This scientific commentary refers to ‘Interictal epileptiform activity outside the seizure onset zone impacts cognition’, by Ung et al. (doi:10.1093/brain/awx143).

Objective

Considerable 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.

Method

Fifty-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.

Results

Patients 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.

Conclusion

Patients 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).

Objective

Considerable 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.

Method

Fifty-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.

Results

Patients 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.

Conclusion

Patients 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).

Background

Despite the increasing interest in sex differences in disease manifestations and responses to treatment, very few data are available on sex differences in seizure types and semiology. The Epilepsy Phenome/Genome Project (EPGP) is a large-scale, multi-institutional, collaborative study that aims to create a comprehensive repository of detailed clinical information and DNA samples from a large cohort of people with epilepsy. We used this well-characterized cohort to explore differences in seizure types as well as focal seizure symptoms between males and females.

Methods

We reviewed the EPGP database and identified individuals with generalized epilepsy of unknown etiology (GE) (n = 760; female: 446, male: 314), nonacquired focal epilepsy (NAFE) (n = 476; female: 245, male: 231), or both (n = 64; female: 33, male: 31). Demographic data along with characterization of seizure type and focal seizure semiologies were examined.

Results

In GE, males reported atonic seizures more frequently than females (6.5% vs. 1.7%; p < 0.001). No differences were observed in other generalized seizure types. In NAFE, no sex differences were seen for seizure types with or without alteration of consciousness or progression to secondary generalization. Autonomic (16.4% vs. 26.6%; p = 0.005), psychic (26.7% vs. 40.3%; p = 0.001), and visual (10.3% vs. 19.9%; p = 0.002) symptoms were more frequently reported in females than males. Specifically, of psychic symptoms, more females than males endorsed déjà vu (p = 0.001) but not forced thoughts, derealization/depersonalization, jamais vu, or fear. With corrections for multiple comparisons, there were no significant differences in aphasic, motor, somatosensory, gustatory, olfactory, auditory, vertiginous, or ictal headache symptoms between sexes.

Conclusions

Significant differences between the sexes were observed in the reporting of atonic seizures, which were more common in males with GE, and for autonomic, visual, and psychic symptoms associated with NAFE, which were more common in females.

Objective

We studied the frequency of auras in generalized epilepsy (GE) using a detailed semistructured diagnostic interview.

Methods

In this cross-sectional study, participants with GE were drawn from the Epilepsy Phenome/Genome Project (EPGP). Responses to the standardized diagnostic interview regarding tonic-clonic (grand mal) seizures were then examined. This questionnaire initially required participants to provide their own description of any subjective phenomena before their "grand mal seizures." Participants who provided answers to these questions were considered to have an aura. All participants were then systematically queried regarding a list of specific symptoms occurring before grand mal seizures, using structured (closed-ended) questions.

Results

Seven hundred ninety-eight participants with GE were identified, of whom 530 reported grand mal seizures. Of these, 112 (21.3%) reported auras in response to the open-ended question. Analysis of responses to the closed-ended questions suggested that 341 participants (64.3%) experienced at least one form of aura.

Conclusions

Auras typically associated with focal epilepsy were reported by a substantial proportion of EPGP subjects with GE. This finding may support existing theories of cortical and subcortical generators of GE with variable spread patterns. Differences between responses to the open-ended question and closed-ended questions may also reflect clinically relevant variation in patient responses to history-taking and surveys. Open-ended questions may underestimate the prevalence of specific types of auras and may be in part responsible for the underrecognition of auras in GE. In addition, structured questions may influence participants, possibly leading to a greater representation of symptoms.

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.

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction error (PE) signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from PE signals has been challenging. Critically, while many studies found indirect evidence for PC in the form of PE signals, direct evidence for the prediction signal is mostly lacking. Here, we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex evinces prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The broadband high-frequency amplitude (HFA) was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single-trial HFA predicted deviations and correlated with poststimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of PE signals.

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.