Universal dynamic fitting of magnetic resonance spectroscopy

Purpose: Dynamic (2D) magnetic resonance spectroscopy is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modelling temporal changes in the parameters. Here we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type. Methods: A simple user-interface allows information to be shared and precisely modelled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting. Results: Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data. Conclusion: A toolbox for generalised and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials

Diffusion Tensor Imaging of Dolphin Brains Reveals Direct Auditory Pathway to Temporal Lobe

The brains of odontocetes (toothed whales) look grossly different from their terrestrial relatives. Because of their adaptation to the aquatic environment and their reliance on echolocation, the odontocetes’ auditory system is both unique and crucial to their survival. Yet, scant data exist about the functional organization of the cetacean auditory system. A predominant hypothesis is that the primary auditory cortex lies in the suprasylvian gyrus along the vertex of the hemispheres, with this position induced by expansion of ‘associative0 regions in lateral and caudal directions. However, the precise location of the auditory cortex and its connections are still unknown. Here, we used a novel diffusion tensor imaging (DTI) sequence in archival post-mortem brains of a common dolphin (Delphinus delphis) and a pantropical dolphin (Stenella attenuata) to map their sensory and motor systems. Using thalamic parcellation based on traditionally defined regions for the primary visual (V1) and auditory cortex (A1), we found distinct regions of the thalamus connected to V1 and A1. But in addition to suprasylvian-A1, we report here, for the first time, the auditory cortex also exists in the temporal lobe, in a region near cetacean-A2 and possibly analogous to the primary auditory cortex in related terrestrial mammals (Artiodactyla). Using probabilistic tract tracing, we found a direct pathway from the inferior colliculus to the medial geniculate nucleus to the temporal lobe near the sylvian fissure. Our results demonstrate the feasibility of postmortem DTI in archival specimens to answer basic questions in comparative neurobiology in a way that has not previously been possible and shows a link between the cetacean auditory system and those of terrestrial mammals. Given that fresh cetacean specimens are relatively rare, the ability to measure connectivity in archival specimens opens up a plethora of possibilities for investigating neuroanatomy in cetaceans and other species

Perceptually relevant remapping of human somatotopy in 24 hours

Experience-dependent reorganisation of functional maps in the cerebral cortex is well described in the primary sensory cortices. However, there is relatively little evidence for such cortical reorganisation over the short-term. Using human somatosensory cortex as a model, we investigated the effects of a 24 hr gluing manipulation in which the right index and right middle fingers (digits 2 and 3) were adjoined with surgical glue. Somatotopic representations, assessed with two 7 tesla fMRI protocols, revealed rapid off-target reorganisation in the non-manipulated fingers following gluing, with the representation of the ring finger (digit 4) shifted towards the little finger (digit 5) and away from the middle finger (digit 3). These shifts were also evident in two behavioural tasks conducted in an independent cohort, showing reduced sensitivity for discriminating the temporal order of stimuli to the ring and little fingers, and increased substitution errors across this pair on a speeded reaction time task

Bayesian optimization of large-scale biophysical networks

The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g. derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties (e.g. band-specific functional connectivity); and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. Using five parameters controlling key aspects of our model, we find that this method is able to converge to regions of high functional similarity with real MEG data, with very few samples given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations

Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs

With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation

Using GPUs to accelerate computational diffusion MRI: from microstructure estimation to tractography and connectomes

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, it can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performances than 200 CPU cores thanks to our parallel designs

Improved tractography using asymmetric fibre orientation distributions

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity

The relationship between visual acuity loss and GABAergic inhibition in amblyopia

Early childhood experience alters visual development, a process exemplified by amblyopia, a common neurodevelopmental condition resulting in cortically reduced vision in one eye. Visual deficits in amblyopia may be a consequence of abnormal suppressive interactions in the primary visual cortex by inhibitory neurotransmitter γ-aminobutyric acid (GABA). We examined the relationship between visual acuity loss and GABA+ in adult human participants with amblyopia. Single voxel proton magnetic resonance spectroscopy (MRS) data were collected from the early visual cortex (EVC) and posterior cingulate cortex (control region) of twenty-eight male and female adults with current or past amblyopia while they viewed flashing checkerboards monocularly, binocularly, or while they had their eyes closed. First, we compared GABA+ concentrations between conditions to evaluate suppressive binocular interactions. Then, we correlated the degree of visual acuity loss with GABA+ levels to test whether GABAergic inhibition could explain visual acuity deficits. Visual cortex GABA+ was not modulated by viewing condition, and we found weak evidence for a negative correlation between visual acuity deficits and GABA+. These findings suggest that reduced vision in one eye due to amblyopia is not strongly linked to GABAergic inhibition in the visual cortex. We advanced our understanding of early experience dependent plasticity in the human brain by testing the association between visual acuity deficits and visual cortex GABA in amblyopes of the most common subtypes. Our study shows that the relationship was not as clear as expected and provides avenues for future investigation

Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain

While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks

GABA and glutamate in hMT+ link to individual differences in residual visual function after occipital stroke

BACKGROUND: Damage to the primary visual cortex following an occipital stroke causes loss of conscious vision in the contralateral hemifield. Yet, some patients retain the ability to detect moving visual stimuli within their blind field. The present study asked whether such individual differences in blind field perception following loss of primary visual cortex could be explained by the concentration of neurotransmitters γ-aminobutyric acid (GABA) and glutamate or activity of the visual motion processing, human middle temporal complex (hMT+). METHODS: We used magnetic resonance imaging in 19 patients with chronic occipital stroke to measure the concentration of neurotransmitters GABA and glutamate (proton magnetic resonance spectroscopy) and functional activity in hMT+ (functional magnetic resonance imaging). We also tested each participant on a 2-interval forced choice detection task using high-contrast, moving Gabor patches. We then measured and assessed the strength of relationships between participants’ residual vision in their blind field and in vivo neurotransmitter concentrations, as well as visually evoked functional magnetic resonance imaging activity in their hMT+. Levels of GABA and glutamate were also measured in a sensorimotor region, which served as a control. RESULTS: Magnetic resonance spectroscopy-derived GABA and glutamate concentrations in hMT+ (but not sensorimotor cortex) strongly predicted blind-field visual detection abilities. Performance was inversely related to levels of both inhibitory and excitatory neurotransmitters in hMT+ but, surprisingly, did not correlate with visually evoked blood oxygenation level–dependent signal change in this motion-sensitive region. CONCLUSIONS: Levels of GABA and glutamate in hMT+ appear to provide superior information about motion detection capabilities inside perimetrically defined blind fields compared to blood oxygenation level–dependent signal changes—in essence, serving as biomarkers for the quality of residual visual processing in the blind-field. Whether they also reflect a potential for successful rehabilitation of visual function remains to be determined