Genetic-based patient stratification in Alzheimer's disease.

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

Monitoring differences in the function of the autonomic nervous system in patients with chronic insomnia using a wearable device.

STUDY OBJECTIVES: to characterize possible differences in the function of the ANS in patients with chronic insomnia compared to a control group, using a wearable device, in order to determine whether those findings allow diagnosing this medical entity. METHODS: Thirty-two patients with chronic insomnia and nineteen patients without any sleep disorder, as a control group, were recruited prospectively. Both groups of patients underwent an in-patient night with simultaneous polysomnography and wearable device recording Empatica E4 a diverse array of physiological signals, including electrodermal activity, temperature, accelerometry, and photoplethysmography, providing a comprehensive resource for in-depth sleep analysis. RESULTS: In polysomnography, patients suffering from insomnia showed a significant decrease in sleep efficiency and total sleep time, prolonged sleep latency, and increased wakefulness after sleep onset. Accelerometry results were statistically significant differences in the three axis (x, y, z) just in stage N3, no differences were observed between both groups in REM sleep. The lowest temperature was reached in REM sleep in both groups. Peripheral temperature did not decrease during the different sleep phases compared to wakefulness in insomnia, unlike in the control group. Heart rate was higher in patients with insomnia than in controls during wakefulness and sleep stage. Heart rate variability was lower in stage N3 and higher in REM sleep compared to wakefulness in both groups. Sweating was significantly higher in patients with insomnia compared to the control group, as indicated by Skin Conductance Variability values and Sudomotor Nerve. CONCLUSIONS: Our study suggests that patients with insomnia have increased sympathetic activity during sleep, showing a higher heart rate. Temperature and sweating significantly influence the different sleep phases.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.

AIMS: The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. METHODS: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aß(1-42), Aß(1-42)/Aß(1-40) ratio, tTau, and pTau. RESULTS: The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia. CONCLUSION: We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

On the limits of graph neural networks for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biological interactions between the genes used as input features, thus neglecting important information about the disease mechanisms at play. To mitigate this, we first extracted AD subnetworks from several protein-protein interaction (PPI) databases and labeled these with genotype information (number of missense variants) to make them patient-specific. Next, we trained Graph Neural Networks (GNNs) on the patient-specific networks for phenotype prediction. We tested different PPI databases and compared the performance of the GNN models to baseline models using classical machine learning techniques, as well as randomized networks and input datasets. The overall results showed that GNNs could not outperform a baseline predictor only using the APOE gene, suggesting that missense variants are not sufficient to explain disease risk beyond the APOE status. Nevertheless, our results show that GNNs outperformed other machine learning techniques and that protein-protein interactions lead to superior results compared to randomized networks. These findings highlight that gene interactions are a valuable source of information in predicting disease status.

GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer's disease and frontotemporal dementia using genetic algorithms.

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients' evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD).

Body Complexion and Circulating Lipids in the Risk of TDP-43 Related Disorders.

Objective: Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are two distinct degenerative disorders with overlapping genetics, clinical manifestations, and pathology, including the presence of TDP-43 aggregates in nearly 50% of patients with FTD and 98% of all patients with ALS. Here, we evaluate whether different genetically predicted body lipid metabolic traits are causally associated with the risk of FTD with TDP-43 aggregates, compare it to their causal role in the risk of ALS, and identify genetic variants shared between these two TDP43 related disorders in relation to lipid metabolic traits. Methods: We conducted two-sample Mendelian randomization analyses (2SMR) to evaluate the causal association of 9 body complexion and 9 circulating lipids traits with the risk of FTD with TDP-43 aggregates and the risk of ALS. The inverse-variance weighted method was the primary analysis, followed by secondary sensitive analyses. We then looked for common genetic variants between FTD and ALS in relation to lipid metabolic traits. Results: Genetically increased trunk-predicted mass, fat-free mass, and higher circulating triglycerides levels were suggestively associated with a higher risk of FTD with TDP-43 aggregates. Circulating lipids, mainly LDL cholesterol, were causally associated with a higher risk of ALS. We identified two genetic variants, EIF4ENIF1 and HNRNPK, in relation to body complexion and circulating lipids shared between FTD with TDP-43 aggregates and ALS. Conclusion: This work provides evidence that body complexion and circulating lipids traits impact differentially on the risk of FTD and ALS, suggesting new and specific interventional approaches in the control of body lipid metabolism for FTD and ALS, and identified HNRNPK as a potential link between circulating lipids levels and these disorders.

Cluster-Then-Classify Methodology for the Identification of Pain Episodes in Chronic Diseases.

Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biology, the Internet of Things and Artificial Intelligence. 70% to 80% of all healthcare costs in the EU and US are currently spent on chronic diseases, leading to estimated costs of C=700 billion and $3.5 trillion respectively. The management of symptomatic pain crises in chronic diseases is based on general clinical guidelines that do not take into account the singularities of the crises, such as their intensity or duration, so that the pain of those particular crises may cause the medication to be ineffective and lead the patient to overmedication. Knowing in detail the characteristics of the pain would help the physician to objectively prescribe personalized treatments for each patient and crisis. In this manuscript, we make a step further on the prediction of symptomatic crisis from ambulatory collected data in chronic diseases. We propose a categorization of pain types according to subjective symptoms of real patients. Our approach has been evaluated in the migraine disease. The migraine is one of the most disabling neurological diseases that affects over 12% of the population worldwide and leads to high economic costs for private and public health systems. This study aims to classify pain episodes by the characterization of pain curves reported by patients in real time. Pain curves have been described as a set of morphological features. With these features the pain episodes are clustered then classified by unsupervised and supervised machine learning models. It is shown that the evolution of different pain episodes in chronic diseases can be modeled and clustered. Over a population of 51 migraine patients, it has been found that there are 4 clusters of pain types that can be classified using 4 morphological features with an accuracy of 99.0% using a Logistic Model Tree algorithm.

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms.

BACKGROUND: Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests. METHODS: Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy. RESULTS: Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. CONCLUSIONS: Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

Predictive and diagnosis models of stroke from hemodynamic signal monitoring.

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 min of monitoring, to predict the exitus during the first 3 h of monitoring, and to predict the stroke recurrence in just 15 min of monitoring. Patients with difficult access to a CT scan and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around 98% precision (97.8% sensitivity, 99.5% specificity), exitus prediction with 99.8% precision (99.8% Sens., 99.9% Spec.), and 98% precision predicting stroke recurrence (98% Sens., 99% Spec.). Graphical abstract depicting the complete process since a patient is monitored until the data collected is used to generate models.

Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. RESULTS: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. CONCLUSIONS: Although reducing the dimensionality does not means a general improvement, the set of features is almost halved and results are better or quite similar. Finally, it is interesting how these results expose a finer grain classification of patients according to the neuroanatomy of their disease.

Machine learning in the clinical and language characterisation of primary progressive aphasia variants.

INTRODUCTION: Primary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was to study the language and cognitive features associated with a new proposed classification for PPA. MATERIAL AND METHODS: Sixty-eight patients with PPA in early stages of the disease and 20 healthy controls were assessed with a comprehensive language and cognitive protocol. They were also evaluated with 18F-FDG positron emision tomography (PET). Patients were classified according to FDG PET regional metabolism, using our previously developed algorithm based on a hierarchical agglomerative cluster analysis with Ward's linkage method. Five variants were found, with both the non-fluent and logopenic variants being split into 2 subtypes. Machine learning techniques were used to predict each variant according to language assessment results. RESULTS: Non-fluent type 1 was associated with poorer performance in repetition of sentences and reading of irregular words than non-fluent type 2. Conversely, the second group showed a higher degree of apraxia of speech. Patients with logopenic variant type 1 performed more poorly on action naming than patients with logopenic type 2. Language assessments were predictive of PET-based subtypes in 86%-89% of cases using clustering analysis and principal components analysis. CONCLUSIONS: Our study supports the existence of 5 variants of PPA. These variants show some differences in language and FDG PET imaging characteristics. Machine learning algorithms using language test data were able to predict each of the 5 PPA variants with a relatively high degree of accuracy, and enable the possibility of automated, machine-aided diagnosis of PPA variants.

Prediction of patient's response to OnabotulinumtoxinA treatment for migraine.

Migraine affects the daily life of millions of people around the world. The most well-known disabling symptom associated with this illness is the intense headache. Nowadays, there are treatments that can diminish the level of pain. OnabotulinumtoxinA (BoNT-A) has become a very popular medication for treating migraine headaches in those cases in which other medication is not working, typically in chronic migraines. Currently, the positive response to Botox treatment is not clearly understood, yet understanding the mechanisms that determine the effectiveness of the treatment could help with the development of more effective treatments. To solve this problem, this paper sets up a realistic scenario of electronic medical records of migraineurs under BoNT-A treatment where some clinical features from real patients are labeled by doctors. Medical registers have been preprocessed. A label encoding method based on simulated annealing has been proposed. Two methodologies for predicting the results of the first and the second infiltration of the BoNT-A based treatment are contempled. Firstly, a strategy based on the medical HIT6 metric is described, which achieves an accuracy over 91%. Secondly, when this value is not available, several classifiers and clustering methods have been performed in order to predict the reduction and adverse effects, obtaining an accuracy of 85%. Some clinical features as Greater occipital nerves (GON), chronic migraine time evolution and others have been detected as relevant features when examining the prediction models. The GON and the retroocular component have also been described as important features according to doctors.

To what extent are patients with migraine able to predict attacks?

PURPOSE: Premonitory symptoms (PSs) of migraine are those that precede pain in a migraine attack. Previous studies suggest that treatment during this phase may prevent the onset of pain; however, this approach requires that patients be able to recognize their PSs. Our objectives were to evaluate patients' actual ability to predict migraine attacks based on their PSs and analyze whether good predictors meet any characteristic profile. PATIENTS AND METHODS: This prospective, observational study included patients with migraine with and without aura. Patients' baseline characteristics were recorded. During a 2-month follow-up period, patients used a mobile application to record what they believed to be PSs and later to record the onset of pain, if this occurred. When a migraine attack ended, patients had to complete a form on the characteristics of the episode (including the presence of PSs not identified prior to the attack). RESULTS: Fifty patients were initially selected. A final total of 34 patients were analyzed, recording 229 attacks. Of whom, 158 (69%) were accompanied by PSs and were recorded prior to the pain onset in 63 (27.5%) cases. A total of 67.6% of the patients were able to predict at least one attack, but only 35.3% were good predictors (>50% of attacks). There were only 11 cases in which a patient erroneously reported their PSs (positive predictive value: 85.1%). Good predictors were not differentiated by any specific clinical characteristic. However, a range of symptoms were particularly predictive; these included photophobia, drowsiness, yawning, increased thirst, and blurred vision. CONCLUSION: A large majority of patients with migraine experienced a PS and were able to predict at least one attack. Besides, only a small percentage of patients were considered as good predictors; however, they could not be characterized by any specific profile. Nonetheless, when patients with migraine believed that they were experiencing PSs, they were frequently correct.

A Bio-Inspired Hybrid Thermal Management Approach for 3-D Network-on-Chip Systems.

3-D network-on-chip (NoC) systems are getting popular among the integrated circuit (IC) manufacturer because of reduced latency, heterogeneous integration of technologies on a single chip, high yield, and consumption of less interconnecting power. However, the addition of functional units in the -direction has resulted in higher on-chip temperature and appearance of local hotspots on the die. The increase in temperature degrades the performance, lifetime, and reliability, and increases the maintenance cost of 3-D ICs. To keep the heat within an acceptable limit, floorplanning is the widely accepted solution. Proper arrangement of functional units across different layers can lead to uniform thermal distribution in the chip. For systems with high density of elements, few hotspots cannot be eliminated in the floorplanning approach. To overcome, liquid microchannel cooling technology has emerged as an efficient and scalable solution for 3-D NoC. In this paper, we propose a novel hybrid algorithm combining both floorplanning, and liquid microchannel placement to alleviate the hotspots in high-density systems. A mathematical model is proposed to deal with heat transfer due to diffusion and convention. The proposed approach is independent of topology. Three different topologies: 3-D stacked homogeneous mesh architecture, 3-D stacked heterogeneous mesh architecture, and 3-D stacked ciliated mesh architecture are considered to check the effectiveness of the proposed algorithm in hotspot reduction. A thermal comparison is made with and without the proposed thermal management approach for the above architectures considered. It is observed that there is a significant reduction in on-chip temperature when the proposed thermal management approach is applied.

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data.

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.

Proactive and reactive transmission power control for energy-efficient on-body communications.

In wireless body sensor network (WBSNs), the human body has an important effect on the performance of the communication due to the temporal variations caused and the attenuation and fluctuation of the path loss. This fact suggests that the transmission power must adapt to the current state of the link in a way that it ensures a balance between energy consumption and packet loss. In this paper, we validate our two transmission power level policies (reactive and predictive approaches) using the Castalia simulator. The integration of our experimental measurements in the simulator allows us to easily evaluate complex scenarios, avoiding the difficulties associated with a practical realization. Our results show that both schemes perform satisfactorily, providing overall energy savings of 24% and 22% for a case of study, as compared to the maximum transmission power mode.

Accurate human tissue characterization for energy-efficient wireless on-body communications.

The demand for Wireless Body Sensor Networks (WBSNs) is rapidly increasing due to the revolution in wearable systems demonstrated by the penetration of on-the-body sensors in hospitals, sports medicine and general health-care practices. In WBSN, the body acts as a communication channel for the propagation of electromagnetic (EM) waves, where losses are mainly due to absorption of power in the tissue. This paper shows the effects of the dielectric properties of biological tissues in the signal strength and, for the first time, relates these effects with the human body composition. After a careful analysis of results, this work proposes a reactive algorithm for power transmission to alleviate the effect of body movement and body type. This policy achieves up to 40.8% energy savings in a realistic scenario with no performance overhead.