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  1. Article ; Online: Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI

    Dorian Pustina / Brian Avants / Michael Sperling / Richard Gorniak / Xiaosong He / Gaelle Doucet / Paul Barnett / Scott Mintzer / Ashwini Sharan / Joseph Tracy

    NeuroImage: Clinical, Vol 9, Iss C, Pp 20-

    A multimodal study

    2015  Volume 31

    Abstract: Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. ...

    Abstract Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.
    Keywords Asymmetry ; Classification ; Metabolism ; Resection ; Machine learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
    Subject code 610
    Language English
    Publishing date 2015-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation

    Shennan Aibel Weiss / Ali A Asadi-Pooya / Sitaram Vangala / Stephanie Moy / Dale H Wyeth / Iren Orosz / Michael Gibbs / Lara Schrader / Jason Lerner / Christopher K Cheng / Edward Chang / Rajsekar Rajaraman / Inna Keselman / Perdro Churchman / Christine Bower-Baca / Adam L Numis / Michael G Ho / Lekha Rao / Annapoorna Bhat /
    Joanna Suski / Marjan Asadollahi / Timothy Ambrose / Andres Fernandez / Maromi Nei / Christopher Skidmore / Scott Mintzer / Dawn S Eliashiv / Gary W Mathern / Marc R Nuwer / Michael Sperling / Jerome Engel Jr / John M Stern

    F1000Research, Vol

    Validation and comparison of performance with commercially available software [version 2; referees: 2 approved]

    2017  Volume 6

    Abstract: Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to ... ...

    Abstract Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p<0.01). Fewer readers could lateralize seizure-onset (p<0.05). The confidence measures of the assignments were low (probable-unlikely), but increased using AR2 (p<0.05). The ICC for identifying the time of seizure-onset was 0.15 (95% confidence interval (CI), 0.11-0.18) using AR1 and 0.26 (95% CI 0.21-0.30) using AR2. The EEG interpretations were often consistent with behavioral, neurophysiological, and neuro-radiological findings, with left sided assignments correct in 95.9% (CI 85.7-98.9%, n=4) of cases using AR2, and 91.9% (77.0-97.5%) (n=4) of cases using AR1. Conclusions: EEG artifact reduction methods for localizing seizure-onset does not result in high rates of interpretability, reader confidence, and inter-reader agreement. However, the assignments by groups of readers are often congruent with other clinical data. Utilization of the AR2 software method may improve the validity of ictal EEG artifact reduction.
    Keywords Neuroimaging ; Medicine ; R ; Science ; Q
    Subject code 616
    Language English
    Publishing date 2017-04-01T00:00:00Z
    Publisher F1000 Research Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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