Computer‐automated focus lateralization of temporal lobe epilepsy using fMRI

Chiang, Sharon; Levin, Harvey S.; Haneef, Zulfi

doi:10.1002/jmri.24696

Cited by 37 publications

(26 citation statements)

References 19 publications

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“…These results were comparable to those of previous studies that used machine learning approaches for volumetry, DTI, and fMRI. [2][3][4][5] Although our results for classification accuracy do not exceed those of previous reports that applied machine learning to DTI using voxel-based approaches 3 or a fractional anisotropy (FA) skeleton generated by tract-based spatial statistics (TBSS), 4 the graph-based approach has considerable strengths as follows. First, it is directed to the recent trend to consider diseases of the brain as network disorders.…”

Section: Discussioncontrasting

confidence: 53%

“…To increase the certainty of lateralization of the epileptogenic focus and obviate the need for invasive intracranial electrode placement, prior studies have investigated the utility of quantitative or automated MR image analyses, including voxelbased morphometry, 2,3 diffusion tensor imaging (DTI), 3,4 and functional MR imaging (fMRI). 5 DTI can approximate the white matter architecture by describing the directionality and magnitude of water diffusion. In TLE, the decrease in fractional anisotropy (FA) tends to be maximal at the epileptic zone and subtle at a distance, 6 and decreased FA in the extra-temporal regions as well as within the ipsilateral temporal lobe suggests that the network is altered.…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13][14][15] In recent years, several studies have investigated the performance of machine learning algorithms, such as that of the support vector machine (SVM), for automatic localization of epileptogenic foci using MR voxel-based morphometry (VBM) 2,3 and fMRI. 5 Because graph theory metrics use a subset of numeric parameters to summarize the characteristic properties of huge and complex brain networks, they are mathematically good candidates for a machine learning approach to identify the multivariate feature combinations that best predict an outcome of interest. A combination of machine learning and connectomic measures has been used to distinguish healthy individuals from patients with disorders including autism, 16 schizophrenia, 17 and Alzheimer's disease.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Kamiya

Amemiya

Suzuki

et al. 2016

MRMS

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Background and Purpose: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).Materials and Methods: We analyzed diffusion tensor and 3-dimensional (3D) T 1 -weighted images of 44 patients with TLE (right, 15, left, 29; mean age, 33.0 « 11.6 years) and 14 age-matched controls. We constructed a whole brain structural connectome for each subject, calculated graph theoretical network measures, and used a support vector machine (SVM) for classification among 3 groups (right TLE versus controls, left TLE versus controls, and right TLE versus left TLE) following a feature reduction process with sparse linear regression.Results: In left TLE, we found a significant decrease in local efficiency and the clustering coefficient in several brain regions, including the left posterior cingulate gyrus, left cuneus, and both hippocampi. In right TLE, the right hippocampus showed reduced nodal degree, clustering coefficient, and local efficiency. With use of the leave-one-out crossvalidation strategy, the SVM classifier achieved accuracy of 75.9 to 89.7% for right TLE versus controls, 74.4 to 86.0% for left TLE versus controls, and 72.7 to 86.4% for left TLE versus right TLE.Conclusion: Machine learning of graph theoretical measures from the DTI structural connectome may give support to lateralization of the TLE focus. The present good discrimination between left and right TLE suggests that, with further refinement, the classifier should improve presurgical diagnostic confidence.

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Section: Discussioncontrasting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Kamiya

Amemiya

Suzuki

et al. 2016

MRMS

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“…The higher level of temporal stationarity of betweenness centrality may lead to a higher level of sensitivity in characterizing hub distributions based on static analytic approaches. Betweenness centrality has been consistently implicated in both localizing [7] and lateralizing TLE [61, 62], whereas eigenvector centrality has been less well implicated.…”

Section: Discussionmentioning

confidence: 99%

Time-dependence of graph theory metrics in functional connectivity analysis

et al. 2016

Self Cite

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Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.

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“…8,9 Additionally, some have applied these methods to EEG readings to automate seizure detection and localize epileptogenic focus. 10,11 In the field of neuro-oncology, machine and deep-learning approaches have been used to analyze magnetic resonance images to classify and grade tumors, and predict survival. [12][13][14] These are only some of the applications of Big Data methods that are currently under investigation in neurosurgery, and specifically by our group at the Computational Neuroscience Outcomes Center, and a more in depth review can be found elsewhere.…”

mentioning

confidence: 99%