Systematic Assessment of Multispectral Voxel-Based Morphometry in Previously MRI-Negative Focal Epilepsy

Kotikalapudi, Raviteja; Martin, Pascal; Marquetand, Justus; Lindig, Tobias; Bender, Benjamín; Focke, Niels K.

doi:10.3174/ajnr.a5809

Cited by 20 publications

(26 citation statements)

References 26 publications

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“…It has been previously shown that the selection of smoothing (12 mm with T1 as reference) as well as statistical threshold ( t ‐score = 3.7) affected the detection rates in MRI‐negative cohort (Kotikalapudi et al, ). In the current study, a smoothing of 14 mm at a liberal T‐threshold of 3.3, gave the optimal trade‐off between specificity and concordant rate for the MP2 contrast.…”

Section: Discussionmentioning

confidence: 99%

“…The method to estimate the diagnostically ideal smoothing and T‐threshold has already been explained in detail in our previous work (Kotikalapudi et al, ). In brief, a single‐channel VBM variant is considered as a reference, that is, MP2 in our case.…”

Section: Methodsmentioning

confidence: 99%

“…Technical advances of the static magnetic fields from 1.5 to 3 T and higher field strengths of 7 T can aid in detecting epileptogenic lesions that are otherwise difficult to perceive (De Ciantis et al, ; Knake et al, ; Veersema et al, ; Zijlmans et al, ). Also, computational methods have been shown helpful in detecting subtle lesions, previously missed in the visual analysis (Kotikalapudi et al, ; Martin, Bender, & Focke, ; Martin et al, ). Voxel‐based morphometry (VBM) is a well‐established method for computer‐aided, volumetric MRI processing (Ashburner & Friston, ).…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have confirmed superiority of multispectral over conventional T1‐only segmentation by addressing issues such as overestimation of dura and vessels as GM and also improve cortical GM segmentation (Lindig et al, ; Viviani, Pracht, et al, ; Viviani, Stocker, et al, ). With the addition of T2/T2‐FLAIR weighted, VBM variants (T1 + FLAIR, T1 + T2) have also shown improved lesion detection over T1‐only VBM, in both lesional and non lesional MRI focal epilepsy (Kotikalapudi et al, ; Lindig et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…However, MP2RAGE and the multispectral MP2RAGE variants have not been systematically analyzed for detecting subtle epileptogenic lesions in a VBM approach. Moreover, it was previously shown that the performance of lesion detection is strongly influenced by the choice of smoothing and statistical thresholds or t ‐scores (Kotikalapudi et al, ; Martin et al, ). The ideal parameters for an MP2RAGE VBM are yet unclear in focal epilepsy.…”

Section: Introductionmentioning

confidence: 99%

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MP2RAGE multispectral voxel‐based morphometry in focal epilepsy

Kotikalapudi

Martin

Erb³

et al. 2019

Human Brain Mapping

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We assessed the applicability of MP2RAGE for voxel‐based morphometry. To this end, we analyzed its brain tissue segmentation characteristics in healthy subjects and the potential for detecting focal epileptogenic lesions (previously visible and nonvisible). Automated results and expert visual interpretations were compared with conventional VBM variants (i.e., T1 and T1 + FLAIR). Thirty‐one healthy controls and 21 patients with focal epilepsy were recruited. 3D T1‐, T2‐FLAIR, and MP2RAGE images (consisting of INV1, INV2, and MP2 maps) were acquired on a 3T MRI. The effects of brain tissue segmentation and lesion detection rates were analyzed among single‐ and multispectral VBM variants. MP2‐single‐contrast gave better delineation of deep, subcortical nuclei but was prone to misclassification of dura/vessels as gray matter, even more than conventional‐T1. The addition of multispectral combinations (INV1, INV2, or FLAIR) could markedly reduce such misclassifications. MP2 + INV1 yielded generally clearer gray matter segmentation allowing better differentiation of white matter and neighboring gyri. Different models detected known lesions with a sensitivity between 60 and 100%. In non lesional cases, MP2 + INV1 was found to be best with a concordant rate of 37.5%, specificity of 51.6% and concordant to discordant ratio of 0.60. In summary, we show that multispectral MP2RAGE VBM (e.g., MP2 + INV1, MP2 + INV2) can improve brain tissue segmentation and lesion detection in epilepsy.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MP2RAGE multispectral voxel‐based morphometry in focal epilepsy

Kotikalapudi

Martin

Erb³

et al. 2019

Human Brain Mapping

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The significance of multimodality approach in the management of non‐lesional drug‐resistant focal parietal lobe epilepsies

Hartnett,

Zomorodi,

Goodkin

et al. 2024

Epilepsia Open

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Due to extensive connectivity of the parietal lobe, non‐lesional drug‐resistant (DRE) parietal lobe epilepsies (PLEs) are difficult to localize and often imitate other epilepsies. Therefore, patients with PLEs have low rates of seizure freedom following epilepsy surgery. Previous studies have highlighted the need to combine EEG and semiology for more accurate localization of PLEs. As sophisticated tools for localization become more available, the use of multiple different neuroimaging and neurophysiologic diagnostic tests may more readily identify PLE. We hereby report a unique case of a complex localization in a non‐lesional PLE, which was initially falsely localized to frontal lobe. This case underscores the utility of voxel‐based morphometry (VBM) in identifying an epileptogenic lesion on a non‐lesional MRI and the significance of multimodality approach including PET, magnetoencephalopathy (MEG), interictal and ictal EEG, semiology and cortical stimulation for accurate localization of PLEs. Understanding epilepsy through multimodality approach in this fashion can help with accurate localization especially in difficulty to localize and deceptive non‐lesional PLEs.Plain Language SummaryParietal lobe epilepsies are hard to pinpoint in the brain and can mimic other types of epilepsy, especially when brain MRIs appear normal. As sophisticated tools for locating epilepsies in the brain become more available, using multiple diagnostic tests may help identify parietal lobe epilepsies more easily. We describe a unique case of a parietal lobe epilepsy patient with normal brain MRI whose epilepsy was initially misidentified as being in the frontal lobe. Using various advanced diagnostic tests, we accurately found the epilepsy's true location in the parietal lobe and successfully treated the patient with surgery.

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Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging

Mareček

Říha

Bartoňová

et al. 2021

Human Brain Mapping

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Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand‐alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR‐negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel‐wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion‐weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR‐negative epilepsy patients.

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Systematic Assessment of Multispectral Voxel-Based Morphometry in Previously MRI-Negative Focal Epilepsy

Cited by 20 publications

References 26 publications

MP2RAGE multispectral voxel‐based morphometry in focal epilepsy

MP2RAGE multispectral voxel‐based morphometry in focal epilepsy

The significance of multimodality approach in the management of non‐lesional drug‐resistant focal parietal lobe epilepsies

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging

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