EP 32. Automated high resolution fMRI mapping of the cortical sensory fingertip somatotopy in group examinations. Issue 9 (September 2016)

• Record Type: Journal Article
• Authors: Pfannmöller, J.; Greiner, M.; Balasubramanian, M.; Lotze, M.
• Is Part Of: Clinical neurophysiology. Volume 127:Issue 9(2016:Sep.)
• Page Start: e248
• Page End: e249
• Publication Date: 2016-09
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.05.087
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Objective: The increasing spatial precision in functional brain mapping is a methodological key issue driving the progress in brain research. Mapping of previously invisible fine scale details permits the discovery of unknown functional brain properties. Currently, the available resolution of fMRI examinations is increasing rapidly. However, data analysis techniques lag behind this development, and achieve an effective resolution which is smaller as the maximum possible spatial precision. We present a data analysis technique with maximum spatial precision in group analyses of high resolution functional magnetic resonance imaging (fMRI) data. Methods: The contralateral fingertip representations of thumb, index and middle finger were investigated in a group of 18 participants, for right and left hand. A block design was applied (absolute duration of 200 s) including long counting pulses as a temporal identification task for attention monitoring (Fig. 1 ). Imaging was carried out with a 3 Tesla MRI-scanner (Verio, Siemens, Germany). FreeSurfer recommendations for the anatomy scans were followed (standard MPRAGE sequence). A standard gradient-echo EPI sequence was used for fMRI (resolution 1.5 × 1.5 × 2 mm 3, 17 slices, field of view = 191 × 179 mm 2, TE/TR = 3.6 ms/21 ms). Data analysis was performed with an automated protocol (Pfannmöller et al., 2015 ), including vessel artefact removal. Cortical positions of the fingertips were computed on individual and MNI brains, using volume-based linear or surface-based non-linear normalization. An assessment of cortical distance measures, for mapping of functional properties, was carried out including straight-line distances, using the 3-D Euclidean measure, and shortest connections within the cortical surface, either with the Dijkstra or LOS-Floyd algorithms. Results: In BA3b a distinct somatotopy was found for both hands. The maximum spatial fingertip spread in BA3b after linear normalization was at least two times larger than for the non-linear normalization. All properties of the BA3b fingertip somatotopy were left-right symmetric. Since the representation in BA1 did not exhibit any of those features it was neglected. The LOS-Floyd algorithm achieved an error free result for the distances within measurement accuracy. In contrast, Euclidean distances did not account for the relevant cortical properties. Dijkstra distances were sufficiently precise in the individual brains, but excessively overestimated the left-hand distances after non-linear normalization. Discussion & conclusion: Since the representations in BA1 and BA3b are entirely different, the cytoarchitectonic divisions need to be accounted for. The convergence of the BA3b fingertip positions after non-linear normalization implies a removal of the anatomical variability, leaving exclusively functional variability. In the distance mapping, application of the LOS-Floyd algorithm was mandatory in the group template. The Dijkstra algorithm represents a fast alternative for distance mapping in individual brains, while the precision of Euclidean distances did not suffice for our purposes. In conclusion, our methodology improves the spatial precision of somatotopic mapping at 3 T. At larger field strengths improvements to even higher resolutions are anticipated.