Dissertations / Theses on the topic 'Neuroimaging biomarkers'

Parkinson’s disease (PD) is the neurological disease with the fastest growing rate. 1% of the world population over 60 years have a PD diagnosis. PD presents a complex and heterogenous clinical picture during the disease course, and dementia represents the most severe condition. This thesis investigates the neurobiological mechanisms and cognitive features of PD patients with a severe clinical phenotype – developing cognitive deterioration, reaching dementia condition. The studies included in this dissertation contributed to identifying risk factors, biomarkers, cognitive features, and sources of clinical variability of dementia in Lewy Bodies disorders (LBD) with multiple methodological approaches to neuroimaging data. Moreover, the cognitive picture of the LBD clinical spectrum has been explored by combining cross-sectional and longitudinal approaches. This dissertation provides new evidence on modifiable and non-modifiable risk factors that influence the development of severe phenotypes within LBD and those acting on the timing of dementia symptoms onset. Moreover, we identify valuable biomarker and cognitive marker candidates for dementia risk profiling since early preclinical stages.<br>La malattia di Parkinson è la malattia neurologica con il tasso di crescita più rapida. L'1% della popolazione mondiale oltre i 60 anni ha una diagnosi di Parkinson. Il morbo di Parkinson presenta un quadro clinico complesso ed eterogeneo durante il decorso della malattia, di cui la demenza rappresenta la condizione più grave. Questa tesi indaga i meccanismi neurobiologici e le caratteristiche cognitive dei pazienti affetti da Malattia di Parkinson con un grave fenotipo clinico – che sviluppano un deterioramento cognitivo raggiungendo la condizione di demenza. Gli studi inclusi in questo elaborato hanno contribuito a identificare fattori di rischio, biomarcatori, caratteristiche cognitive e fonti di variabilità clinica della demenza nei disturbi a corpi di Lewy (LBD) con molteplici approcci metodologici ai dati di neuroimaging. Inoltre, è stato esplorato il quadro cognitivo dello spettro clinico LBD combinando approcci cross-sectional e longitudinali. Questa tesi fornisce nuove evidenze sui fattori di rischio modificabili e non modificabili che influenzano lo sviluppo di fenotipi gravi all'interno della LBD e sui fattori che agiscono sui tempi di insorgenza dei sintomi della demenza. Identifica inoltre validi candidati biomarcatori e marcatori cognitivi per la profilazione del rischio di demenza sin dalle fasi precliniche.

Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease of the central nervous system (CNS) that is associated with progressive neurodegeneration. To better understand the dynamics of disease progression in individuals with MS, and to personalise treatment strategies, the development of quantitative in-vivo biomarkers is critical. Magnetic resonance imaging (MRI) is an essential technique that has been successfully embedded in the formal diagnostic criteria for MS since 2001. Conventional MRI techniques facilitate the demonstration of lesion dissemination in both space and time. Furthermore, conventional MRI metrics derived from quantitative analysis can predict disability progression in clinical trials. However, these imaging metrics are often criticised for their weak correlations with clinical outcomes at the individual level; and lack of specificity for the underlying pathological process(es). Despite successes in large group studies, the transition of quantitative neuroimaging to clinical practice as a tool for both monitoring disease progression and guiding therapeutic strategy has progressed slowly. In this thesis, a refined analysis framework that improves the specificity and clinical validity of MRI metrics, is described and evaluated. Specifically, multi-modal approaches that integrate advanced diffusion imaging and conventional structural metrics are investigated; and a potential composite biomarker of MS disease progression is proposed.

La schizophrénie est un trouble mental, chronique et invalidant caractérisé par divers symptômes tels que des hallucinations, des épisodes délirants ainsi que des déficiences dans les fonctions cognitives. Au fil des ans, l'Imagerie par Résonance Magnétique (IRM) a été de plus en plus utilisée pour mieux comprendre les anomalies structurelles et fonctionnelles inhérentes à ce trouble. Les progrès récents en apprentissage automatique et l'apparition de larges bases de données ouvrent maintenant la voie vers la découverte de biomarqueurs pour le diagnostic/ pronostic assisté par ordinateur. Compte tenu des limitations des algorithmes actuels à produire des signatures prédictives stables et interprétables, nous avons prolongé les approches classiques de régularisation avec des contraintes structurelles provenant de la structure spatiale du cerveau afin de: forcer la solution à adhérer aux hypothèses biologiques, produisant des solutions interprétables et plausibles. De telles contraintes structurelles ont été utilisées pour d'abord identifier une signature neuroanatomique de la schizophrénie et ensuite une signature fonctionnelle des hallucinations chez les patients atteints de schizophrénie<br>Schizophrenia is a disabling chronic mental disorder characterized by various symptoms such as hallucinations, delusions as well as impairments in high-order cognitive functions. Over the years, Magnetic Resonance Imaging (MRI) has been increasingly used to gain insights on the structural and functional abnormalities inherent to the disorder. Recent progress in machine learning together with the availability of large datasets now pave the way to capture complex relationships to make inferences at an individual level in the perspective of computer-aided diagnosis/prognosis or biomarkers discovery. Given the limitations of state-of-the-art sparse algorithms to produce stable and interpretable predictive signatures, we have pushed forward the regularization approaches extending classical algorithms with structural constraints issued from the known biological structure (spatial structure of the brain) in order to force the solution to adhere to biological priors, producing more plausible interpretable solutions. Such structured sparsity constraints have been leveraged to identify first, a neuroanatomical signature of schizophrenia and second a neuroimaging functional signature of hallucinations in patients with schizophrenia. Additionally, we also extended the popular PCA (Principal Component Analysis) with spatial regularization to identify interpretable patterns of the neuroimaging variability in either functional or anatomical meshes of the cortical surface

My thesis consisted of two studies. The first study was a part of a wider study; within this, we investigated the modulation of amygdala structure by the val66met BDNF (Brain Derived Neurotrophic Factor) polymorphism. Structural Magnetic Resonance Imaging (MRI) scans were obtained at 1.5 T in 87 Major Depressive Disorder (MDD) patients and 74 age, gender, and verbal IQ matched healthy controls. We used Freesurfer version 5.1.0 to examine the grey matter amygdala volume. In the second study, we investigated neuroendocrine abnormalities˗˗ Hypothalamus-Pituitary-Adrenal Axis (HPA) axis changes˗˗ in MDD and their relation to early life stress (ELS). In total 112 subjects took part, consisting of MDD patients with (n=28) and without (n=15) a history of ELS and healthy controls with (n=26) and without (n=43) a history of ELS. The cortisol awakening response (CAR) was used as an index of HPA axis activity. In both studies, the data were analyzed using Statistical Package for Social Science (SPSS version 22). In the first study, we did not find any modulatory effect of the val66met polymorphism on the grey matter of right and left amygdala volumes. In the second study, we showed that the CAR was most elevated in those who were both depressed and had a history of ELS, which supports the argument that the effects of early life stress and MDD on the HPA axis may be additive.

Les maladies neurodégénératives figurent parmi les principales causes de mortalité dans le monde. Malheureusement, leur diagnostic précoce nécessite un examen médical prescrit souvent trop tardivement et des équipements de laboratoire dédiés. Il repose aussi fréquemment sur des mesures prédictives souffrant d'un biais de sélection. Cette thèse présente une solution prometteuse à ces problèmes: une méthode robuste, directement utilisable en clinique, pour construire des biomarqueurs prédictifs à partir des signaux cérébraux M/EEG, validés contre les troubles neurocognitifs apparaissant après une anesthésie générale. Dans une première contribution (théorique), nous avons évalué des modèles de régression capables d'apprendre des biomarqueurs à partir des matrices de covariance de signaux M/EEG. Notre analyse mathématique a identifié différents modèles garantissant une prédiction parfaite dans des circonstances idéales, lorsque la cible est une fonction (log-)linéaire en la puissance des sources cérébrales. Ces modèles, basés sur les approches mathématiques de filtrage spatial supervisé et de géométrie riemannienne, permettent une prédiction optimale sans nécessiter une coûteuse localisation des sources. Nos simulations confirment cette analyse mathématique et suggèrent que ces algorithmes de régression sont robustes à travers les mécanismes de génération de données et les violations de modèles. Cette étude suggère que les méthodes riemanniennes sont des méthodes de choix pour l'analyse automatisée à grande échelle des données M/EEG en l'absence d'IRM, condition importante pour pouvoir développer des biomarqueurs cliniques. Dans une deuxième contribution (empirique), nous avons validé nos modèles prédictifs sur plusieurs ensembles de données de neuro-imagerie et avons montré qu'ils peuvent être utilisé pour apprendre l'âge du cerveau à partir de signaux cérébraux M/EEG, sans localisation de sources, et avec un prétraitement minimal des données. De plus, la performance de notre méthode riemannienne est proche de celle des méthodes de référence nécessitant une localisation de sources et donc un traitement manuel des données, la disponibilité d'images IRM anatomiques et une expertise en modélisation de sources M/EEG. Une analyse empirique à grande échelle a ensuite permis de démontrer que l'âge du cerveau dérivé de la MEG capture des informations uniques liées à l'activité neuronale et non expliquées par l'IRM anatomique. Conformément aux simulations, ces résultats suggèrent également que l'approche riemannienne est une méthode pouvant s'appliquer dans un large éventail de situations, avec une robustesse considérable aux différents choix de prétraitement, y compris minimaliste. Les bonnes performances obtenues avec la MEG ont ensuite été répliquées avec des EEGs de qualité recherche. Dans une troisième contribution (clinique), nous avons validé le concept d'âge cérébral directement au bloc opératoire de l'hôpital Lariboisière à Paris, à partir d'EEG de qualité clinique recueillis pendant la période de l'anesthésie générale. Nous avons évalué notre mesure de l'âge cérébral comme prédicteur de complications peropératoires liées aux dysfonctions cognitives post opération, validant ainsi l'âge du cerveau comme un biomarqueur clinique prometteur des troubles neurocognitifs. Nous avons également montré que le sédatif utilisé a un impact important sur la prédiction de l'âge du cerveau et avons démontré la robustesse de notre approche à différents types de médicaments. Combinant des concepts précédemment étudiés séparément, notre contribution démontre la pertinence clinique de la notion d'âge du cerveau prédit à partir de l'EEG pour révéler les pathologies des fonctions cérébrales dans des situations où l'IRM ne peut pas être réalisée. Ces résultats fournissent également une première preuve que l'anesthésie générale est une période propice à la découverte de biomarqueurs cérébraux, avec un impact potentiel profond sur la médecine préventive<br>Neurodegenerative diseases are among the top causes of worldwide mortality. Unfortunately, early diagnosis is challenging as it requires a frequently too late indication of biomedical exam and dedicated laboratory equipments. It also often relies on research-based predictive measures suffering from selection bias. This thesis investigates a promising solution to tackle these problems : a robust method to build predictive biological markers from M/EEG brain signals, directly usable in the clinic, and validated against neurocognitive disorders following general anaesthesia. In a first (theoretical) contribution, we benchmarked M/EEG regression models that could learn from between-channels covariance matrices as a compact summary of spatial distribution of power of high-dimensional brain M/EEG signal. Mathematical analysis identified different models supporting perfect prediction under ideal circumstances when the outcome is either linear or log-linear in the source power. These models are based on the mathematically principled approaches of supervised spatial filtering and projection with Riemannian geometry, and enjoy optimal prediction guarantees without the need of costly source localization. Our simulation-based findings were consistent with the mathematical analysis and suggested that these regression algorithms were robust across data generating scenarios and model violations. This study suggested that the Riemannian methods have the potential to support automated large-scale analysis of M/EEG data in the absence of MRI scans, which is one condition to be practically used in the clinic for biomarker development. In a second (empirical) contribution, we validated our predictive modeling framework with several publicly available neuroimaging datasets and showed it can be used to learn the surrogate biomarker of brain age from research-grade M/EEG signals, without source localization and with minimal pre-processing. Our results demonstrate that our Riemannian data-driven method does not fall far behind the gold-standard source localization methods with biophysical priors, that depend on manual data processing, the costly availability of anatomical MRI images and specialized knowledge in M/EEG source modeling. Subsequent large-scale empirical analysis provided evidence that brain age derived from MEG captures unique information related to neuronal activity that was not explained by anatomical MRI. They also suggested that, consistent with simulations, Riemannian methods are generally a good bet across a wide range of settings with considerable robustness to different choices of preprocessing including minimalistic preprocessing. The goodperformance obtained on MEG was also reached with research-grade clinical EEG. In a third (clinical) contribution, we validated the concept of M/EEG-derived brain age directly in the operating rooms of Lariboisiere hospital in Paris, from monitoring-grade clinical EEG during the particular period of general anaesthesia. We validated our EEG-based brain age measure against intra-operative complications and brain health in anaesthesia population with a potential link to postoperative cognitive dysfunctions, unveiling it as a promising clinical biomarker of neurocognitive disorders. We also showed that the drug critically impacts brain age prediction and demonstrated the robustness applicability of our approach across different types of drugs. By combining concepts previously investigated separately, our contribution demonstrates the clinical relevance of EEG-brain-age in revealing pathologies of brain function and obtaining brain health assessments in situations where MRI scans cannot be conducted. It also provides early evidence that GA-based modeling has the potential to help biomarker discovery and eventually revolutionize preventive medicine

INTRODUÇÃO: Acidente vascular cerebral (AVC), depressão e prejuízo cognitivo são comorbidades associadas à alta carga mundial de incapacidade. Depressão e prejuízo cognitivo são condições que ocorrem mais frequentemente em pacientes pós-AVC e estão associadas a um aumento da morbimortalidade. OBJETIVO: Investigar potenciais fatores de risco cerebrovasculares associados ao desenvolvimento de depressão pós-AVC (DPA) e prejuízo cognitivo pós-AVC (PC) com base na neuroimagem por ressonância magnética cerebral (RM) e biomarcadores (serotonina, BDNF, IL-6, IL-18) na fase subaguda (1-3 meses) e na fase crônica (até 2 anos de seguimento) após o AVC em sobreviventes do Estudo de Mortalidade e Morbidade do AVC (EMMA), São Paulo, Brasil. MÉTODOS: Amostra de participantes admitidos por AVC no Departamento de Emergências do HU-USP, de abril de 2006 a novembro de 2014, foi submetida a avaliações clínicas e neurológicas. Os principais instrumentos para avaliação de depressão foram: Entrevista Clínica Estruturada para DSM-4 eixo 1 (SCID-I) e Questionário de Saúde do Paciente - versão 9 itens (PHQ-9); e para comprometimento cognitivo: Entrevista Telefônica Modificada para Estado Cognitivo (TICS-M), aplicados em 1-3 meses, 6 meses e anualmente até 2 anos. Além da quantificaçao da DPA e PC e fatores associados em 1-3 meses (fase subaguda), na fase crônica foram realizadas análises de sobrevida pelas curvas de Kaplan-Meier e modelos de regressão logística de Cox (Razão de Risco - RR, interval de confiança- IC95%) para investigar a progressão de DPA ou PC aos 6 meses e 2 anos, de acordo com lateralidade do AVC. RESULTADOS: Dos 103 pacientes elegíveis, 85,4% apresentaram AVC isquêmico e 73,7% foram diagnosticados como primeiro evento. Na fase subaguda, 27,2% apresentaram PC e 13,6% apresentaram DPA (5,8% com \"primeiro episódio\" e 7,8% com depressão \"recorrente\"). DPA e/ou PC foram associados com baixa escolaridade, gênero feminino e idade entre 55 e 74 anos. Em 1-3 meses pós-AVC, a lesão cerebral do hemisfério esquerdo foi mais frequentemente associada ao aumento do PC do que à lesão à direita (71,4% vs. 28,4%, p = 0,005). A DPA não esteve associada à lateralidade do AVC. No geral, os níveis de biomarcadores não apresentaram alterações nos pacientes XX com DPA e PC. No seguimento até 2 anos, foi encontrada uma frequência de 19% de DPA e 38% de PC. A maioria dos participantes (53%) apresentou AVC no hemisfério direito, entretanto o AVC neste hemisfério não esteve associado com DPA ou PC. Confirmando os dados observados em 1-3 meses pós-evento, o AVC do lado esquerdo foi um preditor independente de PC em longo prazo, mas não de DPA. O AVC esquerdo foi associado a uma alta probabilidade de PC (42,6% e 53,2%, respectivamente, aos 6 meses e 2 anos, p-log-rank: 0,002). A RR de PC por AVC à esquerda foi de 3,38 (IC95%, 1,35-8,50) aos 6 meses e foi mantida aos 2 anos (RR 3,38; IC95%, 1,50-7,59). CONCLUSÕES: PC associou-se a uma menor escolaridade, sexo feminino e faixa etária entre 55 a 74 anos. O AVC no hemisfério esquerdo esteve associado a uma maior frequência de PC, apresentando um risco 3 vezes maior para o desenvolvimento de PC ao longo de 2 anos após o AVC<br>INTRODUCTION: Stroke, depression and cognitive impaiment are comorbidities associated with the high burden of disability worldwide. Depression and cognitive impairment are responsible for increased post-stroke morbidity and mortality. OBJECTIVE: To investigate the cerebrovascular risk factors associated with the development of post-stroke depression (PSD) and post-stroke cognitive impairment (PCI) based on brain magnetic resonance imaging (MRI) and biomarkers (serotonin, BDNF, IL-6, IL-18) in the subacute phase (1-3 months) and in the chronic phase (up to 2 years of follow-up) after stroke in survivors of the Stroke Mortality and Morbidity Study (EMMA), São Paulo, Brazil. METHODS: Stroke participants prospectively admitted at HU-USP Emergency Department from April 2006 to November 2014 underwent clinical and neurological evaluations. The main instruments for evaluation of depression were: Structured Clinical Interview for DSM-4 axis 1 (SCID-I) and Patient Health Questionnaire version 9 items (PHQ-9); and cognitive impairment: Modified Telephone Interview for Cognitive Status (TICS-M), applied in 1-3 months, 6 months and annually up to 2 years. In addition of the quantification of PSD and PCI and associated factors in 1-3 months (subacute phase), survival analyzes were performed on Kaplan-Meier curves and Cox logistic regression models (Hazard Ratio-HR, confidence interval-95% CI) to investigate the progression of PSD or PCI at 6 months and 2 years, according to laterality of the stroke. RESULTS: Of the 103 eligible patients, 85.4% had ischemic stroke and 73.7% had a stroke for the first time. In the subacute phase, 27.2% had PCI and 13.6% had current PSD (5.8% with \"first episode\" and 7.8% with \"recurrent\" depression). PSD and / or PCI were associated with low educational level, female gender and 55-74 years old. In 1-3 months, left-sided stroke was more frequently associated with an increase in PCI than right lesion (71.4% vs. 28.4%, p = 0.005). PSD was not associated with laterality of the stroke. Overall, biomarkers levels did not show changes in patients with PSD and PCI. Up to 2 years follow-up, it was found a frequency of 19% of DPA and 38% of PCI. Most participants (53%) presented right-sided stroke, however, it was not associated with PSD or PCI. Confirming the observed data at 1-3 months post-event, left-sided stroke XXI I was an independent predictor of long-term PCI but not PSD. Left-sided stroke was associated with a high probability of PCI (42.6% and 53.2%, respectively at 6 months and 2 years, p-log rank: 0.002). The risk ratio (RR) of PCI due to left-sided stroke was 3.38 (95% CI, 1.35-8.50) at 6 months and maintained at 2 years (RR 3.38, 95% CI, 1, 50-7.59). CONCLUSIONS: PCI was associated with lower educational level, female gender and age group between 55 and 74 years. Stroke in the left hemisphere was associated with a higher frequency of PCI and the risk of developing cognitive impairment over 2 years after stroke was 3

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder involving progressive motor dysfunction, most commonly affecting elderly individuals. A hallmark of disease pathology is a loss of dopaminergic neurons in the substantia nigra and their terminals in the striatum. Neurodegeneration is thought to lead to a destabiliation of the normal signalling that occurs between these, and other cortical and subcortical brain regions. An important challenge for neuroscience lies in differentiating PD neuropathology from similar disorders, as well as those impairments that occur as part of normal aging. The purpose of this thesis was to apply neuroimaging, specifically magnetic resonance imaging (MRI), to identify and evaluate biomarkers of PD. Biomarkers are objective measures of biological state that can be used to quantify a disease, including the symptoms associated with its appearance and progression. Three standalone studies formed the basis of this work. In the first study, outliers in diffusion weighted MRI (DW-MRI) were probed using simulations and real data analyses, to understand their impact on candidate biomarker metrics. Metrics were found to be highly sensitive to error, and data correction was recommended to minimise bias in further analyses. A q-space interpolation method was proposed for identifying and correcting corruptionaffected data, and a decision support system outlined with which to treat this data. In the second study, candidate biomarkers in DW-MRI and resting state functional MRI (rsfMRI) were evaluated to determine their performance as metrics of structural and functional connectivity. A unique, multimodal paradigm demonstrated that anatomical and functional deficits could be attributed to PD for particular connections relevant to the sensorimotor circuit. In the third study, validation of the candidate biomarkers identified in PD was performed in a group of healthy subjects. Here, the objective was to understand the effects of aging on the same associative, limbic and sensorimotor circuitry connecting cortical and subcortical brain regions. Age-related variations in anatomical connectivity were found to differentially affect certain circuits, and nuances in biomarker metrics were observed for particular brain structures. Interestingly, associative circuit functional connections also appeared strengthened. Overall, this thesis successfully identified and evaluated several new candidate biomarkers for PD, as well as furthered an understanding of brain structure and function as it relates to PD, through the development of new analytical pipelines. In addition, tentative conclusions could be drawn regarding the differentiation of PD dysfunction from brain function in healthy elderly subjects<br>La maladie de Parkinson (MDP) est une maladie neurodégénérative qui atteint le plus souvent les personnes âgées et qui se manifeste par des troubles moteurs qui s’aggravent au cours du temps. L'objectif de cette thèse était d'identifier et d'évaluer des biomarqueurs de la MDP grâce à l'imagerie par résonance magnétique (IRM). Ce travail est constitué de trois différentes études. La première étude visait à mieux comprendre l'influence de la qualité des données acquises sur les mesures de biomarqueurs candidats en IRM pondérée en diffusion (IRM-DW) en utilisant un jeu de données simulées dans un premier temps, puis un jeu de données réelles dans un deuxième temps. Dans la deuxième étude, les patrons de connectivité structurelle et fonctionnelle ont été évalués en IRM-DW et en IRM fonctionnelle au repos (IRMf-rs) pour déterminer leur efficacité en tant que biomarqueurs candidats. Un paradigme multimodal unique a permis d'isoler des déficits anatomiques et fonctionnels qui concernaient des connexions isolées du circuit sensorimoteur particulièrement touchées dans la MDP. Dans la troisième étude, la validation des biomarqueurs candidats identifiés précédemment a été effectuée dans un groupe de volontaires sains. Les changements de connectivité anatomiques liés à l'âge affectaient différemment les trois circuits corticales-souscorticales. Dans l'ensemble, cette thèse a permis d'une part d'identifier et d'évaluer plusieurs nouveaux biomarqueurs dans la MDP, d'autre part d'approfondir notre compréhension du rapport entre les anomalies structurelles et fonctionnelles du cerveau en ce qui concerne la MDP, tout en développant de nouveaux outils analytiques de neuroimagerie

Given the recognition that disease-modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient-level longitudinal data of 672 subjects with early-stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed-effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was -3.16 (90% confidence interval [CI] = -0.96 to -5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.

La stimulation magnétique transcrânienne (TMS) est une technique de stimulation corticale non-invasive.Depuis son apparition au milieu des années 1980, les évolutions technologiques qu’elle a connues ontconsidérablement amélioré sa fiabilité, sa précision ainsi que sa reproductibilité. Ces progrès ont favorisél’émergence d’un grand nombre d’applications, tant dans le domaine de la recherche fondamentale enneurosciences cognitives que dans celui de la recherche clinique. Cette thèse a pour objectif d’étudierles apports méthodologiques et fondamentaux de la TMS robotisée, dernière avancée technologique dudomaine. Grâce à un placement et un suivi automatisés de la bobine de stimulation, la TMS robotiséeouvre en effet la voie à l’automatisation des protocoles, ainsi qu’à l’élaboration de nouvelles approchesen neuroimagerie fonctionnelle. Les deux premières études de ce travail abordent ce premier point, enproposant le développement de deux outils nécessaires à l’automatisation du paramétrage des protocolesde TMS : CortExTool et AutoHS. CortExTool est une boîte à outils qui permet l’analyse automatisée dessignaux électromyographiques évalués durant le paramétrage, et AutoHS un modèle bayésien assurantune recherche automatique du point chaud moteur, étape essentielle de la procédure. Testée sur donnéesvirtuelles et comparée expérimentalement à la pratique manuelle d’experts sur 19 volontaires sains, laprocédure automatisée proposée ici apparaît au moins aussi fiable, tout en étant plus rapide et repro-ductible. La troisième et dernière étude de cette thèse s’attache quant à elle aux apports fondamentauxpossibles de cette technologie. Elle propose un protocole qui permet la cartographie extensive des ré-ponses électroencéphalographiques évoquées par la TMS sur 18 aires corticales réparties sur l’ensembledu néocortex. Appliquée sur 22 volontaires sains, l’analyse des propriétés dynamiques de ces réponses faitapparaître des spécificités régionales ainsi que des réseaux corticaux partageant des propriétés communes.Celles-ci étant liées aux caractéristiques cytoarchitecturales des aires stimulées, nos résultats apportent lapreuve de concept pour la cytoarchitectonie fonctionnelle, qui pourrait aboutir à une nouvelle méthodede parcellisation in vivo du cortex chez l’Homme. L’ensemble des résultats de cette thèse confirme l’intérêtde la robotisation de cette technique, qui pourrait à terme faciliter la mise en œuvre des protocoles par lescentres cliniques, et amener de nouveaux outils d’exploration fonctionnelle pour un meilleur diagnosticdes pathologies psychiatriques et neurologiques<br>Transcranial Magnetic Stimulation (TMS) is a non invasive cortical stimulation tool. Major technologicalevolution has continuously increased the spatial reliability and reproducibility of TMS since its beginningin the middle of the 80’s, by minimizing the influence of human and experimental factors. Therefore, TMSestablished itself as a powerful technique for probing and treating the human brain. The aim of this thesisis to study the methodological and basics contribution of robotized TMS, as being the last technologicaladvance to date. By means of the automatic handling of the TMS coil, robotized TMS opens new avenues forthe automation of stimulation protocol, and to new approaches in functional neuroimaging. The two firststudies of this work aim at developing two tools that are still needed to achieve the automation of set-upprocedures of TMS protocols : CortExTool and AutoHS. CortExTool is a toolbox allowing the automaticanalysis of electromyographic signals, while AutoHS is a Bayesian model aiming at automatically finding themotor hotspot, which are two critical ingredients used during such procedures. We validated our automaticset-up procedure on both virtual and real data, during an experimental comparison against manual set-upprocedures on 19 healthy volunteers. Results showed that the automatic procedure was at least as reliableas the manual one, while being faster and more reproducible. The third and last study of this thesis aimed atexploring new basics approaches offered by this technology. We developed a protocol allowing the extensivemapping of evoked electroencephalographic responses on 18 cortical targets covering the whole neocortex,and tested it on 22 healthy volunteers. The analysis of the dynamical properties of these responses revealedregional specificities as well as cortical networks sharing similar properties. Our results provide the proofof concept of functional cytoarchitectonics, that would guide the parcellation of the human cortex in vivobased on its intrinsic responses to local perturbations. The results of this thesis are promising regarding thenew possibilities offered by robotized TMS. Its use could decrease the experimental variability, facilitatethe handling of TMS protocols used for research and clinical routine, and finally offer new functionalexploration approaches that could allow a better diagnosis of psychiatric and neurological pathologies

L'angiopathie amyloïde cérébrale (AAC) sporadique est une microangiopathie cérébrale, dont l'intérêt auprès des cliniciens et des chercheurs est grandissant. L'AAC est fréquente chez les sujets âgés et constitue une cause majeure et croissante d'hémorragie intracérébrale et de démence. Des avancées importantes ont été réalisées ces dernières années dans ce champ de recherche, permettant d'identifier de nouveaux biomarqueurs de la maladie, grâce aux progrès réalisés en neuroimagerie structurelle, fonctionnelle et moléculaire. Des thérapies anti-amyloïdes sont en cours de développement et des essais cliniques évaluant la meilleure stratégie vis à vis des traitements anticoagulants chez ces patients ont débuté, laissant entrevoir des possibilités thérapeutiques. Ainsi, l'évaluation de ces nouveaux biomarqueurs d'AAC semble particulièrement importante. L'objectif principal de cette thèse était d'apporter de nouvelles connaissances sur les biomarqueurs de neuroimagerie les plus récents et leurs applications potentielles en pratique clinique chez les patients avec AAC. Nous avons pour cela conduit 6 études de recherche clinique explorant des nouveaux biomarqueurs hémorragiques (hémosidérose corticale, hémorragie sous arachnoïdienne de la convexité) et non hémorragiques (espaces périvasculaires dilatés, TEP amyloïde et réseau de connectivité cérébrale) de la maladie. Nous évaluons ces biomarqueurs comme outils diagnostiques et leur pertinence clinique comme facteur pronostique<br>Sporadic cerebral amyloid angiopathy (CAA) is a common cerebral small vessel disease with growing interest among clinicians and researchers. CAA occurs frequently in elderly subjects and is a major and increasing cause of intracerebral hemorrhage and dementia. Over the last several years, important advances have been made in this research field, with the development of new biomarkers for the disease, thanks to advances in structural, functional and molecular neuroimaging. Anti-amyloid therapies are currently in development and clinical trial assessing anticoagulant strategy in these patients are ongoing, raising the perspective of future treatments. Hence, evaluating these new biomarkers of CAA seems particularly important. The main objective of this PhD thesis was to get insights into novel neuroimaging biomarkers and their potential clinical applications in patients with CAA. We conducted 6 clinical research studies exploring new hemorrhagic (cortical superficial siderosis, convexity subarachnoid hemorrhage) and non-hemorrhagic (enlarged perivascular spaces, amyloid PET, brain network connectivity) markers of the disease. Biomarkers are evaluated as diagnostic tools and their clinical relevance, as prognostic markers are investigated

Introduction : Issus des données animales, les modèles neurotrophiniques du mécanisme d’action des médicaments antidépresseurs pourraient permettre d’identifier chez l’Homme des biomarqueurs prédictifs de la réponse et de la rémission sous antidépresseurs. Nous évaluons l’intérêt clinique, chez les patients souffrant de trouble dépressif caractérisé unipolaire, de 11 biomarqueurs : polymorphismes nucléotidiques simples (SNP) du Brain Derived Neurotrophic Factor (BDNF) et de son récepteur, le Récepteur Tyrosine-Kinase B (TRKB), taux plasmatiques de BDNF et volume hippocampique sur la réponse/rémission sous antidépresseurs. Méthode : Les données originales de ce travail sont issues de la cohorte METADAP. Il s’agit d’une cohorte, prospective, multicentrique incluant 624 patients présentant un épisode dépressif caractérisé dans le cadre d’un trouble dépressif caractérisé unipolaire et nécessitant l’introduction d’un traitement antidépresseur. Le traitement antidépresseur est prescrit de façon naturaliste (tous antidépresseurs commercialisés en France). Les patients sont évalués 1, 3 et 6 mois après l’introduction du traitement antidépresseur. Les biomarqueurs étudiés sont les polymorphismes Val66Met du BDNF et 8 SNP du TRKB et les dosages de BDNF plasmatiques. Une étude ancillaire est menée à partir de 63 patients ayant bénéficié d’Imagerie par Résonnance Magnétique cérébrale réalisée en pratique courante à l’inclusion de cette cohorte afin d’évaluer les volumes hippocampiques. Résultats : 1) Une revue de la littérature met en évidence une association entre la réponse aux antidépresseurs et 12 SNP du BDNF/TRKB sur 242 étudiés, ainsi qu’une association entre allèle Met du polymorphisme Val66Met du BDNF et meilleure réponse sous antidépresseurs chez les patients asiatiques. 2) Nos données ne mettent pas en évidence d’impact de 8 SNP du TRKB sur la réponse/rémission après traitement antidépresseur, mais un effet différentiel du Val66Met du BDNF selon la classe de traitement antidépresseur. 3) L’étude des dosages de BDNF plasmatiques n’est pas concluante. 4) Concernant les volumes hippocampiques, notre méta-analyse montre que des volumes hippocampiques moindres prédisent une moindre réponse/rémission après traitement antidépresseur. 5) Concernant les liens entre les biomarqueurs étudiés, nous ne mettons pas en évidence d’association. Conclusion : Sur les 11 biomarqueurs étudiés, seuls 2 pourraient présenter une utilité en pratique clinique. Si nos travaux étaient répliqués, le polymorphisme Val66Met du BDNF et le volume hippocampique pourraient conduire à orienter le choix des antidépresseurs dans le traitement des épisodes dépressifs caractérisés. Malgré une littérature cohérente chez l’Animal, nous n’avons pas mis en évidence, dans l’échantillon étudié, de lien entre les biomarqueurs génétiques étudiés et les volumes hippocampiques. Nous poursuivons ce travail d’évaluation des biomarqueurs neurotrophiniques et neurogéniques avec des méthodes d’évaluations nouvelles : séquençage nouvelle génération pour la génétique et imagerie multimodale (acquisition répétée d’IRM structurelle, fonctionnelle et de diffusion) de l’hippocampe. Nous évaluerons également de nouveaux biomarqueurs<br>Introduction: developed with Animal preclinical approachs, neurtrophinic and neurogenic models of antidepressant mechanism of action lead to identify biomarkers in Human which could be predict antidepressant response and remission in depressed patients. We assess the clinical benefit of 11 biomarkers in depressed patients: Brain Derived Neurotrophic Factor (BDNF) and its receptor Tyrosine Receptor -Kinase B (TRKB), Plasma BDNF and Hippocampal volumes to predict antidepressant response/remission. Methods: The original research data of this work are from METADAP cohort. It is a prospective, multicentric cohort including 624 patients with a diagnosis of major depressive disorder and a current major depressive episode at the start of the index antidepressant treatment. Antidepressant treatment is prescribed in naturalistic conditions (all commercialized antidepressant in France). Patient are assessed 1, 3 and 6 months after the start of antidepressant treatment. Studied biomarkers are BDNF Val66Met polymorphism, 8 TRKB SNP and plasma BDNF. Ancillary study are done with 63 patients which benefit in clinical practice of Magnetic Resonnance (MRI) at the inclusion of the cohort. Results: 1) A review of literature reports associations between antidepressant efficacy and 12 BDNF/TRKB SNP on 242 studied SNP and an association with Met allele of Val66Met BDNF polymorphism and a best antidepressant efficacy in Asian patients. 2) Our original data show no impact of 8 TRKB SNP on antidepressant response remission but a differential effect of Val66Met BDNF polymorphism depending on antidepressant treatment class. 3) Plasma BDNF study is not conclusive. 4) Concerning hippocampal volumes, our meta-analysis show that smaller hippocampal volumes predict lower response/remission rate after antidepressant treatment. 5) No association is found between studied biomarkers. Conclusion: 2 of the 11 studied biomarkers could be useful in clinical practice. After replication of our results, Val66Met polymorphism could lead to personalized antidepressant prescription in major depressive disorder. Although the animal prelinical littérature appar strong, we dont report association between genetic biomarker and hippocampal volume in ours ample. We will assess neurotrophinic and neurogenic biomarkers with new methods: next generation sequencing for genetic, multimodal imaging (repeated structural, functional and diffusion MRI) of hippocampus. We also will assess new biomarkers

Indiana University-Purdue University Indianapolis (IUPUI)<br>The pathway leading from soluble and monomeric to hyperphosphorylated, insoluble and filamentous tau protein is at the center of many human neurodegenerative diseases, collectively referred to as tauopathies, such as Alzheimer disease (AD). In this report, we discuss the role of neuroimaging, genetics, and biomarkers in better understanding the underlying brain changes in tauopathies. In Chapters 1 and 2, we review current knowledge of tauopathies, the protein tau and FDG PET studies in AD. In Chapter 3, we investigate glucose metabolism using [18F]FDG PET in a family with multiple systems tauopathy with presenile dementia (MSTD), a primary tauopathy cause by a mutation in MAPT. The results from this study suggest that mutation carriers have lower [18F]FDG uptake, which may precede clinical onset. In Chapter 4, we assessed brain glucose metabolism using [18F]Fluorodeoxyglucose (FDG) positron emission tomography (PET) in individuals with Gerstmann–Sträussler–Scheinker Disease (GSS) with the PRNP F198S mutation. The results from this study suggest hypometabolism in the cerebellar and striatal regions, which may be preceded by hypermetabolism. This chapter also evaluated if [11C]Pittsburgh Compound B (PiB) PET is capable of detecting PrP-amyloid in GSS in individuals with the PRNP P102L and F198S mutations. The results from this study suggest that [11C]PiB is not suitable for in vivo assessment of PrP amyloid plaques in GSS. In Chapter 5, we examine a correlation between two peripheral markers of axonal degeneration, plasma tau and neurofilament light (NFL), and MRI. The results from this study suggest that plasma NFL may be a more specific marker for neurodegeneration relative to plasma tau. In Chapter 6, we attempted to create a tau biological network from gene and protein databases and literature search. We identified over 150 genes that are related to tau protein or MAPT that are involved in different biological functions. Overall, the results of this report support the notion that using a combination of techniques may help model progression of tau pathology. Future studies may establish additional markers that may be used in combination with some of these measures as tools for diagnosis and for the evaluation of treatment efficacy in therapeutic trials.

Parkinson disease (PD) is the second most common neurodegenerative disorder in the world, directly affecting 2-3% of the population over the age of 65. People diagnosed with the disorder can experience motor, autonomic, cognitive, sensory and neuropsychiatric symptoms that can significantly impact quality of life. Uncertainty still exists about the pathophysiological mechanisms that underlie a range of clinical features of the disorder, linked to structural as well as functional brain changes. This thesis thus aimed to uncover neuroimaging biomarkers associated with clinical dysfunction in PD. A 'hubs-and-spokes' neural circuit-based approach can contribute to this aim, by analysing the component elements and also the interconnections of important brain networks. This thesis focusses on structures within basal ganglia-thalamocortical neuronal circuits that are linked to a range functions impacted in the disorder, and that are vulnerable to the consequences of PD pathology. This thesis investigated neuronal 'hubs' by studying the morphology of the caudate nucleus, putamen, thalamus and neocortex. The caudate nucleus, putamen and thalamus are all vital subcortical 'hubs' that play important roles in a number of functional domains that are compromised in PD. The neocortex, on the other hand, has a range of 'hubs' spread across it, regions of the brain that are crucial for neuronal signalling and communication. The interconnections, or 'spokes', between these hubs and other brain regions were investigated using seed-based resting-state functional connectivity analyses. Finally, a morphological analysis was used to investigate possible structural changes to the corpus callosum, the major inter-hemispheric white matter tract of the brain, crucial to effective higher-order brain processes. This thesis demonstrates that the caudate nucleus, putamen, thalamus, corpus callosum and neocortex are all atrophied in PD participants with dementia. PD participants also demonstrated a significant correlation between volumes of the caudate nuclei and general cognitive functioning and speed, while putamina volumes were correlated with general motor function. Cognitively unimpaired PD participants demonstrated minimal morphological alterations compared to control participants, however they demonstrated significant increases in functional connectivity of the caudate nucleus, putamen and thalamus with areas across the frontal lobe, and decreases in functional connectivity with parietal and cerebellar regions. PD participants with mild cognitive impairment and dementia show decreased functional connectivity of the thalamus with paracingulate and posterior cingulate cortices, respectively. This thesis contributes a deeper understanding of the relationship between structures of basal ganglia-thalamocortical neuronal circuits, corpus callosal and neocortical morphology, and the clinical dysfunction associated with PD. This thesis suggests that functional connectivity changes are more common in early stages of the disorder, while morphological alterations are more pronounced in advanced disease stages.

博士<br>國立中山大學<br>生物科學系研究所<br>100<br>Purpose: Excessive day time sleepiness, sleep disorders and neurobehavior changes are common clinical observations in the patients with epilepsy. From literature review, they were highly related with epilepsy risk characteristics (age of onset, types or numbers of drugs, seizure frequency), co-morbidities or neuronal network changes. The serological biomarkers have been reported to reflect the phenomenon of seizure, while their correlations with neurobehavior changes were still not concluded. There were two purposes of this thesis. (1) To understand the relationship between sleep disturbance with day time performances (2) To understand the relationships between serological biomarkers, neurobehavior performances and neuronal networks in patients with temporal lobe epilepsy. Material and Methods: The study enrolled patients from epilepsy outpatient clinic. By using self-appreciated questionnaire (The Pittsburgh Sleep Quality Index, The Epworth Sleepiness Scale, Euroqol Quality of Life Scale-5D), we collected the characteristics of sleep related behavior and life quality changes and explored the relationship with epilepsy risk characteristics. In patients with temporal lobe epilepsy, we assessed the neurobehavior performances, measured the serological biomarkers (heat shock protein 70, S100βprotein, neuron specific enolase, brain derived neurotrophic factor, plasma and mitochondrial DNA) and brain magnetic resonance imaging. In statistical analysis, we compared the differences with age matched controls or performed correlation analysis among the parameters Result: One hundred and seventeen patients with epilepsy completed the sleep quality questionnaires. The results showed that 20 percent of patients had day time sleepiness, while the sleep disorder was prolong sleep latency and impaired sleep efficiency. In epilepsy characteristics, patients with complex partial seizure, intractable seizure or with multi-pharmacy were related with poor sleep quality. A total of 34 patients completed the serological, neurobehavior and brain magnetic resonance analysis. The results showed that patients with temporal lobe epilepsy had higher heat shock protein 70 and S100βprotein levels, while those with attacks more than twice per month had significant higher heat shock protein 70, S100βprotein and neuron specific enolase levels. Compared with the matched controls, the regions showing atrophy included hippocampus and parahippocampus, putamen, thalamus and supplementary motor areas. In correlation study, only heat shock protein 70 showed an inverse correlation with hippocampal volume (R square = 0.22, p = 0.007) after controlling for the effect of age. Conclusion: The study suggested that epilepsy risk characteristics, serological biomarkers, brain atrophic regions were important factors for day time sleepiness, sleep disturbances and neurobehavior changes in patients with epilepsy.

Indiana University-Purdue University Indianapolis (IUPUI)<br>Large-scale genome-wide association studies for Alzheimer’s disease (AD) have identified more than 20 risk loci and several pathways including lipid metabolism. Lipids are fundamental to cellular structure and organization, where they compose biological bilayer membranes surrounding the cell. In their structural role, lipids provide a scaffold for cell signaling, such as neurotransmission. There is a large body of evidence linking lipids and AD, yet the relationship between AD pathogenesis and lipid dyshomeostasis is not well understood. Here, we performed manual PubMed searches to identify the most studied lipid classes and risk genes in AD. We discussed pathological alterations of the key lipids and their potential contribution to the recent NIA-AA “A/T/N” framework. We also summarized what is known between the key lipids and etiological hypotheses of AD. Finally, we characterized relationship of the key lipids with AD genomic risk factors to identify possible downstream mechanisms of lipid dysfunction in AD. There is a large body of evidence linking lipids and AD, yet the relationship between AD pathogenesis and lipid dyshomeostasis is not well understood. In particular, we investigated the association between triglyceride (TG) species and AD. The overall goal was to test the hypothesis that TGs would associate with AD endophenotypes, based on their fatty acid composition. Diagnostic groups (cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD) differed on two principal components extracted from 84 serum TG levels. Fish oil-type and olive oil-type TGs were significantly lower in MCI and AD compared to CN. Next, association analysis of TG principal components with “A/T/N/V” (amyloid-β, tau, neurodegeneration, and cerebrovascular) biomarkers for AD showed that the fish oil-type and olive oil-type TGs were also significantly associated with atrophy on MRI. Finally, a mixed model regression analysis investigated the association between baseline TGs and longitudinal changes of AD endophenotypes to show that olive oil-type TGs predicted changes in AD brain atrophy. Our results indicate that a specific subcategory of TGs is associated with an early prodromal stage of cognitive impairment and early-stage biomarkers for AD, providing the foundation for future therapeutic development related to TG metabolism.<br>2023-05-05

Football safety has increased over time, in part due to improvements in equipment and body mechanics, but there are still inherent risks involved, including exposure to repetitive head impacts (RHI). Significant head impacts can result in a constellation of symptoms including nausea, vomiting, headache, dizziness, and amnesia, which typically assist in the diagnosis of concussion. However, it has been shown that subconcussive impacts may result in microstructural changes and physiological alterations in the brain. This is particularly concerning because athletes may be undergoing changes in the brain in the absence of outwardly visible symptoms. Poorer neurologic outcomes later in life have been associated with cumulative exposure rather than number of diagnosed concussions. Accelerometers installed in helmets have shown that college football players may receive up to 1,850 head impacts throughout the course of one season. The concussion rate is obviously much lower, indicating there are a high number of head impacts per diagnosed concussion. Axons are especially susceptible to damage from RHI because of their extension throughout the nervous system. The subtle changes thought to result from RHI are not easy to measure, but several modalities have been proposed. These include diffusion tensor imaging (DTI), plasma tau protein, and King-Devick testing. The proposed study will look to quantify cumulative head impact exposure in college football players prior to the start of a season and see if this has any impact on the variables. They will then participate in one season of football wearing helmet accelerometers to measure the number of head impacts sustained. Changes in the variables will be compared to non-contact sport college athletes. Data will be analyzed to determine if number of head impacts correlates with changes in variables and if prior head impact exposure has any effect on these changes. Data obtained from this study will have significant implications in the field of head injury. It may strengthen the use of several markers of brain injury that could be utilized in the future. Additionally, the effects of cumulative head impact exposure and one season of head impacts will be thoroughly examined. This information can be provided to trainers, coaches, and athletes to further improve football safety.

Background: Given that individuals with subjective cognitive decline (SCD) report a change that is not yet measurable with standard neuropsychological assessment measures, they are thought to be the earliest along the cognitive continuum between healthy aging and Alzheimer’s disease (AD). The current study used a neuroimaging approach to examine differences in brain function and structure between individuals with SCD and healthy controls (HC). Method: 3T resting state functional MRI and high resolution anatomical images were retrieved from 23 individuals with SCD (mean age = 72.9 years, SD = 5.4, 12 females) and 23 HC (mean age = 74.3 years, SD = 5.0, 12 females) from the screening time point from the AD Neuroimaging Initiative database. All data were processed using the FMRIB Software Library. Seed-based analyses of the default mode network (DMN) were used to compare differences in brain function between SCD and HC groups (Z > 2.3; cluster significance: p < 0.05, corrected). Voxel-based morphometry (VBM) was used to examine differences in grey matter volume between the SCD and HC groups. Results: The SCD and HC groups were not significantly different in age or education level. Results revealed significantly greater activity in the DMN including the bilateral precuneus cortex, bilateral thalamus, and right hippocampal regions in individuals with SCD relative to controls. Conversely, those with SCD showed decreased activation in the bilateral frontal pole, caudate, angular gyrus, lingual gyrus, right superior frontal gyrus, right occipital pole, right superior temporal gyrus, left superior temporal gyrus in the posterior division, left precuneus cortex, left precentral gyrus, left occipital fusiform gyrus, left temporal pole, and left cerebellum compared to HC. Finally, VBM results did not show significant differences in grey matter volume between the groups. Conclusion: Findings revealed changes in brain function but not structure between individuals with SCD and HC. Overall, this study represents a crucial step in characterizing individuals with SCD, a group recognized to be at increased risk for AD. It is imperative to identify biomarkers prior to significant decline on clinical assessment, so that disease-delaying interventions may be delivered at the earliest possible time point.<br>Graduate<br>2020-08-15

The human brain is a complex and powerful organ, directing every aspect of life from somatosensory and motor function to visceral responses to higher order cognition. Neurological and psychiatric disorders often disrupt normal functioning. While the clinical symptoms of such disorders are known, their biological underpinnings are not as clearly characterized. Structural neuroimaging is a powerful, non-invasive tool that can play a critical role in finding biomarkers of these illnesses. Currently, variations in pre-processing techniques yield inconsistent and conflicting results. As neuroimaging is a nascent branch of medical research, gold standards in imaging methodologies have not yet been established. Quantitatively validating and optimizing the way these images are preprocessed is the first step towards standardization. Voxel-based morphometry (VBM) is one technique that is commonly used to compare whole-brain structural differences between groups. Statistical tests are used to compare intensities of voxels throughout all brain scans in each group. In order to ensure that comparable voxels are being tested, the images must be fitted into a common space, which is done through image preprocessing. Spatial normalization to templates is an early pre-processing step that is executed unreliably as many options for both templates and normalization algorithms exist. To determine the effect variations in template usage may cause, we utilized a VBM approach to detect simulated lesions. Template performance was analyzed by comparing the accuracy with which the lesion was detected.

New paradigms are developed in magnetic resonance imaging for the advanced diagnosis of neurodegenerative diseases. In particular, multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS) preclinical research poorly focuses on functional connectivity in brain. Available animal models of MS and ALS are extensively used for analysis such drug testing and discovery of underlying mechanisms of pathogenesis. These diseases present, since neuronal lesions formation and neuroinflammation, a multilevel heterogeneity in mechanism of neurodegeneration and brain connectivity still not well understood. Moreover, they play a key role in pharmacological research, from the identification of a therapy target to the in vivo validation of the efficacy. More recently alterations in synchronized brain activity at rest in MS patients have been reported. At the best of our knowledge, functional imaging has not been applied yet in the assessment of new therapies in the preclinical models for MS and ALS. In this study, we aim to develop an innovative platform based on functional MRI in the resting state (rsfMRI), for the pre-clinical evaluation of new markers in MS and ALS. Moreover, the advancing in MRI techniques could assess new criteria of sensitivity and specificity in diagnosis; an additional analysis on diffusion MRI outcomes in MS preclinical models is added to this study.

<p>Understanding the structure of the brain has been a major goal of neuroscience research over the past century, driven in part by the understanding that brain structure closely follows function. Normative brain maps, or atlases, can be used to understand normal brain structure, and to identify structural differences resulting from disease. Recently, diffusion tensor magnetic resonance imaging has emerged as a powerful tool for brain atlasing; however, its utility is hindered by image resolution and signal limitations. These limitations can be overcome by imaging fixed ex-vivo specimens stained with MRI contrast agents, a technique known as diffusion tensor magnetic resonance histology (DT-MRH). DT-MRH represents a unique, quantitative tool for mapping the brain with unprecedented structural detail. This technique has engendered a new generation of 3D, digital brain atlases, capable of representing complex dynamic processes such as neurodevelopment. This dissertation explores the use of DT-MRH for quantitative brain atlasing in an animal model and initial work in the human brain. </p><p>Chapter 1 describes the advantages of the DT-MRH technique, and the motivations for generating a quantitative atlas of rat postnatal neurodevelopment. The second chapter covers optimization of the DT-MRH hardware and pulse sequence design for imaging the developing rat brain. Chapter 3 details the acquisition and curation of rat neurodevelopmental atlas data. Chapter 4 describes the creation and implementation of an ontology-based segmentation scheme for tracking changes in the developing brain. Chapters 5 and 6 pertain to analyses of volumetric changes and diffusion tensor parameter changes throughout rat postnatal neurodevelopment, respectively. Together, the first six chapters demonstrate many of the unique and scientifically valuable features of DT-MRH brain atlases in a popular animal model.</p><p>The final two chapters are concerned with translating the DT-MRH technique for use in human and non-human primate brain atlasing. Chapter 7 explores the validity of assumptions imposed by DT-MRH in the primate brain. Specifically, it analyzes computer models and experimental data to determine the extent to which intravoxel diffusion complexity exists in the rhesus macaque brain, a close model for the human brain. Finally, Chapter 8 presents conclusions and future directions for DT-MRH brain atlasing, and includes initial work in creating DT-MRH atlases of the human brain. In conclusion, this work demonstrates the utility of a DT-MRH brain atlasing with an atlas of rat postnatal neurodevelopment, and lays the foundation for creating a DT-MRH atlas of the human brain.</p><br>Dissertation

博士<br>國立陽明大學<br>腦科學研究所<br>106<br>Background: The impact of migraine on patient health is complicated by cerebro- and cardio-vascular risk factors and comorbidity. The role of central pulsatile hemodynamics in the pathogenesis of white matter hyperintensities (WMHs) in migraine patients has not been clarified. MicroRNAs (miRs) have emerged as important regulators of vascular endothelial functions. Yet, whether levels of miRs associated with endothelial dysfunction are altered in migraine patients remains unknown. Aims: Our study aims were (1) to shed light on possible mechanism of increased risk of vascular events and WMHs formation in migraine patients, (2) to investigate whether potential vascular parameters correlate with WMHs presence or clinical profiles in migraine patients, and (3) to explore the relationship between circulating endothelial-specific miRs and migraine. Methods: Patients with migraine without overt vascular risk factors and demographically-matched healthy controls were recruited prospectively. Cerebral WMHs volume was determined by T1-weighted magnetic resonance imaging with CUBE-fluid-attenuated-inversion-recovery sequences. Central systolic blood pressure (cSBP), carotid-femoral pulse wave velocity (cf-PWV), and carotid augmentation index (AI) were measured by applanation tonometry. Carotid pulsatility index (CPI) was derived from Doppler ultrasound carotid artery flow analysis. The levels of four endothelial-specific miRs (miR-155, miR-126, miR-21, and Let-7g) were quantified and expressed in terms of fold changes (2-ΔΔct) relative to mean levels in the control group. Associations of miRs levels with headache features and syncope comorbidity were explored. Results: Compared to the controls, the migraine patients had a higher WMHs frequency [odds ratio (OR), 2.75; P = 0.04) and greater mean WMHs volume (0.174 vs. 0.049, cm3, P = 0.04). Multivariable regression analysis showed that WMHs volume in migraine patients was positively associated with cSBP (P = 0.04) and cf-PWV (P < 0.001), but negatively associated with CPI (P = 0.04) after controlling for potential confounding factors. The interaction effects observed indicated that the influence of cf-PWV (P = 0.004) and cSBP (P = 0.03) on WMHs formation was greater for the lower-CPI subgroup of migraine patients. Compared to controls, migraine patients had upregulated expression of miR-155 (6.17-fold, P = 0.018), miR-126 (6.17-fold, P = 0.013), and let-7g (7.37-fold, P = 0.005). Levels of miR-155 (r = 0.375, P = 0.041) and miR-126 (r = 0.375, P = 0.041) were associated with the syncope frequency in the past year in migraine patients. Conclusions: Increased aortic stiffness or CBP in the presence of low intracranial artery resistance may predispose migraine patients to WMHs formation; this suggested pathophysiology differs from that observed in non-migraine elderly subjects. Circulating levels of endothelial-specific miRs were elevated and associated with syncope comorbidity in migraine patients. Our findings may shed light on understanding the mechanism mediating altered vascular dysfunctions in migraine and suggest that quantification of targeted vascular parameters and miRs, could be used for stratification of vascular risks in migraine patients.

Research Doctorate - Doctor of Philosophy (PhD)<br>The aim of the thesis is to study clinical and neuroradiological biomarkers towards individual patient profiling in acute ischaemic stroke (AIS). There is existing evidence that individual patient profiling using a combination of initial stroke severity, infarct topography and surrogate neuroimaging markers provide clinicians with prognostically relevant information that can guide the selection of patients for acute interventions and/or planning and conduct of rehabilitation. Perilesional brain perfusion neuroimaging and cortical venous drain angiography may be additional measures of prognostic relevance. Combinations of these prognostic variables could potentially allow the development of more sophisticated approaches to selection of post-stroke patients for recovery assistance interventions and AIS patients for thrombolytic and/or endovascular treatments. The overall goal of the thesis is to examine specific subgroups of AIS patients and assess functional outcome, where subgroups are defined by, (a) clinically in terms of stroke severity profile (measured using National Institute of Health Stroke Severity (NIHSS) scale), and (b) neuroradiologically depending upon: lesion topography, presence or absence of peri-lesional hyperperfusion, and presence or absence of delayed late venous phase cortical vein filling (delayed-LCVF). Advanced neuroimaging such as arterial spin labelling (ASL) magnetic resonance imaging (MRI) and dynamic computed tomography angiography (dCTA) can be effectively used to discern surrogate imaging biomarkers, investigate underlying post-ischemic pathophysiological mechanisms, identify stroke subtype, and predict the clinical course of AIS patients receiving reperfusion therapy.

Indiana University-Purdue University Indianapolis (IUPUI)<br>Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease. Many researchers believe that an effective AD treatment will prevent the development of disease rather than treat the disease after a diagnosis. Therefore, the development of tools to detect AD-related pathology in early stages is an important goal. In this report, MRI-based markers of neurodegeneration are explored as biomarkers of AD. In the first chapter, the sensitivity of cross-sectional MRI biomarkers to neurodegenerative changes is evaluated in AD patients and in patients with a diagnosis of mild cognitive impairment (MCI), a prodromal stage of AD. The results in Chapter 1 suggest that cross-sectional MRI biomarkers effectively measure neurodegeneration in AD and MCI patients and are sensitive to atrophic changes in patients who convert from MCI to AD up to 1 year before clinical conversion. Chapter 2 investigates longitudinal MRI-based measures of neurodegeneration as biomarkers of AD. In Chapter 2a, measures of brain atrophy rate in a cohort of AD and MCI patients are evaluated; whereas in Chapter 2b, these measures are assessed in a pre-MCI stage, namely older adults with cognitive complaints (CC) but no significant deficits. The results from Chapter 2 suggest that dynamic MRI-based measures of neurodegeneration are sensitive biomarkers for measuring progressive atrophy associated with the development of AD. In the final chapter, a novel biomarker for AD, visual contrast sensitivity, was evaluated. The results demonstrated contrast sensitivity impairments in AD and MCI patients, as well as slightly in CC participants. Impaired contrast sensitivity was also shown to be significantly associated with known markers of AD, including cognitive impairments and temporal lobe atrophy on MRI-based measures. The results of Chapter 3 support contrast sensitivity as a potential novel biomarker for AD and suggest that future studies are warranted. Overall, the results of this report support MRI-based measures of neurodegeneration as effective biomarkers for AD, even in early clinical and preclinical disease stages. Future therapeutic trials may consider utilizing these measures to evaluate potential treatment efficacy and mechanism of action, as well as for sample enrichment with patients most likely to rapidly progress towards AD.

博士<br>國防醫學院<br>醫學科學研究所<br>103<br>Background: Many lines of evidence suggest the role of serotonin transporter (SERT)-mediated reuptake of serotonin in the pathophysiology and treatment of major depressive disorder (MDD). The study aimed to examine whether the pretreatment SERT binding potential or SERT binding ratio between terminal projection regions relative to the midbrain raphe nuclei was associated with suicide attempts and treatment outcome to SERT-targeted antidepressants. Moreover, genetic polymorphisms may modulate serotonergic and noradrenergic function, thereby affecting the treatment efficacy of venlafaxine, a serotonin-norepinephrine reuptake inhibitor. The aim of this study was to examine whether polymorphisms in the norepinephrine transporter gene (SLC6A2) associate with remission after venlafaxine treatment, a serotonin-norepinephrine reuptake inhibitor. Methods: We recruited 39 antidepressant-naïve patients with MDD and 39 heathy controls. Positron emission tomography with N,N-dimethyl-2- (2-amino-4-[18F]fluorophenylthio)benzylamine (4-[18F]-ADAM) was used to measure in vivo SERT availability prior to antidepressant treatment. The 21-item Hamilton Depression Rating Scale (HDRS) was use to assess the severity of depression from baseline to week 6. The Beck Scale for Suicide Ideation (BSS) was used to assess intensity of suicide ideation before treatment. All the patients with MDD had HDRS scores of 18 or more at baseline. In addition, we recruited 243 patients with MDD who were under treatment with venlafaxine. We screened seven single-nucleotide polymorphisms of the SLC6A2 gene in these patients and explored the association between SLC6A2 variants and treatment response by regression model. Results: First, higher SERT binding ratio in the prefrontal cortex (PFC) relative to midbrain was found in suicide attempters compared to non-attempters and healthy controls. Further, the PFC/midbrain binding ratio positively correlated with BSS scores. Second, pretreatment SERT binding in the thalamus and striatum positively correlated with an early reduction in HDRS scores at week 3. Non-responders and dropout patients showed a proportionate reduction in SERT binding in the terminal projection regions and midbrain compared to healthy controls. In contrast, a disproportionate reduction in SERT binding in the terminal projection regions relative to midbrain was observed in responders. SLC6A2 variants (rs28386840, rs40434, and rs187714) may predict remission after treatment with venlafaxine. Conclusions: The results of this study suggested that a disproportionate reduction in SERT binding between terminal projection regions and midbrain may predict suicide attempts and treatment outcome in patients with MDD. In addition, variation of the SLC6A2 gene is associated with treatment remission after venlafaxine in patients with MDD.

Indiana University-Purdue University Indianapolis (IUPUI)<br>Neuroimaging biomarkers play a crucial role in our understanding of Alzheimer’s disease. Beyond providing a fast and accurate in vivo picture of the neuronal structure and biochemistry, these biomarkers make up a research framework, defined in a 2018 as the A(amyloid)/T(tau)/N(neurodegeneration) framework after three of the hallmarks of Alzheimer’s disease. I first used imaging measures of amyloid, tau and neurodegeneration to study clinically diagnosed Alzheimer’s disease. After dividing subjects into early (onset younger than 65) and late-onset (onset of 65 and older) amyloid-positive (AD) and amyloid-negative (nonAD) groups, I saw radically differing topographical distribution of tau and neurodegeneration. AD subjects with an early disease onset had a much more severe amyloid, tau and neurodegeneration than lateonset AD. In the nonAD group, neurodegeneration was found only in early-onset FDG PET data and in a nonAlzheimer’s-like MRI and FDG pattern for late-onset. The late-onset nonAD resembled that of limbic-predominant age-related TDP-43 encephalopathy. I next utilized an imaging genetics approach to associate genome-wide significant Alzheimer’s risk variants to structural (MRI), metabolic (FDG PET) and tau (tau PET) imaging biomarkers. Linear regression was used to select variants for each of the models and included a pooled sample, cognitively normal, mild cognitive impairment and dementia groups in order to fully capture the cognitive spectrum from normal cognition to the most severely impaired. Model selected variants were replicated using voxelwise regression in an exploratory analysis of spatial associations for each modality. For each imaging type, I replicated some associations to the biomarkers previously seen, as well as identified several novel associations. Several variants identified with crucial Alzheimer’s biomarkers may be potential future targets for drug interventions.

<div>Adolescent collision-sport athletes may be exposed to repetitive head impacts over years of practices and competitions without immediately observable symptoms. Despite the growing concerns, these athletes often continue play while at risk. Concrete objective measurements are desired to inform prompt and effective preventative strategies for this vulnerable population. However, adolescent brains are rapidly developing and the accrual of brain injury is often subtle. Prospective screening with sensitive biomarkers is challenging and requires advanced technologies, rigorous data processing, and the interdisciplinary expertise of engineering, neurobiology, and cognitive sciences.</div><div><br></div><div>To address the challenge, we first developed population-specific brain atlases to facilitate reproducible and meaningful statistical analyses. The atlases better characterized the neuroanatomy of early-to-middle adolescent (ages 13-19) collision-sport athletes, reduced deformation introduced during spatial normalization, and exhibited higher sensitivity in image analysis compared to standardized adult or age-appropriate brain templates. The atlases can be further applied to monitor the neuroanatomical trajectory and can serve as a coordinate reference system to retrospectively harmonize data collected from different sites and imaging acquisition parameters, facilitating group analysis at large scale.</div><div><br></div><div>Next, to assess whether the changes of white matter microstructure can be attributed to repetitive head impacts and are reflected by cognitive performance, we analysed the diffusion tensor imaging (DTI) data of high school men’s football and women's soccer across a single season, with accompanying data from head impact sensors and neurocognitive assessments. Within multiple brain regions, we observed significantly altered DTI metrics, both transiently over a season and chronically with more years of high school experience. For the football players, hits with peak translational acceleration over 37 <i>g</i> were sufficient to alter the distributions of DTI changes, and deficits in white matter microstructure correlated with poorer performance of anti-saccade task at one month post-season, suggesting increased vulnerability for inhibitory control. Monitoring repetitive head impacts thus provides a temporal profile for identifying at-risk individuals during the competitive season, informing prompt interventional strategies, therefore protecting the brain and cognitive health of early-to-middle adolescent collision-sport athletes in the long run.</div>