INTRODUCTION: Diagnostic cortical stimulation (CS) in intracranial electroencephalography (iEEG) is an established epilepsy presurgical assessment tool to delineate relevant brain functions and elicit habitual epileptic seizures. Currently, no consensus exists as to whether CS should be routinely performed in pediatric patients. A significant challenge is their limited ability to cooperate during the procedure or to describe non-observable seizure semiology features. Our goal was to identify the spectrum of CS practices in Canada, for both eloquent cortex mapping and seizure stimulation. METHODS: An online survey, answered by all 8 Canadian pediatric epilepsy centers, enquired about implantation, stimulation methods, and use of standardized protocols. A systematic literature review extracted detailed stimulation parameters. RESULTS: Most of the institutions (n = 7/8) reported performing CS during presurgical evaluation. Four institutions indicated they perform stimulation in all implanted patients for the purpose of eloquent cortex mapping and seizure stimulation. The majority of physicians had their individual approach to CS. A largely variable approach to CS, mainly in the choice of stimulation parameters (i.e., train and pulse duration), was observed, with the highest variance concerning the purpose of seizure stimulation. The literature review highlighted an overall small sample size and minimal number of publications. Even though there is a rising trend towards stereotactic iEEG implantation, more data were available on subdural EEGs. CONCLUSION: This study shows individual and sparsely validated approach to CS in pediatric epilepsy. The literature review underscores the urgent need to harmonize pediatric intracranial EEG practices. More multicenter studies are needed to identify safe stimulation thresholds and allow implementation of evidence-based guidelines.

• MeSH
• Child MeSH
• Electroencephalography methods   MeSH
• Electrocorticography methods   MeSH
• Epilepsy surgery   physiopathology   diagnosis   MeSH
• Humans MeSH
• Brain Mapping * methods   MeSH
• Cerebral Cortex physiopathology   MeSH
• Pediatrics methods   MeSH
• Surveys and Questionnaires MeSH
• Seizures * physiopathology   diagnosis   MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH
• Systematic Review MeSH
• Geographicals
• Canada MeSH

Chemosensory learning is a lifelong process of acquiring perceptual expertise and semantic knowledge about chemical stimuli within the everyday environment. In the research context, it is usually simulated using olfactory training, which typically involves repeated exposure to a set of odors over a period of time. Following olfactory training, enhanced olfactory performance has been observed in adults, and similar evidence is beginning to emerge in children. However, the literature is scant concerning the effects of interventions that more closely resemble how chemosensory experience is acquired in daily life. Since children's chemosensory ecology appears to play a crucial role in olfactory development, we investigated whether engaging in activities that stimulate the chemical senses enhances olfactory performance and metacognition. To this end, we invited 20 children aged 9-11 years to participate in teacher-assisted after-school activities for 30-60 minutes a day for six weeks. During the odd weeks, the children appraised herbal and spice blends and used them to prepare dishes and make beauty products. During the even ones, they explored the city by smellwalking and created smellscape maps. The educational outcomes were evaluated using the Sniffin' Sticks test for odor identification and discrimination and the Children's Personal Significance of Olfaction. Bayesian analyses did not reveal any compelling evidence in support of the alternative hypothesis that children in the chemosensory education group outperform those in the comparison group at the post-test. Rates of reliable increase but also decrease in performance on the Sniffin' Sticks identification and discrimination tests were similar in both groups. We corroborated the previous findings regarding girls' and older children's greater proficiency at identifying odors and the female keener interest in the sense of smell. We offer several practical suggestions researchers may want to consider to tailor their research protocols to reflect more closely the broader context in which chemosensory learning takes place and better capture the nuanced outcomes of such interventions.

• MeSH
• Bayes Theorem MeSH
• Smell * physiology   MeSH
• Olfactory Perception * physiology   MeSH
• Child MeSH
• Humans MeSH
• Odorants * MeSH
• Schools MeSH
• Learning physiology   MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.

• MeSH
• Child MeSH
• Electroencephalography methods   MeSH
• Electrocorticography methods   MeSH
• Epilepsies, Partial * diagnosis   surgery   MeSH
• Epilepsy * MeSH
• Humans MeSH
• Drug Resistant Epilepsy * diagnosis   surgery   MeSH
• Seizures diagnosis   MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH

"Genetics and Genomics in Medicine is a new textbook written for undergraduate and graduate students, as well as medical researchers, which explains the science behind the uses of genetics and genomics in medicine today. It is not just about rare inherited and chromosomal disorders, but how genetics affects the whole spectrum of human health and disease. DNA technologies are explained, with emphasis on the modern techniques that have revolutionized the use of genetic information in medicine and are indicating the role of genetics in common complex diseases. The detailed, integrative coverage of genetic approaches to treatment and prevention includes pharmacogenomics and the prospects for personalized medicine. Cancers are essentially genetic diseases and are given a dedicated chapter that includes new insights from cancer genome sequencing. Clinical disorders are covered throughout and there are extensive end-of-chapter questions and problems"--Provided by publisher.

BACKGROUND: Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PURPOSE: To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters. MATERIALS AND METHODS: We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively. RESULTS: Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. CONCLUSION: Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

• MeSH
• Anisotropy MeSH
• Diffusion Magnetic Resonance Imaging methods   MeSH
• Humans MeSH
• Meningeal Neoplasms * pathology   MeSH
• Meningioma * diagnostic imaging   pathology   MeSH
• Diffusion Tensor Imaging methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n=30) while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affect decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that (1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, (2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain vs. loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

• MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Brain Mapping MeSH
• Brain MeSH
• Prefrontal Cortex MeSH
• Risk-Taking MeSH
• Decision Making * MeSH
• Choice Behavior MeSH
• Feedback MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: Treating memory and cognitive deficits requires knowledge about anatomical sites and neural activities to be targeted with particular therapies. Emerging technologies for local brain stimulation offer attractive therapeutic options but need to be applied to target specific neural activities, at distinct times, and in specific brain regions that are critical for memory formation. METHODS: The areas that are critical for successful encoding of verbal memory as well as the underlying neural activities were determined directly in the human brain with intracranial electrophysiological recordings in epilepsy patients. We recorded a broad range of spectral activities across the cortex of 135 patients as they memorised word lists for subsequent free recall. FINDINGS: The greatest differences in the spectral power between encoding subsequently recalled and forgotten words were found in low theta frequency (3-5 Hz) activities of the left anterior prefrontal cortex. This subsequent memory effect was proportionally greater in the lower frequency bands and in the more anterior cortical regions. We found the peak of this memory signal in a distinct part of the prefrontal cortex at the junction between the Broca's area and the frontal pole. The memory effect in this confined area was significantly higher (Tukey-Kramer test, p<0.05) than in other anatomically distinct areas. INTERPRETATION: Our results suggest a focal hotspot of human verbal memory encoding located in the higher-order processing region of the prefrontal cortex, which presents a prospective target for modulating cognitive functions in the human patients. The memory effect provides an electrophysiological biomarker of low frequency neural activities, at distinct times of memory encoding, and in one hotspot location in the human brain. FUNDING: Open-access datasets were originally collected as part of a BRAIN Initiative project called Restoring Active Memory (RAM) funded by the Defence Advanced Research Project Agency (DARPA). CT, ML, MTK and this research were supported from the First Team grant of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

• MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Mapping MeSH
• Brain physiology   MeSH
• Memory * physiology   MeSH
• Prefrontal Cortex * physiology   MeSH
• Mental Recall physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Hallucinatory experiences (HEs) can be pronounced in psychosis, but similar experiences also occur in nonclinical populations. Cognitive mechanisms hypothesized to underpin HEs include dysfunctional source monitoring, heightened signal detection, and impaired attentional processes. Using data from an international multisite study on non-clinical participants (N = 419), we described the overlap between two sets of variables - one measuring cognition and the other HEs - at the level of individual items. We used a three-step method to extract and examine item-specific signal, which is typically obscured when summary scores are analyzed using traditional methodologies. The three-step method involved: (1) constraining variance in cognition variables to that which is predictable from HE variables, followed by dimension reduction, (2) determining reliable HE items using split-halves and permutation tests, and (3) selecting cognition items for interpretation using a leave-one-out procedure followed by repetition of Steps 1 and 2. The results showed that the overlap between HEs and cognition variables can be conceptualized as bi-dimensional, with two distinct mechanisms emerging as candidates for separate pathways to the development of HEs: HEs involving perceptual distortions on one hand (including voices), underpinned by a low threshold for signal detection in cognition, and HEs involving sensory overload on the other hand, underpinned by reduced laterality in cognition. We propose that these two dimensions of HEs involving distortions/liberal signal detection, and sensation overload/reduced laterality may map onto psychosis-spectrum and dissociation-spectrum anomalous experiences, respectively.

• MeSH
• Hallucinations * MeSH
• Cognition MeSH
• Humans MeSH
• Multivariate Analysis MeSH
• Attention MeSH
• Psychotic Disorders * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH

OBJECTIVE: Verbal memory dysfunction is common in focal, drug-resistant epilepsy (DRE). Unfortunately, surgical removal of seizure-generating brain tissue can be associated with further memory decline. Therefore, localization of both the circuits generating seizures and those underlying cognitive functions is critical in presurgical evaluations for patients who may be candidates for resective surgery. We used intracranial electroencephalographic (iEEG) recordings during a verbal memory task to investigate word encoding in focal epilepsy. We hypothesized that engagement in a memory task would exaggerate local iEEG feature differences between the seizure onset zone (SOZ) and neighboring tissue as compared to wakeful rest ("nontask"). METHODS: Ten participants undergoing presurgical iEEG evaluation for DRE performed a free recall verbal memory task. We evaluated three iEEG features in SOZ and non-SOZ electrodes during successful word encoding and compared them with nontask recordings: interictal epileptiform spike (IES) rates, power in band (PIB), and relative entropy (REN; a functional connectivity measure). RESULTS: We found a complex pattern of PIB and REN changes in SOZ and non-SOZ electrodes during successful word encoding compared to nontask. Successful word encoding was associated with a reduction in local electrographic functional connectivity (increased REN), which was most exaggerated in temporal lobe SOZ. The IES rates were reduced during task, but only in the non-SOZ electrodes. Compared with nontask, REN features during task yielded marginal improvements in SOZ classification. SIGNIFICANCE: Previous studies have supported REN as a biomarker for epileptic brain. We show that REN differences between SOZ and non-SOZ are enhanced during a verbal memory task. We also show that IESs are reduced during task in non-SOZ, but not in SOZ. These findings support the hypothesis that SOZ and non-SOZ respond differently to task and warrant further exploration into the use of cognitive tasks to identify functioning memory circuits and localize SOZ.

• MeSH
• Electroencephalography MeSH
• Electrocorticography MeSH
• Epilepsies, Partial * surgery   MeSH
• Humans MeSH
• Brain MeSH
• Drug Resistant Epilepsy * surgery   MeSH
• Seizures MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two distinctive examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; potentially due to topographical, rather than topological, specificity of the differences. In the easier task of seizure discrimination from scalp electroencephalography data, classification based on persistent homology features generally reached comparable performance to using raw connectivity, albeit with typically smaller accuracies obtained for the directed (effective) connectivity compared to the undirected (functional) connectivity. Specific applications for topological data analysis may open when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

• MeSH
• Electroencephalography MeSH
• Epilepsy diagnostic imaging   physiopathology   MeSH
• Connectome * MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Mapping MeSH
• Models, Neurological * MeSH
• Brain diagnostic imaging   physiopathology   MeSH
• Nerve Net diagnostic imaging   physiopathology   MeSH
• Schizophrenia diagnostic imaging   physiopathology   MeSH
• Seizures diagnostic imaging   physiopathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH