Pseudo resting-state investigation from task-evoked functional MRI signals

Functional connectomics studies the way different regions of the brain are functionally connected by means of time-series correlation of functional magnetic resonance imaging data (fMRI) based on Blood Oxygen Level-Dependent (BOLD) contrast. Typically, it is based on resting-state fMRI, which is meant to measure the effect of the spontaneous neural firing in absence of external stimuli. In this scenario, there is an effort in the scientific community to retrieve a “pseudo-resting” state from task-based fMRI and a need of a methodological consensus among task-state connectivity studies. This work takes place within this context and investigates whether a task-free “resting-state” connectivity can be inferred from task-fMRI and examine its impact upon behavioural traits on a clinical context, namely in the comparison of Typical Readers and children with Developmental Dyslexia. Connectomes are derived with several processing options from the two administered tasks, namely the Sinusoidal Gratings and Coherent Motion detection, and analysed by means of classification experiments. The “pseudo-resting” state connectomes, derived from the regression of the task design information, were able to discriminate between the two tasks. Both “task” and “pseudo-resting” state connectomes successfully allows the discrimination between children with Developmental Dyslexia and Typical Readers (accuracy > 70%). Group discrimination seems to be driven by task-related features, independently from the processing pipeline. Taken together, our results suggest that regression of task-fMRI signals does not lead to a reliable “pseudo-resting” state connectivity. Task based connectomics can still be used to highlight alterations in connectivity underpinning the different behavioral traits.