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Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. This paper describes recent efforts on the Simon project to capture information from ICU care providers about patient state and therapy in response to alerts, in order to assess the value of event definitions and progressively refine alerting algorithms. Event definitions for intracranial pressure and cerebral perfusion pressure were studied by implementing a reliable system to automatically deliver alerts to clinical users' alphanumeric pagers, and to capture associated documentation about patient state and therapy when the alerts occurred. During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility. [ABSTRACT FROM AUTHOR]

Background and Objectives: Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) motor symptoms. DBS is also associated with postoperative cognitive change in some patients. Previous studies found associations between medial active electrode contacts and overall cognitive decline. Our current aim is to determine the relationship between active electrode contact location and domain-specific cognitive changes., Methods: A single-institution retrospective cohort study was conducted in patients with PD who underwent subthalamic nucleus (STN) DBS from August 05, 2010, to February 22, 2021, and received preoperative and postoperative neuropsychological testing. Standardized norm-referenced test z-scores were categorized into attention, executive function, language, verbal memory, and visuospatial domains. SD change scores were averaged to create domain-specific change scores. We identified anterior commissure/posterior commissure coordinates of active electrode contacts in atlas space. We evaluated differences in active electrode contact location between patients with a domain score decrease of at least 1 SD and less than 1 SD. We performed multiple variable linear regression controlling for age, sex, education, time from surgery to postoperative neuropsychological testing (follow-up duration), disease duration, preoperative unified Parkinson's disease rating scale off medication scores, and preoperative memory scores to determine the relationship between active electrode contact location and domain change., Results: A total of 83 patients (male: n = 60, 72.3%) were included with a mean age of 63.6 ± 8.3 years, median disease duration of 9.0 [6.0, 11.5] years, and median follow-up duration of 8.0 [7.0, 11.0] months. More superior active electrode contact location in the left STN (P = .002) and higher preoperative memory scores (P < .0001) were associated with worsening memory. Active electrode contact location was not associated with change in other domains., Conclusion: In patients with PD who underwent STN DBS, we found an association between superior active electrode contacts in the left STN and verbal memory decline. Our study increases understanding of factors associated with cognitive change after DBS and may help inform postoperative programming., (Copyright © Congress of Neurological Surgeons 2024. All rights reserved.)

Objective: Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally., Methods: We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types., Results: We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures., Significance: This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Transcranial-focused ultrasound (tFUS) procedures such as neuromodulation and blood-brain barrier (BBB) opening require precise focus placement within the brain. MRI is currently the most reliable tool for focus localization but can be prohibitive for procedures requiring recurrent therapies. We designed, fabricated, and characterized a patient-specific, 3-D-printed, stereotactic frame for repeated tFUS therapy. The frame is compact, with minimal footprint, can be removed and re-secured between treatments while maintaining sub-mm accuracy, and will allow for precise and repeatable transcranial FUS treatment without the need for MR-guidance following the initial calibration scan. Focus localization and repeatability were assessed via MR-thermometry and MR-acoustic radiation force imaging (ARFI) on an ex vivo skull phantom and in vivo nonhuman primates (NHPs), respectively. Focal localization, registration, steering, and re-steering were accomplished during the initial MRI calibration scan session. Keeping steering coordinates fixed in subsequent therapy and imaging sessions, we found good agreement between steered foci and the intended target, with target registration error (TRE) of 1.2 ± 0.3 ( n = 4 , ex vivo) and 1.0 ± 0.5 ( n = 3 , in vivo) mm. Focus position (steered and non-steered) was consistent, with sub-mm variation in each dimension between studies. Our 3-D-printed, patient-specific stereotactic frame can reliably position and orient the ultrasound transducer for repeated targeting of brain regions using a single MR-based calibration. The compact frame allows for high-precision tFUS to be carried out outside the magnet and could help reduce the cost of tFUS treatments where repeated application of an ultrasound focus is required with high precision.

Objective: Deep brain stimulation (DBS) targeting the globus pallidus interna (GPi) has been shown to significantly improve motor symptoms for the treatment of medication-refractory Parkinson's disease. Yet, heterogeneity in clinical outcomes persists, possibly due to suboptimal target identification within the GPi. By leveraging robust sampling of the GPi and 6-month postsurgical outcomes, this study aims to determine optimal symptom-specific GPi DBS targets., Methods: In this study, the authors analyzed the anatomical lead location and 6-month postsurgical, double-blinded outcome measures of 86 patients who underwent bilateral GPi DBS. These patients were selected from the multicenter Veterans Affairs (VA)/National Institutes of Neurological Disorders and Stroke (NINDS) Cooperative Studies Program (CSP) 468 study to identify the optimal target zones ("sweet spots") for the control of overall motor (United Parkinson's Disease Rating Scale [UPDRS]-III), axial, tremor, rigidity, and bradykinesia symptoms. Lead coordinates were normalized to Montreal Neurological Institute space and the optimal target zones were identified and validated using a leave-one-patient-out approach., Results: The authors' findings revealed statistically significant optimal target zones for UPDRS-III (R = 0.37, p < 0.001), axial (R = 0.22, p = 0.042), rigidity (R = 0.20, p = 0.021), and bradykinesia (R = 0.23, p = 0.004) symptoms. These zones were localized within the primary motor and premotor subdivisions of the GPi. Interestingly, these zones extended beyond the GPi lateral border into the GPi-globus pallidus externa (GPe) lamina and into the GPe, but they did not reach the GPi ventral border, challenging traditional surgical approaches based on pallidotomies., Conclusions: Drawing upon a robust dataset, this research effectively delineates specific optimal target zones for not only overall motor improvement but also symptom subscores. These insights hold the potential to enhance the precision of targeting in subsequent bilateral GPi DBS surgical procedures.

Deep brain stimulation of the subthalamic nucleus is an effective treatment for the clinical motor symptoms of Parkinson's disease, but may alter the ability to learn contingencies between stimuli, actions and outcomes. We investigated how stimulation of the functional subregions in the subthalamic nucleus (motor and cognitive regions) modulates stimulus-action-outcome learning in Parkinson's disease patients. Twelve Parkinson's disease patients with deep brain stimulation of the subthalamic nucleus completed a probabilistic stimulus-action-outcome task while undergoing ventral and dorsal subthalamic nucleus stimulation (within subjects, order counterbalanced). The task orthogonalized action choice and outcome valence, which created four action-outcome learning conditions: action-reward, inhibit-reward, action-punishment avoidance and inhibit-punishment avoidance. We compared the effects of deep brain stimulation on learning rates across these conditions as well as on computed Pavlovian learning biases. Dorsal stimulation was associated with higher overall learning proficiency relative to ventral subthalamic nucleus stimulation. Compared to ventral stimulation, stimulating the dorsal subthalamic nucleus led to a particular advantage in learning to inhibit action to produce desired outcomes (gain reward or avoid punishment) as well as better learning proficiency across all conditions providing reward opportunities. The Pavlovian reward bias was reduced with dorsal relative to ventral subthalamic nucleus stimulation, which was reflected by improved inhibit-reward learning. Our results show that focused stimulation in the dorsal compared to the ventral subthalamic nucleus is relatively more favourable for learning action-outcome contingencies and reduces the Pavlovian bias that could lead to reward-driven behaviour. Considering the effects of deep brain stimulation of the subthalamic nucleus on learning and behaviour could be important when optimizing stimulation parameters to avoid side effects like impulsive reward-driven behaviour., Competing Interests: B.M.D. is a founder and equity holder in Neurotargeting, LLC, that licences the technology from Vanderbilt University described in this article., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Objective: Subthalamic nucleus (STN) and globus pallidus internus (GPI) deep brain stimulation (DBS) effectively treat motor symptoms in Parkinson's disease (PD) but may be associated with cognitive and psychiatric changes in some patients. Evaluation of changes in cognitive and psychiatric symptoms following DBS is complicated by changes in these symptoms that occur as part of the natural disease course. The aim of this study was to evaluate whether electrode position was associated with changes in neurocognitive symptoms in patients who underwent STN and GPI DBS., Methods: A single-institution retrospective cohort study was conducted on patients with PD who underwent DBS from 2008 to 2019. Cognitive and psychiatric outcomes included Beck Depression Inventory II (BDI-II) score, presence of impulsive-compulsive behavior (ICB), Mini-Mental State Examination (MMSE) score, and overall cognitive status grade determined by comprehensive neuropsychology testing (normal, mild impairment, moderate impairment, and dementia). Pre- and postoperative comparisons were performed using a Wilcoxon signed-rank test or paired t-test. Patients with and without cognitive decline were compared using a Mann-Whitney U-test or unpaired t-test. A chi-square test was used for categorical comparisons., Results: One hundred thirty patients were included (mean age 62.5 ± 7.9 years). At a mean postoperative follow-up from DBS of 13.0 ± 12.7 (range 6-66) months, there was an improvement in ICB (26.3% preoperatively vs 15.0% postoperatively, p = 0.017), but a decline in MMSE score (28.6 ± 1.6 vs 27.6 ± 2.0, p < 0.001) and overall cognitive status (normal: 66.2% vs 39.2%; mild: 12.3% vs 17.7%; moderate: 21.5% vs 33.1%; dementia: 0.0% vs 10.0%; p < 0.001). Patients undergoing STN DBS had a worse decline in overall cognitive status than patients who underwent GPI DBS (p = 0.006). Postoperative cognitive decline was associated with a more medial electrode position only for patients who underwent STN DBS., Conclusions: Cognitive change was observed in some patients with PD who underwent both GPI and STN DBS, likely due partly to underlying disease progression. Compared with GPI DBS, STN DBS was associated with a greater likelihood of cognitive decline. In STN but not GPI DBS, cognitive decline was associated with medialized electrode position, suggesting modulation of nonmotor STN divisions may contribute to cognitive changes following STN DBS.

Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150 Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40 Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Background: Chronic graft-versus-host disease (cGVHD) is a significant cause of long-term morbidity and mortality in patients after allogeneic hematopoietic cell transplantation. Skin is the most commonly affected organ, and visual assessment of cGVHD can have low reliability. Crowdsourcing data from nonexpert participants has been used for numerous medical applications, including image labeling and segmentation tasks., Objective: This study aimed to assess the ability of crowds of nonexpert raters-individuals without any prior training for identifying or marking cGHVD-to demarcate photos of cGVHD-affected skin. We also studied the effect of training and feedback on crowd performance., Methods: Using a Canfield Vectra H1 3D camera, 360 photographs of the skin of 36 patients with cGVHD were taken. Ground truth demarcations were provided in 3D by a trained expert and reviewed by a board-certified dermatologist. In total, 3000 2D images (projections from various angles) were created for crowd demarcation through the DiagnosUs mobile app. Raters were split into high and low feedback groups. The performances of 4 different crowds of nonexperts were analyzed, including 17 raters per image for the low and high feedback groups, 32-35 raters per image for the low feedback group, and the top 5 performers for each image from the low feedback group., Results: Across 8 demarcation competitions, 130 raters were recruited to the high feedback group and 161 to the low feedback group. This resulted in a total of 54,887 individual demarcations from the high feedback group and 78,967 from the low feedback group. The nonexpert crowds achieved good overall performance for segmenting cGVHD-affected skin with minimal training, achieving a median surface area error of less than 12% of skin pixels for all crowds in both the high and low feedback groups. The low feedback crowds performed slightly poorer than the high feedback crowd, even when a larger crowd was used. Tracking the 5 most reliable raters from the low feedback group for each image recovered a performance similar to that of the high feedback crowd. Higher variability between raters for a given image was not found to correlate with lower performance of the crowd consensus demarcation and cannot therefore be used as a measure of reliability. No significant learning was observed during the task as more photos and feedback were seen., Conclusions: Crowds of nonexpert raters can demarcate cGVHD images with good overall performance. Tracking the top 5 most reliable raters provided optimal results, obtaining the best performance with the lowest number of expert demarcations required for adequate training. However, the agreement amongst individual nonexperts does not help predict whether the crowd has provided an accurate result. Future work should explore the performance of crowdsourcing in standard clinical photos and further methods to estimate the reliability of consensus demarcations., (©Andrew J McNeil, Kelsey Parks, Xiaoqi Liu, Bohan Jiang, Joseph Coco, Kira McCool, Daniel Fabbri, Erik P Duhaime, Benoit M Dawant, Eric R Tkaczyk. Originally published in JMIR Dermatology (http://derma.jmir.org), 26.12.2023.)

Background: Deep brain stimulation (DBS) is commonly performed with patients awake to perform intraoperative microelectrode recordings and/or macrostimulation testing to guide final electrode placement. Supplemental information from atlas-based databases derived from prior patient data and visualised as efficacy heat maps transformed and overlaid onto preoperative MRIs can be used to guide preoperative target planning and intraoperative final positioning. Our quantitative analysis of intraoperative testing and corresponding changes made to final electrode positioning aims to highlight the value of intraoperative neurophysiological testing paired with image-based data to optimise final electrode positioning in a large patient cohort., Methods: Data from 451 patients with movement disorders treated with 822 individual DBS leads at a single institution from 2011 to 2021 were included. Atlas-based data was used to guide surgical targeting. Intraoperative testing data and coordinate data were retrospectively obtained from a large patient database. Medical records were reviewed to obtain active contact usage and neurologist-defined outcomes at 1 year., Results: Microelectrode recording firing profiles differ per track, per target and inform the locations where macrostimulation testing is performed. Macrostimulation performance correlates with the final electrode track chosen. Centroids of atlas-based efficacy heat maps per target were close in proximity to and may predict active contact usage at 1 year. Overall, patient outcomes at 1 year were improved for patients with better macrostimulation response., Conclusions: Atlas-based imaging data is beneficial for target planning and intraoperative guidance, and in conjunction with intraoperative neurophysiological testing during awake DBS can be used to individualize and optimise final electrode positioning, resulting in favourable outcomes., Competing Interests: Competing interests: Some of the technology described in this article has been licensed by Vanderbilt University to FHC, Inc. which distributes it under the name WayPoint Navigator. BD receives royalties for this license. PK has a fiduciary relationship with NeuroTargeting, LLC, but has not received financial compensation in the 5 years prior to this article., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Cochlear implants (CIs) are neuroprosthetics that can provide a sense of sound to people with severe-to-profound hearing loss. A CI contains an electrode array (EA) that is threaded into the cochlea during surgery. Recent studies have shown that hearing outcomes are correlated with EA placement. An image-guided cochlear implant programming technique is based on this correlation and utilizes the EA location with respect to the intracochlear anatomy to help audiologists adjust the CI settings to improve hearing. Automated methods to localize EA in postoperative CT images are of great interest for large-scale studies and for translation into the clinical workflow. In this work, we propose a unified deep-learning-based framework for automated EA localization. It consists of a multi-task network and a series of postprocessing algorithms to localize various types of EAs. The evaluation on a dataset with 27 cadaveric samples shows that its localization error is slightly smaller than the state-of-the-art method. Another evaluation on a large-scale clinical dataset containing 561 cases across two institutions demonstrates a significant improvement in robustness compared to the state-of-the-art method. This suggests that this technique could be integrated into the clinical workflow and provide audiologists with information that facilitates the programming of the implant leading to improved patient care.

In cochlear implant (CI) procedures, an electrode array is surgically inserted into the cochlea. The electrodes are used to stimulate the auditory nerve and restore hearing sensation for the recipient. If the array folds inside the cochlea during the insertion procedure, it can lead to trauma, damage to the residual hearing, and poor hearing restoration. Intraoperative detection of such a case can allow a surgeon to perform reimplantation. However, this intraoperative detection requires experience and electrophysiological tests sometimes fail to detect an array folding. Due to the low incidence of array folding, we generated a dataset of CT images with folded synthetic electrode arrays with realistic metal artifact. The dataset was used to train a multitask custom 3D-UNet model for array fold detection. We tested the trained model on real post-operative CTs (7 with folded arrays and 200 without). Our model could correctly classify all the fold-over cases while misclassifying only 3 non fold-over cases. Therefore, the model is a promising option for array fold detection.

Background and Purpose: The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging., Materials and Methods: Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed., Results: The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P = .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P < .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P = .007)., Conclusions: We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation., (© 2023 by American Journal of Neuroradiology.)

Background: Essential tremor (ET) and Parkinson's disease (PD) are the most common tremor disorders and are common indications for deep brain stimulation (DBS). In some patients, PD and ET symptoms overlap and diagnosis can be challenging based on clinical criteria alone. The objective of this study was to identify structural brain differences between PD and ET DBS patients to help differentiate these disorders and improve our understanding of the different brain regions involved in these pathologic processes., Methods: We included ET and PD patients scheduled to undergo DBS surgery in this observational study. Patients underwent 3T brain MRI while under general anesthesia as part of their procedure. Cortical thicknesses and subcortical volumes were quantified from T1-weighted images using automated multi-atlas segmentation. We used logistic regression analysis to identify brain regions associated with diagnosis of ET or PD., Results: 149 ET and 265 PD patients were included. Smaller volumes in the pallidum and thalamus and reduced thickness in the anterior orbital gyrus, lateral orbital gyrus, and medial precentral gyrus were associated with greater odds of ET diagnosis. Conversely, reduced volumes in the caudate, amygdala, putamen, and basal forebrain, and reduced thickness in the orbital part of the inferior frontal gyrus, supramarginal gyrus, and posterior cingulate were associated with greater odds of PD diagnosis., Conclusions: These findings identify structural brain differences between PD and ET patients. These results expand our understanding of the different brain regions involved in these disorders and suggest that structural MRI may help to differentiate patients with these two disorders., 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 Ltd. All rights reserved.)

Objective: To investigate whether revision surgery with the same device results in a change in three key indicators of electrode positioning: scalar location, mean modiolar distance ( M ¯ ), and angular insertion depth (AID)., Methods: Retrospective analysis of a cochlear implant database at a university-based tertiary medical center. Intra-operative CT scans were obtained after initial and revision implantation. Electrode array (EA) position was calculated using auto-segmentation techniques. Initial and revision scalar location, M ¯ , and AID were compared., Results: Mean change in M ¯ for all ears was -0.07 mm (SD 0.24 mm; P  = 0.16). The mean change in AID for all ears was -5° (SD 67°; P  = 0.72). Three initial implantations with pre-curved EAs resulted in a translocation from Scala Tympani (ST) to Scala Vestibuli (SV). Two remained translocated after revision, while one was corrected when revised with a straight EA. An additional five translocations occurred after revision., Conclusions: In this study examining revision cochlear implantation from a single manufacturer, we demonstrated no significant change in key indicators of EA positioning, even when revising with a different style of electrode. However, the revision EA is not necessarily confined by the initial trajectory and there may be an increased risk of translocation.

Purpose: Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under magnetic resonance imaging (MRI) guidance. CT imaging is used to estimate the acoustic properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients to potentially harmful radiation. A method to estimate acoustic parameters in the skull without the need for CT is desirable., Approach: We synthesized CT images from routinely acquired T1-weighted MRI using a 3D patch-based conditional generative adversarial network and evaluated the performance of synthesized CT (sCT) images for treatment planning with tFUS. We compared the performance of sCT with real CT (rCT) images for tFUS planning using Kranion and simulations using the acoustic toolbox, k-Wave. Simulations were performed for 3 tFUS scenarios: (1) no aberration correction, (2) correction with phases calculated from Kranion, and (3) phase shifts calculated from time reversal., Results: From Kranion, the skull density ratio, skull thickness, and number of active elements between rCT and sCT had Pearson's correlation coefficients of 0.94, 0.92, and 0.98, respectively. Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction ( 12.4 % ± 8.1 % ) and smallest with Kranion phases ( 7.3 % ± 6.0 % ). The distance between peak focal locations between rCT and sCT was < 1.3    mm for all simulation cases., Conclusions: Real and synthetically generated skulls had comparable image similarity, skull measurements, and acoustic simulation metrics. Our work demonstrated similar results for 10 testing cases comparing MR-sCTs and rCTs for tFUS planning. Source code and a docker image with the trained model are available at https://github.com/han-liu/SynCT&#95;TcMRgFUS., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Objective: To develop a machine learning-based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline., Study Design: Retrospective cohort., Setting: Tertiary referral center., Patients: 772 adults undergoing CICE from 2015 to 2020., Interventions: Variables included demographics, unaided thresholds, and word recognition score. A random forest classification model was trained on patients undergoing CICE, and bootstrap cross-validation was used to assess the modeling approach's performance., Main Outcome Measures: The machine learning-based referral tool was evaluated against the 60/60 guideline based on ability to identify CI candidates under traditional and expanded criteria., Results: Of 587 patients with complete data, 563 (96%) met candidacy at our center, and the 60/60 guideline identified 512 (87%) patients. In the random forest model, word recognition score; thresholds at 3000, 2000, and 125; and age at CICE had the largest impact on candidacy (mean decrease in Gini coefficient, 2.83, 1.60, 1.20, 1.17, and 1.16, respectively). The 60/60 guideline had a sensitivity of 0.91, a specificity of 0.42, and an accuracy of 0.89 (95% confidence interval, 0.86-0.91). The random forest model obtained higher sensitivity (0.96), specificity (1.00), and accuracy (0.96; 95% confidence interval, 0.95-0.98). Across 1,000 bootstrapped iterations, the model yielded a median sensitivity of 0.92 (interquartile range [IQR], 0.85-0.98), specificity of 1.00 (IQR, 0.88-1.00), accuracy of 0.93 (IQR, 0.85-0.97), and area under the curve of 0.96 (IQR, 0.93-0.98)., Conclusions: A novel machine learning-based screening model is highly sensitive, specific, and accurate in predicting CI candidacy. Bootstrapping confirmed that this approach is potentially generalizable with consistent results., (Copyright © 2023, Otology & Neurotology, Inc.)

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT)., Approach: In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine self-supervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort ( n = 1189 ) from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence., Results: Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 ( p < 0.01 ). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of PLCO m 2012 risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence ( χ 2 = 7.48 , p = 0.02 )., Conclusions: We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Purpose: This study investigated the relationship between the number of active electrodes, channel stimulation rate, and their interaction on speech recognition and sound quality measures while controlling for electrode placement. Cochlear implant (CI) recipients with precurved electrode arrays placed entirely within scala tympani and closer to the modiolus were hypothesized to be able to utilize more channels and possibly higher stimulation rates to achieve better speech recognition performance and sound quality ratings than recipients in previous studies., Method: Participants included seven postlingually deafened adult CI recipients with Advanced Bionics Mid-Scala electrode arrays confirmed to be entirely within scala tympani using postoperative computerized tomography. Twelve conditions were tested using four, eight, 12, and 16 electrodes and channel stimulation rates of 600 pulse per second (pps), 1,200 pps, and each participant's maximum allowable rate (1,245-4,800 pps). Measures of speech recognition and sound quality were acutely assessed., Results: For the effect of channels, results showed no significant improvements beyond eight channels for all measures. For the effect of channel stimulation rate, results showed no significant improvements with higher rates, suggesting that 600 pps was sufficient for maximum speech recognition performance and sound quality ratings. However, across all conditions, there was a significant relationship between mean electrode-to-modiolus distance and all measures, suggesting that a lower mean electrode-to-modiolus distance was correlated with higher speech recognition scores and sound quality ratings., Conclusion: These findings suggest that even well-placed precurved electrode array recipients may not be able to take advantage of more than eight channels or higher channel stimulation rates (> 600 pps), but that closer electrode array placement to the modiolus correlates with better outcomes for these recipients.

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.

Lack of reliable measures of cutaneous chronic graft-versus-host disease (cGVHD) remains a significant challenge. Non-expert assistance in marking photographs of active disease could aid the development of automated segmentation algorithms, but validated metrics to evaluate training effects are lacking. We studied absolute and relative error of marked body surface area (BSA), redness, and the Dice index as potential metrics of non-expert improvement. Three non-experts underwent an extensive training program led by a board-certified dermatologist to mark cGVHD in photographs. At the end of the 4-month training, the dermatologist confirmed that each trainee had learned to accurately mark cGVHD. The trainees' inter- and intra-rater intraclass correlation coefficient estimates were "substantial" to "almost perfect" for both BSA and total redness. For fifteen 3D photos of patients with cGVHD, the trainees' median absolute (relative) BSA error compared to expert marking dropped from 20 cm 2 (29%) pre-training to 14 cm 2 (24%) post-training. Total redness error decreased from 122 a*·cm 2 (26%) to 95 a*·cm 2 (21%). By contrast, median Dice index did not reflect improvement (0.76 to 0.75). Both absolute and relative BSA and redness errors similarly and stably reflected improvements from this training program, which the Dice index failed to capture., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT 1 ) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT 2 ) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT 1 scans (N=105) and unpaired non-annotated hrT 2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT 2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to full supervision (median Dice score - VS: 92.5%; Cochleas: 87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image., 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., (Crown Copyright © 2022. Published by Elsevier B.V. All rights reserved.)

Introduction: We sought to evaluate the correlation between whole cochlear T2 signal changes obtained with a novel automated segmentation method and hearing levels, both at diagnosis and over time, in patients with observed vestibular schwannoma., Methods: This retrospective correlation study within an academic medical center neurotology practice evaluated 127 patients with vestibular schwannoma observed over time, each with ≥2 MRI scans (367 total) and ≥2 audiograms (472 total). 86 patients had T2-weighted sequences with sufficient resolution for cochlear signal analysis, yielding 348 unique timepoint intervals. The main outcome measure was correlation of the ipsilateral-to-contralateral ratio of whole cochlear T2 signal with hearing outcomes as measured by pure tone average (PTA) and word recognition score (WRS)., Results: Whole cochlear T2 signal ratios did not show a correlation with hearing levels at diagnosis. Change in signal ratio over time showed weak correlation with changes in PTA, but not WRS, over time. Cochlear signal ratio did not precede changes in hearing but did follow changes in both PTA and WRS., Conclusion: Whole cochlear T2 signal ratios were weakly correlated with changes in hearing in patients with observed vestibular schwannoma. The technology of automated segmentation and signal processing holds promise for future evaluation of clinical entities causing cochlear signal changes., (© 2023 S. Karger AG, Basel.)

Cochlear implantation with acoustic hearing preservation is becoming increasingly prevalent allowing cochlear implant (CI) users to combine electric and acoustic stimulation (EAS) in the implanted ears. Despite a growing EAS population, our field does not have definitive guidance regarding EAS technology optimization and the majority of previous studies investigating hearing aid (HA) and cochlear implant (CI) programming for EAS listeners have been mixed. Thus, the purpose of this exploratory study was to explore the effects of various EAS crossover frequencies-defined as the low-frequency (LF) CI cutoff-relative to the underlying spiral ganglion (SG) characteristic frequency associated with the most distal or apical electrode in the array. Speech recognition in semi-diffuse noise and subjective estimates of listening difficulty were measured for 15 adult CI recipients with acoustic hearing preservation in three listening conditions: 1) CI-alone, 2) bimodal (CI+HA), and best-aided EAS (CIHA+HA). The results showed no effect of LF CI cutoff for any of the three listening conditions such that there was no trend for increased performance or less subjective listening difficulty across LF CI cutoffs, referenced to underlying SG-place frequency. Consistent with past studies, the current results were also consistent with significant speech recognition and subject listening difficulty benefits for both bimodal (CI+HA) and best-aided EAS (CIHA+HA) as compared to CI-alone listening as well as significant additional benefits for best-aided EAS (CIHA+HA) compared to bimodal hearing (CI+HA). Future studies are necessary to investigate the efficacy of SG-place-based fittings for i) large samples of experienced EAS listeners for whom perceptual adaptation has occurred to the frequency mismatch provided by standard CI frequency allocations, and ii) EAS users at or close to CI activation as place-based approaches may ultimately yield greater outcomes, particularly for newly activated CI users for whom SG-place-based approaches may afford a steeper trajectory to performance asymptote., (Copyright © 2022. Published by Elsevier B.V.)

Objective: Deep brain stimulation (DBS) is an effective treatment to improve motor symptoms in Parkinson's disease (PD). The Globus Pallidus (GPi) and the Subthalamic Nucleus (STN) are the most targeted brain regions for stimulation and produce similar improvements in PD motor symptoms. However, our understanding of stimulation effects across targets on inhibitory action control processes is limited. We compared the effects of STN (n = 20) and GPi (n = 13) DBS on inhibitory control in PD patients., Methods: We recruited PD patients undergoing DBS at the Vanderbilt Movement Disorders Clinic and measured their performance on an inhibitory action control task (Simon task) before surgery (optimally treated medication state) and after surgery in their optimally treated state (medication plus their DBS device turned on)., Results: DBS to both STN and GPi targets induced an increase in fast impulsive errors while simultaneously producing more proficient reactive suppression of interference from action impulses., Conclusions: Stimulation in GPi produced similar effects as STN DBS, indicating that stimulation to either target increases the initial susceptibility to act on strong action impulses while concomitantly improving the ability to suppress ongoing interference from activated impulses., Significance: Action impulse control processes are similarly impacted by stimulating dissociable nodes in frontal-basal ganglia circuitry., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

This study investigated the number of channels required for asymptotic speech recognition for ten pediatric cochlear implant (CI) recipients with precurved electrode arrays. Programs with 4-22 active electrodes were used to assess word and sentence recognition in noise. Children demonstrated significant performance gains up to 12 electrodes for continuous interleaved sampling (CIS) and up to 22 channels with 16 maxima. These data are consistent with the latest adult CI studies demonstrating that modern CI recipients have access to more than 8 independent channels and that both adults and children exhibit performance gains up to 22 channels.

This study investigated the number of channels needed for maximum speech understanding and sound quality in 15 adult cochlear implant (CI) recipients with Advanced Bionics (AB) Mid-Scala electrode arrays completely within scala tympani. In experiment I, CI programs used a continuous interleaved sampling (CIS)-based strategy and 4-16 active electrodes. In experiment II, CI programs used an n-of-m strategy featuring 16 active electrodes with either 8- or 12-maxima. Speech understanding and sound quality measures were assessed. For CIS programs, participants demonstrated performance gains using up to 4-10 electrodes on speech measures and sound quality ratings. For n-of-m programs, there was no significant effect of maxima, suggesting 8-maxima is sufficient for this sample's maximum performance and sound quality. These results are largely consistent with previous studies using straight electrode arrays [e.g., Fishman, Shannon, and Slattery (1997). J. Speech Lang. Hear. Res. 40, 1201-1215; Friesen, Shannon, Baskent, and Wang (2001). J. Acoust. Soc. Am. 110, 1150-1163; Shannon, Cruz, and Galvin (2011). Audiol. Neurotol. 16, 113-123; Berg, Noble, Dawant, Dwyer, Labadie, and Gifford (2020). J. Acoust. Soc. Am. 147, 3646-3656] and in contrast with recent studies looking at cochlear precurved electrode arrays [e.g., Croghan, Duran, and Smith (2017). J. Acoust. Soc. Am. 142, EL537-EL543; Berg, Noble, Dawant, Dwuer, Labadie, and Gifford (2019b). J. Acoust. Soc. Am. 145, 1556-1564], which found continuous improvements up to 16 independent channels. These findings suggest that Mid-Scala electrode array recipients demonstrate similar channel independence to straight electrode arrays rather than other manufacturer's precurved electrode arrays.

Introduction: Translocation of precurved cochlear implant (CI) electrodes reduces hearing outcomes, but it is not known whether it is possible to correct scalar translocation such that all electrodes reside fully in the scala tympani (ST)., Methods: Six cadaveric temporal bones were scanned with CT and segmented to delineate intracochlear anatomy. Mastoidectomy with facial recess was performed. Precurved CI electrodes (CI532; Cochlear Limited) were implanted until scalar translocation was confirmed with postoperative CT. Then, electrodes were removed and replaced. CT scan was repeated to assess for translocation correction. Scalar position of electrode contacts, angular insertion depth (AID) of the electrode array, and M- (average distance between each electrode contact and the modiolus) were measured. An in vivo case is reported in which intraoperative translocation detection led to removal and replacement of the electrode., Results: Five of 6 cadaveric translocations (83%) were corrected with 1 attempt, resulting in full ST insertions. AID averaged 285 ± 77° for translocated electrodes compared to 344 ± 28° for nontranslocated electrodes (p = 0.109). M- averaged 0.75 ± 0.18 mm for translocated electrodes and 0.45 ± 0.11 mm for nontranslocated electrodes (p = 0.016). Reduction in M- with translocation correction averaged 38%. In the in vivo case, translocation was successfully corrected in a single attempt., Conclusion: Scalar translocation of precurved CI electrodes can be corrected by removal and reinsertion. This significantly improves the perimodiolar positioning of these electrodes. There was a high rate of success (83%) in this cadaveric model as well as a successful in vivo attempt., (© 2021 S. Karger AG, Basel.)

With the rapid development of deep learning approaches, tremendous progress has been made in computer- assisted analysis of minimally-invasive, videoscopic surgery. However, surgery through open incisions ("open surgery"), which constitutes a much larger portion of surgical procedures performed, is rarely investigated because of the difficulty in obtaining high-quality open surgical video footage. Automated detection of surgical instruments shows promise for evaluating surgical activities, and provides a foundation for quality/safety review, education, and identification of surgical performance. In this paper, we present results using YOLOv3 to successfully identify an electrocautery surgical instrument in a library of images derived from 22 open neck procedures (an 887-image training/validation set, and a 1149-image testing set) captured using a wearable surgical camera. We show that our method effectively detects the spatial bounds of the electrocautery pencil in still images and we further demonstrate the ability of our method to detect the location of this instrument in video footage. Our work serves as the first demonstration of open surgical instrument detection using first-person video footage from a wearable camera and sets the stage for further work in this field.Clinical Relevance- Detection of instrumentation in surgical video is the necessary first step towards automating surgical task identification and skills assessment, which will be useful for surgical quality improvement and training.

There is evidence that cochlear MR signal intensity may be useful in prognosticating the risk of hearing loss after middle cranial fossa (MCF) resection of acoustic neuroma (AN), but the manual segmentation of this structure is difficult and prone to error. This hampers both large-scale retrospective studies and routine clinical use of this information. To address this issue, we present a fully automatic method that permits the segmentation of the intra-cochlear anatomy in MR images, which uses a weighted active shape model we have developed and validated to segment the intra-cochlear anatomy in CT images. We take advantage of a dataset for which both CT and MR images are available to validate our method on 132 ears in 66 high-resolution T2-weighted MR images. Using the CT segmentation as ground truth, we achieve a mean Dice (DSC) value of 0.81 and 0.79 for the scala tympani (ST) and the scala vestibuli (SV), which are the two main intracochlear structures.Clinical Relevance- The proposed method is accurate and fully automated for MR image segmentation. It can be used to support large retrospective studies that explore relations between MR signal in preoperative images and outcomes. It can also facilitate the routine and clinical use of this information.

Purpose: Robust and accurate segmentation methods for the intracochlear anatomy (ICA) are a critical step in the image-guided cochlear implant programming process. We have proposed an active shape model (ASM)-based method and a deep learning (DL)-based method for this task, and we have observed that the DL method tends to be more accurate than the ASM method while the ASM method tends to be more robust. Approach: We propose a DL-based U-Net-like architecture that incorporates ASM segmentation into the network. A quantitative analysis is performed on a dataset that consists of 11 cochlea specimens for which a segmentation ground truth is available. To qualitatively evaluate the robustness of the method, an experienced expert is asked to visually inspect and grade the segmentation results on a clinical dataset made of 138 image volumes acquired with conventional CT scanners and of 39 image volumes acquired with cone beam CT (CBCT) scanners. Finally, we compare training the network (1) first with the ASM results, and then fine-tuning it with the ground truth segmentation and (2) directly with the specimens with ground truth segmentation. Results: Quantitative and qualitative results show that the proposed method increases substantially the robustness of the DL method while having only a minor detrimental effect (though not significant) on its accuracy. Expert evaluation of the clinical dataset shows that by incorporating the ASM segmentation into the DL network, the proportion of good segmentation cases increases from 60/177 to 119/177 when training only with the specimens and increases from 129/177 to 151/177 when pretraining with the ASM results. Conclusions: A hybrid ASM and DL-based segmentation method is proposed to segment the ICA in CT and CBCT images. Our results show that combining DL and ASM methods leads to a solution that is both robust and accurate., (© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).)

We propose an atlas-based method to segment the intracochlear anatomy (ICA) in the post-implantation CT (Post-CT) images of cochlear implant (CI) recipients that preserves the point-to-point correspondence between the meshes in the atlas and the segmented volumes. To solve this problem, which is challenging because of the strong artifacts produced by the implant, we use a pair of co-trained deep networks that generate dense deformation fields (DDFs) in opposite directions. One network is tasked with registering an atlas image to the Post-CT images and the other network is tasked with registering the Post-CT images to the atlas image. The networks are trained using loss functions based on voxel-wise labels, image content, fiducial registration error, and cycle-consistency constraint. The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks. Our model can learn the underlying geometric features of the ICA even though they are obscured by the metal artifacts. We show that our end-to-end network produces results that are comparable to the current state of the art (SOTA) that relies on a two-steps approach that first uses conditional generative adversarial networks to synthesize artifact-free images from the Post-CT images and then uses an active shape model-based method to segment the ICA in the synthetic images. Our method requires a fraction of the time needed by the SOTA, which is important for end-user acceptance.

Cutaneous erythema is used in diagnosis and response assessment of cutaneous chronic graft-versus-host disease (cGVHD). The development of objective erythema evaluation methods remains a challenge. We used a pre-trained neural network to segment cGVHD erythema by detecting changes relative to a patient's registered baseline photo. We fixed this change detection algorithm on human annotations from a single photo pair, by using either a traditional approach or by marking definitely affected ("Do Not Miss", DNM) and definitely unaffected skin ("Do Not Include", DNI). The fixed algorithm was applied to each of the remaining 47 test photo pairs from six follow-up sessions of one patient. We used both the Dice index and the opinion of two board-certified dermatologists to evaluate the algorithm performance. The change detection algorithm correctly assigned 80% of the pixels, regardless of whether it was fixed on traditional (median accuracy: 0.77, interquartile range 0.62-0.87) or DNM/DNI segmentations (0.81, 0.65-0.89). When the algorithm was fixed on markings by different annotators, the DNM/DNI achieved more consistent outputs (median Dice indices: 0.94-0.96) than the traditional method (0.73-0.81). Compared to viewing only rash photos, the addition of baseline photos improved the reliability of dermatologists' scoring. The inter-rater intraclass correlation coefficient increased from 0.19 (95% confidence interval lower bound: 0.06) to 0.51 (lower bound: 0.35). In conclusion, a change detection algorithm accurately assigned erythema in longitudinal photos of cGVHD. The reliability was significantly improved by exclusively using confident human segmentations to fix the algorithm. Baseline photos improved the agreement among two dermatologists in assessing algorithm performance., Competing Interests: The authors declare they have no conflicts of interest., (© 2021 International Academy for Clinical Hematology. Publishing services by Atlantis Press International B.V.)

This study investigated the number of channels available to cochlear implant (CI) recipients for maximum speech understanding and sound quality for lateral wall electrode arrays-which result in large electrode-to-modiolus distances-featuring the greatest inter-electrode distances (2.1-2.4 mm), the longest active lengths (23.1-26.4 mm), and the fewest number of electrodes commercially available. Participants included ten post-lingually deafened adult CI recipients with MED-EL electrode arrays (FLEX28 and STANDARD) entirely within scala tympani. Electrode placement and scalar location were determined using computerized tomography. The number of channels was varied from 4 to 12 with equal spatial distribution across the array. A continuous interleaved sampling-based strategy was used. Speech recognition, sound quality ratings, and a closed-set vowel recognition task were measured acutely for each electrode condition. Participants did not demonstrate statistically significant differences beyond eight channels at the group level for almost all measures. However, several listeners showed considerable improvements from 8 to 12 channels for speech and sound quality measures. These results suggest that channel interaction caused by the greater electrode-to-modiolus distances of straight electrode arrays could be partially compensated for by a large inter-electrode distance or spacing.

Objective: Deep brain stimulation (DBS) improves motor symptoms in Parkinson's disease (PD), but it can also disrupt verbal fluency with significant costs to quality of life. The current study investigated how variability of bilateral active electrode coordinates along the superior/inferior, anterior/posterior, and lateral/medial axes in the subthalamic nucleus (STN) or the globus pallidus interna (GPi) contribute to changes in verbal fluency. We predicted that electrode location in the left hemisphere would be linked to changes in fluency, especially in the STN., Methods: Forty PD participants treated with bilateral DBS targeting STN (n = 23) or GPi (n = 17) completed verbal fluency testing in their optimally treated state before and after DBS therapy. Normalized atlas coordinates from left and right active electrode positions along superior/inferior, anterior/posterior, and lateral/medial axes were used to predict changes in fluency postoperatively, separately for patients with STN and GPi targets., Results: Consistent with prior studies, fluency significantly declined pre- to postsurgery (in both DBS targets). In STN-DBS patients, electrode position along the inferior to superior axis in the left STN was a significant predictor of fluency changes; relatively more superior left active electrode was associated with the largest fluency declines in STN. Electrode coordinates in right STN or GPi (left or right) did not predict fluency changes., Interpretation: We discuss these findings in light of putative mechanisms and potential clinical impact., (© 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Objective: In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity., Methods: In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity., Results: Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes., Significance: Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation., (© 2020 International League Against Epilepsy.)

Background: Parkinson's disease (PD) and essential tremor (ET) are commonly encountered movement disorders. Pathophysiologic processes that localize to the cerebellum are described in both. There are limited studies investigating cerebellar structural changes in these conditions, largely because of inherent challenges in the efficiency of segmentation., Methods: We applied a novel multiatlas cerebellar segmentation method to T1-weighted images in 282 PD and 111 essential tremor patients to define 26 cerebellar lobule volumes. The severity of postural and resting tremor in both populations and gait and postural instability in PD patients were defined using subscores of the UPDRS and Washington Heights-Inwood Genetic Study motor scales. These clinical measurements were related to lobule volume size. Multiple comparisons were controlled using a false discovery rate method., Results: Group differences were identified between ET and PD patients, with reductions in deep cerebellar nucleus volume in ET versus reduced lobule VI volume in PD. In ET patients, lobule VIII was negatively correlated with the severity of postural tremor. In PD patients, lobule IV was positively correlated with resting tremor and total tremor severity. We observed differences in cerebellar structure that localized to sensorimotor lobules of the cerebellum. Lobule volumes appeared to differentially relate to clinical symptoms, suggesting important clinicopathologic distinctions between these conditions. These results emphasize the role of the cerebellum in tremor symptoms and should foster future clinical and pathologic investigations of the sensorimotor lobules of the cerebellum. © 2020 International Parkinson and Movement Disorder Society., (© 2020 International Parkinson and Movement Disorder Society.)

Background: Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures., Objective: To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings., Methods: In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions., Results: Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%., Conclusion: Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings., (Copyright © 2019 by the Congress of Neurological Surgeons.)

Objective: Studies suggest lateral wall (LW) scala tympani (ST) height decreases apically, which may limit insertion depth. No studies have investigated the relationship of LW ST height with translocation rate or location., Study Design: Retrospective review., Setting: Cochlear implant program at tertiary referral center., Subjects and Methods: LW ST height was measured in preoperative images for patients with straight electrodes. Scalar location, angle of insertion depth (AID), and translocation depth were measured in postoperative images. Audiologic outcomes were tracked., Results: In total, 177 ears were identified with 39 translocations (22%). Median AID was 443° (interquartile range [IQR], 367°-550°). Audiologic outcomes (126 ears) showed a small, significant correlation between consonant-nucleus-consonant (CNC) word score and AID ( r = 0.20, P = .027), although correlation was insignificant if translocation occurred ( r = 0.11, P = .553). Translocation did not affect CNC score ( P = .335). AID was higher for translocated electrodes (503° vs 445°, P = .004). Median translocation depth was 381° (IQR, 222°-399°). Median depth at which a 0.5-mm electrode would not fit within 0.1 mm of LW was 585° (IQR, 405°-585°). Median depth at which a 0.5-mm electrode would displace the basilar membrane by ≥0.1 mm was 585° (IQR, 518°-765°); this was defined as predicted translocation depth (PTD). Translocation rate was 39% for insertions deeper than PTD and 14% for insertions shallower than PTD ( P = .008)., Conclusion: AID and CNC are directly correlated for straight electrodes when not translocated. Translocations generally occur around 380° and are more common with deeper insertions due to decreasing LW ST height. Risk of translocation increases significantly after 580°.