Brain imaging of mild cognitive impairment and Alzheimer's disease

The rapidly increasing prevalence of cognitive impairment and Alzheimer＇s disease has the potential to create a major worldwide healthcare crisis. Structural MRI studies in patients with Alzheimer＇s disease and mild cognitive impairment are currently attracting considerable interest. It is extremely important to study early structural and metabolic changes, such as those in the hippocampus, entorhinal cortex, and gray matter structures in the medial temporal lobe, to allow the early detection of mild cognitive impairment and AIzheimer＇s disease. The microstructural integrity of white matter can be studied with diffusion tensor imaging. Increased mean diffusivity and decreased fractional anisotropy are found in subjects with white matter damage. Functional imaging studies with positron emission tomography tracer compounds enable detection of amyloid plaques in the living brain in patients with Alzheimer＇s disease. In this review, we will focus on key findings from brain imaging studies in mild cognitive impairment and Alzheimer＇s disease, including structural brain changes studied with MRI and white matter changes seen with diffusion tensor imaging, and other specific imaging methodologies will also be discussed.

Individual identification using multi-metric of DTI in Alzheimer's disease and mild cognitive impairment

Accurate identification of Alzheimer＇s disease （AD） and mild cognitive impairment （MCI） is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply the machine learning method to individual identification and identify the discriminate features associated with AD and MCI. Diffusion tensor imaging scans of 48 patients with AD, 39 patients with late MCI, 75 patients with early MCI, and 51 age-matched healthy controls （HCs） are acquired from the Alzheimer＇s Disease Neuroimaging Initiative database. In addition to the common fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity metrics, there are two novel metrics, named local diffusion homogeneity that used Spearman＇s rank correlation coefficient and Kendall＇s coefficient concordance, which are taken as classification metrics. The recursive feature elimination method for support vector machine （SVM） and logistic regression （LR） combined with leave-one-out cross validation are applied to determine the optimal feature dimensions. Then the SVM and LR methods perform the classification process and compare the classification performance. The results show that not only can the multi-type combined metrics obtain higher accuracy than the single metric, but also the SVM classifier with multi-type combined metrics has better classification performance than the LR classifier. Statistically, the average accuracy of the combined metric is more than 92% for all between-group comparisons of SVM classifier. In addition to the high recognition rate, significant differences are found in the statistical analysis of cognitive scores between groups. We further execute the permutation test, receiver operating characteristic curves, and area under the curve to validate the robustness of the classifiers, and indicate that the SVM classifier is more stable and efficient than the LR classifier. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule, and internal capsule have been regarded as the most important white matter tracts to identify AD, MCI, and HC. Our findings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.

Cognitive and Functional Profiles in Mild-to-Moderate Alzheimer’s Disease and Mild Cognitive Impairment Compared to Healthy Elderly

Background: Amnestic mild cognitive impairment (aMCI) and mild-to-moderate Alzheimer’s disease (AD) are clinically distinct but impact cognitive and functional ability similarly. Comprehensive assessment of cognitive and functional deficits may prove useful in informing differential diagnosis in early stages of dementia and in informing endpoint selection in therapeutic AD trials. Objective: The objective of this study was to characterize patterns of cognitive and functional impairment in aMCI and mild-to-moderate AD subjects compared to cognitively intact healthy elderly (HE). Methods: Thirty-one healthy elderly, 20 aMCI and 19 AD participants were administered a cognitive test battery that included the ADAS-Cog and functional assessments. Z-scores were calculated for all endpoints based on the HE reference group. Results: Cognitive deficits were observed in AD and aMCI participants relative to the referent group. On average, aMCI participants performed 1 - 2 standard deviations below HE on cognitive tests, and AD participants performed 2 - 3 standard deviations below HE. Domain-specific functional deficits among AD participants (z- score -0.4 to -6.4) were consistently greater than those of aMCI participants (z-score 0 to -1.7). Conclusion: This study provides further support for comprehensive assessment and monitoring of cognitive and functional domain scores in the diagnosis and treatment of aMCI and mild AD. Domain-specific cognitive scores may be more useful than composite scores in characterizing impairment and decline. Measuring domains such as attention, processing speed and executive function may increase the sensitivity of detecting disease progression and therapeutic effects, particularly in mild-moderate AD where memory decline may be too slow to detect drug effects during a typical clinical trial.

Evaluation of Montreal cognitive assessment for the differential diagnosis of mild cognitive impairment and Alzheimer’s disease in elderly patients with more than 5 years of schooling: Data from a Brazilian sample

Background: Diagnostic investigation of dementia is based on a series of tests which lie the neuropsychological evaluations. The Montreal Cognitive Assessment (MoCA) was developed as an instrument to recognize Mild Cognitive Impairment (MCI) and initial cases of Alzheimer’s disease. The present study aims to evaluate the predictive value of Brazilian MoCA test version in a sample of elderly above 5 years of education. Methods: Cross-sectional study with 136 elderly, above 60 years old at least 5 years of education. Diagnostic criteria is based on clinical and neuropsychological data classified Alzheimer’s disease n = 52, MCI n = 45 e normal controls n = 39. MoCA test was compared with Cambridge Cognitive Examination, Mini-Mental State Exam, Verbal Fluency, Clock Drawing Test, Geriatric Depression Scale and Pfeffer Functional Activities Questionnaire. Accuracy was evaluated by receiver operating characteristic (ROC) curve analyses. Pearson correlation coefficient was used to compare the MoCA with the other tests. It was also used logistic regression analysis to identify the main risk factors for the diagnostic groups. Results: MoCA was the best test to differentiate Alzheimer’s disease cases from MCI with 86.5% sensitivity and 75.6% specificity. Furthermore, analyzes of correlation test showed that MoCA correlates robust way of already validated with other tests and wide application inBrazil. Conclusions: It can be concluded that MoCA is a good screening tool for investigation of MCI among the elderly in Brazil with over 5 years of schooling. Studies with larger numbers of participants are needed to further validate the test also for elderly people with low education.

Proteomics of serum exosomes identified fibulin-1 as a novel biomarker for mild cognitive impairment

Mild cognitive impairment(MCI)is a prodrome of Alzheimer’s disease pathology.Cognitive impairment patients often have a delayed diagnosis because there are no early symptoms or conventional diagnostic methods.Exosomes play a vital role in cell-to-cell communications and can act as promising biomarkers in diagnosing diseases.This study was designed to identify serum exosomal candidate proteins that may play roles in diagnosing MCI.Mass spectrometry coupled with tandem mass tag approach-based non-targeted proteomics was used to show the differentially expressed proteins in exosomes between MCI patients and healthy controls,and these differential proteins were validated using immunoblot and enzyme-linked immunosorbent assays.Correlation of cognitive performance with the serum exosomal protein level was determined.Nanoparticle tracking analysis suggested that there was a higher serum exosome concentration and smaller exosome diameter in individuals with MCI compared with healthy controls.We identified 69 exosomal proteins that were differentially expressed between MCI patients and healthy controls using mass spectrometry analysis.Thirty-nine exosomal proteins were upregulated in MCI patients compared with those in control patients.Exosomal fibulin-1,with an area under the curve value of 0.81,may be a biomarker for an MCI diagnosis.The exosomal protein signature from MCI patients reflected the cell adhesion molecule category.In particular,higher exosomal fibulin-1 levels correlated with lower cognitive performance.Thus,this study revealed that exosomal fibulin-1 is a promising biomarker for diagnosing MCI.

How random is the random forest ? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative(ADNI) database

Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines（behavioural neuroscience, genetics, cognitive psychology, etc.） working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest（RF） algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer＇s disease（AD）, the conversion from mild cognitive impairment（MCI） and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 ^（st） position in an international challenge for automated prediction of MCI from MRI data.

Virtual Cognitive Screenings and Interviews of Patients with Neurodegenerative Conditions Associated with Alzheimer’s Disease and Parkinson’s Disease

The current pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly referred to as COVID-19, brings myriad challenges to research conducted among those more susceptible to the virus. According to the United States (US) Centers for Disease Control (CDC), eight out of ten re-ported COVID-19 deaths are among people > 65 years of age and older. Nonetheless, researchers must continue the crucial work of investigating and understanding diseases that affect the elderly. The focus of this white paper is to assess the challenges associated with research within the elderly population with neurocognitive conditions. Specifically, this paper addresses the need for the standardized administration of performance measures (e.g., neurocognitive assessments) among a dementia population while ensuring the physical safety of participants. Consideration is given to the administration of performance measures and the availability and feasibility of administering these measures remotely to a population that may have difficulty using novel technologies. In implementing remote research assessments, it is suggested that researchers fol-low a GAMMA approach by: 1) establishing clear Guidance on remote visit expectations and processes;2) establishing Appropriate exclusionary criteria in the development of the study design;3) providing subjects Appropriate study Materials for visual processing;4) incorporating Multiple data sources in the overall study design (e.g., caregiver input);and 5) Acknowledging that there will be study limitations as researchers use emerging technology with this patient population, and using mitigation strategies for these limitations where possible.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.

Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study

Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects of acupuncture therapy on MCI patients.Eleven healthy individuals and eleven MCI patients were recruited for this study.Oxy-and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy.Before acupuncture treatment,working-memory experiments were conducted for healthy control(HC)and MCI groups(MCI-0),followed by 24 sessions of acupuncture for the MCI group.The acupuncture sessions were initially carried out for 6 weeks(two sessions per week),after which experiments were performed again on the MCI group(MCI-1).This was followed by another set of acupuncture sessions that also lasted for 6 weeks,after which the experiments were repeated on the MCI group(MCI-2).Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.The highest classification accuracies obtained using binary connectivity maps were 85.7%HC vs.MCI-0,69.5%HC vs.MCI-1,and 61.69%HC vs.MCI-2.The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum(i.e,max(5:28 seconds))values were 60.6%HC vs.MCI-0,56.9%HC vs.MCI-1,and 56.4%HC vs.MCI-2.The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture.This was reflected by a reduction in the classification accuracy after the therapy,indicating that the patients’brain responses improved and became comparable to those of healthy subjects.A similar trend was reflected in the classification using the image feature.These results indicate that acupuncture can be used for the treatment of MCI patients.

Modulatory effects of acupuncture on brain networks in mild cognitive impairment patients

Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong（EX-HN1）, Yintang（EX-HN3）, Neiguan（PC6）, Taixi（KI3）, Fenglong（ST40）, and Taichong（LR3）, or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.

Psychophysiological Effects of Sideritis and Bacopa Extract and Three Combinations Thereof—A Quantitative EEG Study in Subjects Suffering from Mild Cognitive Impairment (MCI)

Mild cognitive impairment (MCI) is regarded as a transitional stage during the development of Alzheimer’s disease. Diagnosis of MCI can be obtained by the questionnaire “DemTect” in German speaking countries. Quantitative assessment has been successfully performed using psychometric testing concomitantly with quantitative EEG recording. The present investigation aimed at the possible treatment of MCI with two botanicals, namely extracts from Sideritis scardica (500 mg) or Bacopa monnieri (320 mg) and three combinations thereof using this method in order to find a new treatment. The performance of the d2-test, an arithmetic calculation test (CPT) and a memory-test revealed better performance for the d2-test only in the presence of Sideritis extract or the combinations with Bacopa extract. Quantitative EEG assessment during the different experimental conditions showed massive differences between both extracts. Whereas Sideritis extract and its combination with a low amount of Bacopa extract (160 mg) induced increases of spectral power in fronto-temporal brain areas, Bacopa and the combination of Sideritis with high amounts of Bacopa extract produced attenuation of all waves except for delta in fronto-temporal brain areas. These differences were also documented by quantitative EEG maps in comparison to Placebo. A different action of both extracts was confirmed by discriminant analysis, where Sideritis extract and its combination with low Bacopa grouped together quite at distance to Bacopa and the combination of Sideritis with high Bacopa. A combination of Sideritis extract with a low amount of Bacopa should be tested with daily repetitive dosing for at least 4 weeks as a consequence.

Neurophysiological Biomarker of Mild Cognitive Impairment

Mild cognitive impairment is sometimes regarded as related to aging. However, statistically every second case turns into full dementia, which still is resistant to any treatment. It is therefore desir-able to recognize deviations from normality as early as possible. This might be feasible by using quantitative EEG analysis in the presence of mental work. The present retrospective data analysis revealed a new quantitative biomarker indicating the degree of impairment. Current source density was calculated from 16 channel EEG using CATEEM?? software. Four different conditions were analyzed: relaxed state, performing a d2-concentration test, a calculation performance test and a memory test for 5 min each. Subjects older than 40 years were divided into two groups according to their DemTect score: 13 - 18 (HC;n = 44) or 8 - 12 (MCI;n = 45). Spectral power was chopped into six frequency ranges (delta, theta, alpha 1, alpha 2, beta 1 and beta 2). Average spectral power was enhanced in the MCI group in comparison to healthy subjects with respect to delta (p = 0.05) during relaxed state when all electrode positions were regarded. With respect to EEG recording during performance of three different psychometric tests it was recognized that mainly spectral changes during performance of the d2-concentration test were related to mild cognitive impairment. With regard to all electrode positions statistically significantly lower spectral power values were reached during the d2-test for delta (p = 0.001), theta (p = 0.0001) and alpha 1 waves (p = 0.08) in impaired subjects in comparison to healthy subjects. Regarding regions of interest increases of delta and theta power were seen in the fronto-temporal brain during performance of the d2-concentration test. These increases disappeared when looking at MCI data. In the centro-parietal region decreases of alpha and beta 1 power emerged, which were even larger in MCI subjects. No MCI-dependent changes were observed in the other two tests. A correlation was found between psychometric performance of the d2-test and the DemTect score (r = 0.51). MCI subjects had statistically significant worse performance in all three mental challenges in comparison to healthy volunteers. It is concluded that MCI can be characterized at an early stage by EEG recording in the relaxed state. High spectral delta and theta power in general and specifically at fronto- temporal electrode positions (especially at T3) was recognized as a biomarker for MCI. A DemTect score of 8-12 was validated as indicative for MCI.

Abnormal characterization of dynamic functional connectivity in Alzheimer’s disease

Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer’s disease(AD)or amnestic mild cognitive impairment(aMCI).However,most studies examined traditional resting state functional connections,ignoring the instantaneous connection mode of the whole brain.In this case-control study,we used a new method called dynamic functional connectivity(DFC)to look for abnormalities in patients with AD and aMCI.We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant,and then used a support vector machine to classify AD patients and normal controls.Finally,we highlighted brain regions and brain networks that made the largest contributions to the classification.We found differences in dynamic function connectivity strength in the left precuneus,default mode network,and dorsal attention network among normal controls,aMCI patients,and AD patients.These abnormalities are potential imaging markers for the early diagnosis of AD.

An abnormal resting-state functional brain network indicates progression towards Alzheimer's disease

Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer＇s disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations （nor- mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer＇s disease） using the Alzheimer＇s Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer＇s disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest- ing-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addi- tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and AIz- heimer＇s disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.

Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

Filipin Levels as Potential Predictors of Alzheimer’s Disease Risk

To date, therapies to prevent or treat Alzheimer’s disease (AD) have largely focused on removing excess aggregation-prone amyloid peptide Aβ from the brain, an approach that has produced disappointing clinical outcomes. An alternative hypothesis proposes that Aβ production and aggregation is a symptom of a larger, systemic disease affecting the regulation of lipids, including cholesterol. In this scenario, lipid dysregulation would likely occur early in the disease process, making it an ideal target for predicting risk of mild cognitive impairment (MCI) to AD conversion. Here, we report that levels of filipin, a fluorescent polyene macrolide widely used as a diagnostic tool for diseases of lipid dysregulation, correlate with cellular damage caused by 27-hydroxycholesterol and with dementia status in human peripheral blood cells. These results provide strong preliminary data suggesting that filipin could be of use in the development of a quick and inexpensive method to measure the risk of AD conversion in patients with MCI, supplementing existing testing strategies that focus on the consequences of Aβ accumulation.

Regional patterns of atrophy on MRI in Alzheimer’s disease: Neuropsychological features and progression rates in the ADNI cohort

Background: Discrete clinical and pathological subtypes of Alzheimer’s disease (AD) with variable presentations and rates of progression are well known. These subtypes may have specific patterns of regional brain atrophy, which are identifiable on MRI scans. Methods: To examine distinct regions which had distinct underlying patterns of cortical atrophy, factor analytic techniques applied to structural MRI volumetric data from cognitively normal (CN) (n = 202), amnestic mild cognitive impairment (aMCI) (n = 333) or mild AD (n = 146) subjects, in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was applied. This revealed the existence of two neocortical (NeoC-1 and NeoC-2), and a limbic cluster of atrophic brain regions. The frequency and clinical correlates of these regional patterns of atrophy were evaluated among the three diagnostic groups, and the rates of progression from aMCI to AD, over 24 months were evaluated. Results: Discernable patterns of regional atrophy were observed in about 29% of CN, 55% of aMCI and 83% of AD subjects. Heterogeneity in clinical presentation and APOE ε4 frequency were associated with regional patterns of atrophy on MRI scans. The most rapid progression rates to dementia among aMCI subjects (n = 224), over a 24-month period, were in those with NeoC-1 regional impairment (68.2%), followed by the Limbic regional impairment (48.8%). The same pattern of results was observed when only aMCI amyloid positive subjects were examined. Conclusions: The neuroimaging results closely parallel findings described recently among AD patients with the hippocampal sparing and limbic subtypes of AD neuropathology at autopsy. We conclude that NeoC-1, Limbic and other patterns of MRI atrophy may be useful markers for predicting the rate of progression of aMCI to AD and could have utility selecting individuals at higher risk for progression in clinical trials.

AB099.Cognitive impairment and age-related macular degeneration:a possible genetic link

Background:The number of older adults affected by age-related macular degeneration(AMD)and early cognitive changes is on the rise.Recent studies have shown a high co-occurrence of these conditions.This,along with shared risk factors and similar histopathology suggests they may share genetic risk factors as well.The goal of this study was to explore the possibility of known AMD SNPs contributing to the co-morbidity.Methods:Participants(AMD and controls)aged 70 years or older with no known neurological or cognitive impairments were recruited for this study.Visual function was evaluated using ETDRS visual acuity,Mars Contrast sensitivity and the scanning laser ophthalmoscope.Cognitive status was measured using the Mini-Mental State Exam(MMSE)and the Montreal Cognitive Assessment(MoCA).Genotyping was conducted using a panel of AMD single nucleotide polymorphisms(SNPs).Analysis was focused on the CFH Y402H and ARMS2 A69S SNPs due their association with drusen and evidence of their association with cognitive impairment.Results:According to the MMSE,two participants from the AMD group(N=21)and none from the control group(N=18)scored positive for cognitive impairment.The MoCA indicated 33.3%of the AMD group and 27.7%of the control group had MCI.There were no significant differences between MoCA scores based on the carrier versus non-carrier status of either the CFH or ARMS SNPs.The SNP in FADS1(rs174547)that was part of the original panel,but not in the analysis,was found in a large number of participants.All those who scored positive for MCI were homozygous carriers of the FADS1 SNP.Conclusions:Although more people from the AMD group scored positive for MCI,scores between groups were significantly different.The AMD and control groups did differ on which cognitive domains they had difficulty with,indicating those with AMD and MCI may be at a higher risk of converting to AD.There were no significant differences on cognitive scores between CFH and ARMS2 SNP carriers and non-carriers.The FADS1 SNP,not originally intended to be part of this study,will be included in future analyses to explore the possibility of a founder effect and a potential link to mild cognitive impairment(MCI).

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible,non-curable,and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention.It is a frequently occurring mental illness that occurs in about 60%–80%of cases of dementia.It is usually observed between people in the age group of 60 years and above.Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal(CN),Mild Cognitive Impairment(MCI)and Alzheimer’s Disease(AD).Alzheimer’s disease is the last phase of the disease where the brain is severely damaged,and the patients are not able to live on their own.Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms.Here,105 number of radiomic features are extracted and used to predict the alzhimer’s.This paper uses Support Vector Machine,K-Nearest Neighbour,Gaussian Naïve Bayes,eXtreme Gradient Boosting(XGBoost)and Random Forest to predict Alzheimer’s disease.The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%.This proposed approach also achieved 88%accuracy,88%recall,88%precision and 87%F1-score for AD vs.CN,it achieved 72%accuracy,73%recall,72%precisionand 71%F1-score for AD vs.MCI and it achieved 69%accuracy,69%recall,68%precision and 69%F1-score for MCI vs.CN.The comparative analysis shows that the proposed approach performs better than others approaches.