This double-blind, placebo-controlled, cross-over, within-subjects study (n=22) investigated the effects of ketamine (30mg/70kg) on whole-brain functional connectivity in healthy male participants while attenuating pre-synaptic glutamate release directly via pretreatments with the sodium-channel modulator lamotrigine (300 mg), and indirectly via pretreatment with the 5-HT2A receptor antagonist risperidone (2mg). Ketamine induced robust changes in the functional connectivity pattern and produced a shift from a cortically-centered to a sub-cortically-centered brain state. Pre-treatment with risperidone, but not lamotrigine, resulted in a strong modulation of the ketamine-induced hub changes, which suggests that these changes are likely a result of NMDA blockade and possible serotonergic modulation rather than purely modulation of glutamate release.
Introduction
Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, has been studied in relation to the glutamate hypothesis of schizophrenia and increases dissociation, positive and negative symptom ratings. Ketamine effects brain function through changes in brain activity; these activity patterns can be modulated by pre-treatment of compounds known to attenuate the effects of ketamine on glutamate release. Ketamine also has marked effects on brain connectivity; we predicted that these changes would also be modulated by compounds known to attenuate glutamate release.
Methods
Here, we perform task-free pharmacological magnetic resonance imaging (phMRI) to investigate the functional connectivity effects of ketamine in the brain and the potential modulation of these effects by pre-treatment of the compounds lamotrigine and risperidone, compounds hypothesised to differentially modulate glutamate release. Connectivity patterns were assessed by combining windowing, graph theory and multivariate Gaussian process classification.
Results
We demonstrate that ketamine has a robust effect on the functional connectivity of the human brain compared to saline (87.5 % accuracy). Ketamine produced a shift from a cortically centred, to a subcortically centred pattern of connections. This effect is strongly modulated by pre-treatment with risperidone (81.25 %) but not lamotrigine (43.75 %). Based on the differential effect of these compounds on ketamine response, we suggest the observed connectivity effects are primarily due to NMDAR blockade rather than downstream glutamatergic effects.
Discussion
The connectivity changes contrast with amplitude of response for which no differential effect between pre-treatments was detected, highlighting the necessity of these techniques in forming an informed view of the mechanistic effects of pharmacological compounds in the human brain.
Papers cited by this study that are also in Blossom
Corlett, P. R., Honey, G. D., Krystal, J. H. et al. · Neuropsychopharmacology (2010)
William Deakin, J. F., Lees, J., McKie, S. et al. · JAMA Psychiatry (2008)
Papers in Blossom that reference this study
Moujaes, F., Lisa, J., Rahmati, M. et al. · eLife (2024)
Veraart, J. K. E., Smith-Apeldoorn, S. Y., Bakker, I. M. et al. · International Journal of Neuropsychopharmacology (2021)
Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, induces transient glutamatergic dysfunction and acutely produces psychotomimetic effects in healthy volunteers. Earlier neuroimaging work has documented robust ketamine effects on the blood oxygen level-dependent (BOLD) signal, typically analysed in terms of amplitude, and pre-treatment with compounds that reduce glutamate release has been shown to attenuate those amplitude changes. However, ketamine also affects functional connectivity across the brain, and it remains uncertain whether connectivity changes are driven primarily by direct NMDAR blockade or by downstream glutamatergic mechanisms. Joules and colleagues set out to characterise whole-brain task-free functional connectivity changes induced by an acute sub-anaesthetic ketamine infusion and to test whether two pharmacological probes with different mechanisms—lamotrigine (a presynaptic inhibitor of glutamate release via sodium channel modulation) and risperidone (an atypical antipsychotic with 5-HT2A antagonism and reported NMDAR potentiation)—differentially modulate those connectivity effects. Using a multivariate pattern-recognition approach applied to graph-theory centrality metrics, the study aims to identify ketamine-induced connectivity patterns and to determine whether pre-treatment with lamotrigine or risperidone attenuates or alters these patterns, thereby informing mechanistic interpretation.
The investigators conducted a double-blind, placebo-controlled, cross-over study in 20 initially recruited right-handed male volunteers; after withdrawals, 16 participants (age range 20–37, mean 25.8, SD 5.7) completed all sessions. Each participant attended four sessions separated by at least 10 days. Oral pre-treatments were lamotrigine 300 mg, risperidone 2 mg, or placebo (ascorbic acid, given in two sessions). Following oral dosing at times chosen to coincide with maximal plasma exposure, an intravenous infusion of either racemic ketamine (target plasma level 75 ngml-1) or saline was administered. The ketamine dosing protocol consisted of a bolus of approximately 0.12 mgkg-1 in the first minute followed by a pseudocontinuous infusion of ~0.31 mgkg-1 h-1; plasma samples for ketamine, risperidone and lamotrigine concentrations were taken at specified intervals. Functional MRI data were acquired on a 3T scanner, collecting 450 task-free EPI volumes over 15 min (TR=2000 ms). The infusion began 5 min into the scan. Pre-processing included slice-timing correction, realignment, co-registration to structural images, spatial normalisation, smoothing (8 mm FWHM), tissue segmentation, nuisance regression (mean CSF, mean white matter and six motion parameters) and band-pass filtering (0.01–0.1 Hz). The time series for each session were divided into pre- and post-infusion conditions defined as the first and last 150 volumes (5 min each). For network analysis, the pre-processed data were parcellated using the automated anatomical labelling (AAL) atlas into 116 regions; node time series were the mean voxel time course per region. To account for temporal dynamics of resting-state networks, a tapered Gaussian moving window of length 120 volumes (240 s) was applied in increments equivalent to 2 s, yielding eight windowed time series per condition. Pairwise linear correlations between nodes produced adjacency matrices per window. Degree centrality (weighted degree) was computed for each node and normalised by network density to quantify regional connectedness. Multivariate analysis employed Gaussian process classification (GPC) for binary contrasts and ordinal regression using Gaussian processes (ORGP) for rank-ordered analyses. Feature sets comprised subject-wise mean-centred degree centrality measures across all windows. Classifier performance was assessed with leave-one-out cross-validation (LOOCV); statistical significance was estimated by permutation testing with Nperm=1000 and Bonferroni correction applied across multiple contrasts. For significant models, multivariate maps (g-maps) visualised node weights driving classification; univariate paired t-tests on node differences were performed qualitatively without formal multiple comparison correction.
Ketamine produced a robust and classifiable change in whole-brain degree centrality (DC) compared with control conditions. Binary classification separating the post-infusion ketamine condition from the pre-infusion placebo condition (PLA+KET) achieved 81.25% accuracy (p<0.003), and ketamine versus saline achieved 87.5% accuracy (p<0.001). By contrast, classifiers could not reliably separate the two pre-infusion placebo sessions (Acc=68.75%, p>0.05) nor the pre- versus post-infusion saline session (Acc=62.5%, p>0.05), supporting within-session and between-session stability in the absence of active pharmacology. The spatial pattern driving ketamine classification reflected a shift from cortical to subcortical and cerebellar dominance. Nodes with increased DC in the ketamine state (positive weights) were concentrated in basal ganglia and cerebellar regions, while nodes with decreased DC (negative weights) were primarily cortical, notably occipital, temporal, medial temporal and frontal areas. Post hoc univariate tests confirmed these directions of change across nodes. Risperidone pre-treatment modulated the ketamine-induced DC pattern. Classification distinguished the risperidone-plus-ketamine (RIS+KET) condition from placebo pre-treated ketamine (PLA+KET) with 81.25% accuracy (p<0.001), indicating risperidone altered the ketamine effect. RIS+KET was also discriminable from saline (Acc=81.25%, reported p>0.05 in the extracted text), and direct comparison of lamotrigine and risperidone post-ketamine states showed strong discrimination (lam+ketamine v ris+ketamine, Acc=93.75%, p<0.001). Univariate contrasts suggested risperidone increased DC in frontal and temporal cortices and decreased DC in basal ganglia, occipital and parietal regions relative to placebo-pre-treated ketamine. The classifier could not reliably separate pre- and post-infusion for the risperidone session (risperidone v ris+ketamine, Acc=62.5%, p>0.05), and when a GPC trained on saline and ketamine was used to classify RIS+KET samples, RIS+KET was variably confused with both classes. Lamotrigine pre-treatment did not show evidence of modulating ketamine-induced DC patterns. Classification could not distinguish lamotrigine from placebo (Acc=32.125%, p>0.05) nor ketamine from lamotrigine-plus-ketamine (ketamine v lam+ketamine, Acc=43.75%, p>0.05). Within-session ketamine effects remained detectable for both placebo and lamotrigine pre-treated sessions (placebo pre-ket v ketamine Acc=81.5%, p<0.001; lamotrigine pre v lam+ketamine Acc=87.5%, p=0.01). Direct lamotrigine v risperidone comparisons were not significant (Acc=62.5%, p>0.05). Ketamine plasma concentrations did not show a significant correlation with subject-level classifier posterior probabilities (no significant correlation at p<0.05). Ordinal regression analyses did not reveal a clear attenuative ordinal trend for either pre-treatment. In one configuration saline was well identified but ketamine and lamotrigine-plus-ketamine were confused; in the other, saline and ketamine were distinguished while risperidone-plus-ketamine was confused with both classes.
Joules and colleagues interpret their findings as showing that a sub-anaesthetic ketamine infusion reliably reconfigures whole-brain functional connectivity, shifting from a cortically centred organisation toward increased centrality in subcortical structures and the cerebellum. The authors note that this multivariate centrality change complements prior reports of increased ‘‘global connectivity’’ under ketamine and expands those observations by mapping a coherent spatial pattern of increased subcortical and decreased cortical hubness. Mechanistically, the investigators propose that NMDAR antagonism underlies the observed connectivity reorganisation. They discuss a plausible microcircuit mechanism in which ketamine disinhibits parvalbumin-positive GABAergic interneurons, impairing pyramidal synchrony and reducing cortical hub connectivity; the thalamus may contribute to disrupted cortical synchrony and thus appear with increased centrality. The pattern of connectivity alteration is linked by the authors to ketamine's acute experiential effects—perceptual distortion, cognitive disorganisation and anhedonia—via affected parietal, visual, fronto-parietal and striatal/ventromedial nodes. The differential effects of the two pre-treatments are emphasised. Lamotrigine, despite attenuating ketamine-evoked BOLD amplitude in prior work and being administered at a clinically effective 300 mg dose here, did not alter the ketamine-induced DC pattern; the authors infer that lamotrigine’s presynaptic reduction of glutamate release does not account for the connectivity reorganisation. In contrast, risperidone substantially modulated ketamine-induced DC, producing a DC pattern distinct from both saline and ketamine. The authors suggest risperidone's combined pharmacology—5-HT2A antagonism reducing downstream glutamate and reported NMDAR potentiation—may oppose or interact with ketamine’s action, and that NMDAR-related mechanisms are a likely contributor to the DC effects. They acknowledge that serotonergic and dopaminergic receptor actions could also play a role and indicate that studies with more selective ligands would be required to disentangle receptor-specific contributions. Methodological strengths and caveats are discussed. The multivariate graph-theory plus machine-learning approach demonstrated stability in the absence of pharmacological challenge, supporting interpretation of drug effects. Limitations include the focus on a single connectivity metric (degree centrality) and temporal scale, insensitivity to phase-delayed or non-linear interregional relationships, and the inability of centrality measures to localise changes in specific connections. The authors also note that low ketamine doses produced sparse subjective effects, limiting correlation of connectivity changes with phenomenology, and that broader sets of connectivity measures and alternative windowing scales would be informative in future work. Overall, the authors conclude that the connectivity findings favour a primary role for NMDAR blockade in generating the observed reorganisation and that multivariate connectivity analyses yield mechanistic information distinct from amplitude-based BOLD measures, with implications for interpreting pharmacological manipulations of brain networks.
The study demonstrates a robust acute connectivity signature of sub-anaesthetic ketamine characterised by reduced cortical hub connectivity and increased degree centrality in basal ganglia and cerebellar nodes. Pattern-recognition analysis showed no spurious changes in the absence of pharmacological stimulus. Pre-treatment with risperidone, but not lamotrigine, markedly modulated the ketamine-induced hub changes, a result the authors attribute to the distinct mechanisms of the two compounds and which suggests the connectivity alterations are more likely driven by NMDAR blockade and possible serotonergic modulation rather than by presynaptic suppression of glutamate release alone. The authors emphasise that connectivity analyses offer complementary mechanistic insight to conventional BOLD amplitude measures and can inform on neural-circuit correlates of specific pharmacological mechanisms.
The N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine induces glutamatergic dysfunction in healthy humans which can lead to the acute onset of psychomimetic symptoms resembling schizophrenia. Whilst not a phenocopy of the disorder, ketamine causes an increase in both positive and negative symptom scores as well as impairing cognition). Hence, it has been widely used to study the role of glutamatergic dysfunction in relation to schizophrenia. Ketamine has been shown to elicit robust and reliable effects on the blood oxygen level-dependent (BOLD) signal in healthy volunteers. To date, these BOLD effects have been primarily studied in terms of their amplitude). Moreover, pre-treatment with compounds expected to reduce glutamate release has been demonstrated to attenuate the ketamine-induced BOLD response. Previously, we demonstrated that acute pre-treatment with clinically effective doses of either lamotrigine or risperidone attenuated the BOLD response to ketamine infusion in healthy volunteers via different mechanisms of action. Lamotrigine, an anticonvulsant, acts pre-synaptically through sodium ion channel modulation, attenuating glutamate release directly. In contrast, risperidone, an atypical antipsychotic, is thought to alter cortical glutamate levels pre-synaptically through 5-HT 2A receptor antagonism, indirectly reducing glutamate release). Moreover, risperidone has been shown to potentiate NMDARs directlyproviding an additional potential mechanism of action to attenuate the ketamine-evoked effect. Acute NMDAR blockade has been shown to alter the functional connectivity of several neural systems in both rodents) and humans. The mechanism by which this is achieved is not understood. It is presumed that the downstream effects on the glutamate system drive connectivity changes, but evidence for this is lacking. Here, we address the question of how an acute ketamine infusion affects the pattern of whole-brain connectivity and how this is altered through different modulatory mechanisms using risperidone and lamotrigine as pharmacological probes. If purely a result of direct NMDA blockade, we hypothesise that any changes in connectivity will be insensitive to the effects of lamotrigine whilst attenuated by risperidone. We would interpret shared effects by both lamotrigine and risperidone as resulting from the presumed attenuation of glutamate release. Pattern recognition (PR) techniques have been used to perform single subject inference using whole brain functional connectivity data to model disease states, cognitive states) and age groups). In the current study, we use whole-brain measures of functional connectivity with PR techniques to identify spatial patterns of whole-brain connectivity underlying the effects of acute ketamine and to investigate the extent of the effect of pre-treatment on this induced connectivity pattern. In accord with our BOLD signal amplitude response findings, we expect both compounds to exhibit modulation of the ketamine-induced change. We predict that the extent and pattern of each compound's modulatory effect will provide insight into the neuronal systems underlying the ketamine effect in the human brain.
Twenty right handed male volunteers were recruited for this double blind, placebo controlled, cross-over design study, approved by the Wandsworth Research Ethics Committee (09/H0803/48). Participants provided written informed consent and were screened for any history of physical, neurological and psychiatric illness. Further exclusion criteria included MRI-incompatibilities, illicit drug abuse and excessive alcohol, cigarette or caffeine consumption, in addition to normal ranges on standard haematology and biochemistry assessments. Further details are available in. Twenty participants passed screening and were entered into the study, of which four withdrew, one due to fainting upon cannulation in session 1, one experiencing nausea at session 3 and two participants were withdrawn at session 2 and session 3 due to violating the lifestyle guidelines. Thus, 16 participants (range=20-37; mean=25.8; SD=5.7) completed all sessions in this study.
Data were collected over four sessions, each separated by a period of at least 10 days. During each session, participants received a single oral dose of either lamotrigine (300 mg), risperidone (2 mg) or placebo (ascorbic acid; two sessions). This was followed by an intravenous infusion of either saline or ketamine, to a target plasma level of 75 ngml -1 , timed to coincide with the approximate expected time of maximum plasma concentration of both compounds. The administered pre-treatment and infusion combinations were placebo and saline, placebo and ketamine, risperidone and ketamine and lamotrigine and ketamine. The compound combinations and session timelines are illustrated in Fig.. Imaging procedures for risperidone and lamotrigine sessions were undertaken during the broad maximum plasma exposure between 1 h 30 m post-dose (oral drug alone) and 4 h 15 m post-dose during ketamine infusion. At 30 min, 1, 1.5, 4 and 8 h following the administration of the oral compounds, blood samples were taken to measure risperidone and lamotrigine concentrations in plasma.
A sub-anaesthetic dose of racemic ketamine was administered intravenously to achieve a target plasma level of 75 ngml -1 , adjusted for the participant's weight and height measured at each session. Intravenous administration was achieved using a Graseby 3400 pump based on the Clements 250 model and was controlled using Stanpump (pkpd). A dose of 0.12 (±0.003 SD) mgkg -1 was administered in the first minute followed by a pseudocontinuous infusion of approximately 0.31 mgkg -1 h -1 . Blood samples were taken at 15 and 75 min after the start of the infusion to measure ketamine concentration in the plasma.
MR images were acquired using a 3T GE HDx scanner. Gradient-echo echo-planar imaging (EPI) was used to acquire 450 task-free volumes of 38 near-axial slices over a period of 15 min for each session (3 mm thickness, 0.3 mm inter-slice gap, TE=30 ms, TR=2000 ms, FA=75°, in-plane resolution= 3.3 mm, matrix size=64×64, field of view=21.1×21.1 cm). The infusion of ketamine or saline was administered 5 min into the 15 min scan. Additionally, a high-resolution gradientecho scan was performed resulting in 43 near-axial slices (3 mm thickness, 0.3 mm inter-slice gap, TE=30 ms, TR= 2000 ms, FA=90°, in-plane resolution=3.3 mm, matrix size= 128×128, field of view=24×24 cm).
The 450 volumes of the EPI time series acquired at each session were divided into pre-and post-infusion conditions, defined as the first and last 150 volumes (5 min) of data, respectively. The pre-infusion condition corresponds to the period during which participants had only the oral compound present and the post-infusion condition corresponds to the period 5-10 min after the administration of bolus. We refer to pre-and post-infusion conditions in each compound combination session using the naming scheme described in Table.
Imaging data were pre-processed using SPM5 (www.fil.ion. ucl.ac.uk/spm). The BOLD time series were slice-time corrected, realigned to the first volume then to the mean volume, co-registered to the high-resolution structural scan, spatially normalised to the SPM EPI template and smoothed using a Gaussian kernel of 8 mm at full-width half-maximum. The structural images were segmented into grey matter, white matter and cerebrospinal fluid (CSF) tissue types. Linear regression was used to regress out nuisance signal parameters from the imaging time-series data, specifically the mean CSF signal, mean WM signal and the six motion parameters. The mean and maximum head motion parameters were compared between compound infusion conditions within each session. No differences in motion were observed at p< 0.05 (uncorrected). A band pass filter of 0.01-0.1 Hz was subsequently applied to reduce the effect of high-frequency (>0.1 Hz) physiological signals and low-frequency (<0.01 Hz) noise such as scanner drift.
The pre-processed data were parcellated into regions to facilitate network analysis where each parcellated region represents a node in a graph. The automated anatomical labelling (AAL) atlaswas selected to parcellate the brain into 116 anatomical nodes due to its prevalence in neuroimaging studies. A finer scale parcellation would provide increased regional homogeneity but complicates the interpretation of the model. Node time series were calculated as the mean of voxel time courses in each atlas region. In this manner, the entire brain can be represented as a graph, G=(V, E), where V describes the collection of 116 nodes (defined in the AAL Atlas) and E the edges between each node pair.
Task-free or "resting state" networks (RSNs) display complex spatiotemporal dynamics. Networks identified from resting state data are highly dependent on the temporal scale used. In order to account for these dynamics, temporal windowing may be applied. RSNs derived from long windows (>~120 s) allow investigations of the core network and minimise the effects of spurious correlations which occur with brief acquisitions. Furthermore, it has been demonstrated that RSNs can be considered stable when windowing at >~240 s. To this end, a moving window of length T=120 volumes (240 s) window was applied over the parcellated time series data for each condition in order to compute temporal connectivity. A tapered window was preferred to a box car in order to minimise overlap between windows and reduce the contribution of any outliers not central to the window. We define the window using the Gaussian window function: where t takes values {-T/2 .. T/2} and s is defined as T/6. This window was applied in increments of 2s, the effective length of the window, resulting in eight windowed time-series for each condition. In the present study, although no specific task was applied during the functional scan, the paradigm likely induces some departure from the "resting state" due to the expectation of drug delivery pre-infusion and putative subjective effects of ketamine following its infusion.
The edges, E, were calculated between all pairs of nodes in V using linear correlation resulting in a series of adjacency matrices for each window position, as illustrated in Fig.. In this manner, we express the functional connections between regions across the whole brain whilst accounting for low-frequency network dynamics thought to represent "core" connections.
Using graph theory centrality measures, it is possible to quantify the importance or "connectedness" of node within a graph. The most intuitive of these measures is weighted degree centrality which is simply the sum of the weights of all edges impingent on a given node and has been previously demonstrated to be well-suited to elucidate node properties within brain networks. As a local measure, it provides an indication of the connectedness of a brain region with the rest of the brain. Degree centrality was selected for this investigation as it provides an intuitive measure of the overall connectedness of a region. Given the wide spread effects of glutamatergic TableNaming convention for each individual drug state in each of the four compound combinations dysfunction, providing a measure of how the mean connectivity of a region with the rest of the brain is affected. Here, the degree centrality (DC) was calculated for each node, i, in each adjacency matrix using: where N is the number of nodes (N=116) and a ij is the edge weighting between nodes i and j in the adjacency matrix A. Measures at each network slice were normalised by network density to account for inter-subject differences in network densities.
To perform comparisons between session conditions, Gaussian process classification (GPC) was employed. This binary classification algorithm provides probabilistic predictions of class membership for unseen test data based on a model defined from a set of training data and their respective labels (either +1 or -1). It has been demonstrated to provide robust performance in several neuroimaging studies. For a more detailed account of this technique, please refer to. The subject-wise mean centred DC measures from all window positions in each session condition were used as a feature set for classification, allowing for the comparison of regional network connectedness between conditions. In order to assess the performance of the classifier, leaveone-out cross validation (LOOCV) was used. This is an iterative process whereby data from all but one participant is used for training; data from the withheld participant is then used for testing. The reported classification accuracy is the mean accuracy value obtained across all LOOCV folds. This accuracy serves to quantify the degree to which the two groups can be separated in terms of their functional connectivity as measured by DC. The statistical significance of the classification accuracy was assessed by permutation. A distribution of permuted accuracies was generated by re-training the classifier N perm times using randomly permuted class labels. The number of times the permuted accuracy was found to be greater than the true accuracy was counted and divided by N perm to provide an estimate of the p value (here, N perm =1000). As several contrasts were investigated, Bonferroni correction was applied to correct for the effect of multiple comparisons. Classification was performed using the following two configurations: (1) between PLA+SAL and PLA+KET sessions in order to investigate the effect of ketamine on whole-brain connectivity and (2) using lamotrigine and risperidone pre-treated sessions (LAM+KET and RIS+ KET) in contrast to the PLA+KET and PLA+SAL sessions to investigate their modulation of ketamine-induced connectivity. Ordinal regression using Gaussian processes (ORGP)) was applied to investigate the relationship between experimental conditions, identifying an ordinal trend across classes under the proviso that classes are rank ordered. Classes may be accurately distinguished from one another only if an ordinal progression exists between classes, else poor discrimination may be observed. We refer the reader tofor further details on this multi-class classification algorithm.
Multivariate classification maps (g-maps) were generated for models with significant classification accuracy (identified by permutation tests), allowing for the visualisation of the discriminating pattern of spatial covariance driving the classification. Samples were weighted in relation to their class typicality, resulting in a visualisation of the distribution of the classes relative to one another. For each map, node weights were depicted as spheres positioned in MNI space at the centre of mass for the corresponding AAL regions. The size of these spheres represents the contribution of the node in driving the discriminatory performance. The colour, blue or red, represents the sign of the weights, recalling that the class labels are encoded as either +1 or -1 (e.g. +1=PLA+KET and -1=PLA+SAL). A high positive weight implies that the weighted average DC for that node was higher for the class with the positive label +1 (e.g. the PLA+KET class). We note this weighted average is influenced by a combination of the DC value, its variance and the classification parameterisation. The direction of change in node strength between classification groups was confirmed using paired t tests. One sample t tests were performed on the node deltas between classes for the purpose of qualitative analysis only. Significance was indicated at p<0.05; we have not used formal multiple comparisons correction to balance type I and type II errors as the core inferences of this paper were drawn from the multivariate analysis and not this supplementary univariate analysis.
In order to determine the effect of ketamine on the pattern on degree centrality (DC), the ketamine condition was compared against both pre-infusion placebo (PLA+KET) and saline conditions which involved no active pharmacology. These comparisons yielded high classification accuracies reflecting significant differences in functional connectivity (Acc=81.25 and 87.5 %, respectively, p<0.003 and p<0.001; Table). Importantly, the classifier was unable to significantly distinguish between both the two pre-infusion conditions following administration of oral placebo (placebo pre-ketamine v placebo pre-saline) with a classification accuracy (68.75 %, p>0.05) and conditions before and after a saline infusion (placebo (pre-saline) v saline, Acc=62.5 %, p>0.05) (Table). These findings indicate that within the context of our study design, we cannot discriminate between placebo (pre-saline) and saline conditions; this is akin to within-session stability. Furthermore, it was not possible to discriminate between placebo conditions which are akin to between-session stability. Figureillustrates the pattern of weights (g-map) driving the classification between the placebo (pre-ketamine) and ketamine conditions. Qualitatively, the pattern of node weights favouring the ketamine condition (depicted in red) is strongly represented in sub-cortical and cerebellar regions whereas the pattern of weights favouring placebo (pre-ketamine, depicted in blue) is predominantly cortical, especially in occipital, temporal, medial temporal and frontal cortical regions. A regional visualisation of the weight distributions is presented in Fig.. This pattern of features was highly similar to that of the g-map of saline infusion compared to ketamine infusion, suggesting a similar effect driving the classification in both comparisons. Post hoc t tests performed directly on the DC values confirm the regions exhibiting increased DC, in relation to placebo, fit with the pattern of positive weights favouring ketamine, and vice versa for the pattern of negative weights. A summary figure illustrating the univariate change in DC values for each node within each region is shown in Fig..
To investigate the effect of modulating the ketamine response by risperidone, classification was performed by comparing each of the RIS+KET sessions against both the placebo sessions (PLA+KET and PLA+SAL) as shown in Table. The DC pattern for the risperidone pre-treated ketamine condition was significantly discriminated from saline (saline v ris+ketamine, Acc=81.25 %, p>0.05) suggesting a dissimilar pattern of DC between conditions. Furthermore, the risperidone pre-treated ketamine condition was significantly discriminated from the placebo pre-treated ketamine condition (ketamine v ris+ketamine, Acc=81.25 %, p<0.001) indicating risperidone modulates the ketamine effect on DC; classification map is shown in Fig.. This suggests a distinct risperidone state, different from both ketamine and saline. The spatial pattern of weights for ketamine pre-treated with risperidone was to some degree similar to ketamine pre-treated with placebo (Fig.) with the notable exception of the weighting of nodes in occipital regions; this can best be observed in Fig.. To investigate the univariate change in DC values, t tests were performed on the delta of ris+ketamine and ketamine conditions, Fig.. Risperidone pre-treatment was observed to modulate ketamine-induced connectivity resulting in increased DC in the frontal and temporal cortices and decreased DC in the basal ganglia, occipital and parietal regions, when compared to placebo pre-treated ketamine. Moreover, when comparing the pre-and post-ketamine infusion conditions for the risperidone pre-treated session, the classifier was unable to significantly separate these conditions (risperidone v ris+ketamine, Acc=62.5 %, p>0.05). The results of these three comparisons are all consistent with risperidone pre-treatment modulating the ketamine-induced functional connectivity pattern as measured by DC. Whilst the results of these comparisons are consistent with risperidone modulating the ketamine-induced connectivity pattern, it is also possible that the classifications are driven by a common risperidone signature. That is, the main effect of risperidone may be present during both the risperidone and the ris+ketamine conditions. Thus, we propose that we are observing an interaction effect between risperidone and the ketamine response. This is in keeping with our hypothesis that risperidone has a substantial effect on the pattern of ketamine DC. The risperidone pre-treated ketamine condition was classified using the GPC model trained using saline and ketamine classes (Table). Risperidone pre-treated ketamine was confused with both saline and ketamine conditions with chance level accuracies reported. A marginally higher accuracy was reported for the confusion with the ketamine condition than the saline. TableConfusion matrix for the classification of risperidone+ ketamine using the GPC model obtained from training on saline and ketamine conditions Testing was performed using leave-one-subject-out cross validation
To investigate the effect of lamotrigine pre-treatment on ketamine-induced changes in functional connectivity, we compared both the lamotrigine conditions against the placebo post-infusion sessions (PLA+KET and PLA+SAL) as shown in Table. It was not possible to discriminate lamotrigine from placebo (lamotrigine v placebo (pre-sal), Acc = 32.125 %, p>0.05), suggesting similar patterns of DC in both conditions. There was no supportive evidence of a significant modulation effect of the ketamine-induced DC pattern by lamotrigine (ketamine v lam+ketamine, Acc = 43.75 %, p > 0.05). Furthermore, similar trends were observed when comparing the pre-infusion and post-infusion ketamine states for the placebo sessions (placebo (pre-ket) v ketamine, Acc=81.5 %, p<0.001) and lamotrigine pre-treatment (lamotrigine v Lam+ketamine, Acc=87.5 %, p=0.01). Taken together, our results suggest lamotrigine pre-treatment, at the administered dosage, does not have an effect on ketamine-induced DC patterns. We were unable to distinguish between both lamotrigine and risperidone sessions when compared directly (lamotrigine v risperidone, Acc=62.5 %, p>0.05). However, a significant classification accuracy was observed for the comparison between post-ketamine conditions following a pre-treatment of lamotrigine and risperidone (lam+ketamine v ris+ketamine, Acc=93.75 %, p<0.001). This suggests both compounds differentially affect the pattern of ketamine-induced DC. Ketamine plasma concentrations in the experimental sessions were correlated to participant posterior probabilities for the within session classification (pre-infusion vs. post-infusion). No significant correlation was observed at a level of p <0.05. This suggests the observed patterns of DC do not linearly scale with the ketamine plasma concentrations.
Ordinal regression was performed to investigate any potential attenuative effects of lamotrigine and risperidone on the ketamine-induced change in connectivity. Specifically, two analyses were performed with conditions rank ordered: (1) saline v (Ris+ketamine) v ketamine and (2) saline v (Lam+ketamine) v ketamine. As shown in Table, no ordinal trend was observed between saline and ketamine for (ris+ketamine) and (lam+ketamine) conditions. For configuration 1, saline is distinctly and accurately identified; however, the ketamine and the lam+ketamine classes exhibit confusion with each other. In configuration 2, saline and ketamine are well distinguished from each other; however, the ris+ketamine condition reports confusion with both conditions and is not well identified.
Using whole brain centrality metrics in a multivariate framework, we were able to show that ketamine induces a robust, predictable change in the functional connectivity pattern in the human brain, producing a shift from a cortically centred to a sub-cortically centred brain state. Risperidone pre-treatment significantly modulated the ketamine-induced centrality changes; however, lamotrigine did not. The distinct modulatory effects of these two compounds on ketamine-induced DC changes stands in contrast to their similar effects in attenuating the ketamine-induced BOLD amplitude changes.
Consistent with previous work, during a task-free acute ketamine challenge, a distributed pattern of functional connectivity changes were observed across the brain. On contrasting ketamine with placebo, nodes in the basal ganglia and TableConfusion matrices for ORGP classification of (A) saline v lam+ketamine v ketamine and (B) saline v ris+ketamine v ketamine, no significant discrimination was observed for the pre-treated classes in relation to saline and ketamine, as indicated by the less than chance accuracy shown in the central cell (<33 %) (2015) 232:4205-4218 cerebellum were predominantly weighted in favour of the ketamine group whilst nodes in the frontal, occipital, parietal, temporal and medial temporal lobe regions were weighted for the placebo state. Post hoc univariate tests confirm the directionality of these effects, revealing decreasing cortical centrality and an increase in centrality of the cerebellum and basal ganglia in the ketamine state relative to the control. Previous studies have revealed findings consistent with observations from this study. An overall increase in "global connectivity" as a result of ketamine infusion was observed in healthy volunteers. Regions exhibiting the greatest increase in this global connectivity for ketamine included the thalamus, parietal regions and cerebellum. Whilst our analysis is multivariate, it is noteworthy that the same regions are present in the pattern of features describing ketamine and have increased degree centrality in relation to placebo. Our analysis extends these previous works, revealing the pattern on changes across the whole brain between groups. Ketamine has also been reported to strongly affect the connectivity of cerebellar, visual, auditory, somatosensory and subcortical regions in relation to pre-defined networks of interest. The cerebellum was shown to have increased connectivity in relation to visual networks as well as the intra-network connectivity of the visual cortex when under the influence of ketamine. Furthermore, the thalamus, PFC, temporal cortex and cingulum were shown to have decreasing connectivity in relation to the auditory and somatosensory network. The multivariate results presented here provide an alternative perspective on the effect of acute ketamine on wholebrain connectivity. The task-free connectivity organisation of the brain is radically altered by acute ketamine with both increases and decreases in the centrality pattern of the brain. This is supported by previous studies demonstrating ketamine administration can cause long-range decoupling of neural population activity in mouse neocortical slices and has been shown to cause both increases and decreases in connectivity, e.g.. We can summarise the observed effects of acute ketamine in humans as a shifting of the pattern of connectivity from a cortically centred state to a sub-cortically centred state. It is possible that cortical regions have a global reduction in connectivity with the rest of the brain, resulting in a reduction in the connectivity of cortical hubs; alternatively, it is possible that dominant regional couplings are reduced, resulting in a more equally distributed connectivity. Both mechanisms would result in the DC pattern observed under ketamine. The presented methodology is not designed to identify changes in individual connections; instead, we reveal consistent patterns of DC change across the whole brain which reveals a subcortical contribution for the ketamine connectivity pattern. The observed pattern of disconnectivity of the cortical hubs is consistent with many of the experiential effects observed with ketamine; interruption of connectivity between cortical hubs would be predicted to result in a reduction contextual processing, impaired memory, spatial representation and sensorimotor processing. Whilst not a phenocopy of the disorder, ketamine has been used as pharmacological model for schizophrenia which has been shown to exhibit similar alterations in connectivity, notably a reduction in high DC cortical hubs) and wide spread disconnections across the brain. The main effects of ketamine at sub-anaesthetic doses are typically attributed to its blockade of the NMDA receptor). It has high affinity for NMDA and D 2 receptor sites as well as a slightly lower affinity for the 5-HT 2A receptor, muscarinic and opioid receptors although these are thought to have notable effects at higher doses only. A possible mechanism by which ketamine effects connectivity changes is through interruption of NMDARmediated transmission, namely the known cortical microcircuit effects where parvalbumin-positive GABAergic interneurons disinhibited by ketamine. Microdialysis confirms the NMDA antagonists PCP and MK-801 increase extracellular GABA levels. These interneurons inhibit pyramidal cells and synchronise their oscillations. The disinhibition of these GABAergic interneurons may result in impaired pyramidal synchrony and hence in decreased connectivity of cortical nodes, leading to the observed pattern of centrality. It is suggested that the lateral posterior pulvinar in the thalamus may strongly contribute to pyramidal synchronywhich may explain its inclusion in the pattern for ketamine exhibiting increased centrality. Alternatively, if ketamine increases cortical connectivity globally whilst reducing strong regional connections, this may also result in increased centrality of the basal ganglia and cerebellum whilst reducing cortical centrality. The radical alteration of the pattern of centrality during a task-free ketamine infusion has not been demonstrated previously and suggests a complex network reorganisation underpinning the effect of ketamine, even at the low dose administered in this study. This reorganisation is a plausible candidate for the variety of effects produced by ketamine, such as perceptual distortion (via parietal and visual regions as well as cerebellar and basal ganglia timing nodes), cognitive disorganisation (via fronto-patietal nodes) and anhedonia (via striatal and ventromedial nodes). The important contribution of the current study is to inform on the reorganisation of brain networks by ketamine and to what degree these can be pharmacologically modulated by different pharmacological mechanisms. In order to facilitate multiple administrations of ketamine, doses were kept relatively low; as such, we did not observe any strong and consistent subjective effects. Furthermore, the sparseness and inconsistencies of participant subjective responses, which were collected, prohibited investigation of correlation between functional connectivity effects and subjective ratings. Replication using higher doses producing stronger subjective changes in all participants would be important and also allow testing of the relationship between the multivariate changes in network patterns with the profiles of changes in subjective reporting of drug effects.
Lamotrigine causes a reduction in glutamate release primarily through Na + channel modulation; we hypothesised that this would partially counter the downstream glutamatergic effects of ketamine, thus attenuating any ketamine-induced changes in functional connectivity. Indeed, it has been demonstrated that lamotrigine pre-treatment attenuates the ketamine BOLD effect. Furthermore, it has been suggested that ketamine's effect on functional connectivity may reflect glutamatergic modulation. In contrast to our previous findings on the ketamine-induced BOLD signal, we found no significant effects of lamotrigine on the pattern of DC in the brain. The administered dose of lamotrigine (300 mg) is clinically effective and the data analysed is the same as that used in our previous studywhere clear effects on the local BOLD signal were demonstrated. Instead, our combined results show that lamotrigine induces an amplitude change in signal rather than affecting the coupling between regions; although we cannot preclude more subtle, localised changes in connectivity which our method which interrogates whole brain DC patterns would be insensitive. This lack of DC modulation by lamotrigine is further supported by the ordinal regression of saline, lamotrigine+ketamine and ketamine conditions, where we did not observe an ordinal progression between classes. This is consistent with our conclusion that lamotrigine does not attenuate the ketamine-induced pattern of DC. The strong confusion between the lamotrigine+ketamine and ketamine conditions as observed using binary classification whereby the discrimination between the lamotrigine+ketamine conditions was not significant (Table 3) and for ordinal regression (Table) whereby ~41 % of the lamotrigine + ketamine cases were classified as ketamine. These results have important mechanistic implications for the ketamine-induced connectivity response and support the conclusion that lamotrigine pre-treatment has a similar effect to placebo on ketamine-induced DC. Furthermore, since lamotrigine, which block glutamate release, does not significantly modulate the ketamine-induced effects then this suggests that the observed pattern of hub connectivity induced by ketamine reflects NMDAR antagonism per se rather than downstream glutamatergic effects at non-NMDA sites. Instead, this would favour the NMDA blockade of ketamine as responsible for the observed connectivity changes. This proposal could be confirmed in preclinical experiments using specific NMDA subunit antagonists and non-NMDA antagonists for the AMPA and kainate receptors.
Risperidone (2 mg) is expected to achieve high occupancy of 5-HT 2A and D 2 receptors) and reduces glutamate levels downstream through 5-HT 2A antagonism. Furthermore, risperidone has also been reported to potentiate NMDAR function directly. Given this profile, we expected that risperidone pre-treatment would attenuate the pattern of DC induced by ketamine. Indeed, our observations indicate that, in contrast to lamotrigine, risperidone modulates the effect of ketamine resulting in a pattern of DC distinct to both ketamine and saline. The classification maps for ketamine compared to saline and ketamine compared to ris+ketamine also reveal a strong overlap in the spatial distribution, but not amplitude, of weights with the exception of occipital regions. This is consistent with risperidone pre-treatment reversing the ketamine-induced node strength changes. Comparing the pre-and post-infusion conditions for ketamine pre-treated with risperidone reveal risperidone effects some more subtle changes that did not achieve statistical significance. Direct comparison of the ketamine and ris + ketamine states reveal strong effects of the risperidone pre-treatment on the ketamine-induced pattern of DC which appear to oppose the ketamine effect. Furthermore, the ORGP results suggest risperidone does not attenuate the ketamine DC response in an ordinal manner; instead, the combined results support the conclusion that risperidone may have an opposing effect on the ketamine response. We theorise that it is the twofold mechanism of risperidone acting upon ketamine that is responsible for the observed DC effects. It is likely that the potentiation of the NMDAR is the primary mechanism due to the dissimilarity in resultant effects between risperidone and lamotrigine pre-treatment. Our results are suggestive that risperidone may interact with, and in opposition to, the ketamine resulting in a pattern of DC dissimilar to that of ketamine; the dissimilarity from the saline state and ordinal regression results preclude a linear attenuation effect of risperidone on ketamine. These observations may be predominantly due to serotonergic effects; however, further work is required using a compound with selective 5-HT 2A binding. In addition to the NMDA receptor, ketamine also has affinity for other receptors including dopamine, D2 and opioid receptors, particularly at high doses, effects can also be elicited through downstream serotonergic and muscarinic receptors). Whilst we cannot preclude effects at other receptors contributing to our findings, we would favour a major contribution from glutamatergic effects. The opposing effects of ketamine and risperidone in the striatum may conceivably be related to their opposing effects at the D2 receptor, where risperidone acts as an antagonist and ketamine an agonist. The use of selective compounds would be required to understand the contribution of individual receptor types to the observed pattern of effects.
Importantly, our methodology did not detect changes in connectivity pattern when no pharmacological stimulus was applied. Comparisons between pre-infusion scans from the two oral placebo sessions (PLA+SAL vs. PLA+KET), as well as between the pre-and post-infusion scans from the saline infusion session (PLA+SAL), both revealed no significant differences with classification accuracies approximating chance level. This test-retest stability provides confidence in our interpretation of the effects of ketamine and its modulation by risperidone and lamotrigine as reflecting the pharmacological action of these interventions.
The results presented here provide an informative insight into the mechanisms of effect of ketamine on the human brain; however, the limitations of this analysis must also be considered. The use of graph theory combined with machine learning is a novel approach; it provides a principled means of investigation pharmacological compounds on the human brain. The use of centrality, whilst providing an intuitive measure of regional connectedness and providing an elegant solution to the interpreting spatial patterns of discrimination, prohibits investigation into specific regional coupling. As such, this methodology is insensitive to subtle changes in regional coupling which may be induced by the administered compounds. Univariate methodsare available for testing hypotheses about specific connections, but these might also be more sensitive to non-pharmacological effects. Our choice of examining the patterns of change across the brain was motivated by the widespread expression of NMDA receptors. The use of a single connectivity metric and temporal scale is a further limitation. The use of correlation allows for the identification of positive and negative synchronous relationships between regions, however, is naive to phase-delayed and non-linear relationships between regions which are likely to exist in a complex biological system such as the brain. By comparing the results of analysis using both correlation and a phase insensitive measures such as coherence, it may be possible to infer the effects of ketamine on BOLD signal synchronisation between regions. It is likely that analysis performed using different windowing scales would provide further insight into the effects of ketamine on the dynamics of connectivity in the human brain. The use of degree centrality provides an initial investigation into the connectivity effects of ketamine in the brain. However, in order to generate a complete view of the connectivity effects of ketamine, a range comprehensive range of measures should be compared. Several graph theory metrics exist informing differential aspects of function connectivity; however, many of these measures are highly correlated and present challenges in biological interpretation. Further investigation is required in order to identify an optimal set of orthonormal connectivity measure suitable for use in connectivity analysis which provides a broad overview of connectivity effects.
We have demonstrated a robust pattern of acute connectivity changes resulting from administration of a sub-anaesthetic dose of ketamine, comprising a reduction in connectivity of cortical nodes and an increase in DC of nodes in the basal ganglia and cerebellum. Importantly, our methodology detected no changes in the connectivity pattern for paired comparisons involving no pharmacological stimulus. Furthermore, pre-treatment with risperidone, but not lamotrigine, resulted in a strong modulation of the ketamine-induced hub changes. This likely reflects the distinct mechanisms of the two compounds and suggests that the observed changes in connectivity are more likely a result of NMDA blockade and possible serotonergic modulation rather than purely modulation of glutamate release. The differential modulatory effects of these two compounds are in contrast to the finding that both compounds similarly attenuate the ketamine-evoked BOLD signal amplitude changes. This connectivity analysis provides distinct information regarding the effects of neurologically active compounds and inform on the neural circuit correlates of specific pharmacological mechanisms.
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