Partition the human brain with binary trees using ...

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Partition the human brain with binary trees using simulated annealing Presented During:

Tuesday Poster Stand-By Session Tuesday, June 12, 2012: 1:30 PM - 3:30 PM Room: Plenary Hall, Level 4 Poster No:

853 On Display:

Monday, June 11 & Tuesday, June 12 Authors: 1,2

Johnson GadElkarim

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, Dan Schonfeld , Olusola Ajilore , Jamie Feusner , Donatello Arienzo , Liang Zhan ,

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Teena Moody , Anand Kumar , Alex Leow Institutions: 1

Department of Electrical and Computer Engineering, University of Illinois-Chicago, Chicago, IL,

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Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, UCLA Department of Psychiatry and

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Biobehavioral Sciences, Los Angeles, CA, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA Poster Presenter(s):

Johnson GadElkarim - Contact Me University of Illinois at Chicago Chicago, United States Johnson GadElkarim - Contact Me University of Illinois at Chicago Chicago, United States Introduction:

Brain network analysis has emerged as one of the latest methods to study the human brain's complex structure using diffusion MRI or fMRI. Mathematical quantitative metrics based on the graph theory have been developed to analyze those networks. One important aspect of the brain when analyzed as a network is its community structure. To date, several papers have used modularity as a metric for probing such community structure. In this study, we explored intra- versus inter-modularity path length as new metrics to partition structural human brain networks. Methods:

We scanned 21 healthy subjects (13 male / 8 female; age: 48.33+/-14.3). Brain MRI data were acquired using a Philips 3.0T Achieva scanner supplied with 8-channel head-coil. T1-weighted images were acquired with MPRAGE sequence (FOV=240mm; TR/TE = 8.4/3.9ms; flip angle = 8o; voxel size = 1.1X1.1X1.1 mm). DWI images were acquired using SS-SE-EPI sequence (FOV = 240 mm; voxel size = 0.83 X 0.83X2.2 mm; TR/TE = 6994/71 ms; flip angle = 90o, 32 gradient directions, b = 700 s/mm2). Parallel imaging was also used with a SENSE factor of 2 to reduce scan time to ~ 4 min. Structural brain networks were produced using a pipeline consisting of eddy current correction for DWI data followed by diffusion tensor extraction and full brain white matter fiber tracts using DTI-Studio (http://www.mristudio.org). T1-weighted images were used to generate whole brain gray matter label maps using the Freesurfer software (http://surfer.nmr.mgh.harvard.edu/). Each label map is composed of 68 cortical ROIs. Structural networks were generated by counting the number of fibers connecting each node pair. Here we propose to use the intra- and inter-modular path length as measures of network integration between modules and are defined as follows: intra-PL

Mi

=∑

n,m∈Mi;n≠m

2

d

nm

/[({Mi} -{Mi})/2]

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inter-PL

Mi↔Mj

=∑

n∈Mi;m∈Mj

d

nm

/({Mi}{Mj})

Here {Mi} denotes the number of nodes in module Mi, and d

nm

denotes the length of the shortest path

connecting nodes n and m. The element inverse of the structural network was used to determine edge length and to compute shortest path lengths. To partition the 68 cortical regions into modules, we conducted hierarchical clustering to minimize the mean intra-modular path length while maximizing the mean inter-modular path length. At the first level, brain regions were randomly assigned to one of two modules, and optimal assignment was determined by minimizing the proposed cost function using simulated annealing [1]. This process is repeated at each level until a 4-level binary tree or dendrogram is reached (a total of 16 modules). Results:

Hierarchical clustering was first performed on the mean connectivity matrix by averaging across all 21 subjects' connectivity matrices. Our results suggested that at the first level, brain community structure revealed an anterior-rostral/posterior-caudal partitioning, followed by left/right in the next stage (Figure 1). To determine locally highly consistent community structures, we computed the scaled inclusivity, a measure of partition consistency proposed in [2] by comparing each individual's dendrogram to that obtained using the mean connectivity matrix. Figure 2 visualizes the mean scaled inclusivity, showing that the most consistent community structures are in the pre-frontal cortex, limbic structures, the visual cortex, and cortical regions surrounding the central sulcus.

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Conclusions:

We have presented a method for partitioning the human brain using binary trees. This was achieved using a hierarchical clustering approach by optimizing a metric based on intra/inter modular path lengths with simulated annealing. Our results revealed a natural anterior-rostral/posterior-caudal partition of the human brain. Future studies are needed to further validate the proposed partitioning method and to elucidate the clinical relevance of our results. Neuroanatomy:

Brain Networks Abstract Information References

1. Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. (1983), 'Optimization by Simulated Annealing', Science, vol. 220, no. 4598, pp. 671–680. 2. Steen, M.; Hayasaka, S.; Joyce, K.; Laurienti, P. (2011), 'Assessing the consistency of community structure in complex networks', Physical Review, E 84, 016111.

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