This study investigated the effects of two different drugs, psilocybin and ... received IV psilocybin and placebo on two separate occasions in a double-âblind.
The effects of psilocybin and MDMA on hippocampal resting state functional connectivity L. T. J. Williams
Internal supervisor: Marty Sereno External supervisor: Robin Carhart-‐Harris September 2013 MSc Cognitive Neuroscience
The effects of psilocybin and MDMA on hippocampal resting state functional connectivity L. T. J. Williams
Internal supervisor: Marty Sereno External supervisor: Robin Carhart-‐Harris
Abstract This study investigated the effects of two different drugs, psilocybin and
MDMA on hippocampal resting state functional connectivity using functional magnetic resonance imaging (fMRI). In the psilocybin study, 15 healthy subjects received IV psilocybin and placebo on two separate occasions in a double-‐blind design. On each occasion they underwent a 12-‐minute resting state scan. The interaction between the infusion and hippocampal functional connectivity was analysed. Increases and decreases in connectivity were found, with decreases to the cortical nodes of the default mode network, and increases to the right insular. In the MDMA study, 25 healthy subjects received an oral dose of MDMA or placebo on two separate occasions in a double-‐blind design. On each occasion they underwent two resting state scans, and the differences in hippocampal connectivity were analysed. Increases and decreases were found, with increases to limbic regions, hippocampus and amygdala and the midbrain, and decreases to lateral and medial frontal regions. The differences in changes in connectivity due to the two drugs reflect both their different pharmacological mechanisms of action and distinct subjective effects.
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Introduction
This paper investigates changes in resting state functional connectivity
(RSFC), as measured via fMRI, between the hippocampus and the rest of the brain, in healthy human subjects under the influence of two different drugs, psilocybin (4-‐phosphoryloxy-‐N,N-‐dimethyltryptamine) and MDMA (3,4-‐ methylenedioxy-‐N-‐methamphetamine). Both substances are well known recreational drugs, currently controlled under the most stringent classification, as drugs with “no known medical application”. Both however have also shown promise in the treatment of a variety of mental health disorders. In this introduction, I will present a brief history of each drug, then discuss the methodology of RSFC, and summarize research into the so called “default mode network” (DMN) that has been implicated in these drug’s mechanisms action.
In the subsequent sections the methods and results for each of the two
experiments will be laid out, followed by a discussion on the significance of these results, drawing on a variety of strands of neuroscientific research to shed light on the possible mechanisms at work. Psilocybin
Psilocybin is the principle active ingredient in so-‐called “magic”
mushrooms of the genus psilocybe, and one of the most common naturally occurring psychedelic substances worldwide (Ott, 1993; Stamets, 1996). Psilocybin is part of the class of so-‐called “classic” psychedelics that also includes the naturally occurring mescaline and the semi-‐synthetic lysergic acid diethylamide (LSD). Psilocybe fungi also contain lower levels of the related compounds psilocin (4-‐hydroxy-‐N.N-‐ dimethyltryptamine) and baeocystin (4-‐ phosphoryloxy-‐N-‐methyltryptamine), and while psilocybin is pharmacologically active it its own right, in humans when consumed orally or administered via IV injection, it functions as a produg of psilocin (Hasler, Bourquin, Brenneisen, Bär, & Vollenweider, 1997). Figure 1 shows the molecular structure of these substances, and highlights their similarity to the modulatory neurotransmitter, serotonin (5-‐hydroxy-‐tryptamine, 5-‐HT). The neuropharmacology of psilocybin will be fully explored in the discussion section of this paper, but it should come
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as no surprise from its chemical structure that it acts principally on the serotonin system in the human brain, and acts as an agonist at a variety of serotonin receptors (Nichols, 2004).
The subjective effects of psilocybin have been extremely well
documented, and although there can be a large variation between subjects, these effects include perceptual distortions, from mild colour alterations and geometric patterns at low to medium doses, to elborate and detailed illusions of animals, people, places and objects at higher doses. Higher doses can also induce vivid memory recall and profound alterations in a person’s sense of subjective self, time and space (Hasler, Grimberg, Benz, Huber, & Vollenweider, 2004; Studerus, Kometer, Hasler, & Vollenweider, 2011). It has also been reported that high doses can induce so-‐called “mystical experiences”, which include feelings of total loss of self (so-‐called “ego death”), experiences of “oneness” and the divine (Griffiths, Richards, Johnson, McCann, & Jesse, 2008; MacLean, Johnson, & Griffiths, 2011).
Figure 1 – The molecular structure of: A -‐ Psilocybin, B -‐ Psilocin, C -‐ Baeocystin and D -‐ Serotonin
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There is a long history of traditional use of psychedelic mushrooms in
Mesoamerica, within ritual shamanic contexts for the purposes of healing and divination (Ott, 1993; Stamets, 1996), and while some researchers have put forward theories that trace their use far back into prehistory, the historical data remains unclear (Letcher, 2007; Wasson, 1980). In modern times, the pioneering ethnomycologist R. Gordon Wasson was the first Western researcher to travel to Mexico to seek out and consume mushrooms of the species psilocybe mexicana in their traditional setting (Wasson, 1957). He subsequently sent samples to Sandoz Pharmaceuticals in Switzerland, where the psychoactive ingredients were determined by the chemist Albert Hofmann, who had some years earlier made the serendipitious discovery of LSD, and was regarded as the world expert on the chemistry of psychedelic drugs (Hofmann, Heim, Brack, & Kobel, 1958; Hofmann, 1980).
During the first flush of psychedelic research in the 1950s and ‘60s,
hundreds of research papers were published looking into the potential therapeutic applications of psychedelics, principally LSD but also psilocybin, which was marketed for this purpose by the pharmaceutical company Sandoz under the trade name “Indocybin” for this purpose (Grinspoon & Bakalar, 1979). The socio-‐cultural upheavals of the ‘60s in the United States led to intense political pressure to “do something” about the spread of psychedelic drug use into the mainstream, and in 1968 LSD was made illegal to posses in the US, swiftly followed in 1970 by the Controlled Substances Act which banned a whole range of psychedelic drugs, including psilocybin; and the UN Convention on Psychotropic Substances of 1971 which codified this into international law (Grinspoon & Bakalar, 1979; Lee & Shlain, 1992; Stevens, 1987).
The legal control of psychedelics imposed, almost overnight, a
moratorium on human research with these substances worldwide, a problem that remains to this day (Nutt, King, & Nichols, 2013). Animal studies continued, as did pockets of research in Switzerland (Gasser, 1995), but it was not until Dr Rick Strassman pioneered the investigation of N,N-‐dimethyltryptamine (DMT) in healthy human subjects that cracks appeared in the once seemingly impenetrable barriers to psychedelic research (Strassman, 1991; Strassman, 1996; Williams, 1999).
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Since then, there has been a second blooming of research into the
therapeutic potential of psychedelic drugs. Psilocybin in particular has been the focus of this work, partially because of its shorter duration of effects than LSD, typically 3-‐6 hours (Shulgin & Shulgin, 1997), although the fact that LSD still carries with it negative historical connotations has surely been a factor too. Studies have been carried out investigating the use of psilocybin in treating anxiety and depression in end-‐stage cancer patients (Grob et al., 2011); the safety, tolerability and ability of high dose psilocybin to induce “mystical” experiences, and these experiences to positively alter personality traits (Griffiths, Richards, McCann, & Jesse, 2006; Griffiths et al., 2008; MacLean et al., 2011); and the potential for treating obsessive-‐compulsive disorder (Moreno, Wiegand, Taitano, & Delgado, 2006). Moreover, further pilot studies are also under way exploring the use of psilocybin as an adjunct to smoking cessation, with promising preliminary results (Johnson, 2013) and a trial investigating the potential of psilocybin to combat treatment-‐resistant depression is soon to commence (R. Carhart-‐Harris, personal communication, June 2013).
This therapeutic renaissance has so far only been accompanied by rather
limited amount of basic neuroimaging research – very little is known about how these substances affect the brain on the levels of cognitive and systems neuroscience. In Zurich, positron emission tomography (PET) studies measuring brain metabolism via radiolabelled [F-‐18]-‐fluorodeoxyglucose (FDG) showed increases in brain activity in prefrontal, limbic and thalamic regions (Gouzoulis-‐ Mayfrank et al., 1999; Vollenweider et al., 1997). In stark contrast to these results, our lab, conducting the first ever functional magnetic resonance imaging (fMRI) study on healthy subjects with psilocybin, showed only decreases in activation, as measured in two separate resting state studies via the modalities of arterial spin labelling (ASL) and blood oxygen level dependent (BOLD) signal (Carhart-‐Harris et al., 2012). The overlapping areas of decreased activity were confined to medial prefrontal cortex (mPFC), posterior parietal cortex (PCC) and subthalamic nuclei. The mPFC and PCC are known to be important nodes in the so-‐called default mode network (DMN) (Buckner, Andrews-‐Hanna, & Schacter, 2008; Raichle et al., 2001), which is discussed in more detail below. In the same report, the results of a functional connectivity analysis using a region of the
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mPFC as the seed region were presented, showing a decrease in connectivity between the mPFC and PCC (Carhart-‐Harris et al., 2012). MDMA
MDMA is an exemplar of the class of psychoactive drugs termed
“empathogens/entactogens”, and has unique profile of subjective effects. The terms were coined by Ralph Metzner and David Nichols, respectively, to refer to a class of psychoactive drugs that share properties with both stimulants and hallucinogens/psychedelics (Nichols, Hoffman, Oberlender, Jacob, & Shulgin, 1986; Nichols, 1986). MDMA, 3,4-‐methylenedioxy-‐N-‐methylamphetamine, shares subjective properties with both amphetamine stimulants (amphetamine, AMP, and methamphetamine, METH) and psychedelic phenethylamines (mescaline, DOM, 2C-‐B etc. – see Shulgin & Shulgin, 1992 for a full exploration of this class of substances). Likewise, its chemical structure is closely related to the above substances – see figure 2.
Figure 2 – Molecular structures of: A – MDMA, B – Mescaline, C – Methamphetamine
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The unique subjective effects of MDMA are noted in the literature and in
thousands of anecdotal reports as being extremely pro-‐social, inducing feelings of euphoria, empathy and positive mood in most subjects. Duration is typically 4-‐ 5 hours, with peak intensity reached about one hour post oral administration, and a plateau phase lasting 3-‐4 hours (for scientific reports see Gouzoulis-‐ Mayfrank, Hermle, Kovar, & Sass, 1996; Hysek, Domes, & Liechti, 2012; and Liechti, Gamma, & Vollenweider, 2001. For anecdotal reports see Holland, 2001 and Shulgin & Shulgin, 1992). Unlike the classic psychedelics, MDMA does not usually produce hallucinogenic effects such as the geometric patterns, alterations in colours, etc., although minor visual disturbances can happen at higher dose ranges (Gouzoulis-‐Mayfrank et al., 1996). Pharmacologically, MDMA functions as a releaser of 5-‐HT, working at monoamine transporter proteins to produce a massive efflux of 5-‐HT into the synaptic cleft (Liechti & Vollenweider, 2001) – more specific details on the neuropharmacology will be covered in the discussion section of this paper.
MDMA was first developed in 1912 by the German pharmaceutical
company, Merck, but only as a precursor to synthesis of a styptic, and no pharmacological studies were done until the 1950s (Bernschneider-‐Reif, Oxler, & Freudenmann, 2006). It was not until much later that underground chemist and so-‐called “Godfather of MDMA”, Alexander Shulgin, rediscovered it and described its subjective properties in humans (Shulgin & Shulgin, 1992). During the 1970s and early ‘80s, the drug gained popularity in two quite different arenas: Firstly, as an adjunct to psychotherapy, often used under the radar by therapists who had often originally used LSD but been forced to stop when it became illegal in 1968 (Stolaroff, 1997, 2004). Secondly, it gained infamy as a recreational dance drug, originally in Texas before spreading via Ibiza to the UK, where it helped ignite a new counterculture based around “rave” music, much as LSD had done over a decade earlier (albeit to a quite different tune) in the US (Collin, 1998; Holland, 2001).
For once the UK got there first, making MDMA illegal in 1977, to be
followed by the US in 1984. The move was heavily criticised in the US by a group of research scientists, psychiatrists and therapists, who petitioned the US Drug Enforcement Agency, to try to prevent it being listed in schedule 1, the most
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restrictive classification of illicit substances, with no known medical use. Despite a judge’s recommendation that MDMA be placed in the less restrictive schedule 3, pleas fell on deaf ears and the DEA placed it into schedule 1, where it languishes to this day (Holland, 2001). Just like the restrictions on psilocybin, such classification continues to hamstring research, despite criticism from prominent neuroscientists (Blakemore, 2009; Nutt et al., 2013).
From early on after its rediscovery, MDMA’s potential as an adjunct to
psychotherapy was recognised, but is now finally being investigated in modern clinical trials. Interest has focused on its potential benefits for post-‐traumatic stress disorder (PTSD), because of its subjective euphoric and empathic effects enabling patients to talk and think about their trauma, without being overwhelmed by negative thoughts -‐ as is so often an issue in therapy for this disorder (Sessa, 2011). Both pilot and follow up studies have now been completed, and offer very promising, if preliminary results (Mithoefer et al., 2013; Mithoefer, Wagner, Mithoefer, Jerome, & Doblin, 2011).
Despite an increasing number of cognitive and behavioural studies of
acute MDMA intoxication (Hysek et al., 2012; Liechti & Vollenweider, 2001; van Wel et al., 2012) there have been no RSFC fMRI studies completed, and the results presented hear form part of the first ever study in this area (Carhart-‐ Harris et al., 2013). Functional Connectivity
Neuroimaging techniques have, in the last 20 years, explored the
principles that the brain is both functionally segregated and at the same time, for every brain process, specific regions recruit other, sometimes anatomically remote segregated regions in a process of integration. It is specifically the phenomenon of integration that functional connectivity is concerned with (Friston, 2011; Horwitz, 2003). Resting state functional connectivity, particularly for fMRI, has become increasingly popular in recent decades (Biswal, 2012). For example, using BOLD fMRI, one can extract the average time series of fluctuating activity from a particular brain region and then assess how its activity covaries with that of the rest of the brain (Biswal, Zerrin Yetkin, Haughton, & Hyde, 1995).
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Areas which show a high degree of covariance between their time series can be said to have positive functional connectivity or positive coupling. Functional connectivity does not allow one to test causal hypothesis about the direction of information transfer, for this “effective connectivity” methodologies such as Dynamic Causal Modelling (DCM) are required. The particular attraction of functional connectivity however is that, in theory, it can be used to determine neural endophenotypes or biomarkers for particular pathologies – or in present study, different conscious states (Friston, 2011). The Default Mode Network
During the 1980s, the popularity of functional PET as a neuroimaging
technique in cognitive psychology and neuroscience came into its own. The methodology of subtractive analysis, whereby a subject’s control scan is subtracted from their task scan, became very popular, with the majority of results reporting functional increases in brain activity due to the imposition of cognitive tasks, and these increases in activity used to support ideas of functional segregation within the brain (Raichle & Snyder, 2007). However, as research programmes expanded, a rather unexpected phenomena was noted – that along with increases in activation, related to the particular modality or type of task, decreases in activation were also seen. Not only were these decreases observed over a variety of different task paradigms, but the regions where decreases were located had a degree of consistency across the different experiments, and were reviewed in a number of meta-‐analyses (Binder et al., 1999; Mazoyer et al., 2001; Shulman, Fiez, & Corbetta, 1997). The regions involved in these task related decreases were to become known as the “default mode network” and include the now familiar trio of medial frontal cortex, PCC and lateral parietal regions.
Given the discovery of these decreases in activation, two possible
explanations presented themselves. Firstly -‐ there was a hitherto undetected increase in activation during the resting state, so that the control state was in effect an activated one, just like the task state. Secondly – that the brain had a baseline state of activation, and during cognitive tasks the shift in energy resources within the brain resulted in patterns of deactivation. The pioneers in
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this field, Raichle and colleagues, set about to test the second hypothesis using PET (Raichle et al., 2001). They measured the oxygen extraction fraction (OEF) – that is, the ratio between blood flow and oxygen consumption. It is well established that brain areas showing increased activation show a decreased OEF – that is, blood flow increases are much higher compared to increases in oxygen consumption in activated brain regions.
Raichle and colleagues were able to show that the average OEF within the
DMN regions previously identified in the meta-‐analyses was no higher than any other region of the brain, suggesting that at rest there was indeed a baseline of brain activity. They also analysed the rest of the brain, and found no areas of activation, as measured by the change in OEF, anywhere. This leant strong support to the second hypothesis over the first. It is also important to note that the regions of the DMN, although they shows consistent deactivation between different tasks, are not the only regions active at rest, rather they are part of an overall baseline of activity in the whole brain (Raichle & Snyder, 2007). Further research in this area lead to investigation of the slow (below 0.1 Hz) spontaneous fluctuations that can be observed in the BOLD signal, which had been previously mostly removed as noise. The discovery that regions which tended to be activated or deactivated together also shared a high degree of coherence in these slow spontaneous signals, even without the presentation of tasks or stimuli, i.e. in the resting state, allowed the determination of a “task positive” network, anti-‐correlated to the DMN (Fox et al., 2005). Later studies have dissembled this general network into more specific salience and executive networks (Seeley et al., 2007).
The discovery of the DMN of course naturally lead on to questions of what
its functional significance is… and there is no shortage of speculation on this matter. A growing number of researchers have suggested that the DMN may be involved in supporting a subjective sense of self (Carhart-‐Harris & Friston, 2010; Gusnard & Raichle, 2001; Gusnard, 2005; Qin & Northoff, 2011); a second strand suggests that the DMN is related to “internal” abstract awareness, opposed to anti-‐correlated to networks supporting “external” sensory awareness (Vanhaudenhuyse et al., 2011). This idea is lent further support by recent work that demonstrates that the DMN, along with other cortical hubs, are the furthest
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removed in connection terms from the sensory cortices (Sepulcre, Sabuncu, Yeo, Liu, & Johnson, 2012). Buckner et al., (2008) detail two possible functions – as a monitor of the external environment, or as a system responsible for simulation in diverse situations such as autobiographical memory and theory of mind perspective taking. This second idea, of a flexible simulation system, will be relevant in the subsequent discussion section of this paper. The DMN & the hippocampus
Since the initial proposal of the DMN, a number of studies have put
forward the idea that the hippocampus is an important element of the DMN (Andrews-‐Hanna, Reidler, Sepulcre, Poulin, & Buckner, 2010; Kahn, Andrews-‐ Hanna, Vincent, Snyder, & Buckner, 2008; Vincent et al., 2006). Studies have examined the RSFC of the hippocampus, and found it to have significant coupling to the PCC, mPFC and iLPC (Buckner et al., 2008; Vincent et al., 2006) and there is also evidence of structural anatomical connections between the PCC and medial temporal lobe regions (Greicius, Supekar, Menon, & Dougherty, 2009; Kobayashi & Amaral, 2003). Direct stimulation of the hippocampus has been observed to increase glucose metabolism in the PCC and mPFC, which may suggest a role for the hippocampus as a driving node of the DMN (Laxton et al., 2010).
It is also clear that trying to delimit the DMN precisely is impossible –
there appear to be sub-‐networks within the DMN, that show greater or lesser degrees of correlation. Within the DMN, the nodes of the PCC, vmPFC and iLPC seem to be common to all methods of defining the DMN – whereas on the other hand, depending on the method of investigation, other nodes may or may not be also coupled. For instance the hippocampus is anti-‐correlated with the dmPFC, so it appears that there can be either activation of the DMN with hippocampus, or with the dmPFC, but not both simultaneously (Buckner et al., 2008). Recent magnetoencephalography (MEG) studies point towards the complex temporal dynamic nature of these networks, something that tends to be lost in fMRI analysis, because of its poor temporal resolution (de Pasquale et al., 2012). Given the existence of sub-‐networks within the range of regions that are candidates for
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inclusion in the DMN, and the emerging picture of these networks as constantly shifting over time, it would be possible to describe a number of DMN “variants”. Such variants may produce different contributions to aspects of cognition, in a fluid, dynamic nature.
Also of note is that Buckner and colleagues used a seed they labelled the
hippocampal formation (HF+) to indicate that it also contained a portion of the parahippocampal gyrus. Other, very recent work supports and develops this idea – Ward and colleagues have shown evidence that the connection of the hippocampus to the DMN is dynamic, not static, and that the parahippocampal gyrus (PHG) is crucial in mediating connections to the DMN, specifically to the PCC (Ward et al., 2013). Study Aims
The first analysis presented here, of the psilocybin experiment, utilised
the same fMRI dataset previous published by Carhart-‐Harris and colleagues (Carhart-‐Harris et al., 2012), to further examine changes in functional connectivity to brain regions associated with the default mode network. The previous research had produced two salient observations, which were contrary to earlier hypotheses and PET neuroimaging studies that administration of psilocybin would produce increases in brain activity – firstly, as determined by fMRI, there were only overall decreases in brain activity, as measured by changes in CBF and BOLD signal. Secondly, the overlap in decreases between the two methodologies were mainly confined to high level cortical regions, such as those that belong to the DMN.
As a secondary part of that study, functional connectivity analysis was
performed using a major node of the DMN, the vmPFC, as a ROI, and an average time series signal was abstracted from that region and compared to the whole brain. This analysis showed decreases in positive coupling between the vmPFC and the PCC, another important node within the DMN (Carhart-‐Harris et al., 2012).
The aims of the current study were to further investigate changes in RSFC
under psilocybin. The hippocampus was chosen for two reasons: Firstly, as
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previously discussed, it appears to be a major sub-‐cortical node of the DMN. Secondly, early work with LSD and mescaline suggested a possible role for medial temporal regions in the mechanism of action of psychedelics (Baldwin, Lewis, & Bach, 1959; Monroe, Heath, Mickle, & Llewellyn, 1957; Schwarz, Sem-‐ Jacobsen, & Petersen, 1956; Serafetinides, 1965). Given that the parahippocampal gyrus appears to play an important role in mediating the connections between the hippocampus and cortical nodes of the DMN (Ward et al., 2013), the seed region used in the analysis was composed of bilateral hippocampi and parahippocampal gyri.
Subsequently, our lab also conducted the first RSFC study in healthy
subjects under the influence of MDMA. As part of the analyses, a number of seed regions were used, and we chose to use the same region as in the psilocybin study, to be able to contrast the two drug effects. Analyses using other ROIs were conducted by other researchers and are not reported here (Carhart-‐Harris et al 2013).
Our hypotheses in these analyses were twofold – firstly, we proposed that
hippocampal RSFC would be altered in both cases, and secondly, that there would be observable differences in these changes to connectivity, due to the two drugs reported different subjective effects and different pharmacological mechanisms.
Experiment 1 – Functional connectivity of the hippocampus under psilocybin Methods
The study design was a within-‐subjects, placebo controlled double-‐blind
protocol, approved by the local National Health Service research ethics committee and Imperial College research and development body, and conducted in accordance with good clinical practice guidelines. A Home Office license was
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obtained for storage and handling of a schedule 1 drug. The University of Bristol sponsored this research.
Fifteen healthy subjects were recruited by word-‐of-‐mouth, 13 males and
2 females (mean age = 32, SD = 8.9). All subjects gave informed consent and were screened prior to enrolment. Entry criteria included the following: at least 21 years of age, no personal or immediate family history of major psychiatric disorder, no cardiovascular disease, no history of substance abuse, and no history of severe adverse response to psychedelic drugs. All participants had used psilocybin (in the form of fruiting bodies of the psilocybe genus) previously, but had no psychedelic drug use in the six weeks prior to the study. The mean number of uses of psilocybin per subject was 16.4, SD = 27.2.
All MRI imaging was performed using a 3T GE HDx system. Each
functional scan was proceeded by an anatomical scan, which were 3D FSPGR scans in an axial orientation, with a field of view = 256 x 256 x 192 and matrix = 256 x 256 x 192, giving an isotropic voxel resolution of 1mm (TR/TE = 7.9/3.0 ms; inversion time = 450 ms; flip angle = 20°).
BOLD-‐weighted fMRI data were acquired using a gradient-‐echo EPI
sequence, TR/TE 3000/35 ms, field-‐of-‐view = 192 mm, 64 × 64 acquisition matrix, parallel acceleration factor = 2, 90° flip angle. Fifty-‐three oblique axial slices were acquired in an interleaved fashion, each 3 mm thick with zero slice gap (3 × 3 × 3-‐mm voxels). A total of 240 volumes were acquired.
Each subject underwent two 12-‐minute resting state scans, with eyes
closed, at least 7 days apart. At the 6 minute mark, they received a manual IV infusion over the course of 60 seconds, either of 10ml of saline (placebo condition) or 2mg of psilocybin HCl dissolved in 10ml of saline (drug condition). Subjects were asked, post-‐scan, to rate the subjective peak intensity of the experience on a 10 point scale with 10 being “extremely intense effects”, and 0 being “no effects”. Subjects also completed responses to a variety of more detailed items on the subjective experience, details of which are not relevant for the current study (see supplementary materials in Carhart-‐Harris et al., 2012).
All analyses were performed using Oxford University’s Oxford Centre for
Functional MRI of the Brain (FMRIB) software, FMRIB Software Library (FSL) version 5.0 (FMRIB Analysis Group, 2012a; Jenkinson, Beckmann, Behrens,
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Woolrich, & Smith, 2012; Smith et al., 2004; Woolrich et al., 2009). For each of the analysis performed, an ROI mask was created, based on FSL’s Oxford-‐ Harvard cortical and subcortical anatomical atlas (FMRIB Analysis Group, 2012b). The ROI was composed of the bilateral hippocampi and parahippocampal gyri.
For each subject, on placebo and drug conditions a short script was used
to generate an average time series for the region of interest, using the “fslmeants” command:
fslmeants –i filtered.func.datastandard.nii.gz –m [mask
file location] –o [output filename].txt
This command was run within each subject’s relevant data directory, and
took the whole dataset for each patient, applied the mask to it, and outputted the average time series for the set of voxels within the mask. Grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) were also anatomically segmented from each subject and masks used to generate average time series for each.
Subsequent to that, FSL’s Expert Analysis Tool (FEAT) (FMRIB Analysis
Group, 2012c) was used to analyse the data. Firstly, first level analyses were performed on each individual subject. At this level, a high-‐pass filter of 100 seconds was used, and each volume was spatially smoothed (6mm FWHM). FEAT uses a general linear model (GLM) based analysis, called FMRIB's Improved Linear Model (FLIM). Essentially, FEAT uses a GLM of the form: ! ! = !×! ! + ! + !(!) Where y(t) = data, x(t) = model, a = parameter b = constant and e(t) = error in model fit. This is formulated in matrix notation, with a design matrix, X, and a parameter matrix, A. The design matrix is composed of a number of explanatory variables (EVs). In the case of the first level subject analyses, the GLM included six different EVs:
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1. Infusion – a basic square-‐wave model with gamma convolution, informed by the time course of the subjective effects of IV psilocybin (Carhart-‐ Harris et al., 2011), modelled the drug effects. 2. ROI – average time series of the voxels in the ROI mask 3. Interaction – the interaction between EVs 1 and 2. 4. GM – average grey matter time series 5. WM – average white matter time series 6. CSF – average cerebrospinal fluid time series
The final three EVs – GM, WM, CSF – were designated as regressors of no
interest. FEAT also automatically generates further regressors of no interest based on motion confounds for each subject.
Four contrasts were set up – positive interaction, negative interaction,
positive ROI and negative ROI. The contrasts of primary interest in this study were the interaction terms, as they showed the interaction between the drug infusion and the changes in the correlation between the ROI and the rest of the brain. The interaction EV was thus weighted 1 and -‐1 for the positive and negative interaction contrasts respectively, while the ROI EV was weighted 1 and -‐1 for the positive and negative ROI contrasts respectively. All regressors of no interest were weighted as zero for all contrasts.
Note that the inclusion of GM as a regressor of no interest is somewhat
controversial (Murphy, Birn, Handwerker, Jones, & Bandettini, 2009) – as a validity check, the analysis was re-‐run without the GM regressor, the differences (or rather, lack thereof) are discussed in the results section.
These first level subject results where then fed into a second level FEAT
design to calculate the group mean for each condition, using the 15 subject scans as inputs into another GLM, with one EV, the group mean. Group means were generated for the placebo and drug conditions. Following that, all 30 scans were put into another second level FEAT analysis, to subtract each subject’s placebo scan from their drug scan.
All results were produced as z score maps, cluster thresholded at z > 2.3,
with whole brain correction for multiple comparisons at p = 0.05.
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Results
The principle subjective effects of 2mg IV psilocybin have been previously
reported, and included: altered visual perception (such as geometric patterns, alterations in colour hue and intensity, warping/melting movements in static objects); altered senses of space, time and self; and increased freedom of thought (Carhart-‐Harris et al., 2011; Carhart-‐Harris et al., 2012).
Figure 3 shows the baseline connectivity of the hippocampal ROI, warm
colours indicating a positive coupling. This image was produced from the group mean of the placebo scans, and shows the coupling of the ROI EV. In the placebo group mean, the infusion interaction generated no significant voxels, as expected.
The results for the group mean (interaction contrast) in the drug
condition with the hippocampal seed demonstrated both increases and decreases in functional connectivity. Increases were seen to right insular and temporal cortex, whereas decreases were seen to the vmPFC, the PCC and putamen, visual cortex and iLPC, shown in figure 4. Unfortunately, the second level analysis using all 30 drug and placebo conditions as inputs, and looking at drug > placebo as the EV of interest, produced no significant voxels.
A correlation analysis, using Pearson’s r was done, comparing the
subjective ratings of the peak intensity of psilocybin’s effects, with the decreases in RSFC between the hippocampal seed and the DMN, and the results are displayed in figure 5. There was a trend level correlation that just failed to reach significance, despite the removal of a single outlier, who was removed as he was judged to be understating the intensity of the effects.
The validity check -‐ re-‐running the analyses without GM as a regressor of
no interest, revealed an almost identical z score map, and is not shown here.
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Figure 3 – Baseline RSFC between the ROI and the rest of the brain during the 12 minute placebo scan. The ROI is shown in yellow, orange regions are those positively coupled to the ROI. Thresholded with cluster size z > 2.3, whole brain corrected p = 0.05.
Figure 4 – Changes to RSFC under infusion of psilocybin. The ROI is shown in yellow, regions with increased coupling are shown in orange, and regions with
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decreased coupling are shown in blue. Thresholded with cluster size z > 2.3, whole brain corrected p = 0.05.
Figure 5 – correlation between subjective ratings of intensity and decreases in hippocampal-‐DMN RSFC
Experiment 2 – Functional connectivity of the hippocampus under MDMA Methods This was a within-‐subjects, double-‐blind, randomised, placebo-‐controlled study. Participants were scanned twice, 7 days apart, once after MDMA and once
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after placebo. The study was approved by NRES West London Research Ethics Committee, Imperial College London’s Joint Compliance and Research Office (JCRO), Imperial College’s Research Ethics Committee (ICREC), IMANOVA Centre for Imaging Science and Imperial College London’s Faculty of Medicine, and was conducted in accordance with Good Clinical Practice guidelines. A Home Office Licence was obtained for the storage and handling of a Schedule 1 drug and Imperial College London sponsored the research.
Twenty-‐five healthy participants were recruited by word of mouth, 18
males and 7 females (mean age = 35, SD = 11). All had at least one prior experience with MDMA, had used MDMA previously an average of 35 times (SD = 51, range = 1 to 200), with a mean time to last use of 1400 days (SD = 2351, range = 7 to 7300 days). Participants were screened for drug use via urine sample before the scans, and were confirmed as no having used MDMA for at least 7 days and no other drugs for 48 hours. A breathalyser test was also used to confirm that no subjects had consumed alcohol. Participants were also screened for general health, MR-‐compatibility and present mental health. Screening involved routine blood tests, electrocardiogram, heart rate, blood pressure and a brief neurological exam.
MR images were acquired on a 3T Siemens Tim Trio (Siemens Healthcare,
Erlangen, Germany) using a 32-‐channel phased array head coil. Anatomical reference images were acquired using the ADNI-‐GO recommended MPRAGE parameters (1mm isotropic voxels, TR = 2300ms, TE = 2.98ms, 160 sagittal slices, 256x256 in-‐plane resolution, flip angle = 9 degrees, bandwidth = 240Hz/pixel, GRAPPA acceleration = 2). T2*-‐weighted echo-‐planar (EPI) images were acquired for the resting state functional scan using 3mm isotropic voxels in a 192mm in-‐plane FOV, TR=2s, echo time = 31ms, 80 degree flip angle, 36 axial slices in each TR, bandwidth = 2298 Hz/pixel, and a GRAPPA acceleration of 2. 180 volumes were acquired during the functional imaging paradigm, which took 6 minutes to complete, and there were 2 of these resting state scans.
Each session consisted of 60 minutes of functional scanning. During that
period, two ASL and two BOLD resting state scans were performed. The protocol was as follows – at t = 0, subjects were orally administered either a capsule containing 100mg MDMA HCl or a placebo. At t = 50 minutes, the first ASL scan
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was performed, then at t = 60 minutes, the first BOLD scan was begun. During the following 40 minutes, behavioural paradigms were completed by the subjects. Then, at t = 103 minutes, the second ASL scan began, and was followed at t = 113 minutes by the second BOLD scan. The ASL scans and behavioural tasks performed are not relevant to the present study, and will not be discussed further. The schedule of events is illustrated in figure 6, below.
Figure 6 – Schedule of events during the MDMA scan sessions.
Subjects gave ratings of intensity using a simple visual analogue scale
(VAS), completed in the scanner. The bottom of the scale was marked “no effects” and the maximum “extremely intense effects”. Subjects could increase and decrease the sliding scale in 5% increments, using their right middle and index finger whilst in the scanner, and the scale always started at the 0% position. Subjects were asked to rate the intensity at 59, 67, 102, 112 and 120 minutes during the scanning session.
Subjects also completed a more extensive list of VAS-‐style items to assess
more specific subjective effects. This was completed 4.5 hours after capsule ingestion, when most of the subjective effects of MDMA had subsided. Some items were tailored to refer to commonly reported subjective effects of MDMA, expressed in colloquial terms (e.g. ‘I felt loved up’), and others were items used in the previous experiment with psilocybin (Carhart-‐Harris et al., 2012), selected in order to assist comparisons with this classic ‘psychedelic’ state. The VAS scales had a bottom anchor of ‘no, not more than usually’ and a top anchor of ‘yes, much more than usually’ with the exception of a control item that read ‘I felt entirely normal’. In this case, the bottom anchor read ‘No, I experienced a
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different state altogether and the top anchor read ‘Yes, just as I usually feel’. There were 32 items in total in this questionnaire and its basic format was based on the APZ questionnaire for altered states of consciousness (Dittrich, 1998).
Correlations between changes and RSFC and subjective ratings of drug
intensity and positive affect were performed on data from the ROI. Multiple ROIs were tested by other researchers, and for each ROI, the region that showed the most marked change in connectivity was used for the correlation analysis. Pearson’s r was used to calculate the statistical significance of correlations and statistical thresholds were corrected for multiple comparisons using Bonferonni correction. To look at positive affect, five items related to positive mood effects were collapsed into one single factor, taking the mean for these items for each subject. These items were: ‘I felt amazing’, ‘I felt loved-‐up’, ‘I felt energised and enthusiastic’, ‘I felt a profound inner peace’, ‘I felt an inner warmth’. Tests were corrected for multiple comparisons -‐ .05/2 because 2 different subjective rating parameters were explored. For the correlations using positive affect ratings, all data points were included in the analyses; however, five participants gave ratings of zero for effects intensity while on MDMA in the scanner, despite reporting noticeable subjective effects on exiting it, and so these were considered null and removed.
As in the previous experiment with psilocybin, FSL was used to analyse
the resting state BOLD data collected, although the set up of the model was slightly different due the differences in method of drug administration. The same anatomically defined hippocampal/parahippocampal mask was used to generate average time series for the voxels under the mask, and this was entered into the first level GLM, however in this case there was no infusion or interaction EV. Instead, the only EVs were the ROI time series and the three regressors of no interest – GM, WM and CSF.
To generate a map of baseline functional connectivity of the ROI, a second
level GLM analysis was run in FEAT, using the placebo scans as inputs, to produce a group mean. To compare the resting states functional connectivity between the drug and placebo conditions, the first level results were fed into a second level GLM in FEAT. This included as EVs the 25 subjects, each of which had four inputs (the two drug condition scans, and the two placebo condition
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scans, and one final EV, “placebo vs. drug”). Two contrasts were created, “placebo > drug” and “drug < placebo”, with each subjects placebo scans weighted as 1 and drug scans weighted as -‐1, and a mixed effects analysis was run to produce z score maps, thresholded at z > 2.3, p = 0.05. Two scans out of the total 100 had to be removed because of excessive movement. Results
Figure 7 shows at the top, the regions positively coupled to the ROI at
baseline, taken from the group mean. The bottom half of the figure shows the changes in RSFC due to the MDMA condition – increases are shown in warm colours, whilst decreases are shown in cool colours. Increases in connectivity were seen to right amygdala, right hippocampus and regions in the brainstem, whilst decreases were seen to the mPFC and lateral frontal and temporal regions.
Fig 8 shows the correlation between the subjective drug intensity and the
grouped positive mood ratings, and shows significant correlations for both (given the correction for multiple comparisons reducing the p value for significance to 0.025). The mPFC was chosen for these analyses as the region showing the greatest decrease in connectivity with the ROI.
Discussion
The two experiments presented here form part of the very first studies
using fMRI to investigate RSFC changes produced by the drugs psilocybin and MDMA, and begin to cast light on the acute systems level effects of these substances in the human brain. Focusing on one particular parameter – hippocampal RSFC – we can clearly see two very different patterns of altered connectivity with these two different drugs.
Under psilocybin, decreases in connectivity were observed that were
mostly confined to regions associated with the DMN – the PCC, mPFC and iLPC, although there were also some decreases in visual cortical areas. Increases in connectivity were seen to bilateral insular and temporal cortices. The fact that decreases were mostly confined to areas of the DMN is further evidence that this
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network is implicated in the action of psilocybin (Carhart-‐Harris et al., 2012; Carhart-‐Harris et al., 2012; Carhart-‐Harris, et al., 2012).
The situation is quite different for MDMA – here, instead we see a broader
mixture of increases and decreases in connectivity, including increases to a number of limbic regions – right hippocampus and amygdala – as well as the midbrain, and decreases to frontal regions including the anterior cingulate cortex and medial frontal regions. The only real overlap between the two substances is in the decreases to connectivity in the mPFC.
Although not fully understood, there is now a large amount of data on the
differing neuropharmacological mechanisms of action of the two drugs at synaptic levels. On administration, psilocybin is immediately dephosphorylated to its prodrug, psilocin (Hasler et al., 1997). Psilocin is a fairly potent partial agonist at serotonin receptors, including the 5-‐HT1A, 5-‐HT2A and 5-‐HT2C receptor subtypes with affinities in the tens of nM (Blair et al., 2000; Halberstadt & Geyer, 2011; Sard et al., 2005). It is believed that it is the 5-‐HT2A receptor that is responsible for the unique subjective effects of classic psychedelics, and there is a strong correlation between 5-‐HT2A affinity and potency (Glennon, Titeler, & McKenney, 1984; Nichols, 2004). This is further supported by studies that have shown the ability of ketanserin, a 5-‐HT2A antagonist, to attenuate the effects of psilocybin in human subjects (Quednow, Kometer, Geyer, & Vollenweider, 2012; Vollenweider, Vollenweider-‐Scherpenhuyzen, Bäbler, Vogel, & Hell, 1998).
The 5-‐HT2A receptor is part of the larger class of G-‐protein coupled
receptors (GPCRs), metabotropic receptors that unlike ionotropic receptors, do not directly influence the flow of ions in and out of the cell to contribute to the cell depolarizing sufficiently to generate an action potential. Rather, their influence is indirect, mediated by a number of secondary messenger cascades, where the G-‐proteins react with an effector, activating enzymes such as adenylyl cyclase, in turn producing cAMP and eventually modulating ion transport by phosphorylating ion channels (Roth, 2011).
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Figure 7 – Baseline RSFC to the hippocampal seed is shown in the top half of the figure, while the bottom half shows changes in connectivity due to the MDMA condition. All images thresholded and z > 2.3, and whole brain corrected at p = 0.05.
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Figure 8 – Correlation between subjective ratings of intensity and positive mood with changes in RSFC between the hippocampal seed and the mPFC region.
For some time, pharmacological orthodoxy was that a GPCR could be in
one of two states, active or inactive. Every agonist ligand bound preferentially to the active state, shifting the equilibrium towards more active receptors. However, in recent years, the idea that a receptor could only be in one of two states, active and inactive has come under close scrutiny. To take the 5-‐HT2A receptor as our example, it appears that there are a number of G-‐proteins coupled to the receptor, and they can be differentially activated to produce a
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complex cascade of secondary messenger molecules (Kurrasch-‐Orbaugh, Parrish, Watts, & Nichols, 2003). Contemporary models suggest that in fact a receptor can take up a number of different conformations, each one stabilized by a different agonist, and producing a specific pattern of secondary messenger activation. This has become known as receptor “functional selectivity” (Kenakin, 2003; Urban et al., 2007).
This area has been well studied with regard to the psychedelic drugs, as
one of the puzzling questions that remains is why some 5-‐HT2A agonists, like LSD and psilocybin, produce a psychedelic state, whereas other agonists such as 5-‐HT itself, tryptamine and lisuride do not. Although very much a live area of research, it appears that evidence is emerging that psychedelic drugs may activate specific receptor states and thus specific secondary messengers, producing their global brain effects (González-‐Maeso & Sealfon, 2009; González-‐Maeso et al., 2007; Kurrasch-‐Orbaugh, Watts, Barker, & Nichols, 2003). It seems reasonable that the synaptic level effects are the distal cause of the observed changes to global blood flow and RSFC, and more tentatively that it is the specific secondary messenger profile downstream from the receptor that eventually generates these effects. Much further work needs to be done here, including investigating what changes to resting state brain function non-‐psychedelic agonists might have, and whether under certain conditions endogenous ligands, such as 5-‐HT can be made to activate 5-‐HT2A into the psychedelic conformation. It is also currently unclear to what extent other serotonin receptor subtypes play in the effects of psychedelics – while in vitro and animal research continues apace, developing ever more potent and selective 5-‐HT2A agonists (Braden, Parrish, Naylor, & Nichols, 2006; Juncosa et al., 2013) which suggest that selectivity for 5-‐HT2A over other receptor subtypes may be important for psychedelic effects, human subjective data for these highly specific ligands is non-‐existent, and the potential for a role for 5-‐ HT2C and 5-‐HT1A receptors remains (Nichols, 2004).
MDMA, on the other hand, elicits a broader range of neurochemical
effects, and rather than being a direct agonist it is a potent releaser of monoamines. Evidence suggests that it is the massive efflux of serotonin that is responsible for MDMA’s cognitive effects, and what sets it apart from other stimulants (Liechti & Vollenweider, 2001), mediated by its effects at serotonin
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transporter proteins (SERT) and intracellular vesicle transporter proteins (VMAT) (Rudnick & Wall, 1992). However it is also clear that MDMA also causes the release of norepinephrine (NE), dopamine (DA) and also acetylcholine (ACh), subsequent to its 5-‐HT releasing effects (Gudelsky & Yamamoto, 2008; Rothman et al., 2001). These neurotransmitters contribute significantly to the subjective effects (Hysek et al., 2011).
Given MDMA’s non-‐selective monoamine releasing properties, it is
perhaps not surprising that we see a more varied profile of changes to hippocampal RSFC compared to that seen with psilocybin, and indeed to global changes in brain blood flow (Carhart-‐Harris et al., 2013). Such massive efflux of modulatory neurotransmitters will have multiple indirect agonist effects on a large number of 5-‐HT, DA and NE receptors. Further research involving the co-‐ administration of different selective receptor antagonists will help to tease apart the relative contributions of the different modulatory systems to the subjective effects. For instance, appears that 5-‐HT2A receptors are involved in the subjective perceptual changes, emotional excitation and adverse responses produced by MDMA, whilst having limited effect on subjective positive mood and well-‐being (Liechti, Saur, Gamma, Hell, & Vollenweider, 2000). On the other hand, NE release appears to contribute more prominently to subjective drug high, stimulation, emotional excitation and physiological measures (Hysek et al., 2011). It would be an obvious step to investigate the effects of selectively blocking the different transporter proteins for each neurotransmitter system, and to correlate this with changes in functional neuroimaging measures.
Although the molecular, synaptic level of the effects of psychedelics and
empathogens/entactogens has been the focus of much study, the systems level effects are much more poorly understood, and the results presented here contribute to bridging the knowledge gap between these two levels. With regard to psilocybin, it is known that the 5-‐HT2A receptor is heavily expressed in both the PCC and the mPFC, but relatively little in the hippocampus (Carhart-‐Harris et al., 2012; Erritzoe et al., 2009). 5-‐HT2A receptors are most dense on the post-‐ synaptic dendrites of layer 5 pyramidal neurons (Weber & Andrade, 2010). It has been theorized that the decreases in activity and RSFC within the DMN are caused by psilocybin’s interactions with 5-‐HT2A receptors on these layer 5
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neurons, increasing their spiking rate, leading to downstream activation of GABA interneurons and subsequent drop in net activity within these dense 5-‐HT2A regions (Carhart-‐Harris et al., 2012).
Since MDMA has such a broad range of releasing effects, it is hard to make
any firm claims about what synaptic level interactions might be at work, however it is know that the hippocampus has a high density of 5-‐HT1A receptors (Köhler, Radesäter, Lang, & Chan-‐Palay, 1986). It is also thought that anti-‐anxiety medications, such as selective serotonin re-‐uptake inhibitors and direct 5-‐HT1A agonists like buspirone, work through stimulation of post-‐synaptic 5-‐HT1A receptors, to normalise limbic activity (Gordon & Hen, 2004). Therefore this might suggest a role for hippocampal 5-‐HT1A in the pro-‐social aspects of MDMA’s subjective effects. However, research using the 5-‐HT1A blocker pindolol has produced mixed results on its effects on the MDMA subjective state (van Wel et al., 2012), which would not support this theory.
Both limbic hyperactivity and increased connectivity between the
hippocampus and the mPFC have been implicated in anxiety disorders (Adhikari, Topiwala, & Gordon, 2010; Engel, Bandelow, Gruber, & Wedekind, 2009). Since we observed a correlation between subjective positive effects and decreased coupling between the hippocampal ROI and the mPFC, this would suggest the possibility that at least part of MDMA’s subjective effects are caused via this reduction in connectivity. Given that there is evidence of MDMA’s utility as an adjunct to PTSD psychotherapy (Mithoefer et al., 2013, 2011), this may help to explain, in part, the mechanism of those benefits in reducing anxiety and fear during therapy.
The final section of this discussion will attempt to look at more detail at
the psilocybin result, and place the observed changes in hippocampal RSFC in the context of resent theories of hippocampal/DMN interaction, to offer potential causal mechanisms for psilocybin’s subjective effects. Psilocybin, the default mode and scene construction
The hippocampus is involved in a broad range of cognitive functions,
chiefly those of declarative and spatial memory (Burgess, Maguire, & O’Keefe,
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2002). In rodents, the discovery of place cells lead to the proposal that the hippocampus plays a key role in spatial representation with place cells the basis for spatial maps, backed up by multiple studies in mammalian species (Moser, Kropff, & Moser, 2008; O’Keefe & Dostrovsky, 1971; Tsodyks, 1999). While it is difficult to conduct single cell recordings to confirm the existence of place cells in humans, some limited research has shown cells that apparently respond to specific locations (Ekstrom et al., 2003) and the hippocampus certainly appears to be related to spatial navigation abilities in humans (Hartley, Maguire, Spiers, & Burgess, 2003; Maguire et al., 2003).
The hippocampus is also well recognised as being important for episodic
memory (Burgess et al., 2002; Eichenbaum & Cohen, 2001; Tulving & Markowitsch, 1998). Neuropsychological evidence has shown that hippocampal lesions are associated with impaired performance at recall of episodic memory, whist retaining implicit procedural memory and working memory (Scoville & Milner, 1957; Spiers, Maguire, & Burgess, 2001).
It has recently been proposed that the hippocampus and is part of a
network responsible for “scene construction” (Hassabis & Maguire, 2007). This network essentially covers the same regions as the DMN, containing the PCC, vmPFC, iLPC, PHG, retrosplenial cortex and middle temporal cortices. In essence, the idea of a “scene construction system” builds on the discussion previously mentioned, where it was suggested by Buckner and colleagues that the DMN supports functions of mental simulation -‐ the “imaginative constructions of hypothetical events or scenarios” including autobiographical memory, envisioning the future, theory of mind and moral decision making (Buckner et al., 2008).
Hassabis & Maguire however take this idea further. In an earlier paper
Buckner & Caroll, put forward the concept of “self projection”, which they describe as the ability to place oneself in future imagined and alternative environments (Buckner & Carroll, 2007). Hassabis & Maguire additionally propose that fictitious experiences, not related to self, a subjective sense of time or autonoetic consciousness are supported by the same brain network. They define scene construction thus:
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“We define scene construction as the process of mentally generating and maintaining a complex and coherent scene or event. This is achieved by the retrieval and integration of relevant informational components, stored in their modality-‐specific cortical areas, the product of which has a coherent spatial context, and can then later be manipulated and visualized.” (Hassabis & Maguire, 2007) Immediately we can see this is a much more general and broad concept, which underlies a variety of everyday states of consciousness. The authors go on to map various component processes to cognitive tasks, and list scene construction as being vital to: episodic memory recall, episodic future thinking, navigation, imagination, the default network, viewer replay, vivid dreaming and in some cases theory of mind (depending on the circumstances). They put forward a range of evidence for the fundamental importance of scene construction. Neuropsychological studies show that patients with hippocampal amnesia also suffer in their ability to create new imagined fictitious experiences, and the descriptions they were able to provide were fragment, lacking in coherence and especially spatial structure (Hassabis, Kumaran, Vann, & Maguire, 2007). Neuroimaging studies presented by Hassabis and Maguire delineate the regions of the scene construction network to the nodes of the DMN and medial temporal regions mentioned above.
For Hassabis & Maguire, episodic recollection then becomes actually a
“re-‐construction” – memories are rebuilt using the process of scene construction every time they are accessed. Therefore, the degree of involvement of the hippocampus and the scene construction network reflects the degree of re-‐ construction and re-‐experiencing in memory retrieval (Addis, Moscovitch, Crawley, & McAndrews, 2004). Outstanding questions remain as to the exact relationship between scene construction and scene perception. Some research suggests that MTL regions may have important roles in perception too (Graham & Gaffan, 2005; Lee et al., 2005) and it would seem likely that the systems for construction and perception of scenes share common brain regions.
We therefore propose that under the influence of psilocybin, there is a
general disruption to the coherence of the brain network that this system of
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scene construction, as evidenced by reductions in RSFC between the primary nodes of the system, the HF, PCC, iLPC and vmPFC. This disruption of scene construction may be part of the causal mechanism that produces the subjective alterations in visual and auditory perception, and changes to concepts of self, time and space reported by subjects. This would support a tight relationship between scene construction and scene perception.
The subjective experiences of imagination and episodic memory recall
clearly involve the integration of information into a coherent gestalt, however unlike sensory perception, such representations are wholly abstract and not driven by incoming sensory information. Thus, the creation and maintenance of such internal representations must be driven by nodes within the scene construction network. It may be the case that the hippocampus is such a driver of the network. Some evidence for this is provided by studies which show that hippocampal stimulation can induce déjà vu type experiences (Bartolomei et al., 2012), and chronic hippocampal stimulation increases glucose metabolism within the PCC and mPFC (Laxton et al., 2010).
However, given that we know that the hippocampus has relatively low
levels of 5HT2A receptors, there might seem to be a mechanistic disconnect between the synaptic and systems level effects. If the scene construction network is a self-‐maintaining and regulating dynamic system, it will require bi-‐ directional feedback connections between nodes to maintain its coherence. The PCC and vmPFC do have high concentrations of 5HT2A receptors (Carhart-‐Harris et al., 2012), and also dense connections to the hippocampus and parahippocampus (Greicius et al., 2009), so in this case it is possible that psilocybin disrupts the feedback connections to the hippocampus via agonism in the PCC and vmPFC, producing changes to the network.
Apart from the decreases in RSFC, increases in coupling to the right
insular were also seen. The insular is known to be part of the salience network (Seeley et al., 2007) and usually anti-‐correlated with regions associated with the DMN. However, it has previous been shown that psilocybin tends to reduce the differences in between-‐network connectivity, in effect making them less orthogonal, blurring the separation between networks (Carhart-‐Harris et al., 2012b) and this has been suggested as another causal factor in psilocybin’s
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subjective effects. In light of this, increased RSFC between hippocampus and the insular fits the overall picture of psilocybin’s effects in the brain. Limitations and future directions
The principle limitation in comparing these studies is that they employed
different methodologies, the psilocybin experiment using IV administration, allowing and immediate before and after comparison, whereas the MDMA experiment used oral administration and the averaging of two scans during the period of the drug’s effects, so caution must be taken in drawing any strong conclusions in comparing the two drugs effects.
The lack of any significant results in the drug > placebo scan analyses for
psilocybin is also disappointing, and can possibly be attributed to increased noise added when entering data from scans a week apart into the analysis. However, the fact that we were able to show significant changes due to the immediate infusion of psilocybin shows there is an effect. Further studies, ideally with a large number of subjects, would help clarify these results. Increased subjects might also help to tip the correlation between intensity and changes in functional connectivity into significance, as the results presented here fall just short of being significant.
As yet unpublished experiments using MEG to investigate the effects of
psilocybin have also been conducted, and will help to shed more light on the temporal dynamics of RSFC changes. Our lab is currently planning an experimental protocol that will investigate RSFC changes in subjects under the influence of LSD, also via IV administration – being able to compare two classic psychedelics will help bolster (or weaken) our case.
Conclusions
These two studies investigated changes in hippocampal RSFC under the
influence of the two different drugs. With psilocybin, changes were mostly confined to the cortical nodes of the DMN, also believed to support the “scene construction” system. We propose that these changes reflect the action of the
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drug on fundamental mechanisms of consciousness, by weakening the coherence of this resting state network. These changes in connectivity may explain a possible mechanism for the drug’s profound subjective effects on perception and sense of self, time and space.
With MDMA, changes in connectivity did not so easily coincide with a
single particular network, so only limited conclusions can be drawn. However, reduced connectivity between the hippocampus and the mPFC may reflect the drugs noted pro-‐social and anxiolytic effects
The differences in patterns of alterations in connectivity reflect the two
different drugs different mechanisms of action, and may serve as potential biomarkers for their respective subjective states.
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