Multivariate single-subject analysis of short-term reorganization in the language network

Gesa Hartwigsen (1) , Danilo Bzdok (2, 3)

1 IMPNSC - Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig]
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
3 Department of Psychiatry, Psychotherapy and Psychosomatics [Aachen]

Numerous functional neuroimaging studies in humans used univariate group analyses to identify brain areas that show increased activity across participants during specific tasks. However, there is increasing evidence that group-level analyses may obscure important parts of the signal response (Margulies, 2017; Poldrack, 2017). Here, we re-analyzed data from our recent study (Hartwigsen et al., 2017) that investigated functional reorganization in the language network. We show that across-voxel pattern-learning approaches are useful to isolate plastic changes in neural networks underlying cognitive functions at the individual subject level. Using predictive machine-learning tools to identify and exploit subject-specific neural activity patterns have been argued to become an important cornerstone of precision medicine in psychiatry and neurology.

Domaines: Sciences cognitives; Neurosciences

Gesa Hartwigsen, Danilo Bzdok. Multivariate single-subject analysis of short-term reorganization in the language network. Cortex, 2018, pp.4. ⟨10.1016/j.cortex.2018.06.013⟩. ⟨hal-01824229⟩