Biology subjects

Morgan, A. T.

Publications and source records attributed to Morgan, A. T..

3 recordsLinked to original sources

bioRxiv2020

Multi-centre, multi-vendor reproducibility of 7T QSM and R2* in the human brain: results from the UK7T study

We present the reliability of ultra-high field T2* MRI at 7T, as part of the UK7T Networks "Travelling Heads" study. T2*-weighted MRI images can be processed to produce quantitative susceptibility maps (QSM) and R2* maps. These reflect iron and myelin concentrations, which are altered in many pathophysiological processes. The relaxation parameters of human brain tissue are such that R2* mapping and QSM show particularly strong gains in contrast-to-noise ratio at ultra-high field (7T) vs clinical field strengths (1.5 - 3T). We aimed to determine the inter-subject and inter-site reproducibility of QSM and R2* mapping at 7T, in readiness for future multi-site clinical studies. MethodsTen healthy volunteers were scanned with harmonised single- and multi-echo T2*-weighted gradient echo pulse sequences. Participants were scanned five times at each "home" site and once at each of four other sites. The five sites had 1x Philips, 2x Siemens Magnetom, and 2x Siemens Terra scanners. QSM and R2* maps were computed with the Multi-Scale Dipole Inversion (MSDI) algorithm (https://github.com/fil-physics/Publication-Code). Results were assessed in relevant subcortical and cortical regions of interest (ROIs) defined manually or by the MNI152 standard space. Results and DiscussionMean susceptibility ({chi}) and R2* values agreed broadly with literature values in all ROIs. The inter-site within-subject standard deviation was 0.001 - 0.005 ppm ({chi}) and 0.0005 - 0.001 ms-1 (R2*). For {chi} this is 2.1-4.8 fold better than 3T reports, and 1.1-3.4 fold better for R2*. The median ICC from within- and cross-site R2* data was 0.98 and 0.91, respectively. Multi-echo QSM had greater variability vs single-echo QSM especially in areas with large B0 inhomogeneity such as the inferior frontal cortex. Across sites, R2* values were more consistent than QSM in subcortical structures due to differences in B0-shimming. On a between-subject level, our measured {chi} and R2* cross-site variance is comparable to within-site variance in the literature, suggesting that it is reasonable to pool data across sites using our harmonised protocol. ConclusionThe harmonized UK7T protocol and pipeline delivers on average a 3-fold improvement in the coefficient of reproducibility for QSM and R2* at 7T compared to previous reports of multi-site reproducibility at 3T. These protocols are ready for use in multi-site clinical studies at 7T.

neuroscience↗

bioRxiv2020

An Unsupervised Deep Neural Network for Image Completion Resembles Early Visual Cortex fMRI Activity Patterns for Occluded Scenes

AO_SCPLOWBSTRACTC_SCPLOWThe promise of artificial intelligence in understanding biological vision relies on the comparison of computational models with brain data with the goal of capturing functional principles of visual information processing. Convolutional neural networks (CNN) have successfully matched the transformations in hierarchical processing occurring along the brains feedforward visual pathway extending into ventral temporal cortex. However, we are still to learn if CNNs can successfully describe feedback processes in early visual cortex. Here, we investigated similarities between human early visual cortex and a CNN with encoder/decoder architecture, trained with self-supervised learning to fill occlusions and reconstruct an unseen image. Using Representational Similarity Analysis (RSA), we compared 3T fMRI data from a non-stimulated patch of early visual cortex in human participants viewing partially occluded images, with the different CNN layer activations from the same images. Results show that our self-supervised image-completion network outperforms a classical object-recognition supervised network (VGG16) in terms of similarity to fMRI data. This provides additional evidence that optimal models of the visual system might come from less feedforward architectures trained with less supervision. We also find that CNN decoder pathway activations are more similar to brain processing compared to encoder activations, suggesting an integration of mid- and low/middle-level features in early visual cortex. Challenging an AI model and the human brain to solve the same task offers a valuable way to compare CNNs with brain data and helps to constrain our understanding of information processing such as neuronal predictive coding.

neuroscience↗

bioRxiv2019

Two distinct feedback codes in V1 for \"real\" and \"imaginary\" internal experiences

Visual illusions and visual imagery are conscious sensory events that lack a corresponding physical input. But while everyday mental imagery feels distinct from incoming stimulus input, visual illusions, like hallucinations, are under limited volitional control and appear indistinguishable from physical reality. Illusions are thought to arise from lower-level processes within sensory cortices. In contrast, imagery involves a wide network of brain areas that recruit early visual cortices for the sensory representation of the imagined stimulus. Here, we combine laminar fMRI brain imaging with psychophysical methods and multivariate pattern analysis to investigate in human participants how seemingly real and imaginary non-physical experiences are processed in primary visual cortex (V1). We find that the content of mental imagery is only decodable in deep layers, whereas illusory content is only decodable at superficial depths. This suggests that feedback to the different layers may serve distinct functions: low-level feedback to superficial layers might be responsible for shaping perception-like experiences, while deep-layer feedback might serve the formation of a more malleable inner world, separate from ongoing perception.

neuroscience↗