Mild Cognitive Impairment Alzheimer Research Articles

Longitudinal association between nutritional status and behavioral and psychological symptoms of dementia in older women with mild cognitive impairment and early-stage Alzheimer's disease

While cross-sectional studies report associations between behavioral and psychological symptoms of dementia (BPSD) and nutritional status as a modifiable factor, their causal relationship remains unclear. Therefore, this study investigated the impact of nutritional status on BPSD. This study included women with mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD) from Memory Clinic, National Center for Geriatrics and Gerontology. The participants were assessed for nutritional status and BPSD using the Mini Nutritional Assessment Short-Form (MNA-SF) and the Dementia Behavior Disturbance Scale (DBD), respectively. Based on their MNA-SF scores, the subjects were classified as well-nourished, at risk of malnutrition, or malnourished. Nutritional status and change in BPSD was examined for association by univariate and multivariate linear regression analyses. This study analyzed 181 women (79 with MCI and 102 with early-stage AD). The multivariate analysis showed that the malnourished subjects or those at risk of malnutrition (54.1%) were significantly associated with increased DBD scores (β=0.255, P=0.003) during follow-up. In addition, multivariate regression analysis incorporating change in DBD sub-score as a dependent variable showed that the malnourished subjects or those at risk of malnutrition were associated with increased DBD sub-scores for "verbal aggressiveness/emotional disinhibition" (β=0.247, P=0.005). Poor nutritional status increased BPSD, especially verbal aggressiveness/emotional disinhibition, in those with MCI and early-stage AD during 2.5-year follow-up. Patients with MCI and early-stage AD may need to be assessed for nutritional status from early on, at the onset of mild cognitive decline, and require intervention to prevent worsening of BPSD. Further intervention studies in large prospective cohorts are needed to establish nutritional measures to prevent progression of BPSD in older adults with cognitive impairment.

Effects of Sport Stacking on Neuropsychological, Neurobiological, and Brain Function Performances in Patients With Mild Alzheimer's Disease and Mild Cognitive Impairment: A Randomized Controlled Trial

ObjectiveTo investigate the effects of sport stacking on the overall cognition and brain function in patients with mild Alzheimer's disease (AD) and mild cognitive impairment (MCI).MethodsA single-blind randomized controlled design was performed using sport stacking for 30 min, 5 days/week for 12 weeks. Forty-eight subjects with mild AD or MCI were randomly divided into the sport stacking group (T-mAD = 12, T-MCI = 12) and the active control group (C-mAD = 11, C-MCI = 13). Auditory Verbal Learning Test (AVLT), Alzheimer's Disease Cooperative Study–Activities of Daily Living scale (ADCS-ADL), Geriatric Depression Scale (GDS-30), and Pittsburgh Sleep Quality Index (PSQI) were performed, the level of amyloid β-protein-40 (Aβ-40), Aβ-42, brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1(IGF-1), tumor necrosis factor-alpha (TNF-α), Interleukin-6 (IL-6), and soluble trigger receptor expressed on myeloid cells 2 (sTREM2) in plasma were tested, and brain functional connectivity in resting state and activation under finger movement task were analyzed by functional near-infrared spectroscopy (fNIRS).ResultsThirty-nine patients completed the trial. After 4 weeks, we found a significant increase in AVLT score in T-MCI (6.36 ± 5.08 vs. −1.11 ± 4.23, p = 0.004), and T-mAD group (4.60 ± 4.77 vs. −0.11 ± 2.89, p = 0.039). After 12 weeks, there was a significantly improved in AVLT (9.64 ± 4.90 vs. −0.33 ± 6.10, p = 0.002) and ADCS-ADL (3.36 ± 3.59 vs. −1.89 ± 2.71, p = 0.003) in T-MCI. There was a significant improvement in AVLT (5.30 ± 5.42 vs. 0.44 ± 2.40) in T-mAD (p < 0.05). Plasma levels of BDNF were upregulated in both T-MCI and T-mAD, and IGF-1 increased in T-MCI (P < 0.05) compared to the control groups. The functional connectivity in MCI patients between DLPFC.R and SCA.R, SMA.L, and SCA.R was decreased. In contrast, in mAD patients, the brain regional function connection was increased between DLPFC.R and Broca's.L. The activation of channel 36 located in the left primary somatosensory cortex was significantly increased after 12-week training, which was correlated with the improved AVLT and the increase of BDNF.ConclusionOur findings suggested that sport stacking is effective for patients with MCI and mild AD, possibly through increasing the expression of neuroprotective growth factors and enhancing neural plasticity to improve neurocognitive performance.Clinical Trial Registrationhttps://www.ClinicalTrials.gov, ChiCTR.org.cn, identifier: ChiCTR-2100045980.

Effects of Orally Administered Nicotinamide Riboside on Bioenergetic Metabolism, Oxidative Stress and Cognition in Mild Cognitive Impairment and Mild Alzheimer's Disease

<h3>Introduction</h3> Alzheimer's Disease (AD) is a neurodegenerative disease affecting millions of people worldwide, representing significant health and economic burdens. There has been a recent drive to develop more effective AD treatments. A potential strategy involves enhancing brain bioenergetics, or the metabolism of fuel molecules to create and use energy through glycolysis and oxidative phosphorylation (OxPhos). The regulation of mitochondrial turnover and function in the brain declines as we age, and it has become increasingly accepted that these metabolic alterations precede hallmark AD pathology, including amyloid-beta (Aβ) plaques and neurofibrillary tangles. An overlapping set of mechanisms involving redox dysregulation, oxidative stress, and mitochondrial dysfunction may be related to mild cognitive impairment (MCI) and AD. Nicotinamide adenine dinucleotide (NAD) has been identified as a promising molecular target for bioenergetic treatments as it is requisite for many redox reactions. The redox ratio (RR, i.e., NAD+/NADH) is a mark of the system's ability to carry out ATP synthesis. NAD+ levels decrease as we age, which compromises energy production in the mitochondria, impairs clearance of damaged mitochondria, and impacts our brain's capacity to react to oxidative stress. Since mitochondrial dysfunction and oxidative stress are believed to play a role in the pathophysiology of age-related diseases, boosting NAD levels could be an effective AD treatment. Nicotinamide Riboside (NR) is a naturally occurring, over the counter, commercially available, and safe vitamin B3-based NAD+ precursor supplement. Several lines of evidence suggest that NR is a mitochondrially-favored NAD+ precursor. Data supports the safety and efficacy of using NR to increase NAD+ levels in humans, but these studies have not measured the RR at critical tissue sites, such as the brain, to determine the therapeutic potential of oral NR supplementation. This study proposes a novel intervention strategy for patients with MCI or mild AD that targets redox dysregulation and oxidative stress in the brain using NR. Our primary objective is to validate the effect of NR supplementation on NAD+ levels and RR in the brain of patients with MCI or mild AD. Our secondary aim is to quantify the impact of NR supplementation on ensuing markers of mitochondrial function and oxidative stress in MCI and mild AD patients. Our third, exploratory aim, is to correlate neuroimaging measurements with clinical assessments. <h3>Methods</h3> A 12-week open-label, clinical trial of NR supported by the National Institute on Aging (R01AG066670, Du/Forester), we plan to enroll a total of 50 participants - 25 with MCI and 25 with mild AD, all with at least one copy of the APOE ε4 allele. Participants will be asked to take 1,000 mg NR daily over 12-weeks, and to come in for three scanning visits. We will use novel neuroimaging methods —spectroscopy on a 4T scanner to enable in vivo measurement of NAD+, NADH and RR, along with markers of mitochondrial function tied to NAD, including creatine kinase (CK), ATPase, and glutathione (GSH)—a molecule essential for cellular repair. Our primary outcome measures are NAD+ levels and RR. Our secondary outcome measures are CK/ATPase activity and GSH levels. Our exploratory outcome measures include the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) for cognition and the Alzheimer's Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) for daily functioning. <h3>Results</h3> Previous clinical trials of NR pharmacokinetics in humans support the safety and efficacy of using NR to increase NAD+ levels in the plasma. Emerging data also highlights important health benefits this supplement may have. We recently received preliminary spectroscopic data following a course of NR supplementation in healthy volunteers (N=4) which observed elevations in NAD+ and RR after two weeks. Only a small difference of downstream effects, CK/ATPase activity and GSH levels, were observed, which could be due to the short period of NR treatment. We hypothesize that oral NR will increase NAD+ and RR in the medial prefrontal cortex and will increase GSH levels and CK/ATPase activity in the brain. For our exploratory aim, we hypothesize that oral NR will improve cognitive status and functional abilities, as measured by the RBANS and ADCS-ADL. <h3>Conclusions</h3> We are investigating NR to target redox dysregulation and oxidative stress in the brain in those with MCI or mild AD. We aim to document and quantify the neurobiological and clinical effects of NR supplementation in MCI and mild AD using in vivo brain imaging. This novel intervention strategy of brain bioenergetics has thus far received minute empirical attention and may represent an alternative strategy for treating MCI and mild AD. Early detection and repair of neural dysfunction at this critical stage could significantly alter disease course and improve functional outcomes. <h3>This research was funded by</h3> National Institutes of Health

Association of β-Amyloid and Basal Forebrain With Cortical Thickness and Cognition in Alzheimer and Lewy Body Disease Spectra.

Cholinergic degeneration and β-amyloid contribute to brain atrophy and cognitive dysfunction in Alzheimer disease (AD) and Lewy body disease (LBD), but their relationship has not been comparatively evaluated. In this cross-sectional study, we recruited 28 normal controls (NC), 55 patients with AD mild cognitive impairment (MCI), 34 patients with AD dementia, 28 patients with LBD MCI, and 51 patients with LBD dementia. Participants underwent cognitive evaluation, brain MRI to measure the basal forebrain (BF) volume and global cortical thickness (CTh), and 18F-florbetaben (FBB) PET to measure the standardized uptake value ratio (SUVR). Using general linear models and path analyses, we evaluated the association of FBB-SUVR and BF volume with CTh or cognitive dysfunction in the AD spectrum (AD and NC) and LBD spectrum (LBD and NC), respectively. Covariates included age, sex, education, deep and periventricular white matter hyperintensities, intracranial volume, hypertension, diabetes, and hyperlipidemia. BF volume mediated the association between FBB-SUVR and CTh in both the AD and LBD spectra, while FBB-SUVR was associated with CTh independently of BF volume only in the LBD spectrum. Significant correlation between voxel-wise FBB-SUVR and CTh was observed only in the LBD group. FBB-SUVR was independently associated with widespread cognitive dysfunction in both the AD and LBD spectra, especially in the memory domain (standardized beta [B] for AD spectrum = -0.60, B for LBD spectrum = -0.33). In the AD spectrum, BF volume was associated with memory dysfunction (B = 0.18), and CTh was associated with language (B = 0.21) and executive (B = 0.23) dysfunction. In the LBD spectrum, however, BF volume and CTh were independently associated with widespread cognitive dysfunction. There is a common β-amyloid-related degenerative mechanism with or without the mediation of BF in the AD and LBD spectra, while the association of BF atrophy with cognitive dysfunction is more profound and there is localized β-amyloid-cortical atrophy interaction in the LBD spectrum.

Discriminating speech traits of Alzheimer's disease assessed through a corpus of reading task for Spanish language

It is estimated that between 50% and 75% of all cases of dementia are due to Alzheimer's disease (AD), the most common neurodegenerative disease among World population. However, a long preclinical period of AD makes it difficult to differentiate between people with Mild Cognitive Impairment (MCI) that would progress to dementia from people with MCI that would not. One of the most promising solutions to detect MCI which will evolve to dementia (preAD) comes from the field of automatic speech analysis. Speech is a complex physiological and neurocognitive language-mediated process, which can be significantly altered in pathological aging and exhibit high levels of sensitivity for the diagnosis of neurological diseases. The purpose of this research is to offer a detailed perspective on the speech changes in MCI and mild AD when compared to healthy aging (HA), that would allow to detect pathological processes prior to the clinical expression of AD. Based on our previous research record on speech in HA, MCI and AD, we provide a global review of dementia-related speech traits and propose a reading-based protocol for assessing ongoing neurodegenerative processes in the elderly. We report the results of speech analysis in elderly people with different cognitive profiles, who performed a standardized reading task and were further analyzed for correlations between neurocognitive assessment indicative of cognitive impairment stage (HA, MCI or AD) and acoustic, temporal and prosodic traits in speech. We show that evolution from HA to AD exhibits a steady pattern of speech changes in parallel to the cognitive decline, which consists in significant increase in duration and phonation time, extension of pauses and voice breaks, intensification of variation in syllabic production, and decrease in speech energy and intensity leading to dysphony. In doing so, we prove that a standardized reading task is a very useful type of stimuli for detecting dementia-related speech traits and, in view of this, we discuss the relevance of reading for preclinical automated diagnosis of AD. The main contribution of this paper is a corpus of recordings of the standardized reading task performed by healthy elderly people and people with MCI and AD in Spanish language, and which can be used for further research purposes. In this respect, our work fills an important gap existing in corpora-based studies of speech and language impairments related to progression to dementia.

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease.

The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.

Mild Cognitive Impairment: Diagnosis and Subtypes

Mild cognitive impairment (MCI) is a clinical diagnosis based on subjective cognitive decline, objective cognitive impairment, and relative preservation of activities of daily living. The diagnosis may be established via clinical interview, collateral history from an informant, and psychometric examination. Various consensus groups have proposed criteria for MCI in Alzheimer's disease (AD), Parkinson's disease, dementia with Lewy bodies, and vascular cognitive impairment. These diagnostic criteria have subtle but important differences. Criteria for subjective decline vary according to whether memory is impaired or whether impairment in any cognitive domain is sufficient. There are also differences with respect to whether the subjective decline is noted by the patient, a carer, or a clinician. The precise thresholds for classifying objective cognitive impairment also vary between various diagnostic criteria. There are also differences in the description of functional abilities. Once established, the diagnosis of MCI may be refined to 1 of 4 subtypes based on the pattern of cognitive impairment. The 4 subtypes are defined according to whether or not memory is impaired and whether 1 or more cognitive domains are impaired. Once a diagnosis of MCI has been made, the patient and the family should be counseled about social and legal implications as well as strategies for reducing the risk of progression to dementia. The main utilities of MCI as a nosology are to understand the natural history of neurodegenerative disorders such as AD, to identify those at increased risk of progressing to develop dementia, and to identifying individuals for putative treatments.

Verification of Trail Making Test in Elderly People with Behavioral and Psychological Symptoms of Dementia

In Alzheimer's disease (AD), attention and executive dysfunction occur early in the disease. However, little is known about the relationship between these disorders and behavioral and psychological symptoms of dementia (BPSD). This study investigated the relationship between BPSD and attention and execution functions. Twenty-five patients with mild cognitive impairment (MCI) and early AD were included. Neuropsychological tests, mini-mental state examination (MMSE), Raven’s colored progressive materials (RCPM), and trail making test (TMT) were conducted for patients with dementia. The dementia behavior disturbance scale (DBD) was used for psychological and behavioral evaluations of patients with dementia. The AD group showed significantly lower MMSE, DBD, and TMT-B scores than the MCI group. Multiple regression analyses revealed a significant correlation between DBD score, MMSE, and TMT-B.Conclusion: BPSD is associated with cognitive function severity in patients with MCI and early AD, suggesting that attentional and executive functions are independent risk factors for these neural substrates.

Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia.

Introduction: Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-min, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients.Methods: We studied the ICA in 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests.Results: The ICA demonstrated convergent validity with MoCA (Pearson r=0.58, p<0.0001) and ACE (r=0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p = 0.01) with education years is considerably smaller than that of MoCA (r = 0.34, p < 0.0001) and ACE (r = 0.41, p < 0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practise effect over the duration of the study.Discussion: The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture, and education.

Astrocyte activation imaging with 11C-acetate and amyloid PET in mild cognitive impairment due to Alzheimer pathology.

Neuroinflammation is a well-known feature of early Alzheimer disease (AD) yet astrocyte activation has not been extensively evaluated with in vivo imaging in mild cognitive impairment (MCI) due to amyloid plaque pathology. Unlike neurons, astrocytes metabolize acetate, which has potential as a glial biomarker in neurodegeneration in response to AD pathologic features. Since the medial temporal lobe (MTL) is a hotspot for AD neurodegeneration and inflammation, we assessed astrocyte activity in the MTL and compared it to amyloid and cognition. We evaluate spatial patterns of in vivo astrocyte activation and their relationships to amyloid deposition and cognition in a cross-sectional pilot study of six participants with MCI and five cognitively normal participants. We measure 11C-acetate and 18F-florbetaben amyloid standardized uptake values ratios (SUVRs) and kinetic flux compared to the cerebellum on PET, with MRI and neurocognitive testing. MTL 11C-acetate SUVR was significantly elevated in MCI compared to cognitively normal participants (P = 0.03; Cohen d = 1.76). Moreover, MTL 11C-acetate SUVR displayed significant associations with global and regional amyloid burden in MCI. Greater MTL 11C-acetate retention was significantly related with worse neurocognitive measures including the Montreal Cognitive Assessment (P = 0.001), word list recall memory (P = 0.03), Boston naming test (P = 0.04) and trails B test (P = 0.04). While further validation is required, this exploratory pilot study suggests a potential role for 11C-acetate PET as a neuroinflammatory biomarker in MCI and early AD to provide clinical and translational insights into astrocyte activation as a pathological response to amyloid.

Psychometric Properties of the Persian Montreal Cognitive Assessment in Mild Cognitive Impairment and Alzheimer Disease

Introduction: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test widely used in clinical practice and suited for the detection of Mild Cognitive Impairment (MCI). The aims were to evaluate the psychometric properties of the Persian MoCA as a screening test for mild cognitive dysfunction in Iranian older adults and to assess its accuracy as a screening test for MCI and mild Alzheimer disease (AD). Method: One hundred twenty elderly with a mean age of 73.52 ± 7.46 years participated in this study. Twenty-one subjects had mild AD (MMSE score ≤21), 40 had MCI, and 59 were cognitively healthy controls. All the participants were administered the Mini-Mental State Examination (MMSE) to evaluate their general cognitive status. Also, a battery of comprehensive neuropsychological assessments was administered. Results: The mean score on the Persian version of the MoCA and the MMSE were 19.32 and 25.62 for MCI and 13.71 and 22.14 for AD patients, respectively. Using an optimal cutoff score of 22 the MoCA test detected 86% of MCI subjects, whereas the MMSE with a cutoff score of 26 detected 72% of MCI subjects. In AD patients with a cutoff score of 20, the MoCA had a sensitivity of 94% whereas the MMSE detected 61%. The specificity of the MoCA was 70% and 90% for MCI and AD, respectively. Discussion: The results of this study show that the Persian version of the MoCA is a reliable screening tool for detection of MCI and early stage AD. The MoCA is more sensitive than the MMSE in screening for cognitive impairment, proving it to be superior to MMSE in detecting MCI and mild AD.

Correlation between cerebral blood flow and olfactory function in mild cognitive impairment and Alzheimer's disease.

Olfactory dysfunction is common in patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We sought to elucidate brain regions associated with olfactory dysfunction in patients with MCI and early AD by using 123I-IMP-SPECT to detect regional cerebral blood flow (CBF). We included 218 patients diagnosed with AD or MCI, who underwent a comprehensive battery of neuropsychiatric and neuropsychological tests, Alzheimer's Disease Assessment Scale-Cognitive Part (ADAS-Cog), and forward- and backward-digit span. Olfactory function was assessed using T&T olfactometry of five odors; patients stated whether they experienced any smell (detection test) and identified the odor (identification test). The association between single-photon emission computerized tomography based regional CBF and olfactory function was examined by voxel-by-voxel multiple regression analysis, considering sex, age, and education as covariate parameters. Of the 218 patients, 78 had mildly impaired olfactory detection and 15 had olfactory detection loss; additionally, 213 had mild olfactory identification impairment. The odor detection score correlated significantly with the ADAS-Cog word recall score (r=0.193, p=0.004). The odor identification score correlated significantly with the ADAS memory (r=0.408, p<0.001) and ADAS orientation (r=0.292, p<0.001) scores. The odor identification score correlated negatively with CBF in the left temporal pole, entorhinal area, and bilateral frontal poles (p<0.001). Olfactory identification dysfunction in patients with MCI and AD is attributable to reduced CBF of the left temporal pole, entorhinal area, and bilateral frontal pole.

Diagnostic accuracy of the Cogstate Brief Battery for prevalent MCI and prodromal AD (MCI A+ T+ ) in a population-based sample.

This study evaluated the diagnostic accuracy of the Cogstate Brief Battery (CBB) for mild cognitive impairment (MCI) and prodromal Alzheimer's disease (AD) in a population-based sample. Participants included adults ages 50+ classified as cognitively unimpaired (CU, n=2866) or MCI (n=226), and a subset with amyloid (A) and tau (T) positron emission tomography who were AD biomarker negative (A-T-) or had prodromal AD (A+T+). Diagnostic accuracy of the Learning/Working Memory Composite (Lrn/WM) for discriminating all CU and MCI was moderate (area under the curve [AUC]=0.75), but improved when discriminating CU A-T- and MCI A+T+ (AUC=0.93) and when differentiating MCI participants without AD biomarkers from those with prodromal AD (AUC=0.86). Conventional cut-offs yielded lower than expected sensitivity for both MCI (38%) and prodromal AD (73%). Clinical utility of the CBB for detecting MCI in a population-based sample is lower than expected. Caution is needed when using currently available CBB normative data for clinical interpretation.

Assessment of the DTI-ALPS Parameter Along the Perivascular Space in Older Adults at Risk of Dementia.

Recently, there has been growing interest in the glymphatic system (the functional waste clearance pathway for the central nervous system and its role in flushing solutes (such as amyloid ß and tau), metabolic, and other cellular waste products in the brain. Herein, we investigate a recent potential biomarker for glymphatic activity (the diffusion tensor imaging along the perivascular space [DTI-ALPS] parameter) using diffusion MRI imaging in an elderly cohort comprising 10 cognitively normal, 10 mild cognitive impairment (MCI), and 16 Alzheimer's disease (AD). All 36 participants imaged on a Siemens 3.0T Tim Trio. Single-SE diffusion weighted Echo-planar imaging scans were acquired as well as T1 magnetization prepared rapid gradient echo, T2 axial, and susceptibility weighted imaging. Three millimeter regions of interest were drawn in the projection and association fibers adjacent to the medullary veins at the level of the lateral ventricle. The DTI-ALPS parameter was calculated in these regions and correlated with cognitive status, Mini-Mental State Examination (MMSE), and ADASCog11 measures. Significant correlations were found between DTI-ALPS and MMSE and ADASCog11 in the right hemisphere adjusting for age, sex, and APoE ε4 status. Significant differences were also found in the right DTI-ALPS indices between cognitively normal and AD groups (P < .026) and MCI groups (P < .025) in a univariate general linear model corrected for age, sex, and APoE ε4. Significant differences in apparent diffusion coefficient between cognitively normal and AD groups were found in the right projection fibers (P = .028). Further work is needed to determine the utility of DTI-ALPS index in larger elderly cohorts and whether it measures glymphatic activity.

A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket.

Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.

Changes in the use of psychotropic drugs during the course of Alzheimer's disease: A large-scale longitudinal study of French medical records.

IntroductionWe aim to understand how patients with Alzheimer's disease (AD) are treated by identifying in a longitudinal fashion the late‐life changes in patients’ medical history that precede and follow AD diagnosis.MethodsWe use prescription history of 34,782 patients followed between 1996 and 2019 by French general practitioners. We compare patients with an AD diagnosis, patients with mild cognitive impairment (MCI), and patients free of mental disorders. We use a generalized mixed‐effects model to study the longitudinal changes in the prescription of eight drug types for a period 15 years before diagnosis and 10 years after.ResultsIn the decades preceding diagnosis, we find that future AD patients are treated significantly more than MCI patients with most psychotropic drugs and that most studied drugs are increasingly prescribed with age. At the time of diagnosis, all psychotropic drugs except benzodiazepines show a significant increase in prescription, while other drugs are significantly less prescribed. In the 10 years after diagnosis, nearly all categories of drugs are less and less prescribed including antidementia drugs.DiscussionPre‐diagnosis differences between future AD patients and MCI patients may indicate that subtle cognitive changes are recognized and treated as psychiatric symptoms. The disclosure of AD diagnosis drastically changes patients’ care, priority being given to the management of psychiatric symptoms. The decrease of all prescriptions in the late stages may reflect treatment discontinuation and simplification of therapeutic procedures. This study therefore provides new insights into the medical practices for management of AD.

Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: an 18F-AV-45 study.

Positron emission tomography (PET) with the radiotracer florbetapir (18F-AV-45) allows the pathophysiology of Alzheimer's disease (AD) to be tracked in vivo. The semi-quantification of amyloid-beta (Aβ) has been extensively evaluated with the standardized uptake value ratio (SUVR) but is susceptible to disturbance from the candidate reference region and the partial volume effect (PVE). In the present study, we applied the parametric estimation of reference signal intensity (PERSI) method to 18F-AV-45 PET images for intensity normalization. We enrolled 479 people with 18F-AV-45 images from the Alzheimer's Disease Neuroimaging Initiative database: 261 healthy controls (HCs), 102 patients with mild cognitive impairment (MCI), and 116 AD patients. We used white matter post-processed by PERSI (PERSI-WM) as the reference region and compared our proposed method with the traditional method for semi-quantification. SUVRs were calculated for eight regions of interest: the frontal lobe, the parietal lobe, the temporal lobe, the occipital lobe, the anterior cingulate cortex, the posterior cingulate cortex, the precuneus, and the global cortex. The SUVRs derived from PERSI-WM and other reference regions were evaluated by effect size and receiver-operator characteristic curve analyses. The SUVRs derived from PERSI-WM showed significantly higher trace retention in the frontal, parietal, temporal, and occipital lobes, as well as in the anterior cingulate, posterior cingulate, precuneus, and global cortex in the AD Aβ-positive (+) group (mean: +43.3%±5.4%, P<0.01) and MCI Aβ+ group (mean: +29.6%±5.3%, P<0.01). For the global cortex, PERSI-WM had the greatest Cohen's d effect size compared with the HC Aβ-negative (-) group (AD Aβ+ and MCI Aβ+: 3.02, AD Aβ+: 3.56, MCI Aβ+: 2.34), and the highest area under the curve (AUC) between the HC Aβ- and AD Aβ+ groups (AUC: 0.983, 95% confidence interval: 0.978-0.998). PERSI-WM could mitigate the influence of PVE and improve the semi-quantification of 18F-AV-45 images; therefore, it could be used for large-scale clinical application in the nuclear medicine domain.

Cerebral amyloid accumulation is associated with distinct structural and functional alterations in the brain of depressed elders with mild cognitive impairment

BackgroundElderly patients with late-life depression (LLD) often report mild cognitive impairment (MCI), so Alzheimer's disease (AD) is hard to identify in these patients. We aimed to identify the structural and functional differences between prodromal AD and LLD-related MCI. MethodsWe performed voxel-based morphometry and functional connectivity (FC) analyses in elderly patients with both LLD and MCI to compare alterations between those with cerebral amyloidopathy and those without. We subdivided patients into subthreshold depression (STD) and major depressive disorder (MDD) groups. Using florbetaben positron emission tomography (PET), we compared volume and connectivity between healthy controls and four STD and MDD groups with or without amyloid deposition(A): STD-MCI-A(+), MDD-MCI-A(+), STD-MCI-A(-), and MDD-MCI-A(-). ResultsSubjects with MDD or amyloid deposition showed greater volume reduction in the left middle temporal gyrus. MDD groups had lower FC than STD groups in the frontal, cortical, and limbic areas. The STD-MCI-A(+) group showed greater FC reduction than the MDD-MCI-A(-) and STD-MCI-A(-) groups, particularly in the hippocampus, parahippocampus, and frontal and temporal cortices. The functional differences associated with amyloid plaques were more evident in the STD group than in the MDD group. LimitationsLimitations include disproportional sex ratios, inability to determine the longitudinal effects of amyloidopathy in large populations. ConclusionsRegional gray matter loss and alterations in brain networks may reflect impairments caused by amyloid deposition and depression. Such changes may facilitate the detection of prodromal AD in elderly patients with both depression and cognitive dysfunction, allowing earlier intervention and more appropriate treatment.

Outcomes and interventions in the elderly with and without cognitive impairment: a longitudinal study.

ABSTRACT.Background:Clinical follow-up studies are necessary for a better understanding of the evolution of cognitive impairment as well as the development of better assessment and intervention tools.Objective:To investigate whether older people with preserved cognition (PC), mild cognitive impairment (MCI) and mild Alzheimer's disease (AD) show differences in clinical outcomes and interventions after a 32-month period.Methods:One hundred twenty-four community-dwelling older people were included and classified in one of three groups (PC, MCI and mild AD). Information on clinical outcomes (deaths, new diagnoses, falls, need for assistance or changes in routine and hospitalizations) and interventions (increased use of medication, physiotherapeutic intervention, practice of physical exercise, etc.) in the 32-month period were collected by telephone or during a home visit on a single day.Results:Ninety-five participants (35 with PC, 33 with MCI and 27 with AD) were reevaluated after 32 months. The need for assistance/changes in routine was significantly higher in the AD group, especially with regard to basic activities of daily living. Unlike the other groups, the PC group did not show “other diagnoses” (urinary incontinence, prolapse, change in vision or autoimmune disease). No significant differences were found regarding other variables.Conclusions:Older people with and without cognitive impairment exhibited differences in some clinical outcomes after 32 months, such as need for assistance or changes in their routine and new diagnoses of specific diseases. Therefore, the multidimensionality of geriatric patients should be considered when planning assessments and interventions.

Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images.

Deep-learning methods (especially convolutional neural networks) using structural magnetic resonance imaging (sMRI) data have been successfully applied to computer-aided diagnosis (CAD) of Alzheimer's disease (AD) and its prodromal stage [i.e., mild cognitive impairment (MCI)]. As it is practically challenging to capture local and subtle disease-associated abnormalities directly from the whole-brain sMRI, most of those deep-learning approaches empirically preselect disease-associated sMRI brain regions for model construction. Considering that such isolated selection of potentially informative brain locations might be suboptimal, very few methods have been proposed to perform disease-associated discriminative region localization and disease diagnosis in a unified deep-learning framework. However, those methods based on task-oriented discriminative localization still suffer from two common limitations, that is: 1) identified brain locations are strictly consistent across all subjects, which ignores the unique anatomical characteristics of each brain and 2) only limited local regions/patches are used for model training, which does not fully utilize the global structural information provided by the whole-brain sMRI. In this article, we propose an attention-guided deep-learning framework to extract multilevel discriminative sMRI features for dementia diagnosis. Specifically, we first design a backbone fully convolutional network to automatically localize the discriminative brain regions in a weakly supervised manner. Using the identified disease-related regions as spatial attention guidance, we further develop a hybrid network to jointly learn and fuse multilevel sMRI features for CAD model construction. Our proposed method was evaluated on three public datasets (i.e., ADNI-1, ADNI-2, and AIBL), showing superior performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.