Venue: CARF
Suhrit Duttagupta
Team: Ecopsy
INCIA
Supervisor: Igor Sibon (INCIA)
Title
Predictive Imaging and Clinical Monitoring of Post-stroke Emotional Disability
Abstract
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 Réflexion 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.
Keywords: Stroke, mood, fatigue, diffusion-weighted imaging, remote monitoring
Publications
- Holistic compression to improve predictivity of post-stroke anxiety and depression at 6 months (https://doi.org/10.21203/rs.3.rs-9313883/v1)
- A comparison of clinical, lesion-based and connectome-based models of post-stroke depression: a prospective longitudinal study (https://doi.org/10.1016/j.nicl.2025.103911)
- Identifying clinico-radiological determinants of post-stroke fatigue 3 months post-stroke in a French hospital-based cohort of non-severe stroke patients without psychiatric comorbidities (https://doi.org/10.1371/journal.pone.0345376)
Jury
- M. Igor Sibon (Thesis director)
- M. Charles Laidi (Reviewer)
- M. Nicolas Farrugia (Reviewer)
- Mme. Solène Moulin (Examiner)
- Mme. Sandra Chanraud (Examiner)