Mild Cognitive Impairment Non-converters Research Articles

Dynamic change of hierarchical core voxels of functional brain network in aging

AbstractBackgroundMany methods were developed to assess the functional intervoxel connectivity to better understand the brain function in individuals. In this study, k‐core percolation method was assessed to reveal the dynamic hierarchical structure on resting‐state fMRI (rsfMRI) on voxel‐level and the feasibility of the method was evaluated by applying it in the aging process.MethodTotal 150 individuals were included in this study; 38 young healthy individuals from Seoul National University (SNU), and 32 aged healthy, 22 mild cognitive impairment‐converter (MCI‐C), 26 mild cognitive impairment non‐converter (MCI‐NC), and 32 Alzheimer’s disease (AD) subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. K‐core percolation method, a voxel‐based approach was applied to reveal the hierarchical structure of voxels in the brain. k max‐core and coreness k values derived from k‐core percolation characterizing the time‐varying core voxels were visualized on various plots to display the dynamic hierarchical structure more intuitively. Analysis was done in both static, dynamic studies, and in positive, negative correlations. Coreness k value map on brain T1 MRI was generated for further evaluation of the distribution of coreness k values.ResultDynamic hierarchical structure of voxels on various plots revealed time‐varying change of k max‐core voxels and coreness k values, reflecting the dynamic change of brain function in an individual, which was not fully reflected on static study. Dynamic flow pattern was different in positive and negative correlations, portraying the dynamic brain function in different neuronal networks. Asymmetric, unsynchronized distribution was deteriorated in the aging process on corneas k map and the asymmetry indexes of coreness k values revealed distinctive difference between young and aged healthy groups. The difference was more evident on dynamic study than static study. Also, as the age increased, coreness k values decreased, representing decreased connectivity in aging. However, asymmetry and decrease of coreness k values were not deteriorated by the severity of cognitive impairment.ConclusionInvestigation of dynamic functional connectivity with k‐core percolation on voxel‐level revealed time‐varying change of hierarchical structure of voxels in the aging process. With this method, characteristics of dynamic hierarchical structure of an individual can be discovered and have shown possibility of further clinical application.

Blood biomarkers in MCI conversion to Alzheimer’s disease: a systematic review and meta-analysis

Background: The predictive effects of blood biomarkers (BBMs) in the progression of Alzheimer’s disease (AD) have been reported recently. However, controversies still exist. In the present study, we aim to identify the predictive performances of BBMs in the conversion from Mild cognitive impairment (MCI) to AD.
 Methods: PubMed, Embase, Cochrane Library, and Web of Science from inception to June 10, 2023 were searched. Predictive potentials were evaluated by pooling the ratio of means (ROMs), relative risks (RRs), and diagnostic indexes from MCI-converters (MCI-c: MCI patients who convert to AD) and MCI-non converters (MCI-nc) based on fixed-effects or random-effects. Newcastle–Ottawa Quality Assessment Scale (NOS) was applied for quality assessment.
 Results: A total of 44 studies with 9343 participants from 28 cohorts were included in the meta-analysis, whereas the other 45 articles were included in the qualitative review. The average score of 44 studies included in the meta-analysis was 7.125. In pooled ROMs, plasma Aβ42/Aβ40 was lower, whereas Aβ40, T-tau, P-tau 181, P-tau 217, NFL, and GFAP were higher in MCI-c than MCI-nc. In pooled RRs, P-tau (RR=2.50, 95%CI: 2.04-3.06) as a continuous variable, Aβ42/Aβ40 as a categorical variable (RR=1.28, 95%CI: 1.01-1.61) could predict future conversion risk of MCI patients. In diagnostic indexes, the diagnostic odds ratio (DOR) was 42 for P-tau 217 (sensitivity: 91%; specificity: 81%), 15 for P-tau 181 (sensitivity: 81%; specificity: 78%), 12.71 for GFAP (sensitivity: 71%; specificity: 86%), 6 for Aβ42/Aβ40 (sensitivity: 86%; specificity: 49%, and 6 for NFL (sensitivity: 80%; specificity: 61%).
 Conclusion: Here, our results indicated that blood biomarkers held promising potential in predicting MCI conversion. However, more prospective cohorts based on particular MCI types and high-sensitivity assays are warranted to validate the results next.

Memory markers in the continuum of the Alzheimer’s clinical syndrome

BackgroundThe individual and complementary value of the Visual Short-Term Memory Binding Test (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) as markers to trace the AD continuum was investigated. It was hypothesised that the VSTMBT would be an early indicator while the FCSRT would inform on imminent progression.MethodsHealthy older adults (n=70) and patients with mild cognitive impairment (MCI) (n=80) were recruited and followed up between 2012 and 2017. Participants with at least two assessment points entered the study. Using baseline and follow-up assessments four groups were defined: Older adults who were healthy (HOA), with very mild cognitive but not functional impairment (eMCI), and with MCI who did and did not convert to dementia (MCI converters and non-converters).ResultsOnly the VSTMBT predicted group membership in the very early stages (HOA vs eMCI). As the disease progressed, the FCSRT became a strong predictor excluding the VSTMB from the models. Their complementary value was high during the mid-prodromal stages and decreased in stages closer to dementia.DiscussionThe study supports the notion that neuropsychological assessment for AD needs to abandon the notion of one-size-fits-all. A memory toolkit for AD needs to consider tools that are early indicators and tools that suggest imminent progression. The VSTMBT and the FSCRT are such tools.

The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification.

Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.

ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Multimodal classification methods using different modalities have great advantages over traditional single-modality-based ones for the diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). With the increasing amount of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become a crucial research direction for AD classification. However, traditional methods usually depict the data structure using pre-defined similarity matrix as a priori, which is difficult to precisely measure the intrinsic relationship across different modalities in high-dimensional space. In this paper, we propose a novel multimodal feature selection method called Adaptive-Similarity-based Multi-modality Feature Selection (ASMFS) which performs adaptive similarity learning and feature selection simultaneously. Specifically, a similarity matrix is learned by jointly considering different modalities and at the same time, an efficient feature selection is conducted by imposing group sparsity-inducing l2,1-norm constraint. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline MRI and FDG-PET imaging data collected from 51 AD, 43 MCI converters (MCI-C), 56 MCI non-converters (MCI-NC) and 52 normal controls (NC), we demonstrate the effectiveness and superiority of our proposed method against other state-of-the-art approaches for multi-modality classification of AD/MCI.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks.

Background: In recent years, predicting and modeling the progression of Alzheimer’s disease (AD) based on neuropsychological tests has become increasingly appealing in AD research.Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data.Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer’s disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers.Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD.Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Self-Administered Gerocognitive Examination: longitudinal cohort testing for the early detection of dementia conversion

BackgroundSignificant cognitive changes as individuals’ age are not being identified in a timely manner, delaying diagnosis and treatments. Use of brief, multi-domain, self-administered, objective cognitive assessment tools may remove some barriers in assessing and identifying cognitive changes. We compared longitudinal Self-Administered Gerocognitive Examination (SAGE) test scores to non-self-administered Mini-Mental State Examination (MMSE) scores in 5 different diagnostic subgroups.MethodsA cohort study evaluating annual rates of change was performed on 665 consecutive patients from Ohio State University Memory Disorders Clinic. Patients with at least two visits 6 months apart evaluated with SAGE and MMSE and classified according to standard clinical criteria as subjective cognitive decline (SCD), mild cognitive impairment (MCI), or Alzheimer’s disease (AD) dementia were included. The pattern of change in SAGE scores was compared to MMSE. One way and repeated measures ANOVA and linear regression models were used.ResultsFour hundred twenty-four individuals (40 SCD, 94 MCI non-converters to dementia, 70 MCI converters to dementia (49 to AD dementia and 21 to non-AD dementia), 220 AD dementia) met inclusion criteria. SAGE and MMSE scores declined respectively at annual rates of 1.91 points/year (p < 0.0001) and 1.68 points/year (p < 0.0001) for MCI converters to AD dementia, and 1.82 points/year (p < 0.0001) and 2.38 points/year (p < 0.0001) for AD dementia subjects. SAGE and MMSE scores remained stable for SCD and MCI non-converters. Statistically significant decline from baseline scores in SAGE occurred at least 6 months earlier than MMSE for MCI converters to AD dementia (14.4 vs. 20.4 months), MCI converters to non-AD dementia (14.4 vs. 32.9 months), and AD dementia individuals (8.3 vs. 14.4 months).ConclusionsSAGE detects MCI conversion to dementia at least 6 months sooner than MMSE. Being self-administered, SAGE also addresses a critical need of removing some barriers in performing cognitive assessments. Limitations of our single-site cohort study include potential referral and sampling biases. Repetitively administering SAGE and identifying stability or decline may provide clinicians with an objective cognitive biomarker impacting evaluation and management choices.

Radiogenomics of Alzheimer's disease: exploring gene related metabolic imaging markers.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and considerably determined by genetic factors. Fluorodeoxyglucose positron emission tomography (FDG-PET) can reflect the functional state of glucose metabolism in the brain, and radiomic features of FDG-PET were considered as important imaging markers in AD. However, radiomic features are not highly interpretable, especially lack of explanation of underlying biological and molecular mechanisms. Therefore, this study used radiogenomics analysis to explore prognostic metabolic imaging markers by associating radiomics features and genetic data. In the study, we used the FDG-PET images and genotype data of 389 subjects (Cohort B) enrolled in the ADNI, including 109 AD, 134 healthy controls (HCs), 72 MCI non-converters (MCI-nc) and 74 MCI converters (MCI-c). Firstly, we performed a Genome-wide association study (GWAS) on the genotype data of 998 subjects (Cohort A), including 632 AD and 366 HCs after quality control (QC) steps to identify susceptibility loci as the gene features. Secondly, radiomics features were extracted from the preprocessed PET images. Thirdly, two-sample t-test, rank sum test and F-score were regarded as the feature selection step to select effective radiomic features. Fourthly, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Finally, we performed the Spearman correlation analysis on the genetic data and radiomic features. As a result, we identified rs429358 and rs2075650 as genome-wide significant signals. The radiomic approach achieved good classification abilities. Two prognostic FDG-PET radiomic features in the amygdala were proven to be correlated with the genetic data.

Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach.

BackgroundGraph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer’s disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning.MethodsWith the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling.ResultsWe found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular.ConclusionsThese results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.

A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification

PurposeAlzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way. Materials and methodsWe proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection-wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters. ResultsThe proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD. ConclusionsDeep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR images which may facilitate early diagnosis and prediction of AD.

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.

There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.

Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease.

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) show that the proposed method has better classification performance than the start-of-the-art multimodality-based methods. Specifically, we achieved higher accuracy and area under the curve (AUC) for AD versus normal controls (NC), MCI versus NC, and MCI converters (MCI-C) versus MCI non-converters (MCI-NC) on ADNI datasets. Therefore, the proposed model not only outperforms the traditional method in terms of AD/MCI classification, but also discovers the characteristics associated with the disease, demonstrating its promise for improving disease-related mechanistic understanding.

Neuropsychological decline up to 20 years before incident mild cognitive impairment

IntroductionSome Alzheimer's disease biomarker studies found amyloid changes 20 years or more in advance of expected symptoms, while cognitive changes lagged for more than a decade, but this apparent lag might reflect the sensitivities of the biomarker and cognitive assays used. How far in advance of incident amnestic mild cognitive impairment (MCI) does cognition begin to decline? MethodsLongitudinal neuropsychological study of an apolipoprotein E e4 enriched cohort of cognitively normal individuals at entry. Linear mixed models for MCI converters (n = 65) and nonconverters (n = 719) fitted for each neuropsychological measure; annual changes compared between groups before and after linear model intersections (inflection points). Results34 of 35 cognitive measures and 9 of 18 behavioral measures declined faster post-inflection in the MCI converters; the earliest cognitive inflection point was nearly 20 years in advance of MCI diagnosis. DiscussionThe preclinical duration of cognitive and behavioral changes approaches the earliest reported biomarker changes.

Association Between Neuropsychiatric Symptom Trajectory and Conversion to Alzheimer Disease.

Neuropsychiatric symptoms (NPS) are both common in mild cognitive impairment and Alzheimer disease (AD). Studies have shown that some NPS such as apathy and depression are a key indicator for progression to AD. We compared Neuropsychiatric Inventory (NPI) total score and NPI subdomain score between mild cognitive impairment-converters (MCI-C) and mild cognitive impairment-nonconverters (MCI-NC) longitudinally for 6 years using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In addition to the NPI, Mini-Mental State Examination (MMSE) scores were also compared to find out if MMSE scores would differ between different NPI groups. Lastly, a linear regression model was done on MMSE and NPI total score to establish a relationship between MMSE and NPI total score. The results in this study showed that NPI total scores between MCI-C and MCI-NC differed significantly throughout 6 years. MCI-C subjects had a higher mean NPI total score and lower MMSE score compared with MCI-NC subjects. In addition, MMSE scores were significantly different between the 3 groups of NPI total score. Subjects who have a high NPI score have the lowest mean MMSE score, thus demonstrating that NPI scores do indeed affect MMSE scores. Further analyses using a regression model revealed that a unit change in NPI total score lead to 0.1 to 0.3 decrease in MMSE. On the basis of the findings, this study showed evidence that increase in NPS burden (reflected by increase in NPI) over time predicts conversion to AD, whereas stability of symptoms (reflected by stable NPI score) favors nonconversion. Further study should investigate the underlying mechanisms that drive both NPS burden and cognitive decline.

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.

Low Cerebrospinal Fluid Levels of Melanotransferrin Are Associated With Conversion of Mild Cognitively Impaired Subjects to Alzheimer's Disease.

The disruption of iron metabolism and iron transport proteins have been implicated in the pathogenesis of Alzheimer’s disease (AD). Serum melanotransferrin (MTf), a transferrin homolog capable of reversibly binding iron, has been proposed as a biochemical marker of AD. MTf has also been shown to be elevated in iron-rich reactive microglia near amyloid plaques in AD. We examined the association of CSF MTf to hippocampal volumes and cognitive tests in 86 cognitively normal, 135 mild cognitive impairment (MCI) and 66 AD subjects. CSF was collected at baseline for MTf, Aβ, total-tau and phosphorylated-tau measurements. Serial cognitive testing with ADAS-Cog13, Rey’s auditory visual learning test (RAVLT), mini-mental state examination (MMSE) were performed alongside hippocampal MRI volumetric analysis for up to 10 years after baseline measurements. High levels of baseline CSF MTf were positively associated with baseline hippocampal volume (R2 = 22%, β = 0.202, and p = 0.017) and RAVLT scores (R2 = 7.30%, β = -0.178, and p = 0.043) and negatively correlated to ADAS-Cog13 (R2 = 17.3%, β = 0.247, and p = 0.003) scores in MCI subjects. Interestingly, MCI subjects that converted to AD demonstrated significantly lower levels of CSF MTf (p = 0.020) compared to MCI non-converters at baseline. We suggest the diminished CSF MTf observed in MCI-converters to AD may arise from impaired transport of MTf from blood into the brain tissue/CSF and/or increased MTf export from the CSF into the blood arising from attenuated competition with reduced levels of CSF Aβ. Further investigations are required to determine the source of CSF MTf and how brain MTf is regulated by cellular barriers, Aβ and activated microglia that surround plaques in AD pathophysiology. In conclusion, low CSF MTf may identify those MCI individuals at risk of converting to AD.

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.

Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment.

Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.

MCI Conversion Prediction Based on Transfer Learning

Amnestic mild cognitive impairment (MCI) commonly represents an intermediate stage situated in the spectrum between normal age-related cognitive decline and dementia. Predicting of MCI conversion to Alzheimer’s Disease (AD) plays critical roles in early diagnosis and disease-modifying therapies. We analyzed baseline 3T MRI scans in 337 MCI patients from the ADNI-GO and ANDI-2 cohorts. The subjects were divided into MCI non-converters (MCInc) and MCI converters (MCIc). To evaluate conversion rates, we aim to first extract intermediate representations of structural MRI (sMRI) by a pre-trained convolutional neural network (CNN) model, then combine principal component analysis (PCA) and sequential feature selection (SFS) for feature selection, and finally adopt support vector machine (SVM) for prediction. The method attained an accuracy of 77.58%, a sensitivity of 90.48%, a specificity of 76.42%, which may be useful and practical for clinical diagnosis.

Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment

IntroductionCurrently, there are no tools that can accurately predict which patients with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD). Texture analysis uses image processing and statistical methods to identify patterns in voxel intensities that cannot be appreciated by visual inspection. Our main objective was to determine whether MRI texture could be used to predict conversion of MCI to AD. MethodsA method of 3-dimensional, whole-brain texture analysis was used to compute texture features from T1-weighted MR images. To assess predictive value, texture changes were compared between MCI converters and nonconverters over a 3-year observation period. A predictive model using texture and clinical factors was used to predict conversion of patients with MCI to AD. This model was then tested on ten randomly selected test groups from the data set. ResultsTexture features were found to be significantly different between normal controls (n = 225), patients with MCI (n = 382), and patients with AD (n = 183). A subset of the patients with MCI were used to compare between MCI converters (n = 98) and nonconverters (n = 106). A composite model including texture features, APOE-ε4 genotype, Mini-Mental Status Examination score, sex, and hippocampal occupancy resulted in an area under curve of 0.905. Application of the composite model to ten randomly selected test groups (nonconverters = 26, converters = 24) predicted MCI conversion with a mean accuracy of 76.2%. DiscussionEarly texture changes are detected in patients with MCI who eventually progress to AD dementia. Therefore, whole-brain 3D texture analysis has the potential to predict progression of patients with MCI to AD.