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This current study aimed to investigate the impact of drum training on behavior and brain function in autistic adolescents with no prior drumming experience. Thirty-six autistic adolescents were recruited and randomly assigned to one of two groups. The drum group received individual drum tuition (two lessons per week over an 8-wk period), while the control group did not. All participants attended a testing session before and after the 8-wk period. Each session included a drumming assessment, an MRI scan, and a parent completing questionnaires relating to the participants’ behavioral difficulties. Results showed that improvements in drumming performance were associated with a significant reduction in hyperactivity and inattention difficulties in drummers compared to controls. The fMRI results demonstrated increased functional connectivity in brain areas responsible for inhibitory control, action outcomes monitoring, and self-regulation. In particular, seed-to-voxel analyses revealed an increased functional connectivity in the right inferior frontal gyrus and the right dorsolateral prefrontal cortex. A multivariate pattern analysis demonstrated significant changes in the medial frontal cortex, the left and right paracingulate cortex, the subcallosal cortex, the left frontal pole, the caudate, and the left nucleus accumbens. In conclusion, this study investigates the impact of a drum-based intervention on neural and behavioral outcomes in autistic adolescents. We hope that these findings will inform further research and trials into the potential use of drum-based interventions in benefitting clinical populations with inhibition-related disorders and emotional and behavioral difficulties.

Background Approximately 188 million people use cannabis yearly worldwide, and it has recently been legalised in 11 US states, Canada, and Uruguay for recreational use. The potential for increased cannabis use highlights the need to better understand its risks, including the acute induction of psychotic and other psychiatric symptoms. We aimed to investigate the effect of the cannabis constituent Δ9-tetrahydrocannabinol (THC) alone and in combination with cannabidiol (CBD) compared with placebo on psychiatric symptoms in healthy people. Methods In this systematic review and meta-analysis, we searched MEDLINE, Embase, and PsycINFO for studies published in English between database inception and May 21, 2019, with a within-person, crossover design. Inclusion criteria were studies reporting symptoms using psychiatric scales (the Brief Psychiatric Rating Scale [BPRS] and the Positive and Negative Syndrome Scale [PANSS]) following the acute administration of intravenous, oral, or nasal THC, CBD, and placebo in healthy participants, and presenting data that allowed calculation of standardised mean change (SMC) scores for positive (including delusions and hallucinations), negative (such as blunted affect and amotivation), and general (including depression and anxiety) symptoms. We did a random-effects meta-analysis to assess the main outcomes of the effect sizes for total, positive, and negative PANSS and BPRS scores measured in healthy participants following THC administration versus placebo. Because the number of studies to do a meta-analysis on CBD's moderating effects was insufficient, this outcome was only systematically reviewed. This study is registered with PROSPERO, CRD42019136674. Findings 15 eligible studies involving the acute administration of THC and four studies on CBD plus THC administration were identified. Compared with placebo, THC significantly increased total symptom severity with a large effect size (assessed in nine studies, with ten independent samples, involving 196 participants: SMC 1·10 [95% CI 0·92–1·28], p

Key Points Question Are clinical and demographic factors associated with brain glutamate or glutamate plus glutamine (Glx) levels in schizophrenia? Findings In this mega-analysis of 1251 patients with schizophrenia and 1197 healthy volunteers, medial frontal cortex glutamatergic metabolite levels were lower in patients and negatively associated with the dose of antipsychotic medication, although a reduction in glutamate levels with age was not accelerated in patients with schizophrenia compared with healthy individuals. Higher medial frontal cortex and medial temporal lobe glutamate levels were associated with more severe symptoms in patients with schizophrenia. Meaning Lower brain glutamate levels may be associated with antipsychotic exposure rather than with greater age-related decline, whereas higher glutamate levels may serve as a biomarker of illness severity in patients with schizophrenia., Importance Proton magnetic resonance spectroscopy (1H-MRS) studies indicate that altered brain glutamatergic function may be associated with the pathophysiology of schizophrenia and the response to antipsychotic treatment. However, the association of altered glutamatergic function with clinical and demographic factors is unclear. Objective To assess the associations of age, symptom severity, level of functioning, and antipsychotic treatment with brain glutamatergic metabolites. Data Sources The MEDLINE database was searched to identify journal articles published between January 1, 1980, and June 3, 2020, using the following search terms: MRS or magnetic resonance spectroscopy and (1) schizophrenia or (2) psychosis or (3) UHR or (4) ARMS or (5) ultra-high risk or (6) clinical high risk or (7) genetic high risk or (8) prodrome* or (9) schizoaffective. Authors of 114 1H-MRS studies measuring glutamate (Glu) levels in patients with schizophrenia were contacted between January 2014 and June 2020 and asked to provide individual participant data. Study Selection In total, 45 1H-MRS studies contributed data. Data Extraction and Synthesis Associations of Glu, Glu plus glutamine (Glx), or total creatine plus phosphocreatine levels with age, antipsychotic medication dose, symptom severity, and functioning were assessed using linear mixed models, with study as a random factor. Main Outcomes and Measures Glu, Glx, and Cr values in the medial frontal cortex (MFC) and medial temporal lobe (MTL). Results In total, 42 studies were included, with data for 1251 patients with schizophrenia (mean [SD] age, 30.3 [10.4] years) and 1197 healthy volunteers (mean [SD] age, 27.5 [8.8] years). The MFC Glu (F1,1211.9 = 4.311, P = .04) and Glx (F1,1079.2 = 5.287, P = .02) levels were lower in patients than in healthy volunteers, and although creatine levels appeared lower in patients, the difference was not significant (F1,1395.9 = 3.622, P = .06). In both patients and volunteers, the MFC Glu level was negatively associated with age (Glu to Cr ratio, F1,1522.4 = 47.533, P, This mega-analysis assesses whether age, symptom severity, level of functioning, and antipsychotic treatment are associated with glutamate or glutamatergic metabolite levels measured with proton magnetic resonance spectroscopy in the medial frontal cortex or medial temporal lobe of patients with schizophrenia.

Background For adults with psychosis, international guidelines recommend individual and family based cognitive behavioural therapy interventions. Recommendations are extended to children and adolescents, based on adult research. It is also recommended that psychological interventions are offered for childhood presentations of psychotic-like or Unusual Experiences (UE), in the absence of a formal diagnosis, when these are Distressing (UEDs). Cognitive models underpinning these interventions require testing in adolescent populations, to further refine therapies. We address this need, by testing for the first time, the application of the adult cognitive model of psychosis to adolescent UEDs. Methods We used baseline data from the Coping with Unusual ExperienceS (CUES+) randomised controlled trial for 122 clinically referred adolescents (12–18 years) with self-reported UEDs. Known psychological mechanisms of adult cognitive models of psychosis; negative life events, affect (anxiety and depression), reasoning (jumping to conclusions bias), and schemas were investigated using multiple linear regression models, alongside variables particularly associated with the development and severity of adolescent UEDs and UE type (dissociation, externalising/behavioural problems, managing emotions). Results The psychological mechanisms of adult cognitive models of psychosis explained 89% of the total variance of adolescent UED severity, F (10, 106) = 99.34, p Conclusions Findings suggest that the psychological components of adult cognitive models of psychosis, particularly schemas, are also implicated in adolescent UEDs.

AimsTo conduct a meta-analysis of the effect of ketamine on psychopathology in healthy volunteers and patients with schizophrenia, and the experimental factors affecting this.BackgroundKetamine is increasingly used to treat depression and other psychiatric disorders but can induce schizophrenia-like symptoms. Despite this, the consistency and magnitude of symptoms induced by ketamine, or what factors influence the effects of ketamine on these remain unknown.MethodMEDLINE, EMBASE and PsychINFO databases were searched for within-subject placebo controlled studies reporting symptoms using the Brief Psychiatric Rating Scale (BPRS) or Positive and Negative Syndrome Scale (PANSS) in response to an acute ketamine challenge in healthy participants or people with schizophrenia. Two independent investigators extracted study-level data for a random-effects meta-analysis. Total, positive and negative BPRS and PANSS scores were extracted. Sub-group analyses were conducted examining the effect of: blinding status, ketamine preparation, infusion method and time between ketamine and placebo condition. Standardized mean change scores were used as effect sizes for individual studies. Standardized mean changes between ketamine and placebo for total, positive and negative BPRS and PANSS were calculated.ResultOf 7819 citations retrieved, 36 studies involving healthy participants were included. The overall sample included 725 healthy volunteers exposed to both the ketamine and placebo condition. Ketamine induced a significant increase in transient psychopathology in healthy participants, for total (Standardized mean change (SMC) = 1.50 (95% CI = 1.23 to 1.77), p < 0.0001), positive (SMC = 1.55 (95% CI = 1.29 to 1.81), p < 0.0001) and negative (SMC = 1.16, (95% CI = 0.96 to 1.35), p < 0.0001) symptom ratings, relative to the placebo condition. This effect was significantly greater for positive symptoms than negative symptoms (p = 0.004). Bolus followed by constant infusion increased ketamine's effect on positive symptoms relative to infusion alone (p = 0.006). Single-day study design increased ketamine's effect on total symptoms (p = 0.007), but age and gender did not moderate effects. There were insufficient studies for meta-analysis of studies in schizophrenia. Of these studies, two found a significant increase in symptoms with ketamine administration in total and positive symptoms. Only one study found an increase in negative symptom severity with ketamine.ConclusionThese findings show that acute ketamine administration induces schizophrenia-like symptomatology with large effect sizes but there is a greater increase in positive than negative symptoms, and when a bolus is used. These findings suggest bolus doses should be avoided in its therapeutic use to minimize the risk of inducing transient positive psychotic symptoms.

Importance Ketamine hydrochloride is increasingly used to treat depression and other psychiatric disorders but can induce schizophrenia-like or psychotomimetic symptoms. Despite this risk, the consistency and magnitude of symptoms induced by ketamine or what factors are associated with these symptoms remain unknown. Objective To conduct a meta-analysis of the psychopathological outcomes associated with ketamine in healthy volunteers and patients with schizophrenia and the experimental factors associated with these outcomes. Data Sources MEDLINE, Embase, and PsychINFO databases were searched for within-participant, placebo-controlled studies reporting symptoms using the Brief Psychiatric Rating Scale (BPRS) or the Positive and Negative Syndrome Scale (PANSS) in response to an acute ketamine challenge in healthy participants or patients with schizophrenia. Study Selection Of 8464 citations retrieved, 36 studies involving healthy participants were included. Inclusion criteria were studies (1) including healthy participants; (2) reporting symptoms occurring in response to acute administration of subanesthetic doses of ketamine (racemic ketamine, s-ketamine, r-ketamine) intravenously; (3) containing a placebo condition with a within-subject, crossover design; (4) measuring total positive or negative symptoms using BPRS or PANSS; and (5) providing data allowing the estimation of the mean difference and deviation between the ketamine and placebo condition. Data Extraction and Synthesis Two independent investigators extracted study-level data for a random-effects meta-analysis. Total, positive, and negative BPRS and PANSS scores were extracted. Subgroup analyses were conducted examining the effects of blinding status, ketamine preparation, infusion method, and time between ketamine and placebo conditions. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. Main Outcomes and Measures Standardized mean differences (SMDs) were used as effect sizes for individual studies. Standardized mean differences between ketamine and placebo conditions were calculated for total, positive, and negative BPRS and PANSS scores. Results The overall sample included 725 healthy volunteers (mean [SD] age, 28.3 [3.6] years; 533 [73.6%] male) exposed to the ketamine and placebo conditions. Racemic ketamine or S-ketamine was associated with a statistically significant increase in transient psychopathology in healthy participants for total (SMD = 1.50 [95% CI, 1.23-1.77]; P Conclusions and Relevance This study found that acute ketamine administration was associated with schizophrenia-like or psychotomimetic symptoms with large effect sizes, but there was a greater increase in positive than negative symptoms and when a bolus was used. These findings suggest that bolus doses should be avoided in the therapeutic use of ketamine to minimize the risk of inducing transient positive (psychotic) symptoms.

The aim of this study was to assess whether mild cognitive impairment (MCI) is associated with disruption in large-scale structural networks in newly diagnosed, drug-naive patients with Parkinson's disease (PD). Graph theoretical analyses were applied to 3T MRI data from 123 PD patients and 56 controls from the Parkinson's progression markers initiative (PPMI). Thirty-three patients were classified as having Parkinson's disease with mild cognitive impairment (PD-MCI) using the Movement Disorders Society Task Force criteria, while the remaining 90 PD patients were classified as cognitively normal (PD-CN). Global measures (clustering coefficient, characteristic path length, global efficiency, small-worldness) and regional measures (regional clustering coefficient, regional efficiency, hubs) were assessed in the structural networks that were constructed based on cortical thickness and subcortical volume data. PD-MCI patients showed a marked reduction in the average correlation strength between cortical and subcortical regions compared with controls. These patients had a larger characteristic path length and reduced global efficiency in addition to a lower regional efficiency in frontal and parietal regions compared with PD-CN patients and controls. A reorganization of the highly connected regions in the network was observed in both groups of patients. This study shows that the earliest stages of cognitive decline in PD are associated with a disruption in the large-scale coordination of the brain network and with a decrease of the efficiency of parallel information processing. These changes are likely to signal further cognitive decline and provide support to the role of aberrant network topology in cognitive impairment in patients with early PD.

In recent years, it has become common practice in neuroscience to use networks to summarize relational information in a set of measurements, typically assumed to be reflective of either functional or structural relationships between regions of interest in the brain. One of the most basic tasks of interest in the analysis of such data is the testing of hypotheses, in answer to questions such as "Is there a difference between the networks of these two groups of subjects?" In the classical setting, where the unit of interest is a scalar or a vector, such questions are answered through the use of familiar two-sample testing strategies. Networks, however, are not Euclidean objects, and hence classical methods do not directly apply. We address this challenge by drawing on concepts and techniques from geometry, and high-dimensional statistical inference. Our work is based on a precise geometric characterization of the space of graph Laplacian matrices and a nonparametric notion of averaging due to Fr\'echet. We motivate and illustrate our resulting methodologies for testing in the context of networks derived from functional neuroimaging data on human subjects from the 1000 Functional Connectomes Project. In particular, we show that this global test is more statistical powerful, than a mass-univariate approach. In addition, we have also provided a method for visualizing the individual contribution of each edge to the overall test statistic., Comment: 34 pages. 5 figures

In this short note, we study the properties of the weighted Frechet mean as a convex combination operator on an arbitrary metric space ( Y , d ) . We show that this binary operator is commutative, non-associative, idempotent, invariant to multiplication by a constant weight and possesses an identity element. We also cover the properties of the weighted cumulative Frechet mean. These tools allow us to derive several types of median inequalities for abstract metric spaces that hold for both negative and positive Alexandrov spaces. In particular, we show through an example that these bounds cannot be improved upon in general metric spaces. For weighted Frechet means, however, such inequalities can solely be derived for weights equal to or greater than one. This latter limitation highlights the inherent difficulties associated with abstract-valued random variables.

Network analysis has become a tool of choice for the study of functional and structural Magnetic Resonance Imaging (MRI) data. Little research, however, has investigated connectivity dynamics in relation to varying cognitive load. In fMRI, correlations among slow (0.1 Hz) fluctuations of blood oxygen level dependent (BOLD) signal can be used to construct functional connectivity networks. Using an anatomical parcellation scheme, we produced undirected weighted graphs linking 90 regions of the brain representing major cortical gyri and subcortical nuclei, in a population of healthy adults (n=43). Topological changes in these networks were investigated under different conditions of a classical working memory task - the N-back paradigm. A mass-univariate approach was adopted to construct statistical parametric networks (SPNs) that reflect significant modifications in functional connectivity between N-back conditions. Our proposed method allowed the extraction of 'lost' and 'gained' functional networks, providing concise graphical summaries of whole-brain network topological changes. Robust estimates of functional networks are obtained by pooling information about edges and vertices over subjects. Graph thresholding is therefore here supplanted by inference. The analysis proceeds by firstly considering changes in weighted cost (i.e. mean between-region correlation) over the different N-back conditions and secondly comparing small-world topological measures integrated over network cost, thereby controlling for differences in mean correlation between conditions. The results are threefold: (i) functional networks in the four conditions were all found to satisfy the small-world property and cost-integrated global and local efficiency levels were approximately preserved across the different experimental conditions; (ii) weighted cost considerably decreased as working memory load increased; and (iii) subject-specific weighted costs significantly predicted behavioral performances on the N-back task (Wald F=13.39,df(1)=1,df(2)=83,p0.001), and therefore conferred predictive validity to functional connectivity strength, as measured by weighted cost. The results were found to be highly sensitive to the frequency band used for the computation of the between-region correlations, with the relationship between weighted cost and behavioral performance being most salient at very low frequencies (0.01-0.03 Hz). These findings are discussed in relation to the integration/specialization functional dichotomy. The pruning of functional networks under increasing cognitive load may permit greater modular specialization, thereby enhancing performance.

The apolipoprotein E (APOE) ε4 allele is the best established genetic risk factor for Alzheimer's disease (AD) and has been previously associated with alterations in structural gray matter and changes in functional brain activity in healthy middle-aged individuals and older non-demented subjects. In order to determine the neural mechanism by which APOE polymorphisms affect white matter (WM) structure, we investigated the diffusion characteristics of WM tracts in carriers and non-carriers of the APOE ε4 and ε2 alleles using an unbiased whole brain analysis technique (Tract Based Spatial Statistics) in a healthy young adolescent (14 years) cohort. A large sample of healthy young adolescents (n = 575) were selected from the European neuro imaging-genetics IMAGEN study with available APOE status and accompanying diffusion imaging data. MR Diffusion data was acquired on 3T systems using 32 diffusion-weighted (DW) directions and 4 non-DW volumes (b-value = 1,300 s/mm2 and isotropic resolution of 2.4×2.4×2.4 mm). No significant differences in WM structure were found in diffusion indices between carriers and non-carriers of the APOE ε4 and ε2 alleles, and dose-dependent effects of these variants were not established, suggesting that differences in WM structure are not modulated by the APOE polymorphism. In conclusion, our results suggest that microstructural properties of WM structure are not associated with the APOE ε4 and ε2 alleles in young adolescence, suggesting that the neural effects of these variants are not evident in 14-year-olds and may only develop later in life.

We consider the problem of distinguishing classical (Erd\H{o}s-R\'{e}nyi) percolation from explosive (Achlioptas) percolation, under noise. A statistical model of percolation is constructed allowing for the birth and death of edges as well as the presence of noise in the observations. This graph-valued stochastic process is composed of a latent and an observed non-stationary process, where the observed graph process is corrupted by Type I and Type II errors. This produces a hidden Markov graph model. We show that for certain choices of parameters controlling the noise, the classical (ER) percolation is visually indistinguishable from the explosive (Achlioptas) percolation model. In this setting, we compare two different criteria for discriminating between these two percolation models, based on a quantile difference (QD) of the first component's size and on the maximal size of the second largest component. We show through data simulations that this second criterion outperforms the QD of the first component's size, in terms of discriminatory power. The maximal size of the second component therefore provides a useful statistic for distinguishing between the ER and Achlioptas models of percolation, under physically motivated conditions for the birth and death of edges, and under noise. The potential application of the proposed criteria for percolation detection in clinical neuroscience is also discussed., Comment: 9 pages and 8 figures. Submitted to Physics Review, Series E

The aim of this study was to assess whether mild cognitive impairment (MCI) is associated with disruption in large‐scale structural networks in newly diagnosed, drug‐naïve patients with Parkinson's disease (PD). Graph theoretical analyses were applied to 3T MRI data from 123 PD patients and 56 controls from the Parkinson's progression markers initiative (PPMI). Thirty‐three patients were classified as having Parkinson's disease with mild cognitive impairment (PD‐MCI) using the Movement Disorders Society Task Force criteria, while the remaining 90 PD patients were classified as cognitively normal (PD‐CN). Global measures (clustering coefficient, characteristic path length, global efficiency, small‐worldness) and regional measures (regional clustering coefficient, regional efficiency, hubs) were assessed in the structural networks that were constructed based on cortical thickness and subcortical volume data. PD‐MCI patients showed a marked reduction in the average correlation strength between cortical and subcortical regions compared with controls. These patients had a larger characteristic path length and reduced global efficiency in addition to a lower regional efficiency in frontal and parietal regions compared with PD‐CN patients and controls. A reorganization of the highly connected regions in the network was observed in both groups of patients. This study shows that the earliest stages of cognitive decline in PD are associated with a disruption in the large‐scale coordination of the brain network and with a decrease of the efficiency of parallel information processing. These changes are likely to signal further cognitive decline and provide support to the role of aberrant network topology in cognitive impairment in patients with early PD. Hum Brain Mapp 36:2980–2995, 2015. © 2015 Wiley Periodicals, Inc.

Comparing weighted networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i) the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii) how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by adopting a mass-univariate approach, which produces a statistical parametric network (SPN). In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks., 16 pages, 5 figures

There is growing interest in the complex topology of human brain functional networks, often measured using resting-state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation (>1,600 studies; 1985–2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous coactivation matrix was used to build a weighted graph to characterize network topology. The coactivation network was modular, with occipital, central, and default-mode modules predominantly coactivated by specific cognitive domains (perception, action, and emotion, respectively). It also included a rich club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were coactivated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich-club node and a deactivated peripheral node. Many aspects of the coactivation network were convergent with a connectivity network derived from resting state fMRI data ( n = 27, healthy volunteers); although the connectivity network was more parsimoniously connected and differed in the anatomical locations of some hubs. We conclude that the community structure of human brain networks is relevant to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.

A statistically principled way of conducting weighted network analysis is still lacking. Comparison of different populations of weighted networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs using global efficiency. We evaluate different approaches to the comparison of weighted networks that differ in mean association weight. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric does not constitute a valid approach to this problem. Indeed, we prove that, under mild conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration., Accepted for publication in PLoS one, in June 2011

Amphibian chytridiomycosis is a disease caused by the fungus Batrachochytrium dendrobatidis (Bd). Whether Bd is a new emerging pathogen (the novel pathogen hypothesis; NPH) or whether environmental changes are exacerbating the host-pathogen dynamic (the endemic pathogen hypothesis; EPH) is debated. To disentangle these hypotheses we map the distribution of Bd and chytridiomycosis across the Iberian Peninsula centred on the first European outbreak site. We find that the infection-free state is the norm across both sample sites and individuals. To analyse this dataset, we use Bayesian zero-inflated binomial models to test whether environmental variables can account for heterogeneity in both the presence and prevalence of Bd, and heterogeneity in the occurrence of the disease, chytridiomycosis. We also search for signatures of Bd-spread within Iberia using genotyping. We show (1) no evidence for any relationship between the presence of Bd and environmental variables, (2) a weak relationship between environmental variables and the conditional prevalence of infection, (3) stage-dependent heterogeneity in the infection risk, (4) a strong association between altitude and chytridiomycosis, (5) multiple Iberian genotypes and (6) recent introduction and spread of a single genotype of Bd in the Pyrenees. We conclude that the NPH is consistent with the emergence of Bd in Iberia. However, epizootic forcing of infection is tied to location and shaped by both biotic and abiotic variables. Therefore, the population-level consequences of disease introduction are explained by EPH-like processes. This study demonstrates the power of combining surveillance and molecular data to ascertain the drivers of new emerging infections diseases.