TY - GEN
T1 - A connectivity difference measure for identification of functional neuroimaging markers for epilepsy
AU - Riaz, Atif
AU - Rajpoot, Kashif
AU - Rajpoot, Nasir
PY - 2013
Y1 - 2013
N2 - Identification of functional brain connectivity differences induced by certain neurological disorders from resting state functional MRI (rfMRI) is generally considered a difficult task. This challenging task requires the identification of discriminative neuroimaging markers. In this paper, we propose a two-stage algorithm to identify functional connectivity differences that can discriminate epileptic patients and healthy subjects. In the first stage, we determine the functional connectivity matrix between brain cortical regions for identification of potentially discriminative neuroimaging markers using a novel affinity propagation clustering method. Next, we propose a difference statistic to select the most discriminative connections between the cortical regions. Using selected connections and a support vector machine classifier, we achieve classification accuracy of 81.33% on unseen dataset. The results demonstrate that the proposed algorithm is capable of determining functional connections between brain regions which aid in discrimination of epileptic patients versus healthy subjects.
AB - Identification of functional brain connectivity differences induced by certain neurological disorders from resting state functional MRI (rfMRI) is generally considered a difficult task. This challenging task requires the identification of discriminative neuroimaging markers. In this paper, we propose a two-stage algorithm to identify functional connectivity differences that can discriminate epileptic patients and healthy subjects. In the first stage, we determine the functional connectivity matrix between brain cortical regions for identification of potentially discriminative neuroimaging markers using a novel affinity propagation clustering method. Next, we propose a difference statistic to select the most discriminative connections between the cortical regions. Using selected connections and a support vector machine classifier, we achieve classification accuracy of 81.33% on unseen dataset. The results demonstrate that the proposed algorithm is capable of determining functional connections between brain regions which aid in discrimination of epileptic patients versus healthy subjects.
UR - http://www.scopus.com/inward/record.url?scp=84897731891&partnerID=8YFLogxK
U2 - 10.1109/NER.2013.6696234
DO - 10.1109/NER.2013.6696234
M3 - Conference contribution
AN - SCOPUS:84897731891
SN - 9781467319690
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 1517
EP - 1520
BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
T2 - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Y2 - 6 November 2013 through 8 November 2013
ER -