Electro-clinical criteria and surgical outcome: Is there a difference between mesial and lesional temporal lobe epilepsy?

OBJECTIVES: Mesial temporal lobe epilepsy syndrome (MTLE) with specific electrophysiological and clinical characteristics and hippocampal sclerosis (HS) on MRI is considered the prototype of a syndrome with good surgical prognosis. Ictal onset zones in MTLE have been found to extend outside the hippocampus and neocortical seizures often involve mesial structures. It can, thus, be questioned whether MTLE with HS is different from lesional temporal epilepsies with respect to electro-clinical characteristics and surgical prognosis. We assessed whether MTLE with HS is distinguishable from lesional TLE and which criteria determine surgical outcome. METHODS: People in a retrospective cohort of 389 individuals with MRI abnormalities who underwent temporal lobectomy, were divided into "HS only" or "lesional" TLEs. Twenty-six presented with dual pathology and were excluded from further analysis. We compared surgical outcome and electro-clinical characteristics. RESULTS: Over half (61%) had "HS only." Four electro-clinical characteristics (age at epilepsy onset, febrile seizures, memory dysfunction and contralateral dystonic posturing) distinguished "HS only" from "lesional" TLE, but there was considerable overlap. Seizure freedom 2 years after surgery (Engel class 1) was similar: 67% ("HS only") vs 69% ("lesional" TLE). Neither presence of HS nor electro-clinical criteria was associated with surgical outcome. CONCLUSIONS: Despite small differences in electrophysiological and clinical characteristics between MTLE with HS and lesional TLE, surgical outcomes are similar, indicating that aetiology seems irrelevant in the referral for temporal surgery

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

Promoting Deep Learning through Online Feedback in SPOCs

Higher education aims for deep learning and increasingly uses a specific form of online education: Small Private Online Courses (SPOCs). To overcome challenges that instructors face in order to promote deep learning through that format, the use of feedback may have significant potential. We interviewed eleven instructors and four students and organized a focus group to formulate scalable design propositions for instructors in SPOCs to promote deep learning. Propositions have been formulated according to the CIMO-logic. This study resulted in identification of four mechanisms by which the desired outcome (deep learning) can be achieved, which we describe here along with proposed interventions. Results show that the “online learning interaction model” can be deepened with these mechanisms: 1) Feeling personally committed, 2) Asking and providing relevant feedback, 3) Probing back and forth, and 4) Understanding one’s own learning process. To activate these mechanisms, scalable feedback interventions are described in three categories. Results at this relatively young field of SPOCs also show that feedback as a dialogical process may contribute to solving the current challenges of instructors in SPOCs to achieve deep learning with their students

Audio peer feedback to promote deep learning in online education

We investigated the relation between providing and receiving audio peer feedback with a deep approach to learning within online education. Online students were asked to complete peer feedback assignments. Data through a questionnaire with 108 respondents and 14 interviews were used to measure to what extent deep learning was perceived and why. Results support the view that both providing and receiving audio peer feedback indeed promote deep learning. As a consequence of the peer feedback method, the following student mechanisms were triggered: “feeling personally committed,” “probing back and forth,” and “understanding one's own learning process.” Particularly important for both providing and receiving feedback is feeling personally committed. Results also show that mechanisms were a stronger predictor for deep learning when providing than when receiving. Given the context in which instructors face an increasing number of students and a high workload, students may be supported by online audio peer feedback as a method to choose a deep approach to learning

Audio peer feedback to promote deep learning in online education

We investigated the relation between providing and receiving audio peer feedback with a deep approach to learning within online education. Online students were asked to complete peer feedback assignments. Data through a questionnaire with 108 respondents and 14 interviews were used to measure to what extent deep learning was perceived and why. Results support the view that both providing and receiving audio peer feedback indeed promote deep learning. As a consequence of the peer feedback method, the following student mechanisms were triggered: “feeling personally committed,” “probing back and forth,” and “understanding one's own learning process.” Particularly important for both providing and receiving feedback is feeling personally committed. Results also show that mechanisms were a stronger predictor for deep learning when providing than when receiving. Given the context in which instructors face an increasing number of students and a high workload, students may be supported by online audio peer feedback as a method to choose a deep approach to learning