With the advent of ultra-high field (7T), high spatial resolution functional MRI (fMRI) has allowed the differentiation of the cortical representations of each of the digits at an individual-subject level in human primary somatosensory cortex (S1). Here we generate a probabilistic atlas of the contralateral SI representations of the digits of both the left and right hand in a group of 22 right-handed individuals. The atlas is generated in both volume and surface standardised spaces from somatotopic maps obtained by delivering vibrotactile stimulation to each distal phalangeal digit using a travelling wave paradigm. Metrics quantify the likelihood of a given position being assigned to a digit (full probability map) and the most probable digit for a given spatial location (maximum probability map). The atlas is validated using a leave-one-out cross validation procedure. Anatomical variance across the somatotopic map is also assessed to investigate whether the functional variability across subjects is coupled to structural differences. This probabilistic atlas quantifies the variability in digit representations in healthy subjects, finding some quantifiable separability between digits 2, 3 and 4, a complex overlapping relationship between digits 1 and 2, and little agreement of digit 5 across subjects. The atlas and constituent subject maps are available online for use as a reference in future neuroimaging studies.

The sensation of touch in the glabrous skin of the human hand is conveyed by thousands of fast-conducting mechanoreceptive afferents, which can be categorised into four distinct types. The spiking properties of these afferents in the periphery in response to varied tactile stimuli are well-characterised, but relatively little is known about the spatiotemporal properties of the neural representations of these different receptor types in the human cortex. Here, we use the novel methodological combination of single-unit intraneural microstimulation (INMS) with magnetoencephalography (MEG) to localise cortical representations of individual touch afferents in humans, by measuring the extracranial magnetic fields from neural currents. We found that by assessing the modulation of the beta (13-30 Hz) rhythm during single-unit INMS, significant changes in oscillatory amplitude occur in the contralateral primary somatosensory cortex within and across a group of fast adapting type I mechanoreceptive 20 afferents, which corresponded well to the induced response from matched vibrotactile stimulation. Combining the spatiotemporal specificity of MEG with the selective single-unit stimulation of INMS enables the interrogation of the central representations of different aspects of tactile afferent signalling within the human cortices. The fundamental finding that single-unit INMS ERD responses are robust and consistent with natural somatosensory stimuli will permit us to more dynamically probe the central nervous system responses in humans, to address questions about the processing of touch from the different classes of mechanoreceptive afferents and the effects of varying the stimulus frequency and patterning.

Previously published results indicate that the accuracy of decoding visual orientation from 7 Tesla fMRI data of V1 peaks at spatial acquisition resolutions that are routinely accessible with more conventional 3 Tesla scanners. This study directly compares the decoding performance between a 3 Tesla and a 7 Tesla dataset that were acquired using the same stimulation paradigm by applying an identical analysis procedure. The results indicate that decoding models built on 3 Tesla data are comparatively impaired. Moreover, we found no evidence for a strong coupling of BOLD signal change magnitude or temporal signal to noise ratio (tSNR) with decoding performance. Direct enhancement of tSNR via multiband fMRI acquisition at the same resolution did not translate into improved decoding performance. Additional voxel selection can boost 3 Tesla decoding performance to the 7 Tesla level only at a 3 mm acquisition resolution. In both datasets the BOLD signal available for orientation decoding is spatially broadband, but, consistent with the size of the BOLD point-spread-function, decoding models at 3 Tesla utilize spatially coarser image components.

Background Within the glabrous skin, touch is supported by mechanoreceptors, with most haptic sensations resulting from a combination of different mechanoreceptors being employed. These receptors can be broken down into two groups, those that are slowly-adapting (SAI, SAII), which detect changes in skin deformation (e.g. pressure), and those that are fast-adapting (FAI, FAII), which detect rapid fluctuations (e.g. vibration). A lot is known about the different receptors’ properties within the peripheral nervous system, but how these individual sensations are represented in the brain in humans has been investigated less. Here, we introduce the first known attempt to image these discrete responses using magnetoencephalography (MEG). Methods 9 participants volunteered to undergo a microneurography procedure, where a tungsten electrode was placed within the median nerve, and single mechanoreceptive afferents were recorded from. Once a single afferent had been identified, the participant received a small current (~5 μA, consisting of 60 200 μs pulses over 1 s) down the electrode to electrically-stimulate the same afferent identified physiologically. If there was a match, the participant underwent a scan using a 275-channel CTF MEG system in a synthetic 3rd order gradiometer configuration, where the same 1 s stimulation was repeated 80 times with ~10 s inter trial intervals. Data were frequency filtered between 13-30 Hz, and source localised using a beamformer and a 3-shell boundary element model. Pseudo-T statistical images were generated contrasting oscillatory power during stimulation against an epoch 8-9 seconds after stimulation. Results Across the cohort we recorded MEG data stimulating 19 mechanoreceptive afferents, and after quality control checks, we were left with 16 units (10 FAI, perceived sensation: point-like vibration; 5 SAI, perceived sensation: point-like pressure; 1 FAII, perceived sensation: diffuse vibration). A group analysis of the 10 FAI afferents show a significant event-related decrease in 13-30 Hz power during stimulation, with the largest change in power occurring in the postcentral gyrus. Permutation testing of the individual receptors’ images reveal that 8/10 of these receptors had significant reductions in power, which were all in spatial agreement with each other. Discussion In this investigation we have shown that it is possible to detect the spatial, temporal, and spectral properties of the cortical representations of individual mechanoreceptive afferents using MEG. Future investigations will interrogate how different afferent types manifest in the cortex in response to stimulation.

Spatial filtering strategies, combined with multivariate decoding analysis of BOLD images, have been used to investigate the nature of the neural signal underlying the discriminability of brain activity patterns evoked by sensory stimulation – primarily in the visual cortex. Previous research indicates that such signals are spatially broadband in nature, and are not primarily comprised of fine-grained activation patterns. However, it is unclear whether this is a general property of the BOLD signal, or whether it is specific to the details of employed analyses and stimuli. Here we applied an analysis strategy from a previous study on decoding visual orientation from V1 to publicly available, high-resolution 7T fMRI on the response BOLD response to musical genres in primary auditory cortex. The results show that the pattern of decoding accuracies with respect to different types and levels of spatial filtering is comparable to that obtained from V1, despite considerable differences in the respective cortical circuitry.

Spatial filtering strategies, combined with multivariate decoding analysis of BOLD images, have been used to investigate the nature of the neural signal underlying the discriminability of brain activity patterns evoked by sensory stimulation – primarily in the visual cortex. Previous research indicates that such signals are spatially broadband in nature, and are not primarily comprised of fine-grained activation patterns. However, it is unclear whether this is a general property of the BOLD signal, or whether it is specific to the details of employed analyses and stimuli. Here we applied an analysis strategy from a previous study on decoding visual orientation from V1 to publicly available, high-resolution 7T fMRI on the response BOLD response to musical genres in primary auditory cortex. The results show that the pattern of decoding accuracies with respect to different types and levels of spatial filtering is comparable to that obtained from V1, despite considerable differences in the respective cortical circuitry.

A decade after it was shown that the orientation of visual grating stimuli can be decoded from human visual cortex activity by means of multivariate pattern classification of BOLD fMRI data, numerous studies have investigated which aspects of neuronal activity are reflected in BOLD response patterns and are accessible for decoding. However, it remains inconclusive what the effect of acquisition resolution on BOLD fMRI decoding analyses is. The present study is the first to provide empirical ultra high-field fMRI data recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) on this topic — in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. We present detailed analysis, in line with predictions from previous simulation studies, about how the performance of orientation decoding varies with different acquisition resolutions. Moreover, we also examine different spatial filtering procedures and its effects on orientation decoding. Here we show that higher-resolution scans with subsequent down-sampling or low-pass filtering yield no benefit over scans natively recorded in the corresponding lower resolution regarding decoding accuracy. The orientation-related signal in the BOLD fMRI data is spatially broadband in nature, includes both high spatial frequency components, as well as large-scale biases previously proposed in the literature. Moreover, we found above chance-level contribution from large draining veins to orientation decoding. Acquired raw data were publicly released to facilitate further investigation.

Multivariate pattern classification methods have been successfully applied to decode orientation of visual grating stimuli from BOLD fMRI activity recorded in human visual cortex (Kamitani and Tong, 2005; Haynes and Rees, 2005) [12], [10]. Though there has been extensive research investigating the true spatial scale of the orientation specific signals (Op de Beeck, 2010; Swisher et al., 2010; Alink et al., 2013; Freeman et al., 2011, 2013) [2], [15], [1], [4], [5], it remained inconclusive what spatial acquisition resolution is required, or is optimal, for decoding analyses. The research article entitled “The effect of acquisition resolution on orientation decoding from V1 BOLD fMRI at 7T” Sengupta et al. (2017) [14] studied the effect of spatial acquisition resolution and also analyzed the strength and spatial scale of orientation discriminating signals. In this article, for the first time, we present empirical ultra high-field fMRI data, obtained as a part of the aforementioned study, which were recorded at four spatial resolutions (0.8mm, 1.4mm, 2mm, and 3mm isotropic voxel size) for orientation decoding in visual cortex. The dataset is compliant with the BIDS (Brain Imaging Data Structure) format, and freely available from the OpenfMRI portal (dataset accession number: http://openfmri.org/dataset/ds000113c ds000113c).

Here we present an update of the studyforrest ( http://studyforrest.org ) dataset that complements the previously released functional magnetic resonance imaging (fMRI) data for natural language processing with a new two-hour 3 Tesla fMRI acquisition while 15 of the original participants were shown an audio-visual version of the stimulus motion picture. We demonstrate with two validation analyses that these new data support modeling specific properties of the complex natural stimulus, as well as a substantial within-subject BOLD response congruency in brain areas related to the processing of auditory inputs, speech, and narrative when compared to the existing fMRI data for audio-only stimulation. In addition, we provide participants' eye gaze location as recorded simultaneously with fMRI, and an additional sample of 15 control participants whose eye gaze trajectories for the entire movie were recorded in a lab setting—to enable studies on attentional processes and comparative investigations on the potential impact of the stimulation setting on these processes.

The studyforrest ( http://studyforrest.org ) dataset is likely the largest neuroimag-ing dataset on natural language and story processing publicly available today. In this article, along with a companion publication, we present an update of this dataset that extends its scope to vision and multi-sensory research. 15 participants of the original cohort volunteered for a series of additional studies: a clinical examination of visual function, a standard retinotopic mapping procedure, and a localization of higher visual areas — such as the fusiform face area. The combination of this update, the previous data releases for the dataset, and the companion publication, which includes neuroimaging and eye tracking data from natural stimulation with a motion picture, form an extremely versatile and comprehensive resource for brain imaging research — with almost six hours of functional neuroimaging data across five different stimulation paradigms for each participant. Furthermore, we describe employed paradigms and present results that document the quality of the data for the purpose of characterising major properties of participants’ visual processing stream.

How to use the DICOM anonymizer step by step

The studyforrest (http://studyforrest.org) dataset is likely the largest neuroimaging dataset on natural language and story processing publicly available today. In this article, along with a companion publication, we present an update of this dataset that extends its scope to vision and multi-sensory research. 15 participants of the original cohort volunteered for a series of additional studies: a clinical examination of visual function, a standard retinotopic mapping procedure, and a localization of higher visual areas-such as the fusiform face area. The combination of this update, the previous data releases for the dataset, and the companion publication, which includes neuroimaging and eye tracking data from natural stimulation with a motion picture, form an extremely versatile and comprehensive resource for brain imaging research-with almost six hours of functional neuroimaging data across five different stimulation paradigms for each participant. Furthermore, we describe employed paradigms and present results that document the quality of the data for the purpose of characterising major properties of participants' visual processing stream.

Table of contents I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller 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, Cécile 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, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne 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 Raúl Hernández-Pérez, 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 Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A20 Self-organization and brain function Jörg P. Pfannmöller, 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 Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A23 DIPY: Brain tissue classification Julio E. Villalon-Reina, Eleftherios Garyfallidis