Mild Cognitive Impairment Individuals Research Articles

Predicting amyloid-PET and clinical conversion in apolipoprotein E ε3/ε3 non-demented individuals with multidimensional factors.

The apolipoprotein E (APOE) ε4 is a well-established risk factor of amyloid-β (Aβ) in Alzheimer's disease (AD). However, because of the high prevalence of APOE ε3, there may be a large number of people with APOE ε3/ε3 who are non-demented and have Aβ pathology. There are limited studies on assessing Aβ status and clinical conversion in the APOE ε3/ε3 non-demented population. Two hundred and ninety-three non-demented individuals with APOE ε3/ε3 from ADNI database were divided into Aβ-positron emission tomography (Aβ-PET) positivity (+) and Aβ-PET negativity (-) groups using cut-off value of >1.11. Stepwise regression searched for a single or multidimensional clinical variables for predicting Aβ-PET (+), and the receiver operating characteristic curve (ROC) assessed the accuracy of the predictive models. The Cox regression model explored the risk factors associated with clinical conversion to mild cognitive impairment (MCI) or AD. The results showed that the combination of sex, education, ventricle and white matter hyperintensity (WMH) volume can accurately predict Aβ-PET status in cognitively normal (CN), and the combination of everyday cognition study partner total (EcogSPTotal) score, age, plasma p-tau 181 and WMH can accurately predict Aβ-PET status in MCI individuals. EcogSPTotal score were independent predictors of clinical conversion to MCI or AD. The findings may provide a non-invasive and effective tool to improve the efficiency of screening Aβ-PET (+), accelerate and reduce costs of AD trial recruitment in future secondary prevention trials or help to select patients at high risk of disease progression in clinical trials.

Alterations in brain functional connectivity in patients with mild cognitive impairment: A systematic review and meta-analysis of functional near-infrared spectroscopy studies.

Emerging evidences suggest that cognitive deficits in individuals with mild cognitive impairment (MCI) are associated with disruptions in brain functional connectivity (FC). This systematic review and meta-analysis aimed to comprehensively evaluate alterations in FC between MCI individuals and healthy control (HC) using functional near-infrared spectroscopy (fNIRS). Thirteen studies were included in qualitative analysis, with two studies synthesized for quantitative meta-analysis. Overall, MCI patients exhibited reduced resting-state FC, predominantly in the prefrontal, parietal, and occipital cortex. Meta-analysis of two studies revealed a significant reduction in resting-state FC from the right prefrontal to right occipital cortex (standardized mean difference [SMD]=-.56; p<.001), left prefrontal to left occipital cortex (SMD=-.68; p<.001), and right prefrontal to left occipital cortex (SMD=-.53; p<.001) in MCI patients compared to HC. During naming animal-walking task, MCI patients exhibited enhanced FC in the prefrontal, motor, and occipital cortex, whereas a decrease in FC was observed in the right prefrontal to left prefrontal cortex during calculating-walking task. In working memory tasks, MCI predominantly showed increased FC in the medial and left prefrontal cortex. However, a decreased in prefrontal FC and a shifted in distribution from the left to the right prefrontal cortex were noted in MCI patients during a verbal frequency task. In conclusion, fNIRS effectively identified abnormalities in FC between MCI and HC, indicating disrupted FC as potential markers for the early detection of MCI. Future studies should investigate the use of task- and region-specific FC alterations as a sensitive biomarker for MCI.

Detection of Early Stage Alzheimer's Disease in Gradient-Based MR Images Using Deep Learning Methods

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Blood-based biomarkers (BBMs) are of growing interest in the field of Alzheimer's disease (AD) and related dementias. This study aimed to assess the ability of plasma biomarkers to 1) predict disease progression from mild cognitive impairment (MCI) to dementia and 2) improve the predictive ability of magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) measures when combined. We used data from the Alzheimer's Disease Neuroimaging Initiative. Machine learning models were trained using the data from participants who remained cognitively stable (CN-s) and with Dementia diagnosis at 2-year follow-up visit. The models were used to predict progression to dementia in MCI individuals. We assessed the performance of models with plasma biomarkers against those with CSF and MRI measures, and also in combination with them. Our models with plasma biomarkers classified CN-s individuals from AD with an AUC of 0.75±0.03 and could predict conversion to dementia in MCI individuals with an AUC of 0.64±0.03 (17.1% BP, base prevalence). Models with plasma biomarkers performed better when combined with CSF and MRI measures (CN versus AD: AUC of 0.89±0.02; MCI-to-AD: AUC of 0.76±0.03, 21.5% BP). Our results highlight the potential of plasma biomarkers in predicting conversion to dementia in MCI individuals. While plasma biomarkers could improve the predictive ability of CSF and MRI measures when combined, they also show the potential to predict non-progression to AD when considered alone. The predictive ability of plasma biomarkers is crucially linked to reducing the costly and effortful collection of CSF and MRI measures.

Validation of the Slovenian version of the Montreal Cognitive Assessment Scale as a screening tool for the detection of mild cognitive impairment

ObjectiveThe Montreal cognitive assessment scale (MoCA) is commonly used for detecting individuals with mild cognitive impairment (MCI). The aim of the present study was to evaluate the validity of the Slovenian MoCA as a screening tool for MCI and to determine the optimal cut-off point to detect MCI in the elderly population.MethodsMini-Mental State Examination (MMSE), MoCA, and neuropsychological testing assessment were conducted on 93 individuals aged ≥ 60 years. MCI was found in 35 individuals with 58 cognitively asymptomatic controls. Cut-off values, sensitivity, and specificity of MoCA were calculated with the receiver operating characteristic curve.ResultsMCI and healthy individuals did not differ with respect to age and education. Healthy individuals (M = 24.5, SD = 1.7) performed significantly better on MoCA compared to MCI individuals (M = 21.4, SD = 3.2) (p < 0.001). The Cronbach’s α of MoCA as an index of internal consistency was 0.64. MoCA distinguished between healthy controls and MCI individuals with a sensitivity of 77% and specificity of 74%, using a cut-off of 23/24 points.ConclusionThe Slovenian version of MoCA demonstrates an optimal cut-off value of 23/24 points for detecting older individuals with MCI. As a screening tool for MCI, its better diagnostic accuracy makes it preferable to using MMSE.

Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan

BackgroundThe early detection of dementia depends on efficient methods for the assessment of cognitive capacity. Existing cognitive screening tools are ill-suited to the differentiation of cognitive status, particularly when dealing with early-stage impairment.MethodsThe study included 8,979 individuals (> 50 years) with unimpaired cognitive functions, mild cognitive impairment (MCI), or dementia. This study sought to determine optimal cutoffs values for the Cognitive Abilities Screening Instrument (CASI) aimed at differentiating between individuals with or without dementia as well as between individuals with or without mild cognitive impairment. Cox proportional hazards models were used to evaluate the value of CASI tasks in predicting conversion from MCI to all-cause dementia, dementia of Alzheimer’s type (DAT), or to vascular dementia (VaD).ResultsOur optimized cutoff scores achieved high accuracy in differentiating between individuals with or without dementia (AUC = 0.87—0.93) and moderate accuracy in differentiating between CU and MCI individuals (AUC = 0.67 – 0.74). Among individuals without cognitive impairment, scores that were at least 1.5 × the standard deviation below the mean scores on CASI memory tasks were predictive of conversion to dementia within roughly 2 years after the first assessment (all-cause dementia: hazard ratio [HR] = 2.81 – 3.53; DAT: 1.28 – 1.49; VaD: 1.58). Note that the cutoff scores derived in this study were lower than those reported in previous studies.ConclusionOur results in this study underline the importance of establishing optimal cutoff scores for individuals with specific demographic characteristics and establishing profiles by which to guide CASI analysis.

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool.

Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer's Association Workgroup, and a MoCA score of 19-25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors' beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods. Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4-13, and high risk ≥ 14). The risk score based on age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

Clinical variables contributing to the identification of biologically defined subgroups within cognitively unimpaired and mild cognitive impairment individuals.

A lack of consensus exists in linking demographic, behavioral, and cognitive characteristics to biological stages of dementia, defined by the ATN (amyloid, tau, neurodegeneration) classification incorporating amyloid, tau, and neuronal injury biomarkers. Using a random forest classifier we investigated whether 27 demographic, behavioral, and cognitive characteristics allowed distinction between ATN-defined groups with the same cognitive profile. This was done separately for three cognitively unimpaired (CU) (112 A-T-N-; 46 A+T+N+/-; 65 A-T+/-N+/-) and three mild cognitive impairment (MCI) (128 A-T-N-; 223 A+T+N+/-; 94 A-T+/-N+/-) subgroups. Classification-balanced accuracy reached 39% for the CU and 52% for the MCI subgroups. Logical Delayed Recall (explaining 16% of the variance), followed by the Alzheimer's Disease Assessment Scale 13 (14%) and Everyday Cognition Informant (10%), were the most relevant characteristics for classification of the MCI subgroups. Race and ethnicity, marital status, and Everyday Cognition Patient were not relevant (0%). The demographic, behavioral, and cognitive measures used in our model were not informative in differentiating ATN-defined CU profiles. Measures of delayed memory, general cognition, and activities of daily living were the most informative in differentiating ATN-defined MCI profiles; however, these measures alone were not sufficient to reach high classification performance.

Machine learning based on functional and structural connectivity in mild cognitive impairment

ObjectiveAlzheimer's disease (AD) is a chronic, degenerative neurological disorder characterized by progressive cognitive decline and mental behavioral abnormalities. Mild cognitive impairment (MCI) is regarded as a transitional stage in the progression from normal elderly individuals to patients with AD. While studies have identified abnormalities in brain connectivity in patients with MCI, including functional and structural connectivity, accurately identifying patients with MCI in clinical screening remains challenging. We hypothesized that utilizing machine learning (ML) based on both functional and structural connectivity could yield meaningful results in distinguishing between patients with MCI and normal elderly individuals, so as to provide valuable information for early diagnosis and precise evaluation of patients with MCI. MethodsFollowing clinical criteria, we recruited 32 patients with MCI for the patient group, and 32 normal elderly individuals for the control group. All subjects underwent examinations for resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). Subsequently, significant functional and structural connectivity features were selected and combined with a support vector machine for classification of the patient and control groups. ResultsWe observed significantly different functional connectivity in the frontal lobe and putamen between the MCI group and normal controls. The results based on functional connectivity features demonstrated a classification accuracy of 71.88% and an area under the curve (AUC) value of 0.78. In terms of structural connectivity, we found that decreased fractional anisotropy in patients with MCI was significantly associated with Montreal Cognitive Assessment scores, specifically in regions such as the precuneus and cingulate gyrus. The classification results using the structural connectivity feature yielded an accuracy of 92.19% and an AUC value of 0.99. Lastly, combining functional and structural connectivity features resulted in a classification accuracy and AUC value of 93.75% and 0.99, respectively. ConclusionsIn this study, we demonstrated a high classification performance, underscoring the potential of both brain functional and structural connectivity in distinguishing patients with MCI from normal elderly individuals. Furthermore, the integration of functional connectivity and structural connectivity features indicated that utilizing rs-fMRI and DTI could enhance the accuracy and specificity of identifying patients with MCI compared with relying on a single neuroimaging technique.

Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study.

A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.

Abnormal dynamics of functional brain network in Apolipoprotein E ε4 carriers with mild cognitive impairment

&lt;abstract&gt; &lt;p&gt;As is well known, the Apolipoprotein E (APOE) ε4 allele is the most pertinent genetic hazardous element for Alzheimer's disease (AD). Mild cognitive impairment (MCI) is considered a prodromal stage of AD. How the APOE ε4 allele modulates functional connectivity of brain network in MCI group is a question worth exploring. At present, some studies have evaluated the relationship between APOE ε4 allele and static functional network connectivity (sFNC) for MCI individuals, while the relationship of dynamic FNC (dFNC) with APOE ε4 allele still remained puzzled. Thus, we aim to detect aberrant dFNC for APOE ε4 carriers in the MCI group. On the basis of the resting-state functional magnetic resonance imaging (rs-fMRI) data, seven intrinsic brain functional networks were first recognized by the group independent component analysis. Then, the technique of sliding window was employed to determine the dFNC, and two dFNC states were detected by the k-means clustering algorithm. Finally, three temporal properties of fraction time, mean dwell time as well as transition numbers in the dFNC states were investigated. The results found that the dFNC and temporal properties in APOE ε4 carriers were abnormal compared with those in APOE ε4 noncarriers. In detail, in the MCI group, compared with APOE ε4 noncarriers, carriers had 9 pairs of abnormal dFNC and had significant differences in all the three temporal properties of the two dFNC states. In addition, two pairs of dFNC were found significantly correlated with clinical measure. This detected abnormal dynamics of temporal properties and dFNC in APOE ε4 carriers were similar with that reported for AD patients in previous studies. These results may suggest that in the MCI group, APOE carriers are more at risk for AD compared to noncarriers. Our findings may offer novel insights into the mechanisms of abnormal brain reconfiguration for individuals at genetic risk for AD, which could also be regarded as biomarkers for the early identification of AD.&lt;/p&gt; &lt;/abstract&gt;

Neural correlates of empathy in mild cognitive impairment.

The occurrence of clinically significant changes in empathy is a matter of debate in Alzheimer's disease (AD). Altered empathic mechanisms observed in AD may be a consequence of cognitive impairment, more specifically of reduced mental flexibility and self-regulation. The present study explored possible changes in empathy for subjects in the prodromal phase of AD, namely mild cognitive impairment (MCI) due to AD, and of their neural substrates. Eighteen MCI patients and 20 healthy controls (HC) were included in the study. The Interpersonal Reactivity Index (IRI) questionnaire was administered to each participant. The IRI encompasses four factors: Perspective Taking; Fantasy; Empathic Concern; Personal Distress. MCI patients underwent a magnetic resonance imaging structural examination and were compared to 30 healthy controls (HC-MRI). A limited number of cortical and subcortical regions involved in social cognition was selected as regions of interest (ROIs). MCI individuals obtained lower scores than HC in the Perspective Taking and Fantasy subscales of the IRI, whereas they obtained higher scores on Empathic Concern. Regarding neuroimaging data, a significant correlation emerged between IRI scores and the neural measurements of different regions involved in empathy, especially covering the temporoparietal junction, which is a critical region engaged in both affective and cognitive dimensions of empathy. The results of the present study suggest that a subtle impairment in regulatory mechanisms of empathy may occur very early during the course of AD, possibly as a consequence of neuropathological changes occurring in brain regions involved in social cognition.

Evidence of validity and reliability of the Colombian version of Addenbroke’s Cognitive Examination Revised (ACE-R)

Objective The purpose of this study is to provide evidence that supports the validity and reliability of the Colombian version of the Addenbrooke’s Cognitive Examination Revised (ACE-R) in comparison to the MMSE at assessing and finding patients with Mild Cognitive Impairment (MCI). Additionally, the study aims to determine the optimal cut-off scores based on the age of a population with a low education level. Method This study included 314 individuals (235 participants diagnosed with MCI and 79 cognitively healthy) who live in two different rural departments (states) in Colombia. The participants were recruited for this study through community clubs for the older adults. Most of the individuals were female (236), the average age was 65.95 years of age (SD= 7.8), and the average education level was of 3.78 years (SD = 1.79). It is important to note that the sample only included people with a maximum of 6 years of schooling. Results A ROC analysis indicated that the ACE-R is more effective than the MMSE at evaluating and finding MCI individuals within the three groups. The cut-off points for the Under 60 years of age group was 83.50 (sensitivity 0.880% and specificity 0.632%); 61–69 years of age 80.50 (sensitivity 0.714% and specificity 0.677%); and Over 70 years of age was 79.50 (sensitivity 0.750% and specificity 0.659%). The internal consistency analysis with MacDonald’s Ω determined reliability indicators ≥70 in the ACE-R, except for the age range of 61 to 69 years. Conclusion The Colombian version of the ACE-R demonstrates to be a valid and reliable global cognitive screening tool. It is effective at discerning MCI individuals from healthy within a group of participants with a low education level.

27-hydroxycholesterol causes cognitive deficits by disturbing Th17/Treg balance and the related immune responses in mild cognitive impairment patients and C57BL/6J mice

BackgroundCognitive impairment is associated with dysregulated immune responses. Emerging evidence indicates that Th17 cells and their characteristic cytokine-IL-17 are receiving growing interest in the pathogenesis of cognitive decline. Here, we focus on the involvement of Th17 cells in mild cognitive impairment (MCI) and the possible mechanism of cholesterol metabolite-27-hydroxycholesterol (27-OHC).Methods100 individuals were recruited into the nested case–control study who completed cognition assessment and the detection of oxysterols and Th17-related cytokines in serum. In addition, mice were treated with 27-OHC and inhibitors of RORγt and Foxp3 (Th17 and Treg transcription factors), and the factors involved in Th17/Treg balance and amyloidosis were detected.ResultsOur results showed there was enhanced 27-OHC level in serum of MCI individuals. The Th17-related cytokines homeostasis was altered, manifested as increased IL-17A, IL-12p70, IL-23, GM-CSF, MIP-3α and TNF-α but decreased IL-13, IL-28A and TGF-β1. Further, in vivo experiments showed that 27-OHC induced higher immunogenicity, which increased Th17 proportion but decreased Treg cells in peripheral blood mononuclear cells (PBMCs); Th17 proportions in hippocampus, and IL-17A level in serum and brain were also higher than control mice. The fluorescence intensity of amyloid-β (Aβ) and the precursor of amyloid A amyloidosis–serum amyloid A (SAA) was increased in the brain of 27-OHC-treated mice, and worse learning and memory performance was supported by water maze test results. While by inhibiting RORγt in 27-OHC-loaded mice, Th17 proportions in both PBMCs and hippocampus were reduced, and expressions of IL-17A and TGF-β1 were down- and up-regulated, respectively, along with a decreased amyloidosis in brain and improved learning and memory decline.ConclusionsAltogether, our results demonstrate that excessive 27-OHC aggravates the amyloidosis and leads to cognitive deficits by regulating RORγt and disturbing Th17/Treg balance.

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

BackgroundThe role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing. MethodFunctional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox. ResultsFunctional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group. ConclusionThese findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning.

Functional Brain Activation in Mild Cognitive Impairment With Defined Small Vessel Disease Burden

AbstractBackgroundVascular dysregulations and changes in functional brain network integrity play a fundamental role in the pathogenesis of dementia. While often identified in individuals with dementia, the role of small vessel disease (SVD) in the development of dementia is not completely understood yet. Previously, structural and functional brain connectivity was shown to be different between individuals with and without cerebral SVD, but a comprehensive measure of SVD has not been used consistently. We aim to analyze functional brain activation differences in mild cognitive impairment (MCI) individuals with defined SVD burden.MethodFunctional brain activation differences were analyzed in MCI individuals with absent or low (n = 34) or high (n = 34) SVD burden using data from the Parelsnoer Institute, a multicenter study involving eight Dutch medical centers. SVD burden was characterized using an ordinal scale ranging from 0 to 4 (Klarenbeek et al., 2013) considering the following markers: lacunes; microbleeds; perivascular spaces in the basal ganglia; white matter hyperintensities based on the Fazekas score. Two groups were identified: the absent or low SVD burden group with a score of 0 or 1 and the high SVD group with a score between 2 and 4. Activation differences were calculated using resting state fMRI data acquired using 3Tesla scanners and analyzed with group‐independent component analysis using the CONN toolbox.ResultActivation of two clusters in the high SVD burden group was lower than in the absent or low SVD group: the cerebellum (p‐FDR &gt;. 001, F = 1.49) and the brainstem (p‐FDR &gt;. 001, F = 2.79). The cerebellar cluster (4541 voxels, +20 ‐74 ‐38) consisted of the left cerebellum crus II, right cerebellum crus II, and left cerebellum lobule VIII. The brainstem cluster (218 voxels, +22 ‐28 +20) involved the right thalamus and the right caudate nucleus.ConclusionBrain activation differences between groups of individuals with MCI and absent or low or high SVD burden were found in the cerebellum and brainstem. These brain areas are mainly responsible for motor, attentional and executive functions, domains which were found in previous studies to be mostly associated with SVD markers.

Amyloid β‐specific T cell response is enhanced in individuals with mild cognitive impairment

AbstractBackgroundNeuroinflammation is a key process in initiating and propagating Alzheimer’s disease (AD). Even though it is widely known that the deposit of amyloid plaques and CSF levels of amyloid distinguishes patients with AD or mild cognitive impairment (MCI) from cognitively normal (CN) individuals, little is known about the role of amyloid‐specific immune response in cognitive decline.MethodUsing a polyfunctionality assay typically used for detecting virus‐specific T cell responses, we tested participants from the Epidemiology of Mild Cognitive Impairment in Taiwan study (EMCIT) and the Taiwan Precision Medicine Initiative of Cognitive impairment and dementia (TPMIC) study to compare the amyloid‐specific T cell responses between CN and MCI individuals. The abilities of T cell response parameters and plasma p‐Tau181 to distinguish MCI from CN were tested.ResultResults from both cohorts showed an enhanced amyloid‐specific T‐cell response in individuals with MCI. In the EMCIT cohort, the individual’s amyloid‐specific CD4+ response frequency of total CD4+ cells was significantly larger in MCI (n = 69, 0.93%) than in CN (n = 69, 0.51%, p &lt; 0.001). CD4+ T cell response discriminated MCI versus CN (area under curve [AUC], 0.72 [0.64‐0.81]) with significantly higher accuracy than p‐Tau181 (AUC: 0.59 [0.5‐0.69], p &lt; 0.01). In the TPMIC cohort, both CD4+ and CD8+ response frequencies were higher in MCI individuals (n = 21, CD4: 1.2%, CD8: 2.02%) than in CN (n = 30, CD4: 0.14%, CD8:0.27%; both p &lt; 0.001). CD4+ T cell response frequency and CD8+ response frequency also outperform p‐Tau181 in their discriminative accuracy of MCI versus NC (CD4+ AUC, 0.97, [0.94‐1.01]; CD8+ AUC, 0.96, [0.92‐1.01]; p‐Tau181 AUC, 0.83, [0.69‐0.96]; both p &lt; 0.05).ConclusionOur study validates the amyloid hypothesis by showing that amyloid‐associated neuroinflammation is involved in the process of neurodegeneration and demonstrated the accuracy of using amyloid‐specific T cell response to discriminate MCI from CN individuals. The TPMIC cohort is an ongoing longitudinal study that includes amyloid PET results and thus we will investigate the prognostic value of amyloid‐T cell response in the future.

Individuals from Memory Clinic were Associated with Severer Amyloid Deposition in the Chinese Population in Urban Cities

AbstractBackgroundAmyloid deposition is considered the initial pathology in Alzheimer’s disease (AD), investigation of amyloid pathology and the risk factor for amyloid pathology and cognitive declines in the Chinese population is critical for personalized management. However, the prevalence of amyloid deposition and the risk factors for amyloid pathology are unknown in the Chinese population.MethodParticipants were recruited from urban communities and memory clinics in Shanghai and Zhengzhou, China. A total of 246 AD individuals, 274 Mild cognitive impairment (MCI) individuals, and 506 Normal cognition (NC) individuals were identified: 106 AD, 156 MCI and 506 NC from the Community‐based cohort (COMC) as well as 140 AD and 118 MCI from the Clinic‐based cohort (CLIC). Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET) scans. We also performed a partial least squares (PLS) analysis to evaluate the associations of cohort, age, sex, education level, and ApoE genotype with amyloid qualification, quantification results and global cognition in all 1026 participants.ResultAcross the two cohorts, the AD group displayed the significantly highest rate of amyloid positivity (211/246, 85.8%), followed by the MCI (122/274, 44.5%) and NC groups (136/506, 26.9%, all P &lt; 0.001). Participants from the CLIC displayed a higher rate of amyloid positivity than those from the COMC in the MCI (68/118, 57.6% vs. 54/156, 34.6%, P &lt; 0.001) and AD groups (128/140, 91.4% vs. 83/106, 78.3%, P = 0.006). MCI (SUVr: 1.37 ± 0.25 vs. 1.25 ± 0.18, P &lt; 0.001), Aβ+ MCI (SUVr: 1.48 ± 0.27 vs. 1.37 ± 0.24, P = 0.03) and AD individuals (SUVr: 1.47 ± 0.23 vs. 1.39 ± 0.23, P = 0.01) in the CLIC displayed significantly greater amyloid deposition than those in the COMC. According to the PLS model, we found that the CLIC was significantly associated with positive amyloid deposition and severer amyloid deposition.ConclusionWe found that individuals from CLIC displayed a higher amyloid positive rate and severer amyloid deposition. Specifically, in a PLS model, The CLIC, was strongly associated with amyloid pathology.