Background: Distinguishing between subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia in a scalable, accessible way is important to promote earlier detection and intervention. Objective: We investigated diagnostic categorization using an FDA-cleared quantitative electroencephalographic/event-related potential (qEEG/ERP)-based cognitive testing system (eVox ® by Evoke Neuroscience) combined with an automated volumetric magnetic resonance imaging (vMRI) tool (Neuroreader ® by Brainreader). Methods: Patients who self-presented with memory complaints were assigned to a diagnostic category by dementia specialists based on clinical history, neurologic exam, neuropsychological testing, and laboratory results. In addition, qEEG/ERP (n = 161) and quantitative vMRI (n = 111) data were obtained. A multinomial logistic regression model was used to determine significant predictors of cognitive diagnostic category (SCD, MCI, or dementia) using all available qEEG/ERP features and MRI volumes as the independent variables and controlling for demographic variables. Area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the prediction models. Results: The qEEG/ERP measures of Reaction Time, Commission Errors, and P300b Amplitude were significant predictors (AUC = 0.79) of cognitive category. Diagnostic accuracy increased when volumetric MRI measures, specifically left temporal lobe volume, were added to the model (AUC = 0.87). Conclusion: This study demonstrates the potential of a primarily physiological diagnostic model for differentiating SCD, MCI, and dementia using qEEG/ERP-based cognitive testing, especially when combined with volumetric brain MRI. The accessibility of qEEG/ERP and vMRI means that these tools can be used as adjuncts to clinical assessments to help increase the diagnostic certainty of SCD, MCI, and dementia.

Background: Strength and mobility are essential for activities of daily living. With aging, weaker handgrip strength, mobility, and asymmetry predict poorer cognition. We therefore sought to quantify the relationship between handgrip metrics and volumes quantified on brain magnetic resonance imaging (MRI). Objective: To model the relationships between handgrip strength, mobility, and MRI volumetry. Methods: We selected 38 participants with Alzheimer’s disease dementia: biomarker evidence of amyloidosis and impaired cognition. Handgrip strength on dominant and non-dominant hands was measured with a hand dynamometer. Handgrip asymmetry was calculated. Two-minute walk test (2MWT) mobility evaluation was combined with handgrip strength to identify non-frail versus frail persons. Brain MRI volumes were quantified with Neuroreader. Multiple regression adjusting for age, sex, education, handedness, body mass index, and head size modeled handgrip strength, asymmetry and 2MWT with brain volumes. We modeled non-frail versus frail status relationships with brain structures by analysis of covariance. Results: Higher non-dominant handgrip strength was associated with larger volumes in the hippocampus (p = 0.02). Dominant handgrip strength was related to higher frontal lobe volumes (p = 0.02). Higher 2MWT scores were associated with larger hippocampal (p = 0.04), frontal (p = 0.01), temporal (p = 0.03), parietal (p = 0.009), and occipital lobe (p = 0.005) volumes. Frailty was associated with reduced frontal, temporal, and parietal lobe volumes. Conclusion: Greater handgrip strength and mobility were related to larger hippocampal and lobar brain volumes. Interventions focused on improving handgrip strength and mobility may seek to include quantified brain volumes on MR imaging as endpoints.

Current Alzheimer's disease (AD) research has a major focus on validating and discovering noninvasive biomarkers that can detect AD, benchmark disease severity, and aid in testing the efficacy of interventions. Structural magnetic resonance imaging (sMRI) is a well-validated tool used in diagnosis and for monitoring disease progression in AD. Much of the sMRI literature centers around hippocampal and other medial temporal lobe structure atrophy, which are strongly associated with cognition and diagnosis. Because atrophy patterns are complex and vary by patient, researchers have made efforts to condense more brain information into validated metrics. Many of these methods use machine learning (ML), which can be difficult to interpret clinically, hampering clinical adoption. Here, we introduce a practical, clinically meaningful and interpretable index which we call an "AD-NeuroScore." Our approach is automated and uses multiple regional brain volumes associated with cognitive decline. We used a modified Euclidean inspired distance function to calculate the differences between each participant and a cognitively normal (CN) older adult template, adjusting for intracranial volume, age, sex, and scanner model. Here we report validation results, including sensitivity to diagnosis (CN, mild cognitive impairment (MCI), and AD) and disease severity (Clinical Dementia Rating Scale Sum of Boxes (CDR-SB), Mini Mental State Exam (MMSE), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-11) in 929 older adults (mean age=72.7 years, SD=6.3, Range=55.1-91.5, 50% Female) drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. To determine if AD-NeuroScore might be predictive of disease progression, we assessed the relationship between the calculated AD-NeuroScore at baseline and change in both diagnosis and disease severity scores at 12, 24, 36, and 48-months. We performed additional validation in all analyses, benchmarking AD-NeuroScore against adjusted hippocampal volume (AHV). We found that AD-NeuroScore was significantly associated with diagnosis and all disease severity scores at baseline. Associations between AD-NeuroScore and disease severity (CDR-SB and ADAS-11) were significantly stronger than with AHV. Baseline AD-NeuroScore was also associated with change in diagnosis and changes in disease severity scores at all time points. Performance was equivalent, or in some cases superior, to AHV. These early validation results suggest that AD-NeuroScore has the potential to be a clinically meaningful biomarker for dementia.

BackgroundHandgrip strength is important for performing activities of daily living[1]. In older adults, weaker handgrip strength and asymmetry are associated with poorer cognition. o better understand mechanisms, we sought to quantify the relationship between handgrip strength and regional volumes quantified on brain MR imaging.MethodWe selected 32 participants (mean age=70.8±7.3 [range 57‐89] years, 53.1% female, 90.6% right‐handed, mean body mass index BMI=23.9±4.1) from the Pacific Brain Health Center at Providence St. John’s Health Center, with Alzheimer dementia biomarker evidence of amyloidosis[2]. Mean Montreal Cognitive Assessment score for all participants was 21.3±3.8 points. Handgrip strength on dominant and non‐dominant hands was measured using the NIH Motor Toolbox[3] as part of a cognitive fitness assessment using a hydraulic hand dynamometer. The resulting scores included handgrip strength and percentile comparisons to normative data. Asymmetry scores were calculated. Regional brain volumes, including lobar structures and the hippocampus, were measured from T1‐weighted MR images using Neuroreader[4]. Partial correlations (rp), adjusting for age, sex, BMI and total intracranial volume modeled handgrip strength, asymmetry, and brain volumes with a significance threshold of p<0.05.ResultIn the dominant hand, higher handgrip strength scores and percentiles were associated with larger volumes in the left frontal lobe (rp=+0.51, p=0.007; rp =+0.47, p=0.01) and right parietal lobe (rp=+0.40, p=0.03; rp=+0.39, p=0.04). In the non‐dominant hand, higher handgrip strength score and percentiles were associated with smaller total cerebral spinal fluid (CSF) volume (rp =‐0.55, p=0.004; rp =‐0.52, p =0.006) and larger volumes within the left hippocampus (rp=+0.45, p=0.01; rp=+.43, p=0.02), right hippocampus (rp=+0.47, p=0.01; rp=+0.43, p=0.02), and right parietal lobe (rp=+0.39, p=0.04; rp=+0.44, p=0.02). Handgrip strength asymmetry was inversely related to right hippocampal volume (rp=‐0.58, p=0.002) and positively correlated to CSF volume (rp=+0.39, p=0.04).ConclusionGreater handgrip strength was related to larger regional brain volumes. A higher number of brain regions were related to the non‐dominant hand. Asymmetry was associated with lower right hippocampal volume and higher CSF volume. Interventions focused on improving handgrip strength may seek to include quantified brain volumes on MR imaging as endpoints.

BackgroundCurrent research is focused on noninvasive biomarkers of neurodegeneration that can detect dementia early, benchmark disease severity, monitor disease progression, and aid in testing the efficacy of therapeutic interventions utilizing a single number1–3. The objective of this study is to create and begin to validate an intuitively meaningful biomarker called the ADNeuro‐Score.MethodCognitively normal (CN) individuals and patients with a mild cognitive impairment (MCI) or dementia diagnosis were drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI)4. Eighty‐four cortical and subcortical regional volumes were estimated from T1‐weighted MR images using FreeSurfer5. To determine which regional volumes were associated with cognitive decline, we randomly selected 150 participants (N=50 each CN, MCI, and dementia) and performed an ANOVA with an alpha=0.05, Bonferroni corrected. A vector containing the resulting 41 regions, consistent with the existing literature6,7,8, was extracted for experimental and template cohorts (Table 1). To compute ADNeuro‐Score, a Hausdorff distance metric was used to estimate the differences between individual and average template vectors. ADNeuro‐Score was benchmarked using adjusted hippocampal volume (AHV), which is a NIA‐AA diagnostic biomarker for Alzheimer’s disease9, the most common form of dementia10. Both ADNeuro‐Score and AHV were harmonized for intracranial volume, age, sex, and scanner model11. Validation used an experimental cohort (N=929, mean age=72.67 years) and tested sensitivity to diagnosis using pairwise t‐tests and an alpha=0.001, Bonferroni corrected. Results were converted to a z‐score. We also tested association with disease severity, operationalized here as MMSE and ADASCog scores, using linear regression.ResultBoth ADNeuro‐Score and AHV differed between all three cognitive groups. ADNeuro‐Score (z=10.0) better differentiated MCI from dementia than AHV (z=8.9). AHV (z=7.3) was slightly better at distinguishing MCI from CN than ADNeuro‐Score (z=6.7). ADNeuro‐Score and AHV were similarly associated with MMSE (RADNS=0.41; RHA=0.41) and ADAS‐Cog (RADNS=0.49; RHA=0.47) scores.ConclusionThe newly developed ADNeuro‐Score seems to be a robust and reliable way to distinguish CN, MCI and AD patients and performs equivalently to AHV in predicting MMSE and ADAS‐Cog assessment scores. We hope ADNeuro‐Score will be highly specific to AD because of its focus on AD‐effected regions. Future research will determine if ADNeuro‐Score can improve the differential diagnosis of AD.