Amyloid-PET Levels in the Precuneus and Posterior Cingulate Cortices Are Associated with Executive Function Scores in Preclinical Alzheimer's Disease Prior to Overt Global Amyloid Positivity

A statistical method for predicting amyloid-β deposits from severity, extend, and ratio indices of the 99mTc-ECD SPECT

BackgroundAmyloid-β (Aβ) deposit prediction accuracy is necessary for clinicians treating patients desiring Alzheimer's disease (AD) modifying drugs. Aβ-PET imaging is useful for diagnosis, but high in cost compared to brain perfusion SPECT. However, SPECT displays regional cerebral blood flow (rCBF) and does not detect Aβ deposits, therefore requiring additional clinical information.ObjectiveThis article describes a novel statistical method to predict amyloid deposits via PET images (Aβ+ or Aβ-) using the three indices of the 99mTc-ECD SPECT - severity, extend, and ratio - for the three ROIs.MethodsCandidate patients (N = 114 patients [55% male], 81 Aβ+ 33 Aβ-, mean age 74.2 ± 6.6 years, mean MMSE score 23.7 ± 2.8) underwent MRI and 99mTc-ECD SPECT scanning. After examining SPECT index, demographic, and age data, age and sex were treated as confounders in one, two, and three-index logistic additive models with severity, extend, and ratio as explanatory variables. Area under curve (AUC), sensitivity and specificity were used as statistical indices for model fitness and accuracies. Three-hold cross validation analyses were conducted to evaluate error rates.ResultsAccording to ROC analysis, best scores for fitness and accuracy were obtained from the three-index model with patients' age and sex for the configured ROIs including precuneus, posterior cingulate and temporal-parietal region of SPECT (AUC: 0.818, Sensitivity: 0.803, Specificity: 0.727).ConclusionsThis technique using 99mTc-ECD SPECT data can predict amyloid deposits with acceptable accuracy. To confirm the reliability and validity, a multicenter SPECT study is needed.

Cerebral perfusion correlates with amyloid deposition in patients with mild cognitive impairment due to Alzheimer's disease

BACKGROUND

Changes in cerebral blood flow (CBF) may contribute to the initial stages of the pathophysiological process in patients with Alzheimer's disease (AD). Hypoperfusion has been observed in several brain regions in patients with mild cognitive impairment (MCI). However, the clinical significance of CBF changes in the early stages of AD is currently unclear.

OBJECTIVES

The aim of this study was to investigate the characteristics, diagnostic value and cognitive correlation of cerebral perfusion measured with arterial spin labeling (ASL) magnetic resonance imaging (MRI) in patients with MCI due to AD.

DESIGN, SETTING AND PARTICIPANTS

A total of fifty-nine MCI patients and 49 cognitively unimpaired controls (CUCs) were recruited and underwent multimodal MRI scans, including pseudocontinuous ASL, and neurocognitive testing. MCI patients were dichotomously classified according to the presence of amyloid deposition on 11C-labelled Pittsburgh compound B (PiB) positron emission tomography (PET).

MEASUREMENTS

The differences in CBF and expression of the AD-related perfusion pattern (ADRP), established by spatial covariance analysis in our previous study, were compared between the PiB+ MCI group and the CUC group and between the PiB+ and PiB- MCI groups. The diagnostic accuracy and correlations with cognitive function scores for CBF and ADRP expression were further analyzed.

RESULTS

Hypoperfusion in the precuneus and posterior cingulate cortex (PCC) was more characteristic of patients with MCI due to AD than of those with non-AD-related MCI. The relative regional CBF value of the left precuneus best distinguished patients with MCI due to AD from CUCs and patients with MCI due to non-AD conditions. Cerebral perfusion, as indicated by either the relative regional CBF or the expression score of the ADRP, was strongly correlated with certain cognitive function scores.

CONCLUSIONS

Here, we show that changes in CBF in the precuneus/PCC are promising MRI biomarkers for the identification of an AD etiology in patients with MCI. ASL, a noninvasive and cost-effective tool, has broad application prospects in the screening and early diagnosis of AD.

Network Diffusion-Constrained Variational Generative Models for Investigating the Molecular Dynamics of Brain Connectomes Under Neurodegeneration

Alzheimer's disease (AD) is a complex and progressive neurodegenerative condition with significant societal impact. Understanding the temporal dynamics of its pathology is essential for advancing therapeutic interventions. Empirical and anatomical evidence indicates that network decoupling occurs as a result of gray matter atrophy. However, the scarcity of longitudinal clinical data presents challenges for computer-based simulations. To address this, a first-principles-based, physics-constrained Bayesian framework is proposed to model time-dependent connectome dynamics during neurodegeneration. This temporal diffusion network framework segments pathological progression into discrete time windows and optimizes connectome distributions for biomarker Bayesian regression, conceptualized as a learning problem. The framework employs a variational autoencoder-like architecture with computational enhancements to stabilize and improve training efficiency. Experimental evaluations demonstrate that the proposed temporal meta-models outperform traditional static diffusion models. The models were evaluated using both synthetic and real-world MRI and PET clinical datasets that measure amyloid beta, tau, and glucose metabolism. The framework successfully distinguishes normative aging from AD pathology. Findings provide novel support for the "decoupling" hypothesis and reveal eigenvalue-based evidence of pathological destabilization in AD. Future optimization of the model, integrated with real-world clinical data, is expected to improve applications in personalized medicine for AD and other neurodegenerative diseases.

The Role of PET Detection of Biomarkers in Early Diagnosis, Progression, and Prognosis of Alzheimer's Disease: A Systematic Review

Alzheimer's disease (AD) is a chronic neurologic disease characterized by the deposition of Aβ amyloid and tau protein in the neural tissue, which leads to gradual and irreversible deterioration of memory. Positron emission tomography (PET) showed high potential in diagnosing AD. It provided a unique opportunity to assess cerebral amyloid plaques and tau neurofibrillary tangle deposits in the brain tissue without invasive procedures in vivo. Many studies have been focused on PET diagnosis of AD in recent years, which has significantly improved diagnosis and treatment strategies. This review study aims to summarize the role and emphasize the benefits of PET detection of AD biomarkers in early stages, clinical and histological progression assessment, and predicting AD outcomes. Relevant articles published in the last five years, from September 1, 2019, to October 30, 2024, were searched through authentic databases such as PubMed, PubMed Central, Europe PubMed Central, Science Direct, Cochrane Library, and Google Scholar. In this systematic review, we included articles published in English, with available full text, based on human trials, with relevant information regarding participants who underwent PET of the brain to diagnose AD biomarkers. The study strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and recommendations. The Joanna Briggs Institute (JBI) critical appraisal methods were used to evaluate all selected cross-sectional research, and the Newcastle-Ottawa Scale (NOS) was used to assess the cohort and longitudinal studies. Eleven relevant articles were included in this systematic review, and 2,203 males and females participated. The study revealed that the detection of beta-amyloid PET showed high-precious results in early diagnosis of AD. The detection of tau protein showed a high potential for estimation of the clinical and histological progression and prognosis of AD in longitudinal studies. Identifying amyloid and tau protein accumulation and glucose metabolism alterations is highly predictive of neurodegeneration in preclinical and mild cognitive impairment stages.

Longitudinal relationships between Aβ and tau to executive function and memory in cognitively normal older adults

The early accumulation of AD pathology such as Aβ and tau in cognitively normal older people is predictive of cognitive decline, but it has been difficult to dissociate the cognitive effects of these two proteins. Early Aβ and tau target distinct brain regions that have different functional roles. Here, we assessed specific longitudinal pathology-cognition associations in seventy-six cognitively normal older adults from the Berkeley Aging Cohort Study who underwent longitudinal PiB PET, FTP PET, and cognitive assessments. Using linear mixed-effects models to estimate longitudinal changes and residual approach to characterizing cognitive domain-specific associations, we found that Aβ accumulation, especially in frontal/parietal regions, was associated with faster decline in executive function, not memory, whereas tau accumulation, especially in left entorhinal/parahippocampal regions, was associated with faster decline in memory, not executive function, supporting an "Aβ-executive function, tau-memory" double-dissociation in cognitively normal older people. These specific relationships between accumulating pathology and domain-specific cognitive decline may be due to the particular vulnerabilities of the frontal-parietal executive network to Aβ and temporal memory network to tau.

Identifying the bioimaging features of Alzheimer's disease based on pupillary light response-driven brain-wide fMRI in awake mice

Pupil dynamics has emerged as a critical non-invasive indicator of brain state changes. In particular, pupillary-light-responses (PLR) in Alzheimer's disease (AD) patients show potential as biomarkers for brain degeneration. To investigate AD-specific PLR and its underlying neuromodulatory sources, we combine high-resolution awake mouse fMRI with real-time pupillometry to map brain-wide event-related correlation patterns based on illumination-driven pupil constriction (P c ) and post-illumination pupil dilation recovery (amplitude,P d , and time, T). TheP c -driven differential analysis reveals altered visual signal processing and reduced thalamocortical activation in AD mice in comparison with wild-type (WT) control mice. In contrast, the post-illumination pupil dilation recovery-based fMRI highlights multiple brain areas associated with AD brain degeneration, including the cingulate cortex, hippocampus, septal area of the basal forebrain, medial raphe nucleus, and pontine reticular nuclei (PRN). Additionally, the brain-wide functional connectivity analysis highlights the most significant changes in PRN of AD mice, which serves as the major subcortical relay nuclei underlying oculomotor function. This work integrates non-invasive pupil-fMRI measurements in preclinical models to identify pupillary biomarkers based on brain-wide functional changes, including neuromodulatory dysfunction coupled with AD brain degeneration.

The unique role of anosognosia in the clinical progression of Alzheimer's disease: a disorder-network perspective

Alzheimer's disease (AD) encompasses a long continuum from a preclinical phase, characterized by neuropathological alterations albeit normal cognition, to a symptomatic phase, marked by its clinical manifestations. Yet, the neural mechanisms responsible for cognitive decline in AD patients remain poorly understood. Here, we posit that anosognosia, emerging from an error-monitoring failure due to early amyloid-β deposits in the posterior cingulate cortex, plays a causal role in the clinical progression of AD by preventing patients from being aware of their deficits and implementing strategies to cope with their difficulties, thus fostering a vicious circle of cognitive decline.

Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.

Iron accumulation/overload and Alzheimer's disease risk factors in the precuneus region: A comprehensive narrative review

Alzheimer's disease (AD) is a neurodegenerative disease that is characterized by amyloid plaques, neurofibrillary tangles, and neuronal loss. Early cerebral and body iron dysregulation and accumulation interact with AD pathology, particularly in the precuneus, a crucial functional hub in cognitive functions. Quantitative susceptibility mapping (QSM), a novel post-processing approach, provides insights into tissue iron levels and cerebral oxygen metabolism and reveals abnormal iron accumulation early in AD. Increased iron deposition in the precuneus can lead to oxidative stress, neuroinflammation, and accelerated neurodegeneration. Metabolic disorders (diabetes, non-alcoholic fatty liver disease (NAFLD), and obesity), genetic factors, and small vessel pathology contribute to abnormal iron accumulation in the precuneus. Therefore, in line with the growing body of literature in the precuneus region of patients with AD, QSM as a neuroimaging method could serve as a non-invasive biomarker to track disease progression, complement other imaging modalities, and aid in early AD diagnosis and monitoring.

Retrospective analysis of Braak stage- and APOE4 allele-dependent associations between MR spectroscopy and markers of tau and neurodegeneration in cognitively unimpaired elderly

PURPOSE

The pathological hallmarks of Alzheimer's disease (AD), amyloid, tau, and associated neurodegeneration, are present in the cortical gray matter (GM) years before symptom onset, and at significantly greater levels in carriers of the apolipoprotein E4 (APOE4) allele. Their respective biomarkers, A/T/N, have been found to correlate with aspects of brain biochemistry, measured with magnetic resonance spectroscopy (MRS), indicating a potential for MRS to augment the A/T/N framework for staging and prediction of AD. Unfortunately, the relationships between MRS and A/T/N biomarkers are unclear, largely due to a lack of studies examining them in the context of the spatial and temporal model of T/N progression. Advanced MRS acquisition and post-processing approaches have enabled us to address this knowledge gap and test the hypotheses, that glutamate-plus-glutamine (Glx) and N-acetyl-aspartate (NAA), metabolites reflecting synaptic and neuronal health, respectively, measured from regions on the Braak stage continuum, correlate with: (i) cerebrospinal fluid (CSF) p-tau181 level (T), and (ii) hippocampal volume or cortical thickness of parietal lobe GM (N). We hypothesized that these correlations will be moderated by Braak stage and APOE4 genotype.

METHODS

We conducted a retrospective imaging study of 34 cognitively unimpaired elderly individuals who received APOE4 genotyping and lumbar puncture from pre-existing prospective studies at the NYU Grossman School of Medicine between October 2014 and January 2019. Subjects returned for their imaging exam between April 2018 and February 2020. Metabolites were measured from the left hippocampus (Braak II) using a single-voxel semi-adiabatic localization by adiabatic selective refocusing sequence; and from the bilateral posterior cingulate cortex (PCC; Braak IV), bilateral precuneus (Braak V), and bilateral precentral gyrus (Braak VI) using a multi-voxel echo-planar spectroscopic imaging sequence. Pearson and Spearman correlations were used to examine the relationships between absolute levels of choline, creatine, myo-inositol, Glx, and NAA and CSF p-tau181, and between these metabolites and hippocampal volume or parietal cortical thicknesses. Covariates included age, sex, years of education, Fazekas score, and months between CSF collection and MRI exam.

RESULTS

There was a direct correlation between hippocampal Glx and CSF p-tau181 in APOE4 carriers (Pearson's r = 0.76, p = 0.02), but not after adjusting for covariates. In the entire cohort, there was a direct correlation between hippocampal NAA and hippocampal volume (Spearman's r = 0.55, p = 0.001), even after adjusting for age and Fazekas score (Spearman's r = 0.48, p = 0.006). This relationship was observed only in APOE4 carriers (Pearson's r = 0.66, p = 0.017), and was also retained after adjustment (Pearson's r = 0.76, p = 0.008; metabolite-by-carrier interaction p = 0.03). There were no findings in the PCC, nor in the negative control (late Braak stage) regions of the precuneus and precentral gyrus.

CONCLUSIONS

Our findings are in line with the spatially- and temporally-resolved Braak staging model of pathological severity in which the hippocampus is affected earlier than the PCC. The correlations, between MRS markers of synaptic and neuronal health and, respectively, T and N pathology, were found exclusively within APOE4 carriers, suggesting a connection with AD pathological change, rather than with normal aging. We therefore conclude that MRS has the potential to augment early A/T/N staging, with the hippocampus serving as a more sensitive MRS target compared to the PCC.

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BACKGROUND

Head motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.

METHODS

FMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.

RESULTS

In total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).

CONCLUSIONS

The motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function

BACKGROUND

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility.

OBJECTIVE

To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study.

DESIGN

ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type).

SETTING

Participants across various sites were involved in the ARMADA study for validating the NIHTB.

PARTICIPANTS

199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers).

MEASUREMENTS

We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.

RESULTS

The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.

CONCLUSION

Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

Mapping the bioimaging marker of Alzheimer's disease based on pupillary light response-driven brain-wide fMRI in awake mice

Pupil dynamics has emerged as a critical non-invasive indicator of brain state changes. In particular, pupillary-light-responses (PLR) in Alzheimer's disease (AD) patients may be used as biomarkers of brain degeneration. To characterize AD-specific PLR and its underlying neuromodulatory sources, we combined high-resolution awake mouse fMRI with real-time pupillometry to map brain-wide event-related correlation patterns based on illumination-driven pupil constriction ( P c ) and post-illumination pupil dilation recovery (amplitude, P d , and time, T ). The P c -driven differential analysis revealed altered visual signal processing coupled with reduced thalamocortical activation in AD mice compared with the wild-type normal mice. In contrast, the post-illumination pupil dilation recovery-based fMRI highlighted multiple brain areas related to AD brain degeneration, including the cingulate cortex, hippocampus, septal area of the basal forebrain, medial raphe nucleus, and pontine reticular nuclei (PRN). Also, brain-wide functional connectivity analysis highlighted the most significant changes in PRN of AD mice, which serves as the major subcortical relay nuclei underlying oculomotor function. This work combined non-invasive pupil-fMRI measurements in preclinical models to identify pupillary biomarkers based on neuromodulatory dysfunction coupled with AD brain degeneration.

Executive Functions and Public Health: A Narrative Review

Executive functions (EFs) skills are necessary for regulating the thoughts, emotions, and actions which are associated with many aspects of daily functioning. Executive dysfunction (EDFs) is present in a wide range of mental disorders. New study indicates that EFs may predict health behavior and make it easier to engage in a variety of healthy activities. In this narrative review, EFs and public health are briefly discussed. In general, 133 articles met the inclusion criteria (published 2018-2023) which were reviewed. EFs affect the mental and physical health. Besides individual problems, people with mental problems have heavy costs to society. Mental health cannot be considered separately from general health. Consequently, preventive and therapeutic approaches to mental health should be considered not only at the level of the whole society, but also at the global level.

Distinguishing Curable from Progressive Dementias for Defining Cancer Care Options

The likelihood of a diagnosis of dementia increases with a person's age, as is also the case for many cancers, including melanoma and multiple myeloma, where the median age of diagnosis is above 60 years. However, patients diagnosed with dementia are less likely to be offered invasive curative therapies for cancer. Together with analysis of diet and medication history, advanced imaging methods and genetic profiling can now indicate more about syndromes causing the neurological symptoms. Cachexia, malnutrition, dehydration, alcohol consumption, and even loneliness can all accentuate or cause the "3Ds" of dementia, delirium and depression. Many common drugs, especially in the context of polypharmacy, can cause cognitive difficulties resembling neurodegenerative disease. These syndromes may be reversed by diet, social and caregiver changes, and stopping potentially inappropriate medications (PIMs). More insidious are immune reactions to many different autoantigens, some of which are related to cancers and tumors. These can induce movement and cognitive difficulties that mimic Alzheimer's and Parkinson's diseases and other ataxias associated with aging. Paraneoplastic neurological syndromes may be reversed by directed immunotherapies if detected in their early stages but are best treated by removal of the causative tumor. A full genetic workup should be done for all individuals as soon as possible after diagnosis, to guide less invasive treatments suitable for frail individuals. While surgical interventions may be contraindicated, genetic profile guided immunotherapies, oral treatments, and radiation may be equally curative in a significant number of cancers.

Amyloid-PET and White Matter Hyperintensities Have Independent Effects on Baseline Cognitive Function and Synergistic Effects on Longitudinal Executive Function

Co-occurrence of beta amyloid (Aβ) and white matter hyperintensities (WMHs) increase the risk of dementia and both are considered biomarkers of preclinical dementia. Moderation and mediation modeling were used to define the interplay between global and regional Aβ and WMHs measures in relation to executive function (EF) and memory composite scores outcomes at baseline and after approximately 2 years across a sample of 714 clinically normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI 2). The moderation regression analysis showed additive effects of Aβ and WMHs over baseline memory and EF scores (p = 0.401 and 0.061, respectively) and synergistic effects over follow-up EF (p < 0.05). Through mediation analysis, the data presented demonstrate that WMHs effects, mediated by global and regional amyloid burden, are responsible for baseline cognitive performance deficits in memory and EF. These findings suggest that Aβ and WMHs contribute to baseline cognition independently while WMHs volumes exert effects on baseline cognitive performance directly and through influences on Aβ accumulation.

The Brain Health Platform: Combining Resilience, Vulnerability, and Performance to Assess Brain Health and Risk of Alzheimer's Disease and Related Disorders

BACKGROUND

It is difficult to assess brain health status and risk of cognitive impairment, particularly at the initial evaluation. To address this, we developed the Brain Health Platform to quantify brain health and identify Alzheimer's disease and related disorders (ADRD) risk factors by combining a measure of brain health: the Resilience Index (RI), a measure of risk of ADRD; the Vulnerability Index (VI); and the Number-Symbol Coding Task (NSCT), a measure of brain performance.

OBJECTIVE

The Brain Health Platform is intended to be easily and quickly administered, providing an overview of a patient's risk of developing future impairment based on modifiable and non-modifiable factors as well as current cognitive performance.

METHODS

This cross-sectional study comprehensively evaluated 230 participants (71 controls, 71 mild cognitive impairment, 88 ADRD). VI and RI scores were derived from physical assessments, lifestyle questionnaires, demographics, medical history, and neuropsychological examination including the NSCT.

RESULTS

Individuals with abnormal scores were 95.7% likely to be impaired, with a misclassification rate of 9.7%. The combined model had excellent discrimination (AUC:0.923±0.053; p < 0.001), performing better than the Montreal Cognitive Assessment.

CONCLUSION

The Brain Health Platform combines measures of resilience, vulnerability, and performance to provide a cross-sectional snapshot of overall brain health. The Brain Health Platform can effectively and accurately identify even the very mildest impairments due to ADRD, leveraging brief yet powerful and actionable indices of brain health and risk that could be used to develop personalized, precision medicine-like interventions.