This Is Your Brain on Psychedelic Drugs (via Discover)

Dr. David Nutt and a team of researchers have published a study on the psychoactive substance in mushrooms, psilocybin, and how it impacts the brain circuits. As you can see in the picture below, the effect of psilocybin is a much more interconnected brain (which suggests some other circuits that limit activity are inactive under the influence of psilocybin).

Interesting stuff.

The summary below is from Discover, then the whole article, which is open access, is also included below.

This Is Your Brain on Psychedelic Drugs

By Ben Thomas | October 29, 2014 4:16 pm


Left, the stable brain activity in a normal brain. Right, under the influence of psilocybin, diverse brain regions not normally in communication become strongly linked.
Psychedelic substances can change a user’s mindset in profound ways — a fact that’s relevant even to those who’ve never touched the stuff, because such altered states of consciousness give scientists a window into how our brains give rise to our normal mental states. But neuroscientists are only beginning to understand how and why those mental changes occur.

Now some mathematicians have jumped into the fray, using a new mathematical technique to analyze the brains of people on magic mushrooms.

Psychedelic Puzzles

Scientists have known for decades that many of psychedelic drugs’ most famous effects — visual hallucinations, heightened sensory and emotional sensitivity, etc. — are linked to elevated levels of the neurotransmitter serotonin.

But increasingly neuroscience researchers are interested not just in single chemicals but also in overall brain activity, because the most complicated brain functions arise from lots of different regions working together. Over the last several years, a branch of mathematics known as network theory has been applied to study this phenomenon.

Paul Expert, a complexity researcher at the Imperial College London, and his team took this approach to analyzing fMRI data from people who’d taken psilocybin, the psychedelic chemical in magic mushrooms. The team had recently been working on a new technique for network modeling — one designed to highlight small but unusual patterns in network connectivity.

Brains on Drugs

The team used fMRI data from a previous study, in which 15 healthy people rested inside an fMRI scanner for 12 minutes on two separate occasions. The volunteers received a placebo in one of those sessions, and a mild dose of psilocybin during the other, but they weren’t told which was which.

The investigators crunched the data, specifically studying the brain’s functional connectivity — the amount of active communication among different brain areas.

They found two main effects of the psilocybin. First, most brain connections were fleeting. New connectivity patterns tended to disperse more quickly under the influence of psilocybin than under placebo. But, intriguingly, the second effect was in the opposite direction: a few select connectivity patterns were surprisingly stable, and very different from the normal brain’s stable connections.

This indicates “that the brain does not simply become a random system after psilocybin injection, but instead retains some organizational features, albeit different from the normal state,” the authors write in their paper in the Journal of the Royal Society Interface.

Far Out
The findings seem to explain some of the psychological experiences of a psilocybin trip. Linear thinking and planning become extremely difficult, but nonlinear “out of the box” thinking explodes in all directions. By the same token, it can become difficult to tell fantasy apart from reality during a psilocybin trip; but focusing on a certain thought or image — real or imagined — often greatly amplifies that thought’s intensity and vividness.

The authors suggest that effects like these may be rooted in the two connectivity traits they spotted, since the connectivity patterns that rapidly disperse may reflect unorganized thinking, while the stable inter-regional connections may reflect information from one sensory domain “bleeding” into other areas of sensory experience. In fact, the researchers also suggest that synesthesia — the sensory blurring that causes users of psychedelics to experience sounds as colors, for example — may be a result of these connectivity changes too.

The researchers hope that the patterns they’ve found will provide neuroscientists with new approaches for studying the brain on psychedelic drugs, and therefore better understand the strange psychological effects their users report.

* * * * *

Full Citation:
Petri, G, Expert, P,  Turkheimer, F, Carhart-Harris, R, Nutt, D, Hellyer, PJ, and Vaccarino, F. (2014, Oct 29). Homological scaffolds of brain functional networks. J. R. Soc. Interface; 11(101). doi: 10.1098/​rsif.2014.0873 [Print: 6 December 2014] 

Homological scaffolds of brain functional networks

G. Petri [1], P. Expert [2], F. Turkheimer [2], R. Carhart-Harris [3], D. Nutt [3], P. J. Hellyer [4] and
F. Vaccarino [1,5]

1. ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy
2. Centre for Neuroimaging Sciences, Institute of Psychiatry, Kings College London, De Crespigny Park, London SE5 8AF, UK
3. Centre for Neuropsychopharmacology, Imperial College London, London W12 0NN, UK
4. Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London W12 0NN, UK
5. Dipartimento di Scienze Matematiche, Politecnico di Torino, C.so Duca degli Abruzzi no 24, Torino 10129, Italy

Abstract

Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186–198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects—homological cycles—associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting-state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.

1. Motivation

The understanding of global brain organization and its large-scale integration remains a challenge for modern neurosciences. Network theory is an elegant framework to approach these questions, thanks to its simplicity and versatility [1]. Indeed, in recent years, networks have become a prominent tool to analyse and understand neuroimaging data coming from very diverse sources, such as functional magnetic resonance imaging (fMRI), electroencephalography and magnetoencephalography [2,3], also showing potential for clinical applications [4,5]. 

A natural way of approaching these datasets is to devise a measure of dynamical similarity between the microscopic constituents and interpret it as the strength of the link between those elements. In the case of brain functional activity, this often implies the use of similarity measures such as (partial) correlations or coherence [6–8], which generally yield fully connected, weighted and possibly signed adjacency matrices. Despite the fact that most network metrics can be extended to the weighted case [9–13], the combined effect of complete connectedness and edge weights makes the interpretation of functional networks significantly harder and motivates the widespread use of ad hoc thresholding methods [7,14–18]. However, neglecting weak links incurs the dangers of a trade-off between information completeness and clarity. In fact, it risks overlooking the role that weak links might have, as shown for example in the cases of resting-state dynamics [19,20], cognitive control [21] and correlated network states [22]. 

In order to overcome these limits, Rubinov & Sporn [13,23,24] recently introduced a set of generalized network and community metrics for functional networks that among others were used to uncover the contrasting dynamics underlying recollection [25] and the physiology of functional hubs [26]. 

In this paper, we present an alternative route to the analysis of brain functional networks. We focus on the combined structure of connections and weights as captured by the homology of the network. A summary of all the keywords and concepts introduced in this paper can be found in table 1.

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Table 1. List of notations.

2. From networks to topological spaces and homology

Homology is a topological invariant that characterizes a topological space X by counting its holes and their dimensions. By hole, we mean a hollow region bounded by the parts of that space. The dimension of a hole is directly related to the dimension of its boundary. The boundary of a two-dimensional hole is a one-dimensional loop; the three-dimensional inner part of a doughnut, where the filling goes, is bounded by two-dimensional surface; for dimensions higher than 2, it becomes difficult to have a mental representation of a hole, but k-dimensional holes are still bounded by (k − 1) dimensional faces. In our work, we start with a network and from it construct a topological space. We now use figure 1 to show how we proceed and make rigorous what we mean by boundaries and holes. 

Figure 1. Panels (a,b) display an unweighted network and its clique complex, obtained by promoting cliques to simplices. Simplices can be intuitively thought as higher-dimensional interactions between vertices, e.g. as a simplex the clique (b,c,i) corresponds to a filled triangle and not just its sides. The same principle applies to cliques—thus simplices—of higher order. (Online version in colour.) 

In a network like that of figure 1a, we want the ring of nodes (a,b,c,d) to be a good candidate for a one-dimensional boundary, whereas the other rings of three nodes should not constitute interesting holes. The reason for this choice comes from the formalization of the notion of hole. One way to formalize this is by opposition that is we define what we mean by a dense subnetwork in order to highlight regions of reduced connectivity, i.e. holes. The most natural and conservative definition we can adopt for a dense subnetwork is that of clique, a completely connected subgraph [27]. Moreover, cliques have the crucial property, which will be important later, of being nested, i.e. a clique of dimension k (k-clique) contains all the m-cliques defined by its nodes with m < k. Using this definition and filling in all the maximal cliques, the network in figure 1a can be represented as in figure 1b: 3-cliques are filled in, becoming tiles, and the only interesting structure left is the square (a,b,c,d). It is important at this point to note that a k-clique can be seen as a k − 1 simplex, i.e. as the convex hull of k-points. Our representation of a network can thus be seen as a topological space formed by a finite set of simplices that by construction satisfy the condition that defines the type of topological spaces called abstract simplicial complexes [28]: each element of the space is a simplex, and each of its faces (or subset in the case of cliques) is also a simplex. 

This condition is satisfied, because each clique is a simplex, and subsets of cliques are cliques themselves, and the intersection of two cliques is still a clique. 

The situation with weighted networks becomes more complicated. In the context of a weighted network, the holes can be thought of as representing regions of reduced connectivity with respect to the surrounding structure. 

Consider, for example, the case depicted in figure 2a: the network is almost the same as figure 1 with the two exceptions that it now has weighted edges and has an additional very weak edge between nodes a and c. The edges in the cycle [a,b,c,d] are all much stronger than the link (a,c) that closes the hole by making (a,b,d) and (b,c,d) cliques and therefore fills them. The loop (e,f,g,h,i) has a similar situation, but the difference in edge weights between the links along the cycle and those crossing, is not as large as in the previous case. It would be therefore useful to be able to generalize the approach exposed earlier for binary networks to the case of weighted networks in such a way as to be able to measure the difference between the two cases (a,b,c,d) and (e,f,g,h,i). As shown by figure 2b, this problem can be intuitively thought of as a stratigraphy in the link-weight fabric of the network, where the aim is to detect the holes, measure their depth and when they appear as we scan across the weights' range. 

Figure 2. Panels (a­–c) display a weighted network (a), its intuitive representation in terms of a stratigraphy in the weight structure according the weight filtration described in the main text (b) and the persistence diagram for H₁ associated with the network shown (c). By promoting cliques to simplices, we identify network connectivity with relations between the vertices defining the simplicial complex. By producing a sequence of networks through the filtration, we can study the emergence and relative significance of specific features along the filtration. In this example, the hole defined by (a,b,c,d) has a longer persistence (vertical solid green bars) implying that the boundary of the cycle are much heavier than the internal links that eventually close it. The other hole instead has a much shorter persistence, surviving only for one step and is therefore considered less important in the description of the network homological properties. Note that the births and deaths are defined along the sequence of descending edge weights in the network, not in time. (Online version in colour.) 

From figure 2b, it becomes clear that the added value of this method over conventional network techniques lies in its capability to describe mesoscopic patterns that coexist over different intensity scales, and hence to complement the information about the community structure of brain functional networks. A way to quantify the relevance of holes is given by persistent homology. We describe it and its application to the case of weighted networks in full detail in §3.

3. A persistent homology of weighted networks

The method that we adopt was introduced in references [29,30] and relies on an extension of the metrical persistent homology theory originally introduced by references [31,32]. Technical details about the theory of persistent homology and how the computation is performed can be found in the works of Carlsson, Zomorodian and Edelsbrunner [28,31–35]. Persistent homology is a recent technique in computational topology developed for shape recognition and the analysis of high dimensional datasets [36,37]. It has been used in very diverse fields, ranging from biology [38,39] and sensor network coverage [40] to cosmology [41]. Similar approaches to brain data [42,43], collaboration data [44] and network structure [45] also exist. The central idea is the construction of a sequence of successive approximations of the original dataset seen as a topological space X. This sequence of topological spaces X₀, X₁, … , X_N = X is such that whenever i < j and is called the filtration. Choosing how to construct a filtration from the data is equivalent to choosing the type of goggles one wears to analyse the data. 

In our case, we sort the edge weights in descending order and use the ranks as indices for the subspaces. More specifically, denote by the functional network with vertices V, edges E and weights . We then consider the family of binary graphs G_ω = (V, E_ω), where an edge e ∈ E is also included in G_ω if its weight ω_e is larger than ω (). 

To each of the G_ω, we associate its clique, or flag complex K_ω, that is the simplicial complex that contains the k-simplex [n₀, n₁, n₂, … n_{k − 1}] whenever the nodes n₀, n₁, n₂, … n_{k −1} define a clique in G_ω [27]. As subsets of cliques and intersections of cliques are cliques themselves, as we pointed out in §2, our clique complex is thus a particular case of a simplicial complex. 

The family of complexes {K_ω} defines a filtration, because we have for ω > ω′. At each step, the simplices in K_ω inherit their configuration from the underlying network structure and, because the filtration swipes across all weight scales in descending order, the holes among these units constitute mesoscopic regions of reduced functional connectivity.

Moreover, this approach also highlights how network properties evolve along the filtration, providing insights about where and when lower connectivity regions emerge. This information is available, because it is possible to keep track of each k-dimensional cycle in the homology group H_k. A generator uniquely identifies a hole by its constituting elements at each step of the filtration process. The importance of a hole is encoded in the form of ‘time-stamps' recording its birth β_g and death δ_g along the filtration {K_ω} [31]. These two time-stamps can be combined to define the persistence π_g = δ_g − β_g of a hole, which gives a notion of its importance in terms of it lifespan. Continuing the analogy with stratigraphy, β_g and δ_g correspond, respectively, to the top and the bottom of a hole and π_g would be its depth. As we said above, a generator , or hole, of the kth homology group H_k is identified by its birth and death along the filtration. Therefore, is described by the point . A standard way to summarize the information about the whole kth persistent homology group is then to consider the diagram obtained plotting the points corresponding to the set of generators. The (multi)set {(β_g,δ_g} is called the persistence diagram of H_k. In figure 2c, we show the persistence diagram for the network shown in figure 2a for H₁. Axes are labelled by weights in decreasing order. It is easy to check that the coordinates correspond exactly to the appearance and disappearance of generators. The green vertical bars highlight the persistence of a generator along the filtration. The further a point is from the diagonal (vertically), the more persistent the generator is. In §4, we introduce two objects, the persistence and the frequency homological scaffolds, designed to summarize the topological information about the system. 

4. Homological scaffolds

Once one has calculated the generators of the kth persistent homology group H_k, the corresponding persistence diagram contains a wealth of information that can be used, for example, to highlight differences between two datasets. It would be instructive to obtain a synthetic description of the uncovered topological features in order to interpret the observed differences in terms of the microscopic components, at least for low dimensions k. Here, we present a scheme to obtain such a description by using the information associated with the generators during the filtration process. As each generator, is associated with a whole equivalence class, rather than to a single chain of simplices, we need to choose a representative for each class, we use the representative that is returned by the javaplex implementation [46] of the persistent homology algorithm [47]. For the sake of simplicity in the following, we use the same symbol to refer to a generator and its representative cycle. 

We exploit this to define two new objects, the persistence and the frequency homological scaffolds and of a graph G. The persistence homological scaffold is the network composed of all the cycle paths corresponding to generators weighted by their persistence. If an edge e belongs to multiple cycles g₀,g₁, … ,g_s, its weight is defined as the sum of the generators' persistence:

4.1

Similarly, we define the frequency homological scaffold as the network composed of all the cycle paths corresponding to generators, where this time, an edge e is weighted by the number of different cycles it belongs to

4.2

where is the indicator function for the set of edges composing g_i. By definition, the two scaffolds have the same edge set, although differently weighted. 

The construction of these two scaffolds therefore highlights the role of links which are part of many and/or long persistence cycles, isolating the different roles of edges within the functional connectivity network. The persistence scaffolds encodes the overall persistence of a link through the filtration process: the weight in the persistence scaffold of a link belonging to a certain set of generators is equal to the sum of the persistence of those cycles. The frequency scaffold instead highlights the number of cycles to which a link belongs, thus giving another measure of the importance of that edge during the filtration. The combined information given by the two scaffolds then enables us to decipher the nature of the role different links have regarding the homological properties of the system. A large total persistence for a link in the persistence scaffold implies that the local structure around that link is very weak when compared with the weight of the link, highlighting the link as a locally strong bridge. We remark that the definition of scaffolds we gave depends on the choice of a specific basis of the homology group, and the choice of a consistent basis is an open problem in itself, therefore the scaffolds are not topological invariants. Moreover, it is possible for an edge to be added to a cycle shortly after the cycle's birth in such a way that it creates a triangle with the two edges composing the cycle. In this way, the new edge would be part of the shortest cycle, but the scaffold persistence value would be misattributed to the two other edges. This can be checked, for example, by monitoring the clustering coefficient of the cycle's subgraph as edges are added to it. We checked for this effect and found that in over 80% of the cases the edges do not create triangles that would imply the error, but instead new cycles are created, whose contribution to the scaffold is then accounted for by the new cycle. Finally, we note also that, when a new triangle inside the cycle is created, the two choices of generator differ for a path through a third strongly connected node, owing to the properties of boundary operators. Despite this ambiguity, we show in the following that they can be useful to gain an understanding of what the topological differences detected by the persistent homology actually mean in terms of the system under study.

5. Results from fMRI networks

We start from the processed fMRI time series (see Methods for details). The linear correlations between regional time series were calculated after covarying out the variance owing to all other regions and the residual motion variance represented by the 24 rigid motion parameters obtained from the pre-processing, yielding a partial-correlation matrix χ^α for each subject. The matrices χ^α were then analysed with the algorithm described in the previous sections. We calculated the generators of the first homological group H₁ along the filtration. As mentioned before, each of these generators identifies a lack of mesoscopic connectivity in the form of a one-dimensional cycle and can be represented in a persistence diagram. We aggregate together the persistence diagrams of subjects belonging to each group and compute an associated persistence probability density (figure 3). These probability density functions constitute the statistical signature of the groups' H₁ features. 

Figure 3. Probability densities for the H₁ generators. Panel (a) reports the (log-)probability density for the placebo group, whereas panel (b) refers to the psilocybin group. The placebo displays a uniform broad distribution of values for the births–deaths of H₁ generators, whereas the plot for the psilocybin condition is very peaked at small values with a fatter tail. These heterogeneities are evident also in the persistence distribution and find explanation in the different functional integration schemes in placebo and drugged brains. (Online version in colour.) 

We find that, although the number of cycles in the groups are comparable, the two probability densities strongly differ (Kolmogorov–Smirnov statistics: 0.22, p-value less than 10⁻¹⁰). 

The placebo group displays generators appearing and persisting over a limited interval of the filtration. On the contrary, most of the generators for the psilocybin group are situated in a well-defined peak at small birth indices, indicating a shorter average cycle persistence. However, the psilocybin distribution is also endowed with a longer tail implying the existence of a few cycles that are longer-lived compared with the placebo condition and that influences the weight distribution of the psilocybin persistence scaffold. The difference in behaviour of the two groups is made explicit when looking at the probability distribution functions for the persistence and the birth of generators (figure 4), which are both found to be significantly different (Kolmogorov–Smirnov statistics: 0.13, p-value < 10⁻³⁰ for persistence and Kolmogorov–Smirnov statistics: 0.14, p-value < 10⁻³⁵ for births). In order to better interpret and understand the differences between the two groups, we use the two secondary networks described in §4, and for the placebo group and and for the psilocybin group. The weight of the edges in these secondary networks is proportional to the total number of cycles an edge is part of, and the total persistence of those cycles, respectively. They complement the information given by the persistence density distribution, where the focus is on the entire cycle's behaviour, with information on single links. In fact, individual edges belonging to many and long persistence cycles represent functionally stable ‘hub’ links. As with the persistence density distribution, the scaffolds are obtained at a group level by aggregating the information about all subjects in each group. These networks are slightly sparser than the original complete χ^α networks

5.1 

and 5.2 

and have comparable densities. A first difference between the two groups becomes evident when we look at the distributions for the edge weights (figure 5a). In particular, the weights of display a cut-off for large weights, whereas the weights of have a broader tail (Kolmogorov–Smirnov statistics: 0.06, p-value < 10⁻²⁰; figure 5a). Interestingly, the frequency scaffold weights probability density functions cannot be distinguished from each other figure 5a (inset) (Kolmogorov–Smirnov statistics: 0.008, p-value = 0.72). Taken together, these two results imply that while edges statistically belong to the same number of cycles, in the psilocybin scaffold, there exist very strong, persistent links. 

Figure 4. Comparison of persistence π and birth β distributions. Panel (a) reports the H₁ generators' persistence distributions for the placebo group (blue line) and psilocybin group (red line). Panel (b) reports the distributions of births with the same colour scheme. It is very easy to see that the generators in the psilocybin condition have persistence peaked at shorter values and a wider range of birth times when compared with the placebo condition. (Online version in colour.) 

Figure 5. Statistical features of group homological scaffolds. Panel (a) reports the (log-binned) probability distributions for the edge weights in the persistence homological scaffolds (main plot) and the frequency homological scaffolds (inset). While the weights in the frequency scaffold are not significantly different, the weight distributions for the persistence scaffold display clearly a broader tail. Panel (b) shows instead the scatter plot of the edge frequency versus total persistence. In both cases, there is a clear linear relationship between the two, with a large slope in the psilocybin case. Moreover, the psilocybin scaffold has a larger spread in the frequency and total persistence of individual edges, hinting to a different local functional structure within the functional network of the drugged brains. (Online version in colour.) 

The difference between the two sets of homological scaffolds for the two groups becomes even more evident when one compares the weights between the frequency and persistence scaffolds of the same group. Figure 5b is a scatter plot of between the weights of edges from both scaffolds for the two groups. The placebo group has a linear relationship between the two quantities meaning that edges that are persistent also belongs to many cycles (R² = 0.95, slope = 0.23). Although the linear relationship is still a reasonable fit for the psilocybin group (R² = 0.9, slope = 0.3), the data in this case display a larger dispersion. In particular, it shows that edges in can be much more persistent/longer-lived than in but still appear in the same number of cycles, i.e. the frequency of a link is not predictive of its persistence or simply put: some connections are much more persistent in the psychedelic state. Moreover, the slopes of linear fits of the two clouds are statistically different (p-value < 10⁻²⁰, n_pla = 13 200 and n_psi = 13 275 [48]) pointing to a starkly different local functional structure in the two conditions. 

The results from the persistent homology analysis and the insights provided by the homological scaffolds imply that although the mesoscopic structures, i.e. cycles, in the psilocybin condition are less stable than in the placebo group, their constituent edges are more stable.

6. Discussion

In this paper, we first described a variation of persistent homology that allows us to deal with weighted and signed networks. We then introduced two new objects, the homological scaffolds, to go beyond the picture given by persistent homology to represent and summarize information about individual links. The homological scaffolds represent a new measure of topological importance of edges in the original system in terms of how frequently they are part of the generators of the persistent homology groups and how persistent are the generators to which they belong to. We applied this method to an fMRI dataset comprising a group of subjects injected with a placebo and another injected with psilocybin. 

By focusing on the second homology group H₁, we found that the stability of mesoscopic association cycles is reduced by the action of psilocybin, as shown by the difference in the probability density function of the generators of H₁ (figure 3). 

It is here that the importance of the insight given by the homological scaffolds in the persistent homology procedure becomes apparent. A simple reading of this result would be that the effect of psilocybin is to relax the constraints on brain function, ascribing cognition a more flexible quality, but when looking at the edge level, the picture becomes more complex. The analysis of the homological scaffolds reveals the existence of a set of edges that are predominant in terms of their persistence although they are statistically part of the same number of cycles in the two conditions (figure 5). In other words, these functional connections support cycles that are especially stable and are only present in the psychedelic state. This further implies that the brain does not simply become a random system after psilocybin injection, but instead retains some organizational features, albeit different from the normal state, as suggested by the first part of the analysis. Further work is required to identify the exact functional significance of these edges. Nonetheless, it is interesting to look at the community structure of the persistence homological scaffolds in figure 6. The two pictures are simplified cartoons of the placebo (figure 6a) and psilocybin (figure 6b) scaffolds. In figure 6a,b, the nodes are organized and coloured according to their community membership in the placebo scaffold (obtained with the Louvain algorithm for maximal modularity and resolution 1 [50]). This is done in order to highlight the striking difference in connectivity structure in the two cases. When considering the edges in the tail of the distribution, weight greater than or equal to 80, in figure 5a, only 29 of the 374 edges present in the truncated psilocybin scaffold are shared with the truncated placebo scaffold (165 edges). Of these 374 edges, 217 are between placebo communities and are observed to mostly connect cortical regions. This supports our idea that psilocybin disrupts the normal organization of the brain with the emergence of strong, topologically long-range functional connections that are not present in a normal state. 

Figure 6. Simplified visualization of the persistence homological scaffolds. The persistence homological scaffolds (a) and (b) are shown for comparison. For ease of visualization, only the links heavier than 80 (the weight at which the distributions in figure 5a bifurcate) are shown. This value is slightly smaller than the bifurcation point of the weights distributions in figure 5a. In both networks, colours represent communities obtained by modularity [49] optimization on the placebo persistence scaffold using the Louvain method [50] and are used to show the departure of the psilocybin connectivity structure from the placebo baseline. The width of the links is proportional to their weight and the size of the nodes is proportional to their strength. Note that the proportion of heavy links between communities is much higher (and very different) in the psilocybin group, suggesting greater integration. A labelled version of the two scaffolds is available as GEXF graph files as the electronic supplementary material. (Online version in colour.)

The two key results of the analysis of the homological scaffolds can therefore be summarized as follows (i) there is an increased integration between cortical regions in the psilocybin state and (ii) this integration is supported by a persistent scaffold of a set of edges that support cross modular connectivity probably as a result of the stimulation of the 5HT2A receptors in the cortex [51]. 

We can speculate on the implications of such an organization. One possible by-product of this greater communication across the whole brain is the phenomenon of synaesthesia which is often reported in conjunction with the psychedelic state. Synaesthesia is described as an inducer-concurrent pairing, where the inducer could be a grapheme or a visual stimulus that generates a secondary sensory output—like a colour for example. Drug-induced synaesthesia often leads to chain of associations, pointing to dynamic causes rather than fixed structural ones as may be the case for acquired synaesthesia [52]. Broadly consistent with this, it has been reported that subjects under the influence of psilocybin have objectively worse colour perception performance despite subjectively intensified colour experience [53]. 

To summarize, we presented a new method to analyse fully connected, weighted and signed networks and applied it to a unique fMRI dataset of subjects under the influence of mushrooms. We find that the psychedelic state is associated with a less constrained and more intercommunicative mode of brain function, which is consistent with descriptions of the nature of consciousness in the psychedelic state.

7. Methods

7.1. Dataset

A pharmacological MRI dataset of 15 healthy controls was used for a proof-of-principle test of the methodology [54]. Each subject was scanned on two separate occasions, 14 days apart. Each scan consisted of a structural MRI image (T1-weighted), followed by a 12 min eyes-close resting-state blood oxygen-level-dependent (BOLD) fMRI scan which lasted for 12 min. Placebo (10 ml saline, intravenous injection) was given on one occasion and psilocybin (2 mg dissolved in 10 ml saline) on the other. Injections were given manually by a study doctor situated within the scanning suite. Injections began exactly 6 min after the start of the 12-min scans, and continued for 60 s. 

7.1.1. Scanning parameters

The BOLD fMRI data were acquired using standard gradient-echo EPI sequences, reported in detail in reference [54]. The volume repetition time was 3000 ms, resulting in a total of 240 volumes acquired during each 12 min resting-state scan (120 pre- and 120 post-injection of placebo/psilocybin). 

7.1.2. Image pre-processing

fMRI images were corrected for subject motion within individual resting-state acquisitions, by registering all volumes of the functional data to the middle volume of the acquisition using the FMRIB linear registration motion correction tool, generating a six-dimension parameter time course [55]. Recent work demonstrates that the six parameter motion model is insufficient to correct for motion-induced artefact within functional data, instead a Volterra expansion of these parameters to form a 24 parameter model is favoured as a trade-off between artefact correction and lost degrees of freedom as a result of regressing motion away from functional time courses [56]. fMRI data were pre-processed according to standard protocols using a high-pass filter with a cut-off of 300 s.

Structural MRI images were segmented into n = 194 cortical and subcortical regions, including white matter cerebrospinal fluid (CSF) compartments, using Freesurfer (http://surfer.nmr.mgh.harvard.edu/), according to the Destrieux anatomical atlas [57]. In order to extract mean-functional time courses from the BOLD fMRI, segmented T1 images were registered to the middle volume of the motion-corrected fMRI data, using boundary-based registration [58], once in functional space mean time-courses were extracted for each of the n = 194 regions in native fMRI space. 

7.1.3. Functional connectivity

For each of the 194 regions, alongside the 24 parameter motion model time courses, partial correlations were calculated between all couples of time courses (i,j), non-neural time courses (CSF, white matter and motion) were discarded from the resulting functional connectivity matrices, resulting in a 169 region cortical/subcortical functional connectivity corrected for motion and additional non-neural signals (white matter/CSF). 

7.2. Persistent homology computation

For each subject in the two groups, we have a set of persistence diagrams relative to the persistent homology groups H_n. In this paper, we use the H₁ persistence diagrams of each group to construct the corresponding persistence probability densities for H₁ cycles. 

Filtrations were obtained from the raw partial-correlation matrices through the Python package Holes and fed to javaplex [46] via a Jython subroutine in order to extract the persistence intervals and the representative cycles. The details of the implementation can be found in reference [30], and the software is available at Holes [59].

Funding statement

G.P. and F.V. are supported by the TOPDRIM project supported by the Future and Emerging Technologies programme of the European Commission under Contract IST-318121. I.D. P.E. and F.T. are supported by a PET methodology programme grant from the Medical Research Council UK (ref no. G1100809/1). The authors acknowledge support of Amanda Feilding and the Beckley Foundation and the anonymous referees for their critical and constructive contribution to this paper.

© 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

References at the Journal of the Royal Society Interface site

Neuroscience’s New Toolbox - Optogenetics

New technology, like optogenetics, is making brain imaging much more precise. I remain unconvinced that pretty pictures of the brain, even highly detailed 3-dimensional images, will reveal the secrets of emotions or consciousness, what it is like to experience red, or what it feels like to be a bat. The technology and the pictures are pretty cool, though.

Via the MIT Technology Review.

Neuroscience’s New Toolbox

With the invention of optogenetics and other technologies, researchers can investigate the source of emotions, memory, and consciousness for the first time.

By Stephen S. Hall on June 17, 2014
Sculpture by Joshua Harker

Why It Matters
A better understanding of how memories, emotions, and cognition work in the brain could lead to ways to improve and manipulate such functions.

What might be called the “make love, not war” branch of behavioral neuroscience began to take shape in (where else?) California several years ago, when researchers in David J. Anderson’s laboratory at Caltech decided to tackle the biology of aggression. They initiated the line of research by orchestrating the murine version of Fight Night: they goaded male mice into tangling with rival males and then, with painstaking molecular detective work, zeroed in on a smattering of cells in the hypothalamus that became active when the mice started to fight.

The hypothalamus is a small structure deep in the brain that, among other functions, coördinates sensory inputs—the appearance of a rival, for example—with instinctual behavioral responses. Back in the 1920s, Walter Hess of the University of Zurich (who would win a Nobel in 1949) had shown that if you stuck an electrode into the brain of a cat and electrically stimulated certain regions of the hypothalamus, you could turn a purring feline into a furry blur of aggression. Several interesting hypotheses tried to explain how and why that happened, but there was no way to test them. Like a lot of fundamental questions in brain science, the mystery of aggression didn’t go away over the past century—it just hit the usual empirical roadblocks. We had good questions but no technology to get at the answers.

By 2010, Anderson’s Caltech lab had begun to tease apart the underlying mechanisms and neural circuitry of aggression in their pugnacious mice. Armed with a series of new technologies that allowed them to focus on individual clumps of cells within brain regions, they stumbled onto a surprising anatomical discovery: the tiny part of the hypothalamus that seemed correlated with aggressive behavior was intertwined with the part associated with the impulse to mate. That small duchy of cells—the technical name is the ventromedial hypothalamus—turned out to be an assembly of roughly 5,000 neurons, all marbled together, some of them seemingly connected to copulating and others to fighting.

“There’s no such thing as a generic neuron,” says Anderson, who estimates that there may be up to 10,000 distinct classes of neurons in the brain. Even tiny regions of the brain contain a mixture, he says, and these neurons “often influence behavior in different, opposing directions.” In the case of the hypothalamus, some of the neurons seemed to become active during aggressive behavior, some of them during mating behavior, and a small subset—about 20 percent—during both fighting and mating.

That was a provocative discovery, but it was also a relic of old-style neuroscience. Being active was not the same as causing the behavior; it was just a correlation. How did the scientists know for sure what was triggering the behavior? Could they provoke a mouse to pick a fight simply by tickling a few cells in the hypothalamus?

A decade ago, that would have been technologically impossible. But in the last 10 years, neuroscience has been transformed by a remarkable new technology called optogenetics, invented by scientists at Stanford University and first described in 2005. The Caltech researchers were able to insert a genetically modified light-sensitive gene into specific cells at particular locations in the brain of a living, breathing, feisty, and occasionally canoodling male mouse. Using a hair-thin fiber-optic thread inserted into that living brain, they could then turn the neurons in the hypothalamus on and off with a burst of light.

Optogenetics: Light Switches for Neurons

Anderson and his colleagues used optogenetics to produce a video dramatizing the love-hate tensions deep within rodents. It shows a male mouse doing what comes naturally, mating with a female, until the Caltech researchers switch on the light, at which instant the murine lothario flies into a rage. When the light is on, even a mild-mannered male mouse can be induced to attack whatever target happens to be nearby—his reproductive partner, another male mouse, a castrated male (normally not perceived as a threat), or, most improbably, a rubber glove dropped into the cage.

“Activating these neurons with optogenetic techniques is sufficient to activate aggressive behavior not only toward appropriate targets like another male mouse but also toward inappropriate targets, like females and even inanimate objects,” Anderson says. Conversely, researchers can inhibit these neurons in the middle of a fight by turning the light off, he says: “You can stop the fight dead in its tracks.”

Moreover, the research suggests that lovemaking overrides war-making in the calculus of behavior: the closer a mouse was to consummation of the reproductive act, the more resistant (or oblivious) he became to the light pulses that normally triggered aggression. In a paper published in Biological Psychiatry, titled “Optogenetics, Sex, and Violence in the Brain: Implications for Psychiatry,” Anderson noted, “Perhaps the imperative to ‘make love, not war’ is hard-wired into our nervous system, to a greater extent than we have realized.” We may be both lovers and fighters, with the slimmest of neurological distances separating the two impulses.

No one is suggesting that we’re on the verge of deploying neural circuit breakers to curb aggressive behavior. But, as Anderson points out, the research highlights a larger point about how a new technology can reinvent the way brain science is done. “The ability of optogenetics to turn a largely correlational field of science into one that tests causation has been transformative,” he says.

What’s radical about the technique is that it allows scientists to perturb a cell or a network of cells with exquisite precision, the key to sketching out the circuitry that affects various types of behavior. Whereas older technologies like imaging allowed researchers to watch the brain in action, optogenetics enables them to influence that action, tinkering with specific parts of the brain at specific times to see what happens.

And optogenetics is just one of a suite of revolutionary new tools that are likely to play leading roles in what looks like a heyday for neuroscience. Major initiatives in both the United States and Europe aspire to understand how the human brain—that tangled three-pound curd of neurons, connective tissue, and circuits—gives rise to everything from abstract thought to basic sensory processing to emotions like aggression. Consciousness, free will, memory, learning—they are all on the table now, as researchers use these tools to investigate how the brain achieves its seemingly mysterious effects (see “Searching for the “Free Will” Neuron”).

Connections
More than 2,000 years ago, Hippocrates noted that if you want to understand the mind, you must begin by studying the brain. Nothing has happened in the last two millennia to change that imperative—except the tools that neuroscience is bringing to the task.

The history of neuroscience, like the history of science itself, is often a story of new devices and new technologies. ­Luigi Galvani’s first accidental electrode, which provoked the twitch of a frog’s muscle, has inspired every subsequent electrical probe, from ­Walter Hess’s cat prod to the current therapeutic use of deep brain stimulation to treat Parkinson’s disease (approximately 30,000 people worldwide now have electrodes implanted in their brains to treat this condition). The patch clamp allowed neuroanatomists to see the ebb and flow of ions in a neuron as it prepares to fire. And little did Paul Lauterbur realize, when he focused a strong magnetic field on a single hapless clam in his lab at the State University of New York at Stony Brook in the early 1970s, that he and his colleagues were laying the groundwork for the magnetic resonance imaging (MRI) machines that have helped reveal the internal landscape and activity of a living brain.

Growing Neurons: Studying What Goes Wrong

But it is the advances in genetics and genomic tools during the last few years that have truly revolutionized neuroscience. Those advances made the genetic manipulations at the heart of optogenetics possible. Even more recent genome-editing methods can be used to precisely alter the genetics of living cells in the lab. Along with optogenetics, these tools mean scientists can begin to pinpoint the function of the thousands of different types of nerve cells among the roughly 86 billion in the human brain.

Nothing testifies to the value of a new technology more than the number of scientists who rapidly adopt it and use it to claim new scientific territories. As Edward Boyden, a scientist at MIT who helped develop optogenetics, puts it, “Often when a new technology comes out, there’s a bit of a land grab.”

And even as researchers grab those opportunities in genomics and optogenetics, still other advances are coming on the scene. A new chemical treatment is making it possible to directly see nerve fibers in mammalian brains; robotic microelectrodes can eavesdrop on (and perturb) single cells in living animals; and more sophisticated imaging techniques let researchers match up nerve cells and fibers in brain slices to create a three-dimensional map of the connections. Using these tools together to build up an understanding of the brain’s activity, scientists hope to capture the biggest of cognitive game: memory, decision-­making, consciousness, psychiatric illnesses like anxiety and depression, and, yes, sex and violence.

In January 2013, the European Commission invested a billion euros in the launch of its Human Brain Project, a 10-year initiative to map out all the connections in the brain. Several months later, in April 2013, the Obama administration announced an initiative called Brain Research through Advanced Innovative Neurotechnologies (BRAIN), which is expected to pour as much as $1 billion into the field, with much of the early funding earmarked for technology development. Then there is the Human Connectome Project, which aims to use electron microscope images of sequential slices of brain tissue to map nerve cells and their connections in three dimensions. Complementary connectome and mapping initiatives are getting under way at the Howard Hughes Medical Institute in Virginia and the Allen Institute for Brain Science in Seattle. They are all part of a large global effort, both publicly and privately funded, to build a comprehensive picture of the human brain, from the level of genes and cells to that of connections and circuits.

Last December, as an initial step in the BRAIN Initiative, the National Institutes of Health solicited proposals for $40 million worth of projects on technology development in the neurosciences. “Why is the BRAIN Initiative putting such a heavy emphasis on technology?” says Cornelia Bargmann, the Rockefeller University neuroscientist who co-directs the planning process for the project. “The real goal is to understand how the brain works, at many levels, in space and time, in many different neurons, all at once. And what’s prevented us from understanding that is limitations in technology.”

Eavesdropping

Optogenetics had its origins in 2000, in late-night chitchat at Stanford University. There, neuroscientists Karl Deisseroth and Edward Boyden began to bounce ideas back and forth about ways to identify, and ultimately manipulate, the activity of specific brain circuits. Deisseroth, who had a PhD in neuroscience from Stanford, longed to understand (and someday treat) the mental afflictions that have vexed humankind since the era of Hippocrates, notably anxiety and depression (see “Shining Light on Madness”). Boyden, who was pursuing graduate work in brain function, had an omnivorous curiosity about neurotechnology. At first they dreamed about deploying tiny magnetic beads as a way to manipulate brain function in intact, living animals. But at some point during the next five years, a different kind of light bulb went off.

Since the 1970s, microbiologists had been studying a class of light-sensitive molecules known as rhodopsins, which had been identified in simple organisms like bacteria, fungi, and algae. These proteins act like gatekeepers along the cell wall; when they detect a particular wavelength of light, they either let ions into a cell or, conversely, let ions out of it. This ebb and flow of ions mirrors the process by which a neuron fires: the electrical charge within the nerve cell builds up until the cell unleashes a spike of electrical activity flowing along the length of its fiber (or axon) to the synapses, where the message is passed on to the next cell in the pathway. Scientists speculated that if you could smuggle the gene for one of these light-sensitive proteins into a neuron and then pulse the cell with light, you might trigger it to fire. Simply put, you could turn specific neurons in a conscious animal on—or off—with a burst of light.

In 2004, Deisseroth successfully inserted the gene for a light-sensitive molecule from algae into mammalian neurons in a dish. Deisseroth and ­Boyden went on to show that blue light could induce the neurons to fire. At about the same time, a graduate student named Feng Zhang joined Deisseroth’s lab. Zhang, who had acquired a precocious expertise in the techniques of both molecular biology and gene therapy as a high school student in Des Moines, Iowa, showed that the gene for the desired protein could be introduced into neurons by means of genetically engineered viruses. Again using pulses of blue light, the Stanford team then demonstrated that it could turn electrical pulses on and off in the virus-modified mammalian nerve cells. In a landmark paper that appeared in Nature Neuroscience in 2005 (after, Boyden says, it was rejected by Science), Deisseroth, Zhang, and Boyden described the technique. (No one would call it “optogenetics” for another year.)

Neuroscientists immediately seized on the power of the technique by inserting light-sensitive genes into living animals. Researchers in Deisseroth’s own lab used it to identify new pathways that control anxiety in mice, and both ­Deisseroth’s team and his collaborators at Mount Sinai Hospital in New York used it to turn depression on and off in rats and mice. And Susumu Tonegawa’s lab at MIT recently used optogenetics to create false memories in laboratory animals.

When I visited Boyden’s office at MIT’s Media Lab last December, the scientist called up his favorite recent papers involving optogenetics. In a rush of words as rapid as his keystrokes, Boyden described second-generation technologies already being developed. One involves eavesdropping on single nerve cells in anesthetized and conscious animals in order to see “the things roiling underneath the sea of activity” within a neuron when the animal is unconscious. Boyden said, “It literally sheds light on what it means to have thoughts and awareness and feelings.”

Boyden’s group had also just sent off a paper reporting a new twist on optogenetics: separate, independent neural pathways can be perturbed simultaneously with red and blue wavelengths of light. The technique has the potential to show how different circuits interact with and influence each other. His group is also working on “insanely dense” recording probes and microscopes that aspire to capture whole-brain activity. The ambitions are not modest. “Can you record all the cells in the brain,” he says, “so that you can watch thoughts or decisions or other complex phenomena emerge as you go from sensation to emotion to decision to action site?”

Brain Mapping: Charting the Information Superhighways

A few blocks away, Feng Zhang, who is now an assistant professor at MIT and a faculty member at the Broad Institute, listed age-old neuroscience questions that might now be attacked with the new technologies. “Can you do a memory upgrade and increase the capacity?” he asked. “How are neural circuits genetically encoded? How can you reprogram the genetic instructions? How do you fix the genetic mutations that cause miswiring or other malfunctions of the neural system? How do you make the old brain younger?”

In addition to helping to invent optogenetics, Zhang played a central role in developing a gene-editing technique called CRISPR (see “10 Breakthrough Technologies: Genome Editing,” May/June). The technology allows scientists to target a gene—in neurons, for example—and either delete or modify it. If it’s modified to include a mutation known or suspected to cause brain disorders, scientists can study the progression of those disorders in lab animals. Alternatively, researchers can use CRISPR in the lab to alter stem cells that can then be grown into neurons to see the effects.

Transparency

Back at Stanford, when he’s not seeing patients with autism spectrum disorders or depression in the clinic, Deisseroth continues to invent tools that he and others can use to study these conditions. Last summer, his lab reported a new way for scientists to visualize the cables of nerve fibers, known as “white matter,” that connect distant precincts of the brain. The technique, called Clarity, first immobilizes biomolecules such as protein and DNA in a plastic-like mesh that retains the physical integrity of a postmortem brain. Then researchers flush a kind of detergent through the mesh to dissolve all the fats in brain tissue that normally block light. The brain is rendered transparent, suddenly exposing the entire three-­dimensional wiring pattern to view.

Together, the new tools are transforming many conventional views in neuroscience. For example, as Deisseroth noted in a review article published earlier this year in Nature, optogenetics has challenged some of the ideas underlying deep brain stimulation, which has been widely used to treat everything from tremors and epilepsy to anxiety and obsessive-­compulsive disorder. No one knows just why it works, but the operating assumption has been that its therapeutic effects derive from electrical stimulation of very specific brain regions; neurosurgeons exert extraordinary effort to place electrodes with the utmost precision.

In 2009, however, Deisseroth and colleagues showed that specifically stimulating the white matter, the neural cables that happen to lie near the electrodes, produced the most robust clinical improvement in symptoms of Parkinson’s disease. In other words, it wasn’t the neighborhood of the brain that mattered so much as which neural highways happened to pass nearby. Scientists often employ words like “surprising” and “unexpected” to characterize such recent results, reflecting the impact that optogenetics has had on the understanding of psychiatric illness.

In the same vein, Caltech’s Anderson points out that the public and scientific infatuation with functional MRI studies over the last two decades has created the impression that certain regions of the brain act as “centers” of neural activity—that the amygdala is the “center” of fear, for example, or the hypothalamus is the “center” of aggression. But he likens fMRI to looking down on a nighttime landscape from an airplane at 30,000 feet and “trying to figure out what is going on in a single town.” Optogenetics, by contrast, has provided a much more detailed view of that tiny subdivision of cells in the hypothalamus, and thus a much more complex and nuanced picture of aggression. Activating specific neurons in that little town can tip an organism to make war, but activating the neurons next door can nudge it to make love.

The new techniques will give scientists the first glimpses of human cognition in action—a look at how thoughts, feelings, forebodings, and dysfunctional mental activity arise from the neural circuitry and from the activity of particular types of cells. Researchers are just beginning to gain these insights, but given the recent pace of technology development, the picture might emerge sooner than anyone dreamed possible when the light of optogenetics first flickered on a few years ago.

~ Stephen S. Hall is a science writer and author in New York City. His last feature for MIT Technology Review was “Repairing Bad Memories.”