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Alzheimer's disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly O 3 and N O 2 ) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer's disease and to confirm the urgent need to further investigate the relationship between the environment and the disease., (© 2024. The Author(s).)

Dementia is on the rise in the world population and has been defined by the World Health Organization as a global public health priority. In Italy, according to demographic projections, in 2051 there will be 280 elderly people for every 100 young people, with an increase in all age-related chronic diseases, including dementia. Currently the total number of patients with dementia is estimated to be over 1 million (mainly with Alzheimer's disease (AD) and Parkinson's disease (PD)). In-depth studies of the etiology and physiology of dementia are complicated due to the complexity of these diseases and their long duration. In this work we present a dataset on mortality rates (in the form of Standardized Mortality Ratios, SMR) for AD e PD in Italy at provincial level over a period of 8 years (2012-2019). Access to long-term, spatially detailed and ready-to-use data could favor both health monitoring and the research of new treatments and new drugs as well as innovative methodologies for early diagnosis of dementia., (© 2023. Springer Nature Limited.)

Alzheimer's disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer's disease., Competing Interests: The authors have declared that no competing interests exist.

Background: Extracting fundamental information from data, thus underlining hidden structures or removing noisy information, is one of the most important aims in different scientific fields especially in biological and medical sciences. In this article, we propose an innovative complex network application able to identify salient links for detecting the effect of Alzheimer's disease on brain connectivity. We first build a network model of brain connectivity from structural Magnetic Resonance Imaging (MRI) data, then we study salient networks retrieved from the original ones., Results: Investigating informative power of the salient skeleton features in combination with those of the original networks we obtain an accuracy of [Formula: see text] for the distinction of Alzheimer disease (AD) patients from normal controls (NC). This performance significantly overcomes accuracy of the original network features. Moreover salient networks are able to correctly discriminate normal controls (NC) from AD patients and NC from subjects with mild cognitive impairment that will convert to AD (cMCI). These evaluations, performed on an independent dataset, give an accuracy of [Formula: see text] and [Formula: see text] respectively for NC-AD and NC-cMCI classifications. Therefore, most of the informative content of the original networks is kept after the 92 [Formula: see text] and 82 [Formula: see text] reduction respectively in the number of nodes and links. In addition, the present approach, applied to a publicly available MRI dataset from the Alzheimer Disease Neuroimaging Initiative (ADNI), brings out also some interesting aspects related to the topologies and hubs of the networks., Conclusions: The experimental results demonstrate how salient networks can highlight important brain network characteristics and structural pathological changes, while reducing considerably data complexity and computational requirements.

Objective: Recent studies have shown that complex networks along with diffusion weighted imaging (DWI) can be efficient and promising techniques for early detection of structural pathological changes in Alzheimer's disease. Besides, connectivity studies, specifically assessing the organization of a graph and its topology, could represent the best chance to discover how brain activity is shaped and driven. Accordingly, we propose a methodology to evaluate how Alzheimer's disease affects brain networks through a novel way to look at graph connectivity. In fact, we use the combination of network features related to brain organization with network features related to the variations in connectivity between several subjects., Approach: From a DWI brain scan we reconstruct a probabilistic tractography by evaluating the number of white matter fibers connecting two anatomical districts, thus obtaining a weighted undirected network. The nodes of this network are the cerebral regions provided by the reference brain atlas, the weights are the intensity of linkage among them. We investigated brain connectivity graphs retrieved from a set of 222 publicly available DWI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 47 Alzheimer's disease (AD) patients, 52 normal controls (NC) and 123 mild cognitive impairment (MCI) subjects, this latter cohort includes 85 early and 38 late MCI subjects, EMCI and LMCI respectively., Main Results: The proposed brain connectivity approach effectively characterizes Alzheimer's disease, especially in its early stages. In fact, MCI is a prodromal phase of Alzheimer's disease. We report a [Formula: see text] accuracy for the discrimination of NC and AD subjects and accuracies of [Formula: see text] and [Formula: see text] for the discrimination of MCI from respectively NC and AD subjects., Significance: Our complex network approach offers an innovative and effective instrument to model brain connectivity and detect in DWI tractographies early changes due to Alzheimer's.

Background: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challenge has been promoted, on the international Kaggle platform, to assess the performance of different classification methods for prediction of MCI and its conversion to AD., New Method: This work presents a classification strategy based on Random Forest feature selection and Deep Neural Network classification using a mixed cohort including the four classes of classification problem, that is HC, AD, MCI and cMCI, to train the model. Moreover, we compare this approach with a novel classification strategy based on fuzzy logic learned on a mixed cohort including only HC and AD., Experiments: A training set of 240 subjects and a test set including mixed cohort of 500 real and simulated subjects were used. The data included AD patients, MCI subjects converting to AD (cMCI), MCI subjects and healthy controls (HC). This work ranked third for overall accuracy (38.8%) over 19 participating teams., Comparison With Existing Method(s): The "International challenge for automated prediction of MCI from MRI data" hosted by the Kaggle platform has been promoted to validate different methodologies with a common set of data and evaluation procedures., Conclusion: DNNs reach a classification accuracy significantly higher than other machine learning strategies; on the other hand, fuzzy logic is particularly accurate with cMCI, suggesting a combination of these approaches could lead to interesting future perspectives., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer's disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance., Materials and Methods: The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available., Results: HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC 1  = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC 2  = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans., Conclusions: The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer's disease and become a useful tool for other neuroimaging applications.

Alzheimer's disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.

Alzheimer's Disease (AD) is the most frequent neurodegenerative form of dementia. Although dementia cannot be cured, it is very important to detect preclinical AD as early as possible. Several studies demonstrated the effectiveness of the joint use of structural Magnetic Resonance Imaging (MRI) and cognitive measures to detect and track the progression of the disease. Since hippocampal atrophy is a well known biomarker for AD progression state, we propose here a novel methodology, exploiting it as a searchlight to detect the best discriminating features for the classification of subjects with Mild Cognitive Impairment (MCI) converting (MCI-c) or not converting (MCI-nc) to AD. In particular, we define a significant subdivision of the hippocampal volume in fuzzy classes, and we train for each class Support Vector Machine SVM classifiers on cognitive and morphometric measurements of normal controls (NC) and AD patients. From the ADNI database, we used MRI scans and cognitive measurements at baseline of 372 subjects, including 98 subjects with AD, and 117 NC as a training set, 86 with MCI-c and 71 with MCI-nc as an independent test set. The accuracy of early diagnosis was evaluated by means of a longitudinal analysis. The proposed methodology was able to accurately predict the disease onset also after one year (median AUC=88.2%, interquartile range 87.2%-89.0%). Besides its robustness, the proposed fuzzy methodology naturally incorporates the uncertainty degree intrinsically affecting neuroimaging features. Thus, it might be applicable in several other pathological conditions affecting morphometric changes of the brain., (Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Diffusion tensor imaging (DTI) is a promising imaging technique that provides insight into white matter microstructure integrity and it has greatly helped identifying white matter regions affected by Alzheimer's disease (AD) in its early stages. DTI can therefore be a valuable source of information when designing machine-learning strategies to discriminate between healthy control (HC) subjects, AD patients and subjects with mild cognitive impairment (MCI). Nonetheless, several studies have reported so far conflicting results, especially because of the adoption of biased feature selection strategies. In this paper we firstly analyzed DTI scans of 150 subjects from the Alzheimer's disease neuroimaging initiative (ADNI) database. We measured a significant effect of the feature selection bias on the classification performance (p-value  <  0.01), leading to overoptimistic results (10% up to 30% relative increase in AUC). We observed that this effect is manifest regardless of the choice of diffusion index, specifically fractional anisotropy and mean diffusivity. Secondly, we performed a test on an independent mixed cohort consisting of 119 ADNI scans; thus, we evaluated the informative content provided by DTI measurements for AD classification. Classification performances and biological insight, concerning brain regions related to the disease, provided by cross-validation analysis were both confirmed on the independent test.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Structural MRI measures for monitoring Alzheimer's Disease (AD) progression are becoming instrumental in the clinical practice, and more so in the context of longitudinal studies. This investigation addresses the impact of four image analysis approaches on the longitudinal performance of the hippocampal volume., Methods: We present a hippocampal segmentation algorithm and validate it on a gold-standard manual tracing database. We segmented 460 subjects from ADNI, each subject having been scanned twice at baseline, 12-month and 24month follow-up scan (1.5T, T1 MRI). We used the bilateral hippocampal volume v and its variation, measured as the annualized volume change Λ=δv/year(mm(3)/y). Four processing approaches with different complexity are compared to maximize the longitudinal information, and they are tested for cohort discrimination ability. Reference cohorts are Controls vs. Alzheimer's Disease (CTRL/AD) and CTRL vs. Mild Cognitive Impairment who subsequently progressed to AD dementia (CTRL/MCI-co). We discuss the conditions on v and the added value of Λ in discriminating subjects., Results: The age-corrected bilateral annualized atrophy rate (%/year) were: -1.6 (0.6) for CTRL, -2.2 (1.0) for MCI-nc, -3.2 (1.2) for MCI-co and -4.0 (1.5) for AD. Combined (v, Λ) discrimination ability gave an Area under the ROC curve (auc)=0.93 for CTRL vs AD and auc=0.88 for CTRL vs MCI-co., Conclusions: Longitudinal volume measurements can provide meaningful clinical insight and added value with respect to the baseline provided the analysis procedure embeds the longitudinal information., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: In the framework of the clinical validation of research tools, this investigation presents a validation study of an automatic medial temporal lobe atrophy measure that is applied to a naturalistic population sampled from memory clinic patients across Europe., Methods: The procedure was developed on 1.5-T magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative database, and it was validated on an independent data set coming from the DESCRIPA study. All images underwent an automatic processing procedure to assess tissue atrophy that was targeted at the hippocampal region. For each subject, the procedure returns a classification index. Once provided with the clinical assessment at baseline and follow-up, subjects were grouped into cohorts to assess classification performance. Each cohort was divided into converters (co) and nonconverters (nc) depending on the clinical outcome at follow-up visit., Results: We found the area under the receiver operating characteristic curve (AUC) was 0.81 for all co versus nc subjects, and AUC was 0.90 for subjective memory complaint (SMCnc) versus all co subjects. Furthermore, when training on mild cognitive impairment (MCI-nc/MCI-co), the classification performance generally exceeds that found when training on controls versus Alzheimer's disease (CTRL/AD)., Conclusions: Automatic magnetic resonance imaging analysis may assist clinical classification of subjects in a memory clinic setting even when images are not specifically acquired for automatic analysis., (Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

Background: Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible., Subjects: A reference group composed of 144AD patients and 189 age-matched controls was used to train and test the procedure. It was then applied on a study group composed of 302 MCI subjects, 136 having progressed to clinically probable AD (MCI-converters) and 166 having remained stable or recovered to normal condition after a 24month follow-up (MCI-non converters). All subjects came from the ADNI database., Methods: We sampled the brain with 7 relatively small volumes, mainly centered on the MTL, and 2 control regions. These volumes were filtered to give intensity and textural MRI-based features. Each filtered region was analyzed with a Random Forest (RF) classifier to extract relevant features, which were subsequently processed with a Support Vector Machine (SVM) classifier. Once a prediction model was trained and tested on the reference group, it was used to compute a classification index (CI) on the MCI cohort and to assess its accuracy in predicting AD conversion in MCI patients. The performance of the classification based on the features extracted by the whole 9 volumes is compared with that derived from each single volume. All experiments were performed using a bootstrap sampling estimation, and classifier performance was cross-validated with a 20-fold paradigm., Results: We identified a restricted set of image features correlated with the conversion to AD. It is shown that most information originate from a small subset of the total available features, and that it is enough to give a reliable assessment. We found multiple, highly localized image-based features which alone are responsible for the overall clinical diagnosis and prognosis. The classification index is able to discriminate Controls from AD with an Area Under Curve (AUC)=0.97 (sensitivity ≃89% at specificity ≃94%) and Controls from MCI-converters with an AUC=0.92 (sensitivity ≃89% at specificity ≃80%). MCI-converters are separated from MCI-non converters with AUC=0.74(sensitivity ≃72% at specificity ≃65%)., Findings: The present automated MRI-based technique revealed a strong relationship between highly localized baseline-MRI features and the baseline clinical assessment. In addition, the classification index was also used to predict the probability of AD conversion within a time frame of two years. The definition of a single index combining local analysis of several regions can be useful to detect AD neurodegeneration in a typical MCI population., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean age +/- standard deviation (SD), 75.8 years +/- 7.8; Mini Mental State Examination (MMSE) score, 24.1 +/- 3.1), 42 patients with aMCI (11 men, 31 women; mean age +/- SD, 75.2 years +/- 4.9; MMSE score, 27.9 +/- 1.9), and 30 elderly healthy controls (10 men, 20 women; mean age +/- SD, 74.7 years +/- 5.2; MMSE score, 29.1 +/- 0.8). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean age +/- SD, 76.3 years +/- 5.8; MMSE score, 23.2 +/- 4.1), 247 patients with aMCI (143 men, 104 women; mean age +/- SD, 75.3 years +/- 6.7; MMSE score, 27.0 +/- 1.8), and 135 elderly healthy controls (61 men, 74 women; mean age +/- SD, 76.4 years +/- 6.1). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences were assessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, 0.28 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001; aMCI vs controls, 0.31 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001). Moreover, the subgroup of aMCI converters was significantly different from controls (0.27 +/- 0.034 vs 0.34 +/- 0.03, P < 0.001). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.