ABSTRACT
Episodic memory - the ability to recall specific past events within the context of space and time - is an integral part our daily lives and perhaps the most important computation attributed to the hippocampal system. While the spatial dimension of episodic memory has been widely researched many questions remain unanswered regarding its temporal aspect. Several studies have shown that hippocampus (HIPP) is able to encode the passage of time, retain information about sequences and organize memories in a temporal structure, playing a particularly important role in the temporal dimension of memory. However, the origin of timing signals in HIPP remains unclear and it is unknown whether the temporal firing patterns critically rely on upstream cortical input. The medial entorhinal cortex (MEC) is a major input and output structure of the HIPP and is commonly associated with spatial representation. However, recent studies have shown that entorhinal inputs to the HIPP are essential for bridging temporal gaps between associated events. Furthermore, there is recent evidence showing that time is also encoded in the firing rate of MEC’s principal cells. In this project, we have used an experimental paradigm that allowed us to test the processing of time independent of other behavioral variables, and in a goal-directed manner. The task, developed in the lab, is called Waiting to Trajectory task (WtT), and in it animals must use their ability to judge the duration of a temporal interval in order to get a reward. In order to test the involvement of MEC in the representation of temporal information, we used DREADDs, a viral-delivered modified muscarinic receptor responding to CNO, to silence MEC during WtT. Our results show that inactivating MEC results in decreased performance due to shorter waiting times. In the correct trials under CNO we observed longer waiting times. These results point to a critical function of MEC-HIPP circuitry in temporal coding accuracy. The results reported here informed present and future experimental plans running in the lab, aiming to dissect the mechanisms responsible for encoding the temporal dimension of episodic memory.
RESUMO
A memória episódica é descrita como sendo a capacidade de recordarmos eventos passados, mais especificamente eventos autobiográficos, que ocorreram num determinado local e momento. Esta é considerada parte integrante das nossas vidas diárias, uma vez que associa numa única memória todos os contextos de um episódio (“o quê”, “onde” e “quando”), e é talvez a computação mais importante atribuída ao sistema hipocampal. Contudo, relembrarmos um episódio no tempo e num determinado espaço, requer a existência de mecanismos responsáveis pela codificação dos contextos temporal e espacial da memória. A dimensão espacial da memória episódica tem sido amplamente explorada e investigada, no entanto, relativamente à dimensão temporal, muitas questões continuam ainda sem resposta.
O hipocampo (HIPP) é descrito como o componente central do sistema de memória, desempenhando papeis cruciais na consolidação da informação de memórias a curto-prazo para memórias a longo-prazo e, também, na memória espacial que permite e auxilia a navegação espacial. No entanto, sendo esta uma região amplamente estudada e investigada, vários estudos têm apontado para o HIPP como uma zona que pode desempenhar um papel crucial na codificação de informações temporais, mostrando que esta zona é capaz de codificar a passagem do tempo, reter informações sobre sequências e organizar memórias numa estrutura temporal. No entanto, a origem da representação temporal do HIPP permanece desconhecida, tal como a dependência dos padrões de disparo temporais nos seus inputs corticais. O córtex entorrinal medial (MEC), além de ser a estrutura mais precocemente afetada em patologias da memória, como a Doença de Alzheimer, é considerada umas das mais importantes estruturas de input e output do HIPP. Pelo que, é provável que certas características funcionais sejam partilhadas pelas duas estruturas. O MEC é regularmente associado à codificação de variáveis espaciais e, portanto, naturalmente pensa-se que o circuito HIPP-MEC é responsável pela navegação espacial e representação de informações espaciais. No entanto, recentemente a função desempenhada por esta região tem sido alargada e vários estudos têm demonstrado que também está associada à dimensão temporal da memória, podendo assim, desempenhar um papel importante na codificação de informação temporal. Estudos reportam que os inputs provenientes das camadas II e III do córtex entorrinal são essenciais para colmatar lacunas temporais entre eventos associados. Para além disso, existem evidencias recentes que estabelecem o tempo como um aspeto predominante nas taxas de disparo dos neurónios principais do MEC.
O principal objetivo deste projeto, consistiu em testar a ação desempenhada pelo MEC na codificação de informação temporal, mais especificamente na produção e estimação da duração de intervalos de tempo. Para isso, foi utilizado um paradigma experimental que permitiu avaliar a dependência no MEC para o desempenho de uma tarefa comportamental centrada no tempo, utilizando ratos como modelo animal. Nesta tarefa, denominada de Waiting to Trajectory task (WtT) e desenvolvida no MRemondes Lab, era requerido aos animais esperar um lapso de tempo definido para, de seguida, obterem recompensa. Os animais eram recompensados sempre que esperavam no interior da região o tempo correspondente à duração do intervalo ou durante um intervalo de tempo de duração superior. Inicialmente, foram utlizadas pistas sonoras e luminosas que marcavam o início do intervalo de tempo e demarcavam a duração do mesmo, de maneira a tornar a aprendizagem dos animais mais rápida e simples. Nas sessões comportamentais de teste, a marcação do início do intervalo de tempo foi realizada, no entanto, a duração do mesmo não foi demarcada. Isto serviu para garantir que os animais julgavam ativamente a passagem do tempo e avaliavam a duração do intervalo de tempo, sem qualquer tipo de pista que pudesse sugerir o final do intervalo. O intervalo de espera foi, inicialmente, definido para 1000 ms, e foi sendo aumentado incrementalmente, até uma duração máxima de 2500 ms, de acordo com os níveis de desempenho dos animais em cada sessão de treino.
De maneira a testar a envolvência do MEC na representação de informações temporais, mais especificamente da perceção da passagem do tempo, manipulamos a atividade neuronal desta região utilizando uma abordagem genética designada por DREADDs (do inglês – Designer Receptors Exclusively Activated by Designer Drugs). Os DREADDs pertencem a uma classe de proteínas geneticamente modificadas que permitem o controlo espacial e temporal in vivo da sinalização da proteína G. Para este controlo são utilizados recetores muscarínicos modificados acoplados à proteína G que respondem exclusivamente a ligandos sintéticos, e não ao seus ligandos naturais. Neste projeto, foi utilizado um DREADD (hD4Mi) inibitório que permitiu a inativação da atividade neuronal do MEC. A ativação dos DREADDs foi realizada através da administração do ligando sintético, clozapina – N – óxido (CNO). A inibição da atividade neuronal do MEC foi combinada com o desempenho da tarefa comportamental. Durante 10 dias consecutivos, foram administradas intercaladamente injeções intraperitoneais de CNO e solução salina antes no início das sessões comportamentais. Os níveis de desempenho nas sessões de CNO e de solução salina foram comparadas.
Os nossos resultados mostram que, a administração de CNO e consequente inibição da atividade neuronal do MEC resulta numa diminuição dos níveis de desempenho da tarefa comportamental. Para além disso, uma análise detalhada e aprofundada dos tempos de espera em cada sessão revelou que, sob o feito de CNO, é visível uma tendência clara por parte dos animais para subestimarem a duração do intervalo de tempo e, assim, realizarem tempos de espera mais curtos e menores que o tempo target (200ms). Esta tendência traduziu-se numa perda de performance ao realizar a tarefa comportamental. Foi também observado um efeito de perda de precisão no julgamento da passagem de tempo e avaliação do intervalo de tempo. Isto é, para além da subestimação da duração do intervalo de tempo já verificada, foi também demonstrado que, após a administração de CNO, os trials corretos eram definidos por tempos de espera mais longos comparativamente com os trials corretos nas sessões de solução salina. Estes resultados demonstram que a administração de CNO conduz a um afastamento do tempo target aquando do julgamento da duração do intervalo de tempo, que se traduz numa sob e sobrestimação do mesmo. Juntos, estes resultados revelam uma dependência direta no MEC para a produção e avaliação da duração de intervalos de tempo e, também que esta região desempenha um papel crucial na codificação de informação temporal. A expressão do recetor hM4Di em regiões significativas do MEC e do HIPP foi verificada post mortem.
Na tentativa de validar os resultados comportamentais reportados, foi implantada uma hyperdrive num dos animais injetados com o construtor viral, com o objetivo de comparar a atividade neuronal do MEC sob o efeito de CNO e de solução salina, e assim verificar o silenciamento do circuito EC-HIPP. Os nossos resultados demonstraram uma diminuição robusta, mas incompleta da atividade neuronal do MEC após a administração de CNO. Este efeito estendeu-se também ao HIPP, que demonstrou uma diminuição mais superficial da sua atividade neuronal. Estes resultados serviram como confirmação de que os DREADDs são uma ferramenta viável para manipular a atividade neuronal do circuito MEC – HIPP e como validação dos nossos resultados comportamentais.
Neste projeto procurou-se contribuir para o esclarecimento da funcionalidade do MEC e da sua influência na codificação de informação temporal no HIPP. Os resultados preliminares revelados nesta dissertação informam e servem como base e ponto de partida para futuras experiências que tenham como objetivo principal, identificar e esclarecer os mecanismos responsáveis por codificar a dimensão temporal da memória.
ACKNOWLEGMENTS
I would like to start by thanking Dr. Miguel Remondes for the opportunity of being part of his lab during these last months. Thank you for the willingness you always had, and for sharing your knowledge, making the course of this project a more enriching path.
To my internal supervisor Dr. Alexandre Andrade, thank you for the help and support during the last year, specially throughout my thesis project.
For all the members of the MRemondes Lab, particularly, Bárbara, Sofia, Gonçalo, Ana Raquel, Marcelo and Inês, for all the support and help, and for making me feel at home. A special thanks to Marcelo, for assisting me whenever I needed help. I have learned so much with you!
To my friends, Inês, Beatriz, Mónica, Filipa and Daniela, a huge thanks. You were responsible for keeping me sane during this process. Thank you so much for your support and for being by my side for so many years.
Without my family none of this would have been possible. To my parents, thank you so much for the huge support and for always believing me and in my dreams. Thank you for letting me fly and run after my dreams and ambitions. All that I am, I owe to you. To my godfather, thank you for listening and for the support, even when you didn't know or understand what I was doing. To my grandparents, thank you for helping me in all ways and forms. This work is dedicated to all of you.
And finally, to Cristofe, your support and presence during the past year was vital for the success of this work. Thank you for everything and for being by my side.
TABLE OF CONTENTS
1.
INTRODUCTION ... 1
1.1. ANATOMICAL BACKGROUND ... 1
1.1.1. The anatomy of HIPP ... 1
1.2. THE HIPP–MEC INTERACTION IN MEMORY AND TIME REPRESENTATION ... 8
1.2.1. Functions of the HIPP... 8
1.2.2. Functions of MEC ... 9
1.2.3. Why study the time processing in HIPP and MEC? ... 11
1.3. HOW TO STUDY THE INVOLVEMENT OF MEC IN HIPP TIME PROCESSING? ... 13
2.
MATERIALS AND METHODS ... 15
2.1. SUBJECTS ... 15
2.2. BEHAVIORAL PARADIGM -WAITING TO TRAJECTORY TASK ... 15
2.3. VIRAL CONSTRUCTS ... 17
2.4. CLOZAPINE N-OXIDE SOLUTION PREPARATION ... 17
2.5. SURGICAL PROCEDURES ... 17
2.5.1. Pre-operative animal preparation ... 17
2.5.2. Bilateral injection of DREADDS ... 17
2.5.3. Implant surgery ... 20
2.6. EXPERIMENTAL PROCEDURES ... 22
2.6.1. Causal manipulation of neural activity ... 22
2.6.2. In vivo electrophysiological recordings and Pharmacogenetics ... 22
2.7. HISTOLOGICAL PROCEDURES ... 23
2.8. DATA ANALYSIS ... 24
3.
RESULTS ... 25
3.1. INHIBITION OF MEC AND HIPP NEURONS IN HM4DI – EXPRESSING ANIMALS BY CNO ADMINISTRATION 27 3.2. MEC IS CRITICAL FOR LEARNING AND RECALLING TEMPORAL GOALS ... 29
4.
DISCUSSION ... 37
4.1. MEC IS CRITICAL FOR INTERVAL TIMING ... 37
LIST OF FIGURES
Figure 1.1. Schematic representation of the three-dimensional structure and organization of the HIPP
in the rodent brain ...2
Figure 1.2. ...3
Figure 1.3. Schematic representation of the organization of the EC and its connectivity ...7
Figure 1.4. Place cell ...9
Figure 1.5. Grid celly ...9
Figure 1.6. Overview of the principles underlying the DREADDs – based tools used to inhibit neural activity... 14
Figure 2.1. Waiting to Trajectory task. ... 16
Figure 2.2. Bilateral injections of DREADDs. ... 19
Figure 2.3. Hyperdrive with independently movable tetrodes targeting MEC, HIPP and Striatum ... 21
Figure 3.1. Sagittal brain slices of the experimental and control animals showing hM4Di expression and the absence of expression ... 26
Figure 3.2. Multiunit spiking activity after Saline and CNO treatment. ... 27
Figure 3.3. MEC LFP activity and Single units’ action potentials after CNO and Saline treatment ... 28
Figure 3.4. Behavioral protocol timeline ... 29
Figure 3.5. Testing the MEC dependence in a temporal goal direct task - Waiting to Trajectory task . 30 Figure 3.6. A – B: Frequency distribution of the waiting times in the CNO sessions and Saline sessions ... 32
Figure 3.7. Differences between the waiting times’ distributions in the CNO and Saline sessions ... 34
LIST OF TABLES
Table 2.1. Subjects. ... 15 Table 2.2. Stereotaxic injections of DREADDs ... 17
1. Introduction
Memory can take different shapes depending on the type of information stored and for how long the memory is retained. In 1972, Endel Tulving described one of these shapes, episodic memory, defined as the remembering of a unique event at a particular time and place (Tulving 2002). Consistent with this definition, neuroscientific evidence suggests that our memories are indeed temporally organized (Howard and Eichenbaum 2015; Eichenbaum 2013). However, storing and recalling an episode require neuronal mechanisms underlying the what, where and when axes, whose mechanisms are largely unknown. In the last decades the what and where components of episodic memory have been intensely researched, but the temporal (when) aspect of memory has been less studied. Since the 1957 report of the H.M. case (Scoville and Milner 1957), who lost the ability to form new memories after the surgical removal of the hippocampus (HIPP) and surrounding tissue, the HIPP has been implicated in the formation, storage and recall of episodic memories in humans (Vargha-Khadem 1997; Steinvorth, Levine, and Corkin 2005) and episodic-like memories in rodents (Morris et al. 1982; Fortin, Wright, and Eichenbaum 2004; Ergorul and Eichenbaum 2004). Furthermore, recent studies show that the HIPP plays an essential role in remembering unique sequences of events, organizing events into a unique experience (Fortin, Agster, and Eichenbaum 2002; Ross, Brown, and Stern 2009; Lehn et al. 2009; Kesner, Gilbert, and Barua 2002). These findings suggest that the HIPP must encode the temporal dimension of memory and prompted us to investigate how external inputs guide internal dynamics in the HIPP, potentially enabling the encoding of time.
The entorhinal cortex (EC) is the main cortical gateway of sensory information into the HIPP. EC regulates spiking and plasticity inside the HIPP circuitry (Remondes and Schuman 2002) and is involved in memory consolidation (Remondes and Schuman 2004). Furthermore, the EC – HIPP network has been closely associated with the formation, storage and recall of episodic memories, including its spatial and temporal dimensions (Eichenbaum 2017). This dissertation aims to understand the involvement of the Medial Entorhinal Cortex (MEC) in the representation of time in the EC-HIPP memory circuit. To do this we silenced MEC activity during the performance of a temporal goal directed task, in which an animal must judge the passage of a time lapse before running down a linear track to collect a reward. This chapter is organized into three sections. In the first section we review the anatomy of HIPP and MEC and describe the functional interactions between these two regions. The second section analyzes the functions of the HIPP and MEC and their involvement in time coding. Finally, the third section presents DREADDs, the tool used in our work to manipulate neural activity in MEC.
1.1.
Anatomical background
1.1.1. The anatomy of HIPP
The rat hippocampus (HIPP) is a curved and elongated structure with its long axis extending in a C-shaped manner from the septal nuclei of the basal forebrain rostrodorsally, over and behind the thalamus, into the incipient temporal lobe caudoventrally, that comprises distinct, closely related, subfields organized sequentially as a circuit (M. Witter and Amaral 2004). There is no consensus concerning which brain regions are included in the term. Some authors define the HIPP as being composed by the dentate gyrus (DG), subiculum and the hippocampus proper, with its three subdivisions: CA1, CA2 and CA3 (M. Witter and Amaral 2004), while others also include the presubiculum, parasubiculum and the
entorhinal cortex (EC) (Amaral and Lavenex 2007). The three-dimensional shape of the HIPP is relatively complex since its distinct subfields extend to different septotemporal levels. At extreme septal levels, only the DG and the CA1-3 subfields are present. About one third of the way towards the temporal pole, the subiculum appears. The presubiculum and parasubiculum are not evident until even more temporal levels. The EC is located caudal and ventral to the HIPP and its dorsolateral limit occurs approximately at the rhinal sulcus (Amaral and Lavenex 2007). These are shown diagrammatically in Figure 1.1.
A
B
Figure 1.1. Schematic representation of the three-dimensional structure and organization of the HIPP in the rodent brain. (A) Schematic rat brain with the HIPP highlighted. (B) Two coronal sections (numbered 1-2)
at different septotemporal levels through the HIPP, with their approximate anteroposterior coordinate relative to bregma. Additional abbreviations: (A) S and T – Septotemporal axis; S – Septal nuclei; T – Temporal cortex; POR – Postrhinal cortex ; EC – Entorhinal cortex; PER – Perirhinal cortex ; RF – Rhinal fissure. (B) CA1, CA2 and CA3 - Hippocampal subfields; S – Subiculum; DG – Dentate gyrus; EC – Entorhinal cortex. Modified from Witter and Amaral, 2004.
A unique feature of the hippocampal formation is its intrinsic circuitry (Figure 1.2). As described in the classical studies of Ramón y Cajal (Ramón y Cajal 1909), the fields of the hippocampal formation are linked by unique unidirectional connections and the EC can be considered as the first step in the hippocampal circuit. This is consistent with the fact that the EC is the main gateway for the neocortical inputs reaching HIPP. Cells in the EC layer II project, among other destinations, to the dentate gyrus and the CA3 field via the so-called perforant pathway. Dentate gyrus (DG) granule cells (GC) project, via their mossy fibers, to pyramidal cells in the CA3 hippocampal field. In turn, these pyramidal cells provide the major input to CA1 via the so-called Schaffer collaterals. CA1 neurons then project to the subiculum (SUB), and finally both CA1 and the subiculum project back to EC’s deep layers, thus closing the hippocampal processing try-synaptic loop. In the last years, several new long-range circuit connections have been mapped and added to this canonical circuit (Basu and Siegelbaum 2015; Zemla and Basu 2017). Several studies revealed that CA2, a region that has historically been thought to be a transitional zone between CA3 and CA1, is strongly excited by direct inputs from EC layer II and could act as a conduit for entorhinal information arriving at CA1 in parallel with the direct pathway to CA1 and the indirect pathway through the DG/CA3 regions. In addition, a weaker mossy fiber excitatory input directly from DG granule cells to the CA2 was also described (M. W. Jones and McHugh 2011; Rowland et al. 2013; Cui, Gerfen, and Young 2013; Kohara et al. 2014; Hitti and Siegelbaum 2014; Mankin et al. 2015). Moreover, another study suggested the existence of an additional direct pathway from EC to CA1 involving projections from MEC layer II pyramidal neurons. These inputs target predominantly local interneurons that drive feedforward inhibition upon CA1 pyramidal neurons, and consequently reduce the excitation provided by the long-known projection from the MEC layer III onto CA1 (Kitamura et al. 2014).
Figure 1.2. Schematic representation of the HIPP circuit. Neurons of layer II of the entorhinal cortex project to
the dentate gyrus and to the CA3 hippocampal field. Cells in layer III of the entorhinal cortex project to the CA1 hippocampal subdivision and the subiculum. This pathway is called perforant path. The cells in the dentate gyrus project to the CA3 hippocampal field, via the mossy fiber projections. The pyramidal cells in the CA3 project to the CA1 via Schaffer collaterals. Finally, Pyramidal cells of the CA1 project to the subiculum. Both CA1 and the subiculum project back to the deep layers of the EC. Dotted lines denote CA2 and CA1 connections that have recently been described but have not been confirmed in additional anatomical studies. Additional abbreviations: CA1, CA2 and CA3 - Hippocampal subfields; DG – Dentate gyrus; S – Subiculum; EC – Entorhinal cortex.
Besides a distinctive anatomical organization and unique intrinsic circuit, HIPP also shows a typical cytoarchitectonic organization which is generally similar for all its fields (Fröhlich 2016; Amaral and Lavenex 2007). The principal cellular layer, called pyramidal cell layer, is densely packed in CA1, and less densely in CA2 and CA3, and contains the cell bodies of the pyramidal neurons, the principal hippocampal excitatory neurons and cell bodies of several types of interneurons, including basket cells, bistratified cells, axo-axonic cells, and radial trilaminar cells. Above the pyramidal layer is the stratum oriens which is composed of basal dendrites from pyramidal cells, cell bodies of inhibitory O-LM cells, horizontal trilaminar cells, and commissural fibers from the contralateral HIPP. In CA3 and CA2, just adjacent to the pyramidal cell layer, there is the stratum lucidum which is occupied by the mossy fibers from the DG. The stratum radiatum is occupied by the recurrent connections within CA3 and the CA3 to CA1 Schaffer collateral projections, which correspond to the major monosynaptic projection from CA3 to CA1. The most superficial layer of the HIPP is called the stratum lacunosum – moleculare and is composed of few Schaffer collateral fibers and mostly perforant path fibers, which correspond to the major monosynaptic projection from the superficial layers of EC to the HIPP.
The anatomy of EC
As described before, EC is the major input and output structure of the HIPP and is considered the nodal point between the HIPP and multimodal cortical association areas (Menno P. Witter et al. 2017). The interest in the EC arose in the early 20th century through the hand of Ramón Y Cajal (Ramón y Cajal
1902), who described a region of the posterior temporal cortex that was so strongly connected to HIPP, that he suggested that the functionality of HIPP had to depend and to be related to it.
The rodent EC is surrounded by a number of cortical areas. It lies in the ventroposterior convexity of the rat cerebral hemisphere, limiting the parasubiculum ventromedially and the piriform cortex and amygdala rostrally. Its dorsal levels are close the rhinal fissure and, at the most rostral levels EC ends ventral to the rhinal fissure. At caudal levels, EC extends within and above the rhinal fissure (M. Witter and Amaral 2004). The laminar organization of EC is distinct from other neocortical regions. Six layers can be distinguished in EC, four cellular layers (layers II, III, V and VI) and two acellular layers (layer I and layer IV, this last also called lamina dissecans). The superficial, cell-containing layers (layers II and III) contain stellate cells and smaller pyramidal cells. The deep layers are composed of pyramidal cells (layer V) and a very heterogeneous population of cell sizes and shapes (layer VI). Layer I is the most superficial molecular layer and is rich in transversely oriented fibers. Layer IV, the lamina dissecans is an acellular layer. Patches of cells invade this layer so that it has an incomplete or dashed appearance. Layer VI of the EC is not easily distinguished from layer V (Schultz and Engelhardt 2014; Menno P. Witter et al. 2017).
In addition to EC’s classification as six-layered cortex, it is generally accepted that the EC is subdivided into two functionally and cytoarchitectonically distinct areas, the lateral entorhinal cortex (LEC) and the medial entorhinal cortex (MEC) (Insausti, Herrero, and Witter 1998; Kerr et al. 2007; Menno P. Witter et al. 2017) . MEC is positioned medially and caudally to LEC, bordering the parasubiculum, while LEC occupies the rostrolateral portion of EC, with the perirhinal cortex as the border. Several cytoarchitectonic features can be used to distinguish the two regions. MEC can be recognized by a very regular six-layered structure and a homogenous distribution of neurons across all layers. Layer II of MEC is composed of a mixture of excitatory pyramidal cells and large stellate cells. In the LEC the laminar structure is comparable, but less regular and layer II comprises smaller cells that seem to cluster into sublayers (Amaral and Lavenex 2007; Canto, Wouterlood, and Witter 2008; Cappaert, Van Strien,
MEC and LEC, being less delineated and clear in LEC than in MEC (Canto, Wouterlood, and Witter 2008; Cappaert, Van Strien, and Witter 2015).
Since this dissertation is primarily concerned with the influence of MEC on time coding in HIPP, we will focus on the connectivity and functionality of this region in the following sections.
Connectivity of the MEC
The rodent EC receives inputs from several cortical, subcortical and hippocampal regions (Fig. 1.3). Generally, these inputs are organized according to the cytoarchitectonic subdivisions of EC (LEC and MEC) (Kerr et al. 2007). More than one-fifth of the total afferent input of the MEC is represented by cortical inputs (Agster 2007). The heaviest cortical input of the MEC originates in olfactory structures, in particular from the olfactory bulb, the anterior olfactory nucleus, and the piriform cortex (Haberly and Price 1978; Kosel, Van Hoesen, and West 1981). At least one third of the input to MEC arises from the piriform cortex (Cappaert, Van Strien, and Witter 2015). Furthermore, MEC also receives moderate contributions from the secondary motor area, the retrosplenial cortex, the anterior cingulate, the posterior parietal cortex, and even visual association areas (Kerr et al. 2007; B. F. Jones and Witter 2007; Czajkowski et al. 2013; Olsen et al. 2017). Finally, a very heavy projection to EC originates in the whole rostrocaudal portion of postrhinal (POR) and perirhinal (PER) cortex, with MEC more strongly connected with the POR and the LEC with the PER (Kerr et al. 2007; Koganezawa et al. 2015). These last projections suggest that MEC and LEC are part of separate information streams. The visual and parietal cortex are the major inputs of POR, thus leading a subset of information to MEC neurons of extreme relevance for their spatial firing properties. The projections from PER to LEC, on the other hand, seem to be related to the object-selective firing in LEC. The cortical efferents are widespread, largely reciprocating the afferents (Cappaert, Van Strien, and Witter 2015).
Besides cortical connections, MEC is also strongly influenced and connected by subcortical regions (Agster 2007; Cappaert, Van Strien, and Witter 2015), receiving considerable input from the claustrum, dorsal thalamus, dorsal thalamic nuclei, amygdala, olfactory structures and hypothalamus, more specifically the mammillary bodies and the lateral zone. MEC subcortical efferents mostly target the basal ganglia, but also significantly the olfactory regions, amygdala, septal structures, striatum, claustrum and the hypothalamus (Eid et al. 1996; Macdonald 1998; Insausti, Herrero, and Witter 1998; Kerr et al. 2007; Tomás Pereira, Agster, and Burwell 2016; Agster et al. 2016).
Finally, as described before, EC is strongly and reciprocally connected to all HIPP subregions and the subiculum (Kerr et al. 2007; Canto, Wouterlood, and Witter 2008; Van Strien, Cappaert, and Witter 2009; Cappaert, Van Strien, and Witter 2015; Menno P. Witter et al. 2017), which constitute about one-half of its afferents (Kerr et al. 2007). The strongest hippocampal input arises from caudal parasubiculum, followed by dorsal presubiculum and postrhinal cortex. Moreover, another substantial input arises from the dorsal and ventral HIPP, more specifically the CA1 field (Canto et al. 2012; Agster and Burwell 2013; Koganezawa et al. 2015). MEC’s efferent projections target the dorsal DG and CA1, subiculum, CA2 and CA3, with its strongest projection targeting postrhinal and perirhinal cortices, and pre- and parasubiculum receiving modest inputs (Kloosterman, Witter, and Van Haeften 2003; M.P. Witter 2007; Agster and Burwell 2013).
Substantial differences in the cortical, subcortical and hippocampal connectivity of MEC and LEC indicate that information is distinctively represented in these two subregions. As described before, the MEC is strongly connected with spatial coding areas (Cho and Sharp 2001; Lever et al. 2009; Boccara et al. 2010; Knierim and Hamilton 2011). In contrast, LEC receives major inputs from PER, an area that
is well known for its role in object recognition memory and familiarity (Murray, Bussey, and Saksida 2007). For this reason LEC is thought to have a significant role in processing nonspatial information, more specifically object representation (Deshmukh and Knierim 2011; Wilson et al. 2013), with MEC processing spatial information (Kropff et al. 2015; Sargolini et al. 2006).
Figure 1.3. Schematic representation of the organization of the EC and its connectivity. (a)
Position of the LEC and MEC subdivisions of the EC, surrounding cortices and HIPP in the rat brain. (b) Summary of the major afferent and efferent connections of the MEC. Arrowheads
indicate connectivity direction. Additional abbreviations: PER – Perirhinal cortex; POR – Postrhinal cortex; PaS – Parasubiculum; LEC – Lateral Entorhinal Cortex; MEC – Medial
1.2.
The HIPP – MEC interaction in memory and time representation
1.2.1. Functions of the HIPP
Two major theories guide the research of HIPP functions. The first one states that HIPP is a critical component of a memory system primarily required for the formation, storage and recall of declarative memories (Scoville and Milner 1957), while the other posits that the HIPP is the source of a cognitive map which enables navigation (John O’Keefe 1976; J. O’Keefe and Dostrovsky 1971; Morris et al. 1982). HIPP research began in the field of cognitive neuropsychology, following 1957 Scoville and Milner’s report on patient H.M., and is supported mainly by observations in which bilateral lesions in the HIPP resulted in a loss of the ability to form new declarative memories (Penfield 1958; Squire 2009; Scoville and Milner 1957). O’Keefe and Dostrovsky ‘s seminal discovery of hippocampal neurons firing at specific places in a given environment, later named as “place cells”, led to the spatial navigation hypothesis (Figure 1.4) (J. O’Keefe and Dostrovsky 1971). The discovery of place cells led researchers to focus on the HIPP as a cognitive map taking the form of a Euclidian coordinate system dedicated to encode allocentric physical space, where place cells encode one’s location, and to propose that spatial processing is the predominant function of the HIPP. This was later supported by results of hippocampal damage causing profound deficits in some types of spatial memory in rodents (Sutherland and McDonald 1990; Sutherland, Whishaw, and Kolb 1983; Olton, Becker, and Handelmann 1979), and led to the notion that spatial memory is the main function of HIPP, also supported by experiments in humans (Parslow et al. 2005; Ekstrom et al. 2003). However, changes in spatial location are not sufficient to explain the firing pattern of the hippocampal neurons. Already in their seminal study on place cells (J. O’Keefe and Dostrovsky 1971), O’Keefe and Dostrovsky reported the existence of neural activity associated with non-spatial information in the HIPP, and subsequent studies have consistently shown that HIPP neurons represent non-spatial variables, including objects (Manns and Eichenbaum 2009), odors (Alvarez 2001) and time (Eichenbaum 2014).
Following these discoveries, a reconciliation of the two theories was advanced by Howard Eichenbaum. In his view, the HIPP supports memory by providing a relational processing system that connects the elements of an experience, and then links memories via their common elements, composing a memory space. Eichenbaum advanced that hippocampal functions go beyond spatial navigation in allocentric space to the organization of events in spatial, as well as non-spatial contexts, including the temporal organization of events in associative networks, in a memory space where navigation and declarative memory are linked. In sum, he proposed that the HIPP could act as « relational processing system », in which events are encoded as a relational map of objects and actions within spatial contexts, representing episodes (Eichenbaum and Cohen 2014).
Figure 1.4. Place cell. (Left) Spike locations (red dots) are overlapped on the animal's trajectory in the recording surface
(black traces). Place cells display increased firing rates at specific locations in the environment or "place field" (cluster ed red dots). (Right) Color – coded rate map with white showing high activity and black showing low activity (Marcelo Dias &
Miguel Remondes unpublished work).
1.2.2. Functions of MEC
Given the dense reciprocal projections connecting MEC with HIPP, some functional similarity would be expected. Indeed, MEC is known to process spatial information, and is considered the major source of spatial information to the HIPP neurons. This is supported by the findings of Hafting et al. (Hafting et al. 2005) and the discovery of a new type of MEC neurons, named grid cells, that fire when a freely moving animal traverses a tessellated set of regions (also named firing fields), roughly equal in size, and arranged in a periodic triangular array (grids) covering the available spatial (Figure 1.5). Subsequent discovery of head direction cells, speed cells and boundary cells have also cemented this hypothesis (Taube 2007; Solstad et al. 2008; Sargolini et al. 2006).
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Figure 1.5. Grid cell. (Left) The blue trace shows the animals’ trajectory in the recording enclosure.
Spike locations of the grid cells are superimposed on the trajectory (red dots). Each red dot corresponds to one spike. The red equilateral triangles draw on top of the firing fields illustrate the regular hexagonal structure of the grid pattern. (Right) Color – coded rate map with red showing high activity and blue showing low activity. Adapted from (Pilly and Grossberg 2013).
Since the discovery of MEC’ grid-cells, many models have been developed regarding their role in spatial processing (John O’Keefe and Burgess 2005; Fuhs 2006; McNaughton et al. 2006; Burak and Fiete 2009; Burgess, Barry, and O’Keefe 2007). These models proposed that grid cells may be responsible for conferring spatial selectivity to hippocampal place cells. However, it is not entirely clear that the location firing of the hippocampal place cells is inherited or conferred by the grid cells, especially since there is evidence demonstrating that place cells develop before grid cells (Wills et al. 2010). The possible causal link between MEC spatial firing and HIPP activity can be clarified and tested by interfering with MEC and determining the effects on hippocampal activity. Van Cauter et al. (Van Cauter, Poucet, and Save 2008) performed bilateral lesions on the EC prior to place cell recordings and showed that EC is necessary for the stability of hippocampal representations across exposures to a familiar environment and in response to manipulations of contextual cues. However, they only found modest changes in hippocampal spatial firing. In another study (Brun et al. 2008) the direct input from MEC to HIPP (EC -CA1) was severed, leaving only indirect input through CA3, and found that place cells were still present within CA1, although with larger and sparser fields. Finally, Navawangse and Eichenbaum (2013) reported temporary MEC inactivation causing a change of preferred firing location in a subset of place-cells, but maintenance thereof in their majority. Thus, while it is clear that MEC likely plays a role in spatial processing within the HIPP, it seems no less so that such role is not determinant. It is possible that HIPP or other structure compensate for the disruption of MEC such that enough spatial information reaches the HIPP via direct projections from presubiculum, parasubiculum, perirhinal cortex, postrhinal cortex and thalamic nuclei, which would explain the robustness of hippocampal spatial representations to MEC disruption.
Recent studies have gone beyond place-selectivity, to identify and explore MEC time-processing (Kraus et al. 2015; Heys and Dombeck 2018). In the Kraus et al. report, firing patterns of grid cells in MEC were recorded while animals performed a spatial alternation task in which, on each trial, they ran on a treadmill in the center of the maze. The authors found multiple grid cells that exhibited firing fields at particular moments during the treadmill runs, indicating that these neurons can also encode time information. More recently, Heys et al. (Heys and Dombeck 2018) optically recorded from populations of layer II MEC neurons in head-fixed animals performing a virtual reality task, in which animals had to stop and wait for a specific interval in a specific location. The authors found that MEC neurons exhibited increased firing fields at specific moments during the immobile period, suggesting that MEC contains a representation of the passage of time during immobility, with individual time-cells firing at specific moments of the waiting interval. These results indicate that, besides the spatial information representation, the role of the MEC extends to the representation of time in virtual reality, and that a code for elapsed time may exist in MEC. In line with this discoveries, recent findings suggested that MEC may play a role and be involved in time processing within the HIPP (Suh et al. 2011; Kitamura et al. 2014; Robinson et al. 2017).
1.2.3. Why study the time processing in HIPP and MEC?
The earliest evidence for time representation in the HIPP came from a lesion study (Chiba, Kesner, and Reynolds 1994), showing that hippocampal damage impairs storage and retrieval of a sequence of locations in an eight - arm radial maze. These findings generated considerable interest but were initially criticized as being confounded by the animal’s location, so that they could reflect spatial rather than temporal dimensions of memory. However, later, Kesner et al. (Kesner, Gilbert, and Barua 2002) and Fortin et al. (Fortin, Agster, and Eichenbaum 2002) found that the same lesions severely impair memory performance for sequences in a task in which time is dissociated from space. More recently, Yin et al. (Yin and Meck 2014) demonstrated that hippocampal lesions also impair the judgment of time intervals. In line with lesion findings, more recent studies revealed the involvement of HIPP neurons in the processing of time. The first direct evidence for temporal coding by hippocampal neurons came from a study (Manns, Howard, and Eichenbaum 2007), in which ensembles of CA1 principal neurons were recorded in rodents performing a memory task that consisted in the presentation of a sequence of odors at alternative sides of an enclosure followed by a delay period. After the delay animals had to remember which odor was presented earlier. The authors found that the pattern of activity of CA1 neurons changed across the presentation of the sequence of odors, indicating that these pattern differences could be associated with a temporal context. Thus, these findings suggested that memory for order is associated with the sequencing of the stimulus taking into account their temporal context. A subsequent study (Pastalkova et al. 2008) revealed the existence of hippocampal neurons that fire in sequence while rats run on a running wheel between left and right alternations on a T-maze. More recently, MacDonald et al. (MacDonald et al. 2011) recorded the firing patterns of CA1 neurons during a nonspatial object-odor associative memory task, in which the animals were exposed to an object and, after a delay, dug in a cup for a food reward depending on the odor of the sand in the cup. The authors found that CA1 time cells fired at specific moments across the delay between the object and the odor presentation. Here, the animals remained steady inside a restricted area during the delay period suggesting that these findings are dissociated, to some degree, from space or the animals’ movement. Finally, another report (Kraus et al. 2013) showed that CA1 neurons fired at specific time points during a run on a treadmill. By varying the treadmill speed, the authors were able to dissociate the distance ran from the elapsed time, and thus found that CA1 principal neurons also represent a self-generated time-flow. Despite this progress, the origin of timing in the HIPP and the relevant inputs for time processing in this region, remain unclear. During the last few years recent studies have enlarged our understanding of the information representation carried out by the MEC-HIPP. The most striking report (Aronov, Nevers, and Tank 2017) on the mapping of a non-spatial dimension showed that, very much like tilling of space by place and grid cells, HIPP and EC cells also tile continuous sound sweeps into consecutive firing fields. In line with these discoveries, recent studies suggest that MEC is also involved in the processing of time and can also be crucial for timing in HIPP. Suh et al. (Suh et al. 2011) showed that inhibition of MEC layer III inputs to HIPP (MEC III) leads to a disruption in spatial working memory and in the encoding phase of trace fear-conditioning. Later, Kitamura et al. (Kitamura et al. 2014) mapped and optogenetically manipulated the EC – HIPP network in order to examine its role on a temporal association memory. The authors identified a new type of cell in MEC layer II, called Island Cells, that form clusters and send their projections to CA1. The optogenetic inactivation of Island cells inhibit the excitatory MECIII input to control the strength and duration of temporal association in trace fear memory. Together, these studies revealed that projections from EC layers II and III to the HIPP, play an essential role in the formation and storage of temporal association memories. A subsequent study (Kraus et al. 2015) showed that MEC grid cells, that traditionally fire at particular places, also fire at particular moments providing
timing-related information during treadmill running. Robinson et al. (Robinson et al. 2017) examined the effects of the optogenetic inactivation of MEC on memory, temporal, spatial and object coding by hippocampal neurons during a behavioral task that required the animal to remember a certain object during a delay period. The authors demonstrated that MEC is crucial for CA1 temporal coding and for memory across time. Finally, a recent study reported the existence of a sub-circuit within MEC dedicated to track elapsed time during immobile periods (Heys and Dombeck 2018).
Despite these findings, recently, Sabariego et al. (Sabariego et al. 2019) performed hippocampal neuronal activity recordings during spatial working memory, in order to test the dependence of time cell occurrence on MEC inputs. They demonstrated that MEC lesions lead to a disruption in working memory, but no differences were found in the occurrence of temporal time cells compared to uninjured animals. These results suggested that MEC inputs do not influence hippocampal time cell activity. These discoveries have established temporal coding as a prevalent aspect of hippocampal firing patterns but shed doubt on both the involvement of MEC in HIPP temporal information, and on the need for this type of information processing on memory-dependent behaviors.
1.3.
How to study the involvement of MEC in HIPP time processing?
Selective manipulation of specific neurons and circuits has been a longstanding goal of neuroscience. Already in 1979, Francis Crick suggested that controlling all cells of one type in the brain, leaving others more or less unaltered, would be invaluable, but a major challenge for neuroscience (Crick 1979). Historically, the manipulation of neural activity in specific brain regions was achieved through chemical, physical, and pharmacological lesions, some of them permanent. However, while these approaches are powerful, they lack temporal and anatomical resolution. The introduction of newer methodologies, such as pharmacogenetics (Designer Receptor Exclusively Activated by Designer Drugs – DREADDs) and optogenetics, are leading us to overcome these limitations. These newer methodologies have enabled cell-type specificity, real-time control of neural activity, and the ability to manipulate circuits in a projection-pathway specific manner. Moreover, an obvious advantage of these approaches over permanent lesions is the ability of manipulating neuronal activity in a reversible manner, thereby considerably decreasing the likelihood of compensatory plasticity.
Naturally, there are differences between DREADDs and optogenetics, and both have inherent advantages and disadvantages. The technique that is most useful on a particular experiment depends on the research question and pertinent considerations of the spatiotemporal scale of manipulations. The primary difference between DREADDS’ and optogenetics’ control of neural activity is their degree of temporal specificity and anatomical width. Optogenetics offers the ability to regulate specific populations of neurons within a range of milliseconds. However, for this project that depends upon longer-lasting modulations such temporal resolution becomes a disadvantage. In contrast, DREADDS are ideally suited for prolonged modulation of cell activity in the range of minutes-hours. In addition, in optogenetics there is the need to deliver light into the brain, which traditionally occurs via an implanted optical fiber in targeted regions. Besides anatomically restricted, this manipulation may result in light-induced off-target activity, and heat-associated tissue damage. According to this, we used DREADDs to manipulate the MEC neural activity and thereby test the involvement of MEC in time processing. DREADDs belong to a class of engineered proteins that allow the control of G protein signaling in vivo by using engineered G-protein-coupled-receptors (GPCRs). When expressed in neurons, DREADDs are not responsive or only very weakly responsive to their endogenous ligands and can be activated upon interaction with clozapine N-oxide (CNO). In the present work, we used hM4Di, which is an engineered
version of the human M4 muscarinic receptor coupled with the Gi protein, with the power of silencing
neuronal activity via two distinct mechanisms: (a) hyperpolarization by means of decreased cyclic adenosine monophosphate (cAMP) signaling, as well as increased activation of inwardly rectifying potassium channels and (b) via inhibition of the presynaptic release of neurotransmitters. Physiologically, this results in the temporary suppression of neuronal activity (Roth 2016).
In this work, we used the viral construct ܣܣܸͺ െ ܥܽܯܭܫܫܽ െ ݄ܯͶܦሺܩሻ െ ݉ܥ݄݁ݎݎݕ that, besides the DREADD, carries a promoter and fluorescent tag. The ܥܽ݉ܭܫܫܽ promoter allows the specific targeting of excitatory neurons (Figure 1.6). The fluorescent tag ݉ܥ݄݁ݎݎݕ optimizes the visualization of DREADD – expressing cells via fluorescent microscopy.
Figure 1.6. Overview of the principles underlying the DREADDs – based tools used to inhibit neural activity. The figure illustrates the stereotaxic delivery of the AVV virus vector carrying an
hM4Di DREADD and a fluorescent tag to a target population of cells. When expressed in the neurons, DREADDS are not responsive to their native ligand, but they allow receptor binding and subsequent activation by the small pharmacologically inert molecule, CNO. The DREADDS activation triggers the Gi cascade, silencing neuronal activity of targeted neural population.
2. Materials and Methods
2.1.
Subjects
Four Long Evans rats (Rattus Norvegicus), 6 to 10 months old and weighing between 400 and 700g (Charles River Laboratories) were used in all experiments (Table 2.1). At the time of the experiments, the animals were housed individually and were kept in the animal house facility with controlled temperature and humidity, under a 12/12-hour light/dark cycle and ad libitum food and water before experiments started.
Before behavioral experiments, animals were food deprived to 85 – 90% of their ad libitum weight. During this period, the daily food ration was bathed with chocolate milk, used as a reward, in order to allow the subjects to adapt and recognize the new flavor. During this phase, animals were handled for one week and familiarized with the behavior room. The initial handling consisted in inserting gloved hands inside the cage, in daily sessions of 15 to 20 minutes until the animals were no longer afraid of being touched and held.
All experiments and procedures were approved by the Portuguese National Authority for Animal Health, Direção – Geral de Alimentação e Veterinária (DGAV).
Table 2.1. Subjects.
2.2.
Behavioral paradigm - Waiting to Trajectory task
At the start of the behavioral protocol, the rats were introduced to the maze and received reward every time they performed a complete trajectory – defined as a full traversal of the maze from the initial position (H) to the reward port (R) and back to the initial position (Figure 2.1). This procedure was repeated until the animals performed the trajectory with minimal interruptions (between 1 and 2 sessions). After this initial stage, the rats were introduced to the Waiting to Trajectory task (WtT) in which they were trained to perform pre-trained waiting period inside a region of interest (ROI) and, then to perform a simple spatial trajectory to access reward (see Figure 2.1). Every time that an entry in the ROI is detected, a short tone (50ms beep) is played to mark the beginning of the waiting interval and, simultaneously, a light was turned on for the duration of target time. Only trials in which the animals wait inside the ROI until the light is turned off or longer are rewarded.
In the first training sessions, a transparent door was used to keep the animals inside the ROI for the waiting interval. After that, the door was only used in the first three trials of each session, as a refresher. Whenever the animals performed 50% of trials correct in a given session, the target time was increased incrementally until a maximum duration of 2500 milliseconds. To increase the waiting interval, the median of correct trials times was calculated and then used as the new interval duration for the
Animal Weight Age
#1 449g 8 months
#2 580g 9 months
#3 515g 8 months
subsequent session. When the animals performed 50% of correct trials with the duration of 2500 milliseconds, the light cue was removed, and henceforth the animals had to perform the waiting period without any end-of-range cue. As such, at this stage the task is fully self-paced and the reproduction of the target interval, by waiting inside the ROI, is dependent on the animal’s judgment of time. The animals were trained for 8 – 10 weeks until they reached 50% of correct trials without any end-of-range cue and with the 2500 milliseconds interval. After reaching this performance criterion, stereotaxic injections of DREAADs were performed on 3 animals (experimental group).
To capture and record an overhead image of the behavior room we used a Flea 3 Point Grey camera under the control of Bonsai (Lopes et al. 2015), an open source software platform developed to monitor and process multiple data streams. Using Bonsai, head direction and XY position, as well as the times that the animals stayed inside the ROI in each trial were extracted from the video stream, based on a color threshold segmentation process. In order to facilitate the position tracking, a LED fixed to the animal’s head was used to create a high contrast with the behavior apparatus. The light and sound cues used to mark the beginning and passage of the waiting interval are generated by a system that is controlled by a remote computer via a modified firmata protocol. The features of this system are exposed within the Bonsai workflow. The system controls the activation of light and sound cues by detecting the animal's entry into the ROI. ROI was selected using the Crop node of Bonsai and the Threshold, FindContours, BinaryRegionAnalysis and LargestBinaryRegion nodes were used to detect the presence of the animal and entries in the ROI. Finally, to prevent the triggering of the light and sound cues when the animals cross the ROI in the opposite direction (from the reward to the home location), the direction of entry in the ROI was computed by calculating the angle of the line formed by the center of the ROI and the center of the animals. An Arduino Uno board was used to run the firmata protocol for communication with the remote computer and Bonsai software.
Figure 2.1. Waiting to Trajectory task. H - Home location; ROI - Region of interest; R -
Reward port. The interval of durations of the waiting time are stated in the figure. The timing interval was set to 1000 ms, and it was gradually increased over weeks of training to 2500 ms, as each animal reach the criterium of 50% of correct trials in three consecutive sessions.
2.3.
Viral Constructs
The AAV8.CaMKIIα.hM4D(Gi) – mCherry used for the neural activity manipulation was acquired from
the University of North Carolina at Chapel Hill Vector Core.
2.4.
Clozapine N-oxide solution preparation
Clozapine N-oxide (CNO 25 mg, Enzo Life Sciences) was dissolved in 0.125 mL dimethyl sulfoxide (DMSO) and then further diluted in 24.875 mL isotonic saline to a final concentration of 1mg/mL. Care was taken to obtain a solution with the lowest percentage possible of DMSO due to its off-target effects, in this case final volume of DMSO was 0.5%.
2.5.
Surgical Procedures
2.5.1. Pre-operative animal preparation
Before surgery, the animals were placed in an induction chamber saturated with 5% isoflurane until absence of the righting reflex. Once the animals were deeply anesthetized, they were weighted and transferred to a heating pad, where they received an intraperitoneal injection of ketamine/xylazine (0.3 mg per 100g of body weight) and their heads were shaved. After that, rats were placed in the stereotaxic apparatus (Figure 2.2 (C)) with Lidocaine applied to the ears and their eyes were covered with a lubricant and protection gel (Lacryvisc) to prevent drying of the cornea. The surgical site was scrubbed 3 times with 10% povidone iodine followed by 70% ethanol. During the surgery animals received subcutaneous injections of Ringer’s lactate (approximately 5 mL per hour) to maintain adequate levels of hydration.
2.5.2. Bilateral injection of DREADDS Table 2.2. Stereotaxic injections of DREADDs
Animal Time of viral expression Coordinates
(A/P*,M/L*, D/V**) #1 30 days Bilateral Injection (-9.0, +/-5.2, -5.0) ; 1µL (-8.4, +/-4.5, -6.0) ; 0.5µL #2 56 days Bilateral Injection (-8.8, +/-5.1, -5.0) ; 1µL (-8.4, +/-4.5, -6.0) ; 0.5µL #3 48 days Bilateral Injection (-9.0, +/-5.2, -5.0) ; 1µL (-8.4, +/-4.5, -6.0) ; 0.5µL
During the surgery, the animals were kept anesthetized using a mask delivering 1 – 2% of isoflurane in oxygen. The first step of the surgery was to make a single and clean incision, starting in the midline of the inter-orbital line and extending back to the inter-aural line. Once the incision was made, the skin was retracted laterally, the periosteum was completely removed from the skull and the soft tissue was completely retracted laterally. Then the bone was repeatedly cleaned with sterile saline solution until the surface was completely clean and dry, after which, known structures and sutures, such as bregma and lambda, became visible on the bone surface (see Figure 2.2 (A)). First, bregma and lambda were identified and marked on the skull surface. These two landmarks were used to carefully level the skull until the difference between both was less than 0.10 mm. Once the bone was leveled, bregma was used to delineate and mark the craniotomy coordinates on the bone. Before drilling, the bone surface was treated with Baytril to minimize possible future infections.
A high-power micro drill tool was used to perform a single craniotomy on each hemisphere over the microinjection spots. The loose bone was carefully removed and, then a small needle was used to remove the dura mater and to expose the brain surface. The viral construct was injected using a microinjection control system attached to the stereotaxic frame and glass micropipettes. Micropipettes were filled with mineral oil, placed in the microinjector after which 1µL of DREADDs was aspirated. Then, they were positioned at the microinjection coordinates and slowly lowered until they reach the target depths. The virus started to be delivered after 5 min at a rate of 100 nl/min. To avoid back contamination, we waited
10 minutes before slowly retracting the micropipette. The viral construct
AAV8.CaMKIIα.hM4D(Gi)mCherry was injected in three animals. The coordinates used for the
injections and the time of the viral expression of each animal are depicted in Table 2.2. The injection coordinates should be the same for the three animals to minimize possible differences in viral expression, however in the second rat, due to anatomical differences, namely the presence of large vessels on the brain surface above the injection coordinates, slightly different coordinates were used for one of the most posterior injections.
Once all the injections were made, the micropipettes were removed, the surface of the brain was rinsed with saline solution and the skin edges were approximated using absorbable surgical sutures. Animals received a last injection of Ringer’s lactate, as well as Carprofen for analgesia. Finally, rats were placed in a heated cage during the first 24h post-operative recovery. The subjects were maintained in recovery for at least a week and their motor activity, water and food intake were periodically monitored. During this period, ad libitum food, nutritional gel and water were provided. During the firsts two days, a postoperative analgesic (Buprenorphine, 0.3 mg/kg) dissolved in chocolate milk was administered to the subjects when necessary.
The microinjection surgery of one of the animals used in this project was performed following the protocol described above but using a Hamilton syringe and an automatic microinjector, in order to test a different method and adapt the protocol accordingly with the results of the two methods.
C
D
Figure 2.2. Bilateral injections of DREADDs. A: Rat skull - Surgical field. A1. Bregma; A2.
Lambda. B: Surgical field schematic. B1. Bregma; B2. Injections Craniotomies; B3. Lambda. C: Animal positioned on the stereotaxic apparatus. D: Schematic illustration of a sagittal section of rat
brain showing the position of the MEC at the approximate M/L coordinate of the DREADDs injections.
2.5.3. Implant surgery
Electrophysiological data was recorded from of one of the animals that underwent viral injection surgery (rat 3). For this purpose, after full recovery of the injection surgery and after administration of CNO during behavior, a hyperdrive with 32 independently movable tetrodes was implanted in the animal for tetrode recording from the MEC, HIPP and Striatum (Figure 2.3). Tetrode bundles were targeted at the following coordinates: MEC (9 tetrodes) between -9.0 and -8.0 AP; -4.0 and -5.0 ML; the HIPP (18 tetrodes) between -3.2 and -4.2 AP; -2.4 and -4.4 ML and Striatum (5 tetrodes) between -1.32 and -1.9 AP; -3.5 and -4.5 ML.
The implant surgery was performed as described for the bilateral injection of DREADDS, with some modifications. The pre-operative preparation of the animal, the preparation of the surgical field and the post-operative care of the animal were performed following the protocol described previously (see Section 2.5.1 – 2.5.2). Once the surgical field was ready, 9 anchor screws were fixed to the skull (one of which was used as a ground) and the craniotomy for the implant was opened. The dura matter was removed, and mineral oil was applied to the surface of the brain. Finally, the hyperdrive was carefully lowered and firmly fixed to the skull and to the anchor screws with dental acrylic. Once the dental acrylic dried, the surgical wound was closed, and the animal was left to recover over the period of one week. This protocol was based on (Remondes and Wilson 2013, 2015).
C D B A 2 3 1 D C
Figure 2.3. Hyperdrive with independently movable tetrodes targeting MEC, HIPP and Striatum. (A) Chronic implantable drive composed of 32 movable tetrodes used to record electrophysiological data
in freely moving animals. (B) Tetrode bundles. 1. Striatum’ bundle (5 tetrodes); 2. HIPP’ bundle (18 tetrodes); 3. MEC’ bundle (9 tetrodes). (C) In order to discriminate the activity from different neurons firing with similar waveforms, four wires were joined together, and the activity of a single neuron was thus recorded by multiple electrodes of the same group (in this case, tetrode) at varying distances. By using tetrodes, which are composed of four microelectrodes, the spatial position of each neuron can be triangulated, and it is possible to detect and record spikes from individual neurons. (D) Rat skull – Implant craniotomy (dashed rectangle) and anchor screws scheme (red dots). 1. Anchor screws; 2. Implant craniotomy. Figure 8 - C was adapted from Buzsáki (2004).
2.6.
Experimental procedures
2.6.1. Causal manipulation of neural activity
After recovery from the injection surgery, animals were food deprived in preparation for the experimental sessions. In order to ensure optimal viral expression, all injected animals with the viral construct were tested on the behavioral task at least 30 days after the day of the surgery (Table 2.2). Animals were reintroduced to the WtT task and retrained following the protocol described above (see section 2), until they reached 50% of correct trials in a session without any end-of-range cue and at the 2.5 s target interval. Typically, the post-surgery retraining took between 2-3 weeks.
In the behavioral experiment, animals that underwent viral injection surgery were tested on their ability to perform the WtT task following the rules under two different conditions. The hM4D (Gi) was activated
through the intraperitoneal administration of CNO (3mg/Kg). A 0,9% of sterile saline solution containing the same volume of DMSO as the CNO solution, was injected in control sessions, which were interleaved with CNO sessions on a daily schedule. As such, during 10 consecutive days, systemic injections of CNO or saline were performed 60 minutes before behavioral testing to inhibit MEC neuronal activity.
Besides the 3 injected animals, this protocol was also performed on one animal not injected with DREADDs that served as a control.
2.6.2. In vivo electrophysiological recordings and Pharmacogenetics
As mentioned above, electrophysiological data was recorded from one of the injected animals in order to test the involvement of MEC in the performance of the task, as well distal (HIPP) effects of the inhibition of MEC projecting neurons on the encoding of time. Furthermore, this also served to verify if there was effectively silencing of MEC activity.
During the first week post-surgery, tetrodes were adjusted daily to reach the target brain regions. Once the tetrodes tips start to penetrate the brain tissue, some dimpling occur. Because of this, the tetrodes adjustment routine always guaranteed at least 12 hours between the adjustment and the recording to ensure the units’ stability.
Approximately one-week post-implant, the animal was reintroduced to the maze and started the retraining following the protocol of the WtT task. Two daily sessions were performed, one in the morning and one in the afternoon, during which neural data was recorded. Additionally, shortly after the morning session an open-field session was also recorded in order to establish spatial firing patterns of the recorded neurons. After the afternoon session, a small sleep session was further recorded to assess local-field potential (LFP) patterns and establish tetrode depth based on known physiological markers. Furthermore, tetrodes were adjusted daily to minimize the possibility of recording the same neurons on consecutive days. During this period the animal was again food deprived to ensure its engagement in task. Once the animal reached 50% of correct trials in a session without any end-of-range cue, with the 2.5 seconds interval and the tetrodes reached the target regions, the pharmacogenetics experiments started. During the experiment, the neural activity of the injected animal was recorded while the pharmacogenetic experiment was performed. During the silencing experiment, tetrodes were adjusted every two days to maximize overlap between the populations of neurons recorded on each pair of
Single units’ action potentials and local field potential (LFP), were recorded using an Open Ephys system based on Intan acquisition boards. Extracellular action potentials and continuous LFP were acquired at 30 kHz per channel, digitized and amplified using RHD2164 amplifier boards, and transmitted to the acquisition computer under the control of Bonsai (Lopes et al. 2015). UltraMega Sort 2000 (Hill, Mehta, and Kleinfeld 2012), as well as code written in Matlab (Mathworks) was used offline for single unit detection and sorting.
2.7.
Histological procedures
Expression of the hM4Di receptor was confirmed port-mortem. At the end of the experiments, the
animals were sacrificed through isoflurane overdose and transcardially perfused with 250 mL of PBS followed by 250 mL of 4% paraformaldehyde (PFA). PBS was used to remove the blood of the vasculature of the brain tissue and PFA was used as fixative. Brains were dissected, postfixed with a PFA solution for 24 hours at room temperature, then placed in a solution of 15% sucrose in PBS until the brains sunk, followed by 30% sucrose in PBS. After sinking in the solution, the brains were embedded in gelatin and frozen. Frozen brains were sectioned sagittally in 50 µm slices using a cryostat (LEICA, CM3050 S), mounted on microscope slides, and coverslipped with DAPI Fluoromount. Once the slices were prepared, we waited 5 days until they dried.
In order to amplify the signal, anti-mCherry immunohistochemical staining was performed. Fixed brain slices were de-gelatinized at 37ºC, permeabilized and blocked in a block solution containing 1% bovine serum albumin (BSA), 10% fetal bovine serum (FBS) and Tris-buffered saline (TBST, 0,2% Tween) for 1 hour. Brain sections were then incubated in anti-mCherry primary antibody diluted in block solution (0.1% TBST, FBS 4% and 1% BSA) at 4ºC for 48 hours. The sections were subsequently washed three times for 15 minutes in TBST and then incubated overnight at 4ºC in secondary Alexa-546 diluted in block solution. Following further washing, the sections were mounted, and cover slipped with DAPI Fluor mount and left to dry in a dark place.
The expression of the hM4Di viral construct in the regions of interest was confirmed using an Axio
Observer widefield fluorescence microscope (ZEISS) equipped with an Axiocam 506 mono CCD (ZEISS) and an LSM 880 confocal point-scanning microscope with Airyscan (ZEISS).
The histology protocol was also performed on a non-injected animal that served as a control, in order to show the absence of hM4Di-expression.
