From Blood Oxygenation Level Dependent (BOLD) signals to brain temperature maps

A theoretical framework is presented for converting Blood Oxygenation Level Dependent (BOLD) images to temperature maps, based on the idea that disproportional local changes in cerebral blood flow (CBF) as compared with cerebral metabolic rate of oxygen consumption (CMRO2) during functional brain activity, lead to both brain temperature changes and the BOLD effect. Using an oxygen limitation model and a BOLD signal model we obtain a transcendental equation relating CBF and CMRO2 changes with the corresponding BOLD signal, which is solved in terms of the Lambert W function. Inserting this result in the dynamic bio-heat equation describing the rate of temperature changes in the brain, we obtain a non autonomous ordinary differential equation that depends on the BOLD response, which is solved numerically for each brain voxel. In order to test the method, temperature maps obtained from a real BOLD dataset are calculated showing temperature variations in the range: (-0.15, 0.1) which is consistent with experimental results. The method could find potential clinical uses as it is an improvement over conventional methods which require invasive probes and can record only few locations simultaneously. Interestingly, the statistical analysis revealed that significant temperature variations are more localized than BOLD activations. This seems to exclude the use of temperature maps for mapping neuronal activity as areas where it is well known that electrical activity occurs (such as V5 bilaterally) are not activated in the obtained maps. But it also opens questions about the nature of the information processing and the underlying vascular network in visual areas that give rise to this result

Rapid Quantification of White Matter Disconnection in the Human Brain

With an estimated five million new stroke survivors every year and a rapidly aging population suffering from hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for assessing the impact of lesions consider only location, type, and extent, while ignoring how the affected region was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity. Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion-based white matter disconnection maps that require only moderate computational resources. We achieve this by creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small regions. We then interrupt these connectivity models by projecting patients' lesions into them to compute predicted white matter disconnection. A quantified disconnection map can be computed for an individual patient in approximately 35 seconds using a single core CPU-based computation. In comparison, a similar quantification performed with other tools provided by MRtrix3 takes 5.47 minutes.Comment: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies

CAPTURE ALS: The comprehensive analysis platform to understand, remedy and eliminate ALS

The absence of disease modifying treatments for amyotrophic lateral sclerosis (ALS) is in large part a consequence of its complexity and heterogeneity. Deep clinical and biological phenotyping of people living with ALS would assist in the development of effective treatments and target specific biomarkers to monitor disease progression and inform on treatment efficacy. The objective of this paper is to present the Comprehensive Analysis Platform To Understand Remedy and Eliminate ALS (CAPTURE ALS), an open and translational platform for the scientific community currently in development. CAPTURE ALS is a Canadian-based platform designed to include participants\u27 voices in its development and through execution. Standardized methods will be used to longitudinally characterize ALS patients and healthy controls through deep clinical phenotyping, neuroimaging, neurocognitive and speech assessments, genotyping and multisource biospecimen collection. This effort plugs into complementary Canadian and international initiatives to share common resources. Here, we describe in detail the infrastructure, operating procedures, and long-term vision of CAPTURE ALS to facilitate and accelerate translational ALS research in Canada and beyond

Brain resilience across the general cognitive ability distribution: Evidence from structural connectivity

Resting state functional connectivity research has shown that general cognitive ability (GCA) is associated with brain resilience to targeted and random attacks (TAs and RAs). However, it remains to be seen if the finding generalizes to structural connectivity. Furthermore, individuals showing performance levels at the very high area of the GCA distribution have not yet been analyzed in this regard. Here we study the relation between TAs and RAs to structural brain networks and GCA. Structural and diffusion-weighted MRI brain images were collected from 189 participants: 60 high cognitive ability (HCA) and 129 average cognitive ability (ACA) individuals. All participants completed a standardized fluid reasoning ability test and the results revealed an average HCA-ACA difference equivalent to 33 IQ points. Automated parcellation of cortical and subcortical nodes was combined with tractography to achieve an 82x82 connectivity matrix for each subject. Graph metrics were derived from the structural connectivity matrices. A simulation approach was used to evaluate the effects of recursively removing nodes according to their network centrality (TAs) versus eliminating nodes at random (RAs). HCA individuals showed greater network integrity at baseline and prior to network collapse than ACA individuals. These effects were more evident for TAs than RAs. The networks of HCA individuals were less degraded by the removal of nodes corresponding to more complex information processing stages of the PFIT network, and from removing nodes with larger empirically observed centrality values. Analyzed network features suggest quantitative instead of qualitative differences at different levels of the cognitive ability distributionThe study reported here was supported by research project ‘PSI2017-82218-P’ funded by ‘Ministerio de Economía, Industria y Competitividad’ (Spain

Neocortical age and fluid ability: greater accelerated brain aging for thickness, but smaller for surface area, in high cognitive ability individuals

Resting state functional connectivity research has shown that general cognitive ability (GCA) is associated with brain resilience to targeted and random attacks (TAs and RAs). However, it remains to be seen if the finding generalizes to structural connectivity. Furthermore, individuals showing performance levels at the very high area of the GCA distribution have not yet been analyzed in this regard. Here we study the relation between TAs and RAs to structural brain networks and GCA. Structural and diffusion-weighted MRI brain images were collected from 189 participants: 60 high cognitive ability (HCA) and 129 average cognitive ability (ACA) individuals. All participants completed a standardized fluid reasoning ability test and the results revealed an average HCA-ACA difference equivalent to 33 IQ points. Automated parcellation of cortical and subcortical nodes was combined with tractography to achieve an 82x82 connectivity matrix for each subject. Graph metrics were derived from the structural connectivity matrices. A simulation approach was used to evaluate the effects of recursively removing nodes according to their network centrality (TAs) versus eliminating nodes at random (RAs). HCA individuals showed greater network integrity at baseline and prior to network collapse than ACA individuals. These effects were more evident for TAs than RAs. The networks of HCA individuals were less degraded by the removal of nodes corresponding to more complex information processing stages of the PFIT network, and from removing nodes with larger empirically observed centrality values. Analyzed network features suggest quantitative instead of qualitative differences at different levels of the cognitive ability distributionThe study reported here was supported by research project ‘PSI2017-82218-P’ funded by ‘Ministerio de Economía, Industria y Competitividad’ (Spain