Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46–96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions

Application of machine learning in dementia diagnosis: a systematic literature review

According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer’s disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.info:eu-repo/semantics/publishedVersio

Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis

Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on "correlation's correlation" has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely "hybrid high-order FC networks" by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome

Transfer learning for Alzheimer’s disease through neuroimaging biomarkers: A systematic review

Producción CientíficaAlzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.Ministerio de Industria, Energía y Turismo (AAL-20125036

Structural and Functional Brain Connectivity in Middle-Aged Carriers of Risk Alleles for Alzheimer\u27s Disease

Single nucleotide polymorphisms (SNPs) in APOE, COMT, BDNF, and KIBRA have been associated with age-related memory performance and executive functioning as well as risk for Alzheimer’s disease (AD). The purpose of the present investigation was to characterize differences in brain functional and structural integrity associated with these SNPs as potential endophenotypes of age-related cognitive decline. I focused my investigation on healthy, cognitively normal middle-aged adults, as disentangling the early effects of healthy versus pathological aging in this group may aid early detection and prevention of AD. The aims of the study were 1) to characterize SNP-related differences in functional connectivity within two resting state networks (RSNs; default mode network [DMN] and executive control network [ECN]) associated with memory and executive functioning, respectively; 2) to identify differences in the white matter (WM) microstructural integrity of tracts underlying these RSNs; and 3) to characterize genotype differences in the graph properties of an integrated functional-structural network. Participants (age 40-60, N = 150) underwent resting state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), and genotyping. Independent components analysis (ICA) was used to derive RSNs, while probabilistic tractography was performed to characterize tracts connecting RSN subregions. A technique known as functional-by-structural hierarchical (FSH) mapping was used to create the integrated, whole brain functional-structural network, or resting state structural connectome (rsSC). I found that BDNF risk allele carriers had lower functional connectivity within the DMN, while KIBRA risk allele carriers had poorer WM microstructural integrity in tracts underlying the DMN and ECN. In addition to these differences in the connectivity of specific RSNs, I found significant impairments in the global and local topology of the rsSC across all evaluated SNPs. Collectively, these findings suggest that integrating multiple neuroimaging modalities and using graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment

Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey

Alzheimer’s is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual’s cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer’s disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and non-image biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017. Alzheimer’s disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer’s disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer’s disease

Multimodal and multiscale brain networks : understanding aging, Alzheimer’s disease, and other neurodegenerative disorders

The human brain can be modeled as a complex network, often referred to as the connectome, where structural and functional connections govern its organization. Several neuroimaging studies have focused on understanding the architecture of healthy brain networks and have shed light on how these networks evolve with age and in the presence of neurodegenerative disorders. Many studies have explored the brain networks in Alzheimer’s disease (AD), the most common type of dementia, using various neuroimaging modalities independently. However, most of these studies ignored the complex and multifactorial nature of AD. The aim of this thesis was to investigate and analyze the brain’s multimodal and multiscale network organization in aging and in AD by using different multilayer brain network analyses and different types of data. Additionally, this research extended its scope to incorporate other dementias, such as Lewy body dementias, allowing for a comparison of these disorders with AD and normal aging. These comparisons were made possible through the application of protein co-expression networks. In Study I, we investigated sex differences in healthy individuals using multimodal brain networks. To do this we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion-weighted imaging (DWI) data from the Human Connectome Project (HCP) to perform multilayer and deep learning analyses. These analyses identified differences between men's and women's underlying brain network organization, showing that the deep-learning analysis with multilayer network metrics (area under the curve, AUC, of 0.81) outperforms the classification using single-layer network measures (AUC of 0.72 for functional networks and 0.70 for anatomical networks). Furthermore, we integrated the multilayer brain networks methodology and neural network models into a software package that is easy to use by researchers with different backgrounds and is also easily expandable for researchers with different levels of programming experience. Then, we used the multilayer brain networks methodology to study the interaction between sex and age on the functional network topology using a large group of people from the UK Biobank (Study II). By incorporating multilayer brain network analyses, we analyzed both positive and negative connections derived from functional correlations, and we obtained important insights into how cognitive abilities, physical health, and even genetic factors differ between men and women as they age. Age and sex were strongly associated with multiplex and multilayer measures such as the multiplex participation coefficient, multilayer clustering, and multilayer global efficiency, accounting for up to 89.1%, 79.9%, and 79.5% of the variance related to age, respectively. These results indicate that incorporating separate layers for positive and negative connections within a complex network framework reveals sensitive insights into age- and sex-related variations that are not detected by traditional metrics. Furthermore, our functional metrics exhibited associations with genes that have previously been linked to processes related to aging. In Study III, we assessed whether multilayer connectome analyses could offer new perspectives on the relationship between amyloid pathology and gray matter atrophy across the AD continuum. Subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were divided into four groups based on cerebrospinal fluid (CSF) amyloid-β (Aβ) biomarker levels and clinical diagnosis. We compared the different groups using weighted and binary multilayer measures that assess the strength of the connections, the modularity, as well as the multiplex segregation and integration of the brain connectomes. Across Aβ-positive (Aβ+) groups, we found widespread increases in the overlapping connectivity strength and decreases in the number of identical connections in both layers. Moreover, the brain modules were reorganized in the mild cognitive impairment (MCI) Aβ+ group and an imbalance in the quantity of couplings between the two layers was found in patients with MCI Aβ+ and AD Aβ+. Using a subsample from the same database, ADNI, we analyzed rs-fMRI data from individuals at preclinical and clinical stages of AD (Study IV). By dividing the time series into different time windows, we built temporal multilayer networks and studied the modular organization across time. We were able to capture the dynamic changes across different AD stages using this temporal multilayer network approach, obtaining outstanding areas under the curve of 0.90, 0.92 and 0.99 in the distinction of controls from preclinical, prodromal, and clinical AD stages, respectively, on top and beyond common risk factors. Our results not only improved the discrimination between various disease stages but, importantly, they also showed that dynamic multilayer functional measures are associated with memory and global cognition in addition to amyloid and tau load derived from positron emission tomography. These results highlight the potential of dynamic multilayer functional connectivity measures as functional biomarkers of AD progression. In Study V, we used in-depth quantitative proteomics to compare post-mortem brains from three key brain regions (prefrontal cortex, cingulate cortex, and the parietal cortex) directly related to the disease mechanisms of AD, Parkinson’s disease with dementia (PDD), dementia with Lewy bodies (DLB) in prospectively followed patients and older adults without dementia. We used covariance weighted networks to find modules of protein sets to further understand altered pathways in these dementias and their implications for prognostic and diagnostic purposes. In conclusion, this thesis explored the complex world of brain networks and offered insightful information about how age, sex, and AD influence these networks. We have improved our understanding of how the brain is organized in different imaging modalities and different time scales, as well as developing software tools to make this methodology available to more researchers. Additionally, we assessed the connections among various proteins in different areas of the brain in relation to health, Alzheimer's disease, and Lewy body dementias. This work contributes to the collective effort of unraveling the mysteries of the human brain organization and offers a foundation for future research to understand brain networks in health and disease