Association between structural connectivity and generalized cognitive spectrum in alzheimer’s disease

Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer’s disease (AD). Several scores such as Alzheimer’s Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7)

How Acute and Chronic Alcohol Consumption Affects Brain Networks: Insights from Multimodal Neuroimaging

Background— Multimodal imaging combining 2 or more techniques is becoming increasingly important because no single imaging approach has the capacity to elucidate all clinically relevant characteristics of a network. Methods— This review highlights recent advances in multimodal neuroimaging (i.e., combined use and interpretation of data collected through magnetic resonance imaging [MRI], functional MRI, diffusion tensor imaging, positron emission tomography, magnetoencephalography, MR perfusion, and MR spectroscopy methods) that leads to a more comprehensive understanding of how acute and chronic alcohol consumption affect neural networks underlying cognition, emotion, reward processing, and drinking behavior. Results— Several innovative investigators have started utilizing multiple imaging approaches within the same individual to better understand how alcohol influences brain systems, both during intoxication and after years of chronic heavy use. Conclusions— Their findings can help identify mechanism-based therapeutic and pharmacological treatment options, and they may increase the efficacy and cost effectiveness of such treatments by predicting those at greatest risk for relapse

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases

Sparse reduced-rank regression for imaging genetics studies: models and applications

We present a novel statistical technique; the sparse reduced rank regression (sRRR) model which is a strategy for multivariate modelling of high-dimensional imaging responses and genetic predictors. By adopting penalisation techniques, the model is able to enforce sparsity in the regression coefficients, identifying subsets of genetic markers that best explain the variability observed in subsets of the phenotypes. To properly exploit the rich structure present in each of the imaging and genetics domains, we additionally propose the use of several structured penalties within the sRRR model. Using simulation procedures that accurately reflect realistic imaging genetics data, we present detailed evaluations of the sRRR method in comparison with the more traditional univariate linear modelling approach. In all settings considered, we show that sRRR possesses better power to detect the deleterious genetic variants. Moreover, using a simple genetic model, we demonstrate the potential benefits, in terms of statistical power, of carrying out voxel-wise searches as opposed to extracting averages over regions of interest in the brain. Since this entails the use of phenotypic vectors of enormous dimensionality, we suggest the use of a sparse classification model as a de-noising step, prior to the imaging genetics study. Finally, we present the application of a data re-sampling technique within the sRRR model for model selection. Using this approach we are able to rank the genetic markers in order of importance of association to the phenotypes, and similarly rank the phenotypes in order of importance to the genetic markers. In the very end, we illustrate the application perspective of the proposed statistical models in three real imaging genetics datasets and highlight some potential associations

Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: a challenge for developing theranostic biomarkers

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., theranostic biomarker) is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce recent approach for creating a theranostic biomarker, which consists mainly of two parts: (i) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (ii) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.Comment: 46 pages, 5 figure

Assessing early white matter predictors of syntactic abilities in post-stroke aphasia using HARDI-based tractography

La recherche de prédicteurs d’habilités langagières en aphasie post-accident vasculaire cérébral (AVC) basés sur la matière blanche a récemment vu un élan. Cela a été motivé par l’émergence du modèle à double-voie où des faisceaux de matière blanche dorsaux et ventraux jouent un rôle important dans le langage, ainsi que par l’avènement de la tractographie basée sur l’imagerie par résonance magnétique (IRM) de diffusion permettant l’étude in-vivo des faisceaux de matière blanche et de leurs propriétés structurelles. Les caractéristiques structurelles et la charge lésionnelle des faisceaux de matière blanche ont permis de prédire les troubles langagiers dans la phase chronique dans quelques études. Cependant, les prédicteurs aigus de matière blanche des habilités syntaxiques en aphasie post-AVC chronique sont méconnus. L’exploitation de la tractographie dans l’étude des faisceaux langagiers de matière blanche a été limitée par plusieurs défis méthodologiques, dont la difficulté de reconstruire des faisceaux ayant une architecture complexe. Des progrès méthodologiques ont été récemment introduits afin d’adresser ces limites, dont le plus important est la tractographie basée sur l’imagerie à haute résolution angulaire (« HARDI »). Cependant, la fiabilité test-retest de la reconstruction et des propriétés structurelles d’une approche de tractographie HARDI de pointe n’a pas encore été évaluée. Le premier article de cette thèse visait à évaluer la fiabilité test-retest de la reconstruction et des propriétés structurelles (anisotropie fractionnelle, FA; diffusivité moyenne, axiale et radiale, MD, AD, RD; nombre d’orientations de fibres, NuFO; volume du faisceau; longueur moyenne des « streamlines ») de faisceaux langagiers majeurs (arqué, inférieur fronto-occipital, inférieur longitudinal, unciné, AF, IFOF, ILF, UF) obtenus avec une approche de tractographie HARDI de pointe. La majorité des mesures de propriétés structurelles ont montré une bonne ou excellente fiabilité. Ces résultats ont des implications importantes pour l’utilisation d’une telle approche pour l’étude des faisceaux langagiers de matière blanche, car ils renforcent la confiance dans la stabilité des reconstructions et les propriétés structurelles obtenus avec la tractographie HARDI. Le second article de cette thèse visait à déterminer si et quelles propriétés structurelles (FA, AD, volume du faisceau), et la charge lésionnelle, de l’AF et l’UF gauches dans la phase aigüe (≤ 3 jours), obtenus avec l’approche de tractographie HARDI utilisée dans le premier article, prédisent les habilités syntaxiques dans le discours spontané en aphasie post-AVC chronique (≥ 6 mois). Des régressions multiples ascendantes ont révélé que le volume de l’AF prédit la production des verbes, la complexité des phrases et la complexité de la structure argumentale du verbe. Le volume de l’UF a amélioré la prédiction de cette dernière. Ces résultats indiquent que le volume semble être un bon prédicteur précoce des habilités syntaxiques dans le discours spontané en aphasie post-AVC chronique. Mis ensemble, les résultats de cette thèse soulignent l’utilité d’une approche de tractographie HARDI de pointe et son potentiel pour le développement futur de biomarqueurs précoces pouvant améliorer le pronostic de patients ayant une aphasie post-AVC chronique. Cela pourrait promouvoir l’optimisation des soins et le développement de thérapies pour le bienfait des patients et leurs familles.The search for white matter predictors of language abilities in post-stroke aphasia has gained momentum in recent years. This growing interest has been driven by the emergence of the dual-stream framework where dorsal and ventral white matter bundles play an important functional role in language, as well as the advent of diffusion magnetic resonance imaging (MRI)-based tractography which allows the in-vivo investigation of white matter bundles and their structural properties. Structural characteristics, as well as the lesion load, of white matter bundles have been previously found to predict language impairments in the chronic phase. However, little is known about acute white matter predictors of syntactic abilities in chronic post-stroke aphasia. Leveraging tractography to study white matter language bundles has been limited by several methodological challenges, such as the difficulty of reconstructing white matter bundles with a complex fiber architecture. A number of methodological advances have been introduced fairly recently to address these limitations, the most important of which is the advent of tractography based on High Angular Resolution Imaging (HARDI). However, the test-retest reliability of the reconstruction and structural properties of a state-of-the-art HARDI-based tractography pipeline has not been previously assessed. The first article of the present thesis aimed to assess the test-retest reliability of the reconstruction and structural properties (fractional anisotropy, FA; mean, axial, radial diffusivity, MD, AD, RD; number of fiber orientations, NuFO; bundle volume; mean length of streamlines) of major white matter language bundles (arcuate, inferior fronto-occipital, inferior longitudinal, and uncinate fasciculi, AF, IFOF, ILF, UF) obtained using a state-of-the-art HARDI-based tractography pipeline. Most measures of structural properties showed good to excellent test-retest reliability. These findings have important implications for the use of such a pipeline for the study of white matter language bundles, as they increase our confidence that the reconstructions and structural properties obtained from the tractography pipeline are stable and not due to random variations in measurement. The second article of the thesis aimed to determine whether and which structural properties (FA, AD, bundle volume), as well as the lesion load, of the left AF and UF in the acute phase post-stroke (≤ 3 days), obtained with the same state-of-the-art HARDI-based tractography pipeline used in the first article, predict syntactic abilities in connected speech in chronic post-stroke aphasia (≥ 6 months). Forward multiple regressions revealed that the left AF’s volume predicted the percentage of verbs produced, the structural complexity of sentences, as well as verb-argument structure complexity. The left UF’s volume improved the prediction of verbs with a complex argument structure. These findings indicate that the bundle volume may be a good early predictor of syntactic ability in connected speech in chronic post-stroke aphasia. Overall, the findings of this thesis highlight the usefulness of a state-of-the-art HARDI-based tractography approach and its potential for the future development of early biomarkers that could improve the prognosis and personalized care of patients with chronic post-stroke aphasia. This would promote the optimization of patient care and the development of therapies for the benefit of patients and their families

Developmental neurocognitive pathway of psychosis proneness and the impact of cannabis use

Cette thèse fait la promotion d’une nouvelle approche ciblant le risque de psychose qui consiste à identifier les enfants et les jeunes adolescents de la communauté appartenant à différentes trajectoires développementales d’expériences psychotiques. Une identification très précoce du risque de psychose chez des jeunes de la communauté pourrait ainsi diminuer la période où les symptômes cliniques ne sont pas traités, mais aurait également le potentiel de prévenir efficacement l’émergence de symptômes avérés, et ce, si les facteurs de risque sont identifiés. Étant donné que la consommation de cannabis s’avère un important facteur de risque de la psychose et le contexte actuel où les états en sont à réviser leurs politiques de réglementation du cannabis, il s’avère primordial de mieux comprendre comment la consommation peut mener à la psychose chez les individus vulnérables. Tout d’abord, j’ai investigué la séquence temporelle entre la consommation de cannabis et les expériences psychotiques chez une population de 4000 adolescents, suivis pendant 4 ans, au moment de l’adolescence où les deux phénomènes s’initient. Ensuite, j’ai examiné, chez des adolescents suivant une trajectoire de vulnérabilité, le rôle d’un moins bon fonctionnement cognitif ainsi que celui d’une exacerbation des symptômes anxieux et dépressifs comme médiateurs du lien entre cannabis et risque de psychose. Enfin, j’ai investigué la présence de marqueurs neurocognitifs précoces (fonctionnels et structurels) qui seraient associés à l’émergence de symptômes psychotiques chez des adolescents, et exploré si la consommation de cannabis pourrait modérer l’ampleur de ces marqueurs. Les données proviennent de deux cohortes longitudinales suivant des adolescents de la population générale, l’étude Co-Venture (n=4000, âgés de 12 ans, suivis annuellement pendant 4 ans) et l’étude de neuroimagerie IMAGEN (n=2200, âgés de 14 ans, suivis pendant 2 ans), ainsi qu’un sous-échantillon de l’étude Co-Venture ayant complété des mesures de neuroimagerie (n=151, âgés de 12 ans, suivis annuellement pendant 4 ans). Les résultats ont montré que la consommation de cannabis précédait systématiquement l’augmentation des expériences psychotiques, et non l’inverse. Chez les jeunes suivant une trajectoire de vulnérabilité, la relation entre la consommation de cannabis et le risque de psychose était davantage expliquée par une augmentation des symptômes de dépression et d’anxiété. Une réduction du volume de l’hippocampe et de l’amygdale en combinaison avec une hyperactivité de ces mêmes régions en réponse à des expressions neutres étaient tous associés à l’émergence de symptômes psychotiques. Or, la consommation de cannabis n’a pas exacerbé les altérations structurelles observées chez les adolescents rapportant des expériences psychotiques. Ces résultats ont mis en évidence le rôle primordial d’un hyperfonctionnement du système limbique pouvant mener à l’attribution aberrante d’une importance émotionnelle aux stimuli de l’environnement, et ce, chez des adolescents vulnérables. Il semble que le mécanisme par lequel la consommation de cannabis mène à l’émergence de symptômes cliniques passe par son influence sur les symptômes de dépression et d’anxiété ainsi que leurs mécanismes neuronaux sous-jacents d’une hypersensibilité au stress. Enfin, de par ces résultats, cette thèse permet de contribuer au développement de nouvelles interventions visant à réduire la demande de cannabis chez des adolescents vulnérables.Following the worldwide initiative on intervening early in clinical high-risk individuals for psychosis, this thesis promotes a novel approach to identify those at risk for psychosis by studying children and adolescents from the community who report different trajectories of subclinical psychosis symptoms (i.e., psychotic-like experiences) without the confounds of iatrogenic effects such as major social and cognitive impairments. Early identification from this approach may not only reduce harm by shortening the duration of untreated symptoms, but may also have the capacity to prevent the emergence of clinically validated symptoms, particularly if early risk factors can be identified. Considering the long-standing notion that cannabis misuse is an important risk factor for psychosis and that jurisdictions around the world are currently revising their cannabis regulatory policies, there is a need to better understand how cannabis use may lead to psychosis in vulnerable youths. This thesis examined different mechanisms that may explain the complex relationship between cannabis use and psychosis risk. I first explored the temporal sequence between cannabis use and self-reported psychotic-like experiences in a population-based sample of 4000 adolescents, over a 4-year period when both phenomena have their onset. Second, in vulnerable youths, I investigated the role of impaired cognitive functioning as well as increased affective and anxious symptoms as mediators of the cannabis-to-psychosis relationship. And third, I explored the presence of early neurocognitive markers (both functional and structural) associated with the emergence of psychotic symptoms, and how cannabis use moderates these markers. Two longitudinal cohorts from the general population, the Co-Venture Study (n=4000, aged 12 years old, followed annually for 4 years) and the neuroimaging IMAGEN Study (n=2200, aged 14 years old, followed for 2 years), as well as the neuroimaging subsample from the Co-Venture Study (n=151, aged 12 years old, followed annually for 4 years) were used. It was found that an increase in cannabis use always preceded an increase in reported psychotic-like experiences throughout adolescence, but an increase in psychotic-like experiences rarely predicted an increase in cannabis use. Then, in vulnerable adolescents, the cannabis-to-psychosis risk relationship was better explained by increases in depression and anxiety symptoms relative to changes in cognitive functioning. It was demonstrated that reduced hippocampus and amygdala volumes, combined with hyperactivity of the same regions during neutral cues processing were associated with the emergence of psychotic symptoms in young adolescents reporting psychotic-like experiences. However, cannabis use did not exacerbate the structural alterations observed in youths with psychotic-like experiences. These findings have improved our understanding of the relationship between cannabis use and vulnerability to psychosis. They have also highlighted the important role of an impaired limbic network leading to an aberrant emotional salience attribution in vulnerable adolescents. Although cannabis use did not exacerbate brain structural alterations observed in vulnerable youths, it appears that cannabis will more likely interfere with depression and/or anxiety symptoms and their associated brain mechanisms underlying vulnerability to stress in the path towards psychosis risk. This thesis may inform the development of new evidence-based interventions that reduce demand for cannabis among vulnerable youths

ACTIVATED CARBON NANOFIBERS FROM RENEWABLE (LIGNIN) AND WASTE RESOURCES (RECYCLED PET) AND THEIR ADSORPTION CAPACITY OF REFRACTORY SULFUR COMPOUNDS FROM FOSSIL FUELS

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. Dementia diagnosis is difficult since neither anatomical indicator nor functional testing are currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated aging is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal aging (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types