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Section: New Results

Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling

Participants : Gaƫl Varoquaux [Correspondant] , Pierre Fillard, Bertrand Thirion.

Functional brain connectivity, as revealed through distant correlations in the signals measured by functional Magnetic Resonance Imaging (fMRI), is a promising source of biomarkers of brain pathologies. However, establishing and using diagnostic markers requires probabilistic inter-subject comparisons. Principled comparison of functional-connectivity structures is still a challenging issue. We give a new matrix-variate probabilistic model suitable for inter-subject comparison of functional connectivity matrices on the cone of Symmetric Positive Definite (SPD) matrices endowed with a suitable metric. We show that this model leads to a new algorithm for principled comparison of connectivity coefficients between pairs of regions. We apply this model to comparing separately post-stroke patients to a group of healthy controls. We find neurologically-relevant connection differences and show that our model is more sensitive that the standard procedure. To the best of our knowledge, these results are the first report of functional connectivity differences between a single-patient and a group and thus establish an important step toward using functional connectivity as a diagnostic tool.

Figure 6. Significant differences on two patients (p<0.05 uncorrected), represented as connections between regions: increased connectivity appears in red, and decreased in blue. The lesion, manually segmented from anatomical images, is represented in green. ROIs fully covered by a lesion are marked with a black cross on the correlation matrix.
IMG/subject20_graph

For more information, please see [26] .


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