Mechanisms of memory consolidation : Analyzing the coordinated activity of concept neurons in the human medial temporal lobe during waking and sleep

The aim of this thesis is to investigate the role of human concept neurons in memory consolidation during sleep. Memory consolidation is a process by which memories initially dependent on the hippocampus are transferred to cortical areas, thereby gradually becoming independent of the hippocampus. Theories of memory consolidation posit that memory traces encoding autobiographic episodes are rapidly formed in the hippocampus during waking, and reactivated during subsequent slow-wave sleep to be transformed into a long-lasting form. Concept neurons in the human medial temporal lobe are neurons tuned to semantic concepts in a selective, sparse, and invariant manner. These neurons respond to pictures or written and spoken words representing their preferred concept (for example, a person, an animal, an object), regardless of physical stimulus properties. Concept neurons have been speculated to be building blocks for episodic memory. We used whole-night recordings from concept neurons in the medial temporal lobe of epilepsy patients implanted with depth electrodes for presurgical monitoring to test the hypothesis that the coordinated activity of concept neurons during sleep is a neurophysiological correlate of memory consolidation in humans. To conduct this study, we developed software methods for artifact removal and spike sorting of long-term recordings from single neurons. In an evaluation on both simulated model data and visual stimulus presentation experiments, our software outperformed previous methods. Starting from the conceptual analogy between rodent place cells and human concept neurons, we developed an episodic memory task in which participants learned a story eliciting sequential activity in concept neurons. We found that concept neurons preserved their semantic tuning across whole-night recordings. Hippocampal concept neurons had, on average, lower firing rates during rapid-eye-movement (REM) sleep than during waking. During slow-wave sleep, firing rates did not significantly differ from waking. The activity of concept neurons increased during ripples in the local field potential. Furthermore, concept neurons whose preferred stimuli participated in the memorized story were conjointly reactivated after learning, most pronouncedly during slow-wave sleep. Cross-correlations of concept neurons were most asymmetric during slow-wave sleep. Cross-correlation peak times were often in the range believed to be relevant for spike-timing-dependent plasticity. However, time lags of peak cross-correlations did not correlate with the positional order of stimuli in the memorized story. Our findings support the hypothesis that concept neurons rapidly encode a memory trace during learning, and that the reactivation of the same neurons during subsequent slow-wave sleep and ripples contributes to the consolidation of the memory episode. However, the consolidation of the temporal order of events in humans appears to differ from what rodent research suggests.Mechanismen der Gedächtniskonsolidierung : Analyse der Aktivität von Konzeptzellen im menschlichen Schläfenlappen während Wachheit und Schlaf In dieser Arbeit wird die Rolle von Konzeptzellen ("concept neurons") im Gehirn des Menschen bei der Gedächtniskonsolidierung im Schlaf untersucht. Gedächtniskonsolidierung ist ein Prozess, durch den Gedächtnisinhalte, die zunächst vom Hippokampus abhängen, in die Großhirnrinde übertragen werden. Dadurch reduziert sich im Laufe der Zeit die Abhängigkeit der Gedächtnisinhalte vom Hippokampus. In der Theorie der Gedächtniskonsolidierung wird angenommen, dass während wachem Erleben sehr schnell Gedächtnisspuren im Hippokampus entstehen, welche im darauffolgenden Tiefschlaf reaktiviert werden, um so eine langfristig stabile Gedächtnisspur zu erzeugen. Konzeptzellen im Schläfenlappen des Menschen sind Nervenzellen, die auf den semantischen Inhalt eines Stimulus selektiv und semantisch invariant reagieren. Konzeptzellen antworten auf Abbildungen ihres präferierten Konzepts (zum Beispiel einer Person, eines Tieres oder eines Objekts) oder auf geschriebene und gesprochene Wörter, die das gleiche Konzept darstellen, unabhängig von den speziellen Eigenschaften des Stimulus, wie zum Beispiel Bildgröße oder -farbe. Auf jedes Konzept reagiert dabei nur ein sehr kleiner Teil dieser Zellen. Man vermutet, dass Konzeptzellen Bausteine des episodischen Gedächtnisses sind. Die vorliegende Studie nutzt Aufzeichnungen der Aktivität einzelner Konzeptzellen während ganzer Nächte, um zu untersuchen, inwiefern die koordinierte Aktivität von Konzeptzellen im Schlaf ein neurophysiologisches Korrelat der Gedächtniskonsolidierung darstellt. Die Teilnehmer der Studie waren Epilepsiepatienten, in deren mediale Schläfenlappen aus klinischen Gründen Tiefenelektroden zur Anfallsaufzeichnung implantiert worden waren. Zur Analyse der Daten wurde zunächst eine Software entwickelt, die eine Artefaktbereinigung und das Spike-Sorting von neuronalen Langzeitaufzeichnungen leistet. Diese Software zeigte deutliche Vorteile gegenüber vorhandenen Methoden, und zwar sowohl in Tests mit simulierten Modelldatensätzen als auch im Falle tatsächlicher Aufzeichnungen (hier Experimente, in denen visuelle Stimuli auf einem Laptop dargestellt wurden). Ausgehend von einer Analogie zwischen Ortszellen ("place cells") bei Nagetieren und Konzeptzellen bei Menschen wurde ein Experiment entwickelt, das episodisches Gedächtnis operationalisierte. Darin lernten die Teilnehmer eine kurze Geschichte auswendig, was sequentielle Aktivität von Konzeptzellen auslöste. Konzeptzellen zeigten ein stabiles Antwortverhalten: am Abend und nächsten Morgen antworteten sie auf die gleichen Stimuli. Konzeptzellen im Hippokampus hatten im Mittel im Rapid-Eye-Movement-Schlaf (REM-Schlaf) niedrigere Feuerraten als während Wachheit. Im Tiefschlaf unterschieden sich die Feuerraten nicht signifikant von Wachheit. Die Aktivität der Konzeptzellen war während "ripples" im lokalen Feldpotential erhöht, und Konzeptzellen, deren präferierte Stimuli in der erinnerten Geschichte auftauchten, feuerten im darauffolgenden Schlaf gemeinsam, ein Effekt, der im Tiefschlaf besonders ausgeprägt war. Die Kreuzkorrelationen von Konzeptzellen waren im Tiefschlaf asymmetrischer als während Wachheit und REM-Schlaf, und die typischen Zeitabstände des Feuerns von Konzeptzellen lagen in einem Bereich, der als relevant für "spike-timing-dependent plasticity" gilt. Die Zeitabstände waren jedoch nicht mit dem Abstand der präferierten Stimuli in der erinnerten Geschichte korreliert. Diese Befunde stützen die Theorie, dass die Aktivität von Konzeptzellen während des Lernens instantan eine Gedächtnisspur erzeugt, und dass die Reaktivierung der gleichen Nervenzellen im Tiefschlaf nach dem Lernen zur Konsolidierung der Gedächtnisinhalte beiträgt. Die zeitliche Reihenfolge von Ereignissen wird offenbar im menschlichen Gehirn nicht auf die Weise konsolidiert, die sich aus der Forschung an Nagetieren nahelegte

Recollection in the human hippocampal-entorhinal cell circuitry

The hippocampus is involved both in episodic memory recall and scene processing. Here, the authors show that hippocampal neurons first process scene cues before coordinating memory-guided pattern completion in adjacent entorhinal cortex

Neurons in the human amygdala encode face identity, but not gaze direction

The amygdala is important for face processing, and direction of eye gaze is one of the most socially salient facial signals. Recording from over 200 neurons in the amygdala of neurosurgical patients, we found robust encoding of the identity of neutral-expression faces, but not of their direction of gaze. Processing of gaze direction may rely on a predominantly cortical network rather than the amygdala

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

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

Our algorithm applied to recordings from the human medial temporal lobe (MTL).

<p><b>A</b> 15 seconds of bandpass filtered data (passband 300 Hz to 1000 Hz) from a micro-electrode in the right anterior hippocampus. Extraction threshold is marked in red. Several artifact events are clearly visible. <b>B</b> This recording channel is extremely noisy: Our pre-sorting artifact detection removed ≈ 77% of all spikes from the recorded data. The pink lines depict the cumulative count of events over the course of the recording (28 minutes). <b>C</b> Cluster sizes at different temperatures. Left panel, input to the first clustering step were all non-artifact spikes. Right panel, input to the second clustering step were residual spikes not assigned to any cluster in the first clustering step. Color code of marked dots as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166598#pone.0166598.g003" target="_blank">Fig 3</a>. <b>D</b> Output of our sorting algorithm. Post-sorting artifact detection correctly identified several artifact clusters, but missed one (number 4). Six non-artifact clusters remain. <b>E</b> Result that expert operators generated manually with WaveClus. Two clusters were identified, ≈ 17000 spikes were left unassigned. <b>F</b> Results of the picture presentation experiment. Displayed are raster plots corresponding to Clusters 1 and 2 from D, and to Cluster 1 from E. Responses to four different pictures are shown. It is clearly visible that Cluster D 2 responds sharply to pictures of four male celebrities, while the responses of Cluster E 1 to the second, third and fourth picture are barely recognizable. No other cluster from D responded to any stimulus. S, score of the response; R, rating given to the response by human raters (see main text for details). Stimulus pictures displayed here have been replaced by similar pictures for legal and privacy reasons. Copyright notes: <b>F</b>.1 Photographer: Armin Kübelbeck, CC BY-SA 3.0, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:Campino_02.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:Campino_02.jpg</a>) <b>F</b>.2 “Mel Gibson at the Cannes film festival” by Georges Biard is licensed under CC BY-SA 3.0, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:Mel_Gibson_2011_cropped.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:Mel_Gibson_2011_cropped.jpg</a>) <b>F</b>.3 cropped from “German actor Jan Josef Liefers at the Cinema for Peace gala” by Thore Siebrands, licensed under CC BY 3.0, ipernity (<a href="http://www.ipernity.com/doc/siebbi/10852332" target="_blank">http://www.ipernity.com/doc/siebbi/10852332</a>) <b>F</b>.4 cropped from “Letztes Training von Olli Kahn beim FC Bayern München” by Dirk Vorderstraße, licensed under CC BY 2.0, Flickr (<a href="https://www.flickr.com/photos/dirkvorderstrasse/10560731386" target="_blank">https://www.flickr.com/photos/dirkvorderstrasse/10560731386</a>).</p

Graphical user interface.

<p><b>A</b> Screenshot of the graphical user interface (GUI) used for channel selection. Raw and filtered data traces of all channels in a recording session are displayed along with spike sorting results from every channel for which sorting has already been performed. <b>B</b> Screenshot of the GUI used for visualization and manual optimization of spike sorting results. The interface shows several informative statistics for one unit. The individual elements are explained in panels C through I. <b>C</b> Density plot of all spike waveforms within a cluster group. <b>D</b> Same as C, but using a logarithmic scale. <b>E</b> Overlay plot of all mean spike waveforms in a group of merged clusters. <b>F</b> Histogram of inter-spike intervals. <b>G</b> Cumulative spike counts over time (700 minutes in this example). Note that the unit in this example appears to become more active after the first 200 minutes of recording. Detailed inspection of the other cluster groups is necessary to decide whether this is really the case or merely an effect of over-clustering and false re-grouping. <b>H</b> Distribution of spike maxima. The three vertical pink lines indicate the minimum, median, and maximum of the detection thresholds over time. Note that in this example, spike maxima are clearly separated from the detection threshold. <b>I</b> Spike amplitude maxima over time. The pink line is the extraction threshold. Note that the extraction threshold is relatively stable, while the maxima show considerable drift.</p

Evaluation of our algorithm on a visual stimulus presentation experiment.

<p><b>A</b> Distribution of response scores. Some scores are extremely small, but the majority of scores lies in the interval [10⁻⁴, 10⁻³] (1939 out of 2672; 72.6%) <b>B</b> Each response’s rating is defined as the sum of the binary votes of five human raters. Of all 2672 ratings, 1596 (59.7%) were < 3, and 1076 (40.3%) were ≥ 3. <b>C</b> Mean score and standard deviation of responses at each rating. The relationship between mean scores and ratings is strictly monotonic, but the variance of scores at each rating is large. <b>D</b> Histogram of the numbers of clusters that were generated, on the same recordings, by Combinato and WaveClus. On average, Combinato generated more clusters. <b>E</b> Analysis of possible over-clustering. Displayed is the number of stimuli for which a response was detected in more than one cluster of the same recording channel. <b>F</b> Total numbers of detected responses. The numbers were corrected for possible over-clustering: only one response was counted per stimulus and channel, even if the response was detected in multiple clusters. Of all responses, 620 were detected both by Combinato and WaveClus. An additional 289 responses were detected only by Combinato, and further 158 responses only by WaveClus. The opaque parts of the bars correspond to responses that were rated 3 or better by expert raters. <b>G</b> Distribution of recording channels and responses across regions. Opaque parts of the bars as in F.</p

Performance of our algorithm on simulated data.

<p><b>A</b> Cluster sizes at different temperatures for one of 95 simulations (simulation_5). Each marked location corresponds to an automatically selected cluster; up to <i>C</i>_max = 7 clusters are selected at each temperature. Left panel, input to this clustering step were all spikes in one simulated channel. Right panel, input to this clustering step were all spikes not assigned to any cluster during the first clustering step. <b>B</b> Performance of our algorithm on all simulated datasets. Each simulated dataset contained action potentials from 2 to 20 neurons. For each simulation, we calculated the number of <i>hits</i>: a unit <i>U</i> generated by our spike sorting method was considered a hit if at least 50% of the spikes in <i>U</i> belonged to one neuron and at least 50% of the spikes of that neuron were in <i>U</i>. Displayed is the number of hits as a function of the number of neurons in the simulations (error bars denote s.e.m.). Note that our algorithm is capable of detecting more than eight neurons, a typical maximum for manual operation of WaveClus [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166598#pone.0166598.ref008" target="_blank">8</a>]. <b>C</b> All automatically generated clusters from simulation_5. Shown are spike counts and the percentage of spikes in the detected unit that actually belonged to the corresponding neuron in the simulation. Eleven clusters were hits, two clusters were no hits. Note that cluster C11 was perfectly detected despite its low firing rate of 0.12 Hz. <b>D</b> Undoing an automatic merge in cluster C1 with our graphical interface generated another hit.</p

Responses detected by only one spike sorting algorithm.

<p>Displayed are five different visual stimuli, and corresponding neuronal responses. Each row (<b>A</b>–<b>E</b>) shows the visual stimulus presented and two raster plots. The raster plots on the left correspond to a unit in the Combinato sorting, and the raster plots on the right correspond to a unit in the WaveClus sorting, on the same channel. Differences in spike sorting become apparent. <b>A</b> Combinato generated a sparse unit that enabled detection of the neuronal response. The unit generated by manual operators of WaveClus was not detected as a response. <b>B</b>, <b>C</b> Tiny differences in the units’ composition led to a large difference in the numeric response score. <b>D</b> The unit generated by Combinato violates the requirement that one spike has to be fired during at least four picture presentations. <b>E</b> Differences in unit composition led to a large difference in the numeric response score. S, numeric score of the response; R, rating given to the response by human raters. Stimulus pictures displayed here have been replaced by similar pictures for legal and privacy reasons. Copyright notes: <b>A</b> Superbass, CC BY-SA 3.0, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:Tatort_Keppler_Saalfeld.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:Tatort_Keppler_Saalfeld.jpg</a>)<b>B</b> “Violet” by J. Niediek is licensed under CC BY 4.0 <b>C</b> cropped from “Pakistani journalist Hamid Mir interviewing Osama bin Laden” by Hamid Mir, CC BY-SA 3.0, Wikimedia Commons (<a href="https://commons.wikimedia.org/wiki/File:Hamid_Mir_interviewing_Osama_bin_Laden.jpg" target="_blank">https://commons.wikimedia.org/wiki/File:Hamid_Mir_interviewing_Osama_bin_Laden.jpg</a>) <b>D</b> “Cathedral” by J. Niediek is licensed under CC BY 4.0 <b>E</b> “Photo Wall” by J. Niediek is licensed under CC BY 4.0.</p