Thesis
Reference
Influence of sleep-wake states on human memory and underlying neural plasticity: insights from EEG recordings and parasomnia
CONSTANTINESCU, Irina Oana
Abstract
L'objectif de cette thèse est d'intégrer plusieurs niveaux d'observation (comportement, réseaux neuronaux, potentiels neuraux locaux) de la plasticité du cerveau humain lie à l'apprentissage et a la formation de la mémoire. Nous avons évalué la réorganisation des représentations neuronales post-apprentissage au cours du sommeil et de l'éveil. Nous apportons la preuve, pour la première fois, d'une réactivation comportementale pendant le sommeil chez les humains, en étudiant des patients présentant des épisodes d'activité motrice pendant le sommeil. Nous avons aussi montré chez l'homme, qu'une stimulation faible et répétée de balancement, modifie l'architecture du sommeil et rends le sommeil plus stable. Les résultats de cette thèse de doctorat ouvre de nouvelles perspectives de recherches pluridisciplinaires sur le sommeil et la mémoire.
CONSTANTINESCU, Irina Oana. Influence of sleep-wake states on human memory and underlying neural plasticity: insights from EEG recordings and parasomnia. Thèse de doctorat : Univ. Genève et Lausanne, 2011, no. Neur. 60
URN : urn:nbn:ch:unige-161683
DOI : 10.13097/archive-ouverte/unige:16168
Available at:
http://archive-ouverte.unige.ch/unige:16168
Disclaimer: layout of this document may differ from the published version.
DOCTORAT EN NEUROSCIENCES des Universités de Genève
et de Lausanne
UNIVERSITÉ DE GENÈVE FACULTÉ DE MÉDECINE
Docteure Sophie Schwartz, directrice de thèse Professeure Margitta Seeck, co-directrice de thèse
TITRE DE LA THÈSE
INFLUENCE OF SLEEP-WAKE STATES ON HUMAN MEMORY AND UNDERLYING NEURAL PLASTICITY:
INSIGHTS FROM EEG RECORDINGS AND PARASOMNIA.
THÈSE Présentée à la Faculté de Médecine de l‟Université de Genève
pour obtenir le grade de Docteure en Neurosciences
par
Irina CONSTANTINESCU
de Roumanie
Thèse N° 60 Genève
Editeur ou imprimeur : Université de Genève
2011
To my family, my friends and my mentors
Eu nu strivesc corola de minuni a lumii şi nu ucid cu mintea tainele, ce le-ntâlnesc în calea mea în flori, în ochi, pe buze ori morminte.
I do not crush the aura of world's wonders and do not obliterate within my mind the unrevealed
crossed along my path, be it on flowers, eyes, lips, or graves.
*Lucian Blaga (1895 – 1961)*
* Romanian philosopher, poet, playwright, translator, journalist, university professor and diplomat. Impressive and multidimensional personality of the European culture, Blaga also approached the philosophical problematic of science.
The dream (1937) by Salvador Dali
Remerciements
Mes remerciements vont tout d‟abord a Sophie Schwartz, de m‟avoir accueillie il y a 5 ans dans son groupe en m‟offrant le privilège de découvrir l‟univers passionnant de la recherche sur le sommeil. Un grand merci pour l‟inépuisable enthousiasme, générateur des idées innovatrices, pour son support permanent et sa confiance en moi et en nos projets, pour la grande ouverture d‟esprit, pour la créativité, l‟humanisme, pour l‟écoute et les conseils précieux qui m‟ont aide a évolué pas seulement dans le plan scientifique, mais aussi personnel. Un grand merci pour tout, Sophie ! Et merci la petite Elsa pour son doux sourire.
Je tiens à remercier vivement Margitta Seeck, qui m‟a offert la possibilité d‟accueillir des précieuses données des patients épileptiques implantes dans l‟unité d‟évaluation prechirurgicale de l‟épilepsie, et qui a accepte d‟être co-conductrice de cette thèse.
Je remercie chaleureusement Isabelle Arnulf, qui dirige le laboratoire du sommeil, à l‟hôpital Pitié-Salpêtrière à Paris, qui m‟a offert l‟occasion de m‟impliquer dans un projet passionnant sur les patients parasomniaques. Merci pour l‟enthousiasme communicatif et pour son support. Merci aussi a Delphine Oudiette, pour les discussions très constructives sur le sommeil et la cognition, et pas seulement, mais également pour sa bonne humeur et sa promptitude.
Mes remerciements s‟adressent en particulier à Marzia De Lucia pour son aide précieuse sur les données intracrâniennes, sa rigueur scientifique et sa grande patience avec moi.
Je remercie également Laurence Bayer pour sa collaboration fructueuse sur le projet « hamac », son calme, son écoute, et sa confiance dans notre projet.
Merci a Virginie Sterpenich pour son écoute, sa désarmante perspicacité et ses conseils éclairés sur la dernière ligne droite de la thèse.
Merci a Gilles Pourtois, pour sa vivacité, son enthousiasme contagieux, et ses conseils avisés sur les données EEG.
Je tiens à remercier Michel Mühlethaler pour ses conseils et son soutien.
Un grand merci a Patrik Vuilleumier, pour son son accueil parmi les « Labnics », son support constant, ses conseils précieux a travers ces années.
Mes remerciements s‟adressent à Stephen Perrig pour son enthousiasme, et les échanges constructifs sur le sommeil pendant la durée de la thèse.
Je tiens également à remercier chaleureusement Philippe Peigneux et Dimitri Van De Ville d‟avoir accepte d‟être rapporteurs de cette thèse et suis heureuse de pouvoir soumettre ce travail a leur critique.
Je remercie également Pierre Maquet pour ses conseils avisés.
Mes remerciements vont aussi vers Laurent Spinelli et Denis Brunet pour leur aide précieux concernant les données EEG.
Je remercie vivement mes collègues, Karsten, Stéphanie, Amal, Camille, Karim, Hamdi, Wiebke, Agustina, Tonia, Julie pour leur support, leur gentillesse et la bonne humeur a travers ces années. Je n‟oublie pas l‟équipe du laboratoire du sommeil de Belle-Idée pour leur support et leur accueil.
Je tiens à remercier le Professeur Landis pour son grand support et pour ses encouragements a poursuivre les travaux de neurosciences.
Je remercie Martine Collart pour son soutien avisé.
Je remercie également le Professeur Lücking, Université de Freiburg, Allemagne, pour son soutien et sa confiance en moi à travers mes années d‟étude.
Merci a mes mentors de l‟Université de Iasi en Roumanie pour leur chaleureux support et l‟ouverture d‟esprit.
Un grand merci a mes amis qui ont su m‟écouter et me soutenir pendant ces années, qui ont été près de moi et ont eu confiance en moi.
Je tiens enfin à adresser toute ma reconnaissance et mon affection à ma chère famille et a Xavier pour leur énorme soutien inconditionné, leur attention, leur patience et leur confiance en mes rêves.
Resumé
L'objectif du présent travail est d'étudier l'influence des états de veille-sommeil sur la mémoire humaine et les mécanismes sous-jacents de la plasticité neuronale en utilisant des enregistrements electro-encephalographiques (EEG) et des modèles neurologiques comme la parasomnie.
Mieux comprendre comment les états du sommeil et de l'éveil interfèrent avec des processus de la mémoire pourrait également révéler des indices nouveaux et fondamentaux sur le fonctionnement du cerveau humain. Malgré un grand nombre d'études, il ya encore des questions ouvertes sur ce sujet, qui a motivé le présent travail. Nous avons observé des processus physiologiques et cognitifs liés à la consolidation des connaissances récemment acquises dans différents états de vigilance, en appliquant un schéma convergente des études expérimentales. L'objectif était d'intégrer plusieurs niveaux d‟observation (comportement, réseaux neuronaux, potentiels neuraux locaux) de la plasticité du cerveau humain. Nous avons utilisé le concept de réactivations neuronales liées à l‟expérience récemment acquise pour étudier la dynamique du cerveau sous-tendant la mémoire. Nous avons évalué la réorganisation des représentations neuronales au cours du sommeil post-formation et de l'éveil, qu‟elle soit produite spontanément ou induite par la présentation des stimuli externes, associes. Plus précisément, nous avons étudié la dynamique neuronale liées à l'expérience acquise chez des patients épileptiques pharmaco-résistants, pendant la procédure d'apprentissage des séquences de mouvements. Nous avons également étudié la réactivation induite et la réorganisation des traces mnésiques pendant l'éveil post-apprentissage en utilisant des enregistrements EEG du scalp, de haute densité chez l'homme. La réactivation au niveau du comportement des événements récemment acquis a été évaluée pendant le sommeil post-apprentissage chez des patients parasomniaques, présentant des épisodes d‟activité motrice pendant le sommeil. Nous apportons la preuve, pour la première fois à notre connaissance, d‟une réactivation comportementale pendant le sommeil chez l‟homme. Sur le plan méthodologique, la présente thèse combine de manière innovante les différentes techniques EEG
(enregistrements intracrâniennes, des enregistrements de haute densité au niveau du scalp, des enregistrements des rythmes de sommeil) qui fournissent une fenêtre unique, directe et précise, sur le fonctionnement cérébral de l'homme. En outre, nous avons donné à des techniques déjà existantes de traitement du signal de nouvelles applications; parmi les techniques appliquées, la présente thèse met en évidence la valeur unique de l'EEG intracrânien pour la cartographie du cerveau humain.
Une des façons les plus courantes et archaïques pour faciliter le sommeil est le balancement : nous nous endormons irrésistiblement dans une balançoire et, depuis des temps immémoriaux, le bercement aide l‟endormissement des bébés. Pourtant, aucune explication claire physiologique à cet effet n‟a pas encore été fournie. Dans notre étude, nous avons montré chez l'homme, en utilisant un lit-balançoire pendant les siestes l'après-midi, qu'une stimulation faible et répétée de balancement, modifie l'architecture du sommeil et a un effet bénéfique sur le sommeil en rendant le sommeil plus stable. Notre travail montre que le sommeil peut être amélioré d‟une manière instrumentale et motive le développement de nouveaux dispositifs pour aider à dormir.
Les résultats de cette thèse de doctorat contribuent à une meilleure compréhension des réorganisations de l'activité cérébrale liés à l'apprentissage et ouvre de nouvelles perspectives de recherches pluridisciplinaires sur le sommeil et la mémoire.
Abstract
The aim of the present work was to study the influence of sleep-wake states on human memory and underlying neural plasticity by using EEG recordings and neurological models as parasomnia.
Understanding how sleep and wakefulness states impact memory processes may reveal new and fundamental cues on human brain functioning. Despite a large number of studies, there are still opened questions on this topic, which motivated the present work.
We observed physiological and cognitive processes related to the consolidation of recently acquired knowledge across different vigilance states, by applying a convergent diagram of studies. The aim was to integrate multilevel (behavior, brain networks, local field potentials) views of plasticity in the human brain. We used the concept of experience-related neural reactivations to study brain dynamics subtending memory. We assessed re-shaping of neural representations during post-training sleep and wakefulness, either produced spontaneously or induced by presentation of learning-related external cues. More precisely, we studied experience-related neural dynamics in pharmaco-resistant epileptic patients while procedurally learning sequences of movements. We also studied induced reactivation and reorganization of neural traces during post-training wakefulness at scalp level by using high-density recordings in humans. Experience-related behavioral re-enactment during post- training sleep was assessed in parasomnia patients. We bring evidence, for the first time to our knowledge, of a learning-related behavioral replay in humans.
Methodologically, the present thesis combines in an innovative manner different EEG techniques (intracranial recordings, high-density scalp recordings, sleep rythms recordings) which provide a particularly direct and accurate window onto human brain function (Michel 2009). Furthermore, we conferred to already existent signal processing techniques new applications: in project 1, a multivariate decoding algorithm captured learning-dependent changes in intracranial EEG signal at the single trial level. Among the techniques applied, the present thesis highlights the unique value of the intracranial EEG for human brain mapping.
One of the most common and archaic way to facilitate sleep is rocking: we irresistibly fall asleep in a rocking-chair and, since immemorial times, we cradle our babies to sleep. Yet, no clear physiological explanation has been provided to this observed effect. In our study, we showed by using using a swinging-bed during afternoon naps that a low and repeated stimulation mimicking rocking modifies sleep architecture in humans and has a beneficial effect on sleep by rendering sleep more stable. Our work shows that sleep may be improved instrumentally and motivates the development of new devices to help sleep.
The present results may contribute at further understanding of learning-related reorganization of brain activity and opens new perspectives of multidisciplinary research.
INDEX
1. INTRODUCTION ... 8
2. LEARNING AND MEMORY SYSTEMS ... 9
2.1. Memory systems: theoretical and experimental approaches ... 10
2.2. Perceptual and motor skill learning ... 14
Perceptual learning ... 14
Motor learning ... 15
Attentional influences on learning ... 16
2.3. The temporal dynamics of perceptual and motor skill learning .. 17
3. ANATOMO-FUNCTIONAL CORRELATES OF MEMORY ... 19
3.1. Memory and brain plasticity ... 19
3.2. Brain systems for memory processes ... 21
Hippocampus and related structures ... 21
Implications of the hippocampus in memory functions ... 22
Amygdala and emotional memory ... 24
Diencephalon and declarative memory deficits ... 24
Motor areas, basal ganglia, cerebellum and memory for motor skills ... 24
Prefrontal cortex and memory consolidation ... 25
3.3. Distinct steps in memory consolidation ... 25
4. THE INFLUENCE OF SLEEP-WAKE STATES ON MEMORY PROCESSES ... 28
4.1. Sleep functions: a beneficial role for memory consolidation? .... 28
Aspects of sleep physiology... 28
Sleep functions ... 30
Napping in humans ... 31
4.2. Sleep and wakefulness: recent hypotheses on memory consolidation ... 32
4.2.1. Neural reactivations during sleep ... 32
Reactivations at the cellular and neuronal networks levels ... 32
Reactivations at the brain areas level ... 36
4.2.2. Neural reactivations during wakefulness... 38
4.3. Sleep homeostasis hypothesis ... 39
5. RATIONALE OF THE PRESENT WORK: MAIN QUESTION AND METHODOLOGICAL APPROACHES ... 40
Experimental part OVERVIEW ... 42
THESIS EXPERIMENTAL DESIGN ... 42
Experiment 1 Neurophysiological evidence from human intracranial recordingsof sequenced knowledge consolidation CONTEXT ... 43
METHODOLOGICAL HIGHLIGHTS ... 43
SUMMARY OF RESULTS ... 44
CONCLUSION ... 44
Article submitted to NeuroImage ... 45
ABSTRACT ... 46
1. INTRODUCTION ... 46
2. MATERIAL AND METHODS... 48
2.1. Patients ... 48
2.2. Behavioral task and experimental procedure ... 49
2.3. Intracranial EEG recording ... 50
2.4. Data analysis ... 51
2.4.1. Multivariate decoding approach to iEEG ... 51
Hidden Markov Model of single-trial iEEG ... 52
2.4.2. Accuracy estimation ... 54
2.4.3. Estimating contacts with higher classification power ... 55
2.4.4. Event-related potentials ... 55
3. RESULTS ... 55
3.1. Behavioral results ... 57
3.2. Results from the multivariate decoding approach to single-trial iEEG ... 57
3.2.1. Accuracy estimation ... 57
3.2.2. Estimating contacts with higher classification power ... 58
3.3.3. Relation between classification accuracy and spatio-temporal pattern of single-trial events ... 59
3.3. Intracranial event-related potentials ... 60
4. DISCUSSION ... 61
4.1. Learning-related changes in the stability of momentary neural states ... 62
4.2. Role of the hippocampus in sequence learning ... 63
4.3. Multivariate decoding approach to single-trial iEEG data ... 64
5. CONCLUSIONS ... 66
Acknowledgments ... 66
Reference list ... 66
Figures ... 71
Experiment 2 Evidence of overt replay of a recently learned motor sequence during human sleep CONTEXT ... 75
METHODOLOGICAL HIGHLIGHTS ... 75
SUMMARY OF RESULTS ... 75
CONCLUSION ... 75
Article submitted to PLoS Biology ... 76
ABSTRACT ... 76
INTRODUCTION ... 77
METHODS ... 78
Ethic Statement ... 78
Subjects ... 78
Behavioral task and experimental procedure ... 79
Sleep and nocturnal behavior monitoring ... 81
Statistical analyses of motor performance ... 81
Assessment of sequence replay during sleep ... 82
RESULTS ... 82
Sleep and cognitive performances ... 82
Dreams content after the post-training night of sleep ... 83
Behaviors exhibited during post-training sleep ... 83
RBD patients ... 83
Sleepwalkers ... 84
Evidence for behavioral replay during sleep ... 84
DISCUSSION... 85
Acknowledgements ... 87
References ... 87
Figures ... 89
SUPPLEMENTAL DATA ... 92
Questions to judges ... 92
Experiment 3 Experience-dependent induced reactivations during post-training wakefulness CONTEXT ... 94
METHODOLOGICAL HIGHLIGHTS ... 94
SUMMARY OF RESULTS ... 94
CONCLUSIONS ... 95
Article in preparation... 95
INTRODUCTION ... 95
EXPERIMENTAL PROCEDURE ... 96
Subjects ... 96
Stimuli and task ... 97
Behavioral task and experimental procedure ... 97
Electrophysiological data acquisition ... 100
Event-related potential analysis ... 101
Segmentation analyses ... 101
RESULTS ... 102
Behavioral results ... 102
ELECTROPHYSIOLOGICAL RESULTS ... 103
ERPs ... 103
Perception condition... 103
Mental imagery condition ... 104
Scalp topography analysis ... 105
DISCUSSION... 108
SUPPLEMENTARY DATA ... 109
Instructions ... 109
Eyes Opened ... 110
Eyes Closed ... 111
References ... 112
Experiment 4 Instrumental modulation of electrophysiological features of sleep CONTEXT ... 114
METHODOLOGICAL HIGHLIGHTS ... 114
SUMMARY OF RESULTS ... 114
CONCLUSION ... 115
Article submitted in Current Biology ... 116
SUPPLEMENTAL DATA ... 120
SUPPLEMENTAL EXPERIMENTAL PROCEDURES ... 120
Participants ... 120
Protocol ... 121
EEG data analyses ... 121
References and Notes ... 122
Supplemental References ... 123
DISCUSSION ... 124
MULTIMODAL APPROACH TO MEMORY CONSOLIDATION ... 124
NOVELTY OF THE TECHNIQUES ... 126
CONCLUSIONS AND PERSPECTIVES ... 127
BIBLIOGRAPHY ... 129
1. INTRODUCTION
The aim of this preamble is to provide a rationale for the present research work.
It offers an overview of the cognitive and neural processes underlying learning and memory, as well as the influence of sleep on these processes. This introduction therefore highlights a selection of key issues which support the theoretical questions and the methodological approaches of the thesis.
Neuroscience has provided lately some astonishing breakthroughs in our understanding of sleep and memory (Kandel 2009), from noninvasive large-scale imaging of the human brain during sleep to revealing the molecular machinery of complex processes like cerebral plasticity. This evolving knowledge about brain functioning together with the recent development of investigation techniques foster translations between distinct levels of description, such as the molecular/cellular level, the macroscopic systems level, and the behavioral level. The main motivation for the present work comes from these new possibilities to study the interaction between sleep and memory both at the neural and behavioral levels.
The introduction begins with a conceptual framework for learning and memory.
In the second part, some key aspects of memory consolidation and supporting plasticity mechanisms are introduced, with a special emphasis on interacting neural circuits. In the third part of the introduction, recent contributions on the role of sleep and wakefulness in memory formation are reviewed. Some aspects concerning local brain activity and regulatory processes during sleep are also detailed, as well as their possible functional links with memory and plasticity processes. To conclude the introduction, the research questions and hypotheses investigated in this thesis are briefly presented.
After the introduction, the experimental part reports in detail each of the four projects conducted in the context of this thesis: the hypotheses tested, the methodological approaches used, the main findings and their interpretation. A general discussion concludes the thesis by providing an integrated overview of all the results and opening some new perspectives for further research.
2. LEARNING AND MEMORY SYSTEMS
Learning is the process of acquiring new information, while memory refers to the persistence of learning in a state that can be revealed at a later time. Memory is the usual consequence of learning.
Squire, Memory and Brain, 1987, p.3
Almost every aspect of our thinking and behavior depends on the acquisition and retention of new knowledge: our sense of identity, how we perceive the world, how we react to various stimuli, perform simple or complex activities, such as eat or drive, organize our daily agenda. Thus, learning and memory play a fundamental role in cognitive functioning and behavioral adjustment.
Learning most often refers to the gradual acquisition of the information, while memory refers more to specific processes manipulating the established information, which may impact further behavior. Remembering – the direct effect of memory- enables one to compare the newly learned information with the already existent memory content and decide the relevance of the information for the system (thus maintaining the system‟s efficiency). Therefore, although memory holds information about the past, its main function is to allow predictions and adaptive responses to events yet to come (Schacter and Addis 2007; Schacter, Addis et al. 2008).
La mémoire du passé n'est pas faite pour se souvenir du passé, elle est faite pour prévenir le futur. La mémoire est un instrument de prédiction.
Alain Berthoz, www.automatesintelligents.com/interviews/2003/octobre/berthoz.html
Because many elements in the future are produced according to probabilistic laws, the machinery that encodes memories in the brain has to operate fast and recurrent updating of the stored information. This built-in capacity to anticipate change is reflected at multiple brain levels (Addis, Wong et al. 2007; Bar 2009). As stated by Bar et al (2009), memory-related brain dynamics actually reflect ongoing generation of predictions, which relies on acquired experience and aim at providing permanent and adequate adjustment to our interaction with the changing environment.
2.1. Memory systems:
theoretical and experimental approaches
The topic of memory is broad and accumulating. Much theoretical and experimental effort has been allocated in the last years in the attempt to clearly define and characterize the neural systems that are recruited during the acquisition of different tasks and subsequent memory processes (Squire 2004).
Theoretically, memory is not currently viewed and studied as a unitary process.
The concept of memory system was first promoted by Tulving et al. (1985), who defined it as “a set of correlated processes, more closely related to one another than they are to processes outside the system” (p.386).
More precisely, memory systems can be characterized according to the kind of processed information and to the contributing brain mechanisms (Schacter and Tulving, 1994). According to these aspects, early work on memory postulated two distinct systems. On one hand, a system that deals with explicitly acquired information, generically termed declarative memory. Declarative memory, usually defined as the memory for facts and events (“know what”), can be further divided into episodic memory, reflecting autobiographical events, and semantic memory, dealing with general knowledge about the world. The other classically accepted memory system operates with implicitly acquired information, in the absence of conscious awareness and is termed procedural memory (Eustache and Desgranges 2008).
Procedural memory (“know how”) is mainly based on the acquisition of perceptual and motor skills.
Another way to look at memory processes is by considering the time axis.
Therefore, R. Atkinson and R. Shiffrin proposed in the late 60ies a “multi-store”
model, which assumed that human memory is formed through three consequent stages: sensory memory, short-term memory and long-term memory (Atkinson 1968).
According to the authors, sensory memory corresponds approximately to the initial 200-500 milliseconds after a stimulus is perceived. Short-term memory refers to memory for information currently “held” in mind and has limited capacity. It allows recall for a period of several seconds to a minute without rehearsal, allowing the temporarily saving of a limited quantity of information. Long-term memory refers to information that is stored for an extended period of time. It has a potentially unlimited duration and capacity. In the present thesis we do not deal with short-term memory,
but with long-term memory aspects, which implies changes in widely distributed neural connections throughout the brain. Among all areas, the hippocampus seems particularly important for the transformation of information from short-term to long- term memory. In humans, studies in neurological patients with focal brain damage remain critical in the attempt to define which brain regions are contributing to certain memory processes. Furthermore, as lesion studies address disruption instead of engagement of memory systems, they can reveal structures that are indispensably required for a certain function. Maybe the first influential observation which turned into a landmark for the development of the neuroscience of memory systems, was the case of the patient H.M. (Squire 2009). In 1953, an experimental brain surgery intended to control severe seizure disorder, left patient H.M. unable to form new memories. A finger-sized piece of the temporal lobes on both sides of the brain, including most of the hippocampus, amygdala, and nearby parahippocampal gyrus, was removed. Patient H.M. could remember facts he had learned and namesof people he had met before the surgery, but virtually nothing after it. For half a century, the findings from testing the patient H.M. highlighted memory as a distinct cerebral function, accessible for evaluation, independently from other perceptual and cognitive capacities. Since the case of patient H.M., several theories suggested that memory involves several successive steps leading to stable memory. In particular, it was proposed that new memories formed by the hippocampus are later transferred in the cerebral cortex for long-term storage (Scoville 1957; Scoville 2000). A large body of studies deals with long-term memory in the attempt to understand the basis of information storage and use over time. Studies in amnestic patients showed that damage limited to the hippocampal structure results in selective deficits in recollection and relational memory.
Among the techniques used to approach cognitive and neurophysiological aspects of memory, EEG studies represent a prominent neuroimaging tool. Different effects related to memory processes, such as explicit/implicit, old/new, recognition/familiarity, have been addressed by analyzing modulations in the electrical activity of the brain, time-locked to an event (such as a stimulus to be encoded). Event-related brain potentials (ERPs) have been employed in memory research to identify neural activity associated with both encoding of information and the later retrieval of stored information. Different correlates between amplitudes and/or latencies of event-related
brain potentials and cognitive tasks are quantified depending on the memory paradigm applied (Voss 2008; Friedman 2000; Wieser 2003).
For example, it has been shown in terms of ERP activity, that implicit and explicit learning of event sequences yield different neural representations in the brain (Russeler 2000). In this EEG study, event-related brain potentials of 21 subjects were recorded while they performed a choice reaction time task. In this task, a repetitive sequence of eight successive stimuli (e.g. a sequence of letters) was presented on a computer screen and subjects had to react as quickly and accurately as possible when a stimulus appeared on the screen, by pressing a button, either with the index or middle finger, of either left or right hand, according to the stimulus position on the screen. There were therefore four finger press possibilities and eight “standard”
stimuli, which meant that the same finger coded for two stimuli. The regularity of the sequence was unknown to the subjects. Within the regular event sequence, a
“perceptual” or a “motor” deviant stimulus replaced sometimes an expected stimulus:
a “perceptual” deviant was a different letter from the expected one in the sequence, but which required the same motor response, while a “motor” deviant was a letter from those requiring a response with the opposite hand. Post-experimental free recall and recognition test, assigned subjects to either an explicit or implicit group, according to the either explicit or implicit knowledge of the sequence regularity. The ERPs showed different brain potential modulation for different types of stimuli (perceptual and motor deviants as compared to expected stimuli) between the two groups. In the group of explicit learners, a larger N200 component (negative component peaking at 200 ms post-stimulus) was evoked by both types of deviants (perceptual and motor) as compared to standard stimuli; a larger P300 (positive component peaking at 300 ms post-stimulus) was evoked by “motor” deviants only as compared to “perceptual” and “standard” stimuli. In the group of implicit learners, the N200 and P300 components remained unaffected. In both groups of subjects the lateralized readiness potential (LRP) which accompanied “motor” deviants revealed a different modulation as compared “standard” and “perceptual” stimuli. This meant that in both groups, the preparation for the next motor response dependent on the previous one, therefore on response-response association. These results suggest that while implicit learners acquire knowledge about response dependencies only, explicit learners acquire knowledge about both response and stimulus dependencies.
Moreover, the study of EEG microstates of temporal stability in the brain electrical activity opens promising perspectives concerning the further understanding of the memory architecture (Lehmann 2010; Pascual-Marqui 1995; Van de Ville 2010). Functional microstates (Michel et al. 1992; Lehman 1987) refer to time segments of stable potential map configuration supposed to reflect different steps of information processing. According to the microstate model, the brain activity can be seen as a sequence of non-overlapping microstates of variable duration and strength.
A growing number of studies evaluate functional microstates in the spontaneous EEG and their influence by specific mental conditions, such as memory impairment in Alzheimer disease. Dierks et al (1997) studied alterations of EEG microstates in Alzheimer disease patients compared to healthy controls. The main findings were shorter segment duration and increased number of segments in patients with memory impairment compared to healthy controls.
Another complementary neurophysiological approach to memory related dynamics is represented by the study of synchronous brain oscillations such as theta or gamma activity during subsequent stages of memory formation (Axmacher 2006; Kirk 2003). While EEG provides a high temporal resolution of recordings, it does not allow the unequivocal location of the neural generators responsible for the scalp-recorded potentials. One option is to integrate the ERP findings with the results of functional neuroimaging studies and thus to identify the intracerebral generators of the memory effects. Direct intracranial recordings in animals or humans (in the context of presurgical investigation), which provides a combination of precise spatial and temporal information (Guo 2005; Fell 2008). For example, one intracranial study on seven epileptic pharmaco-resistant patients (Seeck 1997), required precise discrimination between repeated and non-repeated faces. The patients failed to show explicit knowledge of previously seen faces, as measured by the accuracy of motor responses. However, all subjects showed a differential modulation of the intracranial potential to repeated versus non-repeated faces, thus suggesting implicit discrimination between the two types of stimuli. Some considerations need to be taken into account when testing pharmaco-resistant epileptic patients. Although the intracranial recordings performed in these patients provide unique opportunities to observe cognitive processes at a very high spatial and temporal resolution, the neural responses collected may be subject to considerable individual differences (Halgren
1978). Epileptic patients represent a heterogenous population and the presence, the severity and the localization of the epileptic disturbances may influence the interpretation of the cognitive testing; maybe the most contributive variable for the memory research in these patients is the extent to which the implanted tissue (most frequently the temporal lobe) is affected. Also, convulsivant medication may have significant effects on memory functions in these patients. The left and right-sided focus of epileptic discharges has also been related to the presence of more or less important verbal /non verbal memory deficits (Kapur 1994). The important advances in signal processing methods allow a more and more accurate distinction between pathological and physiological data from testing these patients.
2.2. Perceptual and motor skill learning
Skill learning represents “a more or less permanent change in behavior which occurs as a result of practice” (Kimble 1961, p.6). Experimentally, perceptual skill learning is defined as improvement in sensory discrimination after practice (Karni and Sagi 1991); motor skill learning is generally evaluated by improved efficiency and speed of task execution (Poldrack 2005; Doyon and Benali 2005).
Perceptual learning
Perceptual learning essentially implies detecting discriminatory issues between initially very similar stimuli (Mitchell 2009). The behavioral improvement is underlined by stable learning-related changes at the neural level, which persist after the active stimulation. A broad literature in the perceptual field deals with visual tasks and practice-induced improvement in visual performance (Ahissar and Hochstein 2004). This involves training-related increased expertise to detect and extract the task- relevant information from a mixture of input signals. A simple example is provided by the experimented radiologists who can readily identify abnormalities in highly complex images, whereas this task is rather impossible for an untrained person (Skrandies and Fahle 1994; Sasaki, Nanez et al. 2010). Experimentally, it has been shown that experience-dependent changes in neural patterns may happen at the earliest cortical stage of visual processing, i.e., in primary visual cortex (Karni and Sagi 1991; Schwartz, Maquet et al. 2002; Furmanski, Schluppeck et al. 2004; Li,
Piech et al. 2004; Pourtois, Rauss et al. 2008). This has been shown by using cell recordings in animals (monkeys) and brain imaging in humans. These data suggest that perceptual learning is highly localized as it essentially depends on selective local changes, specifically driven by the task performance (Seitz and Watanabe 2005).
However, these findings do not rule out the involvement of a broader spectrum of areas, as the strength of neural connections between low-level visual areas and higher integrative areas might also undergo task-specific modulations (Schwartz, Maquet et al. 2002; Ahissar and Hochstein 2004). This in agreement with general learning theories, which state that the interactions between bottom-up sensory inputs and top- down goal-directed influences are critical for consolidating memory traces and therefore lead to a better performance (Gilbert, Sigman et al. 2001). In the above studies dealing with visual tasks, it has been proposed that perceptual learning recruits a rather highly localized network.
Motor learning
Motor learning generally refers to the gradual process by which more or less complex motor behaviors come to be performed without effort through repeated practice and/or interactions with the environment (Willingham 1998). Motor skill may refer to either simple, repetitive movements (such as when playing the „„ball and cup”
(Milner, Fogel et al. 2006), or to more complex sequences of movements (such as when playing the piano) (Krings, Topper et al. 2000). The learning of sequences of movements and the underlying neural dynamics across vigilance states represents one of the research topics addressed in this thesis. Motor sequences can be explicitly known or not, but even in the latter case, the regularity of the motor sequence can still be learned, even though it remains implicit. The multitude of tasks used to experimentally investigate motor skill learning evaluate either the incremental acquisition of movements that subjects have to execute as quickly and accurately as possible such as finger tapping tasks (Walker 2005; Karni, Meyer et al. 1995; Maquet, Laureys et al. 2000; Maquet, Laureys et al. 2003; Hotermans, Peigneux et al. 2006), or the ability to compensate for environmental changes such as motor adaptation tasks consisting in adapting the motor reaction to externally manipulated conditions (Shadmehr and Holcomb 1997; Huber, Ghilardi et al. 2004).
At the brain level, different anatomical structures are involved in motor learning:
the motor cortex, the cerebellum and the basal ganglia seem to play a key role in skill learning, whether it is about sequential motor learning or about motor adaptation tasks. Depending on the stage of learning, the primary motor cortex, supplementary motor cortex, the cerebellum and the putamen are mainly activated at an early stage, while the supplementary motor area, precuneus and prefrontal cortex are rather active at a more advanced stage of learning.
The task demands (e.g. motor or more cognitive demands) (Doyon, Song et al.
2002; Doyon, Penhune et al. 2003) also modulate the anatomo-functional contributions, with the cortico-striatal pathways classically known to mediate learning of implicit motor behaviors (Doyon, Bellec et al. 2009), while the cortico-cerebellar loop is rather important for motor adaptation. Recent reports suggest that both implicit and explicit learning of a sequence of movements requires the contribution of the hippocampus and its associated medio-temporal limbic areas (Schendan, Searl et al.
2003; Albouy, Sterpenich et al. 2008). Therefore, this type of motor acquisition would require not only the cortico-striatal and cortico-cerebellar systems, but also hippocampal regions. It seems that these differently contributing brain areas interact to create new memories coding for the acquired behavior.
Attentional influences on learning
Attention contributes to the improved performance after perceptual learning by enhancing the selection of task-relevant features of the stimulus. It has been proposed that attention modulates early stages of perceptual processing in a task-relevant manner (Schwartz, Vuilleumier et al. 2005; Klemen, Buchel et al. 2009). It is equally accepted, in the visual domain at least, that attentional processes may be engaged in a top-down control of early stages of sensory information processing (Lamme and Roelfsema 2000). However, attention may not be mandatory for learning as demonstrated by recent studies showing performance improvements outside the focus of attention (task-irrelevant learning) (Watanabe, Nanez et al. 2001; Watanabe, Nanez et al. 2002; Seitz and Watanabe 2005).
2.3. The temporal dynamics of perceptual and motor skill learning
In any type of learning, be it perceptual or motor, the factor time represents a critical dimension, because during learning the brain learns new routines gradually, through practice and interactions with the environment. At the behavioral level, the gradual increase in accuracy and speed level does not only benefit from the intra- practice sessions, but also from the inter-practice periods, with no additional training (Karni and Sagi 1991; Karni, Meyer et al. 1995; Karni, Meyer et al. 1998; Doyon, Penhune et al. 2003; Press, Casement et al. 2005; Halsband and Lange 2006). At the brain level, the neural representations of the newly acquired skill, whether referring to perceptual or to motor behaviors, undergo time-dependent re-shaping, leading to better performance.
The time-course of skill learning can be divided into several distinct stages. A well-studied, „typical” example is that of a visuo-motor learning task, namely the serial reaction time task. Subjects are facing a computer screen where visual cues could appear successively at four different spatial locations, arranged horizontally.
Each of the four possible positions of the visual cue on the screen corresponds to one of four response buttons on a response pad. When a visual cue appears on one of the position, the subjects are instructed to react as quickly and accurately as possible by pressing the spatially corresponding response button. The succession of the visual cues appearance follows a sequential order, pre-defined or probabilistically established. Firstly, considerable improvement occurs within one session of intense training; this is an initial, “fast” stage, where the performance is under sensory (e.g.
visual) control. A second, intermediate, “slow” stage follows, where there is gradual learning of the sensori-motor (e.g. visual-keypress response) associations across repetitive training. A third “consolidation” stage following after a certain time interval without training (Ungerleider, Doyon et al. 2002) leads to further performance increases in the absence of additional training. A following, “automatic” stage is proposed, where the task is correctly executed with minimal effort (Doyon and Benali 2005; Halsband and Lange 2006). Thus, it takes time for long-lasting plastic brain changes necessary for consolidating the motor routine to occur.
The individual contribution of the brain regions involved in motor skill learning varies across different stages of the acquisition: the cerebellum seems more involved in the early stages of acquisition, while the basal ganglia is important for later stages of learning (Doyon and Benali 2005). Plasticity-related changes during motor skill learning occur at both intra- and inter-system levels. Besides, it has been shown, by using functional brain imaging, that motor representations may shift from the associative cortex to the striatum during the explicit learning of a motor sequence (Hikosaka, Nakamura et al. 2002). A transfer of activity from the cerebellar cortex to the dentate nucleus may accompany the implicit acquisition of a known sequence of movements (Jueptner, Frith et al. 1997).
Perceptual learning, much like motor skill learning, is accompanied by time- dependent changes across distinct cortical areas, over shorter or longer time scales.
Yotsumoto et al. (2008) trained subjects during few weeks on a visual task and have showed an increase in both execution performance and brain activity in early vision regions corresponding to the trained visual quadrant. Once performance reached an optimal level and remained constant, the brain activation in the corresponding areas decreased. These results suggest that distinct temporal phases characterize perceptual learning. Mukai et al. (2007) showed in participants who were trained on a visual learning task, that once a certain level of performance is reached, the activation in early visual areas as well as in higher-level regions associated with attentional processes decreased. It has been shown that stimulus specificities found in perceptual learning were maintained across time (Parkosadze, Otto et al. 2008). For instance improvement for vertical bisection stimuli (a central element bisecting an interval) did not transfer to horizontal bisection. So, the time-related improvement for this perceptual task is specific for the stimulus type. Similarly, it has been shown in the motor domain, by studying intermanual transfer of a visuo-motor sequence, that what is acquired is the specific regularity of the motor sequence and not a spatial pattern, and intermanual transfer of implicit learning was found only in a mirror image of the originally trained sequence (Wachs 1994).
3. ANATOMO-FUNCTIONAL CORRELATES OF MEMORY 3.1. Memory and brain plasticity
It is known that activity-dependent modulation of synaptic efficiency (e.g.
synaptic strengthening) and also structural changes (e.g. changes in cell dimensions and number and in axonal length) represent the neurobiological bases of memory formation. The activity-driven molecular and cellular changes support consequent remodeling of functional neural circuits at larger scales in the brain (Bruel-Jungerman, Davis et al. 2007). While neural plasticity is most evident during development, experience can reshape neural networks throughout all life span.
The plasticity mechanisms which contribute conjointly to the formation of long- term memory are represented by long-term potentiation (LTP, rapid and enduring synaptic strengthening), long-term depression (LTD, weakening of synaptic strength), synaptogenesis (growth of new synapses) and neurogenesis (formation and growth of new neurons) (Bruel-Jungerman, Davis et al. 2007).
One of the most extensively investigated forms of synaptic change related to learning and memory is the LTP. For example, it has been shown in rats that the synaptic strengthening (LTP) implying a modulation of amygdaloidal synaptic transmission is critical for fear conditioning (Shaban, Humeau et al. 2006). Further demonstration of the LTP as a necessary condition for the maintenance of memory is brought by studies where suppression of LTP after learning, disrupts a previously established memory trace (Pastalkova, Serrano et al. 2006).
The LTD mechanism based on the rule of “use it or lose it”, by weakening unused connections, may promote the consolidation of patterns of intensely used specific neural connections, which could contribute to the memory storage.
Synaptogenesis, i.e. the actual formation of new viable synapses, has been a subject of debate: does this process reflect a true synaptic growth or just morphological changes at the existent synaptic level? For instance, exposure to a “rich” environment or certain learning paradigms have been shown to determine an increase in synapses number (Markham and Greenough 2004). Several studies promote the idea that hippocampal neurogenesis may be triggered by learning of specific tasks and furthermore, that the formation of new neurons is related to the task difficulty and
hippocampal involvement in the learning process (Leuner, Mendolia-Loffredo et al.
2004). Moreover, it has been shown that a blockage of neurogenesis in the adult hippocampus specifically disrupts the eye-blink conditioning, which is a form of learning depending on the hippocampus (Shors, Miesegaes et al. 2001). Plasticity mechanisms engaged in learning and memory processes are also associated with rapid gene regulation in different brain areas, depending on the type of the informational input. For example, the expression of a class of immediate early genes (IEGs) seems particularly important for triggering transcriptional control mechanisms subserving the stabilization of different types of memory, especially fear related memories, olfactory and spatial memories (Davis, Bozon et al. 2003; Bailey, Wade et al. 2009; Loebrich and Nedivi 2009).
All in all, the plasticity mechanisms highlighted above contribute to memory related functional and structural changes across different brain areas. As an example, the primary visual cortex (V1) of the adult mammalian brain has provided one of the clearest evidence of the experience-dependent plasticity processes at cortical circuits level, which could be modeled according to a variety of manipulations, such as perceptual learning and visual deprivation (Schwartz, Maquet et al. 2002; Karmarkar and Dan 2006).
The concept of learning as “organized knowledge which grows and becomes better organized” (McDermott 1985) also suggests that learning mechanisms may operate at some relatively abstract levels, which would imply accumulating, organizing and re-organizing information, leading to improved processing efficiency and thus changes in behavior. At the brain level, learning can be viewed as
“experience-dependent lasting modification in neural representations” (Dudai 1989).
This definition highlights two key principles: first, the changes in brain activity are
“experience-dependent” (therefore, the changes are caused by experience) and secondly, this definition promotes the notion of “neural representation”, in the sense of an acquired “code” or “trace” of the learned knowledge (for example, newly encountered stimuli, or the forming of new associations between already learned stimuli). Furthermore, the formed neural representations are of dynamic nature in the sense of flexible processing rather than fixed encoding of information. Thus, learning and memory involve cognitive and neural reorganization at multiple levels of integration (which is a key notion for the experimental part of the thesis).
3.2. Brain systems for memory processes
In the following sections, I will briefly overview same anatomical and functional characteristics of different brain regions involved in memory, with a special emphasis on the hippocampus as a key contributor to memory formation. As shown above, there are distinct memory systems. Until recently, it was considered that these distinct memory systems also involve selective brain systems: procedural memory would involve cortical regions (motor or sensory cortices), the striatum, and the cerebellum among others (Grafton, Mazziotta et al. 1992; Doyon, Gaudreau et al. 1997;
Ungerleider, Doyon et al. 2002; Doyon, Penhune et al. 2003; Monchi, Petrides et al.
2006), whereas declarative memory would involve the medial temporal lobes, in particular the hippocampus (Squire and Zola-Morgan 1991; Squire 1992; Reber and Squire 1994; Squire and Zola 1996; Squire 2004; Squire, Stark et al. 2004). However, recent studies challenge the above schema. For instance, there is evidence for a hippocampus role in the learning of a sequence of movements (Albouy, Sterpenich et al. 2008) (Schendan, Searl et al. 2003), which is classically considered as a procedural task (see also below).
Hippocampus and related structures
Together with adjacent perirhinal, entorhinal and parahippocampal cortices, the hippocampus is often referred to as the medial temporal lobe (MTL) memory system (Squire and Zola-Morgan 1991). The organization of the MTL system involves bidirectional pathways between the cerebral cortex and the hippocampus, and these pathways are largely conserved across mammalian species (Manns and Eichenbaum 2006).
Neurons within the hippocampus form a network different from that found anywhere else in the nervous system (Andersen, Morris et al. 2007). Firstly, the relatively simplified two- or three-layered architecture, with its strict layering of synapses in the dendritic arborisation is a characteristic proper to the hippocampus.
Therefore, the hippocampal neuroanatomy sustains a unique internal network organization, taking into account the large unidirectional information flow through hippocampal circuits and the highly distributed organization of within hippocampus associative connections. This high degree of neuronal interconnectivity within the
hippocampus allows the consequent integration and comparison of the information (Amaral, Ishizuka et al. 1990). These anatomical properties may help to understand why the hippocampus is one of the only few brain regions that receive highly processed, multimodal information from different neocortical regions. Across species, the cortical association areas do not target directly the hippocampus, but instead connect to a sum of related areas within the parahippocampus region. Therefore, the flow of information from cerebral cortex is first directed to the parahippocampal region, whose outputs converge to the hippocampus; the outputs of hippocampal processing are directed back to the parahippocampus, which, in turn, send its outputs to the same original cortical areas: therefore, the cerebral cortex, the parahippocampal region and the hippocampus can be viewed as a hierarchy of connectivity (Lavenex and Amaral 2000; Eichenbaum and Lipton 2008).
Small neuronal ensembles in the hippocampus form local circuits which may generate spatiotemporal patterns of spiking activity, therefore converting relatively unstructured inputs into specific patterns of activation (Takahashi and Sakurai 2009). In other words, the local connectivity within the hippocampus may provide the mechanism by which any initial input can “organize” the response to any subsequent input.
Implications of the hippocampus in memory functions
It is known that the hippocampal structure plays a key role in the episodic memory formation (Alvarez and Squire 1994; Eichenbaum 2004; Ergorul and Eichenbaum 2004; Squire 2004), as part of the explicit declarative memory system.
However, recent neuroimagery results provide a new perspective upon hippocampus- related memory processes, by demonstrating the direct hippocampal involvement in the processing of non-declarative memory (Schendan, Searl et al. 2003; Albouy, Sterpenich et al. 2008). In a functional magnetic resonance study, Albouy and colleagues showed by training subjects on an implicit oculomotor sequence learning task and testing them at different time intervals, that both the hippocampus and striatum interact and are involved in the consolidation of motor sequence memory, thus bringing support for the hippocampal involvement in long-term formation of procedural memory. These results confirm the cell-level evidence for the hippocampal capacity to associate temporally fragmented, but structured information to form integrative, coherent memory representations (Burgess, Maguire et al. 2002).
Moreover, recent theoretical approaches suggest a potential role of hippocampal circuitry in representing sequences of events, based on its anatomical and functional capacity to mediate three prominent cognitive features: associative representation, sequential organization and relational networking (Wallenstein, Eichenbaum et al.
1998; Eichenbaum 2004). Rich experimental animal data supports this perspective (Agster, Fortin et al. 2002; Fortin, Agster et al. 2002; Lee and Wilson 2002; Ergorul and Eichenbaum 2004) and provides further evidence for firing patterns in the hippocampus time-locked to individual sequential events (Louie and Wilson 2001;
Foster and Wilson 2006). Navigation studies in rodents report specialized neural networks co-mapping visual and spatial features into flexible representations (Ekstrom, Kahana et al. 2003), thus confirming the hippocampus relevance for navigation, path integration and cognitive mapping (McNaughton, Battaglia et al.
2006). Furthermore, while initial studies showed the role of hippocampal neurons in signaling relative to the position of the animal in the environment (“place cells”), thus forming a spatial cognitive map of the environment (O'Keefe and Dostrovsky 1971), the idea also emerged that spatial information encoded in the “place cells” actually reflects a more general category of relational information, which depends on the hippocampus (Eichenbaum 2001). It has been therefore proposed that the hippocampus has the capacity to build and store “configural” associations between separate events. Studies on associative memory provided further insights concerning the hippocampus capacity to form flexible new associations between different stimuli irrespective of modality (Henke 2010). Chadwick et al (2010) presented three distinct short videoclips of every day life episodes to healthy human subjects and than asked them during an fMRI scanning session to recall as much details as possible of each episode a number of times. The authors have than directly examined neural computations within hippocampus related to the three episodic representations and were able to indentify from the pattern of fMRI hemodynamic response whioch specific episode of memory has been recalled. They therefore showed that neural traces of recently encoded episodic memories are detectable from the BOLD patterns of fMRI.
The role of hippocampus in relational memory is a central research question addressed in the personal part of the thesis.
Amygdala and emotional memory
Since the demonstration in monkeys by Mishkin et al. (1978) that conjoint lesions of amygdala and hippocampus resulted in greater impairments on declarative memory tests than did individual lesions of each structure, the contribution of amygdala to memory either as a proper storage area or as a modulator of mnemonic processes has become an important topic of research. Fear conditioning, a special form of procedural memory based on emotional learning and on Pavovlian conditioning, has been the initial paradigm use to study amygdala function in memory (LeDoux 2003). Recent long-term memory consolidation theories implying amygdalo- hippocampal connections, demonstrate that amygdala also plays an enhancing role in the encoding and long-term recall of emotional stimuli (McGaugh 2004; Sterpenich, Albouy et al. 2009).
Diencephalon and declarative memory deficits
Damage to diencephalic regions (e.g. the thalamus, the hypothalamus) is currently believed to impact memory not by actually disrupting memory processing per se, but rather by altering communication between the medial temporal lobe and other regions, responsible for memory processes. For instance, the mammillothalamic and the amygdalofugal tracts are two pathways through which structures of the medial temporal lobe are connected to memory structures. This explains the presence of amnesic elements in patients with Korsakoff's syndrome, who do not show significant damage of medial temporal lobe structures, but rather present damage to diencephalic structures, including mammillary bodies and the dorsomedial nucleus of the thalamus (Hampstead and Koffler 2009).
Motor areas, basal ganglia, cerebellum and memory for motor skills
There is extensive evidence for the involvement of motor brain regions such as supplementary motor area, primary motor and premotor cortices in the memory for motor behaviors (Doyon, Penhune et al. 2003). Moreover, it has also been suggested that areas previously acknowledged to subserve pure motor executive and control functions (e.g. the basal ganglia), are also mediating the memory formation of learned
sensory-motor associations (White 2009). The other aspects related to motor skill learning and contributing areas has already been tackled in previous sections, and will be further discussed in the main experimental part.
Prefrontal cortex and memory consolidation
The dorsolateral prefrontal cortex may play a critical role in memory consolidation processes, particularly for hippocampal-dependent spatial and contextual information (Frankland and Bontempi 2005). Because of its dense interactions with both MTL structures and the basal ganglia, the prefrontal cortex may support hippocampal-prefrontal interactions in memory processes. Namely, it may participate to the extraction of regularities based on internal representations, so as to improve behavioral control (Robertson 2007; Shima, Isoda et al. 2007). In line with this idea, the next section will detail memory consolidation models, based on reactivation of hippocampal-prefrontal neural patterns across different vigilance states to allow stabilization of the acquired knowledge and subsequent performance improvement (Peyrache, Khamassi et al. 2009).
3.3. Distinct steps in memory consolidation
The term “consolidation” was first associated with memory processes by the German researchers Müller and A. Pilzecker, in their work, Experimentelle Beiträge zur Lehre vom Gedächtnis [Experimental contributions to the science of memory].
Zeitschrift für Psychologie Ergänzungsband, 1, 1–300 (1900). The authors noticed that learning new stimuli immediately after training on series of non-sense syllables interfered with the recall of the items learned first. They postulated therefore that it takes time for the memory trace to become stable and resistant to interferences.
Memory consolidation defines a continuum of experience-driven processes, both at synaptic and systemic level, that render an initially fragile memory trace resistant to interferences through stabilization. In other words, it operates a spatial and temporal reorganization of recently acquired information into a stable representation (Dudai 2004; Buzsaki and Chrobak 2005). At the synaptic level, memory consolidation involves a cascade of molecular events, implying the restructuring of existing synapses (Dudai 2004) and the formation of new connections (Malenka and Nicoll
