Emotion recognition in objects in patients with neurological disease.

ObjectiveConsiderable research indicates that individuals with dementia have deficits in the ability to recognize emotion in other people. The present study examined ability to detect emotional qualities of objects.MethodFifty-two patients with frontotemporal dementia (FTD), 20 patients with Alzheimer's disease (AD), 18 patients awaiting surgery for intractable epilepsy, and 159 healthy controls completed a newly developed test of ability to recognize emotional qualities of art (music and paintings), and pleasantness in simple sensory stimuli (tactile, olfactory, auditory), and to make aesthetic judgments (geometric shapes, room décor). A subset of participants also completed a test of ability to recognize emotions in other people.ResultsPatients with FTD showed a marked deficit in ability to recognize the emotions conveyed in art, compared with both healthy individuals and patients with AD (relative to controls, deficits in patients with AD only approached significance). This deficit remained robust after controlling for FTD patients' ability to recognize pleasantness in simple sensory stimuli, make aesthetic judgments, identify odors, and identify emotions in other people. Neither FTD nor AD patients showed deficits in recognizing pleasant sensory stimuli or making aesthetic judgments. Exploratory analysis of patients with epilepsy revealed no deficits in any of these domains.ConclusionPatients with FTD (but not AD) showed a significant, specific deficit in ability to interpret emotional messages in art, echoing FTD-related deficits in recognizing emotions in other people. This finding adds to our understanding of the impact these diseases have on the lives of patients and their caregivers. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

DNM1 encephalopathy: A new disease of vesicle fission.

ObjectiveTo evaluate the phenotypic spectrum caused by mutations in dynamin 1 (DNM1), encoding the presynaptic protein DNM1, and to investigate possible genotype-phenotype correlations and predicted functional consequences based on structural modeling.MethodsWe reviewed phenotypic data of 21 patients (7 previously published) with DNM1 mutations. We compared mutation data to known functional data and undertook biomolecular modeling to assess the effect of the mutations on protein function.ResultsWe identified 19 patients with de novo mutations in DNM1 and a sibling pair who had an inherited mutation from a mosaic parent. Seven patients (33.3%) carried the recurrent p.Arg237Trp mutation. A common phenotype emerged that included severe to profound intellectual disability and muscular hypotonia in all patients and an epilepsy characterized by infantile spasms in 16 of 21 patients, frequently evolving into Lennox-Gastaut syndrome. Two patients had profound global developmental delay without seizures. In addition, we describe a single patient with normal development before the onset of a catastrophic epilepsy, consistent with febrile infection-related epilepsy syndrome at 4 years. All mutations cluster within the GTPase or middle domains, and structural modeling and existing functional data suggest a dominant-negative effect on DMN1 function.ConclusionsThe phenotypic spectrum of DNM1-related encephalopathy is relatively homogeneous, in contrast to many other genetic epilepsies. Up to one-third of patients carry the recurrent p.Arg237Trp variant, which is now one of the most common recurrent variants in epileptic encephalopathies identified to date. Given the predicted dominant-negative mechanism of this mutation, this variant presents a prime target for therapeutic intervention

Spatiotemporal Dynamics of Word Processing in the Human Brain

We examined the spatiotemporal dynamics of word processing by recording the electrocorticogram (ECoG) from the lateral frontotemporal cortex of neurosurgical patients chronically implanted with subdural electrode grids. Subjects engaged in a target detection task where proper names served as infrequent targets embedded in a stream of task-irrelevant verbs and nonwords. Verbs described actions related to the hand (e.g, throw) or mouth (e.g., blow), while unintelligible nonwords were sounds which matched the verbs in duration, intensity, temporal modulation, and power spectrum. Complex oscillatory dynamics were observed in the delta, theta, alpha, beta, low, and high gamma (HG) bands in response to presentation of all stimulus types. HG activity (80–200 Hz) in the ECoG tracked the spatiotemporal dynamics of word processing and identified a network of cortical structures involved in early word processing. HG was used to determine the relative onset, peak, and offset times of local cortical activation during word processing. Listening to verbs compared to nonwords sequentially activates first the posterior superior temporal gyrus (post-STG), then the middle superior temporal gyrus (mid-STG), followed by the superior temporal sulcus (STS). We also observed strong phase-locking between pairs of electrodes in the theta band, with weaker phase-locking occurring in the delta, alpha, and beta frequency ranges. These results provide details on the first few hundred milliseconds of the spatiotemporal evolution of cortical activity during word processing and provide evidence consistent with the hypothesis that an oscillatory hierarchy coordinates the flow of information between distinct cortical regions during goal-directed behavior

A continuous mapping of sleep states through association of EEG with a mesoscale cortical model

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time

Development of a longitudinal integrated clerkship at an academic medical center

In 2005, medical educators at the University of California, San Francisco (UCSF), began developing the Parnassus Integrated Student Clinical Experiences (PISCES) program, a year-long longitudinal integrated clerkship at its academic medical center. The principles guiding this new clerkship were continuity with faculty preceptors, patients, and peers; a developmentally progressive curriculum with an emphasis on interdisciplinary teaching; and exposure to undiagnosed illness in acute and chronic care settings. Innovative elements included quarterly student evaluation sessions with all preceptors together, peer-to-peer evaluation, and oversight advising with an assigned faculty member. PISCES launched with eight medical students for the 2007/2008 academic year and expanded to 15 students for 2008/2009. Compared to UCSF's traditional core clerkships, evaluations from PISCES indicated significantly higher student satisfaction with faculty teaching, formal didactics, direct observation of clinical skills, and feedback. Student performance on discipline-specific examinations and United States Medical Licensing Examination step 2 CK was equivalent to and on standardized patient examinations was slightly superior to that of traditional peers. Participants' career interests ranged from primary care to surgical subspecialties. These results demonstrate that a longitudinal integrated clerkship can be implemented successfully at a tertiary care academic medical center

Ultra-rare genetic variation in common epilepsies: a case-control sequencing study

BACKGROUND: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. METHODS: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. FINDINGS: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. INTERPRETATION: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. FUNDING:National Institute of Neurological Disorders and Stroke (NINDS), and Epilepsy Research UK

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

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