Electroclinical correlates of flumazenil and fluorodeoxyglucose PET abnormalities in lesional epilepsy

Juhász, Csaba; Chugani, Diane C.; Muzik, Otto; Watson, Craig; Shah, Jagdish; Shah, Aashit; Chugani, Harry T.

doi:10.1212/wnl.55.6.825

Cited by 90 publications

(50 citation statements)

References 50 publications

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“…First, perilesional abnormalities might represent areas with normal tissue architecture but intrinsic epileptic activity due to aberrant connections. This hypothesis is supported by findings of a PET study [16] where perilesional areas with abnormal flumazenil binding showed interictal spiking during intraoperative electrocorticography. Second, perilesional abnormalities might represent microscopic abnormal brain regions, e.g., small clusters of heterotopic neurons in otherwise normal white matter regions.…”

Section: Discussionsupporting

confidence: 55%

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Metabolic characteristics of cortical malformations causing epilepsy

et al. 2005

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Purpose-Cortical malformations (CMs) are increasingly recognized as the epileptogenic substrate in patients with medically refractory neocortical epilepsy (NE). The aim of this study was to test the hypotheses that: 1. CMs are metabolically heterogeneous. 2. The structurally normal appearing perilesional zone is characterized by similar metabolic abnormalities as the CM.Methods-Magnetic resonance spectroscopic imaging (MRSI) in combination with tissue segmentation was performed on eight patients with NE and CMs and 19 age-matched controls. In controls, NAA, Cr, Cho, NAA/Cr and NAA/Cho of all voxels of a given lobe were expressed as a function of white matter content and thresholds for pathological values determined by calculating the 95% prediction intervals. These thresholds were used to identify metabolically abnormal voxels within the CM and in the perilesional zone.Results-30% of all voxels in the CMs were abnormal, most frequently because of decreases of NAA or increases of Cho. Abnormal voxels tended to form metabolically heterogeneous clusters interspersed in metabolically normal regions. Furthermore, 15% of all voxels in the perilesional zone were abnormal, the most frequent being decreases of NAA and Cr. Conclusion-InCMs metabolically normal regions are interspersed with metabolically heterogeneous abnormal regions. Metabolic abnormalities in the perilesional zone share several characteristics of CMs and might therefore represent areas with microscopic malformations and/or intrinsic epileptogenicity.

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Section: Discussionsupporting

confidence: 55%

“…Perilesional spectroscopic abnormalities have been previously reported [4,31], and two PET studies also showed perilesional changes [16,12]. Previous MRS studies found perilesional alterations to be less severe than those in the CM, a finding that would be consistent with partial voluming.…”

Section: Discussionmentioning

confidence: 66%

Metabolic characteristics of cortical malformations causing epilepsy

et al. 2005

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“…In pediatric as in adult focal epilepsy with negative MRI results, hypometabolic areas are known to image the epileptogenic network (27,28). Indeed, the hypometabolic network we found in patients in the FIRES group corresponds to the electroclinical features of seizures that involve the temporofrontal cortex bilaterally.…”

Section: Temporal-orbitofrontal Dysfunction In Fires: From Areas To Nmentioning

confidence: 70%

“…Epileptogenic pathways and dysfunctioning cognitive networks are associated with hypometabolism, although the hypometabolic areas are usually more extensive than the EEG focus (27,28).…”

Section: F-fdg Pet and Detection Of Hypometabolic Areas In Epileptic mentioning

confidence: 99%

¹⁸F-FDG PET Reveals Frontotemporal Dysfunction in Children with Fever-Induced Refractory Epileptic Encephalopathy

Mazzuca¹,

Jambaqué²,

Hertz-Pannier³

et al. 2010

J Nucl Med

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Fever-induced refractory epileptic encephalopathy in schoolage children (FIRES) is a recently described epileptic entity whose etiology remains unknown. Brain abnormalities shown by MRI are usually limited to mesial-temporal structures and do not account for the catastrophic neuropsychologic findings. Methods: We conducted FIRES studies in 8 patients, aged 6-13 y, using 18 F-FDG PET to disclose eventual neocortical dysfunction. Voxel-based analyses of cerebral glucose metabolism were performed using statistical parametric mapping and an age-matched control group. Results: Group analysis revealed a widespread interictal hypometabolic network including the temporoparietal and orbitofrontal cortices bilaterally. The individual analyses in patients identified hypometabolic areas corresponding to the predominant electroencephalograph foci and neuropsychologic deficits involving language, behavior, and memory. Conclusion: Despite clinical heterogeneity, 18 F-FDG PET reveals a common network dysfunction in patients with sequelae due to fever-induced refractory epileptic encephalopathy.

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“…While ADD1020 [ 11 C]FMZ PET images (Juhász et al, , 1999(Juhász et al, , , 2000(Juhász et al, , , 2001Muzik et al, 2000;Niimura et al, 1999), ADD2040 images (Mishina et al, 2000;Ryvlin et al, 1998Ryvlin et al, , 1999 and BP images (Lamusuo et al, 2000) have been widely used in the study of epilepsy, further alternatives to quantification with kinetic modeling using Spectral Analysis are available. It is possible to distinguish between analytical approaches without a reference (ADD images), methods using a reference region from the images themselves (e.g., pons or brainstem (Lamusuo et al, 2000)), including those with equilibrium scanning (Szelies et al, 2000) and partial saturation (Delforge et al, 1997), and those using an external reference (e.g., an arterial plasma input function).…”

Section: Reductions In [mentioning

confidence: 99%

[¹¹C]Flumazenil PET in Temporal Lobe Epilepsy: Do We Need an Arterial Input Function or Kinetic Modeling?

Hammers

Panagoda

Heckemann

et al. 2007

J Cereb Blood Flow Metab

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11 C]FMZ PET datasets and arterial plasma input functions were available for 15 patients with medically refractory medial temporal lobe epilepsy (TLE) and histologically verified unilateral HS and for 13 control subjects. SPM2 was used for analysis. ADD1020 and ADD2040 images showed decreased FMZ uptake ipsilateral to the epileptogenic hippocampus in 13/15 cases; 6/13 had bilateral decreases in the ADD1020 analysis and 5/13 in the ADD2040 analysis. BP-SRTM images detected ipsilateral decreases in 12/15 cases, with bilateral decreases in three. In contrast, VD images showed ipsilateral hippocampal decreases in all 15 patients, with bilateral decreases in three patients. Bilateral decreases in the ADD images tended to be more symmetrical and in one case were more marked contralaterally. Full quantification with an image-independent input should ideally be used in the evaluation of FMZ PET; at least in TLE, intrasubject correlations do not predict equivalent clinical usefulness.

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Electroclinical correlates of flumazenil and fluorodeoxyglucose PET abnormalities in lesional epilepsy

Cited by 90 publications

References 50 publications

Metabolic characteristics of cortical malformations causing epilepsy

Metabolic characteristics of cortical malformations causing epilepsy

¹⁸F-FDG PET Reveals Frontotemporal Dysfunction in Children with Fever-Induced Refractory Epileptic Encephalopathy

[¹¹C]Flumazenil PET in Temporal Lobe Epilepsy: Do We Need an Arterial Input Function or Kinetic Modeling?

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Electroclinical correlates of flumazenil and fluorodeoxyglucose PET abnormalities in lesional epilepsy

Cited by 90 publications

References 50 publications

Metabolic characteristics of cortical malformations causing epilepsy

Metabolic characteristics of cortical malformations causing epilepsy

18F-FDG PET Reveals Frontotemporal Dysfunction in Children with Fever-Induced Refractory Epileptic Encephalopathy

[11C]Flumazenil PET in Temporal Lobe Epilepsy: Do We Need an Arterial Input Function or Kinetic Modeling?

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¹⁸F-FDG PET Reveals Frontotemporal Dysfunction in Children with Fever-Induced Refractory Epileptic Encephalopathy

[¹¹C]Flumazenil PET in Temporal Lobe Epilepsy: Do We Need an Arterial Input Function or Kinetic Modeling?