Mild Cognitive Impairment Population Research Articles

Sulcal Morphometry Predicts Mild Cognitive Impairment Conversion to Alzheimer's Disease.

Being able to differentiate mild cognitive impairment (MCI) patients who would eventually convert (MCIc) to Alzheimer's disease (AD) from those who would not (MCInc) is a key challenge for prognosis. This study aimed to investigate the ability of sulcal morphometry to predict MCI progression to AD, dedicating special attention to an accurate identification of sulci. Twenty-five AD patients, thirty-seven MCI and twenty-five healthy controls (HC) underwent a brain-MR protocol (1.5T scanner) including a high-resolution T1-weighted sequence. MCI patients underwent a neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 2.3 years. At follow-up, 12 MCI were classified as MCInc and 25 as MCIc. Sulcal morphometry was investigated using the BrainVISA framework. Consistency of sulci across subjects was ensured by visual inspection and manual correction of the automatic labelling in each subject. Sulcal surface, depth, length, and width were retrieved from 106 sulci. Features were compared across groups and their classification accuracy in predicting MCI conversion was tested. Potential relationships between sulcal features and cognitive scores were explored using Spearman's correlation. The width of sulci in the temporo-occipital region strongly differentiated between each pair of groups. Comparing MCIc and MCInc, the width of several sulci in the bilateral temporo-occipital and left frontal areas was significantly altered. Higher width of frontal sulci was associated with worse performances in short-term verbal memory and phonemic fluency. Sulcal morphometry emerged as a strong tool for differentiating HC, MCI, and AD, demonstrating its potential prognostic value for the MCI population.

Alzheimer’s disease classification algorithm based on fusion of channel attention and densely connected networks

Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer’s disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD.

Impact of serum leptin and adiponectin levels on brain infarcts in patients with mild cognitive impairment and Alzheimer's disease: a longitudinal analysis.

The adipokines leptin and adiponectin have been associated with atherosclerosis and the risk of cerebral infarcts. Pre-clinical studies, however, suggest a protective role against ischemic brain damage. In this study we analyzed the relationship between serum leptin and adiponectin levels and the onset or progression of brain infarcts in subjects with mild cognitive impairment (MCI) and Alzheimer's disease (AD). All data were extracted from the ADNI database. The final population included 566 subjects, with 58 healthy controls, 396 MCI and 112 AD. All patients with available serum leptin and adiponectin levels at baseline were selected. Demographics, neuropsychological test results, CSF biomarkers, regional brain metabolism with FDG-PET data and the number of brain infarcts on longitudinal MRI scans were extracted. Leptin levels were significantly lower in patients with MCI than controls at baseline, while adiponectin levels were not different between the groups. Multivariate logistic regression analysis at baseline for the presence of brain infarcts showed a predictive value for leptin but not for adiponectin. Multivariate longitudinal analysis showed that age was the only significant predictor of brain infarcts development at 15-year follow-up, while serum leptin and adiponectin levels did not play a role in this population. The evidence on the pathogenetic or protective role of adipokines on ischemic brain damage is mixed. In this MCI and AD population, serum leptin and adiponectin were not associated with the development of brain infarcts; therefore, these results do not support the use of adipokines as biomarkers of cerebrovascular pathology in this population.

Association Between Long-Term Exposure to Ambient Air Pollution and the Risk of Mild Cognitive Impairment in a Chinese Urban Area: A Case-Control Study.

As a prodromal stage of dementia, significant emphasis has been placed on the identification of modifiable risks of mild cognitive impairment (MCI). Research has indicated a correlation between exposure to air pollution and cognitive function in older adults. However, few studies have examined such an association among the MCI population inChina. We aimed to explore the association between air pollution exposure and MCI risk from the Hubei Memory and Aging Cohort Study. We measured four pollutants from 2015 to 2018, 3 years before the cognitive assessment of the participants. Logistic regression models were employed to calculate odds ratios (ORs) to assess the relationship between air pollutants and MCI risk. Among 4,205 older participants, the adjusted ORs of MCI risk for the highest quartile of PM2.5, PM10, O3, and SO2 were 1.90 (1.39, 2.62), 1.77 (1.28, 2.47), 0.56 (0.42, 0.75), and 1.18 (0.87, 1.61) respectively, compared with the lowest quartile. Stratified analyses indicated that such associations were found in both males and females, but were more significant in older participants. Our findings are consistent with the growing evidence suggesting that air pollution increases the risk of mild cognitive decline, which has considerable guiding significance for early intervention of dementia in the older population. Further studies in other populations and broader geographical areas are warranted to validate these findings.

The Associations of Phosphorylated Tau 181 and Tau 231 Levels in Plasma and Cerebrospinal Fluid with Cognitive Function in Alzheimer's Disease: A Systematic Review and Meta-Analysis.

Cerebrospinal fluid (CSF) or blood biomarkers like phosphorylated tau proteins (p-tau) are used to detect Alzheimer's disease (AD) early. Increasing studies on cognitive function and blood or CSF p-tau levels are controversial. Our study examined the potential of p-tau as a biomarker of cognitive status in normal control (NC), mild cognitive impairment (MCI), and AD patients. We searched PubMed, Cochrane, Embase, and Web of Science for relevant material through 12 January 2023. 5,017 participants from 20 studies-1,033 AD, 2,077 MCI, and 1,907 NC-were evaluated. Quantitative analysis provided continuous outcomes as SMDs with 95% CIs. Begg tested publication bias. MCI patients had lower CSF p-tau181 levels than AD patients (SMD =-0.60, 95% CI (-0.85, -0.36)) but higher than healthy controls (SMD = 0.67). AD/MCI patients had greater plasma p-tau181 levels than healthy people (SMD =-0.73, 95% CI (-1.04, -0.43)). MCI patients had significantly lower p-tau231 levels than AD patients in plasma and CSF (SMD =-0.90, 95% CI (-0.82, -0.45)). MCI patients showed greater CSF and plasma p-tau231 than healthy controls (SMD = 1.34, 95% CI (0.89, 1.79) and 0.43, (0.23, 0.64)). Plasma p-tau181/231 levels also distinguished the three categories. MCI patients had higher levels than healthy people, while AD patients had higher levels than MCI patients. CSF p-tau181 and p-tau231 biomarkers distinguished AD, MCI, and healthy populations. Plasma-based p-tau181 and p-tau231 biomarkers for AD and MCI need further study.

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.

Changes in mild cognitive impairment and sociodemographic disparity among adults aged 55 years and above in 4 provinces of China

To investigate the changes in mild cognitive impairment(MCI) and sociodemographic disparity among adults aged 55 years and above in 4 provinces of China. A total of 4687 adults aged 55 years and above from Community-based Cohort Study on Nervous System Disease who did not have Alzheimer&apos;s disease, participated in both rounds of the survey, and had complete baseline sociodemographic data and two rounds of data on cognitive function were selected. Generalized estimation equations were used to analyse the effect of sociodemographic factors on MCI. The detection rates of MCI in adults aged 55 years and above without Alzheimer&apos;s disease in 4 provinces of China in 2018 and 2020 were 48.56% and 42.56% respectively. MCI occurred in 30.11% of those with normal cognition(NC) at baseline, and 44.24% of those with MCI at baseline reverted to NC. The risk of MCI increased and the likelihood of MCI reversion decreased with increasing age and decreasing per capita monthly household income. In the baseline NC population, the risk of MCI in the junior high school and above group was 35% lower than that in the illiterate group(RR=0.65, 95%CI 0.53-0.80), the risk of MCI was lower in those living in rural areas(RR=0.56, 95%CI 0.49-0.65), and the risk of MCI was 1.17 times(95%CI 1.03-1.32) higher in those with a history of chronic diseases than in those without it. In the baseline MCI population, the likelihood of MCI reversion increased with education, the likelihood of MCI reversion was 1.04 times higher for workers than for non-workers(95%CI 1.00-1.08). The incidence and reversal rates of MCI were high in adults aged ≥55 years in four provinces of China. Advanced age, low education and low income level are risk factors for cognitive dysfunction.

Multidimensional digital biomarker phenotypes for mild cognitive impairment: considerations for early identification, diagnosis and monitoring.

Mild Cognitive Impairment (MCI) poses a challenge for a growing population worldwide. Early identification of risk for and diagnosis of MCI is critical to providing the right interventions at the right time. The paucity of reliable, valid, and scalable methods for predicting, diagnosing, and monitoring MCI with traditional biomarkers is noteworthy. Digital biomarkers hold new promise in understanding MCI. Identifying digital biomarkers specifically for MCI, however, is complex. The biomarker profile for MCI is expected to be multidimensional with multiple phenotypes based on different etiologies. Advanced methodological approaches, such as high-dimensional statistics and deep machine learning, will be needed to build these multidimensional digital biomarker profiles for MCI. Comparing patients to these MCI phenotypes in clinical practice can assist clinicians in better determining etiologies, some of which may be reversible, and developing more precise care plans. Key considerations in developing reliable multidimensional digital biomarker profiles specific to an MCI population are also explored.

SLEEP MICROARCHITECTURE IN MILD COGNITIVE IMPAIRMENT: A HIGH‐DENSITY EEG STUDY

AbstractBackgroundSleep disturbance and sleep disorders are potential risk factors for dementia. Adults with mild cognitive impairment (MCI) are at increased risk of developing dementia and many report disturbed sleep. Altered sleep neurophysiology including deficits in sleep spindles and slow wave activity (SWA) during NREM sleep have been reported in MCI, though studies are sparse. These NREM EEG oscillations are crucial for overnight memory consolidation and optimal daytime functioning. Abnormal EEG activity during sleep in MCI may adversely impact sleep‐dependent memory processes and cognitive trajectory. This study is the first to examine sleep microarchitecture using high‐density EEG technology in MCI.MethodOlder adults with multi‐domain MCI (n = 19, mean age 68.5 years) and cognitively‐intact controls (n = 22, 69 years) had 256‐channel high‐density EEG recordings during overnight polysomnography and resting wakefulness. Declarative (word paired associates) and procedural (finger tapping) memory tasks were administered before and after sleep. Neuropsychological testing and brain MRI scans were also performed. After artefact processing, spectral analysis was performed for high‐density EEG recordings. Topographical power maps were calculated for standard frequency ranges for NREM sleep (N2 and N3 (slow wave sleep)). Maps were compared between groups using normalized power (z‐scores computed for each subject).ResultThe MCI group had worse declarative (% retention, MCI vs Controls: 84.2 ± 16.6 vs. 94.5 ± 7.8, p = 0.024) and procedural (% early improvement: 92.9 ± 11.0 vs. 103.0 ± 12.0%, p = 0.017) overnight memory consolidation than the controls. Topographic analysis of high‐density EEG data revealed a regional increase in SWA (1‐4.5Hz) EEG power during N3 in a cluster of 18 electrodes overlying the centroparietal cortex in the MCI group (cluster mean t‐value = 3.11, p = 0.0036, uncorrected) and a regional reduction in SWA in the left frontal (5 channels, t = ‐3.75, p&lt;0.001) and right temporal areas (5 channels, t = ‐3.46, p = 0.001), Figure 1.ConclusionOvernight memory consolidation was impaired in this MCI population. Regional differences in SWA during deep sleep were present in distinct brain regions and may indicate pathology. Future analysis exploring associations between EEG abnormalities with underlying brain structures may provide mechanistic insights into the impact of altered sleep neurophysiology on cognitive outcomes in MCI.

Handwriting characteristics analysis for Alzheimer’s Disease and Mild Cognitive Impairments Assessment

AbstractBackgroundMotor changes are early signs of neurodegenerative diseases such as Alzheimer’s disease (AD), but they are often difficult to detect, especially in the early stages. In the past, the examination of handwriting has been used to investigate motoric changes in different neurodegenerative diseases. In this study, we explore a wide array of explainable characteristics of the handwriting of patients obtained with signal processing, and study their potential for assessment of Mild Cognitive Impairment (MCI) and AD.MethodWe collected the written tasks from 37 participants, 7 with AD, 17 with MCI, and 13 for the control group (CTL) on a digital tablet. We analyzed 14 different tasks, including holding the pen over a point, copying images, free writing/drawing, and memory copying. We extracted multiple movement characteristics, including the length of each stroke, the ratio of the time the pen spent on‐air vs in‐tablet and the standard deviation of the distance between consecutive points. The drawings of each subject were manually supervised, then processed to extract multiple characteristics, and compared to identify the ones providing the most significant differences between the three studied groups using Welch’s t‐test, with false discovery rate (FDR) correction.ResultPreliminary results showed that the ratio between the time with the pen in contact with the tablet and without (on‐air vs in‐tablet) for writing tasks, significantly differentiate between AD and CTL patients (p‐value&lt;0.01), as well as the position standard deviation when asked to maintain the pen at a fixed position (p‐value&lt;0.05). The comparison between MCI and CTL populations also showed that the length of the strokes for drawings and copying tasks can deliver clues to distinguish both groups (p‐value&lt;0.05).ConclusionAutomated analysis of a large variety of handwriting segments employing machine learning techniques can provide an assessment of patients with AD and MCI that may be useful in their early diagnosis and assessment of progression.

Development of a patient‐reported outcome measure for Alzheimer’s disease: the Alzheimer’s Disease‐Health Index (AD‐HI), a fully validated tool to bolster clinical trial and research infrastructure

AbstractBackgroundAs therapeutic advancements are made in Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI), it is important to have a fully validated, sensitive, and reliable disease‐specific patient‐reported outcome measure (PRO) to accurately measure changes in AD and MCI symptomatic disease burden. The U.S. Food and Drug Administration has encouraged the use of PROs to measure therapeutic benefit of potential new treatments. This research describes the development, testing, and validation of a novel, multifactorial PRO: The Alzheimer’s Disease‐Health Index (AD‐HI).MethodsWe conducted interviews and a large cross‐sectional study involving individuals with AD and MCI and their caregivers to determine what symptoms and issues are most important to patients (Figure 1). Questions were selected for the AD‐HI based on their importance to patients with MCI and AD, potential to respond to therapeutic intervention, and generalizability to the broader MCI and AD patient population as determined through qualitative interviews and a cross‐sectional study. We beta tested the initial version of the AD‐HI with individuals with MCI and AD to determine its clarity, ease of use, and relevance to those with MCI and AD. We performed test‐retest reliability, known groups validity, internal consistency, and area under the curve analyses of the AD‐HI to assess the statistical characteristics, performance, and clinical meaningfulness of the outcome measure.ResultsThe content of the AD‐HI was validated using a cross‐sectional study of 104 patients. The final version of the AD‐HI contains 9 subscales that measure disease burden in the following areas: 1) Memory, 2) Cognitive health, 3) Fatigue, 4) Sleep, 5) Social health, 6) Communication, 7) Emotional health, 8) Physical health, and 9) Pain. The AD‐HI was identified as easy to use, relevant to the patient population, responsive, and clear during beta testing. The ADCR‐HI was shown to be reliable during test‐retest reliability testing with 22 individuals and able to distinguish between patient cohorts with different disease severities.ConclusionThe AD‐HI is a is a valid, sensitive, and reliable marker of disease burden in AD This disease‐specific outcome measure is available for use by researchers, clinicians, patient organizations, and companies to measure changes in disease burden during therapeutic trials.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review.

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

Cognition and Functionality Were Not Affected Due to the COVID-19 Pandemic in People with Mild Cognitive Impairment and AD Dementia Attending Digital Non-Pharmacologic Interventions.

The majority of previous studies showed that older adults with mild cognitive impairment (MCI) as well as Alzheimer's disease dementia (ADD) had impaired cognition and mood status, as well as increased behavioral disturbances after the first wave of the COVID-19 pandemic. However, there are still controversial data as regards the multifactorial impact of the restrictive measures on cognition, mood and daily function in older adults with MCI and ADD. In the current study, the scope is to identify possible deterioration by means of cognitive and functional level due to mood and behavioral alterations during the second quarantine imposed in Greece between November 2020 and May 2021, as well as one year after the second quarantine, in May 2022. Participants were recruited from the two day centers of the Greek Association of Alzheimer Disease and Related Disorders (GAADRD). They underwent three yearly follow up assessments from May 2020 to May 2022 and participated in cognitive training interventions (through digital online means) during the aforementioned period. Mixed measures analyses of variance as well as path models were used for the study's purposes. The study sample comprised 210 participants (175 people with MCI and 35 people with ADD). The mean age was 71.59 and 77.94 for people with MCI and mild ADD, respectively, whereas the average number of years of education was 12.65 for those with MCI and 9.83 for people with mild ADD. The results show that participants' deterioration rate (D), calculated by means of their performance in neuropsychological and functional assessments between 2020 and 2021 (D1) and 2021 and 2022 (D2), did not change significantly, except for the Rey Auditory Verbal Learning Test (RAVLT), since both groups displayed a larger D2 across the test conditions (immediate recall, fifth trial and delayed recall). Trail Making Test-B (TMT-B) performance, applied only in the MCI group, decreased more in relation to the deterioration rate D2. Additionally, two path models were applied to measure the direct relationships between diagnosis, performance in tests measuring mood and neuropsychiatric disturbances (NPI) and cognition, as measured by the RAVLT, in the 2020-2022 assessments. TMT-B was administered only in the MCI population, and therefore was not included in path models. The results show that participants' scores in RAVLT conditions were related to diagnosis and NPI performance, which was positively affected by diagnosis. No other relationships between RAVLT with mood tests were observed. Our results show that after the second lockdown period, the neuropsychological performance of people with MCI and ADD, calculated by means of their D2, did not change, except from their verbal memory, as well as visual scanning and information processing, measured using the TMT-B. Therefore, it can be assumed that those who were enrolled in digital non-pharmacological interventions during the COVID-19 pandemic home restrictions did not experience increased cognitive and functional deterioration due to mood and behavioral alterations after the pandemic.

Four‐year cognitive decline in mild cognitive impairment patients with distinct baseline neuroanatomical atrophy profiles: A MEMENTO and QyScore® study

AbstractBackgroundNeuroanatomical atrophy profile is increasingly recognised as a valuable characterisation in mild cognitive impairment (MCI) individuals and may provide prognostic information about cognitive decline.Method1153 MCI participants from the MEMENTO memory clinic study with 48‐month longitudinal follow‐up were included (mean (sd) Age = 70.6 (8.2), 60% female, CDR‐SB = 0.63 (0.72)). QyScore® volumetric quantification was applied to baseline 3DT1 images and four recognized neuroanatomical subtypes defined based on regional atrophy with respect to an internal normative database (z←1.64) [1,2]: no/minimal atrophy [N‐] (n = 1,012), limbic predominant: LP (n = 37), hippocampal sparing: HS (n = 84) and combined hippocampal and cortical atrophy [typical/diffuse: TD] (n = 20). 145 participants were amyloid positive (A+) via CSF or PET (Age = 73.7 (5.9): 47% female: N‐ = 114, LP = 8, HS = 21 and TD = 2). All participants underwent cognitive and functional assessments at baseline and 48 months, including mini‐mental state examination (MMSE), clinical dementia rating (CDR), Free and Cued Selective Reminding Test (FCRST) and Instrumental Activities of Daily Living (IADL) scale. Linear regression analysis (correcting for age, sex and baseline performance) investigated the relationship between baseline neuroanatomical subtype and subsequent 4‐year cognitive decline. Bonferroni correction was applied.ResultAt month 48, all subtypes demonstrated significant decline across memory, orientation, and executive function. Severity of decline varied across subscales and subtype and was particularly marked in A+ LP individuals (Figure 1). IADL remained stable for the N‐ and TD subgroups. 143 participants converted to dementia [102 = N‐, 18 = LP, 15 = HS, and 8 = TD subtypes, representing 10%, 49%, 18% and 40% of the total N‐, LP, HS and TD atrophy subtypes respectively]. HS and N‐ individuals also demonstrated the least decline across all domains and did not significantly differ on any tests. LP atrophy subtype demonstrated the greatest decline across all cognitive domains as well as global CDR and IADL (p&lt;0.001 compared with N‐ and HS subtypes). Similarly, TD phenotypes showed significant decline across multiple cognitive subscales compared with N‐ and HS subtypes (p&lt;0.001) particularly for orientation and global CDR.ConclusionEarly atrophy profiles in MCI populations can provide insights into the domains and severity of future patient progression over 4 years.[1] Planche et al., (2020)[2] Schwarz et al., (AAIC2022)

Dietary pattern, food, and nutritional supplement effects on cognitive outcomes in mild cognitive impairment: a systematic review of previous reviews.

Nutritional interventions may benefit cognition in people with mild cognitive impairment (MCI). However, evidence is yet to be synthesized in a way that can inform recommendations for clinical and public health settings. To systematically review evidence on the effect of dietary patterns, foods, and nutritional supplements on cognitive decline in individuals with MCI. Guided by the Preferred Reporting items for Systematic Review and Meta-Analysis Protocols 2015 statement, the Medline, EMBASE, and CINAHL databases, the JBI Database of Systematic Reviews and Implementation Reports, Cochrane Database of Systematic Reviews, and Database of Abstracts of Reviews of Effects were searched (publication years 2005 to 2020). Included studies were English-language systematic reviews and meta-analyses of randomized controlled trials and cohort studies reporting on the effectiveness of nutritional interventions on cognition of individuals with MCI. Two reviewers independently selected studies and extracted data on cognitive outcomes and adverse events. Review quality was assessed using AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews-2). Primary study overlap was managed following Cochrane Handbook guidelines. Of the 6677 records retrieved, 20 reviews were included, which, in turn, reported on 43 randomized controlled trials and 1 cohort study that, together, addressed 18 nutritional interventions. Most reviews were limited by quality and the small number of primary studies with small sample sizes. Reviews were mostly positive for B vitamins, omega-3 fatty acids, and probiotics (including 12, 11 and 4 primary studies, respectively). Souvenaid and the Mediterranean diet reduced cognitive decline or Alzheimer's disease progression in single trials with <500 participants. Findings from studies with a small number of participants suggest vitamin D, a low-carbohydrate diet, medium-chain triglycerides, blueberries, grape juice, cocoa flavanols, and Brazil nuts may improve individual cognitive subdomains, but more studies are needed. Few nutritional interventions were found to convincingly improve cognition of individuals with MCI. More high-quality research in MCI populations is required to determine if nutritional treatments improve cognition and/or reduce progression to dementia. Open Science Framework protocol identifier DOI:10.17605/OSF.IO/BEP2S.

Varied performance of picture description task as a screening tool across MCI subtypes.

A picture description task is a component of Miro Health's platform for self-administration of neurobehavioral assessments. Picture description has been used as a screening tool for identification of individuals with Alzheimer's disease and mild cognitive impairment (MCI), but currently requires in-person administration and scoring by someone with access to and familiarity with a scoring rubric. The Miro Health implementation allows broader use of this assessment through self-administration and automated processing, analysis, and scoring to deliver clinically useful quantifications of the users' speech production, vocal characteristics, and language. Picture description responses were collected from 62 healthy controls (HC), and 33 participants with MCI: 18 with amnestic MCI (aMCI) and 15 with non-amnestic MCI (naMCI). Speech and language features and contrasts between pairs of features were evaluated for differences in their distributions in the participant subgroups. Picture description features were selected and combined using penalized logistic regression to form risk scores for classification of HC versus MCI as well as HC versus specific MCI subtypes. A picture-description based risk score distinguishes MCI and HC with an area under the receiver operator curve (AUROC) of 0.74. When contrasting specific subtypes of MCI and HC, the classifiers have an AUROC of 0.88 for aMCI versus HC and and AUROC of 0.61 for naMCI versus HC. Tests of association of individual features or contrasts of pairs of features with HC versus aMCI identified 20 features with p-values below 5e-3 and False Discovery Rates (FDRs) at or below 0.113, and 61 contrasts with p-values below 5e-4 and FDRs at or below 0.132. Findings suggest that performance of picture description as a screening tool for MCI detection will vary greatly by MCI subtype or by the proportion of various subtypes in an undifferentiated MCI population.

The effects of white matter hyperintensities on MEG power spectra in population with mild cognitive impairment.

Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer's disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.

M1/M4-Preferring Muscarinic Cholinergic Receptor Agonist Xanomeline Reverses Wake and Arousal Deficits in Nonpathologically Aged Mice.

Degeneration of the cholinergic basal forebrain is implicated in the development of cognitive deficits and sleep/wake architecture disturbances in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Indirect-acting muscarinic cholinergic receptor agonists, such as acetylcholinesterase inhibitors (AChEIs), remain the only FDA-approved treatments for the cognitive impairments observed in AD that target the cholinergic system. Novel direct-acting muscarinic cholinergic receptor agonists also improve cognitive performance in young and aged preclinical species and are currently under clinical development for AD. However, little is known about the effects of direct-acting muscarinic cholinergic receptor agonists on disruptions of sleep/wake architecture and arousal observed in nonpathologically aged rodents, nonhuman primates, and clinical populations. The purpose of the present study was to provide the first assessment of the effects of the direct-acting M1/M4-preferring muscarinic cholinergic receptor agonist xanomeline on sleep/wake architecture and arousal in young and nonpathologically aged mice, in comparison with the AChEI donepezil, when dosed in either the active or inactive phase of the circadian cycle. Xanomeline produced a robust reversal of both wake fragmentation and disruptions in arousal when dosed in the active phase of nonpathologically aged mice. In contrast, donepezil had no effect on either age-related wake fragmentation or arousal deficits when dosed during the active phase. When dosed in the inactive phase, both xanomeline and donepezil produced increases in wake and arousal and decreases in nonrapid eye movement sleep quality and quantity in nonpathologically aged mice. Collectively, these novel findings suggest that direct-acting muscarinic cholinergic agonists such as xanomeline may provide enhanced wakefulness and arousal in nonpathological aging, MCI, and AD patient populations.

Decreased circulating branched-chain amino acids are associated with development of Alzheimer's disease in elderly individuals with mild cognitive impairment.

Nutritional epidemiology has shown that inadequate dietary protein intake is associated with poor brain function in the elderly population. The plasma free amino acid (PFAA) profile reflects nutritional status and may have the potential to predict future changes in cognitive function. Here, we report the results of a 2-year interim analysis of a 3-year longitudinal study following mild cognitive impairment (MCI) participants. In a multicenter prospective cohort design, MCI participants were recruited, and fasting plasma samples were collected. Based on clinical assessment of cognitive function up to 2 years after blood collection, MCI participants were divided into two groups: remained with MCI or reverted to cognitively normal ("MCI-stable," N = 87) and converted to Alzheimer's disease (AD) ("AD-convert," N = 68). The baseline PFAA profile was compared between the two groups. Stratified analysis based on apolipoprotein E ε4 (APOE ε4) allele possession was also conducted. Plasma concentrations of all nine essential amino acids (EAAs) were lower in the AD-convert group. Among EAAs, three branched-chain amino acids (BCAAs), valine, leucine and isoleucine, and histidine (His) exhibited significant differences even in the logistic regression model adjusted for potential confounding factors such as age, sex, body mass index (BMI), and APOE ε4 possession (p < 0.05). In the stratified analysis, differences in plasma concentrations of these four EAAs were more pronounced in the APOE ε4-negative group. The PFAA profile, especially decreases in BCAAs and His, is associated with development of AD in MCI participants, and the difference was larger in the APOE ε4-negative population, suggesting that the PFAA profile is an independent risk indicator for AD development. Measuring the PFAA profile may have importance in assessing the risk of AD conversion in the MCI population, possibly reflecting nutritional status. [https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000025322], identifier [UMIN000021965].

The Effects of Transcutaneous Vagus Nerve Stimulation on Functional Connectivity Within Semantic and Hippocampal Networks in Mild Cognitive Impairment.

Better treatments are needed to improve cognition and brain health in people with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Transcutaneous vagus nerve stimulation (tVNS) may impact brain networks relevant to AD through multiple mechanisms including, but not limited to, projection to the locus coeruleus, the brain's primary source of norepinephrine, and reduction in inflammation. Neuropathological data suggest that the locus coeruleus may be an early site of tau pathology in AD. Thus, tVNS may modify the activity of networks that are impaired and progressively deteriorate in patients with MCI and AD. Fifty patients with MCI (28 women) confirmed via diagnostic consensus conference prior to MRI (sources of info: Montreal Cognitive Assessment Test (MOCA), Clinical Dementia Rating scale (CDR), Functional Activities Questionnaire (FAQ), Hopkins Verbal Learning Test - Revised (HVLT-R) and medical record review) underwent resting state functional magnetic resonance imaging (fMRI) on a Siemens 3T scanner during tVNS (left tragus, n = 25) or sham control conditions (left ear lobe, n = 25). During unilateral left tVNS, compared with ear lobe stimulation, patients with MCI showed alterations in functional connectivity between regions of the brain that are important in semantic and salience functions including regions of the temporal and parietal lobes. Furthermore, connectivity from hippocampi to several cortical and subcortical clusters of ROIs also demonstrated change with tVNS compared with ear lobe stimulation. In conclusion, tVNS modified the activity of brain networks in which disruption correlates with deterioration in AD. These findings suggest afferent target engagement of tVNS, which carries implications for the development of noninvasive therapeutic intervention in the MCI population.