Association of brain network efficiency with aging, depression, and cognition.
Ajilore O, Lamar M, Kumar A.
Am J Geriatr Psychiatry. 2014 Feb;22(2):102-10. doi: 10.1016/j.jagp.2013.10.004.
Altered structural brain connectome in young adult fragile X premutation carriers
A Leow, D Harvey, N Goodrich-Hunsaker, J GadElkarim, A Kumar, L Zhan, S.M Rivera, and T Simon. Human Brain Mapping (in press)
Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space.
Kim WH, Adluru N, Chung MK, Charchut S, GadElkarim JJ, Altshuler L, Moody T, Kumar A, Singh V,Leow AD.
Med Image Comput Comput Assist Interv. 2013;16(Pt 3):643-51.
Constructing the resting state structural connectome.
Ajilore O, Zhan L, Gadelkarim J, Zhang A, Feusner JD, Yang S, Thompson PM, Kumar A, Leow A.
Front Neuroinform. 2013 Dec 5;7:30. doi: 10.3389/fninf.2013.00030
Graph theory analysis of cortical-subcortical networks in late-life depression
Ajilore OA, Lamar M, Leow AD, Zhang A, Yang S, Kumar A
American Journal of Geriatric Psychiatry, 2013 Jul 3
Investigating Brain Community Structure Abnormalities in Bipolar Disorder Using Path Length Associated Community Estimation
GadElkarim JJ, Ajilore O, Schonfeld D, Zhan L, Thompson PM,Feusner JD, Kumar A, Altshuler L, Leow AD.
Human Brain Mapping, 2013 Jun 25
Abnormal brainnetwork organization in body dysmorphic disorder.
Arienzo D, Leow A, Brown JA, Zhan L, Gadelkarim J, Hovav S, Feusner JD.
Neuropsychopharmacology, 2013 May; 38(6):1130-9
Impaired inter-hemispheric integration in bipolar disorder revealed with brain network analyses.
Leow A, Ajilore O, Zhan L, Arienzo D, GadElkarim J, Zhang A, Moody T, Van Horn J, Feusner J, Kumar A, Thompson P, Altshuler L.
Biol Psychiatry. 2013 Jan 15;73(2):183-9
Hierarchical structural mapping for globally optimized estimation of functional networks.
Leow AD, Zhan L, Arienzo D, GadElkarim JJ, Zhang AF, Ajilore O, Kumar A, Thompson PM, Feusner JD.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):228-36.
A framework for quantifying node-level community structure group differences in brain connectivity networks.
GadElkarim JJ, Schonfeld D, Ajilore O, Zhan L, Zhang AF, Feusner JD, Thompson PM, Simon TJ, Kumar A, Leow AD.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):196-203.
MEASURING INTER-HEMISPHERIC INTEGRATION IN BIPOLAR AFFECTIVE DISORDER USING BRAIN NETWORK ANALYSES AND HARDI.
Leow A, Zhan L, Ajilore O, Gadelkarim J, Zhang A, Arienzo D, Moody T, Feusner J, Kumar A, Thompson P, Altshuler L.
Proc IEEE Int Symp Biomed Imaging. 2012:5-8.
TDF-TRACT: Probabilistic tractography using the tensor distribution function
GadElkarim, J.J. ; Zhan, L. ; Yang, S.L. ; Zhang, A.F. ; Altshuler, L. ; Lamar, M. ; Ajilore, O. ; Thompson, P.M. ; Kumar, A.;
Leow, A.Proc IEEE Int Symp Biomed Imaging 2011:812-816
Immersive 3D visualization of Whole-Brain Tractography
This video displays a 3D interactive immersive environment for visualizing tractography from a single subject derived with standard diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI). The visualization in CAVE2™ is sensitive to the user's point of view through the use of head-tracking 3D glasses. Movement through the virtual brain is controlled by the user with a modified Playstation Move controller. By visualizing tractography data in CAVE2, differences between DTI and HARDI in reconstructed fiber density and fiber geometry are easily appreciated.
CAVE2 is approximately 24 feet in diameter and 8 feet tall, and consists of 72 near-seamless passive stereo off-axis-optimized 3D LCD panels, a 36-node high-performance computer cluster, a 20-speaker surround audio system, a 10-camera optical tracking system and a 100-Gigabit/second connection to the outside world. CAVE2 provides users with a 320-degree panoramic environment for displaying information at 37 Megapixels in 3D or 74 Megapixels in 2D with a horizontal visual acuity of 20/20.
Path Length Associated Community Estimation (PLACE)
Path length associated community estimation (PLACE) is a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, ?PL , which measures the difference between intercommunity versus intracommunity path lengths PLACE consists of the following: (1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, (2) constructing and assessing mean group community structure, and (3) detecting node-level changes in community between groups.. In this video, we see how PLACE iteratively creates community structures over 4 stages.
The Brain IsoMap (BRISOMAP)
The Brain Isomap is an innovative technique for conceptualizing and visualizing the intrinsic geometry of brain connectomes. Using a non-linear dimensionality reduction techniqe applied to the graph distance matrix, the Isomap represents the intrinsic geometry of the structural connectome in a d-dimensional Euclidean space. The video demonstrates that the optimal embedding was achieved with d=3 (3-dimensional space). The spatial distance of nodes in the Isomap reflect the graph distance of those nodes. Thus, highly connected nodes or rich-club nodes tend to be centrally located in the Isomap.
WTTW Coverage of the CAVE2 Connectome Visualization Project
Functional by Structural Hierarchical (FSH) Mapping
FSH Mapping is a novel multimodal integration method for creating connectomes from structural and functional imaging data. In brief, the method estimates the white matter structure underlying resting-state functional connectivity. FSH assumes that the resting-state functional connectivity between two regions can be modeled as an exponential decay function of the "modified" graph distance of the structural connectivity matrix subject to a utilization matrix that is estimated using simulated annealing.
Video demonstration of dimensionality reduction in the CAVE2(R)