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Patients with frontal lobe damage and cognitively normal elderly individuals demonstrate increased susceptibility to false facial recognition. In this paper we review neuropsychological evidence consistent with the notion that the common functional impairment underlying face memory distortions in both subject populations is a context recollection/source monitoring deficit, coupled with excessive reliance on relatively preserved facial familiarity signals in recognition decisions. In particular, we suggest that due to the breakdown of strategic memory retrieval, monitoring, and decision operations, individuals with frontal lobe impairment caused by focal damage or age-related functional decline do not have a reliable mechanism for attributing the experience of familiarity to the correct context or source. Memory illusions are mostly apparent under conditions of uncertainty when the face cue does not directly elicit relevant identity-specific contextual information, leaving the source of familiarity unspecified or ambiguous. Based on these findings, we propose that remembering faces is a constructive process that requires dynamic interactions between temporal lobe memory systems that operate in an automatic or bottom-up fashion and frontal executive systems that provide strategic top-down control of recollection. Executive memory control functions implemented by prefrontal cortex play a critical role in suppressing false facial recognition and related source memory misattributions. [ABSTRACT FROM AUTHOR]

Transient epileptic amnesia (TEA) is a recently recognised syndrome of epilepsy in which the principle manifestation of seizures is recurrent episodes of isolated memory loss. In this article, we describe the clinical and cognitive profile of this emerging syndrome, and present new data that provide at most weak support for its proposed relationship to cerebrovascular disease. TEA is often associated with two unusual forms of interictal memory impairment: accelerated long-term forgetting and remote memory impairment. We discuss the clinical and theoretical implications of these relatively novel cognitive deficits. [ABSTRACT FROM AUTHOR]

We report the investigation of the organisation of proper names in two aphasic patients (NBC and FBI). The performance of both patients on spoken word to written word matching tasks was inconsistent, affected by presentation rate and semantic relatedness of the competing responses, all hallmarks of a refractory semantic access dysphasia. In a series of experiments we explored the semantic relatedness effects within their proper name vocabulary, including brand names and person names. First we demonstrated the interaction between very fine grain organisation and personal experience, with one patient with a special interest in the cinema demonstrating higher error rates when identifying the names of actors working in a similar film genre (e.g. action movies: Arnold Schwarzenegger, Bruce Willis, Sylvester Stallone, Mel Gibson) than those working in different genres (e.g. Arnold Schwarzenegger, Gregory Peck, Robin Williams, Gene Kelly). Second we compared directly two potential principles of semantic organisation - taxonomic and thematic. Furthermore we considered these principles of organisation in the context of the individuals' personal knowledge base. We selected topics matching the interests and experience of each patient, namely cinema and literature (NBC) and naval history (FBI). The stimulus items were arranged in taxonomic arrays (e.g. Jane Austen, Emily Bronte, Agatha Christie), thematic arrays (e.g. Jane Austen, Pride and Prejudice, Mr Darcy), and unrelated arrays (e.g. Jane Austen, Wuthering Heights, Hercule Poirot). We documented that different patterns of taxonomic and thematic organisation were constrained by whether the individual has limited knowledge, moderate knowledge or detailed knowledge of a particular vocabulary. It is suggested that moderate proper name knowledge is primarily organised by taxonomy whereas extensive experience results in a more detailed knowledge base in which theme is a powerful organising principle. [ABSTRACT FROM AUTHOR]

Existing empirical data on proper names processing are critically reviewed in trying to understand which tasks may involve the left temporal pole, which proper name related functions are supported by this structure and eventually offer some speculations about why these functions might have developed in this location in the course of human evolution. While clinical group studies support the idea that proper name processing takes place in the left temporal pole, single case studies of selective proper name anomia or sparing, as well as neuroimaging studies, suggest the involvement of a larger neural network. Within this network, an important role may be played by the ventro-medial prefrontal cortex, including areas critical in social interaction. The differentiation in the brain of proper name processing from common names processing could in part be due to social pressure, favouring a neural system able to more efficiently and unambiguously sustain designating categories or designating individual entities. The activation of the left temporal pole in proper name processing is shown to increase with age. Longer social interaction may thus contribute to convey proper names processing toward areas closer to those supporting social cognition. [ABSTRACT FROM AUTHOR]

There is good evidence that aphasia therapy is effective if sufficiently prolonged or intensive and that chronic aphasic individuals can also benefit from therapy, but data on chronic aphasia are scanty. The aim of this retrospective study was to investigate whether chronic aphasia benefits from a very intensive therapeutic regimen. We revised the files (January 2000 to December 2008) of the chronic subjects whom we suggested have periodic sessions in our Unit (generally once a week) and 2-3 hours daily of homework with the help of a family member, supervised and controlled by the speech-therapist. Treatment would go on as long as amelioration is evident. Results for 23 chronic aphasic subjects are reported. All subjects had undergone previous therapy and 10 had been dismissed because no further recovery was expected. Recovery was significant in oral and written nouns and actions naming, oral and written sentence production and Token Test scores. Only 4 subjects did not improve. Severity of the disorder did not predict success or failure. We conclude that recovery was due to the intense work done. Further, we believe such a regimen could be successful in a number of patients for whom a less intensive regimen would not be effective. [ABSTRACT FROM AUTHOR]

In this study we describe a patient, TA, who developed a chronic amnesic-confabulatory syndrome, following rupture of a right internal carotid siphon aneurysm. Our aim was to elucidate as fully as possible the nature of TA's impairment and to test the hypothesis of confabulation as reflecting a dysfunction of Temporal Consciousness, i.e. to become aware of something as part of a personal past, present or future. TA's confabulations were present in answers to questions tapping Temporal Consciousness, i.e. autobiographical episodic memory, orientation in time and place, and foresight of personal future. In contrast, confabulations were not observed in answers to questions tapping Knowing Consciousness, i.e. to become aware of something as a meaning or as an element of impersonal knowledge. In fact, he had normal access to semantic knowledge, including foresight of impersonal future. TA's brain MRI showed lesions involving the right hippocampus, parahippocampal gyrus, fornix, mammillary bodies, and thalamus. Moreover TA showed sub-cortical lesions involving the caudate and putamen nuclei bilaterally, a lesion site not commonly described in amnesic-confabulatory syndrome. We suggest that this pattern of results is better accounted for within the framework of the Memory, Consciousness and Temporality Theory and reflects a specific distortion of Temporal Consciousness. [ABSTRACT FROM AUTHOR]

Objective: Intracranial EEG can identify epilepsy-related networks in patients with focal epilepsy; however, the association between network organization and post-surgical seizure outcomes remains unclear. Hubness serves as a critical metric to assess network organization by identifying brain regions that are highly influential to other regions. In this study, we tested the hypothesis that favorable post-operative seizure outcomes are associated with the surgical removal of interictal network hubs, measured by the novel metric "Resection-Hub Alignment Degree (RHAD).", Methods: We analyzed Phase II interictal intracranial EEG from 69 patients with epilepsy who were seizure-free (n = 45) and non-seizure-free (n = 24) 1 year post-operatively. Connectivity matrices were constructed from intracranial EEG recordings using imaginary coherence in various frequency bands, and centrality metrics were applied to identify network hubs. The RHAD metric quantified the congruence between hubs and resected/ablated areas. We used a logistic regression model, incorporating other clinical factors, and evaluated the association of this alignment regarding post-surgical seizure outcomes., Results: There was a significant difference in RHAD in fast gamma (80-200 Hz) interictal network between patients with favorable and unfavorable surgical outcomes (p = .025). This finding remained similar across network definitions (i.e., channel-based or region-based network) and centrality measurements (Eigenvector, Closeness, and PageRank). The alignment between surgically removed areas and other commonly used clinical quantitative measures (seizure-onset zone, irritative zone, high-frequency oscillations zone) did not reveal significant differences in post-operative outcomes. This finding suggests that the hubness measurement may offer better predictive performance and finer-grained network analysis. In addition, the RHAD metric showed explanatory validity both alone (area under the curve [AUC] = .66) and in combination with surgical therapy type (resection vs ablation, AUC = .71)., Significance: Our findings underscore the role of network hub surgical removal, measured through the RHAD metric of interictal intracranial EEG high gamma networks, in enhancing our understanding of seizure outcomes in epilepsy surgery., (© 2024 International League Against Epilepsy.)

Physical exercise is an emerging target for improving cognition in aging and neurological disease. Due to the beneficial impact of exercise on hippocampal health and the vulnerability of the hippocampus in medication-resistant temporal lobe epilepsy (TLE), exercise could present a promising intervention in TLE. We investigated whether exercise engagement is associated with verbal memory function and hippocampal integrity in 29 young to middle-aged adults with refractory TLE and 21 demographically matched controls. Participants completed a self-reported questionnaire of weekly exercise, three tests of verbal memory, and a subset ( n  = 44) underwent structural MRI. Individuals with TLE self-reported lower exercise scores than controls across all levels of exercise intensity ( p  < 0.001). In TLE, greater exercise engagement was associated with better verbal memory (word-list recall and associative learning; rho  = 0.46-0.47; p s FDR  < 0.05), and with larger contralateral hippocampal volumes ( rho  = 0.61; p  < 0.01). These effects remained significant when controlling for epilepsy-related and demographic factors. Within the limitations of a cross-sectional observational study, these findings suggest that exercise may be a cognitive reserve factor in TLE, potentially mitigating memory decline by enhancing contralateral hippocampal integrity. With future replication and longitudinal studies to clarify the causal pathways of these relationships, exercise holds promise as a low-cost, accessible, and modifiable lifestyle target for improving cognitive health in individuals with refractory TLE., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. McDonald is a consultant for Neurona Therapeutics. The remaining authors do not have any conflicts of interest to disclose., (© 2024 The Author(s).)

Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI. Using 1178 T1-weighted images (589 temporal lobe epilepsy, 589 healthy controls) from 12 surgical centres, we trained 3D and 2D CNNs for temporal lobe epilepsy versus healthy control classification, using feature visualization to identify important regions. The 3D CNN was compared to the 2D model and to a randomized model (comparison to chance). Further, we explored the effect of sample size with subsampling, examined model performance based on single-subject clinical characteristics, and tested the impact of image harmonization on model performance. Across 50 datapoints (10 runs with 5-folds each) the 3D CNN median accuracy was 86.4% (35.3% above chance) and the median F 1-score was 86.1% (33.3% above chance). The 3D model yielded higher accuracy compared to the 2D model on 84% of datapoints (median 2D accuracy, 83.0%), a significant outperformance for the 3D model (binomial test: P < 0.001). This advantage of the 3D model was only apparent at the highest sample size. Saliency maps exhibited the importance of medial-ventral temporal, cerebellar, and midline subcortical regions across both models for classification. However, the 3D model had higher salience in the most important regions, the ventral-medial temporal and midline subcortical regions. Importantly, the model achieved high accuracy (82% accuracy) even in patients without MRI-identifiable hippocampal sclerosis. Finally, applying ComBat for harmonization did not improve performance. These findings highlight the value of 3D CNNs for identifying subtle structural abnormalities on MRI, especially in patients without clinically identified temporal lobe epilepsy lesions. Our findings also reveal that the advantage of 3D CNNs relies on large sample sizes for model training., Competing Interests: None of the authors have any conflict of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-centre study for model development; two-centre study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using several interictal EEG features derived from recent publications. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. Forty-seven patients (30 female; ages 20-69; 20 left-sided, 10 right-sided and 17 bilateral seizure onsets) were analysed for model development and internal validation. Nineteen patients (10 female; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analysed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. We replicated the finding that right temporal lobe epilepsy was harder to distinguish in a separate modality of resting-state functional MRI. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome. A potential clinical application of these findings could be to either support or oppose a hypothesis of unilateral temporal lobe epilepsy when deciding to pursue surgical resection or ablation as opposed to device implantation., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Among stroke survivors, linguistic and non-linguistic impairments exhibit substantial inter-individual variability. Stroke lesion volume and location do not sufficiently explain outcomes, and the neural mechanisms underlying the severity of aphasia or non-verbal cognitive deficits remain inadequately understood. Converging evidence supports the idea that white matter is particularly susceptible to ischaemic injury, and long-range fibres are commonly associated with verbal and non-verbal function. Here, we investigated the relationship among post-stroke aphasia severity, cognition, and white matter integrity. Eighty-seven individuals in the chronic stage of stroke underwent diffusion MRI and behavioural testing, including language and cognitive measures. We used whole-brain structural connectomes from each participant to calculate the ratio of long-range fibres to short-range fibres. We found that a higher proportion of long-range fibres was associated with lower aphasia severity, more accurate picture naming, and increased performance on non-verbal semantic memory/processing and non-verbal reasoning while controlling for lesion volume, key damage areas, age, and years post stroke. Our findings corroborate the hypothesis that, after accounting for age and lesion anatomy, inter-individual differences in post-stroke aphasia severity, verbal, and non-verbal cognitive outcomes are related to the preservation of long-range white matter fibres beyond the lesion., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: The piriform cortex has been implicated in the initiation, spread and termination of epileptic seizures. This understanding has extended to surgical management of epilepsy, where it has been shown that resection or ablation of the piriform cortex can result in better outcomes. How and why the piriform cortex may play such a crucial role in seizure networks is not well understood. To answer these questions, we investigated the functional and structural connectivity of the piriform cortex in both healthy controls and temporal lobe epilepsy (TLE) patients., Methods: We studied a retrospective cohort of 55 drug-resistant unilateral TLE patients and 26 healthy controls who received structural and functional neuroimaging. Using seed-to-voxel connectivity we compared the normative whole-brain connectivity of the piriform to that of the hippocampus, a region commonly involved in epilepsy, to understand the differential contribution of the piriform to the epileptogenic network. We subsequently measured the inter-piriform coupling (IPC) to quantify similarities in the inter-hemispheric cortical functional connectivity profile between the two piriform cortices. We related differences in IPC in TLE back to aberrations in normative piriform connectivity, whole brain functional properties, and structural connectivity., Results: We find that relative to the hippocampus, the piriform is functionally connected to the anterior insula and the rest of the salience ventral attention network (SAN). We also find that low IPC is a sensitive metric of poor surgical outcome (sensitivity: 85.71%, 95% CI: [19.12%, 99.64%]); and differences in IPC within TLE were related to disconnectivity and hyperconnectivity to the anterior insula and the SAN. More globally, we find that low IPC is associated with whole-brain functional and structural segregation, marked by decreased functional small-worldness and fractional anisotropy., Conclusions: Our study presents novel insights into the functional and structural neural network alterations associated with this structure, laying the foundation for future work to carefully consider its connectivity during the presurgical management of epilepsy., Competing Interests: Competing Interests The authors report no competing interests.

Background and Objectives: Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties., Methods: We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time., Results: Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes., Discussion: Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d : 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks., Competing Interests: E.C.C. performs consulting work for Epiminder, an EEG device company. The remaining authors have no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Objective: Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms., Methods: We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts., Results: We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations., Significance: iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide., (© 2023 International League Against Epilepsy.)

Introduction: Dorsal root ganglion stimulation (DRG-S) is a viable interventional option for intractable pain management. Although systematic data are lacking regarding the immediate neurologic complications of this procedure, intraoperative neurophysiological monitoring (IONM) can be a valuable tool to detect real-time neurologic changes and prompt intervention(s) during DRG-S performed under general anesthesia and deep sedation., Materials and Methods: In our single-center case series, we performed multimodal IONM, including peripheral nerve somatosensory evoked potentials (pnSSEPs) and dermatomal somatosensory evoked potentials (dSSEPs), spontaneous electromyography (EMG), transcranial motor evoked potentials (MEPs), and electroencephalogram (EEG) for some trials and all permanent DRG-S lead placement per surgeon preference. Alert criteria for each IONM modality were established before data acquisition and collection. An IONM alert was used to implement an immediate repositioning of the lead to reduce any possible postoperative neurologic deficits. We reviewed the literature and summarized the current IONM modalities commonly applied during DRG-S, including somatosensory evoked potentials and EMG. Because DRG-S targets the dorsal roots, we hypothesized that including dSSEP would allow more sensitivity as a proxy for potential sensory changes under generalized anesthesia than would including standard pnSSEPs., Results: From our case series of 22 consecutive procedures with 45 lead placements, one case had an alert immediately after DRG-S lead positioning. In this case, dSSEP attenuation was seen, indicating changes in the S1 dermatome, which occurred despite ipsilateral pnSSEP from the posterior tibial nerve remaining at baselines. The dSSEP alert prompted the surgeon to reposition the S1 lead, resulting in immediate recovery of the dSSEP to baseline status. The rate of IONM alerts reported intraoperatively was 4.55% per procedure and 2.22% per lead (n = 1). No neurologic deficits were reported after the procedure, resulting in no postoperative neurologic complications or deficits. No other IONM changes or alerts were observed from pnSSEP, spontaneous EMG, MEPs, or EEG modalities. Reviewing the literature, we noted challenges and potential deficiencies when using current IONM modalities for DRG-S procedures., Conclusions: Our case series suggests dSSEPs offer greater reliability than do pnSSEPs in quickly detecting neurologic changes, and subsequent neural injury, during DRG-S cases. We encourage future studies to focus on adding dSSEP to standard pnSSEP to provide a comprehensive, real-time neurophysiological assessment during lead placement for DRG-S. More investigation, collaboration, and evidence are required to evaluate, compare, and standardize comprehensive IONM protocols for DRG-S., (Copyright © 2023 International Neuromodulation Society. Published by Elsevier Inc. All rights reserved.)

Objective: This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response., Methods: We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints., Results: We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa)., Significance: Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research., 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 © 2023 Elsevier Inc. All rights reserved.)

Background and Objectives: A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input., Methods: Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients., Results: Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification., Discussion: These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention., Classification of Evidence: This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality., (© 2023 American Academy of Neurology.)

In post-stroke aphasia, language improvements following speech therapy are variable and can only be partially explained by the lesion. Brain tissue integrity beyond the lesion (brain health) may influence language recovery and can be impacted by cardiovascular risk factors, notably diabetes. We examined the impact of diabetes on structural network integrity and language recovery. Seventy-eight participants with chronic post-stroke aphasia underwent six weeks of semantic and phonological language therapy. To quantify structural network integrity, we evaluated the ratio of long-to-short-range white matter fibers within each participant's whole brain connectome, as long-range fibers are more susceptible to vascular injury and have been linked to high level cognitive processing. We found that diabetes moderated the relationship between structural network integrity and naming improvement at 1 month post treatment. For participants without diabetes (n = 59), there was a positive relationship between structural network integrity and naming improvement (t = 2.19, p = 0.032). Among individuals with diabetes (n = 19), there were fewer treatment gains and virtually no association between structural network integrity and naming improvement. Our results indicate that structural network integrity is associated with treatment gains in aphasia for those without diabetes. These results highlight the importance of post-stroke structural white matter architectural integrity in aphasia recovery., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)