Estimating Brain Connectivity for Pattern Analysis

Onal, Itir
Aksan, Emre
Velioglu, Burak
Firat, Orhan
Ozay, Mete
Yarman Vural, Fatoş Tunay
In this study, the degree of connectivity for each voxel, which is the unit element of functional Magnetic Resonance Imaging (fMRI) data, with its neighboring voxels is estimated. The neighborhood system is defined by spatial connectivity metrics and a local mesh of variable size is formed around each voxel using spatial neighborhood. Then, the mesh arc weights, called Mesh Arc Descriptors (MAD), are used to represent each voxel rather than its own intensity value measured by functional Magnetic Resonance Images (fMRI). Finally, the optimal mesh size of each voxel is estimated using various information theoretic criteria. fMRI measurements are obtained during a memory encoding and retrieval experiment performed on a subject who is exposed to the stimuli from 10 semantic categories. Using the Mesh Arc Descriptors (MAD) having the variable mesh sizes, a k-NN classifier is trained. The classification performances reflect that the suggested variable-size Mesh Arc Descriptors represent the cognitive states better than the classical multi-voxel pattern representation and fixed-size Mesh Arc Descriptors. Moreover, it is observed that the degree of connectivities in the brain greatly varies for each voxel.


Effect of Voxel Selection on Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-05-19)
In this study, we combine a voxel selection method with temporal mesh model to decode the discriminative information distributed in functional Magnetic Resonance Imaging (fMRI) data. We first employ one way Analysis of Variance (ANOVA) feature selection to select the most informative voxels. Then, we form meshes around selected voxels with their spatial and functional neighbors by employing the Mesh Model with Temporal Measurements (MM-TM). We estimate the arc weights of meshes, which represent the relation...
Bici, M. Oguz; Akar, Gözde (2010-09-29)
In this paper, we deal with layered predictive compression of animated meshes represented by series of 3D static meshes with same connectivity. We propose two schemes to improve the prediction. First improvement is using weighted spatial prediction rather than averaging neighbor vertices. The second improvement is a novel predictor based on rotation angle of incident triangles in current and previous frames. The experimental results show that around 6- 10 % bitrate reduction can be achieved by replacing the...
Modeling the Brain Connectivity for Pattern Analysis
Onal, Itir; Aksan, Emre; Velioglu, Burak; Firat, Orhan; Ozay, Mete; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2014-08-28)
An information theoretic approach is proposed to estimate the degree of connectivity for each voxel with its neighboring voxels. The neighborhood system is defined by spatial and functional connectivity metrics. Then, a local mesh of variable size is formed around each voxel using spatial or functional neighborhood. The mesh arc weights, called Mesh Arc Descriptors (MAD), are estimated by a linear regression model fitted to the voxel intensity values of the functional Magnetic Resonance Images (fMRI). Final...
Estimating Partially Observed Graph Signals by Learning Spectrally Concentrated Graph Kernels
Turhan, Gulce; Vural, Elif (2021-01-01)
© 2021 IEEE.Graph models provide flexible tools for the representation and analysis of signals defined over irregular domains such as social or sensor networks. However, in real applications data observations are often not available over the whole graph, due to practical problems such as sensor failure or connection loss. In this paper, we study the estimation of partially observed graph signals on multiple graphs. We learn a sparse representation of partially observed graph signals over spectrally concentr...
Measurement of the production cross section for pairs of isolated photons in pp collisions at root s=7 TeV
Chatrchyan, S.; et. al. (Springer Science and Business Media LLC, 2012-01-01)
The integrated and differential cross sections for the production of pairs of isolated photons is measured in proton-proton collisions at a centre-of-mass energy of 7TeV with the CMS detector at the LHC. A data sample corresponding to an integrated luminosity of 36 pb(-1) is analysed. A next-to-leading-order perturbative QCD calculation is compared to the measurements. A discrepancy is observed for regions of the phase space where the two photons have an azimuthal angle difference Delta phi less than or sim...
Citation Formats
I. Onal et al., “Estimating Brain Connectivity for Pattern Analysis,” 2014, Accessed: 00, 2020. [Online]. Available: