When: January 30th, 2020 – 3:00 pm

Where: VIMM Seminar Hall

Abstract: Multi-subject functional Magnetic Resonance Image (fMRI) studies are critical to test the validity of findings across subjects. However, the anatomical and functional structure varies across subjects, hence the image alignment is a fundamental step. One anatomical alignment is the Talairach Atlas, thus, it doesn’t account for functional topography. For that, Haxby et al. (2011) developed a functional approach called Hyperalignment, using sequential Procrustes orthogonal transformations. The inter-subject classification of functional response is improved. However, any constraint isn’t imposed to the transformation, losing results interpretability.

When: January 23rd, 2020 – 3:00 pm

Where: VIMM Seminar Hall

Abstract: Animal studies show that the adult brain shows remarkable plasticity in response to learning or recovery from injury. Non-invasive brain imaging techniques can be used to detect systems-level structural and functional plasticity in the human brain.

This talk will focus on how brain imaging has allowed us to monitor healthy brains learning new motor skills, to assess how brains recover after damage, such as stroke, and how they adapt to change, such as limb amputation.

When: January 16th, 2020 – 3:00 pm

Where: VIMM Seminar Hall

Abstract: Psychoses are the most severe and devastating psychiatric disorders that cut through the classical nosological categories and include schizophrenia spectrum disorders as well as affective disorders. Genetic and environmental factors have been associated with their etiology, but their pathophysiology is still unknown. Neuroimaging studies have investigated structural and functional changes associated with the risk for these disorders along with their treatment response.

In this presentation, functional connectivity-related phenotypes associated with the risk for the disorders, their modulation by genetic variation and treatment will be discussed.

When: January 9th, 2020 – 3:00 pm

Where: VIMM Seminar Hall

Abstract: Information Retrieval (IR) is the complex of theories, models, and technologies aiming to retrieve relevant information to user’s information needs. IR has recently made significant advances in understanding the content of multimedia documents and user queries.
In this talk, I’ll illustrate some noticeable advances, in particular, how topic modeling helps understand content and how the use of deep learning helps overcome some obstacles.