HR and QTc increase with ictal activity, whereupon modulation of QTc, but not of HR, is asymmetrically lateralized.

<p>(A) Absolute HR at different timepoints from all patients was averaged (based on a mean HR per timepoint and side of seizure-onset per patient). Paired data for right- (white bars) and left-hippocampal onset (grey bars) were available from all 15 patients at all timepoints. (B) Relative HR changes from all patients were averaged with no significant difference of ictal modulation of HR between left- and right-onset seizures. (C) QT intervals corrected with all four formulas (grey bars, left-hippocampal seizures; white bars, right-hippocampal seizures) were plotted versus three timepoints (1, preictal; 2, unilateral ictal activity; 3, postictal). (D) The absolute ictal changes of QT intervals using all four correction formulas (Ba, Bazett; Fri, Fridericia; Ho, Hodges; Fra, Framingham) were separately plotted for left- (grey bars) and right-hippocampal seizures (white bars). QT lengthening was significantly greater during left-hippocampal activity as assessed with all 4 correction formulas, suggesting an asymmetric ictal modulation of cardiac repolarization. All data expressed as mean±S.E.M.</p

Clinical characteristics of patients.

*<p>At telemetry.</p>§<p>follow-up in months.</p>#<p>according to Engel classification.</p><p>c, electrode contacts; ExHipp, extrahippocampal; Hipp, hippocampal; HS, hipppocampal sclerosis; L, left; n.a., not applicable; R, right; SAHE, selective amygdala-hippocampectomie; TL, temporal lobe.</p

Implantation scheme and flowchart of patient selection.

<p>(A) Scheme of implantation of intracranial electrodes to assess hippocampal activity and (B) flowchart of selection and inclusion of patients.</p

Ictal Modulation of Cardiac Repolarization, but Not of Heart Rate, Is Lateralized in Mesial Temporal Lobe Epilepsy

<div><p>Objectives</p><p>Human and animal studies provided controversial data on asymmetric cortical representation of cardiac function, which may partially be due to different study designs and inter-individual variability. Here, we investigated whether seizure-related changes in heart rate (HR) and cardiac repolarization depend on the side of seizure-activity in people with mesial temporal lobe epilepsy (mTLE).</p><p>Methods</p><p>To account for inter-individual variability, EEG and ECG data were reviewed from patients with medically refractory mTLE undergoing pre-surgical video-EEG telemetry with at least 2 seizures arising from each hippocampus as assessed by bilateral hippocampal depths electrodes. RR and QT intervals were determined at different timepoints using a one-lead ECG. QT intervals were corrected for HR (QTc) using 4 established formulas.</p><p>Results</p><p>Eighty-two seizures of 15 patients were analyzed. HR increased by ∼30% during hippocampal activity irrespective of the side (p = 0.411). QTc intervals were lengthened to a significantly greater extent during left hippocampal seizures (e.g. difference of QT intervals between preictal and ictal state using Bazett’s formula; left side 32.0±5.3 ms, right side 15.6±7.7 ms; p = 0.016). Abnormal QTc prolongation occurred in 7 of 41 left hippocampal seizures of 4 patients, and only in 2 of 37 right hippocampal seizures of 2 patients.</p><p>Conclusions</p><p>Seizure-related modulation of cardiac repolarization, but not of HR, appears to depend on the side of ictal activity, strengthening the hypothesis of asymmetric cerebral representation of cardiac function. The clinical relevance of this is unclear, but may indicate an increased risk of abnormal ictal QT prolongation in people with left mTLE.</p></div

Summary of seizure-related QT alterations.

*<p>Luo S, Michler K, Johnston P, Macfarlane PW. A comparison of commonly used QT correction formulae: the effect of heart rate on the QTc of normal ECGs. J Electrocardiol. 2004;37 Suppl: 81–90 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064765#pone.0064765.s003" target="_blank">table S1</a>). In 5 of the 82 included seizures, ictal QT intervals could not been reliably analyzed.</p

Plot of HR and QTc changes per patient.

<p>(A) Relative ictal HR changes and (B) absolute QTc differences using Bazett’s formula were plotted separately for each patient and side of seizure activity. Corresponding data pairs from each patient were connected with a line. Note that only in two patients, QTc increased by more than 10 ms during right hippocampal seizures as compared to left hippocampal seizures (B, highlighted in red). (C) Individual QTc values (Bazett) did not correlate with corresponding absolute ictal heart rates (linear regression, p = 0.67). Examples were illustrated using Bazett’s formula, as this correction formula is known to overestimate corrected QT values, so that a potential artificial bias, if present, should be clearly visible.</p

Example of original EEG- and ECG-traces during a focal seizure with right-sided hippocampal onset.

<p>(A) Implantation scheme of intracranial electrodes (patient no. 119). (B–E) EEG-traces in bipolar montage (localization as given in panel A, the lower numbers apply to the contacts opposite to the cable outlet of the respective strip or depths electrodes) and ECG-traces (last trace, labeled as EKG1-EKG2, represents derivation Einthoven II with inverted polarity). The time period of the recordings is indicated in panel F. (B) Arrow indicates seizure-onset in the right hippocampus. (C) The arrow indicates onset of ictal activity in the left hippocampus. (D) Note the compromised ECG trace due to movement artifacts of the patient. (E) The arrow indicates the abrupt termination of seizure activity. (F) Time course of HR during this focal seizure with impaired responsiveness and complex automatisms. The arrows indicate the time periods from which example panels B–E have been selected. Note the missing values after propagation of ictal activity to the left hemisphere (time period between arrows “C” and “E”).</p

Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

<div><p>Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed <i>Combinato</i>: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning.</p></div

Tracking of selectively responding neurons over an entire night.

<p><b>A</b>–<b>H</b> show data from eight patients. Continuous unit recordings started in the evening and ended the next morning. “Screening sessions” were performed at the beginning and in the end of each recording. Displayed are raster plots for one stimulus image per screening session. Inter-stimulus interval histograms for the evening and morning are displayed. In all patients but A, written names corresponding to the images were also presented. The middle column (“Night”) shows the activity of units tracked automatically during the entire recording. Each small dot marks the time point and maximal voltage of one action potential. Colors correspond to the raster plots from the screening sessions: units marked in gray do not respond to the images/written names. Units marked in blue, red, or yellow respond to the images/written names as shown in the raster plots. Mean waveforms of all responsive units are displayed for each hour recorded. <b>A</b> Stable waveform and response pattern. <b>B</b> Amplitude variations are visible. As typical for parahippocampal units, unit does not respond to the written name. <b>C</b> An amplitude shift in the responsive neuron (possibly caused by micro-movement of the electrode) results in the detection of two different units, most likely belonging to one neuron. <b>D</b> Two responsive clusters are generated. No response to the written name. <b>E</b> Stable waveform, but very weak response in the morning. <b>F</b> Solid response in the evening and morning, but with separate units. No definite conclusion about the success of tracking can be made. <b>G</b> The blue cluster generates most of the response. The red cluster also contributes to the response. Both clusters are tracked with a stable waveform. <b>H</b> Similar to G, with three responsive clusters. The red cluster generates most of the response. The blue and yellow clusters contribute to the response. All three clusters have a stable waveform. Hipp., hippocampus; Para., parahippocampal cortex; Amyg., amygdala. Stimulus pictures displayed here have been replaced by similar pictures for legal and privacy reasons. Copyright notes: <b>A</b> “Lake” by J. Niediek is licensed under CC BY 4.0 <b>B</b> “Antalya” by J. Schmidtkunz is licensed under CC BY 4.0 <b>C</b> cropped from “[…] Sebastian Vettel (Ferrari)” by Morio, CC BY-SA 4.0, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:Sebastian_Vettel_2015_Malaysia_podium_2.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:Sebastian_Vettel_2015_Malaysia_podium_2.jpg</a> <b>D</b> cropped from “50th Munich Security Conference 2014: Vitali Klychko and Frank-Walter Steinmeier […]” by Mueller / MSC (Marc Müller), CC BY 3.0 DE, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:MSC_2014_Klychko-Steinmeier3_Mueller_MSC2014.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:MSC_2014_Klychko-Steinmeier3_Mueller_MSC2014.jpg</a>) <b>E</b> “[…] Horst Schimanski […]” by H. Schrapers is licensed under CC BY-SA 2.5, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:HorstSchimanski.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:HorstSchimanski.jpg</a>) <b>F</b> cropped from “Simpsons 20 Years” by Gabriel Shepard, CC BY-SA 3.0, DeviantArt (<a href="http://gabrielshepard.deviantart.com/art/Simpsons-20-Years-124858104" target="_blank">http://gabrielshepard.deviantart.com/art/Simpsons-20-Years-124858104</a>) <b>G</b> “Avocado” by D. E. Bruschi is licensed under CC BY 4.0 <b>H</b> “My friend” by J. Niediek is licensed under CC BY 4.0.</p

Performance of our algorithm on simulated data at different parameter settings.

<p>Results for a total of 48 different parameter settings are displayed. Each column of panels corresponds to one value of <i>R</i>_min (indicated above each column), and each row of panels corresponds to one pair of values for <i>N</i>_rep and <i>C</i>_max (indicated left of each row). Colors correspond to four different values of <i>C</i>_stop, as indicated by the legend in the lower left. Each line plot shows the number of hits as a function of the number of neurons in the simulation (error bars denote s.e.m.), compare <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166598#pone.0166598.g003" target="_blank">Fig 3B</a>. Each bar plot represents the number of hits as a fraction of the number of neurons in the simulation (error bars denote standard deviation). The presence of asterisks or ‘ns’ above each bar indicate that the fraction of hits obtained at this particular choice of parameters is higher than the one obtained by manual expert operators in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166598#pone.0166598.ref008" target="_blank">8</a>] (‘ns’ if <i>P</i> ≥ .05; * if <i>P</i> < .05; ** if <i>P</i> < .01; *** if <i>P</i> < .001). A Wilcoxon signed-rank test was used for all comparisons. Bars without ‘ns’ or asterisks indicate parameters at which the fraction of hits was lower than the one obtained by manual expert operators.</p