The effect of learning to drum on behavior and brain function in autistic adolescents.

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.

Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis.

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan's unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p < 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p < 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = -0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = -0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate (g = -0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p < 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p < 0.001). Proportion of males was negatively associated with MFC glutamate (z = -0.02, p < 0.001) and frontal white matter Glx (z = -0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG (z = 0.01, p = 0.01) and temporal lobe (z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.

Dose-response modeling in mental health using stein-like estimators with instrumental variables.

A mental health trial is analyzed using a dose-response model, in which the number of sessions attended by the patients is deemed indicative of the dose of psychotherapeutic treatment. Here, the parameter of interest is the difference in causal treatment effects between the subpopulations that take part in different numbers of therapy sessions. For this data set, interactions between random treatment allocation and prognostic baseline variables provide the requisite instrumental variables. While the corresponding two-stage least squares (TSLS) estimator tends to have smaller bias than the ordinary least squares (OLS) estimator; the TSLS suffers from larger variance. It is therefore appealing to combine the desirable properties of the OLS and TSLS estimators. Such a trade-off is achieved through an affine combination of these two estimators, using mean squared error as a criterion. This produces the semi-parametric Stein-like (SPSL) estimator as introduced by Judge and Mittelhammer (2004). The SPSL estimator is used in conjunction with multiple imputation with chained equations, to provide an estimator that can exploit all available information. Simulated data are also generated to illustrate the superiority of the SPSL estimator over its OLS and TSLS counterparts. A package entitled SteinIV implementing these methods has been made available through the R platform. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Cognitive relevance of the community structure of the human brain functional coactivation network.

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.

Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder.

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions that are accompanied by atypical brain connectivity. So far, in vivo evidence for atypical structural brain connectivity in ASD has mainly been based on neuroimaging studies of cortical white matter. However, genetic studies suggest that abnormal connectivity in ASD may also affect neural connections within the cortical gray matter. Such intrinsic gray-matter connections are inherently more difficult to describe in vivo but may be inferred from a variety of surface-based geometric features that can be measured using magnetic resonance imaging. Here, we present a neuroimaging study that examines the intrinsic cortico-cortical connectivity of the brain in ASD using measures of "cortical separation distances" to assess the global and local intrinsic "wiring costs" of the cortex (i.e., estimated length of horizontal connections required to wire the cortex within the cortical sheet). In a sample of 68 adults with ASD and matched controls, we observed significantly reduced intrinsic wiring costs of cortex in ASD, both globally and locally. Differences in global and local wiring cost were predominantly observed in fronto-temporal regions and also significantly predicted the severity of social and repetitive symptoms (respectively). Our study confirms that atypical cortico-cortical "connectivity" in ASD is not restricted to the development of white-matter connections but may also affect the intrinsic gray-matter architecture (and connectivity) within the cortical sheet. Thus, the atypical connectivity of the brain in ASD is complex, affecting both gray and white matter, and forms part of the core neural substrates underlying autistic symptoms.

Aberrant cerebral network topology and mild cognitive impairment in early Parkinson's disease.

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.

White matter development and early cognition in babies and toddlers.

The normal myelination of neuronal axons is essential to neurodevelopment, allowing fast inter-neuronal communication. The most dynamic period of myelination occurs in the first few years of life, in concert with a dramatic increase in cognitive abilities. How these processes relate, however, is still unclear. Here we aimed to use a data-driven technique to parcellate developing white matter into regions with consistent white matter growth trajectories and investigate how these regions related to cognitive development. In a large sample of 183 children aged 3 months to 4 years, we calculated whole brain myelin volume fraction (VFM ) maps using quantitative multicomponent relaxometry. We used spatial independent component analysis (ICA) to blindly segment these quantitative VFM images into anatomically meaningful parcels with distinct developmental trajectories. We further investigated the relationship of these trajectories with standardized cognitive scores in the same children. The resulting components represented a mix of unilateral and bilateral white matter regions (e.g., cortico-spinal tract, genu and splenium of the corpus callosum, white matter underlying the inferior frontal gyrus) as well as structured noise (misregistration, image artifact). The trajectories of these regions were associated with individual differences in cognitive abilities. Specifically, components in white matter underlying frontal and temporal cortices showed significant relationships to expressive and receptive language abilities. Many of these relationships had a significant interaction with age, with VFM becoming more strongly associated with language skills with age. These data provide evidence for a changing coupling between developing myelin and cognitive development.

Group analysis of self-organizing maps based on functional MRI using restricted Frechet means.

Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering and independent component analysis (ICA) have been used to identify such spatio-temporal networks. While these approaches have been useful for estimating these networks at the subject-level, comparisons over groups or experimental conditions require further methodological development. In this paper, we tackle this problem by showing how self-organizing maps (SOMs) can be compared within a Frechean inferential framework. Here, we summarize the mean SOM in each group as a Frechet mean with respect to a metric on the space of SOMs. The advantage of this approach is twofold. Firstly, it allows the visualization of the mean SOM in each experimental condition. Secondly, this Frechean approach permits one to draw inference on group differences, using permutation of the group labels. We consider the use of different distance functions, and introduce one extension of the classical sum of minimum distance (SMD) between two SOMs, which take into account the spatial pattern of the fMRI data. The validity of these methods is illustrated on synthetic data. Through these simulations, we show that the two distance functions of interest behave as expected, in the sense that the ones capturing temporal and spatial aspects of the SOMs are more likely to reach significance under simulated scenarios characterized by temporal, spatial [and spatio-temporal] differences, respectively. In addition, a re-analysis of a classical experiment on visually-triggered emotions demonstrates the usefulness of this methodology. In this study, the multivariate functional patterns typical of the subjects exposed to pleasant and unpleasant stimuli are found to be more similar than the ones of the subjects exposed to emotionally neutral stimuli. In this re-analysis, the group-level SOM output units with the smallest sample Jaccard indices were compared with standard GLM group-specific z-score maps, and provided considerable levels of agreement. Taken together, these results indicate that our proposed methods can cast new light on existing data by adopting a global analytical perspective on functional MRI paradigms.

Statistical parametric network analysis of functional connectivity dynamics during a working memory task.

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,p<0.001), 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.

Association of Age, Antipsychotic Medication, and Symptom Severity in Schizophrenia With Proton Magnetic Resonance Spectroscopy Brain Glutamate Level: A Mega-analysis of Individual Participant-Level Data.

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 < .001; cerebrospinal fluid-corrected Glu, F1,1216.7 = 5.610, P = .02), showing a 0.2-unit reduction per decade. In patients, antipsychotic dose (in chlorpromazine equivalents) was negatively associated with MFC Glu (estimate, 0.10 reduction per 100 mg; SE, 0.03) and MFC Glx (estimate, -0.11; SE, 0.04) levels. The MFC Glu to Cr ratio was positively associated with total symptom severity (estimate, 0.01 per 10 points; SE, 0.005) and positive symptom severity (estimate, 0.04; SE, 0.02) and was negatively associated with level of global functioning (estimate, 0.04; SE, 0.01). In the MTL, the Glx to Cr ratio was positively associated with total symptom severity (estimate, 0.06; SE, 0.03), negative symptoms (estimate, 0.2; SE, 0.07), and worse Clinical Global Impression score (estimate, 0.2 per point; SE, 0.06). The MFC creatine level increased with age (estimate, 0.2; SE, 0.05) but was not associated with either symptom severity or antipsychotic medication dose. Conclusions and Relevance: Findings from this mega-analysis suggest that lower brain Glu levels in patients with schizophrenia may be associated with antipsychotic medication exposure rather than with greater age-related decline. Higher brain Glu levels may act as a biomarker of illness severity in schizophrenia.

Stein-like estimators for causal mediation analysis in randomized trials.

Causal mediation analysis aims to estimate natural direct and natural indirect effects under clearly specified assumptions. Traditional mediation analysis based on Ordinary Least Squares assumes an absence of unmeasured causes to the putative mediator and outcome. When these assumptions cannot be justified, instrumental variable estimators can be used in order to produce an asymptotically unbiased estimator of the mediator-outcome link, commonly referred to as a Two-Stage Least Squares estimator. Such bias removal, however, comes at the cost of variance inflation. A Semi-Parametric Stein-Like estimator has been proposed in the literature that strikes a natural trade-off between the unbiasedness of the Two-Stage Least Squares procedure and the relatively small variance of the Ordinary Least Squares estimator. The Semi-Parametric Stein-Like estimator has the advantage of allowing for a direct estimation of its shrinkage parameter. In this paper, we demonstrate how this Stein-like estimator can be implemented in the context of the estimation of natural direct and natural indirect effects of treatments in randomized controlled trials. The performance of the competing methods is studied in a simulation study, in which both the strength of hidden confounding and the strength of the instruments are independently varied. These considerations are motivated by a trial in mental health, evaluating the impact of a primary care-based intervention to reduce depression in the elderly.

Psychiatric symptoms caused by cannabis constituents: a systematic review and meta-analysis.

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<0·0001); positive symptom severity (assessed in 14 studies, with 15 independent samples, involving 324 participants: SMC 0·91 [95% CI 0·68-1·14], p<0·0001); and negative symptom severity with a large effect size (assessed in 12 studies, with 13 independent samples, involving 267 participants: SMC 0·78 [95% CI 0·59-0·97], p<0·0001). In the systematic review, of the four studies evaluating CBD's effects on THC-induced symptoms, only one identified a significant reduction in symptoms. INTERPRETATION: A single THC administration induces psychotic, negative, and other psychiatric symptoms with large effect sizes. There is no consistent evidence that CBD induces symptoms or moderates the effects of THC. These findings highlight the potential risks associated with the use of cannabis and other cannabinoids that contain THC for recreational or therapeutic purposes. FUNDING: UK Medical Research Council, Maudsley Charity, Brain and Behavior Research Foundation, Wellcome Trust, and the UK National Institute for Health Research.

Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure.

Importance: Autism spectrum disorder (ASD) is 2 to 5 times more common in male individuals than in female individuals. While the male preponderant prevalence of ASD might partially be explained by sex differences in clinical symptoms, etiological models suggest that the biological male phenotype carries a higher intrinsic risk for ASD than the female phenotype. To our knowledge, this hypothesis has never been tested directly, and the neurobiological mechanisms that modulate ASD risk in male individuals and female individuals remain elusive. Objectives: To examine the probability of ASD as a function of normative sex-related phenotypic diversity in brain structure and to identify the patterns of sex-related neuroanatomical variability associated with low or high probability of ASD. Design, Setting, and Participants: This study examined a cross-sectional sample of 98 right-handed, high-functioning adults with ASD and 98 matched neurotypical control individuals aged 18 to 42 years. A multivariate probabilistic classification approach was used to develop a predictive model of biological sex based on cortical thickness measures assessed via magnetic resonance imaging in neurotypical controls. This normative model was subsequently applied to individuals with ASD. The study dates were June 2005 to October 2009, and this analysis was conducted between June 2015 and July 2016. Main Outcomes and Measures: Sample and population ASD probability estimates as a function of normative sex-related diversity in brain structure, as well as neuroanatomical patterns associated with low or high ASD probability in male individuals and female individuals. Results: Among the 98 individuals with ASD, 49 were male and 49 female, with a mean (SD) age of 26.88 (7.18) years. Among the 98 controls, 51 were male and 47 female, with a mean (SD) age of 27.39 (6.44) years. The sample probability of ASD increased significantly with predictive probabilities for the male neuroanatomical brain phenotype. For example, biological female individuals with a more male-typic pattern of brain anatomy were significantly (ie, 3 times) more likely to have ASD than biological female individuals with a characteristically female brain phenotype (P = .72 vs .24, respectively; χ21 = 20.26; P < .001; difference in P values, 0.48; 95% CI, 0.29-0.68). This finding translates to an estimated variability in population prevalence from 0.2% to 1.3%, respectively. Moreover, the patterns of neuroanatomical variability carrying low or high ASD probability were sex specific (eg, in inferior temporal regions, where ASD has different neurobiological underpinnings in male individuals and female individuals). Conclusions and Relevance: These findings highlight the need for considering normative sex-related phenotypic diversity when determining an individual's risk for ASD and provide important novel insights into the neurobiological mechanisms mediating sex differences in ASD prevalence.

Percolation under noise: Detecting explosive percolation using the second-largest component.

We consider the problem of distinguishing between different rates of 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 nonstationary 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 (Erdos-Rényi) percolation is visually indistinguishable from a more rapid form of percolation. In this setting, we compare two different criteria for discriminating between these two percolation models, based on the interquartile range (IQR) 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 IQR 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 different rates of percolation, under physically motivated conditions for the birth and death of edges, and under noise. The potential application of the proposed criteria for the detection of clinically relevant percolation in the context of applied neuroscience is also discussed.

Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy.

Abnormalities of tau protein are central to the pathogenesis of progressive supranuclear palsy, whereas haplotype variation of the tau gene MAPT influences the risk of Parkinson disease and Parkinson's disease dementia. We assessed whether regional MAPT expression might be associated with selective vulnerability of global brain networks to neurodegenerative pathology. Using task-free functional magnetic resonance imaging in progressive supranuclear palsy, Parkinson disease, and healthy subjects (n = 128), we examined functional brain networks and measured the connection strength between 471 gray matter regions. We obtained MAPT and SNCA microarray expression data in healthy subjects from the Allen brain atlas. Regional connectivity varied according to the normal expression of MAPT. The regional expression of MAPT correlated with the proportionate loss of regional connectivity in Parkinson's disease. Executive cognition was impaired in proportion to the loss of hub connectivity. These effects were not seen with SNCA, suggesting that alpha-synuclein pathology is not mediated through global network properties. The results establish a link between regional MAPT expression and selective vulnerability of functional brain networks to neurodegeneration.

Statistical network analysis for functional MRI: summary networks and group comparisons.

Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges in 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 summary 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 either 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.

Brain network analysis: separating cost from topology using cost-integration.

A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain 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 that differ in mean association weight, using global efficiency. 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, and for any choice of distribution over cost levels. 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 is generally not a valid approach to this problem. Indeed, we prove that, under weak 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 with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. 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.

Taxometric investigation of the latent structure of nicotine dependence: an epidemiological sample.

A considerable number of daily tobacco users do not fulfill the DSM-IV and ICD-10 diagnostic criteria for nicotine dependence (ND). This suggests that such a diagnostic boundary may be arbitrary. This paper addresses this question empirically by comparing the viability of two models, respectively hypothesizing a dimensional and a categorical latent structure of ND. An epidemiological sample of 6,926 individuals was selected from a cross-sectional probabilistic stratified sampling design. All participants having smoked in the past 30 days were included in the study. Half of this sample was used to select appropriate composite indicators of tobacco consumption. A factor analysis with oblique PROMAX rotation was used as well as the MAXCOV (Maximum Covariance) procedure to identify indicators that maximized between-class distance, and minimize within-class variance. The remaining half of the sample was submitted to a set of three mathematically independent taxometric procedures: Mean Above Minus Below A Cut (MAMBAC), MAXCOV and Maximum Eigenvalues (MAXEIG). In line with the original hypothesis, the results supported a dimensional latent structure for ND. These findings are discussed in terms of their clinical implications for the validation of adequate screening procedures and the etiology and maintenance of ND.