Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs.

To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist.

Volumetric Analysis of the Sinus and Orbit in Silent Sinus Syndrome After Endoscopic Sinus Surgery.

OBJECTIVE: The term "silent sinus syndrome" (SSS) describes spontaneous enophthalmos secondary to subclinical maxillary sinus atelectasis. Debate remains on whether treatment with endoscopic maxillary antrostomy alone is adequate in reversing atelectasis and globe displacement. This study aims to determine the degree of volume change of the diseased sinus and orbit as well as the change in orbital height in patients treated with endoscopic antrostomy without orbital floor augmentation. STUDY DESIGN: Retrospective review with image analysis. SETTING: Single tertiary care institution. METHODS: Three-dimensional (3D) analysis of computed tomographic imaging data was performed using 3D Slicer. 3D models of the maxillary sinus and orbit of the diseased and normal sides were created, and volume measurements were calculated using the segmentation program. RESULTS: Thirteen patients with SSS who underwent endoscopic sinus surgery (ESS) and had follow-up computed tomographic imaging were analyzed. After endoscopic antrostomy, the mean volume of the diseased maxillary sinus significantly increased by 9.82%, from 6.37 to 7.00 cm3 (p = .0302). There was no significant change in mean orbital volume; however, the mean orbital height decreased by 5.67%, from 38.09 to 35.93 mm from pretreatment to posttreatment samples (p = .0101). All patients had resolution of clinical or radiographic enophthalmos and orbital displacement with ESS alone. CONCLUSION: Endoscopic maxillary antrostomy alone in the treatment of SSS significantly increased maxillary sinus volume and decreased diseased orbital height. These changes were associated with clinical and radiographic improvement in globe displacement. These findings support performing ESS alone, reserving orbital augmentation for patients who do not exhibit adequate clinical improvement.

Graph theoretical measures of fast ripples support the epileptic network hypothesis.

The epileptic network hypothesis and epileptogenic zone hypothesis are two theories of ictogenesis. The network hypothesis posits that coordinated activity among interconnected nodes produces seizures. The epileptogenic zone hypothesis posits that distinct regions are necessary and sufficient for seizure generation. High-frequency oscillations, and particularly fast ripples, are thought to be biomarkers of the epileptogenic zone. We sought to test these theories by comparing high-frequency oscillation rates and networks in surgical responders and non-responders, with no appreciable change in seizure frequency or severity, within a retrospective cohort of 48 patients implanted with stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye movement sleep and semi-automatically detected and quantified high-frequency oscillations. Each electrode contact was localized in normalized coordinates. We found that the accuracy of seizure onset zone electrode contact classification using high-frequency oscillation rates was not significantly different in surgical responders and non-responders, suggesting that in non-responders the epileptogenic zone partially encompassed the seizure onset zone(s) (P > 0.05). We also found that in the responders, fast ripple on oscillations exhibited a higher spectral content in the seizure onset zone compared with the non-seizure onset zone (P < 1 × 10-5). By contrast, in the non-responders, fast ripple had a lower spectral content in the seizure onset zone (P < 1 × 10-5). We constructed two different networks of fast ripple with a spectral content >350 Hz. The first was a rate-distance network that multiplied the Euclidian distance between fast ripple-generating contacts by the average rate of fast ripple in the two contacts. The radius of the rate-distance network, which excluded seizure onset zone nodes, discriminated non-responders, including patients not offered resection or responsive neurostimulation due to diffuse multifocal onsets, with an accuracy of 0.77 [95% confidence interval (CI) 0.56-0.98]. The second fast ripple network was constructed using the mutual information between the timing of the events to measure functional connectivity. For most non-responders, this network had a longer characteristic path length, lower mean local efficiency in the non-seizure onset zone, and a higher nodal strength among non-seizure onset zone nodes relative to seizure onset zone nodes. The graphical theoretical measures from the rate-distance and mutual information networks of 22 non- responsive neurostimulation treated patients was used to train a support vector machine, which when tested on 13 distinct patients classified non-responders with an accuracy of 0.92 (95% CI 0.75-1). These results indicate patients who do not respond to surgery or those not selected for resection or responsive neurostimulation can be explained by the epileptic network hypothesis that is a decentralized network consisting of widely distributed, hyperexcitable fast ripple-generating nodes.

Endotracheal Tube Position Assessment on Chest Radiographs Using Deep Learning.

PURPOSE: To determine the efficacy of deep learning in assessing endotracheal tube (ETT) position on radiographs. MATERIALS AND METHODS: In this retrospective study, 22 960 de-identified frontal chest radiographs from 11 153 patients (average age, 60.2 years ± 19.9 [standard deviation], 55.6% men) between 2010 and 2018 containing an ETT were placed into 12 categories, including bronchial insertion and distance from the carina at 1.0-cm intervals (0.0-0.9 cm, 1.0-1.9 cm, etc), and greater than 10 cm. Images were split into training (80%, 18 368 images), validation (10%, 2296 images), and internal test (10%, 2296 images), derived from the same institution as the training data. One hundred external test radiographs were also obtained from a different hospital. The Inception V3 deep neural network was used to predict ETT-carina distance. ETT-carina distances and intraclass correlation coefficients (ICCs) for the radiologists and artificial intelligence (AI) system were calculated on a subset of 100 random internal and 100 external test images. Sensitivity and specificity were calculated for low and high ETT position thresholds. RESULTS: On the internal and external test images, respectively, the ICCs of AI and radiologists were 0.84 (95% CI: 0.78, 0.92) and 0.89 (95% CI: 0.77, 0.94); the ICCs of the radiologists were 0.93 (95% CI: 0.90, 0.95) and 0.84 (95% CI: 0.71, 0.90). The AI model was 93.9% sensitive (95% CI: 90.0, 96.7) and 97.7% specific (95% CI: 96.9, 98.3) for detecting ETT-carina distance less than 1 cm. CONCLUSION: Deep learning predicted ETT-carina distance within 1 cm in most cases and showed excellent interrater agreement compared with radiologists. The model was sensitive and specific in detecting low ETT positions.© RSNA, 2020.

Consequences of mesial temporal sparing temporal lobe surgery in medically refractory epilepsy.

OBJECTIVE: We compared long-term seizure outcome, neuropsychological outcome, and occupational outcome of anterior temporal lobectomy (ATL) with and without sparing of mesial structures to determine whether mesial sparing temporal lobectomy prevents memory decline and thus disability, with acceptable seizure outcome. METHODS: We studied patients (n = 21) and controls (n = 21) with no evidence of mesial temporal sclerosis (MTS) on MRI who had surgery to treat drug-resistant epilepsy. Demographic and pre- and postsurgical clinical characteristics were compared. Patients had neuropsychological assessment before and after surgery. Neuropsychological analyses were limited to patients with left-sided surgery and available data (n = 14 in each group) as they were at risk of verbal memory impairment. The California Verbal Learning Test II (CVLT-II) (sum of trials 1-5, delayed free recall) and the Logical Memory subtest of the Wechsler Memory Scale III or IV (WMS-III or WMS-IV) (learning and delayed recall of prose passages) were used to assess verbal episodic learning and memory. Seizure and occupational outcomes were assessed. RESULTS: The chance of attaining seizure freedom was similar in the two groups, so sparing mesial temporal structures did not lessen the chance of stopping seizures. Sparing mesial temporal structures mitigated the extent of postoperative verbal memory impairment, though some of these individuals suffered decline as a consequence of surgery. Occupational outcome was similar in both groups. SIGNIFICANCE: Mesial temporal sparing resections provide a similar seizure outcome as ATL, while producing a better memory outcome. Anterior temporal lobectomy including mesial structure resection did not increase the risk of postoperative disability.

The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity.

BACKGROUND: Researchers have used direct electrical brain stimulation to treat a range of neurological and psychiatric disorders. However, for brain stimulation to be maximally effective, clinicians and researchers should optimize stimulation parameters according to desired outcomes. OBJECTIVE: The goal of our large-scale study was to comprehensively evaluate the effects of stimulation at different parameters and locations on neuronal activity across the human brain. METHODS: To examine how different kinds of stimulation affect human brain activity, we compared the changes in neuronal activity that resulted from stimulation at a range of frequencies, amplitudes, and locations with direct human brain recordings. We recorded human brain activity directly with electrodes that were implanted in widespread regions across 106 neurosurgical epilepsy patients while systematically stimulating across a range of parameters and locations. RESULTS: Overall, stimulation most often had an inhibitory effect on neuronal activity, consistent with earlier work. When stimulation excited neuronal activity, it most often occurred from high-frequency stimulation. These effects were modulated by the location of the stimulating electrode, with stimulation sites near white matter more likely to cause excitation and sites near gray matter more likely to inhibit neuronal activity. CONCLUSION: By characterizing how different stimulation parameters produced specific neuronal activity patterns on a large scale, our results provide an electrophysiological framework that clinicians and researchers may consider when designing stimulation protocols to cause precisely targeted changes in human brain activity.

Ripples Have Distinct Spectral Properties and Phase-Amplitude Coupling With Slow Waves, but Indistinct Unit Firing, in Human Epileptogenic Hippocampus.

Ripple oscillations (80-200 Hz) in the normal hippocampus are involved in memory consolidation during rest and sleep. In the epileptic brain, increased ripple and fast ripple (200-600 Hz) rates serve as a biomarker of epileptogenic brain. We report that both ripples and fast ripples exhibit a preferred phase angle of coupling with the trough-peak (or On-Off) state transition of the sleep slow wave in the hippocampal seizure onset zone (SOZ). Ripples on slow waves in the hippocampal SOZ also had a lower power, greater spectral frequency, and shorter duration than those in the non-SOZ. Slow waves in the mesial temporal lobe modulated the baseline firing rate of excitatory neurons, but did not significantly influence the increased firing rate associated with ripples. In summary, pathological ripples and fast ripples occur preferentially during the On-Off state transition of the slow wave in the epileptogenic hippocampus, and ripples do not require the increased recruitment of excitatory neurons.

Osteoblastoma in the occipital bone: A case report of a rare tumor in the calvarium.

Osteoblastomas infrequently occur in the calvarium, displaying a preference for temporal and frontal bones when it does. We present an unusual case of a large, expansile osteoblastoma in the occipital bone of a 23-year-old man who presented with a nontender lump at the back of his head. Initial computed tomography scan showed a large occipital bone mass, and after additional imaging, a gross total resection was performed. Histopathological examination revealed an osteoblastoma. Although these tumors are benign, overlapping imaging characteristics of lesions affecting the calvarium often present a diagnostic dilemma. This case emphasizes the importance of imaging in the management and work-up of these patients to decrease the risk of complications and assists surgeons in their preoperative planning.

White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions.

Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain's response to stimulation.

Functional control of electrophysiological network architecture using direct neurostimulation in humans.

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

Application of Diffusion Tensor Imaging in Forecasting Neurological Injury and Recovery after Human Cervical Spinal Cord Injury.

The aim of this study is to determine the strength and accuracy of diffusion tensor imaging (DTI) parameters to predict neurological injury and recovery in adult cervical spinal cord injury (SCI). DTI magnetic resonance imaging (MRI) was performed on 23 acute cervical SCI patients within 12 h after injury and on 45 controls utilizing a rapid DTI sequence (∼5 min). Neurological assessments were conducted from within 24 h of injury up to 6 months utilizing detailed International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) examinations. Spearman correlation and receiver operating characteristic (ROC) analysis were used to identify relationships between the DTI parameters at the lesion epicenter and adjacent regions, with injury severity and recovery. In proximity to the anatomical injury (within one level above and below epicenter), there was significant reduction in fractional anisotropy (FA), and elevation in mean diffusivity (MD) and radial diffusivity (RD). DTI values measured one level rostral to the injury epicenter showed stronger correlations with multiple clinical features at several time-points. Area under the curve (AUC) obtained from ROC analysis showed FA (AUC = 0.77) measured at lesion epicenter, and FA (0.83), MD (0.76), and RD (0.83) values measured immediately rostral (one level above) to epicenter discriminate injury severity. Further, MD (0.78) measured at lesion epicenter, and MD (0.79) and RD (0.74) values measured immediately rostral to epicenter discriminate neurological recovery. DTI indices measured immediately rostral to the anatomical level of injury consistently showed better correlation (moderate to strong) and accuracy in predicting neurological injury (FA, r = -0.51 and RD, r = 0.54) and recovery (MD, r = -0.51) than indices measured at the epicenter. There was weak to moderate correlation of all measures at lesion epicenter in predicting neurological injury (FA: r = -0.48; MD: r = 0.23; RD: r = 0.34; axial diffusivity [AD]: r = 0.02) and recovery (FA: r = 0.27; MD: r = -0.44; RD: r = -0.35; AD: r = -0.34).

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p < 0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80-0.94), performed statistically significantly better (p < .001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52-0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p < 0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions.

Dynamic Theta Networks in the Human Medial Temporal Lobe Support Episodic Memory.

The medial temporal lobe (MTL) is a locus of episodic memory in the human brain. It is comprised of cytologically distinct subregions that, in concert, give rise to successful encoding and retrieval of context-dependent memories. However, the functional connections between these subregions are poorly understood. To determine functional connectivity among MTL subregions, we had 131 subjects fitted with indwelling electrodes perform a verbal memory task and asked how encoding or retrieval correlated with inter-regional synchronization. Using phase-based measures of connectivity, we found that synchronous theta (4-8 Hz) activity underlies successful episodic memory. During encoding, we observed a dynamic pattern of connections converging on the left entorhinal cortex, beginning with the perirhinal cortex and shifting through hippocampal subfields. Retrieval-associated networks demonstrated enhanced involvement of the subiculum and CA1, reflecting a substantial reorganization of the encoding network. We posit that coherent theta activity within the MTL marks periods of successful memory, but distinct patterns of connectivity dissociate key stages of memory processing.

Human Verbal Memory Encoding Is Hierarchically Distributed in a Continuous Processing Stream.

Processing of memory is supported by coordinated activity in a network of sensory, association, and motor brain regions. It remains a major challenge to determine where memory is encoded for later retrieval. Here, we used direct intracranial brain recordings from epilepsy patients performing free recall tasks to determine the temporal pattern and anatomical distribution of verbal memory encoding across the entire human cortex. High γ frequency activity (65-115 Hz) showed consistent power responses during encoding of subsequently recalled and forgotten words on a subset of electrodes localized in 16 distinct cortical areas activated in the tasks. More of the high γ power during word encoding, and less power before and after the word presentation, was characteristic of successful recall and observed across multiple brain regions. Latencies of the induced power changes and this subsequent memory effect (SME) between the recalled and forgotten words followed an anatomical sequence from visual to prefrontal cortical areas. Finally, the magnitude of the memory effect was unexpectedly found to be the largest in selected brain regions both at the top and at the bottom of the processing stream. These included the language processing areas of the prefrontal cortex and the early visual areas at the junction of the occipital and temporal lobes. Our results provide evidence for distributed encoding of verbal memory organized along a hierarchical posterior-to-anterior processing stream.

Neuronal phase consistency tracks dynamic changes in acoustic spectral regularity.

The brain parses the auditory environment into distinct sounds by identifying those acoustic features in the environment that have common relationships (e.g., spectral regularities) with one another and then grouping together the neuronal representations of these features. Although there is a large literature that tests how the brain tracks spectral regularities that are predictable, it is not known how the auditory system tracks spectral regularities that are not predictable and that change dynamically over time. Furthermore, the contribution of brain regions downstream of the auditory cortex to the coding of spectral regularity is unknown. Here, we addressed these two issues by recording electrocorticographic activity, while human patients listened to tone-burst sequences with dynamically varying spectral regularities, and identified potential neuronal mechanisms of the analysis of spectral regularities throughout the brain. We found that the degree of oscillatory stimulus phase consistency (PC) in multiple neuronal-frequency bands tracked spectral regularity. In particular, PC in the delta-frequency band seemed to be the best indicator of spectral regularity. We also found that these regularity representations existed in multiple regions throughout cortex. This widespread reliable modulation in PC - both in neuronal-frequency space and in cortical space - suggests that phase-based modulations may be a general mechanism for tracking regularity in the auditory system specifically and other sensory systems more generally. Our findings also support a general role for the delta-frequency band in processing the regularity of auditory stimuli.

Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding.

BACKGROUND: We sought to determine if ripple oscillations (80-120 Hz), detected in intracranial electroencephalogram (iEEG) recordings of patients with epilepsy, correlate with an enhancement or disruption of verbal episodic memory encoding. METHODS: We defined ripple and spike events in depth iEEG recordings during list learning in 107 patients with focal epilepsy. We used logistic regression models (LRMs) to investigate the relationship between the occurrence of ripple and spike events during word presentation and the odds of successful word recall following a distractor epoch and included the seizure onset zone (SOZ) as a covariate in the LRMs. RESULTS: We detected events during 58,312 word presentation trials from 7630 unique electrode sites. The probability of ripple on spike (RonS) events was increased in the SOZ (p < 0.04). In the left temporal neocortex, RonS events during word presentation corresponded with a decrease in the odds ratio (OR) of successful recall, however, this effect only met significance in the SOZ (OR of word recall: 0.71, 95% confidence interval (CI): 0.59-0.85, n = 158 events, adaptive Hochberg, p < 0.01). Ripple on oscillation (RonO) events that occurred in the left temporal neocortex non-SOZ also correlated with decreased odds of successful recall (OR: 0.52, 95% CI: 0.34-0.80, n = 140, adaptive Hochberg, p < 0.01). Spikes and RonS that occurred during word presentation in the left middle temporal gyrus (MTG) correlated with the most significant decrease in the odds of successful recall, irrespective of the location of the SOZ (adaptive Hochberg, p < 0.01). CONCLUSION: Ripples and spikes generated in the left temporal neocortex are associated with impaired verbal episodic memory encoding. Although physiological and pathological ripple oscillations were not distinguished during cognitive tasks, our results show an association of undifferentiated ripples with impaired encoding. The effect was sometimes specific to regions outside the SOZ, suggesting that widespread effects of epilepsy outside the SOZ may contribute to cognitive impairment.

Lateralized hippocampal oscillations underlie distinct aspects of human spatial memory and navigation.

The hippocampus plays a vital role in various aspects of cognition including both memory and spatial navigation. To understand electrophysiologically how the hippocampus supports these processes, we recorded intracranial electroencephalographic activity from 46 neurosurgical patients as they performed a spatial memory task. We measure signals from multiple brain regions, including both left and right hippocampi, and we use spectral analysis to identify oscillatory patterns related to memory encoding and navigation. We show that in the left but not right hippocampus, the amplitude of oscillations in the 1-3-Hz "low theta" band increases when viewing subsequently remembered object-location pairs. In contrast, in the right but not left hippocampus, low-theta activity increases during periods of navigation. The frequencies of these hippocampal signals are slower than task-related signals in the neocortex. These results suggest that the human brain includes multiple lateralized oscillatory networks that support different aspects of cognition.

Electrical Stimulation in Hippocampus and Entorhinal Cortex Impairs Spatial and Temporal Memory.

The medial temporal lobe (MTL) is widely implicated in supporting episodic memory and navigation, but its precise functional role in organizing memory across time and space remains elusive. Here we examine the specific cognitive processes implemented by MTL structures (hippocampus and entorhinal cortex) to organize memory by using electrical brain stimulation, leveraging its ability to establish causal links between brain regions and features of behavior. We studied neurosurgical patients of both sexes who performed spatial-navigation and verbal-episodic memory tasks while brain stimulation was applied in various regions during learning. During the verbal memory task, stimulation in the MTL disrupted the temporal organization of encoded memories such that items learned with stimulation tended to be recalled in a more randomized order. During the spatial task, MTL stimulation impaired subjects' abilities to remember items located far away from boundaries. These stimulation effects were specific to the MTL. Our findings thus provide the first causal demonstration in humans of the specific memory processes that are performed by the MTL to encode when and where events occurred.SIGNIFICANCE STATEMENT Numerous studies have implicated the medial temporal lobe (MTL) in encoding spatial and temporal memories, but they have not been able to causally demonstrate the nature of the cognitive processes by which this occurs in real-time. Electrical brain stimulation is able to demonstrate causal links between a brain region and a given function with high temporal precision. By examining behavior in a memory task as subjects received MTL stimulation, we provide the first causal evidence demonstrating the role of the MTL in organizing the spatial and temporal aspects of episodic memory.