Mild Cognitive Impairment Conversion Research Articles

Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI.

Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.

Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Robust prediction of Alzheimer’s disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.

The contribution of cerebrovascular risk factors, metabolic and inflammatory changes to cognitive decline in Parkinson's disease: preliminary observations.

To determine whether systemic medical factors, such as vascular risk factors, metabolic and inflammatory markers contribute to cognitive decline in Parkinson's disease (PD); if confirmed to determine whether a clinically applicable risk factor model can predict the conversion from normal cognition (NC) to mild cognitive impairment (MCI). 58 patients who met the UK Brain Bank Criteria for PD underwent clinical and laboratory assessment at study entry; 47 patients were re-assessed after 2 years. Medical history, vascular risk (QRISK2), blood metabolic and inflammatory factors, brain vessel examinations, activity of daily living, and neuropsychological testing were performed. Forty patients had NC and 18 patients had MCI at baseline. Patients with MCI had higher level of interleukin 6, folic acid below normal range and higher L-dopa equivalent dose compared to cognitive normal patients at baseline. Patients with NC at baseline were classified into two groups: patients who remained cognitively normal (non-converters, n = 23) and patients who progressed to MCI (converters, n = 11). MCI converters were older at baseline and had higher QRISK2 than the non-converters. Patients with higher QRISK2, lower uric acid level and lower activity of daily living scale at baseline had a higher risk of converting from NC to MCI with a sensitivity of 72.2%, a specificity of 87%, and an overall accuracy of 82.4%. Systemic medical factors are associated with cognitive impairment in PD both cross-sectionally and longitudinally. A risk factor model predicting the decline from NC to MCI could be constructed.

δ Scores Identify Subsets of "Mild Cognitive Impairment" with Variable Conversion Risks.

The latent variable "δ" (for "dementia) is a transdiagnostic measure of dementia severity. δ can be reified and applied to individuals as a composite "d-score". Like Spearman's general intelligence factor "g", δ can be constructed from almost any cognitive battery. So many are available that we must further distinguish each composite as a δ "homolog". Fourteen have been validated. All are strongly associated with dementia severity and potentially with mild cognitive impairment (MCI) conversion. To assess δ's impact on MCI conversion risk. A new δ homolog (dDx) was constructed in 1,230 Mexican-American (MA) and 2,215 non-Hispanic White (NHW) participants in the Texas Alzheimer's Research and Care Consortium (TARCC). 1,445 normal controls (NC) and 723 MCI were followed annually for up to 6 years. Each SD decrease in the dDx score increased the risk of conversion sixteen-fold [OR = 16.39 (CI: 5.0-52.6)]. Cases below the optimal diagnostic threshold for Alzheimer's disease (AD) versus NC were labeled as having a functionally salient cognitive impairment (FSCI). Such cases were at a 73-fold increase risk of a diagnosis of AD [OR = 73.19 (95% CI: 58.3-92.0)]. However, 25.6% of MCI cases were also FSCI(+). They accounted disproportionately for prospective conversions. Age <80 years, the absence of an ɛ4 allele, <12 years of education, and MA ethnicity independently increased the risk of diagnosing FSCI as MCI. A sizable minority of MCI cases may be misdiagnosed and they account disproportionately for AD conversions.

Fluoxetine and Vortioxetine Reverse Depressive-Like Phenotype and Memory Deficits Induced by Aβ1-42 Oligomers in Mice: A Key Role of Transforming Growth Factor-β1.

Depression is a risk factor for the development of Alzheimer’s disease (AD), and the presence of depressive symptoms significantly increases the conversion of mild cognitive impairment (MCI) into AD. A long-term treatment with antidepressants reduces the risk to develop AD, and different second-generation antidepressants such as selective serotonin reuptake inhibitors (SSRIs) are currently being studied for their neuroprotective properties in AD. In the present work, the SSRI fluoxetine and the new multimodal antidepressant vortioxetine were tested for their ability to prevent memory deficits and depressive-like phenotype induced by intracerebroventricular injection of amyloid-β (1-42) (Aβ1-42) oligomers in 2-month-old C57BL/6 mice. Starting from 7 days before Aβ injection, fluoxetine (10 mg/kg) and vortioxetine (5 and 10 mg/kg) were intraperitoneally injected daily for 24 days. Chronic treatment with fluoxetine and vortioxetine (both at the dose of 10 mg/kg) was able to rescue the loss of memory assessed 14 days after Aβ injection by the passive avoidance task and the object recognition test. Both antidepressants reversed the increase in immobility time detected 19 days after Aβ injection by forced swim test. Vortioxetine exerted significant antidepressant effects also at the dose of 5 mg/kg. A significant deficit of transforming growth factor-β1 (TGF-β1), paralleling memory deficits and depressive-like phenotype, was found in the hippocampus of Aβ-injected mice in combination with a significant reduction of the synaptic proteins synaptophysin and PSD-95. Fluoxetine and vortioxetine completely rescued hippocampal TGF-β1 levels in Aβ-injected mice as well as synaptophysin and PSD-95 levels. This is the first evidence that a chronic treatment with fluoxetine or vortioxetine can prevent both cognitive deficits and depressive-like phenotype in a non-transgenic animal model of AD with a key contribution of TGF-β1.

Depression of community elderly patients with mild cognitive impairment and its influencing factors

Objective To investigate the depression of community elderly patients with mild cognitive impairment (MCI) and to analyze its influencing factors so as to provide a basis for relieving the depression and delaying the conversion of MCI among elderly patients to dementia. Methods From December 2016 to March 2018, we selected 1 093 community elderly people (≥60 years old) complained of cognition hypofunction by convenience sampling. All of the aged were investigated with the general information questionnaire, Peking Union Medical College Hospital Version of Montreal Cognitive Assessment (MoCA-P) and Activities of Daily Living (ADL) . Outpatient doctors of neurology department at a ClassⅢ Grade A hospital in Beijing provided clinical diagnosis for elderly patients with primary screening of MCI. The Geriatric Depression Scale-30 (GDS-30) was used to assess depression of MCI patients. SPSS 21.0 was used to the date entry and statistical analysis, and multiple Logistic regression was used to analyze the influencing factors of depression. Results A total of 271 community elderly people were diagnosed as MCI. After excluding 30 depression assessment questionnaires with glaring holes and regularity, a total of 241 valid questionnaires were collected finally with 88.93% for the effective rate. Among 241 community elderly patients with MCI, 71 cases of them were with depression and the incidence was 29.46%; the score of depression was (15.99±4.31) ; a total of 59 cases (83.10%) of them had the moderate depression and 12 cases (16.90%) had severe depression. Multiple Logistic regression analysis showed that the risk factors of depression included insomnia (OR=2.09) , negative emotions such as anxiety or agitation (OR=6.25) , recent major events (OR=3.93) and poor ADL (OR=1.45) , the protective factors of depression included ages of over 80 years (OR=0.29) compared with the ages from 60 to 65 years among elderly MCI patients. Conclusions The incidence of elderly MCI patients with depression is high. Based on comprehensive evaluation for elderly MCI patients, nurses should pay attention to their depression emotions and help patients to improve sleep and ADL, and help them positively deal with negative influences brought by major events. Key words: Aged; Mild cognitive dysfunction; Depression; Cognition; Activity of daily living; Influencing factors

Lower Serum Calcium as a Potentially Associated Factor for Conversion of Mild Cognitive Impairment to Early Alzheimer's Disease in the Japanese Alzheimer's Disease Neuroimaging Initiative.

Effect of serum calcium level to the incidence of mild cognitive impairment (MCI) conversion to early Alzheimer's disease (AD) remains uncertain. To investigate association between baseline serum calcium and the MCI conversion in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study cohort. In this sub-analysis of J-ADNI study, we reviewed data from MCI participants at baseline regarding their conversion to early AD during the 3 years of observation period and assessed the associated factors including serum calcium level. In addition, we compared our results from the J-ADNI study with the corresponding results from the North American (NA)-ADNI. Of 234 eligible MCI participants from the J-ADNI cohort, 121 (51.7%) converted to AD during the first 36 months of observation. Using univariate analysis, being female, having shorter years of education, and lower serum calcium level were correlated with increased risk of MCI-to-AD conversion exclusively in J-ADNI cohort. The lower corrected serum calcium level remained as one of conversion-associated factors in the J-ADNI cohort even after adjustment for multiple confounding variables, although this was not observed in the NA-ADNI cohort. Our findings suggest that lower serum calcium may be associated with an increased risk of MCI conversion to AD in Japanese cohorts. The reason for this correlation remains unclear and further external validation using other Asian cohorts is needed. It would be interesting for future AD studies to obtain serum calcium levels and other related factors, such as vitamin D levels, culture-specific dietary or medication information.

Association study of rs3846662 with Alzheimer's disease in a population-based cohort: the Cache County Study

3-Hydroxy-3-methylglutaryl coenzyme A reductase is associated with monitoring cholesterol levels. The presence of the single-nucleotide polymorphism rs3846662 introduces alternative splicing at exon 13; the exclusion of this exon leads to a reduction in total cholesterol levels. Lower cholesterol levels are linked to a reduction in Alzheimer's disease (AD) risk. The major allele of rs3846662, which encourages the splicing of exon 13, has recently been shown to act as a preventative allele for AD, especially in women. The purpose of our research was to replicate and confirm this finding. Using logistic regressions and survival curves, we found a significant association between AD and rs3846662, with a stronger association in individuals who carry the APOE e4 allele, supporting previously published work. The effect of rs3846662 on women is insignificant in our cohort. We confirmed that rs3846662 is associated with reduced risk for AD without gender differences; however, we failed to detect association between rs3846662 and delayed mild cognitive impairment conversion to AD for either of the APOE e4 allelic groups.

ASSOCIATION BETWEEN NEUROPSYCHIATRIC SYMPTOM TRAJECTORY AND PROGRESSION TO ALZHEIMER'S DISEASE

Introduction Objective: Neuropsychiatric symptoms (NPS) are a key characteristic and known biomarker of Alzheimer's disease (AD). Our study investigates the potential of NPS as measured by the Neuropsychiatric Inventory score (NPI) to predict progression from Mild Cognitive Impairment (MCI) to AD. In addition, we have analyzed the trajectory of NPS in comparison to global cognitive scores. Introduction NPS are common in MCI and based on prior studies they are a significant risk factor for progression to AD. Working memory and episodic memory are also known to decline with progression of AD. Cognitive tests such as MMSE have been utilized to track patients’ level of cognition and the decline in these cognitive tests is a well-known predictor of dementia. However, how NPI scores relate to cognitive test scores in predicting progression to AD is still unknown. We propose to analyze NPI (total score and sub scores) longitudinally for 6 years and to compare scores between MCI- converters (MCI-C) and MCI- non converters (MCI-NC). We hypothesize that converters may have higher scores compared to non-converters. In addition, we expect that MMSE score should correlate inversely with NPI score. Symptoms associated with progression to AD that are prevalent in our study should match those that are shown in previous studies (depression, delusion, agitation, and apathy.) Methods We selected 150 subjects from Alzheimer's Disease Neuroimaging Initiative phase 3 (36 MCI-C and 114 MCI-NC). The only inclusion criteria were that subjects must have NPI scores at two or more time points. Gender, age, and education were matched across all subjects. To measure NPI scores longitudinally, subjects were divided into two groups, MCI-C and MCI-NC. We also grouped subjects according to their NPI scores to measure their cognition using MMSE: healthy (N=55), mild (NPI score= 1-9, N=65), and severe (NPI score= 10 and above, N=30). The NPI total score and MMSE were measured across 6 years and compared at 12-month intervals. All statistical analyses were performed using SPSS 25. Analyses of variance (ANOVA) were used to compare MCI-C and MCI-NC group differences. If the assumption of homogeneity was violated, Brown-Forsythe Test p-value was reported instead. Post-hoc analyses was done by using Gabriel and Dunnett's T3 test. The 12 NPI sub scores were also counted as percentage to analyse the progression of NPS. Results The NPI total scores at all time points for the duration of 6 years were significantly different between MCI-C and MCI-NC (p Conclusions Our study has confirmed previous findings that NPI scores increased with progression of clinical symptoms from MCI to AD in MCI-C relative to MCI-NC. In addition, MMSE scores varied between NPI groups at all time points after baseline. Both of the scores showed a deteriorating trend in their respective scale in MCI-C relative to MCI-NC. This research was funded by Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Classification of amyloid PET images using novel features for early diagnosis of Alzheimer's disease and mild cognitive impairment conversion.

New PET tracers could have a substantial impact on the early diagnosis of Alzheimer's disease (AD), particularly if they are accompanied by optimised image analysis and machine learning methods. Fractal dimension (FD) analysis, a measure of shape complexity, has been proven useful in MRI but its application to fluorine-18 amyloid PET has not yet been demonstrated. Shannon entropy (SE) has also been proposed as a measure of image complexity in DTI imaging, but it is not yet widely used in radiology. In this study, one volumetric FD method and one volumetric SE method were applied to fluorine-18-flutemetamol and fluorine-18-florbetapir 3D amyloid images from 65 and 281 participants, respectively, including healthy volunteers, and patients with probable Alzheimer's disease (pAD) or mild cognitive impairment (MCI). The group average FD of white matter surface and SE of white matter volume for healthy volunteers were higher than for pAD patients. Both FD and SE are effective in the identification of MCI patients who progress to pAD during the 2-year follow-up (ground truth). Finally, we developed a support vector machine multimodal classification framework using both PET and MRI features, which showed higher accuracy compared to traditional standard uptake value ratio or using PET alone. The classification accuracy for flutemetamol and florbetapir is 88.9 and 83.3%, respectively, for MCI progression, which is competitive with existing literature. The results presented in this study demonstrate the potential of FD and SE methods for the analysis of brain PET scans in early AD diagnosis and in the prediction of MCI-AD conversion.

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.

A longitudinal study of polysomnographic variables in patients with mild cognitive impairment converting to Alzheimer's disease.

The main condition at increased risk of dementia is considered to be mild cognitive impairment. Mild cognitive impairment has been defined as a transitional state between normal aging and dementia, of which it may represent a prodrome. The aim of our study was to evaluate whether sleep variables (both conventional and microstructural ones) in subjects with mild cognitive impairment correlate with conversion to dementia. Nineteen subjects with amnestic mild cognitive impairment (mean age 68.5 ± 7.0 years) and 11 cognitively intact healthy elderly individuals (mean age 69.2 ± 12.6 years) underwent ambulatory polysomnography for the evaluation of nocturnal sleep architecture and cyclic alternating pattern parameters. Amnestic mild cognitive impairment subjects were clinically and cognitively re-evaluated after 2 years, during routine follow-up, and further classified as amnestic mild cognitive impairment converters (that is, patients developing Alzheimer's disease, N = 11) and amnestic mild cognitive impairment non-converters. Compared with healthy elderly individuals, amnestic mild cognitive impairment showed disrupted sleep with decreased rapid eye movement sleep, cyclic alternating pattern rate and cyclic alternating pattern slow-wave-related phases (A1 index). Standard sleep architecture analysis did not show significant differences between the two subgroups of amnestic mild cognitive impairment, whereas cyclic alternating pattern analysis showed that cyclic alternating pattern rate, A1 index and A3 index are significantly reduced in converters compared with non-converters. Our data confirm that in amnestic mild cognitive impairment subjects there is a sleep impairment, particularly when considering more refined sleep parameters and that sleep variables at baseline are different among converters versus non-converters at the 2-year follow-up. Specific sleep alterations might represent potential further biomarkers for the diagnosis and prognosis of early-phase cognitive impairment.

Differential Associations of IL-4 With Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease.

Background/Aims: We aimed to assess the association between in volumetric measures of hippocampal sub-regions – in healthy older controls (HC), subjects with mild cognitive impairment (MCI) and AD- with circulating levels of IL-4.Methods: From AddNeuroMed Project 113 HC, 101 stable MCI (sMCI), 22 converter MCI (cMCI) and 119 AD were included. Hippocampal subfield volumes were analyzed using Freesurfer 6.0.0 on high-resolution sagittal 3D-T1W MP-RAGE acquisitions. Plasmatic IL-4 was measured using ELISA assay.Results: IL-4 was found to be (a) positively associate with left subiculum volume (β = 0.226, p = 0.037) in sMCI and (b) negatively associate with left subiculum volume (β = -0.253, p = 0.011) and left presubiculum volume (β = -0.257, p = 0.011) in AD.Conclusion: Our results indicate a potential neuroprotective effect of IL-4 on the areas of the hippocampus more vulnerable to aging and neurodegeneration.

Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease.

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Complement protein levels in plasma astrocyte-derived exosomes are abnormal in conversion from mild cognitive impairment to Alzheimer's disease dementia

IntroductionLevels of complement proteins (CPs) in plasma astrocyte-derived exosomes (ADEs) that are abnormal in Alzheimer's disease (AD) have not been assessed in mild cognitive impairment (MCI). MethodsParticipants (n = 20 per group) had either MCI converting to dementia within 3 years (MCIC), MCI remaining stable over 3 years (MCIS), Alzheimer's disease, or were controls. CPs of ADEs isolated from plasmas by anti-human glutamine aspartate transporter antibody absorption were quantified by ELISAs. ResultsADE levels of C1q and C4b of the classical pathway, factor D and fragment Bb of the alternative pathway, and C5b, C3b, and C5b-C9 of both pathways were significantly higher in patients with MCIC than those with MCIS. ADE levels of inhibitory CPs decay-accelerating factor, CD46, CD59, and type 1 complement receptor were significantly lower in patients with MCIC than those with MCIS. DiscussionADE CPs are components of neurotoxic neuroinflammation that may be predictive biomarkers of MCI conversion to Alzheimer's disease.

Visualizing modules of coordinated structural brain atrophy during the course of conversion to Alzheimer's disease by applying methodology from gene co-expression analysis.

ObjectiveWe aimed to identify modularized structural atrophy of brain regions with a high degree of connectivity and its longitudinal changes associated with the progression of Alzheimer's disease (AD) using weighted gene co-expression network analysis (WGCNA), which is an unsupervised hierarchical clustering method originally used in genetic analysis.MethodsWe included participants with late mild cognitive impairment (MCI) at baseline from the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study. We imputed normalized and Z-transformed structural volume or cortical thickness data of 164 parcellated brain regions/structures based on the calculations of the FreeSurfer software. We applied the WGCNA to extract modules with highly interconnected structural atrophic patterns and examined the correlation between the identified modules and clinical AD progression.ResultsWe included 204 participants from the baseline dataset, and performed a follow-up with 100 in the 36-month dataset of MCI cohort participants from the J-ADNI. In the univariate correlation or variable importance analysis, baseline atrophy in temporal lobe regions/structures significantly predicted clinical AD progression. In the WGCNA consensus analysis, co-atrophy modules associated with MCI conversion were first distributed in the temporal lobe and subsequently extended to adjacent parietal cortical regions in the following 36 months.ConclusionsWe identified coordinated modules of brain atrophy and demonstrated their longitudinal extension along with the clinical course of AD progression using WGCNA, which showed a good correspondence with previous pathological studies of the tau propagation theory. Our results suggest the potential applicability of this methodology, originating from genetic analyses, for the surrogate visualization of the underlying pathological progression in neurodegenerative diseases not limited to AD.

Connectome-wide network analysis of white matter connectivity in Alzheimer's disease.

A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients.

Predicting conversion from mild cognitive impairment to Alzheimer's disease using brain 1H-MRS and volumetric changes: A two- year retrospective follow-up study.

This study investigated the ability of magnetic resonance spectroscopy (1H-MRS) of posterior cingulate cortex (PCC) and brain volumetry to predict the progression from mild cognitive impairment (MCI) to Alzheimer's Disease (AD) on the basis of clinical classification at 2 years follow-up. Thirty-eight MCI patients, eighteen healthy older adults and twenty-three AD patients were included in this study. All participants underwent a brain-MR protocol (1.5 T GE scanner) including high-resolution T1-weighted volumetric sequence (isotropic 1mm3). Voxel-wise differences in brain volumetry were evaluated using FreeSurfer software and all volumes were normalized by the total intracranial volume (TIV). Careful localization of 1H-MRS volume of PCC was performed and data were processed with the LCModel program. MCI patients underwent a complete neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 28 months; twenty-six MCI patients (68.4%) converted to AD and twelve remained stable.At baseline these two MCI subgroups did not differ in the global cognitive level (Mini Mental State Examination, MMSE) or in any of the other cognitive domains; the NAA/ mI ratio in the PCC was able to differentiate MCI converters from those MCI that did not develop AD (p = 0.022) with a level of accuracy (AUC area) of 0.779. A significantly reduced volume of parahippocampal gyrus (p = 0.010) and fusiform gyrus (p = 0.026) were found in the converter MCI subgroup compared to the stable MCI subgroup. The combined use of both N- acetyl-aspartate (NAA)/myo-Inositol (mI) ratio and volume of parahippocampal gyrus, increases the overall accuracy (AUC = 0.910) in predicting the conversion to AD two years before the development of clinical symptoms. Additional longitudinal studies with a broader representative sample of MCI patients and longer follow-up might be helpful to confirm these results and to elucidate the role of each parameter in predicting the possible progression to AD, and also to all the other non-AD dementia subtypes.

Risk factors for developing dementia in type 2 diabetes mellitus patients with mild cognitive impairment.

BackgroundDementia and cognitive dysfunction have many causes. There is strong evidence that diabetes mellitus (DM) increases the risk of cognitive impairment and dementia. Optimal glycemic control, identification of diabetic risk factors, and prophylactic approach are essential in the prevention of cognitive complications.AimsThe main purpose of this study was to establish the cognitive impairment in DM patients, cared for in the Diabetes Center from Timisoara. Also, we investigated the prevalence of dementia in our group as well as the risk factors involved in the progression of mild cognitive impairment (MCI) to dementia.Patients and methodsWe considered a sample of 207 type 2 DM (T2DM) patients, aged between 33 and 81 years, mean 57.49 (±11.37) years. We established the diagnosis of dementia based on the Mini-Mental State Examination (MMSE) test, as well as on the psychological testing, psychiatric and neurological investigations, and imaging tests (computerized tomography and MRI).ResultsA percentage of 42.03% of patients presented MCI, mean age 63 (57.00–71.00) years, being older than patients without MCI, mean age 52.00 (45.00–61.00) years, P<0.001. We observed that diabetes duration was a significant risk factor for developing dementia. Also, patients with MCI presented higher values of body fat than patients without MCI. Moreover, we found that glucose levels, low-density lipoprotein cholesterol levels, the presence of stroke events, and the presence of cardiovascular disease were significant risk factors for MCI conversion to dementia.ConclusionPatients with T2DM at early to severe stages of MCI are more likely to develop dementia and should be regularly evaluated for their cognitive status. Regular administrations of the MMSE test can be done to detect early stages of MCI development. Also, to reduce the progression of cognitive impairment to dementia, it is worthwhile to give greater importance to glycemic control and overall DM management.

Self-Weighting Grading Biomarker Based on Graph-Guided Information Propagation for the Prediction of Mild Cognitive Impairment Conversion

Self-Weighting Grading Biomarker Based on Graph-Guided Information Propagation for the Prediction of Mild Cognitive Impairment Conversion