294 research outputs found
Archiving and Sharing Functional MRI data
No full textThis document describes the file structure and metadata requirements followed in the collating of Edinburgh Imaging fMRI results and their preparation for online sharing, as at July 2015
Best practice for fMRI displays, plots and colour maps
No full textSets of illustrations for best practices in displaying tomographic imaging data - applied here to functional MRI.
Images either shows different ways to present the data or show various colour maps with luminance correction.5 figures showing examples of data visualization with fMRI (fMRI_*)
4 figures showing colour maps with/without luminance correction (Colour*
Structural MRI: from Slices to Faces and the effect of defacing
No full textImage (3 formats, png original, psd and jpg size and resolution interpolated and colour edited) of a reconstructed face from a series of MRI slices, along with various 'defacing' for pseudonymization. MRI data were obtained at the Brain Research Imaging Centre, showing the authors' face. Face renders were made with MRICroGL (http://www.cabiatl.com/mricrogl/), and defaced version computed using mask_face (https://nrg.wustl.edu/software/face-masking/usage/), mri_deface from the freesurfer suite (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface) and SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/)
Edinburgh_NIH10
No full textThis data set contains 10 normal healthy subjects ('sub-001' to 'sub-010') from a larger NIH funded project (NIH grant R01 EB004155) investigating changes in water diffusion parameters with age. Subjects underwent structural and diffusion MRI at 1.5 T and provided images suitable for a wide range of analyses, e.g. tractography, connectome etc
Structural MRI: from Slices to Faces and the effect of defacing
No full textImage (3 formats, png original, psd and jpg size and resolution interpolated and colour edited) of a reconstructed face from a series of MRI slices, along with various 'defacing' for pseudonymization. MRI data were obtained at the Brain Research Imaging Centre, showing the authors' face. Face renders were made with MRICroGL (http://www.cabiatl.com/mricrogl/), and defaced version computed using mask_face (https://nrg.wustl.edu/software/face-masking/usage/), mri_deface from the freesurfer suite (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface) and SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/).Pernet, Cyril. (2020). Structural MRI: from Slices to Faces and the effect of defacing, [image]. University of Edinburgh, Center for Clinical Brain Sciences & Edinburgh Imaging. https://doi.org/10.7488/ds/2877
Masks2Metrics (M2M): A Matlab Toolbox for Gold Standard Morphometrics
No full textHuman brains undergo morphometric changes over a lifetime, from conception through to birth, infancy, adolescence, adulthood, and old age (Thambisetty et al. (2010); Madan and Kensinger (2016)). This is further compounded by the changes associated with var-ious brain pathologies such as tumours (e.g. Bauer et al. (2013)) and dementia (e.g., B. C. Dickerson et al. (2011)). It is therefore essential to accurately and scientifically characterise such changes by using an array of morphologic measurements, for a better un-derstanding of the natural progression of ageing and disease (Mills et al. (2016); Madan (2017)). While many existing brain image analysis tools (e.g., FreeSurfer (Fischl et al. (2004); Desikan et al. (2006)), BrainSuite (Shattuck and Leahy (2002)), and BrainVISA (Kochunov et al. (2012))) automatically compute such data from a 3-dimensional (3D) brain image, they lack the ability to do so for the equivalent manually-traced regions of interest (ROIs). This is all the more significant as such ROIs are considered as the gold standard, thus making knowledge of their metrics essential. We have developed an automated Matlab-based tool, Masks2Metrics (Mikhael and Gray (2017)), that calculates three metrics for a given ROI in a 3D image: thickness, volume and suface area. An ROI is defined by a pair of binary masks (in NIfTI file format) representing its outer and inner borders, each of which are drawn continuously along one direction (x-, y-or z-axis). In the specific case of brain images, when the ROI describes a gyrus, its paired masks would correspond to grey matter (GM) and white matter (WM) curves. The paired ROI NIfTI (.nii) masks are expected to be of the form subjroihemgm/wmsegments.nii. For example, a pair corresponding to subject 1's right SFG (superior frontal gyrus) would be 1sfgrgm1.nii and 1sfgrwm1.nii. A special feature of M2M is that multiple pairs, or segments, can be used rather than a single continuous ROI. These segments can be manually or automatically derived. The gener-ated ROI metrics are grey matter thickness (GMth), grey matter volume (GMvol),and white matter surface area (WMsa), also classically calculated by popular existing au-tomated tools (Fischl2000; Shattuck2002) . Additionally, the ROI's corresponding mean Fréchet(Ursell (2013)) and mean Modified Hausdorff Distance (SasiKanth (2011)) are calculated and saved as matrices
Gabapentin for the Management of Chronic Pelvic Pain in Women (GaPP1)
No full textThis is a pilot study looking at the effect of Gabapentin in women with chronic pelvic pain. Participants were assigned at random to the placebo or the drug group. The design follows procedure of double blinding, i.e. neither patients, nor people involved in data collection or analysis knew the participants group. There was two sessions on thermal stimulation, in which a thermal probe was applied to the abdomen or to the hand. After each stimulation are two rating scales were presentated in succession. In a third session, punctuations were applied to he hand and fMRI scans conducted. The full study (not fMRI) report has been published here: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153037 .* The experiment was conducted using "Presentation" (NeuroBehavioural Systems www.neurobs.com) and the scripts are available in Experiment.zip (in the /stimuli directory of the Gapp1.zip archive). Files named *.sce and *.exp are for use with the package "Presentation"; they are plain text, so the contents may be read using any text editor.
* There was two sessions on thermal stimulation, in which a thermal probe was applied to the abdomen or to the hand. After each stimulation are two rating scales were presentated in succession. In a third session, punctuations were applied to he hand. Files subxxxx_ThermalAbdomen.tsv and subxxxx_ThermalHand.tsv show the timing of the thermal probe, rating scales and volume acquisition.
* Files subxxxx_ThermalAbdomen_scales.tsv and subxxxx_ThermalHand_scales.tsv report the answer of the subjects.
* The file subxxxx_punctate.tsv reports the timing of the stimulation.
* Participants.tsv contains the behavioural data collected for each participant
Archiving and Sharing Functional MRI data
No full textThis document describes the file structure and metadata requirements followed in the collating of Edinburgh Imaging fMRI results and their preparation for online sharing, as at July 2015.Pernet, Cyril. (2015). Archiving and Sharing Functional MRI data, [text]. Edinburgh Imaging, University of Edinburgh. http://dx.doi.org/10.7488/ds/283
Quantifying the time course of visual object processing using ERPs: it's time to up the game
No full textHundreds of studies have investigated the early ERPs to faces and objects using scalp and intracranial recordings. The vast majority of these studies have used uncontrolled stimuli, inappropriate designs, peak measurements, poor figures, and poor inferential and descriptive group statistics. These problems, together with a tendency to discuss any effect p < 0.05 rather than to report effect sizes, have led to a research field very much qualitative in nature, despite its quantitative inspirations, and in which predictions do not go beyond condition A > condition B. Here we describe the main limitations of face and object ERP research and suggest alternative strategies to move forward. The problems plague intracranial and surface ERP studies, but also studies using more advanced techniques – e.g., source space analyses and measurements of network dynamics, as well as many behavioral, fMRI, TMS, and LFP studies. In essence, it is time to stop amassing binary results and start using single-trial analyses to build models of visual perception
LIMO EEG v1.5
No full textMagnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses
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