A novel approach of fMRI-guided tractography analysis within a group: construction of an fMRI-guided tractographic atlas.

Maria Giulia Preti, Nikos Makris, Maria Marcella Laganà, George Papadimitriou, Francesca Baglio, Ludovica Griffanti, Raffaello Nemni, Pietro Cecconi, Carl Fredrik Westin, Giuseppe Baselli

Research output: Contribution to journalArticlepeer-review


Diffusion Tensor Imaging (DTI) tractography and functional Magnetic Resonance Imaging (fMRI) investigate two complementary aspects of brain networks: white matter (WM) anatomical connectivity and gray matter (GM) function. However, integration standards have yet to be defined; namely, individual fMRI-driven tractography is usually applied and only few studies address group analysis. This work proposes an efficient method of fMRI-driven tractography at group level through the creation of a tractographic atlas starting from the GM areas activated by a verbal fluency task in 11 healthy subjects. The individual tracts were registered to the MNI space. Selection ROIs derived by GM masking and dilation of group activated areas were applied to obtain the fMRI-driven subsets within tracts. An atlas of the tracts recruited among the population was obtained by selecting for each subject the fMRI-guided tracts passing through the high probability voxels (the voxels recruited by the 90% of the subjects) and merging them together. The reliability of this approach was assessed by comparing it with the probabilistic atlas previously introduced in literature. The introduced method allowed to successfully reconstruct activated tracts, which comprehended corpus callosum, left cingulum and arcuate, a small portion of the right arcuate, both cortico-spinal tracts and inferior fronto-occipital fasciculi. Moreover, it proved to give results concordant with the previously introduced probabilistic approach, allowing in addition to reconstruct 3D trajectories of the activated fibers, which appear particularly helpful in the detection of WM connections.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics


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