Early Stages Of Cognitive Decline Research Articles

Subjective cognitive decline in healthy older adults is associated with altered processing of negative versus positive feedback in a probabilistic learning task

Older adults who worry about their own cognitive capabilities declining, but who do not show evidence of actual cognitive decline in neuropsychological tests, are at an increased risk of being diagnosed with dementia at a later time. Since neural markers may be more sensitive to early stages of cognitive decline, in the present study we examined whether event-related potential responses of feedback processing, elicited in a probabilistic learning task, differ between healthy older adults recruited from the community, who either did (subjective cognitive decline/SCD-group) or did not report (No-SCD group) worry about their own cognition declining beyond the normal age-related development. In the absence of group differences in learning from emotionally charged feedback in the probabilistic learning task, the amplitude of the feedback-related negativity (FRN) varied with feedback valence differently in the two groups: In the No-SCD group, the FRN was larger for positive than negative feedback, while in the SCD group, FRN amplitude did not differ between positive and negative feedback. The P3b was enhanced for negative feedback in both groups, and group differences in P3b amplitude were not significant. Altered sensitivity in neural processing of negative versus positive feedback may be a marker of SCD.

Finger Food Meals as a Means of Improving Mealtimes for People with Motoric Eating Difficulties: A Pilot Study

Motoric eating difficulties affecting the ability to eat according to established norms may result in loss of autonomy, reduced food intake and decreased social interaction. Finger food meals may affect the ability to eat independently and were therefore compared to regular meals for older adults >65 years with major motoric eating difficulties. In this pilot study the screening instrument MEOF-II, including additional questions about use of cutlery and fingers, was used to collect data regarding autonomy, food intake and social interaction through observations. Five women and one man participated in the study. Results showed that finger food meals facilitated autonomous eating since the participants were able to eat independently without relying on help from others. Less energy was spent on eating, which allowed for social interaction. However, finger food meals entail unfamiliar norms and culinary rules which may hinder eating; this is an important factor to consider in the implementation of such meals. Further studies on finger foods for older adults may consider larger and diverse cohorts, including healthy older adults, those with motoric difficulties and those with early stages of cognitive decline. Also, a wider variety of finger foods for specific cultural preferences and situations may be considered.

Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions.

This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.

Skeletal Muscle Mitochondrial Response to Diet Differs by Genotype and Sex in an Alzheimer’s Disease Mouse Model

Introduction: Skeletal muscle mass is reduced in the early stages of cognitive decline. Apolipoprotein ε4 ( APOE4) is the most prevalent genetic risk factor for Alzheimer’s disease (AD) and is associated with faster motor decline in cognitively healthy individuals. APOE4 also increases the risk of insulin resistance and cardiovascular disease, conditions associated with altered bioenergetic function in muscle. However, no studies have examined the role of APOE4 in modulating muscle physiology and the impact this would have on cognitive function. We sought to determine if APOE4 affects skeletal muscle mitochondrial metabolism in response to a high-fat diet (HFD) and if this is reflected in whole-body metabolism. We also examined the role of sex since APOE4 carriers who are female are more likely to develop AD. Methods: Male and female APOE3 (n=32) and APOE4 (n= 29) targeted-replacement mice on a C57BL/6 background purchased from Taconic were fed a standard chow diet until 4 months old. Mice were then switched to a low-fat diet (LFD, 10% kcal fat) or HFD (45% kcal fat) until sacrifice. Glucose tolerance was tested at 7 months by administering an intraperitoneal glucose injection of 1g/kg lean mass. Blood glucose was measured before and 15, 30, 45, 60, 90 and 120 minutes after glucose injection. Resting respiratory exchange ratio (RER) was determined using the Promethion Indirect Calorimetry system at 8 months to determine the relative contribution of carbohydrates versus lipids to whole body metabolism. Mice were sacrificed at 9 months and carbohydrate-driven oxygen consumption was measured at basal, state 3 (ADP), state 3 + glutamate, state 3S (succinate) and uncoupled respiratory states in mitochondria isolated from quadriceps tissue on the Oroboros Oxygraph-2k system. Results: Mice fed a HFD had greater glucose area under the curve (p<0.0001) and reduced RER (p= <0.001) compared to mice fed a LFD, regardless of sex or genotype. At the level of skeletal muscle, APOE4 female mice had increased state 3 (p=0.027) and state 3 + glutamate oxygen consumption (p=0.037) on a HFD compared to a LFD while no diet effect was observed in APOE4 males or any APOE3 mice. Conclusion: We provide evidence that female APOE4 mice are susceptible to diet-induced increases in carbohydrate-driven mitochondrial oxygen consumption in muscle. Future studies are needed to determine the underlying mechanism and consequences of increased muscle mitochondrial activity, which could help identify treatment targets in female APOE4 carriers. BHTP T32 AG078114, P30 AG072973 (Chelsea Johnson). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Cognitive and functional performance and plasma biomarkers of early Alzheimer's disease in Down syndrome.

People with Down syndrome (DS) have a 75% to 90% lifetime risk of Alzheimer's disease (AD). AD pathology begins a decade or more prior to onset of clinical AD dementia in people with DS. It is not clear if plasma biomarkers of AD pathology are correlated with early cognitive and functional impairments in DS, and if these biomarkers could be used to track the early stages of AD in DS or to inform inclusion criteria for clinical AD treatment trials. This large cross-sectional cohort study investigated the associations between plasma biomarkers of amyloid beta (Aβ)42/40, total tau, and neurofilament light chain (NfL) and cognitive (episodic memory, visual-motor integration, and visuospatial abilities) and functional (adaptive behavior) impairments in 260 adults with DS without dementia (aged 25-81 years). In general linear models lower plasma Aβ42/40 was related to lower visuospatial ability, higher total tau was related to lower episodic memory, and higher NfL was related to lower visuospatial ability and lower episodic memory. Plasma biomarkers may have utility in tracking AD pathology associated with early stages of cognitive decline in adults with DS, although associations were modest. Plasma Alzheimer's disease (AD) biomarkers correlate with cognition prior to dementia in Down syndrome.Lower plasma amyloid beta 42/40 was related to lower visuospatial abilities.Higher plasma total tau and neurofilament light chain were associated with lower cognitive performance.Plasma biomarkers show potential for tracking early stages of AD symptomology.

The Value of Subjective Olfactometry as a Predictive Biomarker of Neurodegenerative Diseases: A Systematic Review.

Olfactory disorders (ODs) are reported to be an early non-motor sign before the onset of deterioration in neurodegenerative diseases (NDs) such as Alzheimer's and Parkinson's. This systematic revision aims to review the current literature and the value of subjective olfactometry (SO) in the early diagnosis of cognitive decline and NDs. A systematic literature review was conducted following the PRISMA framework. Four different authors reviewed six different databases. The main variables analyzed were olfactory function and cognitive status. The quality of results was evaluated using the Oxford Centre of Evidence-based Medicine Levels. Twenty-one cross-sectional and cohort studies and six meta-analyses were included. Most of them found an association between ODs and NDs. A prevalence of ODs greater than 80% was shown in Parkinson's disease, proportional to the severity of symptoms. In Alzheimer's, ODs were associated with early diagnosis and prognosis. All SO tests employed in the literature showed enough predictive value to correlate with early stages of cognitive decline. SO should be considered a pivotal tool when diagnosing NDs due to their association with early symptoms and prognosis. However, in the current literature, no firm consensus exists on the optimal SO tests and protocols that should be applied to the study of NDs, which prevents the interpretability and comparability of results among studies.

Using 6-CIT, P300 encephalography, and pro-inflammation assessments for screening age-related cognitive decline and exploring associated risk factors in Egyptian elderly

BackgroundThe elderly population is suffering from many mental health problems that are aggravated as a matter of age and cognitive decline is a serious one of them. The aim of the present work is to screen the cognitive performance among a sample of Egyptian elderly volunteers and to investigate the associated risk factors.ResultsA sample of 88 elderly volunteers from both genders was enrolled in the study according to the specified eligibility criteria after signing the approval consent. Medical history and socio-demographic data were collected from all participants in addition to basic clinical examination. Cognitive performance was assessed using the 6-Item Cognitive Impairment Test (6-CIT) while the endogenous event-related potentials (ERP) was measured using P300. The inflammatory biomarkers; TNF-α and COX-2 levels were assessed in serum using ELISA technique in addition to gene expression of TNF-α, PPAR-γ and CD-36 exploration using qRT-PCR. About half (51%) of the sample under investigation showed cognitive problems with scores on the 6-CIT exceeding the normal level. TNF-α serum levels showed positive correlation with P300 latency and correlated negatively with P300 reaction time. Furthermore, serum COX-2 levels correlated positively with P300 reaction time and negatively with P300 amplitude.ConclusionThe study population is showing early signs of cognitive decline that invites attention to the importance of spreading preventive measures against further deterioration. Inflammatory biomarkers under investigation and 6-CIT are suggested to be used in prediction of early stages of cognitive decline among the elderly population.

Clinical importance in Alzheimer’s disease: effects of anchor agreement and disease severity

ObjectivesMethods of evaluating clinically meaningful decline are critical in research on Alzheimer’s disease. A common method of quantifying clinically meaningful change is to calculate an anchor-based minimal clinically important difference (MCID) score. In this approach, individuals who report a meaningful change serve as the “anchors”, and the mean level of change for this group serves as the MCID. In research on Alzheimer’s disease, there are several possible anchors, including patients, knowledgeable observers (e.g., a family member), and clinicians. The goal of this study was to examine the extent to which agreement among anchors impacts MCID estimation and whether this relationship is moderated by cognitive severity status.MethodsAnalyses were completed on a longitudinal sample of 2247 adults, aged 50–103, from the Uniform Data Set. Outcome measures included the Montreal Cognitive Assessment, Clinical Dementia Rating—Sum of Boxes, and Functional Activities Questionnaire.ResultsFor all of the outcomes, the MCID estimate was significantly higher when meaningful decline was endorsed by all of the anchors compared to when there was disagreement among the anchors. In addition, the MCID estimate was higher with increasing severity of cognitive impairment. Finally, cognitive severity status moderated the influence of agreement among anchors on MCID estimation; as disease severity increased, anchor agreement demonstrated less influence on the MCID.ConclusionsMCID estimates based on one anchor may underestimate meaningful change, and researchers should consider the viewpoints of multiple anchors in constructing MCIDs, particularly in the early stages of cognitive decline.

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods often rely on personal interpretations or biases, may not detect the early stages of cognitive decline, or involve invasive screening procedures; thus, there is a growing interest in developing non-invasive methods benefiting also from the technological advances. Wearable devices and Internet of Things sensors can monitor various aspects of daily life together with health parameters and can provide valuable data regarding people’s behavior. In this paper, we propose a technical solution that can be useful for potentially supporting cognitive decline assessment in early stages, by employing advanced machine learning techniques for detecting higher activity fragmentation based on daily activity monitoring using wearable devices. Our approach also considers data coming from wellbeing assessment questionnaires that can offer other important insights about a monitored person. We use deep neural network models to capture complex, non-linear relationships in the daily activities data and graph learning for the structural wellbeing information in the questionnaire answers. The proposed solution is evaluated in a simulated environment on a large synthetic dataset, the results showing that our approach can offer an alternative as a support for early detection of cognitive decline during patient-assessment processes.

Effects of Cognitive‐motor Rhythmic Training on Executive Functions and Motor Performance in Older Adults with Mild Cognitive Impairment: A Randomized Controlled Trial

AbstractIntroductionIn recent years, prevalence of mild cognitive impairment (MCI) has increased in older adults, exacerbating the potential impact on global health. Cognitive‐motor training is an emerging treatment modality with proven benefits in enhancing both physical and cognitive functions. In recent research findings, training of musical rhythmic skills with auditory‐motor synchronization is beneficial to reactivate the brain’s sensorimotor and perceptual regions as well as to improve cognition. However, there is limited research on combining these modalities.ObjectivesThis pilot study aims to develop a pioneer program on cognitive‐motor rhythmic training (CMRT), and to investigate whether the program would improve the executive functions (EF) and hand functions of patients with MCI.MethodologyThirty patients recruited from the Hong Kong Buddhist Hospital were randomized into two groups: (i) an intervention group receiving a 6‐week training program and (ii) a waitlist control group. The program was designed with reference to the rhythm game “Taiko no Tatsujin”, in which precise timed motor responses were required to hit the circular cues against the target zone. The game was designed with progression of difficulty levels and instant feedback. Classical Cantopop songs that were familiar to Hong Kong older adults were selected. Mental flexibility, inhibitory control, working memory, visuospatial memory, verbal fluency, processing speed and problem‐solving were assessed using domain‐specific cognitive tests, while hand dexterity was measured by the Purdue Pegboard Test. Paired t‐test and ANOVA were used to examine the within group difference and the comparison between groups. The level of significance was set at P<0.05.ResultsThe analysis showed significant Time x Group interaction effects in Color Trail Test (p = 0.001), Color Word Stroop Test (P = 0.002), Digit Span Test (P<0.001), Corsi Block‐Tapping Test (P<0.001), Verbal Fluency Test (P<0.001), Symbol Digit Modalities Test (P = 0.015), Maze Test (P<0.001) and the Purdue Pegboard scores (P<0.001) compared to the control group after training.ConclusionsThe 6‐week CMRT program is effective in improving the executive functions and motor performance in patients with MCI. This innovation can serve as an affordable and quality intervention in early stages of cognitive decline.

Suboptimal sleep efficiency and duration predict faster cognitive decline in cognitively unimpaired older adults

AbstractBackgroundResearch indicates a role for suboptimal sleep, in cognitive ageing. An increasing, though inconsistent body of evidence has associated aspects of suboptimal self‐reported sleep quality with poorer cognitive performance. The current study investigated whether self‐reported sleep quality parameters predict the slope of domain specific cognitive performance.MethodParticipants were drawn from the Australian Imaging, Biomarkers and Lifestyle study of ageing. Linear mixed effect model (LMM) analyses were conducted on 525 cognitively unimpaired, older adults (M = 74.1, SD = 5.5, 56.4% female), characterised by apolipoprotein E (APOE) ε4 status, with baseline self‐reported sleep characteristics. Comprehensive cognitive assessments were conducted over a minimum 3 timepoints (range 29.64 ‐ 98.16 months). All models included random slopes, random intercepts, interaction effects for time, and APOE ε4 carriage, with each model adjusted based on the model of best fit for each cognitive outcome variable of interest.ResultFollowing Bonferroni correction, sleep duration <6 hours and sleep efficiency <65% in the whole cohort, and in APOE ε4 non‐carriers, forecasted accelerated decline in attention and speed of processing. In the same groups, greater sleep efficiency (%) forecasted greater preservation within the attention and speed of processing domain. No associations were observed for other sleep variables or cognitive domains of interest.ConclusionThese findings suggest a role for self‐reported suboptimal sleep efficiency and duration in the early stages of cognitive decline, represented by declining attention and speed of processing performance, in cognitively unimpaired individuals. Additionally, short sleep duration and poor sleep efficiency may increase future risk of cognitive decline independent of APOE ε4 carriage. Our findings also suggest the potential utility of self‐report screening for sleep efficiency and duration as a marker for increased risk of declining attention and speed of processing performance. Individuals with suboptimal sleep may benefit from an intervention to improve sleep efficiency and duration to lessen future risk of cognitive decline.

Fluid Biomarkers of Neurodegeneration in Mild Cognitive Impairment: A Meta‐Analysis

AbstractBackgroundMild cognitive impairment (MCI) is a well‐established prodromal stage of Alzheimer’s disease and is often accompanied by early signs of neurodegeneration (e.g., neuronal loss) that may influence subsequent prognosis. To facilitate a better characterization of the underlying neuropathology, a thorough investigation of proteins associated with neurodegenerative processes in MCI is needed. Thus, the present study systematically assessed the available literature to quantitatively evaluate cerebrospinal fluid (CSF) and peripheral blood concentrations of biomarkers related to neurodegeneration in individuals with MCI compared to healthy controls (HCs).MethodOriginal peer‐reviewed articles that assessed the following CSF and/or peripheral biomarkers: neurofilament light chain (NFL), total‐tau (T‐tau), glial fibrillary acidic protein (GFAP), heart‐type fatty acid binding protein (HFABP), neuron‐specific enolase (NSE), and S100 calcium‐binding protein B (S100B) in both MCI and HCs were included for meta‐analysis. Standardized mean differences (SMDs) and 95% confidence intervals were calculated using a random‐effects model. Heterogeneity was assessed using the I2 statistic.ResultA total of 109 study cohorts were included in the meta‐analysis. In CSF, concentrations of NFL (MCI n = 2771, HC n = 4263, SMD [95% CI] = 0.69 [0.56, 0.83], p < 0.001), GFAP (MCI n = 513, HC n = 1620, SMD [95% CI] = 0.41 [0.08, 0.74], p = 0.02), and HFABP (MCI n = 363, HC n = 245, SMD [95% CI] = 0.57 [0.23, 0.55], p < 0.001) were elevated in MCI. In peripheral blood, concentrations of NFL (MCI n = 6520, HC n = 9439, SMD [95% CI] = 0.41 [0.32, 0.49], p < 0.001), T‐tau (MCI n = 4463, HC n = 5921, SMD [95% CI] = 0.19 [0.09, 0.29], p < 0.001), and GFAP (MCI n = 1758, HC n = 3179, SMD [95% CI] = 0.39 [0.23, 0.55], p < 0.001) were increased in MCI. Significant heterogeneity was identified for all comparisons and was explored through meta regression and subgroup analysis.ConclusionThe present study identified several fluid biomarkers of neurodegeneration in MCI. Importantly, elevated biomarker levels can be detected in both CSF and less‐invasively through peripheral blood. Future studies should focus on assessing the clinical utility of monitoring these biomarkers as they may provide further insight into neuronal and astroglial pathology occurring in the early stages of cognitive decline.

Comparison of sleep and wake EEG biomarkers in mild cognitive impairment and Alzheimer’s disease

AbstractBackgroundEEG slowing, a biomarker of cognitive decline in Alzheimer’s disease (AD), has been reported in wake or sleep EEG [1,2,3]. However, the relation between wake and sleep EEG and their comparative sensitivity to early stage of cognitive decline is less understood. In this study we compared sleep and wake EEG biomarkers using a common set of endpoints.MethodsParticipants included individuals with mild cognitive impairment (MCI, n = 30, ages 53‐88), Alzheimer’s disease dementia (ADem, n = 15, ages 60‐84), and controls with normal cognition (HC, n = 55, ages 50‐84). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). Sleep EEG recordings from AF7‐AF8 were collected in‐home and staged using Sleep ProfilerTM. Power spectral densities (PSD) and the derived Theta‐Alpha ratios were computed for wake EEG sites (counterpart bipolar montage), with the wake FP1‐FP2 compared by stage to the sleep EEG at AF7‐AF8. Significant differences in Theta‐Alpha ratios between MCI or ADem vs. HC groups were identified using independent t‐test (p<0.05) with the normalized effect size measured using Hedges‐g (E.S.).ResultsCorrelation coefficients between wake Alpha, Theta power and non‐REM sleep were: r = 0.83, 0.75 for stage N1, r = 0.71, 0.66 for stage N2, and r = 0.55, 0.50 for stage N3. Based on the frontal Theta‐Alpha ratios, the ADem group showed significant EEG slowing during both wake (E.S. = 0.73) and sleep (E.S. = 1.65, 1.31, 0.61, 1.71 for N1, N2, N3, and REM) as compared to the HC, while MCI group only exhibited EEG slowing during sleep (E.S. = 0.56, 0.77, 0.51 for N1, N2 and REM). Temporal (T5‐T6) wake EEG manifested significant slowing in both ADem (E.S. = 1.21) and MCI (E.S. = 0.56) groups. Compared to the HC, ADem patients exhibited significantly less sleep time (E.S. = 0.91, p = 0.002) and less stage REM (E.S. = 1.0, p = 0.001).ConclusionsCognitive decline in the AD spectrum is associated with common signatures in both sleep and wake EEG. However, the magnitude of these effects and their spatial distribution may be different in sleep versus wakefulness. These findings support a multi modal approach to neurophysiological assessment using both sleep and wakefulness data.

Development of a monoclonal antibody specific for a calpain-generated ∆48 kDa calcineurin fragment, a marker of distressed astrocytes

BackgroundCalcineurin (CN) is a Ca2+/calmodulin-dependent protein phosphatase. In healthy tissue, CN exists mainly as a full-length (∼60 kDa) highly-regulated protein phosphatase involved in essential cellular functions. However, in diseased or injured tissue, CN is proteolytically converted to a constitutively active fragment that has been causatively-linked to numerous pathophysiologic processes. These calpain-cleaved CN fragments (∆CN) appear at high levels in human brain at early stages of cognitive decline associated with Alzheimer’s disease (AD). New methodWe developed a monoclonal antibody to ∆CN, using an immunizing peptide corresponding to the C-terminal end of the ∆CN fragment. ResultsWe obtained a mouse monoclonal antibody, designated 26A6, that selectively detects ∆CN in Western analysis of calpain-cleaved recombinant human CN. Using this antibody, we screened both pathological and normal human brain sections provided by the University of Kentucky’s Alzheimer’s Disease Research Center. 26A6 showed low reactivity towards normal brain tissue, but detected astrocytes both surrounding AD amyloid plaques and throughout AD brain tissue. In brain tissue with infarcts, there was considerable concentration of 26A6-positive astrocytes within/around infarcts, suggesting a link with anoxic/ischemia pathways. Comparison with existing methodThe results obtained with the new monoclonal are similar to those obtained with a polyclonal we had previously developed. However, the monoclonal is an abundant tool available to the dementia research community. ConclusionsThe new monoclonal 26A6 antibody is highly selective for the ∆CN proteolytic fragment and labels a subset of astrocytes, and could be a useful tool for marking insidious brain pathology and identifying novel astrocyte phenotypes.

14 A Potential Behavioral Sign for Detection of Mild HIV-Related Neuropsychological Impairment

Objective:Even though the severity of HIV-associated neurocognitive disorders (HAND) has decreased with the introduction of combination antiretroviral therapy, mild forms of HAND remain prevalent. Many HIV-infected individuals live alone, so mild cognitive impairments are easily missed. It is important to check their neurocognitive and everyday functions during hospital visits; however, it is challenging for Japanese clinicians because many hospitals do not have enough clinical psychologists or neuropsychologists. Additionally, neuropsychological (NP) test results may not detect those mild cognitive impairments. A micro error has been given more attention as a new behavioral sign of the early stages of cognitive decline, especially among people with Mild Cognitive Impairment (MCI). The current study aimed to 1) develop a touch-panel HAND screening battery and 2) evaluate if the micro errors could differentiate individuals with HAND from their counterpart healthy individuals.Participants and Methods:Forty HIV-infected men (age: 49.0±8.51 years old, education: 18.5±2.17 years) and 44 healthy men (age: 45.4±8.49 years old, education: 14.4±2.27 years) completed the touch-panel HAND screening battery which assessed six NP domains by seven subtests, everyday functions, and depression. A micro error is defined as a subtle action disruption or hesitation occurring immediately before making final actions. We evaluated the micro errors in short-term memory (STM) and long-term memory (LTM) of verbal learning tests (VLT).Results:Mann Whitney U tests revealed that the HIV+ group made significantly more micro errors on both STM (HIV+: 1.45±0.90 times, Healthy: 0.52±0.84 times) and LTM (HIV+: 1.85±0.73 times, Healthy: 1.29±0.71 times) than the healthy group (STM: W=1362, p< .001, Effect Size (EF)= .548; LTM: W=1199.5, p= .002, EF= .363). An independent samples T-test showed that the HAND group made significantly more micro errors than the non-HAND group (t=1.822, p= .038, ES= .595) on STM; moreover, the Asymptomatic Neurocognitive Impairment (ANI) group made significantly more micro errors than the healthy group (W=446, p< .001, ES= .689). On LTM, no significant micro error differences between HAND and non-HAND (W=184.5, p= .539, ES= -.189) nor between ANI and healthy group (W=327.5, p= .103, ES= .241) were found.Conclusions:The present study suggests that a novel behavioral measure, micro errors, may be able to help detect even the mildest form of HAND, ANI. Given that the touch-panel HAND screening battery consists of NP and IADL tests, it is important to evaluate micro errors on these various measures. Additionally, the touch-panel screening battery requires minimal administrative staff involvement, which could be beneficial for busy HIV clinicians.

The Relationships between Cerebrospinal Fluid Glial (CXCL12, CX3CL, YKL-40) and Synaptic Biomarkers (Ng, NPTXR) in Early Alzheimer's Disease.

In addition to amyloid and tau pathology in the central nervous system (CNS), inflammatory processes and synaptic dysfunction are highly important mechanisms involved in the development and progression of dementia diseases. In the present study, we conducted a comparative analysis of selected pro-inflammatory proteins in the CNS with proteins reflecting synaptic damage and core biomarkers in mild cognitive impairment (MCI) and early Alzheimer's disease (AD). To our knowledge, no studies have yet compared CXCL12 and CX3CL1 with markers of synaptic disturbance in cerebrospinal fluid (CSF) in the early stages of dementia. The quantitative assessment of selected proteins in the CSF of patients with MCI, AD, and non-demented controls (CTRL) was performed using immunoassays (single- and multiplex techniques). In this study, increased CSF concentration of CX3CL1 in MCI and AD patients correlated positively with neurogranin (r = 0.74; p < 0.001, and r = 0.40; p = 0.020, respectively), ptau181 (r = 0.49; p = 0.040), and YKL-40 (r = 0.47; p = 0.050) in MCI subjects. In addition, elevated CSF levels of CXCL12 in the AD group were significantly associated with mini-mental state examination score (r = -0.32; p = 0.040). We found significant evidence to support an association between CX3CL1 and neurogranin, already in the early stages of cognitive decline. Furthermore, our findings indicate that CXCL12 might be a useful marker for tract severity of cognitive impairment.

Epidemiology of Motoric Cognitive Risk Syndrome in the Kerala Einstein Study: Protocol for a Prospective Cohort Study.

The southern India state of Kerala has among the highest proportion of older adults in its population in the country. An increase in chronic age-related diseases such as dementia is expected in the older Kerala population. Identifying older individuals early in the course of cognitive decline offers the best hope of introducing preventive measures early and planning management. However, the epidemiology and pathogenesis of predementia syndromes at the early stages of cognitive decline in older adults are not well established in India. The Kerala Einstein Study (KES) is a community-based cohort study that was established in 2008 and is based in the Kozhikode district in Kerala state. KES aims to establish risk factors and brain substrates of motoric cognitive risk syndrome (MCR), a predementia syndrome characterized by the presence of slow gait and subjective cognitive concerns in individuals without dementia or disability. This protocol describes the study design and procedures for this KES project. KES is proposing to enroll a sample of 1000 adults ≥60 years old from urban and rural areas in the Kozhikode district of Kerala state: 200 recruited in the previous phase of KES and 800 new participants to be recruited in this project. MCR is the cognitive phenotype of primary interest. The associations between previously established risk factors for dementia as well as novel risk factors (apathy and traumatic brain injury) and MCR will be examined in KES. Risk factor profiles for MCR will be compared between urban and rural residents as well as with individuals who meet the criteria for mild cognitive impairment (MCI). Cognitive and physical function, medical history and medications, sociodemographic characteristics, lifestyle patterns, and activities of daily living will be evaluated. Participants will also undergo magnetic resonance imaging and electrocardiogram investigations. Longitudinal follow-up is planned in a subset of participants as a prelude to future longitudinal studies. KES (2R01AG039330-07) was funded by the US National Institutes of Health in September 2019 and received approval from the Indian Medical Council of Research to start the study in June 2021. We had recruited 433 new participants from urban and rural sites in Kozhikode as of May 2023: 41.1% (178/433) women, 67.7% (293/433) rural residents, and 13.4% (58/433) MCR cases. Enrollment is actively ongoing at all the KES recruitment sites. KES will provide new insights into risk factors and brain substrates associated with MCR in India and will help guide future development of regionally specific preventive interventions for dementia. DERR1-10.2196/49933.

Eye movement changes as an indicator of mild cognitive impairment.

Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data. We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics' odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC). LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840. Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.

Amyloid accumulation and functional connectivity in normal aging and preclinical Alzheimer’s Disease: a longitudinal analysis of the OASIS‐3 cohort

AbstractBackgroundPathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer’s Disease (AD). The deposition of amyloid‐beta (Aβ) proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical AD has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on cross sectional differences in the brains of patients with mild cognitive impairment (MCI) or AD and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes early preclinical phases of neurodegeneration.MethodWe use a longitudinal approach to examine the brain changes that take place during early stages of cognitive decline in the OASIS‐3 dataset. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two repeated measures of cognitive status and amyloid levels). We use mixed‐effect linear models to examine rates of amyloid accumulation in those who showed cognitive decline compared to those who did not change in cognitive status.ResultOver 90% of participants were healthy at baseline. Over 8‐10 years, some participants progressed to very mild dementia (n = 48) while others stayed healthy (n = 226). Participants with cognitive decline have faster amyloid accumulation and greater increases in functional connectivity in similar brain regions, namely among lateral temporal, motor, and some lateral prefrontal cortex areas. These changes were linked to increases in functional connectivity of select default mode, frontoparietal and motor components.ConclusionOur findings advance the understanding of initial amyloid staging in preclinical Alzheimer’s, as well as contribute to the determination of the functional changes that occur among brain network components as the disease progresses.

Combining data and mathematical models for the design of personalised light interventions to improve sleep timing in aging

AbstractBackgroundMore than 70% of people living with dementia (PLWD) experience sleep disturbances (e.g., early bedtime, long time in bed) even in the early stages of cognitive decline. Light interventions have been proposed as a promising non‐pharmacological approach to improve sleep timing, but current implementations are burdensome and not personalised. Quantitative tools for designing pragmatic light interventions for the home‐setting could benefit PLWD and their carers.Here, we report on a quantitative computational tool which combines readily collectable data with a new mathematical model to provide personalised advice on light interventions. Our focus is on designing low‐burden interventions requiring minimal changes to lifestyle or sleep‐wake routines.MethodEighteen older adults (65‐80 y) monitored their light exposure and sleep for a period of 7‐10 days at home using a wristworn monitor (Actiwatch Spectrum) and a sleep diary from which we calculated daily sleep timing and duration.We constructed a new mechanistic mathematical model for sleep timing that incorporates sleep homeostasis, circadian rhythmicity and light data (HCL). For each participant, we fitted the model to their sleep timing and duration data. We then used simulations to predict the effect of different light interventions on sleep timing and used optimisation to determine light exposure patterns that promote a target sleep onset time.ResultIndividuals varied in their sleep duration (07:18 ± 0:56 hh:mm; mean ± SD) and mid‐sleep timing (03:22 ± 01:00 hh:mm). We successfully retrieved ‘personal’ model parameters that accurately captured sleep duration and timing for 17 of the 18 participants. Our simulations indicated that the effect of a given light exposure pattern on sleep timing depends on the personalised model parameters, suggesting that an intervention that works for one person may not work for someone else. We were able to propose light exposure patterns requiring minimal behavioural change, facilitating adherence.ConclusionIt is possible to extract individual physiological parameters for sleep‐wake regulation from data collected in the field. These individual parameters can be used to design personalised light interventions. Our fully‐documented code could be combined with passive sleep monitors and environmental light sensors to unobtrusively recommend personalised light exposure patterns in close‐to real time.