The Laboratory of Neuro Imaging Resource (LONIR) will create and apply innovative solutions for the investigation of imaging, genetics, behavioral and clinical data. The methods that we produce enable population-based analysis in numerous healthy and disease cohorts. We build upon our considerable prior progress in this competitive renewal proposal to focus our LONIR Technology Research and Development (TR&D) projects on three specific areas. TR&D 1 (Image Understanding) focuses on methodological developments for the analysis of brain imagery including robust image segmentation and registration, quality assurance and evaluation of image processing results, and processing of structural and diffusion brain data. TR&D 2 (Connectomics) will advance the study of brain connectivity using diffusion imaging and its powerful extensions. This project will go beyond tensor models of diffusion for assessing fiber integrity and connectivity, develop tract-based statistical analysis tools, introduce novel connectivity mapping approaches, and provide mechanisms for studying the genetics of brain connectivity. TR&D 3 (Data Interpretation) will utilize the imaging feature information extracted using tools from TR&D 1 and 2 and enable the interpretation of the resulting data, to address relevant biologically questions by providing tools for the selection of appropriate statistical models and the visual examination and interpretation of results. These research activities are tightly coupled and address the needs and requirements that have been presented to us by our Driving Biological Projects and Service Collaborators. These projects form a well-integrated program for the characterization, measurement, modeling, analysis, interpretation, and understanding of multifaceted patterns of structural and functional brain data. LONIR will facilitate studies of dynamically changing anatomical frameworks, e.g., developmental, neurodegenerative, traumatic, metastatic, by providing tools for comprehensive understanding of the nature and extent of these processes. These research efforts are supported by integrated Infrastructure, Dissemination, Training and Dissemination cores.
The Laboratory of Neuro Imaging Resource (LONIR) develops, validates and disseminates powerful and user-friendly tools and biomedical analysis protocols for studies of various neurological disorders, e.g., HIV, complex behavior, Alzheimer's disease, and child development. All LONIR data, analysis protocols, computational resources and research findings are openly shared online, enhancing research efforts of a wide community. The research efforts of LONIR investigators and collaborators are centered on the fundamental recognition that the brain is dynamic. LONIR facilitates studies of dynamically changing anatomical frameworks, e.g., developmental, neurodegenerative, traumatic, and metastatic, by providing tools for comprehensive understanding of the nature and extent of these processes.
|Duncan, Dominique; Vespa, Paul; Pitkänen, Asla et al. (2018) Big data sharing and analysis to advance research in post-traumatic epilepsy. Neurobiol Dis :|
|Wang, Junyan; Aydogan, Dogu Baran; Varma, Rohit et al. (2018) Modeling topographic regularity in structural brain connectivity with application to tractogram filtering. Neuroimage 183:87-98|
|Aydogan, Dogu Baran; Shi, Yonggang (2018) Tracking and validation techniques for topographically organized tractography. Neuroimage 181:64-84|
|Kim, Hosung; Caldairou, Benoit; Bernasconi, Andrea et al. (2018) Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling. Front Neuroinform 12:39|
|Duncan, Dominique; Vespa, Paul; Toga, Arthur W (2018) DETECTING FEATURES OF EPILEPTOGENESIS IN EEG AFTER TBI USING UNSUPERVISED DIFFUSION COMPONENT ANALYSIS. Discrete Continuous Dyn Syst Ser B 23:161-172|
|Azevedo, Christina J; Cen, Steven Y; Khadka, Sankalpa et al. (2018) Thalamic atrophy in multiple sclerosis: A magnetic resonance imaging marker of neurodegeneration throughout disease. Ann Neurol 83:223-234|
|Ning, Kaida; Chen, Bo; Sun, Fengzhu et al. (2018) Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework. Neurobiol Aging 68:151-158|
|Coletti, Amanda M; Singh, Deepinder; Kumar, Saurabh et al. (2018) Characterization of the ventricular-subventricular stem cell niche during human brain development. Development 145:|
|Aydogan, Dogu Baran; Jacobs, Russell; Dulawa, Stephanie et al. (2018) When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity. Brain Struct Funct 223:2841-2858|
|Gahm, Jin Kyu; Shi, Yonggang; Alzheimer’s Disease Neuroimaging Initiative (2018) Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space. Med Image Anal 46:189-201|
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