Mild Cognitive Impairment Cases Research Articles

Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is aheterogeneous progressive neurocognitive disorder. Although different neuroimaging modalities have been used for the identification of early diagnostic and prognostic factors of AD, there is no consolidated view of the findings from the literature. Here, we aim to provide acomprehensive account of different neural correlates of cognitive dysfunction via magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI) (resting-state and task-related), positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) modalities across the cognitive groups i.e., normalcognition, mild cognitive impairment (MCI), and AD. A total of 46 meta-analyses met the inclusion criteria, including relevance to MCI, and/or AD along with neuroimaging modality used with quantitative and/or functional data. Volumetric MRI identified early anatomical changes involving transentorhinal cortex, Brodmann area28, followed by the hippocampus, which differentiated early AD from healthy subjects. Aconsistent pattern of disruption in the bilateral precuneus along with the medial temporal lobe and limbic systemwas observed in fMRI, while DTI substantiated the observed atrophic alterations in the corpus callosum among MCI and AD cases. Default mode network hypoconnectivity in bilateral precuneus (PCu)/posterior cingulate cortices (PCC) and hypometabolism/hypoperfusion in inferior parietal lobules and left PCC/PCu was evident. Molecular imaging revealed variable metabolite concentrations in PCC. In conclusion, the use of different neuroimaging modalities together may lead to identification of an early diagnostic and/or prognostic biomarker for AD.

Near-Infrared Optical Spectroscopy In Vivo Distinguishes Subjects with Alzheimer’s Disease from Age-Matched Controls

Background:Medical imaging methods such as PET and MRI aid clinical assessment of Alzheimer’s disease (AD). Less expensive, less technically demanding, and more widely deployable technologies are needed to expand objective screening for diagnosis, treatment, and research. We previously reported brain tissue near-infrared optical spectroscopy (NIR) in vitro indicating the potential to meet this need.Objective:To determine whether completely non-invasive, clinical, NIR in vivo can distinguish AD patients from age-matched controls and to show the potential of NIR as a clinical screen and monitor of therapeutic efficacy.Methods:NIR spectra were acquired in vivo. Three groups were studied: autopsy-confirmed AD, control and mild cognitive impairment (MCI). A feature selection approach using the first derivative of the intensity normalized spectra was used to discover spectral regions that best distinguished “AD-alone” (i.e., without other significant neuropathology) from controls. The approach was then applied to other autopsy-confirmed AD cases and to clinically diagnosed MCI cases.Results:Two regions about 860 and 895 nm completely separate AD patients from controls and differentiate MCI subjects according to the degree of impairment. The 895 nm feature is more important in separating MCI subjects from controls (ratio-of-weights: 1.3); the 860 nm feature is more important for distinguishing MCI from AD (ratio-of-weights: 8.2).Conclusion:These results form a proof of the concept that near-infrared spectroscopy can detect and classify diseased and normal human brain in vivo. A clinical trial is needed to determine whether the two features can track disease progression and monitor potential therapeutic interventions.

Mild Cognitive Impairment and its Lifestyle-Related Risk Factors in the Elderly

Context: Alzheimer's dementia (AD), an irreversible condition is an important cause of disability in old age. Mild cognitive impairment (MCI) is an intermediate state between normal cognition and dementia. Amnestic MCI (aMCI) is the precursor of AD. Identification of modifiable lifestyle risk factors help in the prevention of aMCI, and thereby in the prevention of AD. Aim: The purpose of this study was to determine the prevalence of aMCI and different lifestyle factors associated with types of MCI. Methods: This cross-sectional study was conducted amongst the elderly (≥60 years). A sample of 576 persons was selected using a multistage sampling technique. Vernacular adaptation of Hindi Mini-Mental State Examination tool was used to screen dementia and MCI. Geriatric Depression Scale-15 was used for screening depression. Data were collected using a pre-designed and pretested schedule and SPSS was used for data entry and analysis. Results: The prevalence of MCI was found to be 22.4% among the elderly. Out of all MCI cases, the prevalence of aMCI was 38.8% in this study. The lack of social and leisure engagement was found to be significantly associated with the type of MCI. Conclusion: The comparatively higher prevalence of aMCI is just the tip of the iceberg. Lack of social and leisure engagement is a highly predictive risk factor.

Association of mitochondrial variants and haplogroups identified by whole exome sequencing with Alzheimer's disease.

IntroductionFindings regarding the association between mitochondrial DNA (mtDNA) variants and Alzheimer's disease (AD) are inconsistent.MethodsWe developed a pipeline for accurate assembly and variant calling in mitochondrial genomes embedded within whole exome sequences (WES) from 10,831 participants from the Alzheimer's Disease Sequencing Project (ADSP). Association of AD risk was evaluated with each mtDNA variant and variants located in 1158 nuclear genes related to mitochondrial function using the SCORE test. Gene‐based tests were performed using SKAT‐O.ResultsAnalysis of 4220 mtDNA variants revealed study‐wide significant association of AD with a rare MT‐ND4L variant (rs28709356 C>T; minor allele frequency = 0.002; P = 7.3 × 10−5) as well as with MT‐ND4L in a gene‐based test (P = 6.71 × 10−5). Significant association was also observed with a MT‐related nuclear gene, TAMM41, in a gene‐based test (P = 2.7 × 10−5). The expression of TAMM41 was lower in AD cases than controls (P = .00046) or mild cognitive impairment cases (P = .03).DiscussionSignificant findings in MT‐ND4L and TAMM41 provide evidence for a role of mitochondria in AD.

Developing a cognitive dysfunction risk score for use with opioid-dependent persons in drug treatment

BackgroundCognitive dysfunction is common in persons seeking medication for opioid use disorder (MOUD) and may hinder many addiction-related services. Brief but accurate screening measures are needed to efficiently assess cognitive dysfunction in these resource-limited settings. The study aimed to develop a brief predictive risk score tailored for use among patients in drug treatment. MethodsThe present study examined predictors of mild cognitive impairment (MCI), objectively assessed via the NIH Toolbox, among 173 patients receiving methadone as MOUD at an urban New England drug treatment facility. Predictors of MCI were identified in one subsample using demographic characteristics, medical chart data, and selected items from the Brief Inventory of Neuro-Cognitive Impairment (BINI). Predictors were cross-validated in a second subsample using logistic regression. Receiver operating curve (ROC) analyses determined an optimal cut-off score for detecting MCI. ResultsA cognitive dysfunction risk score (CDRS) was calculated from patient demographics (age 50+, non-White ethnicity, less than high school education), medical and substance use chart data (history of head injury, overdose, psychiatric diagnosis, past year polysubstance use), and selected self-report items (BINI). The CDRS discriminated acceptably well, with a ROC curve area of 70.6 %, and correctly identified 78 % of MCI cases (sensitivity = 87.5 %; specificity = 55.6 %). ConclusionsThe CDRS identified patients with cognitive challenges at a level likely to impede treatment engagement and/or key outcomes. The CDRS may assist in efficiently identifying patients with cognitive dysfunction while requiring minimal training and resources. Larger validation studies are needed in other clinical settings.

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study.

Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.

Progression to Dementia in Mild Cognitive Impairment With Lewy Bodies or Alzheimer Disease.

ObjectiveTo determine whether mild cognitive impairment with Lewy bodies or mild cognitive impairment with Alzheimer disease differ in their rates of clinical progression to dementia, we undertook longitudinal observation of mild cognitive impairment cases with detailed clinical assessment of Lewy body diagnostic characteristics.MethodsTwo prospective longitudinal cohorts including 111 individuals ≥60 years of age with mild cognitive impairment were assessed annually to track cognitive and clinical progression, including the presence or absence of core clinical features and proposed biomarkers of dementia with Lewy bodies. Multistate modeling was used to assess the associations of diagnostic characteristics of dementia with Lewy bodies with clinical progression from mild cognitive impairment to dementia, with death as a competing outcome.ResultsAfter a mean follow-up of 2.2 years (range 1–6.7 years), 38 of the 111 (34%) participants progressed to dementia: 10 with AD, 3 with possible dementia with Lewy bodies, and 25 with probable dementia with Lewy bodies. The presence of any Lewy body disease characteristic was associated with an increased hazard of transition to dementia; this risk further increased as more diagnostic characteristics were observed (hazard ratio 1.33 per characteristic, 95% confidence interval [CI] 1.11–1.60) and was especially high for those experiencing complex visual hallucinations (hazard ratio 1.98, 95% CI 0.92–4.29) or cognitive fluctuations (hazard ratio 3.99, 95% CI 2.03–7.84).ConclusionsDiagnostic characteristics of Lewy body disease are associated with an increased risk of transition from mild cognitive impairment to dementia.

Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a Han population

IntroductionSubjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer’s disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level.MethodsThis study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition.ResultsOnly the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus.ConclusionsThese findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism.

Cross-lingual detection of mild cognitive impairment based on temporal parameters of spontaneous speech

Mild Cognitive Impairment (MCI) is a heterogeneous clinical syndrome, often considered as the prodromal stage of dementia. It is characterized by the subtle deterioration of cognitive functions, including memory, executive functions and language. Mainly due to the tenuous nature of these impairments, a high percentage of MCI cases remain undetected. There is evidence that language changes in MCI are present even before the manifestation of other distinctive cognitive symptoms, which offers a chance for early recognition. A cheap non-invasive way of early screening could be the use of automatic speech analysis. Earlier, our research team developed a set of speech temporal parameters, and demonstrated its applicability for MCI detection. For the automatic extraction of these attributes, a Hungarian-language ASR system was employed to match the native language of the MCI and healthy control (HC) subjects. In practical applications, however, it would be convenient to use exactly the same tool, regardless of the language spoken by the subjects. In this study we show that our temporal parameter set, consisting of articulation rate, speech tempo and various other attributes describing the hesitation of the subject, can indeed be reliably extracted regardless of the language of the ASR system used. For this purpose, we performed experiments both on English-speaking and on Hungarian-speaking MCI patients and healthy control subjects, using English and Hungarian ASR systems in both cases. Our experimental results indicate that the language on which the ASR system was trained only slightly affects the MCI classification performance, because we got quite similar scores (67-92%) as we did in the monolingual cases (67-92% as well). As our last investigation, we compared the proposed attribute values for the same utterances, utilizing both the English and the Hungarian ASR models. We found that the articulation rate and speech tempo values calculated based on the two ASR models were highly correlated, and so were the attributes corresponding to silent pauses; however, noticeable differences were found regarding the filled pauses (still, these attributes remained indicative for both languages). Our further analysis revealed that this is probably due to a difference regarding the annotation of the English and the Hungarian ASR training utterances.

Race/Ethnic Disparities in Mild Cognitive Impairment and Dementia: The Northern Manhattan Study.

Background:Variability in dementia rates across racial and ethnic groups has been estimated at 60%. Studies suggest disparities in Caribbean Hispanic and Black populations, but community-based data are limited.Objective:Estimate the prevalence of mild cognitive impairment (MCI) and dementia in the racially and ethnically diverse community-based Northern Manhattan Study cohort and examine sociodemographic, vascular risk factor, and brain imaging correlates.Methods:Cases of MCI and dementia were adjudicated by a team of neuropsychologists and neurologists and prevalence was estimated across race/ethnic groups. Ordinal proportional odds models were used to estimate race/ethnic differences in the prevalence of MCI or dementia adjusting for sociodemographic variables (model 1), model 1 plus potentially modifiable vascular risk factors (model 2), and model 1 plus structural imaging markers of brain integrity (model 3).Results:There were 989 participants with cognitive outcome determinations (mean age 69±9 years; 68% Hispanic, 16% Black, 14% White; 62% women; mean (±SD) follow-up five (±0.6) years). Hispanic and Black participants had greater likelihood of MCI (20%) and dementia (5%) than White participants accounting for age and education differences. Hispanic participants had greater odds of MCI or dementia than both White and Black participants adjusting for sociodemographic variables, vascular risk factors, and brain imaging factors. White matter hyperintensity burden was significantly associated with greater odds of MCI or dementia (OR = 1.3, 1.1 to 1.6), but there was no significant interaction by race/ethnicity.Conclusion:In this diverse community-based cohort, cross-sectional data revealed significant race/ethnic disparities in the prevalence of MCI and dementia. Longer follow-up and incidence data are needed to further clarify these relationships.

Cognitive subtypes in individuals with essential tremor seeking deep brain stimulation

Objective: Essential tremor (ET) is a common neurological disorder that has been associated with 60% increased risk of developing dementia. The goals of the present study were to: (a) learn whether individuals with advanced ET symptoms seeking deep brain stimulation (DBS) surgery would fall into distinct cognitive subgroups, and (b) learn how empirically derived subgroups map onto criteria for mild cognitive impairment (MCI). Method: Patients with ET (N = 201; mean age = 68.9 ± 8.9 years) undergoing pre-surgical evaluation for DBS completed a multi-domain neurocognitive assessment consisting of memory, executive function, visuospatial skill, language, and processing speed. Two cluster analytic approaches (K-means, hierarchical) were independently conducted to classify cognitive patterns using domain composites. Demographics, clinical characteristics, and proportion of cases meeting neuropsychologically defined criteria for MCI were examined among clusters. Results: A three-cluster solution reflected a Low Executive group (N = 64), Low Memory Multi-Domain group (N = 41), and Cognitively Normal group (N = 96). The Cognitively Normal group was older and more educated, with a higher Dementia Rating Scale-2 score. In total, 27.4% of participants met criteria for MCI. Of the MCI cases, most were in the Low Executive (41.8%) or Low Memory Multi-Domain groups (49.1%). In the latter, 65.9% of its members were classified as MCI versus 35.9% in the Low Executive group. Conclusions: Our study identified three cognitive subtypes of ET patients presenting for DBS. Future work should examine the subgroups for progression to dementia, particularly the Low Memory Multi-Domain subgroup which may be at highest risk.

Long-term exposures to ambient PM1 and NO2 pollution in relation to mild cognitive impairment of male veterans in China

Mild cognitive impairment (MCI) is an intermediate stage of cognitive decline between normal ageing and dementia or Alzheimer’s disease in the elderly. However, evidence is very limited in China for the association between air pollution and MCI. This study aims to examine the associations of long-term exposure to air pollution and MCI, using data from the Chinese Veteran Clinical Research Platform. A national investigation on mental health of veterans was conducted in 277 veteran communities in 18 cities across China. In total, 1,861 MCI cases and 3,188 controls were randomly selected using a stratified cluster sampling strategy from December 2009 to December 2011. Participants’ cognitive function was first assessed using the Mini Mental State Examination and the Montreal Cognitive Assessment in the Chinese version, and then further confirmed by clinical examination. Participants’ mean exposures to PM1 (particulate matter with aerodynamic diameter ⩽1 μm) and NO2 (nitrogen dioxide) during the 3 years before the investigation were estimated using satellite remote sensing data, meteorological variables and land use information. The association between historical exposure to air pollution and MCI was examined using Logistic regression. After controlling for individual-level and regional-level confounders, we found historical exposures to PM1 and NO2 significantly increased the risk of MCI. The odds ratios (ORs associated with per 10 µg m−3 increase in air pollution) and 95% confidence intervals for PM1 and NO2 were 1.08 (1.04, 1.13) and 1.07 (1.02, 1.13), respectively. In the multi-pollutant models, higher OR for PM1 while lower OR for NO2 were observed compared to single-pollutant models. High levels of PM1 and NO2 pollution significantly increased the risk of cognitive decline among male veterans in China. Given the causal air pollution-MCI relationship, good air quality may help to reduce the burden of mental disorders among elderly veterans in China.

Clinical impact of 11C-Pittsburgh compound-B positron emission tomography in addition to magnetic resonance imaging and single-photon emission computed tomography on diagnosis of mild cognitive impairment to Alzheimer's disease.

This study aimed to evaluated the clinical impact of adding [11C] Pittsburgh compound-B (11C-PiB) PET for clinical diagnosis of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia.Twenty six (mean age 78.5 ± 5.18 years, 21 females) AD (n = 7), amnestic MCI (n = 12), non-amnestic MCI (n = 3), vascular dementia, progressive supranuclear palsy (PSP) with frontotemporal dementia (FTD), FTD (n = 1 each), and normal (n = 1) patients underwent 11C-PiB-PET, MRI, and SPECT scanning. 11C-PiB-PET was compared with MRI and SPECT for clinical impact.11C-PiB-PET showed positivity in 6, 9, and 0 of the AD, amnestic MCI, and non-amnestic MCI patients, respectively, and 0 of those with another disease. Parahippocampal atrophy at VSASD was observed in 5 AD patients, 6 amnestic and PiB-positive MCI patients, 1 amnestic and PiB-negative MCI patient, and 1 vascular dementia patient. Parietal lobe hypoperfusion in SPECT findings was observed in 6, 4, and 2 of those, respectively, as well as 1 each of non-amnestic MCI, vascular dementia, and normal cases. Sensitivity/specificity/accuracy for selecting PiB-positive patients among the 15 MCI patients for 11C-PiB-PET were 100% (9/9)/100% (6/6)/100% (15/15), for VSRAD were 66.7% (6/9)/83.3% (5/6)/73.3% (11/15), and for SPECT were 44.4% (4/9)/50.0% (3/6)/46.7% (7/15), while those were 88.9% (8/9)/33.3% (2/6)/66.7% (10/15)/for combined VSRAD and SPECT. 11C-PiB-PET accuracy was significantly higher than that of SPECT.11PiB-PET alone may be useful for selecting patients who will progress from MCI to AD in the future, although follow-up study is necessary to clarify the outcome of MCI patients.

Blood Derived Amyloid Biomarkers for Alzheimer\u2019s Disease Prevention

BackgroundReliable, widely accessible and affordable biomarkers for predicting Alzheimer’s disease (AD) brain pathology status are a necessity to aid development of prevention strategies in cognitively healthy at-risk older adults, at the right timepoint. Measurements of the key neuropathological hallmark beta-amyloid (Aβ) by PET neuroimaging or cerebrospinal fluid measures reflect its accumulation in the brain, yet recent methodological advancements now enable blood-based measures reflecting cerebral amyloid burden.ObjectivesThe current study validated the capacity of plasma Aβ42/Aβ40 measured using six different assays to predict amyloid positivity in a subgroup of cognitively unimpaired (CU) participants in the ADNI study and assessed its ability to discriminate CU from AD cases. We also explored economic viability of using two different plasma amyloid assays for pre-screening in AD prevention trials and as routine clinical diagnostic tool, versus amyloid PET alone.DesignA cross-sectional analysis of plasma and brain amyloid data, including comparative cost analysis of the plasma biomarkers in relation to brain amyloid PET.SettingAlzheimer’s Disease Neuroimaging Initiative (ADNI).ParticipantsADNI participants consisting of 115 CU, mild cognitive impairment and AD cases who had plasma Aβ42/ Aβ40 measured with six platforms.MeasurementsPlasma Aβ42/Aβ40 was measured via six different platforms: three immunoassays (Roche, Quanterix and ADx Neurosciences) and three mass spectrometry (MS) based assays (WashU, Shimadzu and Gothenburg). Aβ-PET imaging was conducted within three months of plasma sampling using [18F]florbetapir.ResultsThere was a weak to moderate correlation of plasma Aβ42/Aβ40 ratio between platforms. The MS-based WashU test had the highest capacity to discriminate between CU and AD (area under the curve, AUC = 0.734, 95% CI: 0.613–0.854; P = 0.008). Within the CU group, the WashU plasma amyloid test had the best discriminative capacity to distinguish Aβ+ from Aβ- (AUC = 0.753, 95% CI: 0.601–0.905; P = 0.003) closely followed by the immunoassay from Roche (AUC = 0.737, 95% CI: 0.597–0.877; P = 0.006). The exploratory economic analyses showed that the use of Roche or WashU plasma amyloid assay as a pre-screening tool prior to Aβ-PET scans for clinical trial recruitment significantly reduced total screening cost (saving up to $5882 per recruited patient) expected in an AD prevention trial.ConclusionsWith few available treatment strategies, dementia prevention is a global priority. CU individuals at risk for AD are the target population for dementia prevention but have been poorly studied. Our findings confirming diagnostic value of ultrasensitive immunoassays and high-performance immunoprecipitation coupled with MS for measurement of plasma Aβ42/β40 to detect PET amyloid positivity in CU participants allude to potential clinical utility of this biomarker. Plasma Aβ42/Aβ40 could be optimal for pre-selecting at-risk candidates for more invasive and expensive investigations across AD prevention clinical trials and clinical care for a rapidly ageing population.

Depressive symptoms and cognitive impairment: A 10-year follow-up study from the Survey of Health, Ageing and Retirement in Europe.

Depressive symptoms and cognitive impairment often coexisted in the elderly. This study investigates the effect of late-life depressive symptoms on risk of mild cognitive impairment (MCI). A total of 14,231 dementia- and MCI free participants aged 60+ from the Survey of Health, Ageing, and Retirement in Europe were followed-up for 10 years to detect incident MCI. MCI was defined as 1.5 standard deviation (SD) below the mean of the standardized global cognition score. Depressive symptoms were assessed by a 12-item Europe-depression scale (EURO-D). Severity of depressive symptoms was grouped as: no/minimal (score 0-3), moderate (score 4-5), and severe (score 6-12). Significant depressive symptoms (SDSs) were defined as EURO-D score ≥ 4. During an average of 8.2 (SD = 2.4)-year follow-up, 1,352 (9.50%) incident MCI cases were identified. SDSs were related to higher MCI risk (hazard ratio [HR] = 1.26, 95% confidence intervals [CI]: 1.10-1.44) in total population, individuals aged 70+ (HR = 1.35, 95% CI: 1.14-1.61) and women (HR = 1.28, 95% CI: 1.08-1.51) in Cox proportional hazard model adjusting for confounders. In addition, there was a dose-response association between the severity of depressive symptoms and MCI incidence in total population, people aged ≥70 years and women (p-trend <0.001). Significant depressive symptoms were associated with higher incidence of MCI in a dose-response fashion, especially among people aged 70+ years and women. Treating depressive symptoms targeting older population and women may be effective in preventing MCI.

Accounting for cognitive practice effects results in earlier detection and more accurate diagnosis of MCI: Biomarker confirmation

AbstractBackgroundPractice effects (PEs) mask true cognitive decline. Even with declines at follow‐up, PEs can still obscure even steeper declines. Accounting for PEs means that impairment cutoffs are reached earlier. Importantly, mild cognitive impairment (MCI) would be detected earlier. If these diagnoses are valid, there should be more biomarker‐positive MCI cases and fewer biomarker‐positive cognitively normal subjects (CNs). We tested this hypothesis using a replacement‐subjects method.MethodWe examined 722 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects who were CN at baseline (mean age=74.9±6.8). MCI at follow‐up was diagnosed by Jak‐Bondi and Petersen criteria. We identified “pseudo‐replacements” whose baseline age was matched to the age of returnees at their 1‐year follow‐up. Education and estimated premorbid IQ were also matched. Difference scores were calculated at Time 2 between returnees on their second testing and the demographically‐matched pseudo‐replacements on their first testing. Attrition effects were estimated by comparing returnee baseline scores with the full baseline sample. PEs = difference scores – attrition effects. PEs are then subtracted from follow‐up scores, with the resultant scores used in diagnosing MCI. CSF Aβ42 and phosphorylated (p)‐tau were measured 6‐12 months prior to retesting.ResultThere were significant PEs on 5 of 6 tests (average Cohen’s d=.27). For Jak‐Bondi diagnoses, accounting for PEs at follow‐up resulted in a 24.8% increase in the proportion of MCI cases (p&lt;.000001). There were increased proportions of Aβ42‐positive (18.8%) and p‐tau‐positive cases (29.8%), and reduced proportions of Aβ42‐positive (5.8%) and p‐tau‐positive (7.9%) CNs (McNemar χ2: Aβ42: p&lt;.0014, p‐tau: p&lt;.028; Figure 1). Results were similar for Petersen diagnoses. Of 26 false negatives (i.e., CNs whose diagnosis changed to MCI after accounting for PEs), 9 (35%) were MCI without PE adjustment at 2‐year follow‐up.ConclusionAccounting for PEs led to earlier detection of MCI at 1‐year follow‐up. Biomarker data support these being more accurate diagnoses because more cases were biomarker‐positive and more CNs were biomarker‐negative. Longer‐term follow‐up indicated that MCI cases may be detected a year or more earlier if performance is adjusted for PEs. Accounting for PEs has meaningful, real‐world implications for early identification, which may, in turn, improve opportunities to slow disease progression.

The association between low social support and risk of cognitive impairment is partially mediated by neuroanatomic biomarkers of Alzheimer’s disease

AbstractBackgroundSocial relationships with less support are linked to greater risk of developing Alzheimer’s disease (AD) and other dementias. Despite growing evidence of this link, to our knowledge, no studies have examined neuropathological processes through which low social support may affect risk of cognitive impairment. We used data from the Women’s Health Initiative Memory Study (WHIMS)‐MRI Study to examine whether neuroanatomical characteristics indicative of AD risk and cerebrovascular diseases underlie the greater risk of cognitive impairment or dementia over time in those with less supportive social relationships.MethodParticipants included 989 women (aged 73‐87 years) from the WHIMS‐MRI study who completed the Medical Outcomes Study Social Support Scale, assessing perceived emotional, tangible, and affectionate support, in 2004‐05. Structural brain scans were performed in 2005‐06 to assess small‐vessel ischemic disease (SVID) volume and determine AD pattern similarity scores (AD‐PS), a brain‐MRI measured neuroanatomical biomarker reflecting diffuse gray matter atrophy in brain regions vulnerable to AD neuropathologies, developed by supervised machine learning and validated with AD Neuroimaging Initiative data. Annual WHIMS follow‐ups included extensive neuropsychological assessments to ascertain centrally‐adjudicated incident cases of mild cognitive impairment (MCI) or all‐cause dementia. Structural equation models were used to assess the association between social support and risk of MCI/dementia (diagnosis data available through June 2018), and whether the association was mediated by global SVID volumes or AD‐PS scores. Models were adjusted for depressive symptoms, demographic, lifestyle, and clinical covariates.ResultLow social support was associated with higher MCI/dementia risk (hazard ratio= 1.23 per 1‐SD; 95% CI: 1.07, 1.44) and also with higher AD‐PS scores (β=.067; 95% CI: .01, .12), but not SVID volume. Higher AD‐PS score accounted for 18.6% (95% CI: 4%, 43%) of the total effect of low social support on risk of MCI/dementia (Figure 1). Higher SVID only accounted for 1.9% (95% CI: ‐1.5%, 8.8%) of the total effect.ConclusionOur study illustrates an association between low social support and higher MCI/dementia risk among older women, which was partly mediated by gray matter atrophy indicative of higher AD risk. Findings also suggest less of a role of cerebrovascular disease in mediating these associations.

A novel visual short‐term memory task differentiates MCI and Alzheimer’s patients from healthy ageing

AbstractBackgroundWe developed a novel delayed reproduction task, the “What was where?” task, which obtains a continuous measure of localization error and is more sensitive compared to conventional span measures of Short‐term Memory (STM).MethodWe recruited 44 MCI (Mild Cognitive Impairment), 41 AD (Alzheimer’s Disease) patients and 109 healthy elderly controls (EHCs) from memory clinics in Oxford, UK and Jena, Germany, where they performed the “What was where?” task (Figure 1). We extracted the following metrics: Identification Accuracy (percentage correctly identified items), Absolute Localization Error (how far the object was misplaced), Misbinding rate (erroneously localizing an item to the remembered location of another item in memory) and Guessing response rate.ResultAbsolute Localization Error and Misbinding rates were greater in MCI and AD patients (p &lt; 0.001) compared to EHCs, while AD and MCI performed similarly on these metrics. However, Identification Accuracy as well as Guessing (p = 0.003) were greater in MCI (p &lt; 0.001) compared to AD patients. Moreover, EHCs identified the correct object more often (p &lt; 0.001) and guessed less (p &lt; 0.001) than MCI patients.ConclusionThis novel task is able to detect STM impairment in MCI cases on a continuous scale, and is a sensitive memory measure which might be a useful index of memory for future clinical trials in AD in its earliest stages.

Resting state functional connectivity of financial capacity in mild cognitive impairment

AbstractBackgroundFinancial capacity (FC) is a complex, multi‐dimensional neurocognitive construct that is commonly impaired and disables older individuals with cognitive impairment but the underlying mechanisms are not well understood. Resting state functional connectivity was measured in individuals with mild cognitive impairment (MCI) and impaired FC compared to cognitively normal controls (NC) in order to identify relevant network components that may help drive future interventions.MethodResting state functional MRI scans were obtained from individuals with MCI (N = 17) and age, gender, and education matched NC (N = 15). Table 1. All participants completed the Financial Capacity Instrument (FCI) and neuropsychological assessment. Based on previously reported imaging findings in this population, the left Angular Gyrus (lAG) was used as the seed region in functional connectivity measurements. Connectivity beta values (Fisher transformed correlation coefficients) were calculated for each connection that showed significantly altered connectivity between control and MCI cases. A Pearson’s correlation was calculated between the connectivity beta values from relevant regions and cognitive measures.ResultA total of 26 brain regions showed significant increased functional connectivity (p &lt;0.05 uncorrected) with the lAG in NC compared to MCI. Of these regions 14 were identified as relevant to higher‐level cognitive function and carried forward in the correlation analysis (Figure 1). Pearson’s correlation showed significant negative association between the FCI and increased connectivity with the right temporal fusiform cortex (rTFC, r = ‐0.455, p = 0.009); negative correlation between Clock drawing and increased connectivity with the intraparietal sulcus (r = ‐0.38, p = 0.03); positive correlation between Trails B and increased connectivity with postcentral gyrus (r = 0.39, p = 0.03) and left central operculum (r = 0.36, p = 0.04). Table 2.ConclusionThere is a significant correlation between a well‐validated measure of FC and increased functional connectivity between the lAG and the rTFC in controls compared to MCI. This novel finding suggests that integration of visuoperception, memory, and mathematical function is critical for FC. No other significant correlation between the lAG and rTFC was found with other tests of executive function or visuospatial cognition.