Mild Cognitive Impairment Subjects Research Articles

Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment

ObjectivesAccurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI.MethodsWe recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network.ResultsThe proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aβ (+) than with Aβ (−).ConclusionThe proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.

No Differences in Urine Bisphenol A Concentrations between Subjects Categorized with Normal Cognitive Function and Mild Cognitive Impairment Based on Montreal Cognitive Assessment Scores

A link between bisphenol A (BPA) exposure and cognitive disorders has been suggested. However, the differences in BPA concentrations between subjects with and without cognitive impairment have not been analysed. Therefore, this observational study aimed to compare urine BPA levels in subjects with normal cognitive function (NCF) and mild cognitive impairment (MCI). A total of 89 MCI subjects and 89 well-matched NCF individuals were included in this study. Cognitive functions were assessed using the Montreal Cognitive Assessment (MOCA) scale. Urine BPA concentrations were evaluated by gas chromatography–mass spectrometry and adjusted for creatinine levels. Moreover, anthropometric parameters, body composition, sociodemographic factors, and physical activity were also assessed. Creatinine-adjusted urine BPA levels did not differ between the NCF and MCI groups (1.8 (1.4–2.7) vs. 2.2 (1.4–3.6) µg/g creatinine, p = 0.1528). However, there were significant differences in MOCA results between groups when the study population was divided into tertiles according to BPA concentrations (p = 0.0325). Nevertheless, multivariate logistic regression demonstrated that only education levels were independently associated with MCI. In conclusion, urine BPA levels are not significantly different between subjects with MCI and NCF, but these findings need to be confirmed in further studies.

High plasma homocysteine levels predict the progression from mild cognitive impairment to dementia

High levels of blood homocysteine (HCy), a well-known cardiovascular risk factor and promoter of oxidative stress, have been associated with the incidence of cognitive impairment and dementia. Nonetheless, contrasting data are still present on its involvement in the progression from Mild Cognitive Impairment (MCI) to overt dementia.In this study we aimed to observe whether blood HCy level are associated with the evolution from MCI, divided into amnestic MCI (aMCI) and non-amnestic MCI (naMCI), to dementia.Blood HCy was measured in 311 MCI subjects (aMCI: 64%, naMCI: 36%) followed-up for a median of 33 months (range 10–155 months). At follow-up, 137 individuals converted to dementia (naMCI, n = 34; aMCI, n = 103). Based on HCy distribution, subjects in the highest tertile had a greater risk to convert to dementia compared to tertile I (Hazard Ratio (95% confidence interval): 2.25 (1.05–4.86); p = 0.04). aMCI subjects did not show increased risk to convert to dementia with increasing HCy concentration, but was significant in naMCI (p = 0.04). We observed a non-significant increase in the risk of progression to dementia from naMCI/low HCy (reference group, HCy cutoff value = 16 μmol/L) to naMCI/high HCy, but it was significant from aMCI/low HCy (HR: 2.73; 95%CI: 1.06–7.0; p:0.03), to aMCI/high HCy (HR: 3.24; 95%CI: 1.17–8.47; p:0.02).Our results suggest that HCy levels are associated with the progression from MCI to dementia. This association seems significant only for the naMCI group, indirectly supporting the notion that hyperhomocysteinemia damages the nervous system through its role as a vascular risk factor.

Low Functional network integrity in cognitively unimpaired and MCI subjects with depressive symptoms: results from a multi-center fMRI study

Evidence suggests that depressive symptomatology is a consequence of network dysfunction rather than lesion pathology. We studied whole-brain functional connectivity using a Minimum Spanning Tree as a graph-theoretical approach. Furthermore, we examined functional connectivity in the Default Mode Network, the Frontolimbic Network (FLN), the Salience Network, and the Cognitive Control Network. All 183 elderly subjects underwent a comprehensive neuropsychological evaluation and a 3 Tesla brain MRI scan. To assess the potential presence of depressive symptoms, the 13-item version of the Beck Depression Inventory (BDI) or the Geriatric Depression Scale (GDS) was utilized. Participants were assigned into three groups based on their cognitive status: amnestic mild cognitive impairment (MCI), non-amnestic MCI, and healthy controls. Regarding affective symptoms, subjects were categorized into depressed and non-depressed groups. An increased mean eccentricity and network diameter were found in patients with depressive symptoms relative to non-depressed ones, and both measures showed correlations with depressive symptom severity. In patients with depressive symptoms, a functional hypoconnectivity was detected between the Anterior Cingulate Cortex (ACC) and the right amygdala in the FLN, which impairment correlated with depressive symptom severity. While no structural difference was found in subjects with depressive symptoms, the volume of the hippocampus and the thickness of the precuneus and the entorhinal cortex were decreased in subjects with MCI, especially in amnestic MCI. The increase in eccentricity and diameter indicates a more path-like functional network configuration that may lead to an impaired functional integration in depression, a possible cause of depressive symptomatology in the elderly.

Predicting the reversion from mild cognitive impairment to normal cognition based on magnetic resonance imaging, clinical, and neuropsychological examinations.

Reversion from mild cognitive impairment (MCI) to normal cognition (NC) is not uncommon and indicates a better cognitive trajectory. This study aims to identify predictors of MCI reversion and develop a predicting model. A total of 391 MCI subjects (mean age=74.3years, female=61%) who had baseline data of magnetic resonance imaging, clinical, and neuropsychological measurements were followed for two years. Multivariate logistic analyses were used to identify the predictors of MCI reversion after adjusting for age and sex. A stepwise backward logistic regression model was used to construct a predictive nomogram for MCI reversion. The nomogram was validated by internal bootstrapping and in an independent cohort. In the training cohort, the 2-year reversion rate was 19.95%. Predictors associated with reversion to NC were higher education level (p=0.004), absence of APOE4 allele (p=0.001), larger brain volume (p<0.005), better neuropsychological measurements performance (p<0.001), higher glomerular filtration rate (p=0.035), and lower mean arterial pressure (p=0.060). The nomogram incorporating five predictors (education, hippocampus volume, the Alzheimer's Disease Assessment Scale-Cognitive score, the Rey Auditory Verbal Learning Test-immediate score, and mean arterial pressure) achieved good C-indexes of 0.892 (95% confidence interval [CI], 0.859-0.926) and 0.806 (95% CI, 0.709-0.902) for the training and validation cohort. Observational duration is relatively short; The predicting model warrant further validation in larger samples. This prediction model could facilitate risk stratification and early management for the MCI population.

Hippocampal Subregions Volume and Texture for the Diagnosis of Mild Cognitive Impairment

ABSTRACT The aim was to examine the diagnostic efficacy of hippocampal subregions volume and texture in differentiating amnestic mild cognitive impairment (MCI) from normal aging changes. Ninety MCI subjects and eighty-eight well-matched healthy controls (HCs) were selected. Twelve hippocampal subregions volume and texture features were extracted using Freesurfer and MaZda based on T1 weighted MRI. Then, two-sample t-test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were developed to select a subset of the original features. Support vector machine (SVM) was used to perform the classification task and the area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the diagnostic efficacy of the model. The volume features with high discriminative power were mainly located in the bilateral CA1 and CA4, while texture feature were gray-level non-uniformity, run length non-uniformity and fraction. Our model based on hippocampal subregions volume and texture features achieved better classification performance with an AUC of 0.90. The volume and texture of hippocampal subregions can be utilized for the diagnosis of MCI. Moreover, we found that the features that contributed most to the model were mainly textural features, followed by volume. These results may guide future studies using structural scans to classify patients with MCI.

Associations of cerebrospinal fluid complement proteins with Alzheimer’s pathology, cognition, and brain structure in non-dementia elderly

BackgroundCerebrospinal fluid (CSF) complement activation is a key part of neuroinflammation that occurs in the early stages of Alzheimer’s disease (AD). However, the associations of CSF complement proteins with AD pathology, cognition, and structural neuroimaging biomarkers for AD have been rarely investigated.MethodsA total of 210 participants (125 mild cognitive impairment [MCI] patients and 85 normal controls) were included from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database who measured AD pathology, cognition, and neuroimaging at baseline and every 12 months. The mixed-effect linear models were utilized to investigate longitudinal associations of CSF complement proteins with AD pathology, cognition, and neuroimaging in cognitively normal (CN) and mild cognitive impairment (MCI) subjects. Causal mediation analyses were conducted to explore the potential mediators between CSF complement proteins and cognitive changes.ResultsWe found that the subjects with low CSF complement protein levels at baseline had worse outcomes in AD pathology, indicated by their lowest concentrations observed in A + and A + T + individuals. The reduced CSF complement proteins were associated with faster accumulation of tau among CN subjects and with cognitive decline and greater brain atrophy of specific regions among MCI subjects. Furthermore, mediation analyses showed that the effects of CSF complement proteins on cognitive performance were partially mediated by regional brain structures (mediation proportions range from 19.78 to 94.92%; p < 0.05).ConclusionsThis study demonstrated that CSF complement proteins were involved in the early progression of AD. Our results indicated that regional brain atrophy might be a plausible way to connect CSF complement protein levels and cognition.

Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status.

Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI). This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status. This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification. We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype. This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging.

Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

Evaluation of the beneficial effects of ETAS® on normal aging or mild cognitive impairment subjects: A pilot randomized controlled trial

Context: Recently, new intervention tools at the mild cognitive impairment (MCI) stage are needed to prevent dementia, including Alzheimer's disease (AD). Aims: To evaluate whether a standardized Asparagus officinalis stem (ETAS) extract, as a functional ingredient, effectively maintains cognitive functions in subjects with normal aging and MCI. Methods: A pilot randomized controlled trial was conducted on thirty subjects that were randomly allocated to the experimental group (n = 15), which received ETAS supplementation (1,000 mg) daily, and a control group, which received placebo (dextrin) in the same form (n = 15) for a period of 12 months. All subjects were evaluated using neuropsychological questionnaires comprised of Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB), Clock Drawing Test (CDT), and Hospital Anxiety and Depression Scale (HADS) at all visit times. Brain tests (MRI, EEG and EPB) and blood tests (hematology, hemochemistry, immunological status and HSP70) were also performed. Primary endpoints were the change in the neuropsychological questionnaire scores. Secondary endpoints such as immunological and molecular biomarkers of dementia, neurological imaging, and electrophysiological outcomes were measured. Results: The ETAS group showed a significant improvement in scores on MMSE, FAB, and HADS, tended to improve in scores on CDT, and could show a slight increase in blood HSP70. The ETAS group also maintained normal levels of CD4/CD8 immune complex. No adverse events were detected during the study period. Conclusions: ETAS supplementation could prevent the progression of cognitive decline and anxiety/depression expression associated with the prevention of AD.

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings.

Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n=17), subjective cognitive decline (SCD, n=58), and mild cognitive impairment (MCI, n=44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87vs 0.50). Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.

Differentiating MCI from depression through verbal memory scores.

The present study aims to assess the differences between major depressive disorder (MDD) and mild cognitive impairment (MCI) in terms of verbal learning profile together with structural changes in the brain on magnetic resonance imaging (MRI) and to reveal predictive factors for MCI. Fifty-six patients with MDD and 31 MCI subjects were assessed using the Turkish Verbal Memory Processes Test (VMPT). Brain MRI was used to evaluate sulcal atrophy (SA), ventricular atrophy, periventricular white matter hyperintensity (WMH), subcortical WMH, basal ganglia infarct, medial temporal lobe atrophy, and infratentorial infarct scores based on the Modified Visual MRI Rating Scale (MVMRS). The symptoms of depression were evaluated with the Beck Depression Inventory in both groups. Demographic factors, VMPT scores, and MVMRS scores between MDD and MCI groups were compared. Also, potential predictors of MCI were analyzed by binary logistic regression analyses. The total scores of VMPT and the scores of VMPT subgroups, including immediate memory, highest learning, total learning, and delayed recall, were significantly higher in the MDD groups compared to MCI patients (Mann-Whitney U, Student's t-test, p < 0.05), indicating that higher scores were associated with better memory. The total MVMRS score and a subgroup of MVMRS, the SA score, were significantly higher in MCI patients compared to the MDD group, suggesting more atrophic changes and a higher burden of infarction in MCI patients. In our statistical analyses, impaired immediate memory (p < 0.001; OR = 6.002; 95% CI: 1.996-18.042), increased SA (p = 0.008; OR = 1.522; 95% CI: 1.118-2.073), and education (p = 0.028; OR = 0.84; 95% CI: 0.719-0.981) were significant predictive values obtained through backward Wald elimination in the binary logistic regression model for detecting MCI. Our findings suggest that VMPT may potentially represent a novel neuropsychiatric test that might be combined with MRI-based morphometric evaluation methods, such as MVMRS.

Alteration of functional connectivity network in population of objectively-defined subtle cognitive decline.

The objectively-defined subtle cognitive decline individuals had higher progression rates of cognitive decline and pathological deposition than healthy elderly, indicating a higher risk of progressing to Alzheimer's disease. However, little is known about the brain functional alterations during this stage. Thus, we aimed to investigate the functional network patterns in objectively-defined subtle cognitive decline cohort. Forty-two cognitive normal, 29 objectively-defined subtle cognitive decline and 55 mild cognitive impairment subjects were included based on neuropsychological measures from the Alzheimer's disease Neuroimaging Initiative dataset. Thirty cognitive normal, 22 objectively-defined subtle cognitive declines and 48 mild cognitive impairment had longitudinal MRI data. The degree centrality and eigenvector centrality for each participant were calculated by using resting-state functional MRI. For cross-sectional data, analysis of covariance was performed to detect between-group differences in degree centrality and eigenvector centrality after controlling age, sex and education. For longitudinal data, repeated measurement analysis of covariance was used for comparing the alterations during follow-up period among three groups. In order to classify the clinical significance, we correlated degree centrality and eigenvector centrality values to Alzheimer's disease biomarkers and cognitive function. The results of analysis of covariance showed significant between-group differences in eigenvector centrality and degree centrality in left superior temporal gyrus and left precuneus, respectively. Across groups, the eigenvector centrality value of left superior temporal gyrus was positively related to recognition scores in auditory verbal learning test, whereas the degree centrality value of left precuneus was positively associated with mini-mental state examination total score. For longitudinal data, the results of repeated measurement analysis of covariance indicated objectively-defined subtle cognitive decline group had the highest declined rate of both eigenvector centrality and degree centrality values than other groups. Our study showed an increased brain functional connectivity in objectively-defined subtle cognitive decline individuals at both local and global level, which were associated with Alzheimer's disease pathology and neuropsychological assessment. Moreover, we also observed a faster declined rate of functional network matrix in objectively-defined subtle cognitive decline individuals during the follow-ups.

DEMENTIA AND MILD COGNITIVE IMPAIRMENT: PREVALENCE, CONVERSION RATES, AND LONGEVITY BETWEEN AGES 70 AND 95

Abstract This study describes the prevalence and conversion rates of Mild Cognitive Impairment (MCI) and dementia between age 70-95, and the association between cognitive status and mortality. The Jerusalem Longitudinal Study (1990-2023) prospectively follows a representative community-dwelling cohort born 1920-21, assessed at home visit during 1990,1998, 2005, 2010, 2015 at ages 70, 78, 85, 90, and 95 (n=437, 806, 1170, 584, 468) respectively. Comprehensive assessment included Mini Mental State Examination (MMSE), defining normal cognition (29-30/30), MCI (24-28/30) and dementia (≤23/30). Mortality data were collected from the Ministry of Interior. Cox proportional hazards ratios (HR) were determined, adjusting for gender, education, self-rated health, physical activity, BADL status, diabetes, hypertension, ischemic heart disease. At ages 70, 78, 85, 85, 90, and 95 the prevalence of MCI was 13.5%, 21.5%, 38.7%, 37.0%, and 50%; prevalence of dementia was 3.2%, 4.2%, 24.3%, 31.3%, 35.7%. Between age 70-78, 78-85, 85-90, 90-95, conversion rates from MCI back to normal were 74.2%, 27.4%, 18.2%, 7.3%; MCI declining to dementia were 6.5%, 29.2%, 37.0%, 50.9%; MCI remaining MCI were 19.4%, 43.4%, 44.8%, 41.8%. Dementia was significantly associated with increased mortality throughout the entire study period: at age 70-78 Hazards Ratio=4.36 (CI 95%: 2.09-9.10), age 90-95 HR=1.5 (CI 95%: 1.01-2.21). In contrast, MCI was not associated with increased mortality, apart from at age 85: HR=1.38 (CI 95%: 1.04-1.82). Our findings show that conversion rates from MCI to dementia are age-dependent, with less than half of MCI subjects progressing to dementia. Unlike dementia, MCI was largely unassociated with mortality.

Macro and microarchitectural features of sleep in Alzheimer’s dementia and mild cognitive impairment in a large clinical cohort

AbstractBackgroundRecent work has characterized polysomnographic changes in Alzheimer’s Dementia (AD) that correlate with disease severity and precede clinical diagnosis. However, relatively small sample sizes and a paucity of studies examining sleep microarchitecture have been limitations. In this study we examined sleep micro‐ and microarchitectural features in large cohorts of patients with AD and mild cognitive impairment (MCI).MethodUsing a database of over 15,000 clinical PSGs with associated clinical metadata, 125 and 173 subjects were identified with mild AD and MCI, respectively. 260 age and sex matched subjects without neurological disease were identified as healthy controls (HC). Sleep staging results were based on AASM criteria, and micro‐architectural features were assessed using an algorithm trained to predict wake (1) or sleep (0) using spectral data from 3 seconds of EEG from a unique healthy adult cohort (Wake EEG Similarity Index, WESI).ResultSleep fragmentation was apparent in both the MCI and AD cohorts (relative to healthy controls), with AD subjects demonstrating significantly more severe PSG disturbances. Healthy controls demonstrated 400 minutes of total sleep time, in contrast to 374 minutes for MCI subjects and 362 minutes for healthy controls. Healthy subjects demonstrated 33 minutes of wake after sleep onset (WASO), compared to 60 and 67 minutes for MCI and AD subjects, respectively. As has been previously reported in smaller cohorts, wakeful bouts and N1 durations were significantly prolonged for AD and (to a lesser extent) MCI subjects, while N2, N3, and REM durations were reduced (with a pronounced reduction in AD N3 percentage). Sleep microarchitectural features assessed using WESI demonstrated that AD and MCI subjects had markedly higher relative wakefulness in all sleep stages except for N3, which showed the same sleep depth as HC.ConclusionWe confirm increased sleep fragmentation and loss of deeper sleep stages, correlating to disease severity. Spectral features of all sleep stages EXCEPT N3 appear more wakeful in AD and MCI subjects ‐ suggesting microarousals or impaired sleep maintenance contribute to sleep pathology. While N3 duration was markedly reduced, depth of sleep was comparable between populations. PSG findings offer promise as early diagnostic and therapeutic targets.

EEG microstates as a “real time” feedback for rTMS treatment efficacy in MCI subjects

AbstractBackgroundNew treatments for the Alzheimer’s disease (AD) are evolving. There is a need for cheap, immediate and available treatment feedback biomarker which can demonstrate rapidly treatment success/failure. We used EEG microstates for this purpose while using repetitive Trans Magnetic stimulation (rTMS) treatment. Four types of microstates are identified according to EEG topography. They are characterized by their duration, frequent appearance and the transitions among the different microstates. The possibility of using microstates properties as a biomarker for rTMS treatment efficacy in mild cognitive impairment (MCI) subjects was explored.MethodsWe recruited 14 MCI subject to receive 16 rTMS sessions. EEG microstates were analyzed immediately before and after each treatment. We used 3 rTMS predefined protocols. The preferred protocol, for each patient, was selected using microstates duration. All patients performed a comprehensive neuro‐cognitive evaluation before rTMS treatment. 11 subjects completed a second neuro‐cognitive evaluation after treatment period ended.ResultsMicrostates duration change was noticed before and after each rTMS treatment. Duration variation aided deciding which rTMS protocol is preferred in all participants. Moreover, new rTMS protocol was designed, for 3 patients, using microstates parameters. Cognitive performance was associated with microstate duration (p&lt;0.05).ConclusionsMicrostates duration may serve as a “real time” treatment efficacy biomarker for MCI subjects. Further work should be done in order to validate this method.

NeuroAI: AI‐based prediction model of Amyloid PET positivity using MR images and APOE genotype

AbstractBackgroundSince acquiring amyloid PET data is costly and long waiting period due to the inaccessibility of PET machine, predicting positivity of amyloid PET data has many benefits. Therefore, we aimed to develop an artificial intelligence (AI) model for predicting amyoloid PET positivity for mild cognitive impairment (MCI) subjects with MR images and APOE genotype.MethodA total 1,742 MCI subjects were enrolled in the Gwangju Alzheimer’s and Related Dementia cohort registry, Korea. We trained the deep learning model using MR images (T1 and Flair) and their APOE genotyping data. Finally, we conducted an external validation with 227 MCI subjects (118 with Aβ(+) and 109 with Aβ(‐)) from Chonnam National University Hospital and Chosun University Hospital in Korea.ResultWith independent test set, our model had the great performance with an AUC of 0.84 (0.78‐0.89), Accuracy of 78% (72‐83%), Sensitivity of 73% (64‐81%), and Specificity of 83% (74‐89%). The Korea Ministry of Food and Drug Safety granted clearance for the Phase III clinical trail of our model (NeuroAI) in March 2023 (NCT05383053).ConclusionOur model could help reduce the need for costly and time‐consuming amyloid PET scans, while also improving the accuracy of Alzheimer’s Disease diagnosis.

Homocysteine, vitamin B12, folic acid levels and its association with Mild Cognitive Impairment in urban and rural dwelling Indians

AbstractBackgroundHomocysteine (Hcy) is a sulfur containing amino acid generated in the metabolism of methionine involving co‐factors such as B vitamins and folic acid. Deficiency of these co‐factors have been associated with increased Hcy levels (Hutto, 1997). Relationship between differing levels of plasma Hcy, vitamin B12, folic acid and cognitive impairment is inconclusive.MethodCross sectional analysis was done using the baseline data from two ongoing longitudinal studies such as Tata Longitudinal Study of Ageing (TLSA, N = 984) and Srinivaspura Aging, Neuro Senescence and COGnition (SANSCOG, N = 4404), an urban and rural cohorts (Sundarakumar et al, 2020) respectively. Hcy, vitamin B12, folic acid levels were compared between the subjects of both cohorts. Comparison of these levels between healthy and MCI (Mild Cognitive Impairment) subjects within and across the cohorts were done. CDR (Clinical Dementia Rating) scale was used to classify subjects having normal cognition and MCI, considering score of 0.5 as MCI. Mann‐Whitney U test was used to compare these levels between the groups.ResultHcy, vitamin B12, folic acid levels between the two cohorts showed significant difference, with higher levels of Hcy (median (IQR)) = (17.08 (11) vs 14.63 (10); p &lt;0.001), vitamin B12 (247 (252) vs 220 (143); p &lt;0.001) and folic acid (8.55 (9) vs 5.55(4); p &lt;0.05) in TLSA than SANSCOG. Significant difference seen in Hcy (17.73(11) vs 14.73 (10); p &lt;0.001), vitamin B12 (246 (249) vs 220 (136); p &lt;0.001) and folic acid (8.42 (9) vs 5.53 (4): p &lt;0.001) levels for healthy subjects between cohorts, higher levels were seen in TLSA subjects. Hcy (16.08 (8) vs 14.43 (12); p &lt;0.001) vitamin B12 (305 (341) vs 216 (174); p = 0.03) and folic acid (8.52 (9) vs 5.34 (4): p &lt;0.001) levels were significantly high in TLSA than SANSCOG for MCI subjects. However, these levels were not significantly different between healthy and MCI subjects within cohorts.ConclusionConsistently elevated levels of Hcy was observed in urban cohort. Though elevated level of Hcy was observed with normal vitamin B12 and folic acid, it was not associated with MCI.

Personality and cognition in mild cognitive impairment: predictive role of neuroticism

AbstractBackgroundPersonality traits are usually defined as recurrent and distinctive patterns of thoughts, sensations and actions that present in response to specific requests. In the elderly, personality functioning has been associated with dementia risk. However, the relationship between personality traits and cognitive function in subject at risk of cognitive decline has not been explored yet. The aim of this study is to assess the predictive role of personality traits on cognitive and psychopathological function in subjects with mild cognitive impairment (MCI).Method45 subjects (mean age: 73.6±8.9; mean education: 11.2±4.7) with a diagnosis of MCI e 50 healthy older adults (OA) (mean age: 70.8±6.9; mean education: 9.7±3.7) were enrolled. All participants underwent a neuropsychometric assessment including the evaluation of visuo‐spatial, executive and memory functions, as well as behavioural symptoms with the Neuropsychiatric Inventory questionnaire. Personality traits were evaluated with the Eysenck Personality Questionnaire (EPQ‐R).ResultMCI subjects showed significantly poorer performances on visuo‐spatial, executive and memory tasks compared to OA, as well as more severe depression, apathy, irritability, and sleep disturbances. MCI subjects had higher prevalence of neurotic and introvert traits on the personality test. In MCI subjects, neuroticism was correlated with performance on word repetition test and stroop test, as well as with higher agitation and depression. Introvertion trait was correlated with MMSE and Trail Making test scores. Regression analysis showed a signtificant association between neuroticism, executive function and depression in MCI.ConclusionSpecific personality traits, especially neuroticism, might represent a predictive factor for executive functions decline and for depression in MCI. A deeper understanding of personality functioning in subjects with MCI might help develop more tailored treatments aimed at dementia prevention in these subjects.

Defining tau pathology and neurodegeneration by dual‐phase 18F‐Flortaucipir PET as diagnostic and prognostic biomarkers in a memory clinic setting

AbstractBackground 18F‐Flortaucipir‐PET allows visualization of tau deposits (T) representing a powerful diagnostic and prognostic tool for Alzheimer’s disease (AD). Dual‐phase 18F‐Flortaucipir‐PET can also evaluate neurodegeneration (N) through the early‐phase images. We aim to assess the diagnostic and prognostic power of visual and semi‐quantitative T assessment and the potential added value of early‐phase images.MethodWe included 245 subjects from the Geneva Memory Clinic who underwent 18F‐Flortaucipir‐PET (157 with a dual‐phase protocol). Amyloid (Aβ) status was available for 207 subjects and clinical follow‐up for 135. T positivity and a Braak staging estimate were evaluated visually and semi‐quantitatively by standardized uptake value ratio (SUVr) in AD‐related regions. N status was semi‐quantitatively defined using early‐phase images as a summary metric in the AD meta‐ROI region. Receiver Operating Characteristic (ROC) analyses were applied to compare T and N discriminative power. Linear mixed‐effects models tested the prognostic values of T alone and T combined with N (T/N profiles).ResultThe mean age of the participants was 70.49±9.13 years, 52.2% were females and 55.7% were Aβ+. 53.4% of subjects had Mild Cognitive Impairment (MCI), 19.8% had dementia and 26.7% were cognitively unimpaired. All T‐assessment modalities significantly discriminated Aβ+ MCI and demented subjects from others (AUC&gt;0.80), Aβ+ from Aβ‐ subjects (AUC&gt;0.85), and cognitive decliners from stable individuals at follow‐up (AUC&gt;0.79). The discriminative power of N, assessed by early‐phase images, was lower compared to T, with AUC ranging from 0.63 in discriminating Aβ+ MCI and demented subjects from others to 0.71 for discriminating Aβ+ T+ from Aβ‐ T‐ subjects. Linear mixed‐effect models showed that T+ individuals presented a significantly faster cognitive decline than the T‐ group (p&lt;0.001) as well as T+/N+ and T+/N‐ compared to T‐/N‐ group (p&lt;0.001), independently from T classification methods.ConclusionOur results show a similar diagnostic and prognostic power of visual and semi‐quantitative T evaluation of 18F‐Flortaucipir‐PET, proving its high potential for clinical applicability. The N status assessment with early‐phase images did not improve the prediction of cognitive decline. Its usefulness for differential diagnosis by the topographical information provided should be investigated.