A Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke Outcomes.

BACKGROUND AND PURPOSE: Predicting long-term clinical outcome in acute ischemic stroke is beneficial for prognosis, clinical trial design, resource management, and patient expectations. This study used a deep learning-based predictive model (DLPD) to predict 90-day mRS outcomes and compared its predictions with those made by physicians. MATERIALS AND METHODS: A previously developed DLPD that incorporated DWI and clinical data from the acute period was used to predict 90-day mRS outcomes in 80 consecutive patients with acute ischemic stroke from a single-center registry. We assessed the predictions of the model alongside those of 5 physicians (2 stroke neurologists and 3 neuroradiologists provided with the same imaging and clinical information). The primary analysis was the agreement between the ordinal mRS predictions of the model or physician and the ground truth using the Gwet Agreement Coefficient. We also evaluated the ability to identify unfavorable outcomes (mRS >2) using the area under the curve, sensitivity, and specificity. Noninferiority analyses were undertaken using limits of 0.1 for the Gwet Agreement Coefficient and 0.05 for the area under the curve analysis. The accuracy of prediction was also assessed using the mean absolute error for prediction, percentage of predictions ±1 categories away from the ground truth (±1 accuracy [ACC]), and percentage of exact predictions (ACC). RESULTS: To predict the specific mRS score, the DLPD yielded a Gwet Agreement Coefficient score of 0.79 (95% CI, 0.71-0.86), surpassing the physicians' score of 0.76 (95% CI, 0.67-0.84), and was noninferior to the readers (P < .001). For identifying unfavorable outcome, the model achieved an area under the curve of 0.81 (95% CI, 0.72-0.89), again noninferior to the readers' area under the curve of 0.79 (95% CI, 0.69-0.87) (P < .005). The mean absolute error, ±1ACC, and ACC were 0.89, 81%, and 36% for the DLPD. CONCLUSIONS: A deep learning method using acute clinical and imaging data for long-term functional outcome prediction in patients with acute ischemic stroke, the DLPD, was noninferior to that of clinical readers.

Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities.

The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models.

Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.

Volumetric glutamate imaging (GluCEST) using 7T MRI can lateralize nonlesional temporal lobe epilepsy: A preliminary study.

INTRODUCTION: Drug-resistant epilepsy patients show worse outcomes after resection when standard neuroimaging is nonlesional, which occurs in one-third of patients. In prior work, we employed 2-D glutamate imaging, Glutamate Chemical Exchange Saturation Transfer (GluCEST), to lateralize seizure onset in nonlesional temporal lobe epilepsy (TLE) based on increased ipsilateral GluCEST signal in the total hippocampus and hippocampal head. We present a significant advancement to single-slice GluCEST imaging, allowing for three-dimensional analysis of brain glutamate networks. METHODS: The study population consisted of four MRI-negative, nonlesional TLE patients (two male, two female) with electrographically identified left temporal onset seizures. Imaging was conducted on a Siemens 7T MRI scanner using the CEST method for glutamate, while the advanced normalization tools (ANTs) pipeline and the Automated Segmentation of the Hippocampal Subfields (ASHS) method were employed for image analysis. RESULTS: Volumetric GluCEST imaging was validated in four nonlesional TLE patients showing increased glutamate lateralized to the hippocampus of seizure onset (p = .048, with a difference among ipsilateral to contralateral GluCEST signal percentage ranging from -0.05 to 1.37), as well as increased GluCEST signal in the ipsilateral subiculum (p = .034, with a difference among ipsilateral to contralateral GluCEST signal ranging from 0.13 to 1.57). CONCLUSIONS: The ability of 3-D, volumetric GluCEST to localize seizure onset down to the hippocampal subfield in nonlesional TLE is an improvement upon our previous 2-D, single-slice GluCEST method. Eventually, we hope to expand volumetric GluCEST to whole-brain glutamate imaging, thus enabling noninvasive analysis of glutamate networks in epilepsy and potentially leading to improved clinical outcomes.

Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states.

A major challenge in neuroscience is determining a quantitative relationship between the brain's white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes' activation patterns' probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM's interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions' distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain's structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

The sensitivity of network statistics to incomplete electrode sampling on intracranial EEG.

Network neuroscience applied to epilepsy holds promise to map pathological networks, localize seizure generators, and inform targeted interventions to control seizures. However, incomplete sampling of the epileptic brain because of sparse placement of intracranial electrodes may affect model results. In this study, we evaluate the sensitivity of several published network measures to incomplete spatial sampling and propose an algorithm using network subsampling to determine confidence in model results. We retrospectively evaluated intracranial EEG data from 28 patients implanted with grid, strip, and depth electrodes during evaluation for epilepsy surgery. We recalculated global and local network metrics after randomly and systematically removing subsets of intracranial EEG electrode contacts. We found that sensitivity to incomplete sampling varied significantly across network metrics. This sensitivity was largely independent of whether seizure onset zone contacts were targeted or spared from removal. We present an algorithm using random subsampling to compute patient-specific confidence intervals for network localizations. Our findings highlight the difference in robustness between commonly used network metrics and provide tools to assess confidence in intracranial network localization. We present these techniques as an important step toward translating personalized network models of seizures into rigorous, quantitative approaches to invasive therapy.

Electrocorticography reveals spatiotemporal neuronal activation patterns of verbal fluency in patients with epilepsy.

Verbal fluency is commonly used to evaluate cognitive dysfunction in a variety of neuropsychiatric diseases, yet the neurobiology underlying performance of this task is incompletely understood. Electrocorticography (ECoG) provides a unique opportunity to investigate temporal activation patterns during cognitive tasks with high spatial and temporal precision. We used ECoG to study high gamma activity (HGA) patterns in patients undergoing presurgical evaluation for intractable epilepsy as they completed an overt, free-recall verbal fluency task. We examined regions demonstrating changes in HGA during specific timeframes relative to speech onset. Early pre-speech high gamma activity was present in left frontal regions during letter fluency and in bifrontal regions during category fluency. During timeframes typically associated with word planning, a distributed network was engaged including left inferior frontal, orbitofrontal and posterior temporal regions. Peri-Rolandic activation was observed during speech onset, and there was post-speech activation in the bilateral posterior superior temporal regions. Based on these observations in the context of prior studies, we propose a model of neocortical activity patterns underlying verbal fluency.

First-Year Radiology Residents Teaching Anatomy to First-Year Medical Students: A Symbiotic Relationship.

OBJECTIVES: Our institution has developed an educational program in which first-year radiology residents teach first-year medical students during gross anatomy laboratory sessions. The purpose of this study is to assess the impact of this program on medical student knowledge and perceptions of radiology, and on resident attitudes toward teaching. MATERIALS AND METHODS: First-year resident pairs taught small groups of medical students during weekly 15-minute interactive sessions, and were evaluated on teaching skills by senior residents. A survey about attitudes toward radiology and a knowledge quiz were sent to the medical students, and a survey about attitudes toward teaching was sent to the first-year radiology residents, both pre-course and post-course. RESULTS: Students' radiology knowledge significantly increased between the pre-course and post-course survey across all categories tested (P < 0.001). Additionally, there were significant improvements in terms of students' confidence in radiologic anatomy skills, perceived importance of radiology for medical training, familiarity with the field of radiology, and perception that radiologists are friendly (P < 0.001). Radiology residents felt more confident in their teaching proficiency (P < 0.001) by the conclusion of the course. CONCLUSIONS: Resident-led small-group teaching sessions during anatomy laboratory are mutually beneficial for medical students and radiology residents. The program also allows radiology residents to be exposed early on in residency to teaching and academic medicine.

Virtual resection predicts surgical outcome for drug-resistant epilepsy.

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients.

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

Characterizing the role of the structural connectome in seizure dynamics.

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.

Patient-Centered and Specialty-Specific Case Work-Up: An Effective Method for Teaching Appropriateness of Imaging to Medical Students.

RATIONALE AND OBJECTIVES: Our institution has developed a mini-course program within the diagnostic radiology elective curriculum that promotes active learning, using patient cases specifically tailored to students' future specialties. The purpose of this study is to evaluate the effectiveness of this mini-course on medical student knowledge of imaging appropriateness and attitude toward radiologist consultation. MATERIALS AND METHODS: During each month-long radiology elective course, students were divided into teams of up to four students based on their specialty interest and assigned recent patient cases with imaging findings relevant to their specialties. The students researched their customized patient cases, integrated pertinent clinical and imaging findings, and presented their findings in a final preceptor-led session. A five-point Likert-type item preprogram and postprogram survey assessing knowledge of imaging appropriateness and attitude toward radiologist consultation was sent to the enrolled medical students. RESULTS: Out of 36 medical students, 33 (92%) completed the preprogram survey and 31 (86%) completed the postprogram survey. Students reported improved confidence in knowledge of imaging appropriateness, such as indications for intravenous contrast (p < 0.0005) and oral contrast (p < 0.0005). Furthermore, students reported an improved understanding of how to utilize radiologists (p < 0.005) and how to provide pertinent clinical historical information when requesting a radiology exam (p < 0.0005). Students reported that researching the patient's historical and clinical information in conjunction with the radiology images made them more invested in the case. CONCLUSION: Assigning customized patient cases to medical students on diagnostic radiology elective, tailored to their future specialties, is an effective and active way to teach imaging appropriateness and to improve attitudes toward radiologist consultation.

Structural and functional asymmetry of medial temporal subregions in unilateral temporal lobe epilepsy: A 7T MRI study.

Mesial temporal lobe epilepsy (TLE) is a common neurological disorder affecting the hippocampus and surrounding medial temporal lobe (MTL). Although prior studies have analyzed whole-brain network distortions in TLE patients, the functional network architecture of the MTL at the subregion level has not been examined. In this study, we utilized high-resolution 7T T2-weighted magnetic resonance imaging (MRI) and resting-state BOLD-fMRI to characterize volumetric asymmetry and functional network asymmetry of MTL subregions in unilateral medically refractory TLE patients and healthy controls. We subdivided the TLE group into mesial temporal sclerosis patients (TLE-MTS) and MRI-negative nonlesional patients (TLE-NL). Using an automated multi-atlas segmentation pipeline, we delineated 10 MTL subregions per hemisphere for each subject. We found significantly different patterns of volumetric asymmetry between the two groups, with TLE-MTS exhibiting volumetric asymmetry corresponding to decreased volumes ipsilaterally in all hippocampal subfields, and TLE-NL exhibiting no significant volumetric asymmetries other than a mild decrease in whole-hippocampal volume ipsilaterally. We also found significantly different patterns of functional network asymmetry in the CA1 subfield and whole hippocampus, with TLE-NL patients exhibiting asymmetry corresponding to increased connectivity ipsilaterally and TLE-MTS patients exhibiting asymmetry corresponding to decreased connectivity ipsilaterally. Our findings provide initial evidence that functional neuroimaging-based network properties within the MTL can distinguish between TLE subtypes. High-resolution MRI has potential to improve localization of underlying brain network disruptions in TLE patients who are candidates for surgical resection.

Impact of Early Radiology Research Experiences on Medical Student Perceptions of Radiology and Research.

RATIONALE AND OBJECTIVES: To promote opportunities for medical students to gain early exposure to radiology and research, our institution has initiated programs which fund summer radiology research projects for rising second-year medical students. This study assesses the impact of these faculty-mentored summer research experiences on medical student perceptions of radiology and research, in terms of both knowledge and interest. MATERIALS AND METHODS: A voluntary, anonymous survey was administered to students both before and after the summer research period. Both the pre-program survey and post-program survey included 7-point Likert-scale questions (1 = strongly disagree; 7 = strongly agree) to evaluate students' perceptions about research and students' perceptions about radiology as a specialty. Faculty mentors were sent an analogous post-program survey that included an evaluation of their student's research skills. RESULTS: The surveys were completed by 9 of 11 students and 10 of 11 mentors. Students' perceived knowledge of radiology as a specialty improved (P = 0.02) between the pre-program survey and post-program survey. Similarly, there was an increase in students' perceived knowledge of research skills (P = 0.02) between the pre-program survey and post-program survey, with student ratings of research skills consistent with those of mentors. High student interest in both radiology and research was maintained over the course of the program. CONCLUSION: Our pilot study suggests that summer research experiences can improve knowledge of radiology and research among medical students. Continued evaluation of this annual program will allow us to enhance the benefit to medical students and thereby bolster interest in academic radiology.

Primary malignant perivascular epithelioid cell neoplasm (PEComa) of the bone mimicking granular cell tumor in core biopsy: A case report and literature review.

The present study investigated the case of a 46-year-old female with primary malignant perivascular epithelioid cell neoplasm (PEComa) of the femur. The patient presented with a 5-month history of right distal thigh pain following trauma. Radiographs of the right distal femur revealed a mixed lytic and sclerotic lesion with subtle areas of cortical destruction and soft tissue extension, consistent with an aggressive tumor. A core biopsy revealed an epithelioid tumor with granular cell features, but a definitive diagnosis could not be made. Due to the aggressive features on radiologic evaluation, the patient underwent a resection of the distal femur and reconstruction with a distal femoral megaprosthesis and hinged knee replacement. The post-resection pathology led to a final diagnosis of primary bone PEComa, with histologic features including epithelioid, granular cell and spindled cell morphologies and biphasic immunoreactivity for melanocytic and smooth muscle markers. The large tumor size (>5 cm), rapid mitotic rate, infiltrative growth pattern, high nuclear grade and cellularity, and the presence of necrosis rendered this a malignant PEComa. The present study discussed the case, including radiographic (radiographs, magnetic resonance imaging and positron emission tomography scans) and histologic appearance and a literature review.

Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI.

Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL-focused T2-weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi-atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter-hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra-hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease-based research.

Evolutionary dynamics of cancer in response to targeted combination therapy.

In solid tumors, targeted treatments can lead to dramatic regressions, but responses are often short-lived because resistant cancer cells arise. The major strategy proposed for overcoming resistance is combination therapy. We present a mathematical model describing the evolutionary dynamics of lesions in response to treatment. We first studied 20 melanoma patients receiving vemurafenib. We then applied our model to an independent set of pancreatic, colorectal, and melanoma cancer patients with metastatic disease. We find that dual therapy results in long-term disease control for most patients, if there are no single mutations that cause cross-resistance to both drugs; in patients with large disease burden, triple therapy is needed. We also find that simultaneous therapy with two drugs is much more effective than sequential therapy. Our results provide realistic expectations for the efficacy of new drug combinations and inform the design of trials for new cancer therapeutics. DOI:http://dx.doi.org/10.7554/eLife.00747.001.