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Purpose: Persons with epilepsy (PWE) report memory deficits as one of the most distressing aspects of their disorder. Recently, a long-term memory deficit known as Accelerated Long-Term Forgetting (ALF) has been described in PWE. ALF is characterized by the initial retention of learned information, followed by an accelerated rate of memory decay. However, the rate of ALF varies widely across literature and it is unclear how it impacts different memory retrieval types. The current study aimed to capture the time course of ALF on both free recall and recognition memory using a movie-based task in PWE., Methods: A sample of 30 PWE and 30 healthy comparison (HC) subjects watched a nature documentary and were tested on their recall and recognition of the film's content immediately after viewing and at delays of 24 hours, 48 hours, and 72 hours. Participants also rated the confidence they had in their recognition memory trial responses., Results: For recall, PWE exhibit ALF at 72 hours (β = -19.840, SE = 3.743, z(226) = -5.301, p < 0.001). For recognition, PWE had decreased performance compared to controls at the 24-hour (β = -10.165, SE = 4.174, z(224) = -3.166, p = 0.004), 48-hour (β = -8.113, SE = 3.701, z(224) = -2.195, p = 0.044), and 72-hour (β = -10.794, SE = 3.017, z(224) = -3.295, p = 0.003) delays. The PWE group showed positive correlations (tau = 0.165, p < 0.001) between confidence ratings and accuracy, with higher confidence reflecting successful recognition. PWE were 49% less likely to answer either retrieval type correctly at 72 hours (OR 0.51, 95% CI [0.35, 0.74], p < 0.001). Left hemispheric seizure onset decreased the odds of successful retrieval by 88% (OR 0.12, 95% CI [0.01, 0.42], p = 0.019)., Conclusions: These findings provide evidence of ALF in PWE, with a differential impact on recall and recognition memory. This further supports the call to include ALF assessments in standard memory evaluations in PWE. Additionally, identifying the neural correlates of ALF in the future will be important in developing targeted therapies to alleviate the burden of memory impairment for PWE., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: B. C. J. serves as an Associate Editor of Neurology and receives funding through the Louis and Ruth Frank Chair of Neurosciences. Dr. Jobst has received research funding from Neuropace Inc., Harvard Pilgrim Inc., the Epilepsy Foundation, the American Epilepsy Society, NIH, CDC and the Department of Defense. No other authors have any disclosures at this time., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: To develop a classifier that predicts reductions in depression severity in people with epilepsy after participation in an epilepsy self-management intervention., Methods: Ninety-three people with epilepsy from three epilepsy self-management randomized controlled trials from the Managing Epilepsy Well (MWE) Network integrated research database met the inclusion criteria. Supervised machine learning algorithms were utilized to develop prediction models for changes in self-reported depression symptom severity. Features considered by the machine learning classifiers include age, gender, race, ethnicity, education, study type, baseline quality of life, and baseline depression symptom severity. The models were trained and evaluated on their ability to predict clinically meaningful improvement (i.e., a reduction of greater than three points on the nine-item Patient Health Questionnaire (PHQ-9)) between baseline and follow-up (<=12 weeks) depression scores. Models tested were a Multilayer Perceptron (ML), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression with Stochastic Gradient Descent (SGD), K-nearest Neighbors (KNN), and Gradient Boosting (GB). A separate, outside dataset of 41 people with epilepsy was used in a validation exercise to examine the top-performing model's generalizability and performance with external data., Results: All six classifiers performed better than our baseline mode classifier. Support Vector Machine had the best overall performance (average area under the curve [AUC] = 0.754, highest subpopulation AUC = 0.963). Our analysis of the SVM features revealed that higher baseline depression symptom severity, study type (i.e., intervention program goals), higher baseline quality of life, and race had the strongest influence on increasing the likelihood that a subject would experience a clinically meaningful improvement in depression scores. From the validation exercise, our top-performing SVM model performed similarly or better than the average SVM model with the outside dataset (average AUC = 0.887)., Significance: We trained an SVM classifier that offers novel insight into subject-specific features that are important for predicting a clinically meaningful improvement in subjective depression scores after enrollment in a self-management program. We provide evidence for machine learning to select subjects that may benefit most from a self-management program and indicate important factors that self-management programs should collect to develop improved digital tools., Competing Interests: Declaration of interests M.S. has received research grants from the following entities in the past 3 years: Nuromate, Otsuka, Alkermes, International Society for Bipolar Disorders (ISBD), National Institutes of Health (NIH), Centers for Disease Control and Prevention (CDC), and Patient-Centered Outcomes Research Institute (PCORI). She has consulted for: Alkermes, Otsuka, Janssen, Myriad, Health Analytics, and Frontline Medical Communications. She has royalties with: Springer Press, Johns Hopkins University Press, Oxford Press, and UpToDate. She also has CME activities in: American Physician’s Institute, MCM Education, CMEology, Potomac Center for Medical Education, Global Medical Education, Creative Educational Concepts, and Psychopharmacology Institute. B.C.J. has received research funding from the following entities: NIH, National Science Foundation, DARPA, Centers for Disease Control and Prevention, Neuropace, Inc., Harvard-Pilgrim, Inc., and the Diamond Foundation. She serves as Associate Editor for the journal Neurology. She also has received travel support for activities in her roles as Committee Chair and Vice Chair of the Council of Education of the American Epilepsy Society. All other authors have no conflicts of interest to report. We confirm that we have read the Journal’s position on issues involved in ethnical publication and affirm that this report is consistent with those guidelines., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algorithm and convolutional neural network (CNN) for the detection of intracranial IEDs ("AiED")., Methods: 1000 intracranial electroencephalogram (EEG) epochs extracted randomly from 307 subjects with refractory epilepsy were annotated independently by two expert neurophysiologists. These annotated epochs were divided into 1062 two-second epochs with IEDs and 1428 two-second epochs without IEDs, which were transformed into spectrograms prior to training the neural network. The highest performing network was validated on an annotated external test set., Results: The final network had an F1-score of 0.95 (95% CI: 0.91-0.98) and an average Area Under the Receiver Operating Characteristic of 0.98 (95% CI: 0.96-1.00). For the external test set, it showed an overall F1-score of 0.71, correctly identifying 100% of all high-amplitude IED complexes, 96.23% of all high-amplitude isolated IEDs, and 66.15% of all IEDs of atypical morphology., Conclusions: Template-matching combined with a CNN offers a fast, robust method for detecting intracranial IEDs., Significance: "AiED" is generalizable and achieves comparable performance to human reviewers; it may support clinical and research EEG analyses., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: Interictal epileptiform discharges (IEDs) were shown to be associated with cognitive impairment in persons with epilepsy. Previous studies indicated that IED rate, location, timing, and spatial relation to the seizure onset zone could predict an IED's impact on memory encoding and retrieval if they occurred in lateral temporal, mesial temporal, or parietal regions. In this study, we explore the influence that other IED properties (e.g., amplitude, duration, white matter classification) have on memory performance. We were specifically interested in investigating the influence that lateral temporal IEDs have on memory encoding., Methods: Two hundred sixty-one subjects with medication-refractory epilepsy undergoing intracranial electroencephalographic monitoring performed multiple sessions of a delayed free-recall task (n = 671). Generalized linear mixed models were utilized to examine the relationship between IED properties and memory performance., Results: We found that increased IED rate, IEDs propagating in white matter, and IEDs localized to the left middle temporal region were associated with poorer memory performance. For lateral temporal IEDs, we observed a significant interaction between IED white matter categorization and amplitude, where IEDs with an increased amplitude and white matter propagation were associated with reduced memory performance. Additionally, changes in alpha power after an IED showed a significant positive correlation with memory performance., Significance: Our results suggest that IED properties may be useful for predicting the impact an IED has on memory encoding. We provide an essential step toward understanding pathological versus potentially beneficial interictal epileptiform activity., (© 2021 International League Against Epilepsy.)

There is growing evidence for the efficacy of music, specifically Mozart's Sonata for Two Pianos in D Major (K448), at reducing ictal and interictal epileptiform activity. Nonetheless, little is known about the mechanism underlying this beneficial "Mozart K448 effect" for persons with epilepsy. Here, we measured the influence that K448 had on intracranial interictal epileptiform discharges (IEDs) in sixteen subjects undergoing intracranial monitoring for refractory focal epilepsy. We found reduced IEDs during the original version of K448 after at least 30-s of exposure. Nonsignificant IED rate reductions were witnessed in all brain regions apart from the bilateral frontal cortices, where we observed increased frontal theta power during transitions from prolonged musical segments. All other presented musical stimuli were associated with nonsignificant IED alterations. These results suggest that the "Mozart K448 effect" is dependent on the duration of exposure and may preferentially modulate activity in frontal emotional networks, providing insight into the mechanism underlying this response. Our findings encourage the continued evaluation of Mozart's K448 as a noninvasive, non-pharmacological intervention for refractory epilepsy., (© 2021. The Author(s).)

The factors that control the occurrence of interictal epileptiform discharges (IEDs) are not well understood. We suspected that this phenomenon reflects an attention-dependent suppression of interictal epileptiform activity. We hypothesized that IEDs would occur less frequently when a subject viewed a task-relevant stimulus compared with viewing a blank screen. Furthermore, IEDs have been shown to impair memory when they occur in certain regions during the encoding or recall phases of a memory task. Although these discharges have a short duration, their impact on memory suggests that they have longer lasting electrophysiological effects. We found that IEDs were associated with an increase in low-frequency power and a change in the balance between low- and high-frequency oscillations for several seconds. We found that the occurrence of IEDs is modified by whether a subject is attending to a word displayed on screen or is observing a blank screen. In addition, we found that discharges in brain regions in every lobe impair memory. These findings elucidate the relationship between IEDs and memory impairment and reveal the task dependence of the occurrence of IEDs., (© The Author(s) 2021. Published by Oxford University Press.)

Objective: This study was undertaken to evaluate the influence that subject-specific factors have on intracranial interictal epileptiform discharge (IED) rates in persons with refractory epilepsy., Methods: One hundred fifty subjects with intracranial electrodes performed multiple sessions of a free recall memory task; this standardized task controlled for subject attention levels. We utilized a dominance analysis to rank the importance of subject-specific factors based on their relative influence on IED rates. Linear mixed-effects models were employed to comprehensively examine factors with highly ranked importance., Results: Antiseizure medication (ASM) status, time of testing, and seizure onset zone (SOZ) location were the highest-ranking factors in terms of their impact on IED rates. The average IED rate of electrodes in SOZs was 34% higher than the average IED rate of electrodes outside of SOZs (non-SOZ; p < .001). However, non-SOZ electrodes had similar IED rates regardless of the subject's SOZ location (p = .99). Subjects on older generation (p < .001) and combined generation (p < .001) ASM regimens had significantly lower IED rates relative to the group taking no ASMs; newer generation ASM regimens demonstrated a nonsignificant association with IED rates (p = .13). Of the ASMs included in this study, the following ASMs were associated with significant reductions in IED rates: levetiracetam (p < .001), carbamazepine (p < .001), lacosamide (p = .03), zonisamide (p = .01), lamotrigine (p = .03), phenytoin (p = .03), and topiramate (p = .01). We observed a nonsignificant association between time of testing and IED rates (morning-afternoon p = .15, morning-evening p = .85, afternoon-evening p = .26)., Significance: The current study ranks the relative influence that subject-specific factors have on IED rates and highlights the importance of considering certain factors, such as SOZ location and ASM status, when analyzing IEDs for clinical or research purposes., (© 2020 International League Against Epilepsy.)

In 2020, the COVID-19 pandemic affected formal and informal education programs in the USA. The pandemic had a devastating impact on programs that required a dedicated physical space and in-person laboratory research. The distinguishing feature of New Hampshire Academy of Science (NHAS) programs is the participation of secondary school students in STEM research projects that emulate university-level research. Moving to a remote format presented various challenges. In this case study, we describe and discuss our experiences transforming a summer STEM research program for secondary school students from on-site and in-person to a remote platform, providing details of the planning phase, the logistics of maintaining the quality of the students' research, and the results of internal and external evaluations. Of the 33 students who participated, 32 completed all central elements of the program, and 25 went further and submitted summary papers and presented their research at the remote annual meeting of the American Association for the Advancement of Science. External evaluation found that students saw their work as similar to that of professional scientists, and perceived themselves to have gained proficiency in the use of scientific techniques and instrumentation. Students expressed they missed elements of in-person lab work including social interactions.

This feature issue of Applied Optics (AO) on Optics Theory and Practice in Iberoamerica (OTPI) collects significantly expanded refereed papers presented at the multiconference RIAO-OPTILAS-MOPM, held in Cancún, Mexico, Sept. 23-27, 2019. All authors who participated at the conference were contacted and invited to contribute to this special issue. Furthermore, the AO dedicated issue on OTPI was open to contributions from other practitioners in all related areas, through a call for papers published in AO.

Background: Electrocorticography studies are typically conducted in patients undergoing video EEG monitoring, but these studies are subject to confounds such as the effects of pain, recent anesthesia, analgesics, drug changes, antibiotics, and implant effects., New Method: Techniques were developed to obtain electrocorticographic (ECoG) data from freely moving subjects performing navigational tasks using the RNS ® System (NeuroPace, Inc., Mountain View, CA), a brain-responsive neurostimulation medical device used to treat focal onset epilepsy, and to align data from the RNS System with cognitive task events with high precision. These subjects had not had recent surgery, and were therefore not confounded by the perioperative variables that affect video EEG studies., Results: Task synchronization using the synchronization marker technique provides a quantitative measure of clock uncertainty, and can align data to task events with less than 4 ms of uncertainty. Hippocampal ECoG activity was found to change immediately before an incorrect response to a math problem compared to hippocampal activity before a correct response. In addition, subjects were found to have variable but significant changes in theta band power in the hippocampus during navigation compared to when subjects were not navigating. We found that there is theta-gamma phase-amplitude coupling in the right hippocampus while subjects stand still during a navigation task., Comparison With Existing Methods: An alignment technique described in this study improves the upper bound on task-ECoG alignment uncertainty from approximately 30 ms to under 4 ms. The RNS System is one of the first platforms capable of providing untethered ambulatory ECoG recording in humans, allowing for the study of real world instead of virtual navigation. Compared to intracranial video EEG studies, studies using the RNS System platform are not subject to confounds caused by the drugs and recent surgery inherent to the perioperative environment. Furthermore, these subjects provide the opportunity to record from the same electrodes over the course of many years., Conclusions: The RNS System enables us to study human navigation with unprecedented clarity. While RNS System patients have fewer electrodes implanted than video EEG patients, the lack of external artifact and confounds from recent surgery make this system a useful tool to further human electrophysiology research., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: Interictal epileptiform discharges (IEDs) have been linked to memory impairment, but the spatial and temporal dynamics of this relationship remain elusive. In the present study, we aim to systematically characterize the brain areas and times at which IEDs affect memory., Methods: Eighty epilepsy patients participated in a delayed free recall task while undergoing intracranial electroencephalography (EEG) monitoring. We analyzed the locations and timing of IEDs relative to the behavioral data in order to measure their effects on memory., Results: Overall IED rates did not correlate with task performance across subjects (r = 0.03, p = 0.8). However, at a finer temporal scale, within-subject memory was negatively affected by IEDs during the encoding and recall periods of the task but not during the rest and distractor periods (p < 0.01, p < 0.001, p = 0.3, and p = 0.8, respectively). The effects of IEDs during encoding and recall were stronger in the left hemisphere than in the right (p < 0.05). Of six brain areas analyzed, IEDs in the inferior-temporal, medial-temporal, and parietal areas significantly affected memory (false discovery rate < 0.05)., Significance: These findings reveal a network of brain areas sensitive to IEDs with key nodes in temporal as well as parietal lobes. They also demonstrate the time-dependent effects of IEDs in this network on memory., (Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.)

In working memory tasks, stimulus presentation induces a resetting of intracranial temporal lobe oscillations in multiple frequency bands. To further understand the functional relevance of this phenomenon, we investigated whether working memory performance depends on the phase precision of ongoing oscillations in the hippocampus. We recorded intra-hippocampal local field potentials in individuals performing a working memory task. Two types of trials were administered. For high memory trials presentation of a list of four letters ("List") was followed by a single letter memory probe ("Test"). Low memory load trials, consisting of four identical letters (AAAA) followed by a probe with the same letter (A), were interspersed. Significant phase locking of ongoing oscillations across trials, estimated by the Pairwise Phase Consistency Index (PPCI) was observed in delta (0.5-4 Hz), theta (5-7 Hz), and alpha (8-12 Hz) bands during stimulus presentation and recall but was increased in low memory load trials. Across patients however, higher delta PPCIs during recall in the left hippocampus were associated with faster reaction times. Because phase locking could also be interpreted as a consequence of a stimulus evoked potential, we performed event related potential analysis (ERP) and examined the relationship of ERP components with performance. We found that both amplitude and latency of late ERP components correlated with both reaction time and accuracy. We propose that, in the Sternberg task, phase locking of oscillations, or alternatively its ERP correlate, synchronizes networks within the hippocampus and connected structures that are involved in working memory.

Cognitive impairment is a common comorbidity in temporal lobe epilepsy (TLE) and is often considered more detrimental to quality of life than seizures. While it has been previously shown that the encoding of memory during behavior is impaired in the pilocarpine model of TLE in rats, how this information is consolidated during the subsequent sleep period remains unknown. In this study, we first report marked deficits in spatial memory performance and severe cell loss in the CA1 layer of the hippocampus lower spatial coherence of firing in TLE rats. We then present the first evidence that the reactivation of behavior-driven patterns of activity of CA1 place cells in the hippocampus is intact in TLE rats. Using a template-matching method, we discovered that real-time (3-5 s) reactivation structure was intact in TLE rats. Furthermore, we estimated the entropy rate of short time scale (∼250 ms) bursting activity using block entropies and found that significant, extended temporal correlations exist in both TLE and control rats. Fitting a first-order Markov Chain model to these bursting time series, we found that long sequences derived from behavior were significantly enriched in the Markov model over corresponding models fit on randomized data confirming the presence of replay in shorter time scales. We propose that the persistent consolidation of poor spatial information in both real time and during bursting activity may contribute to memory impairments in TLE rats., (© 2014 Wiley Periodicals, Inc.)

The Wigner distribution function is investigated as a signal processing tool to detect subsurface targets closely located beneath a randomly rough surface. Information provided by a bistatic arrangement of sources and detectors can be used to discriminate target and surface response based on their scattering behavior. It is shown that the bilinearity of the Wigner distribution function can be exploited for nonlinear amplification of the target response. This is achieved by averaging the Wigner distribution of the detected signal for different source locations. Target detection is further improved by numerically backpropagating the detected signal to the surface. A statistical evaluation based on simulated data sets is used to evaluate the performance of the detection method.

A nonlinear signal processing method is applied to the design of strongly scattering objects to realize a defined angular response. Investigated as the complement of inverse scattering problems, k-space design methods are combined with cepstral filtering to obtain a permittivity distribution that scatters with the desired response. Starting with the rigorously computed angular spectrum of the scattering amplitude of an object of simple geometric shape, the corresponding k-space is modified to provide the desired scattering behavior. In order to account for strong scattering, cepstral filtering is applied to map the associated distribution of secondary sources to a unique permittivity distribution. The inversion process results in a structure that exhibits the desired properties and which can be interpreted as a perturbation of the initial structure. Simulation results are presented which illustrate the usefulness of this method. In particular, objects are modified to enhance forward scattering and suppress scattering in all other direction. Results are verified using a rigorous finite-difference frequency-domain scheme to simulate scattering. The method is demonstrated as a novel means for designing invisible objects that act as electromagnetic cloaks.

A method is proposed to reconstruct signals from incomplete data. The method, which can be interpreted both as a discrete implementation of the so-called prior discrete Fourier transform (PDFT) spectral estimation technique and as a variant of the algebraic reconstruction technique, allows one to incorporate prior information about the reconstructed signal to improve the resolution of the signal estimated. The context of diffraction tomography and image reconstruction from samples of the far-field scattering amplitude are used to explore the performance of the method. On the basis of numerical computations, the optimum choice of parameters is determined empirically by comparing image reconstructions of the noniterative PDFT algorithm and the proposed iterative scheme.

An efficient optimization strategy for the design of diffractive optical elements that is based on rigorous diffraction theory is described. The optimization algorithm combines diffraction models of different degrees of accuracy and computational complexity. A fast design algorithm for diffractive optical elements is used to yield estimates of the optimum surface profile based on paraxial diffraction theory. These estimates are subsequently evaluated with a rigorous diffraction model. This scheme allows one to minimize the need to compute diffraction effects rigorously, while providing accurate design. We discuss potential applications of this scheme as well as details of an implementation based on a modified Gerchberg-Saxton algorithm and the finite-difference time-domain method. Illustrative examples are provided in which we use the algorithm to design Fourier array illuminators.