Voxel‐based morphometry of temporal lobe epilepsy: An introduction and review of the literature

Keller, Simon S.; Roberts, Neil

doi:10.1111/j.1528-1167.2007.01485.x

Cited by 385 publications

(374 citation statements)

References 102 publications

Supporting

Mentioning

328

Contrasting

Unclassified

Order By: Relevance

“…This was as expected given that the inclusion criteria for patients involved a radiological diagnosis of HS. The surface‐based approach, to calculate per‐vertex cortical thickness, normalized FLAIR signal intensity and gray–white matter intensity contrast, demonstrated that while pediatric TLE patients did not show the widespread cortical thinning present in adult cohorts,16, 17, 18, 19 they nevertheless had morphological and intensity changes in ipsi‐lesional temporopolar neocortex. Furthermore, temporopolar FLAIR hyperintensities were more marked in patients with a history of febrile convulsions and blurring of the gray–white matter boundary was correlated with earlier onset of epilepsy.…”

Section: Discussionmentioning

confidence: 99%

“…Beyond the hippocampus, there is a body of neuroimaging literature in adults, utilizing both volumetric and surface‐based methodologies, describing whole‐brain changes in gray matter volume,16 cortical thickness17, 18, 19 and T2/FLAIR intensity 20, 21. These changes are seen after several decades of illness duration, as seizures often start in childhood and adolescence.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Adler

Blackwood

Northam

et al. 2018

Ann Clin Transl Neurol

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Adler

Blackwood

Northam

et al. 2018

Ann Clin Transl Neurol

View full text Add to dashboard Cite

show abstract

“…Historically, TLE imaging findings have confirmed the existence of temporal and extratemporal areas of neuronal loss (Keller & Roberts, 2008); with more recent studies demonstrating that microstructural abnormalities are extensive and pervasively distributed (Bonilha et al., 2015). Since most structural brain studies have investigated group‐wise differences, it remains unclear whether microstructural abnormalities in TLE are consistently observed across all patients (and could therefore be used to classify TLE vs. individuals without TLE), or whether there is a high degree of variability beyond a common underlying pattern.…”

Section: Introductionmentioning

confidence: 91%

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

et al. 2017

View full text Add to dashboard Cite

BackgroundIt is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)‐based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).MethodsThirty‐two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross‐validation (CV) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian‐based method.ResultsMean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 (MK), 0.68 (FA), and 0.51 (MD). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes.ConclusionThese results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE‐associated pathological features than DTI. Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non‐Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE. This is important since treatment outcome varies dependent on type of TLE.

show abstract

“…It has been argued2 that non‐mnestic deficits in TLE might reflect extra‐temporal structural pathology,5, 6, 7 giving rise to the notion of TLE as a structural “network disease” 8. While structural “network disease” is unlikely to explain cognitive impairment in all cases,9 the underlying concept can be expanded to incorporate functional network disease as well.…”

Section: Introductionmentioning

confidence: 99%

Cognitive impairment in epilepsy: the role of reduced network flexibility

Tailby

Kowalczyk

Jackson

2017

Ann Clin Transl Neurol

View full text Add to dashboard Cite

ObjectiveThe dominant model of cognitive impairment in focal epilepsy has emphasised structural bases for cognitive deficits. Current theories of cognition in the healthy brain emphasise the importance of the reweighting of brain network interactions in support of task performance. Here, we explore the hypothesis that cognitive deficits in epilepsy arise through abnormalities of dynamic functional network interactions.MethodWe studied 19 healthy controls and 37 temporal lobe epilepsy (TLE) patients, using a behavioural measure of verbal fluency (the Controlled Oral Word Association Test) and an fMRI verbal fluency paradigm (Orthographic Lexical Retrieval).ResultsBehaviourally, verbal fluency was significantly impaired in TLE. Psychophysiological interaction analyses of the fMRI data, which capture state‐dependent changes in network connectivity, revealed reduced task‐dependent modulations of connectivity from left superior medial frontal cortex to left middle frontal gyrus in TLE patients. Individual differences in verbal fluency among TLE cases was correlated with task‐dependent changes in connectivity from left posterior cingulate to left superior medial frontal cortex, and from left superior medial frontal cortex to a range of right predominant brain areas.InterpretationThese data reveal that the typical pattern of task‐driven shifts in network connectivity is not observed in TLE. Our observations go beyond simple structure‐function associations and suggest that failure of network flexibility can be an important contributor to cognitive impairment in epilepsy.

show abstract

Voxel‐based morphometry of temporal lobe epilepsy: An introduction and review of the literature

Cited by 385 publications

References 102 publications

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

Cognitive impairment in epilepsy: the role of reduced network flexibility

Contact Info

Product

Resources

About