Contact: matthieu.gilson [at]

PhD in neuroimaging biomarkers for psychiatry

Scientific context:

Unlike neurological diseases, the characterization of psychiatric disorders remains at an early stage because several of them share the same symptoms. The identification of symptoms identified by clinicians during interviews are so far the standard to establish a diagnostic, but this empirical procedure is demanding as it must be repeated for follow-ups, and involve variability as it may vary depending on the clinicians’ own subjectivity. This calls for a better prediction for the diagnosis of patients, also for the disease evolution during a medication treatment. A promising direction concerns neuroimaging data that give access to both structure and activity of the brain.
The fusion of heterogeneous data (here functional and structural MRI, as well as clinical data) in automated analysis pipelines remains a crucial challenge in high-dimensional data analysis. The rapid advancements observed in various fields, such as image processing, natural language processing, and speech recognition, are largely attributable to the availability of large-scale datasets. However, in the medical domain, these approaches often encounter significant hurdles, primarily due to the relatively small size of patient cohorts and the high costs associated with data acquisition.
To address these issues, this project aims to benchmark existing methods from artificial intelligence (AI) and develop novel methods for multimodal classification that are suited for application in a clinical context. More precisely, it aims to improve the design of markers for bipolar disorder based on structural and functional MRI in combination with clinical data (cognitive scores, etc.), including both patients and healthy controls.


The goal of the PhD project is to develop computational tools for diagnosis and prognosis, like predicting the evolution of a patient subject to medication or patient stratification to identify subtypes of psychiatric diseases. The objectives of the present project are thus focused on data analysis:

The PhD candidate will be trained to acquire the different techniques required for the project, ranging from data preprocessing to machine-learning tools, as well as data acquisition to some extent.

Supervision team:

The multidisciplinary supervision panel has complementary expertise covering the fields of computational neuroscience, machine learning and medicine:

Student profile:

computer science, biomedical, neuroscience, signal processing

Skills to acquire: