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Behavioral studies have suggested that exaggerated reactivity to food cues, especially those associated with high-calorie foods, may be a factor underlying obesity. This increased motivational potency of foods in obese individuals appears to be mediated in part by a hyperactive reward system. We used a Philips 3T magnet and fMRI to investigate activation of reward-system and associated brain structures in response to pictures of high-calorie and low-calorie foods in 12 obese compared to 12 normal-weight women. A regions of interest (ROI) analysis revealed that pictures of high-calorie foods produced significantly greater activation in the obese group compared to controls in medial and lateral orbitofrontal cortex, amygdala, nucleus accumbens/ventral striatum, medial prefrontal cortex, insula, anterior cingulate cortex, ventral pallidum, caudate, putamen, and hippocampus. For the contrast of high-calorie vs. low-calorie foods, the obese group also exhibited a larger difference than the controls did in all of the same regions of interest except for the putamen. Within-group contrasts revealed that pictures of high-calorie foods uniformly stimulated more activation than low-calorie foods did in the obese group. By contrast, in the control group, greater activation by high-calorie foods was seen only in dorsal caudate, whereas low-calorie foods were more effective than high-calorie foods in the lateral orbitofrontal cortex, medial prefrontal cortex, and anterior cingulate cortex. In summary, compared to normal-weight controls, obese women exhibited greater activation in response to pictures of high-calorie foods in a large number of regions hypothesized to mediate motivational effects of food cues.

Magnetic resonance imaging of hippocampal internal architecture (HIA) at 3T is challenging. HIA is defined by layers of gray and white matter that are less than 1 mm thick in the coronal plane. To visualize HIA, conventional MRI approaches have relied on sequences with high in-plane resolution (≤0.5 mm) but comparatively thick slices (2–5 mm). However, thicker slices are prone to volume averaging effects that result in loss of HIA clarity and blurring of the borders of the hippocampal subfields in up to 61% of slices as has been reported. In this work we describe an approach to hippocampal imaging that provides consistently high HIA clarity using a commonly available sequence and post-processing techniques that is flexible and may be applicable to any MRI platform. We refer to this approach as High Resolution Multiple Image Co-registration and Averaging (HR-MICRA). This approach uses a variable flip angle turbo spin echo sequence to repeatedly acquire a whole brain T2w image volume with high resolution in three dimensions in a relatively short amount of time, and then co-register the volumes to correct for movement and average the repeated scans to improve SNR. We compared the averages of 4, 9, and 16 individual scans in 20 healthy controls using a published HIA clarity rating scale. In the body of the hippocampus, the proportion of slices with good or excellent HIA clarity was 90%, 83%, and 67% for the 16x, 9x, and 4x HR-MICRA images, respectively. Using the 4x HR-MICRA images as a baseline, the 9x HR-MICRA images were 2.6 times and 16x HR-MICRA images were 3.2 times more likely to have high HIA ratings ( p < 0.001) across all hippocampal segments (head, body, and tail). The thin slices of the HR-MICRA images allow reformatting in any plane with clear visualization of hippocampal dentation in the sagittal plane. Clear and consistent visualization of HIA will allow application of this technique to future hippocampal structure research, as well as more precise manual or automated segmentation.

Abstract Background: Seizure outcomes after focal neocortical epilepsy (FNE) surgery are less favorable than after temporal lobectomy, and the reasons for surgical failure are incompletely understood. Few groups have performed an in-depth examination of seizure recurrences to identify possible reasons for failure. Objective: To elucidate factors contributing to FNE surgery failures. Methods: We reviewed resections for drug-resistant FNE performed at our institution between 1998 and 2011. We performed a quantitative analysis of seizure outcome predictors and a detailed qualitative review of failed surgical cases. Results: Of 138 resections in 125 FNE patients, 91 (66%) resulted in freedom from disabling seizures (Engel I outcome). Mean ± SEM patient age was 20.0 ± 1.2 years; mean follow-up was 3.8 years (range, 1–17 years); and 57% of patients were male. Less favorable (Engel II–IV) seizure outcome was predicted by higher preoperative seizure frequency (odds ratio = 0.85; 95% confidence interval, 0.78–0.93), a history of generalized tonic-clonic seizures (odds ratio = 0.42; 95% confidence interval, 0.18–0.97), and normal magnetic resonance imaging (odds ratio = 0.30; 95% confidence interval, 0.09–1.02). Among 36 surgical failures examined, 26 (72%) were related to extent of resection, with residual epileptic focus at the resection margins, whereas 10 (28%) involved location of resection, with an additional epileptogenic zone distant from the resection. Of 16 patients who received reoperation after seizure recurrence, 10 (63%) achieved seizure freedom. Conclusion: Insufficient extent of resection is the most common reason for recurrent seizures after FNE surgery, although some patients harbor a remote epileptic focus. Many patients with incomplete seizure control are candidates for reoperation.

Abstract INTRODUCTION: Intractable focal epilepsy is a devastating disorder with profound effects on cognition and quality of life. Epilepsy surgery can lead to seizure freedom in patients with focal epilepsy; however, sometimes it fails owing to an incomplete delineation of the epileptogenic zone (EZ). Brain networks in epilepsy can be studied with resting-state functional connectivity (RSFC) analysis, yet previous investigations using functional MRI or electrocorticography have produced inconsistent results. Magnetoencephalography (MEG) allows noninvasive whole-brain recordings, and can be used to study both long-range network disturbances in focal epilepsy and regional connectivity at the EZ. METHODS: In MEG recordings from presurgical epilepsy patients, we examined: (1) global functional connectivity maps in patients vs controls, and (2) regional functional connectivity maps at the region of resection, compared with the homotopic nonepileptogenic region in the contralateral hemisphere. RESULTS: Sixty-one patients were studied, including 30 with mesial temporal lobe epilepsy and 31 with focal neocortical epilepsy. Compared with a group of 31 controls, epilepsy patients had decreased RSFC in widespread regions, including perisylvian, posterior temporoparietal, and orbitofrontal cortices (P < .01, false discovery rate-corrected). Decreased mean global connectivity was related to longer duration of epilepsy and higher frequency of consciousness-impairing seizures (P < .01, linear regression). Furthermore, patients with increased regional connectivity within the resection site (n = 24) were more likely to achieve postoperative seizure freedom (87.5% with Engel I outcome) than those with neutral (n = 15, 64.3% seizure free) or decreased (n = 23, 47.8% seizure free) regional connectivity (P < .02, χ2). CONCLUSION: Widespread global decreases in functional connectivity are observed in patients with focal epilepsy and may reflect deleterious long-term effects of recurrent seizures. Furthermore, enhanced regional functional connectivity at the area of resection may help predict seizure outcome and aid surgical planning.

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment. Genome-wide association meta-analyses identify 26 risk loci for epilepsy, including 19 loci specific to genetic generalized epilepsy. Prioritized candidate genes implicate synaptic processes and overlap with targets of antiseizure medications.

Despite progress in understanding the genetics of rare epilepsies, the more common epilepsies have proven less amenable to traditional gene-discovery analyses. We aimed to assess the contribution of ultra-rare genetic variation to common epilepsies. We did a case-control sequencing study with exome sequence data from unrelated individuals clinically evaluated for one of the two most common epilepsy syndromes: familial genetic generalised epilepsy, or familial or sporadic non-acquired focal epilepsy. Individuals of any age were recruited between Nov 26, 2007, and Aug 2, 2013, through the multicentre Epilepsy Phenome/Genome Project and Epi4K collaborations, and samples were sequenced at the Institute for Genomic Medicine (New York, USA) between Feb 6, 2013, and Aug 18, 2015. To identify epilepsy risk signals, we tested all protein-coding genes for an excess of ultra-rare genetic variation among the cases, compared with control samples with no known epilepsy or epilepsy comorbidity sequenced through unrelated studies. We separately compared the sequence data from 640 individuals with familial genetic generalised epilepsy and 525 individuals with familial non-acquired focal epilepsy to the same group of 3877 controls, and found significantly higher rates of ultra-rare deleterious variation in genes established as causative for dominant epilepsy disorders (familial genetic generalised epilepsy: odd ratio [OR] 2·3, 95% CI 1·7–3·2, p=9·1 × 10−8; familial non-acquired focal epilepsy 3·6, 2·7–4·9, p=1·1 × 10−17). Comparison of an additional cohort of 662 individuals with sporadic non-acquired focal epilepsy to controls did not identify study-wide significant signals. For the individuals with familial non-acquired focal epilepsy, we found that five known epilepsy genes ranked as the top five genes enriched for ultra-rare deleterious variation. After accounting for the control carrier rate, we estimate that these five genes contribute to the risk of epilepsy in approximately 8% of individuals with familial non-acquired focal epilepsy. Our analyses showed that no individual gene was significantly associated with familial genetic generalised epilepsy; however, known epilepsy genes had lower p values relative to the rest of the protein-coding genes (p=5·8 × 10−8) that were lower than expected from a random sampling of genes. We identified excess ultra-rare variation in known epilepsy genes, which establishes a clear connection between the genetics of common and rare, severe epilepsies, and shows that the variants responsible for epilepsy risk are exceptionally rare in the general population. Our results suggest that the emerging paradigm of targeting of treatments to the genetic cause in rare devastating epilepsies might also extend to a proportion of common epilepsies. These findings might allow clinicians to broadly explain the cause of these syndromes to patients, and lay the foundation for possible precision treatments in the future. National Institute of Neurological Disorders and Stroke (NINDS), and Epilepsy Research UK.

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology. Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.

Magnetoencephalography (MEG) has developed to the point that it has now entered routine clinical application. Epilepsy MEG studies show that it can accurately localize spike sources--both ictal and interictal--as compared with both directly. Limitations involve difficulties in detecting complex or deep sources when recording spontaneous cerebral activity. MEG not only provides a novel tool to localize and characterize epileptiform disturbances, it also has an important role in determining the significance of abnormalities seen on both structural and functional imaging. Ultimately, MEG should play a major role in totally noninvasive epilepsy surgery evaluation.

INTRODUCTION Intracranial electrode monitoring is fundamentally important for epilepsy surgery evaluations. Currently, there is an emerging debate over the optimal approach between subdural grids and stereoelectroencephalography (SEEG). Our institution utilizes two strategies for intracranial EEG ictal recordings: 1) a “hybrid” approach, whereby patients receive combined subdural grid and intraoperative neuronavigation-based depth electrode implantations, and 2) SEEG. METHODS A retrospective review was performed on consecutive patients that underwent hybrid or SEEG implantation from July 2012 to July 2019. Intracranial recordings were utilized in patients with putative seizure onset regions that were MR-negative, poorly localized, and/or abutting eloquent cortex. Hemorrhagic and non-hemorrhagic complications, neurological deficit at discharge, success of localizing seizure focus, one-year Engel I outcomes, and electrode locations. RESULTS 99 hybrid and 44 SEEG procedures were performed. Hemorrhagic complication rates were similar (SEEG: 8%; hybrid: 7.1%, P = .96). SEEG hemorrhages were all intra-axial, whereas hybrid hemorrhages were all extra-axial. Neurological deficits from hybrid cases were quickly reversed with prompt hematoma evacuation, while SEEG was associated with more severe deficits at discharge (P < .01). There was no difference in non-hemorrhagic complications (P = .24). Hybrid cases had higher density of electrode contacts (hybrid: 122.1±28.5, SEEG: 89.8±34.2, P < .01), with more sampling of eloquent cortices. SEEG electrodes were more likely to be located in white matter (SEEG: 34.0%, hybrid: 9.2%, P < .01). CONCLUSION The hybrid and SEEG approaches resulted in similar, relatively low hemorrhage rates in our institutional series. An important difference, however, is that SEEG-related hemorrhages in eloquent brain regions resulted in lasting neurological deficits. Grid-related subdural hemorrhages did not lead to any long-standing injury. The decision to use hybrid or SEEG should tailor the characteristics of either approach to the localization question(s) for a given patien.

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.