Temporal lobe epilepsy and focal cortical dysplasia in children: A tip to find the abnormality

Bartolini, Luca; Whitehead, Matthew T.; Ho, Cheng‐Ying; Sepeta, Leigh N.; Oluigbo, Chima; Havens, Kathryn; Freilich, Emily R.; Schreiber, John M.; Gaillard, William D.

doi:10.1111/epi.13615

Cited by 31 publications

(33 citation statements)

References 36 publications

(75 reference statements)

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“…Ipsi‐lesional blurring of the gray‐white matter boundary can often be seen on visual inspection of T1w MRI scans of patients with TLE 39, 40, 41. In our study, it was present in 45% of children with HS.…”

Section: Discussionsupporting

confidence: 50%

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Adler

Blackwood

Northam

et al. 2018

Ann Clin Transl Neurol

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ObjectiveIn contrast to adult cohorts, neocortical changes in epileptic children with hippocampal damage are not well characterized. Here, we mapped multimodal neocortical markers of epilepsy‐related structural compromise in a pediatric cohort of temporal lobe epilepsy and explored how they relate to clinical factors.MethodsWe measured cortical thickness, gray–white matter intensity contrast and intracortical FLAIR intensity in 22 patients with hippocampal sclerosis (HS) and 30 controls. Surface‐based linear models assessed between‐group differences in morphological and MR signal intensity markers. Structural integrity of the hippocampus was measured by quantifying atrophy and FLAIR patterns. Linear models were used to evaluate the relationships between hippocampal and neocortical MRI markers and clinical factors.ResultsIn the hippocampus, patients demonstrated ipsilateral atrophy and bilateral FLAIR hyperintensity. In the neocortex, patients showed FLAIR signal hyperintensities and gray–white matter boundary blurring in the ipsilesional mesial and lateral temporal neocortex. In contrast, cortical thinning was minimal and restricted to a small area of the ipsilesional temporal pole. Furthermore, patients with a history of febrile convulsions demonstrated more pronounced FLAIR hyperintensity in the ipsilesional temporal neocortex.InterpretationPediatric HS patients do not yet demonstrate the widespread cortical thinning present in adult cohorts, which may reflect consequences of a protracted disease process. However, pronounced temporal neocortical FLAIR hyperintensity and blurring of the gray–white matter boundary are already detectable, suggesting that alterations in MR signal intensities may reflect a different underlying pathophysiology that is detectable earlier in the disease and more pervasive in patients with a history of febrile convulsions.

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Section: Discussionsupporting

confidence: 50%

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Adler

Blackwood

Northam

et al. 2018

Ann Clin Transl Neurol

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“…Current MR technology provides excellent detection for FCD lesions that occur in extratemporal locations but is unreliable for detecting most FCD lesions, which predominantly occur in the temporal lobe. 33 The lesions of three patients occurred in the temporal lobe The limiting results of one patient, P13, is a special case. The lesion was detected in the axial images but was undetected in the coronal images.…”

Section: B | Localization Resultsmentioning

confidence: 93%

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Feng

Zhao

et al. 2020

J Applied Clin Med Phys

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Purpose Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid‐attenuated inversion recovery (FLAIR)‐negative lesions using convolutional neural network (CNN) technology. Methods The technique involves training a six‐layer CNN named Net‐Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net‐Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR‐negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm. Results The PIBs most similar to an FCD lesion image block were identified by the trained Net‐Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR‐negative lesion images from 12 patients. The subject‐wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68. Conclusion We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR‐negative FCD lesions. This work is the first study to apply a CNN‐based model to detect and segment FCD lesions in images of FLAIR‐negative lesions.

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“…11 This can help identifying the extent of epileptogenic foci in TLE and patients with TLE due to FCD are eighteen times more likely to have more pronounced findings on MRI than patients with extratemporal epilepsy. 11 With the progressive refinement of MR techniques FCDs are being seen in vivo with increasing frequency in patients with epilepsy and many previously considered non lesional epilepsies are now recognized with associated FCD or other malformations of cortical development MCD. Currently there is a growing interest and development in the MRI characterization of FCD and other MCD to assist electroclinical localization and surgical planning with less invasive procedures.…”

Section: Discussionmentioning

confidence: 99%

“…Currently there is a growing interest and development in the MRI characterization of FCD and other MCD to assist electroclinical localization and surgical planning with less invasive procedures. 10,11…”

Section: Discussionmentioning

confidence: 99%

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2018

JNSK

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Focal cortical dysplasia FCD can be detected in the temporal pole TP of patients with temporal lobe epilepsy TLE, The association of TP FCD and hippocampal sclerosis HS is increasingly recognized especially in young patients. High resolution magnetic resonance imaging MRI of the brain can identify macroscopic TP FCD in the preoperative evaluation for epilepsy surgery. Favorable surgical outcome is achieved when both lesions of the dual pathology are removed .In this case report we describe a twelve years old boy with pharmaco-resistant TLE. An initial MRI of the brain showed right HS .The clinical and neurophysiological features were atypical for HS. High resolution Three Tesla MRI brain performed prior to invasive electroencephalography IEEG and identified right TP FCD in additional right HS. The patient underwent directly epilepsy surgery with intraoperative electrocorticography and right temporal lobectomy with resection of the right TP is performed. The histopathology confirmed the right HS and FCD type III a. The patient had no postoperative complications and became seizure free. He remained seizure free on small dose of Lamotrigine after five years of follow up after epilepsy surgery.

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Temporal lobe epilepsy and focal cortical dysplasia in children: A tip to find the abnormality

Cited by 31 publications

References 36 publications

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

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