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Accurate and rapid detection of Alzheimer’s disease (AD) using magnetic resonance imaging (MRI) gained considerable attention among research workers because of an increased number of current researches being driven by deep learning (DL) methods that have accomplished outstanding outcomes in variety of domains involving medical image analysis. Especially, convolution neural network (CNN) is primarily applied for the analyses of image datasets according to the capability of handling massive unstructured datasets and automatically extracting significant features. Earlier detection is dominant to the success and development interferences, and neuroimaging characterizes the potential regions for earlier diagnosis of AD. The study presents and develops a novel Deep Learning-based Magnetic Resonance Image Segmentation and Classification for AD Diagnosis (DLMRISC-ADD) model. The presented DLMRISC-ADD model mainly focuses on the segmentation of MRI images to detect AD. To accomplish this, the presented DLMRISC-ADD model follows a two-stage process, namely, skull stripping and image segmentation. At the preliminary stage, the presented DLMRISC-ADD model employs U-Net-based skull stripping approach to remove skull regions from the input MRIs. Next, in the second stage, the DLMRISC-ADD model applies QuickNAT model for MRI image segmentation, which identifies distinct parts such as white matter, gray matter, hippocampus, amygdala, and ventricles. Moreover, densely connected network (DenseNet201) feature extractor with sparse autoencoder (SAE) classifier is used for AD detection process. A brief set of simulations is implemented on ADNI dataset to demonstrate the improved performance of the DLMRISC-ADD method, and the outcomes are examined extensively. The experimental results exhibit the effectual segmentation results of the DLMRISC-ADD technique.

In recent years, most of the patients with dementia have acquired healthcare systems within the primary care system and they also have some challenging psychiatric and medical issues. Here, dementia-based symptoms are not identified in the primary care center, because they are affected by various factors like psychological symptoms, clinically relevant behavior, numerous psychotropic medications, and multiple chronic medical conditions. To enhance the healthcare-related applications, the primary healthcare system with additional resources like coordination with interdisciplinary dementia specialists, feasible diagnosis, and screening process need to be improved. Therefore, the differentiation between Alzheimer’s Disease (AD) and Lewy Body Dementia (LBD) has been acquired to provide the best clinical support to the patients. In this research work, the deep structure depending on AD and LBD systems has been implemented with the help of an adaptive algorithm to provide promising outcomes over dementia detection. Initially, the input images are collected from online sources. Thus, the collected images are forwarded to the newly designed Multi-Cascaded Deep Learning (MSDL), where the ResNet, Autoencoder, and weighted Long-Short Term Memory (LSTM) networks are serially cascaded to provide effective classification results. Then, the fully connected layer of ResNet is given to the Autoencoder structure. Here, the output from the encoder phase is optimized by using the Adaptive Water Wave Cuttlefish Optimization (AWWCO), which is derived from the Water Wave Optimization (WWO) and Cuttlefish Algorithm (CA), and the resultant selected output is fed to the weight-optimized LSTM network. Further, the parameters in the MSDL network are optimized by using the same AWWCO algorithm. Finally, the performance comparison over different heuristic algorithms and conventional dementia detection approaches is done for the validation of the overall effectiveness of the suggested model in terms of various estimation measures.

This study presents an analytical framework for modeling the propagation of electrical impulses in a human brain affected by Alzheimer’s disease through an adapted FitzHugh–Nagumo model. The model incorporates the effects of amyloid plaques and neurofibrillary tangles on neural activity and connectivity. We include additional terms to account for disease-related effects and to provide a comprehensive approach to understanding the disruptions caused by Alzheimer’s disease. A priori estimates establish the boundedness of solutions, ensuring stability and regularity. The uniqueness of solutions is proven, indicating consistent results under identical initial conditions. Stability criteria is derived from linearizing the model around steady-state solutions. An exploration of specific parameters demonstrates the system’s stability for certain wave numbers, of relevance for maintaining proper neural communication.

The targeted delivery of therapeutic chimeric molecules significantly enhances pharmacological efficiency by triggering strong cellular uptake and prolonging the duration of systemic circulation. Purposeful molecular design incorporating specific chimeric protein–receptor interactions is an essential aspect of understanding biological mechanisms and precision modeling of molecular complexes. In this study, a detailed in-silico analysis was performed on a chimeric fusion protein designed to target Alzheimer’s disease. The fusion protein was created using the amino acid sequences retrieved from the beta-NGF, Nerve Growth Factor protein and the A$\beta$ peptide, with a rigid linker in place that ensures structural integrity. Its physicochemical characterization was predicted using ProtParam along with the evaluation of its secondary and tertiary structures, while its potential toxicity and allergenicity were analyzed through online analytical tools. The structural integrity of the fusion protein was evaluated through Rampage and ERRAT analyses, resulting in an ERRAT quality factor of 85 and indicating that 98.6% of the residues were placed within favored regions as suggested by the corresponding Ramachandran plot. Docking experiments were conducted using the HDOCK Server, followed by molecular simulations with the OpenMM engine, employing the AMBER force field for evaluation. The quality, validity, interaction analysis and stability of the fusion protein reveal that it indeed represents a functional molecular entity. The fact that the beta-NGF-A$\beta$ fusion gene is cloned and expressed in a suitable prokaryotic system may indicate that this is a very promising candidate for novel therapeutic approaches that target Alzheimer’s disease.

A complex network approach is combined with time dynamics in order to conduct a space–time analysis applicable to longitudinal studies aimed to characterize the progression of Alzheimer's disease (AD) in individual patients. The network analysis reveals how patient-specific patterns are associated with disease progression, also capturing the widespread effect of local disruptions. This longitudinal study is carried out on resting electroence phalography (EEGs) of seven AD patients. The test is repeated after a three months' period. The proposed methodology allows to extract some averaged information and regularities on the patients' cohort and to quantify concisely the disease evolution. From the functional viewpoint, the progression of AD is shown to be characterized by a loss of connected areas here measured in terms of network parameters (characteristic path length, clustering coefficient, global efficiency, degree of connectivity and connectivity density). The differences found between baseline and at follow-up are statistically significant. Finally, an original topographic multiscale approach is proposed that yields additional results.

In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer’s disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon’s entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.

Previous studies have shown that the topological organization of the cerebral cortex is altered in Alzheimer’s disease (AD). However, it remains unknown whether different levels of the cortical hierarchy are homogeneously affected during disease progression, and which of these levels are mostly involved in the breakdown of metabolic (functional) connectivity. To fulfill these goals, we acquired structural magnetic resonance images (MRI) and positron emission tomography (PET) with the radiotracer 18F-fludeoxyglucose (FDG) in 29 healthy old (HO) adults, 29 amnestic mild cognitive impairment (aMCI) and 29 mild AD patients. Structural and metabolic connections were obtained from inter-regional correlations of cortical thickness and glucose consumption, respectively. Results showed that AD and HO groups differed at all levels of cortical organization (i.e. whole cortex, hemisphere, lobe and node), whereas differences among the three groups were only evident at the lobe and node levels. The correlation between structural and metabolic connectivity (F–S coupling) was also disturbed during AD progression, affecting to different connectivity scales: it decreased at the local level, revealing a progressive increase of metabolic connections in those local communities with fewer structural connections; whereas it increased at the global level, likely due to a parallel reduction of cortical thickness and glucose consumption between long-distance cortical regions. Collectively, these results reveal that different levels of cortical organization are selectively affected during the transition from normal aging to dementia, which could be helpful to track cortical dysfunctions in the progression to AD.

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer’s disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

Objective: In this work, we introduce Permutation Disalignment Index (PDI) as a novel nonlinear, amplitude independent, robust to noise metric of coupling strength between time series, with the aim of applying it to electroencephalographic (EEG) signals recorded longitudinally from Alzheimer’s Disease (AD) and Mild Cognitive Impaired (MCI) patients. The goal is to indirectly estimate the connectivity between the cortical areas, through the quantification of the coupling strength between the corresponding EEG signals, in order to find a possible matching with the disease’s progression. Method: PDI is first defined and tested on simulated interacting dynamic systems. PDI is then applied to real EEG recorded from 8 amnestic MCI subjects and 7 AD patients, who were longitudinally evaluated at time $T$0 and 3 months later (time $T$1). At time $T$1, 5 out of 8 MCI patients were still diagnosed MCI (stable MCI) whereas the remaining 3 exhibited a conversion from MCI to AD (prodromal AD). PDI was compared to the Spectral Coherence and the Dissimilarity Index. Results: Limited to the size of the analyzed dataset, both Coherence and PDI resulted sensitive to the conversion from MCI to AD, even though only PDI resulted specific. In particular, the intrasubject variability study showed that the three patients who converted to AD exhibited a significantly ($p<0.001$) increased PDI (reduced coupling strength) in delta and theta bands. As regards Coherence, even though it significantly decreased in the three converted patients, in delta and theta bands, such a behavior was also detectable in one stable MCI patient, in delta band, thus making Coherence not specific. From the Dissimilarity Index point of view, the converted MCI showed no peculiar behavior. Conclusions: PDI significantly increased, in delta and theta bands, specifically in the MCI subjects who converted to AD. The increase of PDI reflects a reduced coupling strength among the brain areas, which is consistent with the expected connectivity reduction associated to AD progression.

Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.

Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer’s disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer’s disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were $82.57\pm 7.35$%, $89.76\pm 8.67$% and $95.74\pm 2.31$% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

Alzheimer’s Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Alzheimer’s disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient’s autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the ‘cognitive brain wave’ P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels.

A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach.

In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient’s cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of $3282$ drawings. The proposed method provides an accuracy of $75.65\%$ in the binary case-control classification task, with an AUC of $0.83$. These results are indeed relevant considering the use of the classic version of the CDT. The large size of the sample suggests that the method proposed has a high reliability to be used in clinical contexts and demonstrates the suitability of CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by CI. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed.

Alzheimer’s disease (AD) is the most prevalent form of dementia. Although there is no current cure, medical treatment can help to control its progression. Hence, early-stage diagnosis is crucial to maximize the living standards of the patients. Biochemical markers and medical imaging in combination with neuropsychological tests represent the most extended diagnosis procedure. However, these techniques require specialized personnel and long processing time. Furthermore, the access to some of these techniques is often limited in crowded healthcare systems and rural areas. In this context, electroencephalography (EEG), a non-invasive technique to obtain endogenous brain information, has been proposed for the diagnosis of early-stage AD. Despite the valuable information provided by clinical EEG and high density montages, these approaches are impractical in conditions such as those described above. Consequently, in this study, we evaluated the feasibly of using a reduced EEG montage with only four channels to detect early-stage AD. For this purpose, we involved eight clinically diagnosed AD patients and eight healthy controls. The results we obtained reveal similar accuracies ($p$-value$=$0.66) for the reduced montage (0.86) and a 16-channel montage (0.87). This suggests that a four-channel wearable EEG system could be an effective tool for supporting early-stage AD detection.