 AUC, 0.83, [0.69‐0.96]; both p &lt; 0.05).ConclusionOur study validates the amyloid hypothesis by showing that amyloid‐associated neuroinflammation is involved in the process of neurodegeneration and demonstrated the accuracy of using amyloid‐specific T cell response to discriminate MCI from CN individuals. The TPMIC cohort is an ongoing longitudinal study that includes amyloid PET results and thus we will investigate the prognostic value of amyloid‐T cell response in the future.

Identification of leukocyte surface biomarkers associated with late‐onset Alzheimer’s disease

AbstractBackgroundAltered innate immunity has been long associated with late‐onset Alzheimer’s disease (AD) and its associated blood‐based biomarkers are important for AD diagnosis and prognosis.MethodWe collected 38 participants from the AIBL study, including 22 cognitive normal (CN) individuals with negative amyloid burden (Centiloid [CL] &lt; 15), 5 CN individuals with positive amyloid burden (CL &gt; 15), and 11 mild cognitive impairment (MCI) and AD cases. A total of 17 leukocyte surface antigens were examined by flow cytometry immunophenotyping and compared between healthy controls (CN Ab ‐ve), pre‐clinical patients (CN Ab +ve), and clinical cases (MCI &amp; AD), including CD36, MerTK, Clec7a, RAGE, Tyro3, CR1 (CD35), CX3CR1, CCR2, Axl, LILRB2, LILRB3, LILRB4, PILRA, and P2×7.ResultWe identified leukocyte surface markers differentially expressed between HC, pre‐clinical patients, and clinical cases. Mean fluorescence intensities of CD85d and CD85k were significantly reduced in pre‐clinical patients and cases compared with HC, implicating CD85d and CD85k downregulation in AD pathogenesis. Significant upregulation of RAGE, Tyro3, CCR2, CD85a, and PILRA was also noted in cases compared with HC. Leukocyte surface expressions of RAGE, CCR2, PILRA, and CD85k were significantly associated with the Preclinical Alzheimer’s Cognitive Composite (PACC), which is one of the most accurate estimates of cognition in AD diagnosis.ConclusionOur preliminary investigations into these leukocyte markers demonstrated their dysregulations in pre‐clinical stage of AD, implicating early deficits in innate immunity, as supported by genomic studies of AD. This project will continue collecting more AIBL participants up to 200 to evaluate leukocyte surface markers more comprehensively, which would benefit AD screening and diagnosis.

Motor Sequence Learning in Mild Cognitive Impairment and Alzheimer’s Disease and its Relationship to Biomarkers

AbstractBackgroundWhile mild cognitive impairment (MCI) and Alzheimer’s disease (AD) are primarily associated with cognitive impairments, studies have also demonstrated that individuals may experience motor impairments that can precede cognitive symptoms by several years. Here, we examine potential differences in motor sequence learning abilities in individuals with MCI and AD compared to cognitively normal individuals (CNI). Moreover, we investigate whether motor sequence learning impairments could be an early behavioral biomarker. To that end, we evaluated associations between motor learning ability and existing AD biomarkers.MethodTo date, 26 individuals with AD, 27 with MCI, and 47 CNI performed a serial reaction time task that allowed us to assess the amount of sequence‐specific learning at the end of the task and the rate of such learning across the task. Additionally, participants underwent APOE genotyping to determine the presence of e4 alleles, amyloid PET imaging to quantify amyloid deposition, and structural MRI scanning to obtain hippocampal volume.ResultWhile motor responses of individuals with AD and MCI were generally slower than those of CNI, motor sequence learning occurred regardless of cognitive status and did not significantly differ among the three groups. Results of correlational analyses showed no significant associations between motor learning scores and AD biomarkers.ConclusionOur findings suggest that motor sequence learning abilities are relatively preserved in MCI and AD, which could have implications for the design of rehabilitation programs. However, its observed independence of existing biomarkers indicates that sequence learning may not be suitable as an early disease maker. Our ongoing work focuses on measuring general motor proficiency via a broad assessment spanning multiple motor domains, and evaluating whether such proficiency can serve as a predictor of disease status and progression.

Medial temporal lobe gray matter microstructure alterations provide improved sensitivity to detect neurodegeneration associated with Alzheimer’s disease biomarkers

AbstractBackgroundDespite the strengths conventional structural imaging measures to detect atrophy, cross‐sectional relationships between neurodegeneration and Alzheimer’s disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers.MethodOne‐hundred and sixty‐seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS‐T1 protocol to form anterior and posterior MTL ROIs. Multi‐shell DTI data were collected in 113 non‐collinear directions with 1.5mm isotropic voxels, were pre‐processed using a custom pipeline, and fit with voxel‐wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise‐regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume.ResultNODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p‐Tau‐181, inclusion of NODDI metrics subsumed this effect (F‐change&gt;7.54, p&lt;0.0067), and reduced ODI was the best predictor of plasma pTau‐181 within cognitively impaired individuals, while isoVf best predicted pTau‐181 across all individuals. Posterior MTL volume did not predict plasma GFAP, but inclusion of ODI and icVf both significantly predicted plasma GFAP in both CN and impaired individuals (F‐change&gt;12.68, p&lt;.001). Anterior MTL icVf predicted both pTau‐181 and GFAP.ConclusionMicrostructural alterations measured by NODDI appear more closely linked to AD‐related molecular measures and, thus, may provide a more sensitive cross‐sectional measure of neurodegeneration of the MTL compared to conventional macrostructural metrics.

A multi‐site study of Alzheimer’s disease neuroimaging and cognitive biomarkers in 2819 cognitively normal individuals

AbstractBackgroundDeveloping effective Alzheimer’s disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinical AD. However, the generalizability of these biomarkers in large, multi‐site cohorts and their power to predict disease progression remain unclear. Structural MRI, tau PET and standardized cognitive tests of 2819 CN adults were pooled from three studies to investigate the power of these measures in discriminating β‐amyloid positivity (A+/‐) and predicting disease progression.MethodsT1‐weighted MRI and cognitive data of 2819 CN (A‐/A+: 1180/1540, Table 2) individuals from ADNI, HABS and A4 were included. Baseline medial temporal lobe structural measures were extracted from MRI (complete list in Table 1). For participants with prospective longitudinal MRI or cognitive measures (4.5 years), annualized change rate of each measurement was estimated. ANCOVA and receiver operating characteristic analyses were used to test biomarker differences between A+/A‐. In 563 CN with cross‐sectional tau PET available, stepwise linear mixed effect modeling was performed to identify the subset of baseline cross‐sectional biomarkers (tau, MRI and cognition) that yields the optimal model (smallest Akaike information criterion) in predicting longitudinal atrophy and cognitive decline. The area under the curve (AUC) of each model in discriminating the first (fast) and last (slow progressors) tertiles of each longitudinal measurement was reported.ResultsSignificant differences between A+/A‐ (Table 2) were observed in cross‐sectional tau (most significant), cross‐sectional and longitudinal cognitive, as well as longitudinal structural MRI measures. When predicting disease progression with baseline measures (Figure 1‐a), tau (selected in all the models) and cognitive biomarkers were included in the most predictive models while MRI biomarkers did not provide additional information. The selected biomarkers can identify fast versus slow progressors with AUCs ranging from 0.63‐0.78 (Figure 1‐b).ConclusionsThe results demonstrated that both cross‐sectional (tau PET and cognition) and longitudinal biomarkers (MRI and cognition) are sensitive to amyloid status in CN. In addition, baseline tau PET and cognitive measures provide complementary information in predicting disease progression regardless of amyloid status. This indicates that these biomarkers may have important utility in preclinical AD clinical trials and normal aging studies.

Men show increased brain aging with respect to women among Cognitively Normal Individuals

AbstractBackgroundThere is an increasing interest in using machine learning and artificial intelligence to estimate chronological age using neuroimaging data. The gap between chronological age and estimated brain age (brain age gap, BAG) is used as a measure of accelerated/resilient brain aging. Previously, BAG has been associated with cognitive status. However, whether the BAG varies across sex and cognitive status have been less explored. The present study examines these associations and validates a voxel‐based machine learning approach based on the elastic net regression (ENR) for BAG calculation.MethodUsing data from the Atherosclerosis Risk in Communities Study (ARIC) study, the Wake Forest School of Medicine Alzheimer’s Disease Research Center (WFSM‐ADRC) clinical cohort and Alzheimer’s Disease Neuroimaging Initiative (ADNI), we examined associations of BAG across cognitive status and sex. We used structural MRI scans from 1853 ARIC participants (ages 67‐90, 60% females), 508 from the WFSM‐ADRC (55‐95 yo., 66% females) and 584 ADNI cognitively normal (CN) participants (55‐90 yo., 57% females). All images were aligned into a common template and the derived gray matter (GM) probability maps from ADNI MRIs were used as input to train the machine learning algorithm. Once the model was fitted the ARIC and WFSM‐ADRC GM probability maps were provided as input to the algorithm to estimate the BAG values. Finally, an age bias correction was applied. Linear regression methods were used to investigate differences between groups. Age, race, education, sex, and cognitive status were included in the model.ResultWe found in both ARIC and WFSM‐ADRC participants that differences in BAG values between CN‐MCI and MCI‐Dementia participants were highly significant. In addition, when we examined differences in BAG values across sex per cognitive status, we found again in both cohorts that differences were only significant for CN individuals. See Table 1 for details.ConclusionOur analyses show that our approach to estimate chronological age using high‐dimensional ENR, produces BAG values which are strongly associated with cognitive status. The increased severity of cognitive impairment is related to accelerated brain aging. Differences in BAG between men and women were significant for CN individuals only.

Medial temporal lobe gray matter microstructure alterations provide improved sensitivity to detect neurodegeneration associated with Alzheimer’s disease biomarkers

AbstractBackgroundDespite the strengths conventional structural imaging measures to detect atrophy, cross‐sectional relationships between neurodegeneration and Alzheimer’s disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers.MethodOne‐hundred and sixty‐seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS‐T1 protocol to form anterior and posterior MTL ROIs. Multi‐shell DTI data were collected in 113 non‐collinear directions with 1.5mm isotropic voxels, were pre‐processed using a custom pipeline, and fit with voxel‐wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise‐regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume.ResultNODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p‐Tau‐181, inclusion of NODDI metrics subsumed this effect (F‐change&gt;7.54, p&lt;0.0067), and reduced ODI was the best predictor of plasma pTau‐181 within cognitively impaired individuals, while isoVf best predicted pTau‐181 across all individuals. Posterior MTL volume did not predict plasma GFAP, but inclusion of ODI and icVf both significantly predicted plasma GFAP in both CN and impaired individuals (F‐change&gt;12.68, p&lt;.001). Anterior MTL icVf predicted both pTau‐181 and GFAP.ConclusionMicrostructural alterations measured by NODDI appear more closely linked to AD‐related molecular measures and, thus, may provide a more sensitive cross‐sectional measure of neurodegeneration of the MTL compared to conventional macrostructural metrics.

Machine learning composite of remote cognitive tests for screening of preclinical AD cohorts

AbstractBackgroundComputerized cognitive tests are critical in the screening of preclinical Alzheimer’s Disease (AD) given their ability to be conducted remotely at low cost. Here, we explore whether machine‐learning derived composites of remote cognitive tests can improve sensitivity when detecting brain amyloid (Aβ) accumulation in cognitively normal (CN) cohorts than traditional pen‐and‐paper alternatives.MethodBased on the CogState Brief Battery tests in CN individuals from the A4 dataset (n = 4230, Aβ‐ = 3094), we construct a data‐driven composite, denoted the Remote Empirical Cognitive composite for Alzheimer’s Disease (RECAD), designed to be sensitive to Aβ‐PET positivity. Specifically, the composite uses a random feature elimination logistic regression to identify a subset of predictive tests. We construct and evaluate the model using 10 repeat, 5‐fold cross‐validation in the A4 baseline data. We conduct temporal validation with the A4 second visit and external validation using CN participants from the ADNI (n = 389, Aβ‐ = 143), comparing against the pen‐and‐paper‐based PACC composite and previous computerized composites CBB and C3.ResultRECAD includes three accuracy scores (Identification, One‐Back and One‐card) and one reaction time (Detection). In cross‐validation in the A4 baseline, RECAD show a significantly higher ability to discriminate Aβ status [mean Cohen’s d = 0.26] than PACC [mean d = 0.25, t‐test p&lt;0.001] and computerized composites CBB [median d = 0.13, p&lt;0.001] and C3 [mean d = 0.18, p&lt;0.001]. RECAD significantly improves beyond C3 in the A4 second visit (z‐test p&lt;0.001). Moreover, these trends are replicated in the ADNI at baseline (RECAD d = 0.215, PACC d = 0.061) and 24 months (RECAD d = 0.295, PACC d = ‐0.026) but the small sample and effect size limits our ability to demonstrate statistical significance.ConclusionRECAD, as a machine‐learning derived composite of remotely conducted cognitive tests, shows a comparable or better sensitivity to the established pen‐and‐paper composite test when screening for Aβ+ among CN cohorts. While effect sizes are small, conducting such remote tests repeatedly may be a useful pre‐screening for downstream biofluid evaluation.

Genetic architecture of multiple domains of cognition among SuperAgers

AbstractBackgroundA recently recognized subset of older individuals are an anomaly of cognitive decline; the “SuperAgers”, unsurprisingly named, achieve cognitive scores equivalent to much younger cognitively normal (CN) middle‐aged adults. Using longitudinal cognitive data harmonized across eight cohorts of aging and Alzheimer’s Dementia (AD), we investigated the genetic drivers of SuperAging.MethodHarmonized memory, executive function, and language scores were estimated leveraging latent variable modeling and made available through the ADSP Phenotype Harmonization Consortium. SuperAgers (N = 1,095) were defined as individuals over 80 years with a mean sex‐adjusted memory score equal or exceeding CN individuals aged 50‐60, score within one age and sex‐adjusted standard deviation in the other two cognitive domains, and remain CN for all longitudinal visits. Young Cases (N = 1,906) were defined as individuals aged 50‐75 with a clinical diagnosis of AD. Old Controls (N = 3,247) were defined as CN individuals over 80, scoring within one age‐ and sex‐adjusted standard deviation in all three domains. We performed a GWAS on non‐Hispanic Whites using logistic regression comparing SuperAgers and their counterparts (Young Cases and Old Controls) with covaried adjustment for age, sex, education, and principal components for population substructure.ResultComparing SuperAgers with Young Cases (Figure 1), only variants in the well‐established APOE region were associated with genome‐wide significance (GWAS; P&lt;510−8). Additionally, we observed a locus on chromosome 13 approach GWS (rs138699163, P = 6.5610−8). The locus centered on a relatively uncharacterized ncRNA, MIR4500. Analyses comparing SuperAgers to Old Controls did not find any GWS associations, with the strongest association observed at rs116535931 on chromosome 5 (P = 1.5210−6).ConclusionOur extreme‐phenotype GWAS comparing SuperAgers to Young Cases identified established and novel loci for AD. However, larger sample sizes may allow better characterization of the genetic architecture of SuperAging. Future analyses will extend to Case comparison groups to include Old Cases (age&gt;80 years) and All Cases (age&gt;50 years) and Control comparison groups to include Young Controls (age between 50‐60) and Agnostic Controls (age&gt;50 years) with similar criteria.

Behavioural Deficits and Regional Cerebral Perfusion Map in Cognitively Normal and Pre‐Dementia Stages

AbstractBackgroundOur previous work has shown increased presence of early behavioural impairment in Asian than Western cohorts(Soo et al.,2021). Such impairments include mild behavioural changes, sleep disruptions and higher depression, anxiety and stress, in turn representing an at‐risk state which may predict later development of dementia(Müller‐Spahn,2003;Soo et al.,2021). Additionally, growing evidence illustrates that cerebrovascular changes are a key component in the pathological sequelae and may be detectable years before clinical presentation of dementia (de la Torre,2018). However, the influence of cerebral perfusion on behaviour, especially in pre‐dementia stages remains to be elucidated. Thus, we examined the association between regional grey matter perfusion and behaviour in cognitively normal(CN) and subjects with mild cognitive impairment(MCI) or subjective cognitive decline(SCD). We hypothesized that grey matter perfusion deficits would be related to behaviour impairment even in early stages of SCD/MCI.MethodWe studied 35 CN and 248 pre‐dementia (MCI/SCD) from a community‐based research cohort from the Dementia Research Centre (Singapore) with neuroimaging, neuropsychological and behavioural assessments(Table 1). We investigated the association between regional grey matter perfusion across 31 regions of interest spanning frontal, parietal, temporal and occipital lobes using 2D pulsed Arterial Spin Labelling and performance on the mild behaviour impairment‐checklist(MBI‐C), Pittsburgh Sleep Quality Index(PSQI) and Depression, Anxiety and Stress Scale(DASS). These associations were examined in CN and MCI/SCD groups separately and respective Pearson’s correlation matrices were derived.ResultOverall, greater behavioural impairment on the MBI‐C, DASS and PSQI was associated with hypoperfusion across multiple grey matter regions. This association was stronger in CN than in the pre‐dementia group. Specifically, among CN, hypoperfusion in the frontal, parietal and occipital lobes related most strongly to greater behavioural impairment, especially MBI‐C and DASS depression(Fig.1). In the MCI+SCD group, only hypoperfusion in the frontal and temporo‐occipital regions related to worse behavioural impairment across the MBI, PSQI and DASS(Fig.2).ConclusionIn this study, we illustrate differential associations between cerebral hypoperfusion and behavioural impairment in CN and MCI/SCD groups, with CN individuals showing more widespread and stronger associations between behavioural impairment and hypoperfusion across the brain. In SCD/MCI, hypoperfusion in frontal and temporo‐occipital regions were associated with behavioural impairment.

Memory markers in preclinical Alzheimer’s disease

AbstractBackgroundEpisodic memory impairment is a widely known core clinical symptom of Alzheimer’s disease (AD). However, it is still uncertain if it is possible to detect meaningful signs or changes in memory in the preclinical stage of AD due to their within‐normal‐range performance. In the current study, we investigated both verbal and visuospatial memory as markers to track the disease progression in the preclinical AD.MethodWe selected cognitively normal (CN) participants from the Gwangju Alzheimer’s Disease and Related Dementia Cohort in Korea. We included a group of CN individuals who showed a significant amyloid‐beta (aß) deposition, defined by amyloid PET, at baseline and completed at least two follow‐up evaluations. In the final analysis, we included 130 individuals in the preclinical stage of AD. Episodic memory measures were delayed recall scores from the Rey Complex Figure Test (RCFT_delayed) and Seoul Verbal Learning test (SVLT_delayed). We performed logistic regression analysis with stepwise selection to examine associations between memory tests and progression.ResultThe mean follow‐up month was 36.21. Twenty‐one (16.2%) participants were converted to MCI (n = 19) or AD dementia (n = 2), while the rest stayed as CN. When both RCFT_delayed and SVLT_delayed at baseline were entered in the logistic regression model, only the RCFT_delayed was significantly associated with the progression (OR = 0.288; 95% CI, 0.138‐0.602; p = 0.001). When change scores were added to the model, RCFT_delayed at baseline (OR = 0.223; 95% CI, 0.101‐0.496; p &lt; 0.001), RCFT_delayed change score (OR = 0.470; 95% CI, 0.258‐0.857; p = 0.014), and SVLT_delayed change score (OR = 0.523; 95% CI, 0.290‐0.942; p = 0.031), were significant.ConclusionOur results suggest that visuospatial, not verbal, memory measures may play an important role in sensitively detecting the relevant sign of future progression in asymptomatic individuals. In terms of verbal memory, the longitudinal decline was associated with progression. Our study suggests that the inclusion of a visuospatial memory test and repeated evaluation can help to detect the earliest decline in the preclinical stages of AD.

MRI or 18F-FDG PET for Brain Age Gap Estimation: Links to Cognition, Pathology, and Alzheimer Disease Progression.

Deviations of brain age from chronologic age, known as the brain age gap (BAG), have been linked to neurodegenerative diseases such as Alzheimer disease (AD). Here, we compare the associations of MRI-derived (atrophy) or 18F-FDG PET-derived (brain metabolism) BAG with cognitive performance, neuropathologic burden, and disease progression in cognitively normal individuals (CNs) and individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). Methods: Machine learning pipelines were trained to estimate brain age from 185 matched T1-weighted MRI or 18F-FDG PET scans of CN from the Alzheimer's Disease Neuroimaging Initiative and validated in external test sets from the Open Access of Imaging and German Center for Neurodegenerative Diseases-Longitudinal Cognitive Impairment and Dementia studies. BAG was correlated with measures of cognitive performance and AD neuropathology in CNs, SCD subjects, and MCI subjects. Finally, BAG was compared between cognitively stable and declining individuals and subsequently used to predict disease progression. Results: MRI (mean absolute error, 2.49 y) and 18F-FDG PET (mean absolute error, 2.60 y) both estimated chronologic age well. At the SCD stage, MRI-based BAG correlated significantly with beta-amyloid1-42 (Aβ1-42) in cerebrospinal fluid, whereas 18F-FDG PET BAG correlated with memory performance. At the MCI stage, both BAGs were associated with memory and executive function performance and cerebrospinal fluid Aβ1-42, but only MRI-derived BAG correlated with phosphorylated-tau181/Aβ1-42 Lastly, MRI-estimated BAG predicted MCI-to-AD progression better than 18F-FDG PET-estimated BAG (areas under the curve, 0.73 and 0.60, respectively). Conclusion: Age was reliably estimated from MRI or 18F-FDG PET. MRI BAG reflected cognitive and pathologic markers of AD in SCD and MCI, whereas 18F-FDG PET BAG was sensitive mainly to early cognitive impairment, possibly constituting an independent biomarker of brain age-related changes.

Multipredictor risk models for predicting individual risk of Alzheimer’s disease

BackgroundEarly prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.MethodsA total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction.ResultsDuring the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91).ConclusionsThe risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Prefrontal event-related potential markers in association with mild cognitive impairment.

Alzheimer's disease (AD) is among the leading contributors of dementia globally with approximately 60-70% of its cases. Current research is focused on the mild cognitive impairment (MCI), which is associated with cognitive decline but does not disrupt routine activities. Event-related potential (ERP) research is essential in screening patients with MCI. Low-density channel electroencephalography (EEG) is frequently used due to its convenience, portability, and affordability, making it suitable for resource-constrained environments. Despite extensive research on neural biomarkers for cognitive impairment, there is a considerable gap in understanding the effects on early stages of cognitive processes, particularly when combining physiological and cognitive markers using portable devices. The present study aimed to examine cognitive shortfalls and behavioral changes in patients with MCI using prefrontal selective attention ERP recorded from a prefrontal two-channel EEG device. We assessed cognitive decline using the Mini-Mental State Examination (MMSE) and the Seoul Neuropsychological Screening Battery (SNSB). We administered auditory selective attention tasks to 598 elderly participants, including those with MCI (160) and cognitively normal (CN) individuals (407). We conducted statistical analyses such as independent t-tests, Pearson's correlations, and univariate and multiple logistic regression analyses to assess group differences and associations between neuropsychological tests, ERP measures, behavioral measures, and MCI prevalence. Our findings revealed that patients with MCI demonstrated slower information-processing abilities, and exhibited poorer task execution, characterized by reduced accuracy, increased errors, and higher variability in response time, compared to CN adults. Multiple logistic regression analyses confirmed the association between some ERP and behavioral measures with MCI prevalence, independent of demographic and neuropsychological factors. A relationship was observed between neuropsychological scores, ERP, and behavioral measures. The slower information processing abilities, and poor task execution in the MCI group compared to the CN individuals suggests flawed neurological changes and reduced attentional maintenance during cognitive processing, respectively. Hence, the utilization of portable EEG devices to capture prefrontal selective attention ERPs, in combination with behavioral assessments, holds promise for the identification of mild cognitive deficits and neural alterations in individuals with MCI. This approach could potentially augment the traditional neuropsychological tests during clinical screening for MCI.

Reduced body cell mass and functions in lower extremities are associated with mild cognitive impairment and Alzheimer’s dementia

This study examined the alterations of segmental body composition in individuals with Alzheimer’s pathology (AD), including mild cognitive impairment (MCI) and dementia. A multifrequency bioimpedance analysis (BIA) was used to provide segmental water and impedance variables from 365 cognitively normal (CN), 123 MCI due to AD, and 30 AD dementia participants. We compared the BIA variables between the three groups, examined their correlations with neuropsychological screening test scores, and illustrate their 95% confidence RXc graphs. AD dementia participants were older, more depressive, and had worse cognitive abilities than MCI due to AD and CN participants. Although the BIA variables showed weak partial correlations with the cognitive test scores, we found patterns of an increasing water content in lean mass, increasing extra to intracellular water ratio, and decreasing reactance and phase angle in the lower extremities with effect sizes ranging from 0.26 to 0.51 in the groups of MCI and dementia due to AD compared with CN individuals. The RXc graphs upheld the findings with a significant displacement downward and toward the right, dominantly in the lower extremities. Individuals with AD pathology exhibit a reduced body cell mass or cell strength, an abnormal cellular water distribution, and an overhydration status in lean mass, especially in the lower extremities.

Structural Network Topology Reveals Higher Brain Resilience in Individuals with Preclinical Alzheimer's Disease.

Introduction: The diagnosis of Alzheimer's disease (AD) requires the presence of amyloid and tau pathology, but it remains unclear how they affect the structural network in the pre-clinical stage. We aimed to assess differences in topological properties in cognitively normal (CN) individuals with varying levels of amyloid and tau pathology, as well as their association with AD pathology burden. Methods: A total of 68 CN individuals were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding three groups: A-T- (n = 19), A+T- (n = 28), and A+T+ (n = 21). Topological properties were measured from structural connectivity. Group differences and correlations with A and T were evaluated. Results: Compared with the A-T- group, the A+T+ group exhibited changes in the structural network topology. At the global level, higher assortativity was shown in the A+T+ group and was correlated with greater tau burden (r = 0.29, p = 0.02), while no difference in global efficiency was found across the three groups. At the local level, the A+T+ group showed disrupted topological properties in the left hippocampus compared with the A-T- group, characterized by lower local efficiency (p < 0.01) and a lower clustering coefficient (p = 0.014). Conclusions: The increased linkage in the higher level architecture of the white matter network reflected by assortativity may indicate increased brain resilience in the early pathological state. Our results encourage further investigation of the topological properties of the structural network in pre-clinical AD.

Saccadic eye movement variables as biomarkers for cognitive decline in elderly individuals.

Alzheimer's disease (AD) is the leading cause of Dementia, and mild cognitive impairment (MCI) is often considered a precursor to the development of AD dementia and other types of Dementia. Biomarkers such as amyloid beta are specific and sensitive in identifying AD and can identify individuals who have biological evidence of the disease but have no symptoms, but clinicians and researchers may not easily use them on a large scale. Ocular biomarkers, such as those obtained through eye tracking (ET) technology, have the potential as a diagnostic tool due to their accuracy, affordability, and ease of use. In this study, we show that eye movement (EM) metrics from an interleaved Pro/Anti-saccade (PS/AS) ET task can differentiate between cognitively normal (CN) and MCI subjects and that the presence of Aβ brain deposits, a biomarker of AD, significantly affects performance on these tasks. Individuals with Aβ deposits (Aβ+) performed worse than those without (Aβ-). Our findings suggest that eye-tracking measurements may be a valuable tool for detecting amyloid brain pathology and monitoring changes in cognitive function in CN and MCI individuals over time.Clinical Relevance- The PS/AS paradigm, which measures saccadic eye movements, can accurately detect subtle cognitive impairments and changes in the brain associated with Alzheimer's disease in CN and MCI individuals. This makes it a valuable tool for identifying individuals at risk for cognitive decline and tracking changes in cognitive function over time.