{"id":2117,"date":"2026-10-02T07:44:15","date_gmt":"2026-10-02T05:44:15","guid":{"rendered":"https:\/\/incia.u-bordeaux.fr\/fr\/events\/soutenance-de-these-suhrit-duttagupta\/"},"modified":"2026-10-02T09:45:36","modified_gmt":"2026-10-02T07:45:36","slug":"soutenance-de-these-suhrit-duttagupta","status":"publish","type":"buni_events","link":"https:\/\/incia.u-bordeaux.fr\/fr\/events\/soutenance-de-these-suhrit-duttagupta\/","title":{"rendered":"Soutenance de th\u00e8se – <\/span>Suhrit Duttagupta"},"content":{"rendered":"

Lieu : CARF<\/strong><\/p>\n

Egalement en visio<\/a><\/p>\n

Soutenance en anglais<\/p>\n

\n

\"\"<\/a>Suhrit Duttagupta<\/strong>
\nEquipe Ecopsy
\nINCIA<\/p>\n

Th\u00e8se dirig\u00e9e par Igor Sibon (INCIA)<\/p>\n

Titre<\/h3>\n

Imagerie Pr\u00e9dictive et Suivi Clinique des Troubles \u00c9motionnels Post-AVC<\/p>\n

Predictive Imaging and Clinical Monitoring of Post-stroke Emotional Disability<\/em><\/p>\n

R\u00e9sum\u00e9<\/h3>\n

L\u2019accident vasculaire c\u00e9r\u00e9bral (AVC) est une cause majeure de mortalit\u00e9 et de handicap. La r\u00e9\u00e9ducation cible principalement les d\u00e9ficits moteurs et fonctionnels, mais les troubles de l\u2019humeur post-AVC sont fr\u00e9quents et tout aussi invalidants. La d\u00e9pression (PSD), l\u2019anxi\u00e9t\u00e9 (PSA) et la fatigue (PSF) post-AVC restent difficiles \u00e0 pr\u00e9dire. Les marqueurs conventionnels de neuroimagerie, comme la localisation des l\u00e9sions et les alt\u00e9rations de la substance blanche, ont une valeur pr\u00e9dictive limit\u00e9e, et les techniques d\u2019imagerie avanc\u00e9es sont peu applicables en pratique clinique. Cette th\u00e8se visait \u00e0 am\u00e9liorer la pr\u00e9diction des troubles de l\u2019humeur post-AVC \u00e0 partir de marqueurs de neuroimagerie d\u00e9riv\u00e9s d\u2019IRM r\u00e9alis\u00e9es en routine clinique et \u00e0 \u00e9tudier des mesures cliniques permettant de caract\u00e9riser longitudinalement les sympt\u00f4mes de l\u2019humeur. Les donn\u00e9es provenaient de 1142 patients issus de 5 cohortes hospitali\u00e8res: Brain Before Stroke (BBS), Groupe de R\u00e9flexion pour l\u2019Evaluation COGnitive Vasculaire (GRECOGVasc), High B-Value Diffusion & Stroke (HBS), memoQUEST et MObile Technologies In the preVention of POSt-stroke DEPression (MOTIV-POSDEP). L\u2019\u00c9tude 1 a examin\u00e9 la PSF 3 mois post AVC. Les analyses par voxels ayant fourni des r\u00e9sultats limit\u00e9s, une approche fond\u00e9e sur l\u2019analyse en composantes principales (ACP) a permis de d\u00e9river des r\u00e9seaux c\u00e9r\u00e9braux \u00e0 partir de la distribution des l\u00e9sions. Des r\u00e9seaux distincts de substance blanche et grise ont \u00e9t\u00e9 associ\u00e9s \u00e0 diff\u00e9rentes dimensions de la fatigue, soulignant l\u2019aspect multifactoriel des troubles neuropsychologiques post-AVC. L\u2019\u00c9tude 2 a d\u00e9velopp\u00e9 une approche utilisant des IRM de diffusion pour g\u00e9n\u00e9rer, par ACP, des composantes de diffusivit\u00e9 \u00e0 l\u2019\u00e9chelle du cerveau entier. Celles-ci ont \u00e9t\u00e9 int\u00e9gr\u00e9es \u00e0 des mod\u00e8les de r\u00e9gression non param\u00e9trique pour pr\u00e9dire les scores de PSA et PSD \u00e0 6 mois, et compar\u00e9es \u00e0 des mod\u00e8les cliniques et bas\u00e9s sur les cartes de d\u00e9connexion. Les performances pr\u00e9dictives externes \u00e9taient globalement faibles, les mod\u00e8les cliniques \u00e9tant plus performants principalement gr\u00e2ce \u00e0 l\u2019\u00e9tat \u00e9motionnel initial. N\u00e9anmoins, l\u2019approche de neuroimagerie apportait une valeur pr\u00e9dictive suppl\u00e9mentaire modeste, \u00e9tayant l\u2019int\u00e9r\u00eat de marqueurs \u00e0 l\u2019\u00e9chelle du cerveau entier en compl\u00e9ment des pr\u00e9dicteurs \u00e9tablis. \u00c9tant donn\u00e9e l\u2019importance de l\u2019\u00e9tat \u00e9motionnel initial, l\u2019\u00c9tude 3 a \u00e9valu\u00e9 si des mesures \u00e0 distance de l\u2019humeur apr\u00e8s la sortie de l\u2019h\u00f4pital fournissaient une information suppl\u00e9mentaire. Des \u00e9valuations \u00e9cologiques momentan\u00e9es (EMA) quotidiennes ont recueilli l\u2019humeur et l\u2019activit\u00e9 des patients par smartphone pendant 12 semaines. Les r\u00e9ponses du 2\u00e8me mois pr\u00e9disaient mieux la PSA et la PSD au suivi que les \u00e9valuations initiales. Certains sympt\u00f4mes, comme l\u2019anh\u00e9donie et les inqui\u00e9tudes, \u00e9taient \u00e9galement plus fortement associ\u00e9s \u00e0 l\u2019humeur au suivi que les mesures globales d\u2019anxi\u00e9t\u00e9 et de d\u00e9pression. L\u2019\u00c9tude 4 a \u00e9valu\u00e9 une approche moins contraignante avec des r\u00e9ponses par SMS. De br\u00e8ves \u00e9valuations recueillies avant le suivi pr\u00e9sentaient de fortes associations avec les \u00e9valuations cliniques de PSA, PSD et PSF. En r\u00e9sum\u00e9, les s\u00e9quelles neuropsychologiques post-AVC sont multiformes et interd\u00e9pendantes, ii mais restent difficilement pr\u00e9dictibles. Les composantes de neuroimagerie \u00e0 l\u2019\u00e9chelle du cerveau entier pr\u00e9sentent une valeur pr\u00e9dictive modeste. Une approche holistique int\u00e9grant des mod\u00e8les multidimensionnels et des \u00e9valuations longitudinales \u00e0 distance pourrait am\u00e9liorer la caract\u00e9risation pr\u00e9coce des sympt\u00f4mes individuels de l\u2019humeur.<\/p>\n

Mots cl\u00e9s: AVC, Imagerie de diffusion, \u00e9motions, fatigue, t\u00e9l\u00e9surveillance<\/strong><\/p>\n

 <\/p>\n

Stroke is a leading cause of death and disability. Although patient rehabilitation mainly targets motor and functional impairments, post-stroke mood disruptions are prevalent and equally debilitating. Conditions such as post-stroke depression (PSD), anxiety (PSA), and fatigue (PSF) are difficult to predict. Conventional neuroimaging markers such as lesion location and white matter disruptions have provided limited predictive value, while advanced imaging techniques are difficult to implement in clinical practice. The main purpose of this thesis was to improve the management of post-stroke mood impairments. We aimed to improve the predictability of mood outcomes using neuroimaging markers derived from routine clinical MRI data and evaluate clinical measures that capture mood symptoms longitudinally. Data were acquired from 1085 patients across four French hospital-based cohorts: Brain Before Stroke (BBS; Bordeaux), Groupe de R\u00e9flexion pour l’Evaluation COGnitive Vasculaire (GRECOGVasc; Amiens), memoQUEST (Bordeaux), and MObile Technologies In the preVention of POSt-stroke DEPression (MOTIV-POSDEP; Bordeaux). Four studies were conducted. Study I examined PSF at 3 months post-stroke, where traditional voxel-based analyses provided limited results. Using a data-driven approach with principal component analysis (PCA), whole-brain networks were derived from lesion distributions. We identified distinct white- and gray-matter networks associated with specific fatigue domains, highlighting the multifaceted nature of post-stroke neuropsychological impairments. Building on this framework of data compression, Study II developed a neuroimaging pipeline using diffusion-weighted MRI to generate global diffusivity components through PCA. These components were entered into nonparametric regression models to predict 6-month PSA and PSD scores, comparing performances with clinical and disconnectome-based models. Overall, external predictive performance was low and clinical models performed best largely due to including baseline mood status. Nevertheless, the imaging pipeline added modest predictive value across evaluations, supporting the use of whole-brain markers to complement established predictors. Noting the reliance on baseline mood in the predictive models, we explored whether remote evaluations following hospital discharge could provide more utility. Study III evaluated the use of ecological momentary assessments (EMA), collecting daily measures of patient mood and activity through smartphones for 12 weeks. Based on symptoms matching the clinical diagnostic criteria for mood disorders, anxiety and depression mood indices were created. The association between mood indices and follow-up PSA and PSD scores improved with closer proximity. Compared to baseline clinical scores, averaged EMA mood index values from the second month offered iii better early prediction of follow-up mood disorders with 90.6% adherence. Additionally, responses for specific core symptoms, including anhedonia, sadness, and worrying had stronger associations with follow-up mood than the overall index scores.These observations led to Study IV evaluating a lower-burden approach based on SMS responses shortly before follow-up. Brief symptom ratings collected within two weeks prior to follow-up sessions showed strong associations with clinical evaluations for PSA, PSD, and PSF, demonstrating the potential of simplified remote monitoring to prioritize patients at risk of developing mood disorders.In summary, post-stroke neuropsychological outcomes are multifaceted and interacting syndromes with limited predictability. Whole-brain neuroimaging components derived through a novel framework showed modest predictive value. Our findings are consistent with the growing recognition of the need for holistic approaches, suggesting that incorporating multidomain models and longitudinal remote assessments may improve the early characterization of individual mood symptoms.<\/p>\n

Keywords: Stroke, mood, fatigue, diffusion-weighted imaging, remote monitoring\u00a0<\/strong><\/p>\n

Publications<\/h3>\n