EXplainable Artificial Intelligence: enabling AI in neurosciences and beyond

The adoption of AI models in medicine and neurosciences has the potential to play a significant role not only in bringing scientific advancements but also in clinical decision-making. However, concerns mounts due to the eventual biases AI could have which could result in far-reaching consequences particularly in a critical field like biomedicine. It is challenging to achieve usable intelligence because not only it is fundamental to learn from prior data, extract knowledge and guarantee generalization capabilities, but also to disentangle the underlying explanatory factors in order to deeply understand the variables leading to the final decisions. There hence has been a call for approaches to open the AI `black box' to increase trust and reliability on the decision-making capabilities of AI algorithms. Such approaches are commonly referred to as XAI and are starting to be applied in medical fields even if not yet fully exploited. With this thesis we aim at contributing to enabling the use of AI in medicine and neurosciences by taking two fundamental steps: (i) practically pervade AI models with XAI (ii) Strongly validate XAI models. The first step was achieved on one hand by focusing on XAI taxonomy and proposing some guidelines specific for the AI and XAI applications in the neuroscience domain. On the other hand, we faced concrete issues proposing XAI solutions to decode the brain modulations in neurodegeneration relying on the morphological, microstructural and functional changes occurring at different disease stages as well as their connections with the genotype substrate. The second step was as well achieved by firstly defining four attributes related to XAI validation, namely stability, consistency, understandability and plausibility. Each attribute refers to a different aspect of XAI ranging from the assessment of explanations stability across different XAI methods, or highly collinear inputs, to the alignment of the obtained explanations with the state-of-the-art literature. We then proposed different validation techniques aiming at practically fulfilling such requirements. With this thesis, we contributed to the advancement of the research into XAI aiming at increasing awareness and critical use of AI methods opening the way to real-life applications enabling the development of personalized medicine and treatment by taking a data-driven and objective approach to healthcare

Imaging genetics : Methodological approaches to overcoming high dimensional barriers

Imaging genetics is still a quite novel area of research which attempts to discover how genetic factors affect brain structures and functions. In this thesis, using a various methodological approaches I showed how it can contribute to our understanding of the complex genetic architecture of the human brain

A Journey full of Pain through the General Population

Chronic musculoskeletal pain is a common disabling condition with a great impact on daily functioning. In the Netherlands, 19% of all individuals aged 21 years and older experience chronic pain and in elderly this is more than half. This means that more than 2 million Dutch people experience pain on a daily basis, which is a higher prevalence than most common diseases like diabetes and coronary heart disease. A wide variety of risk factors have been described for the development of chronic pain and pain sensitivity. The overall objective of this thesis was to identify and characterize causal and consequential determinants of chronic musculoskeletal pain and pain sensitivity in the general population. Therefore, we used the Rotterdam Study, a large prospective population based study including more than 15,000 participants, which studies determinants of diseases of the elderly. In this thesis, the genetic background of the heat pain threshold and the determinants which influence temperature sensitivity and the heat pain threshold are described. Also, the influence of sex hormones on the development of chronic pain is presented. In addition, using brain MRI, the differences in brain structure in individuals with chronic pain is studied. Finally, gait analysis was used to differentiate between joint pain caused by osteoarthritis and joint pain caused by other conditions

How complex analyses of large multidimensional datasets advance psychology – examples from large-scale studies on behavior, brain imaging, and genetics

Psychology investigates the interplay of human mind, body, and its environment in health and disease. Fully understanding these complex interrelations requires comprehensive analyses across multiple modalities and multidimensional datasets. Large-scale analyses on complex datasets are the exception rather than the rule in current psychological research. At the same time, large and complex datasets are becoming increasingly available. This thesis points out benefits, challenges and adequate approaches for analyzing complex multidimensional datasets in psychology. We applied these approaches and analysis strategies in two studies. In the first publication, we reduced the dimensionality of brain activation during a working memory task based on data from a very large sample. We observed that a mainly parietally-centered brain network was associated with working memory performance and global measures of white matter integrity. In the second publication, we exhaustively assessed pairwise interaction effects of genetic markers onto epigenetic modifications of the genome. Such modifications are complex traits that can be influenced by the environment and in turn affect development and behavior. The lack of observed strong interaction effects in our study suggested that focusing on additive effects is a suitable approach for investigating the link between genetic markers and epigenetic modifications. Both studies demonstrate how psychological scientists can exploit large complex datasets by applying adequate research practices and methodologies. Further adopting these approaches will prepare psychological research for harnessing large and complex datasets, leading towards a better understanding of mental health and disease

Reading the biomineralized book of life: expanding otolith biogeochemical research and applications for fisheries and ecosystem-based management

Chemical analysis of calcified structures continues to flourish, as analytical and technological advances enable researchers to tap into trace elements and isotopes taken up in otoliths and other archival tissues at ever greater resolution. Increasingly, these tracers are applied to refine age estimation and interpretation, and to chronicle responses to environmental stressors, linking these to ecological, physiological, and life-history processes. Here, we review emerging approaches and innovative research directions in otolith chemistry, as well as in the chemistry of other archival tissues, outlining their value for fisheries and ecosystem-based management, turning the spotlight on areas where such biomarkers can support decision making. We summarise recent milestones and the challenges that lie ahead to using otoliths and archival tissues as biomarkers, grouped into seven, rapidly expanding and application-oriented research areas that apply chemical analysis in a variety of contexts, namely: (1) supporting fish age estimation; (2) evaluating environmental stress, ecophysiology and individual performance; (3) confirming seafood provenance; (4) resolving connectivity and movement pathways; (5) characterising food webs and trophic interactions; (6) reconstructing reproductive life histories; and (7) tracing stock enhancement efforts. Emerging research directions that apply hard part chemistry to combat seafood fraud, quantify past food webs, as well as to reconcile growth, movement, thermal, metabolic, stress and reproductive life-histories provide opportunities to examine how harvesting and global change impact fish health and fisheries productivity. Ultimately, improved appreciation of the many practical benefits of archival tissue chemistry to fisheries and ecosystem-based management will support their increased implementation into routine monitoring.[GRAPHICS]

Mapping genome-wide neuropsychiatric mutation effects on functional brain connectivity : c opy number variants delineate dimensions contributing to autism and schizophrenia

Les recherches menées pour comprendre les troubles du spectre autistique (TSA) et la schizophrénie (SZ) ont communément utilisé une approche dite descendante, partant du diagnostic clinique pour investiguer des phénotypes intermédiaires cérébraux ainsi que des variations génétiques associées. Des études transdiagnostiques récentes ont remis en question ces frontières nosologiques, et suggèrent des mécanismes étiologiques imbriqués. L’approche montante propose de composer des groupes de porteurs d’un même variant génétique afin d’investiguer leur contribution aux conditions neuropsychiatriques (NPs) associées. Les variations du nombre de copies (CNV, perte ou gain d’un fragment d’ADN) figurent parmi les facteurs biologiques les plus associés aux NPs, et sont dès lors des candidats particulièrement appropriés. Les CNVs induisant un risque pour des conditions similaires, nous posons l’hypothèse que des classes entières de CNVs convergent sur des dimensions d’altérations cérébrales qui contribuent aux NPs. L’imagerie fonctionnelle au repos (rs-fMRI) s’est révélée un outil prometteur en psychiatrie, mais presqu’aucune étude n’a été menée pour comprendre l’impact des CNVs sur la connectivité fonctionnelle cérébrale (FC). Nos objectifs étaient de: 1) Caractériser l’effet des CNVs sur la FC; 2) Rechercher la présence des motifs conférés par ces signatures biologiques dans des conditions idiopathiques; 3) Tester si la suppression de gènes intolérants à l’haploinsuffisance réorganise la FC de manière indépendante à leur localisation dans le génome. Nous avons agrégé des données de rs-fMRI chez: 502 porteurs de 8 CNVs associées aux NPs (CNVs-NP), de 4 CNVs sans association établie, ainsi que de porteurs de CNVs-NPs éparses; 756 sujets ayant un diagnostic de TSA, de SZ, ou de trouble déficitaire de l’attention/hyperactivité (TDAH), et 5377 contrôles. Les analyses du connectome entier ont montré un effet de dosage génique positif pour les CNVs 22q11.2 et 1q21.1, et négatif pour le 16p11.2. La taille de l’effet des CNVs sur la FC était corrélée au niveau de risque psychiatrique conféré par le CNV. En accord avec leurs effets sur la cognition, l’effet des délétions sur la FC était plus élevé que celui des duplications. Nous avons identifié des similarités entre les motifs cérébraux conférés par les CNVs-NP, et l’architecture fonctionnelle des individus avec NPs. Le niveau de similarité était associé à la sévérité du CNV, et était plus fort avec la SZ et les TSA qu’avec les TDAH. La comparaison des motifs conférés par les délétions les plus sévères (16p11.2, 22q11.2) à l’échelle fonctionnelle, et d’expression génique, nous a confirmé l’existence présumée de relation entre les mutations elles-mêmes. À l’aide d’une mesure d’intolérance aux mutations (pLI), nous avons pu inclure tous les porteurs de CNVs disponibles, et ainsi identifier un profil d’haploinsuffisance impliquant le thalamus, le cortex antérieur cingulaire, et le réseau somato-moteur, associé à une diminution de mesure d’intelligence générale. Enfin, une analyse d’exploration factorielle nous a permis de confirmer la contribution de ces régions cérébrales à 3 composantes latentes partagées entre les CNVs et les NPs. Nos résultats ouvrent de nouvelles perspectives dans la compréhension des mécanismes polygéniques à l’oeuvre dans les maladies mentales, ainsi que des effets pléiotropiques des CNVs.Research on Autism Spectrum Disorder (ASD) and schizophrenia (SZ) has mainly adopted a ‘top-down’ approach, starting from psychiatric diagnosis, and moving to intermediate brain phenotypes and underlying genetic factors. Recent cross-disorder studies have raised questions about diagnostic boundaries and pleiotropic mechanisms. By contrast, the recruitment of groups based on the presence of a genetic risk factor allows for the investigation of molecular pathways related to a particular risk for neuropsychiatric conditions (NPs). Copy number variants (CNVs, loss or gain of a DNA segment), which confer high risk for NPs are natural candidates to conduct such bottom-up approaches. Because CNVs have a similar range of adverse effects on NPs, we hypothesized that entire classes of CNVs may converge upon shared connectivity dimensions contributing to mental illness. Resting-state functional MRI (rs-fMRI) studies have provided critical insight into the architecture of brain networks involved in NPs, but so far only a few studies have investigated networks modulated by CNVs. We aimed at 1) Delineating the effects of neuropsychiatric variants on functional connectivity (FC), 2) Investigating whether the alterations associated with CNVs are also found among idiopathic psychiatric populations, 3) Testing whether deletions reorganize FC along general dimensions, irrespective of their localization in the genome. We gathered rsfMRI data on 502 carriers of eight NP-CNVs (high-risk), four CNVs without prior association to NPs as well as carriers of eight scarcer NP-CNVs. We also analyzed 756 subjects with idiopathic ASD, SZ, and attention deficit hyperactivity disorder (ADHD), and 5,377 controls. Connectome-wide analyses showed a positive gene dosage effect for the 22q11.2 and 1q21.1 CNVs, and a negative association for the 16p11.2 CNV. The effect size of CNVs on relative FC (mean-connectivity adjusted) was correlated with the known level of NP-risk conferred by CNVs. Consistent with results on cognition, we also reported that deletions had a larger effect size on FC than duplications. We identified similarities between high-risk CNV profiles and the connectivity architecture of individuals with NPs. The level of similarity was associated with mutation severity and was strongest in SZ, followed by ASD, and ADHD. The similarity was driven by the thalamus, and the posterior cingulate cortex, previously identified as hubs in transdiagnostic psychiatric studies. These results raised questions about shared mechanisms across CNVs. By comparing deletions at the 16p11.2 and 22q11.2 loci, we identified similarities at the connectivity, and at the gene expression level. We extended this work by pooling all deletions available for analysis. We asked if connectivity alterations were associated with the severity of deletions scored using pLI, a measure of intolerance to haploinsufficiency. The haploinsufficiency profile involved the thalamus, anterior cingulate cortex, and somatomotor network and was correlated with lower general intelligence and higher autism severity scores in 3 unselected and disease cohorts. An exploratory factor analysis confirmed the contribution of these regions to three latent components shared across CNVs and NPs. Our results open new avenues for understanding polygenicity in psychiatric conditions, and the pleiotropic effect of CNVs on cognition and on risk for neuropsychiatric disorders

Lifestyle, cognitive aging, and brain correlates

Inter-individual differences in level and rate of cognitive decline typically seen in aging have been linked to inter-individual differences in lifestyle factors such as leisure activities, including physical activity. The general aim of this thesis was to further our understanding of how and why leisure activity engagement is related to aging-related changes in cognitive performance. Specifically, we sought to (a) identify lifestyle components that are associated with late-life cognitive performance, (b) identify brain correlates of these lifestyle components that are also relevant for cognitive performance, and (c) explore the relative importance of lifestyle- and health-related factors for predicting cognitive change, as well as interactive effects among these factors. In Study I and II, we investigated associations between 3-year changes in leisure activities and concurrent changes in cognitive performance and white matter microstructure in 563 (Study I) and 442 (Study II) participants aged 81 years and older. Study I documented changes in white matter microstructure in the corticospinal (CS) tract to be associated with changes in perceptual speed. In Study II, we observed that concurrent change in frequency of engagement in social activities (e.g. going out to eat in a restaurant, going to the movies, concerts, or the theater) was related to change in both white matter microstructure in the CS tract and in perceptual speed. Change in white matter microstructure in the CS tract statistically accounted for the association between changes in frequency of social leisure activities and perceptual speed. In Study III, we turned to D2/3 dopamine receptor (D2/3DR) availability as a potential brain correlate of lifestyle and cognition in aging. We investigated D2/3DR availability, cognitive performance, and physical activity in 178 healthy adults aged 64-67 years. Participants completed tests of working memory, episodic memory, and processing speed, and a leisure activity questionnaire. Subjective intensity, but not frequency, across the activities each individual performed was associated with D2/3DR availability in caudate nucleus as well as with episodic and working memory. Episodic memory was also related to D2/3DR availability in the caudate, forming a correlative triad with physical activity intensity and caudate D2/3DR availability. In Study IV, we applied a new data-mining technique called structural equation modelling trees and forests to investigate the relative importance of leisure activity engagement, physical activity, and other age- and health-related factors in predicting subsequent 6-year change in perceptual speed in 1046 participants aged 60 years and older. With regard to variable importance, a measure that subsumes main effects and interactions among predictors, frequency of leisure activities was not unimportant, although less important than age, retirement status, walking speed, and multimorbidity. Conceivably, the association between leisure activity engagement and subsequent cognitive decline is conditional upon age- and health-related factors included in the current analyses. Regarding aim (a), identifying lifestyle components related to cognitive aging, we identified change in social activities to be related to change in perceptual speed (Study II). We also found that subjective intensity, but not frequency, of physical activity was related to episodic and working memory (Study III). Regarding the relative importance of frequency of leisure activity engagement as a predictor of change in cognition, we observed some importance of all types of activities, except for physical activity, in predicting change in perceptual speed (Study IV). Concerning aim (b), identifying brain correlates of lifestyle components and cognitive performance, we observed white matter microstructural changes to be related to changes in both leisure activity and perceptual speed (Study II), and D2/3DR availability (Study III) to be related to both subjective physical activity intensity and episodic memory. Regarding aim (c), exploring the relative importance of lifestyle components as predictors of subsequent cognitive decline (Study IV), we found rather small effects of the lifestyle components investigated in Studies II and III, but still found leisure activities to be informative as predictors when using a data-mining approach that takes interactive effects with other predictors into account. The studies in this thesis contribute with new data on associations between lifestyle and cognitive aging, and on brain measures correlated with these two factors. Specifically, we are the first to show parallel changes in leisure activity, white matter microstructure, and perceptual speed. We are also the first to observe an association between physical activity intensity and D2/3DR availability. In sum, the present results indicate that engaging in social activities in very late life and physical activity intensity around retirement age are related to cognitive performance and associated brain parameters. Although the issue of causal directionality remains unresolved, leisure activities are correlates and informative predictors of cognitive decline

White matter connectivity, cognition, symptoms and genetic risk factors in Schizophrenia

Schizophrenia is a highly heritable complex neuropsychiatric disorder with a lifetime prevalence of around 1%. It is often characterised by impaired white matter structural dysconnectivity. In vivo and post-mortem alterations in white matter microstructure have been reported, along with differences in the topology of the structural connectome; overall these suggest a reduced communication between distal brain regions. Schizophrenia is characterised by persistent cognitive impairments that predate the occurrence of symptoms and have been shown to have a neural foundation reflecting aberrant brain connectivity. So far, 179 independent genome-wide significant single nucleotide polymorphisms (SNPs) have been associated with a diagnosis of schizophrenia. The high heritability and polygenicity of schizophrenia, white matter parameters and cognitive functions provides a great opportunity to investigate the potential relationships between them due to the genetic overlap shared among these factors. This work investigates the psychopathology of schizophrenia from a neurobiological, psychological and genetic perspective. The datasets used here include data from the Scottish Family Mental Health (SFMH) study, the Lothian Birth Cohort 1936 (LBC1936) and UK Biobank. The main goal of this thesis was to study white matter microstructure in schizophrenia using diffusion MRI (dMRI) data. Our first aim was to examine whether processing speed mediated the association between white matter structure and general intelligence in patients diagnosed with schizophrenia in the SFMH study. Secondly, we investigated specific networks from the structural connectome and their topological properties in both healthy controls and patients diagnosed with schizophrenia in the SFMH study. These networks were studied alongside cognition, clinical symptoms and polygenic risk factor for schizophrenia (szPGRS). The third aim of this thesis was to study the effects of szPGRS on the longitudinal trajectories of white matter connectivity (measured using tractography and graph theory metrics) in the LBC1936 over a period of three-years. Finally, we derived the salience network which has been previously associated with schizophrenia and examined the effect of szPGRS on the grey matter nodes associated with this network and their connecting white matter tracts in UK Biobank. With regards to the first aim, we found that processing speed significantly mediates the association between a general factor of white matter structure and general intelligence in schizophrenia. These results suggest that, as in healthy controls, processing speed acts as a key cognitive resource facilitating higher order cognition by allowing multiple cognitive processes to be simultaneously available. Secondly, we found that several graph theory metrics were significantly impaired in patients diagnosed with schizophrenia compared with healthy controls. Moreover, these metrics were significantly associated with intelligence. There was a strong tendency towards significance for a correlation between intelligence and szPGRS that was significantly mediated by graph theory metrics in both healthy controls and schizophrenia patients of the SFMH study. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. In the LBC1936 we found that higher szPGRS showed significant associations with longitudinal increases in MD in several white matter tracts. Significant declines over time were observed in graph theory metrics. Overall these findings suggest that szPGRS confer risk for ageing-related degradation of some aspects of structural connectivity. Moreover, we found significant associations between higher szPGRS and decreases in cortical thickness, in particular, in a latent factor for cortical thickness of the salience network. Taken together, our findings suggest that white matter connectivity plays a significant role in the disorder and its psychopathology. The computation of the structural connectome has improved our understanding of the topological characteristics of the brain’s networks in schizophrenia and how it relates to the microstructural level. In particular, the data suggests that white matter structure provides a neuroanatomical substrate for cognition and that structural connectivity mediates the relationship between szPGRS and intelligence. Additionally, these results suggest that szPGRS may have a role in age-related changes in brain structural connectivity, even among individuals who are not diagnosed with schizophrenia. Further work will be required to validate these results and will hopefully examine additional risk factors and biomarkers, with the ultimate aims of improving scientific knowledge about schizophrenia and conceivably of improving clinical practice