June 23, 2009

BlueMatter

Last week at the 15th Annual Meeting of the Organization of the Human Brain Mapping Conference, Anthony Sherbondy presented a breakthrough result.

Title: BlueMatter: Optimal Estimation of Long Range White Matter Fascicle Networks using Diffusion Tensor Imaging

Authors: AJ Sherbondy, R Ananthanarayanan, RF Dougherty, BA Wandell, DS Modha

Abstract:

Introduction: Fiber tractography algorithms generally use greedy algorithms to estimate one fiber group at a time. Consequently, the full white matter network can have unlikely properties – for example, there can be empty regions or regions of implausibly high white matter density. We describe a new approach to white matter estimation (BlueMatter) that optimizes the full network solution, not just individual fiber groups.  This algorithm makes significant use of the BlueGene/L architecture in order to find optimal solutions to a global scoring criterion from a database of 180 billion candidate connections. The network optimization (a) limits the relative fascicle density across the white matter volume, and (b) predicts the diffusion data from all fibers that pass through a voxel. We demonstrate an implementation of this method, and we show that the network solutions generated by BlueMatter have clear advantages over the greedy algorithms.

Methods: Diffusion-weighted data were acquired from a healthy male adult subject (age 35)  using a single-shot spin-echo, echo planar imaging sequence (260mm FOV; 128x128 matrix size; 2mm thick slices (no skip); b=800 s/mm2; 12 diffusion directions).  The BlueMatter technique comprises three parts: 1) Identification of potential gray matter voxels. The segmentation of neocortex and subcortical gray matter resulted in 50 thousand potential gray matter voxels. 2) Generation of candidate pathways that connect gray matter voxels: We generated 3.6 million potential pathways per voxel in 40 hours on the BlueGene supercomputer.  The total database contained 180 billion candidate pathways. 3) Selection of a network of pathways subject to: (a) predicting the diffusion-weighted data, and (b) producing a uniform fiber group density within the white matter.  The algorithms were run on a BlueGene supercomputer with 2048 processors each allocated 256 MB RAM. Optimal network solutions for each hemisphere were identified within 10 hours.  

Results: We compared the resulting pathway network with a whole brain pathway network solution derived using the Streamlines Tracing Technique (STT) [1]. BlueMatter produced a network that was at least as accurate in predicting the acquired diffusion data at every measurement location.  Within core white matter the pathway density variation for BlueMatter (50-150 fibers/voxel) was much more uniform compared to the STT solution (50-2000 fibers/voxel). The fiber group densities in a slice and along three typical lines within this slice are shown in Figure 1. 

Conclusions: By leveraging software developed for the BlueGene architecture and a novel mathematical model for assigning a likelihood score to a white matter network, BlueMatter provides neuroscientists with a method for identifying fascicle networks that simultaneously optimizes the predictions of the diffusion-weighted measurements and normalizes pathway density to match biological variation.

References:
Basser, P. (2000), 'In vivo fiber tractography using DT-MRI data', Magnetic Resonance in Medicine, vol. 44, no. 4, pp. 625-632.

BlueMatter

June 12, 2009

Nikola Kasabov

Today, I had the honor and pleasure of spending some time with Professor Nikola Kasabov.

Professor Nikola Kasabov is the Founding Director and the Chief Scientist of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland (www.kedri.info/). He holds a Chair of Knowledge Engineering at the School of Computing and Mathematical Sciences at Auckland University of Technology. He is a Fellow of the Royal Society of New Zealand, Fellow of the New Zealand Computer Society and a Senior Member of IEEE. He is the President of the International Neural Network Society (INNS) and a Past President of the Asia Pacific Neural Network Assembly (APNNA). He is a member of several technical committees of the IEEE Computational Intelligence Society and of the IFIP AI TC12. Kasabov is Associate Editor of several international journals, that include Neural Networks, IEEE TrNN, IEEE TrFS, Information Science, J. Theoretical and Computational Nanosciences. He chairs a series of int. conferences ANNES/NCEI in New Zealand. Kasabov holds MSc and PhD from the Technical University of Sofia. His main research interests are in the areas of intelligent information systems, soft computing, neuro-computing, bioinformatics, brain study, speech and image processing, novel methods for data mining and knowledge discovery. He has published more than 400 publications that include 15 books, 120 journal papers, 60 book chapters, 32 patents and numerous conference papers. He has extensive academic experience at various academic and research organisations: University of Otago, New Zealand; University of Essex, UK; University of Trento, Italy; Technical University of Sofia, Bulgaria; University of California at Berkeley; RIKEN and KIT, Japan; TUniversity Kaiserslautern, Germany, and others. More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.info/

June 02, 2009

A Conceptual Cortical Surface Atlas

Today, the journal PLoS ONE published a paper entitled "A Conceptual Cortical Surface Atlas" that I authored. The paper should be useful to neuro-anatomically-challenged lay people ("dummies") seeking a bird's eye view of cortical surface atlas. The key contribution is encapsulated in Figure S1. You can download the atlas in excel format here.

Abstract:

Volumetric, slice-based, 3-D atlases are invaluable tools for understanding complex cortical convolutions. We present a simple scheme to convert a slice-based atlas to a conceptual surface atlas that is easier to visualize and understand. The key idea is to unfold each slice into a one-dimensional vector, and concatenate a succession of these vectors – while maintaining as much spatial contiguity as possible – into a 2-D matrix. We illustrate our methodology using a coronal slice-based atlas of the Rhesus Monkey cortex. The conceptual surface-based atlases provide a useful complement to slice-based atlases for the purposes of indexing and browsing.

Cortical Surface Atlas

 

 

 

 

The key idea is to take slices in a stereotaxic atlas (for example, Paxinos G, Huang XF, Petrides M, Toga AW (2009) The Rhesus Monkey Brain in Stereotaxic Coordinates. Elsevier Science & Technology) and then convert each slice into a one-dimensional vector. The 1D vectors are then concatenated together to create a 2D representation.

The process of creating 1D vectors is hown below for Slices 23 and 22, respectively.

Slice 23

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Slice 22

May 19, 2009

President of United Republic of Tanzania

I had a rare honor and privilege to present Cognitive Computing to The Honorable H. E. Jakaya Mrisho Kikwete, President, United Republic of Tanzania

May 18, 2009

AGEA: Anatomic Gene Expression Atlas

Nature Neuroscience recently published a breakthrough paper from Allen Institute for Brain Science and several collaborating institutions. The study was led by Michael Hawrylycz who is a mathematician by training (advisor: late famed Gian-Carlo Rota).

TITLE: An anatomic gene expression atlas of the adult mouse brain

ABSTRACT: Studying gene expression provides a powerful means of understanding structure-function relationships in the nervous system. The availability of genome-scale in situ hybridization datasets enables new possibilities for understanding brain organization based on gene expression patterns. The Anatomic Gene Expression Atlas (AGEA) is a new relational atlas revealing the genetic architecture of the adult C57Bl/6J mouse brain based on spatial correlations across expression data for thousands of genes in the Allen Brain Atlas (ABA). The AGEA includes three discovery tools for examining neuroanatomical relationships and boundaries: (1) three dimensional expression-based correlation maps, (2) a hierarchical transcriptome-based parcellation of the brain and (3) a facility to retrieve from the ABA specific genes showing enriched expression in local correlated domains. The utility of this atlas is illustrated by analysis of genetic organization in the thalamus, striatum and cerebral cortex. The AGEA is a publicly accessible online computational tool integrated with the ABA (http://mouse.brain-map.org/agea).

The paper brings structure-function together at previously unprecendented scale of 200 micron x 200 micron x 200 micron grid cells.

April 28, 2009

Biomedical Computation Review: Reverse Engineering the Brain

Biomedical Computation Review published by Simbios (funded by NIH) carried a cover story by Roberta Friedman on Reverse Engineering the Brain. You can see it here. It is thoroghly researched, and covers work of Gerald Edelman, Kwabena Boahen, Tomaso Poggio, Thomas Serre, Eric Knudsen, myself, amongst others.

Biomedical Computation Review

April 23, 2009

Humor: Theory of Intelligence from Cheers!

"Well, you see, Norm, it's like this. A herd of buffalo can only move as fast as the slowest buffalo. And when the herd is hunted, it's the slowest and weakest ones at the back that are killed first. This natural selection is good for the herd as a whole, because the general speed and health of the whole group keeps improving by the regular killing of the weakest members.

In much the same way, the human brain can only operate as fast as the slowest brain cells. Now, as we know, excessive intake of alcohol kills brain cells. But naturally, it attacks the slowest and weakest brain cells first. In this way, regular consumption of beer eliminates the weaker brain cells, making the brain a faster and more efficient machine.

And that, Norm, is why you always feel smarter after a few beers."
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