“…5 ). Using both atlases, it can be seen that the most frequently sampled regions are located in frontal and temporal regions which are known to be the major sites for focal epileptogenic zones [ 39 ] while posterior parts of the brain represent more rare cases [ 40 ]. Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Indeed, we present a tool to automatically label each channel in respect to the most probable neuronal source using probabilistic atlases. Using such tool we show that the most frequently sampled brain areas are located in frontal and temporal regions which are known to be the major sites for focal epileptogenic foci [ 39 ], while posterior parts of the brain represent more rare cases [ 40 ].…”
BackgroundIn the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist’s workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors.ResultsIn this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing.ConclusionsThe SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.
“…5 ). Using both atlases, it can be seen that the most frequently sampled regions are located in frontal and temporal regions which are known to be the major sites for focal epileptogenic zones [ 39 ] while posterior parts of the brain represent more rare cases [ 40 ]. Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Indeed, we present a tool to automatically label each channel in respect to the most probable neuronal source using probabilistic atlases. Using such tool we show that the most frequently sampled brain areas are located in frontal and temporal regions which are known to be the major sites for focal epileptogenic foci [ 39 ], while posterior parts of the brain represent more rare cases [ 40 ].…”
BackgroundIn the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist’s workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors.ResultsIn this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing.ConclusionsThe SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.
“…Most studies were retrospective analyses of patient data from national or local databases or medical records. Some studies included only temporal lobe resections, 14,17,21–23,26,27,33 frontal lobe resections, 32 posterior resections, 16,24,31 or all extratemporal resections, 19 while other studies included resections regardless of the location. Several cohorts comprised only epileptogenic lesions.…”
Section: Resultsmentioning
confidence: 99%
“…14,19,22,23,36 Several studies included patients under 5 years of age. 17,18,20,21,24,30–32,34,35 All studies reported seizure freedom (including or excluding aura) in accordance with the eligibility criteria. Various measurements for associations of presurgical epilepsy duration and postsurgical seizure freedom were reported in the studies, such as proportions, odds ratio, and hazard ratio.…”
Section: Resultsmentioning
confidence: 99%
“…Three studies (n = 288) reported data on seizure freedom at epilepsy duration <2 years compared to >2 years, 6,31,34 4 studies (n = 551) reported seizure freedom at epilepsy duration <5 years compared to >5 years, 6,24,30,34 10 studies (n = 1,376) reported data on <10 years compared to >10 years. 6,15,22,24,27,30,33–35,37 Three studies (n = 346) reported data on <20 years compared to >20 years. 6,22,28 Finally, we compared epilepsy duration <5 years to >10 years, in order to investigate if a larger time gap in epilepsy duration resulted in a larger effect.…”
ObjectiveTo conduct a systematic review and meta-analysis on the effect of earlier or later resective epilepsy surgery on seizure outcome.MethodsWe searched the electronic databases PubMed, EMBASE, and Cochrane Library for studies investigating the association of epilepsy duration and seizure freedom after resective surgery. Two reviewers independently screened citations for eligibility and assessed relevant studies for risk of bias. We combined data in meta-analyses using a random effects model. We assessed the certainty of evidence according to Grading of Recommendations Assessment, Development and Evaluation (GRADE).ResultsTwenty-five studies were included, 12 of which had data suitable for meta-analyses. Comparing seizure outcome if epilepsy surgery was performed before vs after 2, 5, 10, and 20 years of epilepsy duration, and comparing epilepsy duration <5 years to >10 years, we found significant effects favoring shorter duration with risk differences ranging from 0.15 to 0.21 and risk ratios ranging from 1.20 to 1.33 (p < 0.01 for all comparisons). According to GRADE, we found low certainty of evidence favoring shorter epilepsy duration before surgery.ConclusionPeople with shorter epilepsy duration are more likely to be seizure-free at follow-up. Furthermore, there is a positive association between shorter duration and seizure freedom also for very long epilepsy durations. Patients who might benefit from epilepsy surgery should therefore be referred for presurgical assessments without further delay, regardless of epilepsy duration. The low certainty of evidence acknowledges concerns regarding study heterogeneity and possible residual confounding.
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