Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation

International audienceThis paper presents a new method for classifying surface datavia spectral representations of shapes. Our approach benefits classificationproblems that involve data living on surfaces, such as in cortical parcellation.For instance, current methods for labeling cortical points into surface parcelsoften involve a slow mesh deformation toward pre-labeled atlases, requiringas much as 4 hours with the established FreeSurfer. This may burden neurosciencestudies involving region-specific measurements. Learning techniquesoffer an attractive computational advantage, however, their representation ofspatial information, typically defined in a Euclidean domain, may be inadequatefor cortical parcellation. Indeed, cortical data resides on surfaces thatare highly variable in space and shape. Consequently, Euclidean representationsof surface data may be inconsistent across individuals. We proposeto fundamentally change the spatial representation of surface data, by exploitingspectral coordinates derived from the Laplacian eigenfunctions ofshapes. They have the advantage over Euclidean coordinates, to be geometryaware and to parameterize surfaces explicitly. This change of paradigm,from Euclidean to spectral representations, enables a classifier to be applieddirectly on surface data via spectral coordinates. In this paper, we decide tobuild upon the successful Random Decision Forests algorithm and improve itsspatial representation with spectral features. Our method, Spectral Forests,is shown to significantly improve the accuracy of cortical parcellations overstandard Random Decision Forests (74% versus 28% Dice overlaps), and produceaccuracy equivalent to FreeSurfer in a fraction of its time (23 secondsversus 3 to 4 hours)

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13–30 Hz) and gamma (31–80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer

Parcellation of the human cerebral cortex using diffusion MRI

Histological methods have long been used to segment the cerebral cortex into structurally distinct cortical areas that have served as a basis for research into brain structure and function and remain in use today. There is great interest in adapting and extending these methods to be able to use non-invasive imaging, so that tighter structure-function relationships can be measured in living subjects. Whilst diffusion neuroimaging methods have been widely applied to white matter, the reduced anisotropy in the thin, complexly folded grey matter of the cortex has so far limited its study. In vivo parcellation pipelines have instead focussed on T1 and T2 weighted MRI. Recent advances in imaging hardware have reignited interest in grey matter diffusion MRI as a viable candidate for characterising architectonic domains. This Thesis explores the capabilities of dMRI as a measure of cortical microstructure using in vivo datasets from healthy adult participants. A cortical parcellation pipeline was developed in which both unsupervised and supervised algorithms were explored. Results were presented at both the group level and single subject level across the entire cortical sheet. The diffusion-based feature space characterised the known variation in cellular composition and fibre density relative to the local cortical surface normal. Thus they remain invariant to the confounding orientation changes associated with cortical folding, which usually inhibit studies of cortical microstructure. The features were compared to the alternative T1w/T2w myelin mapping methods to demonstrate that the diffusion MRI signal provides a complementary mode of contrast. A series of classification experiments were used to determine the most effective methods for utilising diffusion in grey matter applications. Several additional methods from the dMRI literature were compared to highlight the benefit of higher-order tissue representations. Similarly, classification tasks were used to corroborate the benefits of sampling multiple b-values in cortical studies. The experimental chapters provide strong evidence in favour of the future use of diffusion MRI as a measure of the varying microstructure that defines cortical areas

A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.The authors would like to thank Professor Karla Miller (Oxford) for her help with the design of the ex vivo MRI acquisition; Ms. Mercedes I~niguez de Onzo~no and Mr. Francisco Romero (UCLM) for their careful technical laboratory help; and Mr. Gonzalo Artacho (UCLM) for his help with the digitization and curation of his organization of histological data. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska- Curie grant agreement No 654911 (project “THALAMODEL”) and by the European Research Council (ERC) Starting Grant agreement No 677697 (“BUNGEE-TOOLS”). It was also funded by the Spanish Ministry of Economy and Competitiveness(MINECO TEC-2014-51882-P, RYC- 2014-15440, PSI2015-65696, and SEV-2015-0490), the Basque Government (PI2016-12), and UCLM Internal Research Groups grants. Support for this research was also provided in part by the National Institute of Biomedical Imaging and Bioengineering (P41EB015896, 1R01EB023281, R01EB006758, R21EB018907, R01EB019956), the National Institute on Aging (5R01AG008122, R01AG016495), the National Institute of Diabetes and Digestive and Kidney Diseases (1-R21-DK- 108277-01), the National Institute of Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S- 10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multiinstitutional Human Connectome Project. In addition, B.F. has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. B.F.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (DOD award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimers Association; Alzheimers Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimers Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California