Dharmendra S. Modha

My Work and Thoughts.

Nikola Kasabov

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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/

A Conceptual Cortical Surface Atlas

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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.

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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.

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AGEA: Anatomic Gene Expression Atlas

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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.