Supplementation of gamma-aminobutyric acid (GABA) affects temporal, but not spatial visual attention

In a randomized, double-blind, and placebo-controlled experiment, the acute effects of gamma-aminobutyric acid (GABA) supplementation on temporal and spatial attention in young healthy adults were investigated. A hybrid two-target rapid serial visual presentation task was used to measure temporal attention and integration. Additionally, a visual search task was used to measure the speed and accuracy of spatial attention. While temporal attention depends primarily on the distribution of limited attentional resources across time, spatial attention represents the engagement and disengagement by relevant and irrelevant stimuli across the visual field. Although spatial attention was unaffected by GABA supplementation altogether, we found evidence supporting improved performance in the temporal attention task. The attentional blink was numerically, albeit not significantly, attenuated at Lag 3, and significantly fewer order errors were committed at Lag 1, compared to the placebo condition. No effect was found on temporal integration rates. Although there is controversy about whether oral GABA can cross the blood-brain barrier, our results offer preliminary evidence that GABA intake might help to distribute limited attentional resources more efficiently, and can specifically improve the identification and ordering of visual events that occur in close temporal succession

Methods for quantitative susceptibility and R2*mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis

Contains fulltext : 228710.pdf (publisher's version ) (Open Access

Tensor image registration library: Deformable registration of stand‐alone histology images to whole‐brain post‐mortem MRI data

Background: Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data. Methods: Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. Results: All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2–0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. Conclusions: Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies

Post-mortem QSM and R2* maps

This repository contains data associated with the following publication: Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis Authors: Chaoyue Wang, Sean Foxley, Olaf Ansorge, Sarah Bangerter-Christensen, Mark Chiew, Anna Leonte, Ricarda A.L. Menke, Jeroen Mollink, Menuka Pallebage-Gamarallage, Martin R. Turner, Karla L. Miller*, Benjamin C. Tendler* (* indicates equal contribution) The text file dataset_loc_QSM_R2s.txt contains a link to the dataset. Further information about the dataset can be found in dataset_info.txt and the publication

The Digital Brain Bank, an open access platform for post-mortem datasets

Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab’s investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies