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INTRODUCTION Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline. METHODS To evaluate multi‐modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test‐determined memory impairment. RESULTS Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics models. DISCUSSION The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.

INTRODUCTION Mild cognitive impairment (MCI) represents a transitional stage between normal aging and dementia. We investigate associations among cardiovascular and metabolic disorders (hypertension, diabetes mellitus, and hyperlipidemia) and diagnosis (normal; amnestic [aMCI]; and non‐amnestic [naMCI]). METHODS Multinomial logistic regressions of participant data (N = 8737; age = 70.9 ± 7.5 years) from the National Alzheimer's Coordinating Center Uniform Dataset Version 3 protocol cohort were used. RESULTS Controlling for demographic/health variables, individuals with aMCI, though not naMCI, showed a higher likelihood of hypertension, diabetes, and hyperlipidemia compared to cognitively normal counterparts, though no differences between aMCI/naMCI. Black Americans, regardless of cognitive status, were more likely to fall into hypertension and diabetes groups compared to White Americans. DISCUSSION These findings underscore the critical role of diagnosis and race in MCI diagnosis and care, emphasizing the need for tailored interventions to address inequities and reduce the risk of progression to dementia. Highlights The study leverages a large, racially diverse cohort from the NACC database.  Black Americans with non‐amnestic mild cognitive impairment(naMCI) show highest comorbidity burden. No significant differences in comorbidity burden between amnestic MCI (aMCI) and naMCI subtypes.  Education is protective, but less so for Black American individuals. Older age, male sex, body mass index (BMI), and low education associate with increased risk for comorbidities.

Background Identifying individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer’s disease and related dementia disorders (ADRD) would facilitate the development of individualized prevention plans. We investigated the association between MCI and comorbidities of ADRD. We examined the predictive potential of these comorbidities for MCI risk determination using a machine learning algorithm. Methods Using a retrospective matched case-control design, 5185 MCI and 15,555 non-MCI individuals aged ≥50 years were identified from MarketScan databases. Predictive models included ADRD comorbidities, age, and sex. Results Associations between 25 ADRD comorbidities and MCI were significant but weakened with increasing age groups. The odds ratios (MCI vs non-MCI) in 50–64, 65–79, and ≥ 80 years, respectively, for depression (4.4, 3.1, 2.9) and stroke/transient ischemic attack (6.4, 3.0, 2.1). The predictive potential decreased with older age groups, with ROC-AUCs 0.75, 0.70, and 0.66 respectively. Certain comorbidities were age-specific predictors. Conclusions The comorbidity burden of MCI relative to non-MCI is age-dependent. A model based on comorbidities alone predicted an MCI diagnosis with reasonable accuracy.

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.

Objective:The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) is a well validated and reliable clinical assessment tool that can be used for characterizing cognitive function in older adults. The RBANS has been shown to reliably discriminate between Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively healthy (CH) individuals. While the RBANS has traditionally been administered in a face to face setting, administration is also feasible via telehealth. Due to the COVID-19 pandemic, cognitive assessments were unexpectedly moved to telehealth formats. Given this, the current study assessed whether differences emerged between face to face and telehealth RBANS scores in both individuals who were CH and had MCI.Participants and Methods:A total of 61 individuals (NCH = 27, NMCI = 34) completed baseline and 1-year follow-up visits in the current study. The sample was predominantly female (N = 43, 70.5%), identified as white (N = 57, 93.4%), and were well educated (MYears = 15.93). Participants completed the RBANS form B at an in-person baseline visit and form C at a one year follow-up visit. Higher RBANS scores indicate overall better cognitive performance. As expected, CH individuals performed better than those with MCI on immediate memory, language, attention, delayed memory, and total score. There were no significant differences found for the visuospatial index. Repeated measures ANOVAs were conducted to assess whether differences in RBANS performance existed based on test administration method.Results:Group differences between testing formats were observed in CH individuals on immediate memory [F(1,37) =9.10, p < .01)], language [F(1, 37)=9.41=p < .01)], and total score [F(1,37)=6.56, p < .05], with higher performance in those who completed the followup session in-person.There were no differences in baseline performance on any RBANS index between those who received an in person versus telehealth format (p’s > .05). No differences were observed in the MCI group. There were no significant differences observed between the CH and MCI group on demographic factors.Conclusions:Results from the current study suggest that CH counterparts experienced a greater degree of difference in scores between testing formats, whereas individuals with MCI did not. The lack of difference in MCI individuals may be due to less room for variability over time for this group given already low scores. These results suggest that while telehealth has been shown to be a viable option for RBANS administration in some samples, further work needs to be conducted regarding the equivalence of in-person vs. telehealth formats. This study is not without limitations. The small MCI group was segmented into in-person and telehealth groups, further reducing power to detect statistically significant results. The sample was also homogenous with highly educated, Caucasian women. Future research should aim to assess a larger, more diverse sample to identify whether RBANS is a reliable measure alone for assessing cognitive change over time via telehealth for MCI.

Objective:Choice response time (RT) increases linearly with increasing information uncertainty, which can be represented externally or internally. Using a card-sorting task, we previously showed that Alzheimer’s disease (AD) dementia patients were more impaired relative to cognitively normal older adults (CN) under conditions that manipulated internally cued rather than externally driven uncertainty, but this study was limited by a between-subjects design that prevented us from directly comparing the two uncertainty conditions. The objective of this study was to assess internally cued and externally driven cued uncertainty representations in CN and mild cognitive impairment (MCI) patients.Participants and Methods:Older participants (age > 60 years; N=49 CN, N=33 MCI patients) completed a card-sorting task that separately manipulated externally cued uncertainty (i.e., the number of sorting piles with equal probability of each stimulus type) or internally cued uncertainty (i.e., the probability of each stimulus type with fixed number of sorting piles) at three different uncertainty loads (low, medium, high). Exploratory analyses separated MCI patients by etiology into possible/probable cortical neurodegenerative process (i.e., AD, frontotemporal dementia; N=13) or nonneurodegenerative process (i.e., vascular, psychiatric, sleep, medication effect; N=20).Results:CN and MCI patients maintained a high level of accuracy on both tasks (M accuracy > .94 across conditions). MCI patients performed more slowly than CN on the externally and internally cued tasks, and both groups showed a significant positive association between uncertainty load and RT (p’s < .05). There was a group x load x uncertainty condition interaction (p = .05). For CNs, the slope of the linear association between load and RT was significantly steeper in the externally cued compared to internally cued condition. For MCI patients in contrast, RTs increased with load to a similar degree in both conditions. Exploratory analyses showed the MCI-neurodegenerative patients were significantly slower than MCI-nondegenerative and CN (p < .001). While the group x load x condition interaction was significant when comparing all three groups (p < .05), this was driven by the differences between CN and MCI patients described above; the MCI-neurodegenerative and non-neurodegenerative groups did not significantly differ in the strength of the RT-load association between the externally or internally cued conditions.Conclusions:Overall, CN participants showed greater RT slowing with increasing load of externally driven than internally cued uncertainty. Though they were slower than CNs, MCI patients (even those with a possible/probable cortical neurodegenerative condition) were able to accurately perform an internally cued uncertainty task and did not show differential slowing compared to an externally driven task. This provides preliminary evidence that internal representations of probabilistic information are intact in patients with MCI due to a neurodegenerative condition, meaning they may not depend on cortical processes. Future work will increase the sample sizes of the MCI-neurodegenerative and non-degenerative groups.

Objective:Cognitive impairment can affect an individual’s ability to perform routine tasks. In this study, we investigate how cognitive abilities relate to the accuracy and efficiency of performance on a naturalistic cooking task completed in older adults’ home environments. We hypothesized a positive association between task accuracy and global cognitive status, and task efficiency and executive functioning. We further hypothesized a negative association between omission errors and immediate and delayed memory recall.Participants and Methods:Fourteen community-dwelling older adults (Age, M = 73.92 years; Female = 9; Education, M = 16.38 years) along the continuum from normal aging to mild dementia completed a “Cooking Task” in their home environment. Specifically, participants were instructed to fry or scramble an egg, prepare slice of toast with jelly, serve side of sliced apple, pour glass of water, bring prepared items to table, and clean dishes used. Participants received ingredients necessary for task completion and a task list to reference. The task efficiency score (range 0-6) was based on multi-tasking and organizational skills (e.g., beginning the egg task early in session, plating items as prepared). Overall accuracy was computed by identifying error types (e.g., inefficiencies, substitutions, omissions, and subtasks attempted) and scaling accuracy (range 1-5) for each subtask, then summing all six subtask accuracy scores to get overall accuracy (range 6-30). Participants also completed a range of neuropsychological assessments, which included the Telephone Interview for Cognitive Status, Letter and Category Fluency from the Delis-Kaplan Executive Function System, and immediate and delayed recall measures from the Repeatable Battery for the Assessment of Neuropsychological Status. Due to the small sample size, findings are preliminary, and scatterplots were evaluated for outliers that might influence findings.Results:Consistent with hypotheses, as overall accuracy on the Cooking Task increased so did performance on the global cognitive measure (TICS: r = 0.61, p = 0.02). Lower rates of omission errors were also associated with better performance on both immediate (r = -0.75, p < 0.01) and delayed (r = -0.55, p = 0.04) recall indices. However, these findings were not specific, as overall accuracy also significantly correlated with the memory indices and verbal fluency measures (ps < 0.05). Additionally, lower rates of omission errors significantly correlated with performance on the TICS and the D-KEFS Letter Fluency (ps < 0.05). Contrary to our hypothesis, no significant associations were found between cooking task efficiency and executive functioning (D-KEFS subtests). There were also no significant correlations between cooking task efficiency and global cognitive status or memory.Conclusions:The present study supported our hypotheses that better overall task accuracy is associated with higher cognitive status and lower rates of omission errors correlate with better immediate and delayed recall abilities. However, the findings were not specific to these domains of functioning but rather suggest that clinical assessments measuring a range of cognitive abilities are related to the accuracy of daily task performance and omission errors on routine daily tasks. Future research will explore the validity of the efficiency measure.

Objective:Alzheimer’s disease (AD) is expected to affect over 7 million older Americans by 2025. Development of fast and inexpensive screening measures for routine screening is critical for identifying those suffering from the earliest stages of AD including Mild Cognitive Impairment (MCI) and Subjective Cognitive Decline (SCD). Here we assess the validity and utility of a brief, 5-item SCD screener and its associations with neuropsychological performance as compared to an existing objective cognitive screener, the Mini Mental Status Exam (MMSE).Participants and Methods:Development: A brief, 5-item SCD questionnaire was developed based on a more extensive 20-item version previously validated (Chapman et al. 2021). Participants: 27 cognitively diverse (MCI and cognitively normal) community dwelling older adults were recruited for this study. Mean age: 71.9 ± 7. Inclusion criteria include memory concerns. Exclusion criteria include no previous diagnoses of neurodegenerative diseases and/or major stroke. Administration: Participants completed a brief, 5-item SCD screener along with cognitive testing including the MMSE and a clinically validated list-learning test, the Selective Reminding Test (SRT). Statistical Methods: Spearman 2-tailed correlations were conducted to assess the relationship between the two screening measures, and their relationships to the SRT. Outcome measures on the SRT included total recall (max: 72) and delayed recall (max: 12). All cognitive measures were demographically adjusted with normative data.Results:The mean total for the SCD screener was 2.1 ±1.1, and the mean MMSE score was 29.2 ± 1.2. The SCD screener was associated with MMSE scores (r= -.39, p= .043), SRT Total Recall (r= -.43, p= .024) and Delayed Recall (r=-.42, p=.031) measures. MMSE scores did not associate with either SRT outcome (p>.05).Conclusions:Results support the utility and validity of a brief subjective cognitive decline screener for identifying those who may be experiencing memory dysfunction. The brief SCD screener outperformed the MMSE, an existing and widely used objective screening measure. Associations between the SCD screener and SRT outcomes support the validity and utility of the brief screener and recapitulate previous findings with the more extensive version of the SCD questionnaire (Chapman et al. 2021). Ongoing research is focusing on the utility of the screener in frontline clinical settings and translation of the screener into Spanish.

Metabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer's disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities. First, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD. Our results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters. We proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.

While amnestic mild cognitive impairment (aMCI) and non-amnestic mild cognitive impairment (naMCI) are theoretically different entities, only a few investigations studied the structural brain differences between these subtypes of mild cognitive impairment. The aim of the study was to find the structural differences between aMCI and naMCI, and to replicate previous findings on the differentiation between aMCI and healthy controls. Altogether 62 aMCI, naMCI, and healthy control subjects were included into the study based on the Petersen criteria. All patients underwent a routine brain MR examination, and a detailed neuropsychological examination. The sizes of the hippocampus, the entorhinal cortex and the amygdala were decreased in aMCI relative to naMCI and to controls. Furthermore the cortical thickness of the entorhinal cortex, the fusiform gyrus, the precuneus and the isthmus of the cingulate gyrus were significantly decreased in aMCI relative to naMCI and healthy controls. The largest differences relative to controls were detected for the volume of the hippocampus (18% decrease vs. controls) and the cortical thickness (20% decrease vs. controls) of the entorhinal cortex: 1.6 and 1.4 in terms of Cohen's d. Only the volume of the precuneus were decreased in the naMCI group (5% decrease) compared to the control subjects: 0.9 in terms of Cohen's d. Significant between group differences were also found in the neuropsychological test results: a decreased anterograde, retrograde memory, and category fluency performance was detected in the aMCI group relative to controls and naMCI subjects. Subjects with naMCI showed decreased letter fluency relative to controls, while both MCI groups showed decreased executive functioning relative to controls as measured by the Trail Making test part B. Memory performance in the aMCI group and in the entire sample correlated with the thickness of the entorhinal cortex and with the volume of the amygdala. The amnestic mild cognitive impairment/non-amnestic mild cognitive impairment separation is not only theoretical but backed by structural imaging methods and neuropsychological tests. A better knowledge of the MCI subtypes can help to predict the direction of progression and create targeted prevention.