To introduce a standardized and automatized method for functional MRI (fMRI) examinations of the cortical sensory somatotopy in large samples for investigations of the fingertip somatotopy in the primary somatosensory cortex.At 3 Tesla, T2* (spin-spin relaxation time) weighted images (gradient-echo echo planar imaging, voxel size 1.5 × 1.5 × 2 mm3) were acquired during stimulation of the finger tips for thumb, index and middle finger on both hands, in a group of 18 healthy participants. In addition, structural T1 weighted (magnetization prepared rapid gradient echo, isotropic voxel size 1 mm) and MR-angiography (time of flight, voxel size 0.26 × 0.26 × 0.5 mm3) images were recorded. Boundary based register served to combine movement correction and registration in FreeSurfer Functional analysis stream (FS-Fast), resulting in fine scale corrections, as revealed with FSL Possum (FSL FMRIB Software Library Physics-Oriented Simulated Scanner for Understanding MRI) simulations. Automated data analysis was achieved by inclusion of cytoarchitectonic probability maps for calculation of functional activation in Brodmann area 3b. Draining vessel artifacts were identified using the peak value approach and the MR-angiography. Distances were computed as the shortest connection within the gray matter.The fMRI somatotopic maps agreed with the expected fingertip somatotopy in 63% of the investigated subjects, an improvement of 34% compared with FS-Fast. Artifacts have been removed completely. Adjacent fingertips showed average distances of 8 ± 4.3 mm, and between thumb and middle finger 13.4 ± 4.8 mm was found. Distances for both hands were similar as expected from the characteristics of the fingertip spatial tactile resolution.The introduced evaluation procedure allowed automated analysis of the fingertip representation in excellent agreement with preceding results.
I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jorg P. Pfannmoller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cecile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Remi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Etienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raul Hernandez-Perez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johnson A16 Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts Erik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Sook-Lei Liew A17 A cortical surface-based geodesic distance package for Python Daniel S Margulies, Marcel Falkiewicz, Julia M Huntenburg A18 Sharing data in the cloud David O’Connor, Daniel J. Clark, Michael P. Milham, R. Cameron Craddock A19 Detecting task-based fMRI compliance using plan abandonment techniques Ramon Fraga Pereira, Anibal Solon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A20 Self-organization and brain function Jorg P. Pfannmoller, Rickson Mesquita, Luis C.T. Herrera, Daniela Dentico A21 The Neuroimaging Data Model (NIDM) API Vanessa Sochat, B Nolan Nichols A22 NeuroView: a customizable browser-base utility Anibal Solon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A23 DIPY: Brain tissue classification Julio E. Villalon-Reina, Eleftherios Garyfallidis
Brodmann area 4 will be mapped with a state motor_actv.Thus, for a paradigm that works with motor tasks, the plan must contain motor_actv for the given time that the task occurs. DiscussionThe formalization of brain states strongly depends on the discretization of specific region states, which might vary from subject to subject.In order to normalize the signals, a previous tuning phase is required with simple paradigms, depending on which paradigm will be executed.During the scan, an online normalization must be made to a standard space, such as the MNI brain space.This realtime processing is required to map expected active regions to the previously selected brain areas from an atlas.The usage of real-time fMRI methods aggregates to our approach since the tuning and pursuance recognition can be made during the exam.Such real-time fMRI methods can also monitor movements during the scan in order to identify if there is too much subject movement.In the case of fMRI paradigm abandonment, the paradigm can be adapted to induce or interest the subject in a way that the subject proceeds with its tasks, using methods such as demonstrated by [2].Neurofeedback can be used to sustain the subject's interest by letting the paradigm be more challenging, requiring more attention and collaboration from the patient, such as the paradigm from [3]. ConclusionsThis project is in its initial phase.Real-time fMRI methods are being tested, using AFNI's provided tools.In order to use plan abandonment techniques, the next step is to formalize basic stimuli types based on mapped regions.By using these formalizations, paradigms can be converted to a problem of plan abandonment and it becomes possible to evaluate the participation of a subject during the scan.
Intracortical mapping in monkeys revealed a full body map in all four cytoarchitectonic subdivisions of the contralateral primary somatosensory cortex (S1), as well as positive associations between spatio-tactile acuity performance of the fingers and their representation field size especially within cytoarchitectonic Area 3b and Area 1. Previous non-invasive investigations on these associations in humans assumed a monotonous decrease of representation field size from index finger to little finger although the field sizes are known to change in response to training or in disease. Recent developments improved noninvasive functional mapping of S1 by a) adding a cognitive task during repetitive stimulation to decrease habituation to the stimuli, b) smaller voxel size of fMRI-sequences, c) surface-based analysis accounting for cortical curvature, and d) increase of spatial specificity for fMRI data analysis by avoidance of smoothing, partial volume effects, and pial vein signals. We here applied repetitive pneumatic stimulation of digit 1 (D1; thumb) and digit 5 (D5; little finger) on both hands to investigate finger/hand representation maps in the complete S1, but also in cytoarchitectonic Areas 1, 2, 3a, and 3b separately, in 21 healthy volunteers using 3T fMRI. The distances between activation maxima of D1 and D5 were evaluated by two independent raters, blinded for performance parameters. The fingertip representations showed a somatotopy and were localized in the transition region between the crown and the anterior wall of the post central gyrus agreeing with Area 1 and 3b. Participants were comprehensively tested for tactile performance using von Freyhair filaments to determine cutaneous sensory thresholds (CST) as well as grating orientation thresholds (GOT) and two-point resolution (TPD) for spatio-tactile acuity testing. Motor performance was evaluated with pinch grip performance (Roeder test). We found bilateral associations of D1-D5 distance for GOT thresholds and partially also for TPD in Area 3b and in Area 1, but not if using the complete S1 mask. In conclusion, we here demonstrate that 3T fMRI is capable to map associations between spatio-tactile acuity and the fingertip representation in Area 3b and Area 1 in healthy participants.
In analogy to the appreciation of humor, that of tickling is based upon the re-interpretation of an anticipated emotional situation. Hence, the anticipation of tickling contributes to the final outburst of ticklish laughter. To localize the neuronal substrates of this process, functional magnetic resonance imaging (fMRI) was conducted on 31 healthy volunteers. The state of anticipation was simulated by generating an uncertainty respecting the onset of manual foot tickling. Anticipation was characterized by an augmented fMRI signal in the anterior insula, the hypothalamus, the nucleus accumbens and the ventral tegmental area, as well as by an attenuated one in the internal globus pallidus. Furthermore, anticipatory activity in the anterior insula correlated positively with the degree of laughter that was produced during tickling. These findings are consistent with an encoding of the expected emotional consequences of tickling and suggest that early regulatory mechanisms influence, automatically, the laughter circuitry at the level of affective and sensory processing. Tickling activated not only those regions of the brain that were involved during anticipation, but also the posterior insula, the anterior cingulate cortex and the periaqueductal gray matter. Sequential or combined anticipatory and tickling-related neuronal activities may adjust emotional and sensorimotor pathways in preparation for the impending laughter response.
