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Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning.

Yang Z, Choupan J, Reutens D, Hocking J - Front Neurol (2015)

Bottom Line: A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network.A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved.The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia ; Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia.

ABSTRACT
Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

No MeSH data available.


Related in: MedlinePlus

Inter-regional resting-state functional connectivity. (A) shows the matrix of which the entries indicate FCs with significant group difference (U-test, p < 0.01) A 3D rendering of the FCs, 50 in total, is shown on (B,C). The diameter of a node is proportional to the number of identified FCs involving that node and the top five nodes are: right paracentral lobule (degree = 6), left superior temporal gyrus (degree = 5), left superior temporal pole (degree = 5), left paracentral lobule (degree = 4), and right cuneus (degree = 4).
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Figure 2: Inter-regional resting-state functional connectivity. (A) shows the matrix of which the entries indicate FCs with significant group difference (U-test, p < 0.01) A 3D rendering of the FCs, 50 in total, is shown on (B,C). The diameter of a node is proportional to the number of identified FCs involving that node and the top five nodes are: right paracentral lobule (degree = 6), left superior temporal gyrus (degree = 5), left superior temporal pole (degree = 5), left paracentral lobule (degree = 4), and right cuneus (degree = 4).

Mentions: As illustrated in Figure 2, 50 FCs demonstrated significant between-group differences. The top 10 FCs with significant between-group difference and the top 10 selected FCs are shown in Figure 3. It is noted that there was no overlap between the two sets of FCs.


Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning.

Yang Z, Choupan J, Reutens D, Hocking J - Front Neurol (2015)

Inter-regional resting-state functional connectivity. (A) shows the matrix of which the entries indicate FCs with significant group difference (U-test, p < 0.01) A 3D rendering of the FCs, 50 in total, is shown on (B,C). The diameter of a node is proportional to the number of identified FCs involving that node and the top five nodes are: right paracentral lobule (degree = 6), left superior temporal gyrus (degree = 5), left superior temporal pole (degree = 5), left paracentral lobule (degree = 4), and right cuneus (degree = 4).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4553409&req=5

Figure 2: Inter-regional resting-state functional connectivity. (A) shows the matrix of which the entries indicate FCs with significant group difference (U-test, p < 0.01) A 3D rendering of the FCs, 50 in total, is shown on (B,C). The diameter of a node is proportional to the number of identified FCs involving that node and the top five nodes are: right paracentral lobule (degree = 6), left superior temporal gyrus (degree = 5), left superior temporal pole (degree = 5), left paracentral lobule (degree = 4), and right cuneus (degree = 4).
Mentions: As illustrated in Figure 2, 50 FCs demonstrated significant between-group differences. The top 10 FCs with significant between-group difference and the top 10 selected FCs are shown in Figure 3. It is noted that there was no overlap between the two sets of FCs.

Bottom Line: A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network.A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved.The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia ; Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia.

ABSTRACT
Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

No MeSH data available.


Related in: MedlinePlus