Increasing robustness of pairwise methods for effective connectivity in Magnetic Resonance Imaging by using fractional moment series of BOLD signal distributions

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly even when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between of every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the Dynamic Causal Modeling (DCM) generative model. The directionality is inferred based upon statistical dependencies between the two node time series, e.g. assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.Comment: 41 pages, 12 figure

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Interplay between genome-wide implicated genetic variants and environmental factors related to childhood antisocial behavior in the UK ALSPAC cohort

We investigated gene-environment (G x E) interactions related to childhood antisocial behavior between polymorphisms implicated by recent genome-wide association studies (GWASs) and two key environmental adversities (maltreatment and smoking during pregnancy) in a large population cohort (ALSPAC). We also studied the MAOA candidate gene and addressed comorbid attention-deficit/hyperactivity disorder (ADHD). ALSPAC is a large, prospective, ethnically homogeneous British cohort. Our outcome consisted of mother-rated conduct disorder symptom scores at age 7;9 years. G x E interactions were tested in a sex-stratified way (alpha = 0.0031) for four GWAS-implicated variants (for males, rs4714329 and rs9471290; for females, rs2764450 and rs11215217), and a length polymorphism near the MAOA-promoter region. We found that males with rs4714329-GG (P = 0.0015) and rs9471290-AA (P = 0.0001) genotypes were significantly more susceptible to effects of smoking during pregnancy in relation to childhood antisocial behavior. Females with the rs11215217-TC genotype (P = 0.0018) were significantly less susceptible to effects of maltreatment, whereas females with the MAOA-HL genotype (P = 0.0002) were more susceptible to maltreatment effects related to antisocial behavior. After adjustment for comorbid ADHD symptomatology, aforementioned G x E's remained significant, except for rs11215217 x maltreatment, which retained only nominal significance. Genetic variants implicated by recent GWASs of antisocial behavior moderated associations of smoking during pregnancy and maltreatment with childhood antisocial behavior in the general population. While we also found a G x E interaction between the candidate gene MAOA and maltreatment, we were mostly unable to replicate the previous results regarding MAOA-G x E's. Future studies should, in addition to genome-wide implicated variants, consider polygenic and/or multimarker analyses and take into account potential sex stratification

Clinical improvement of DM1 patients reflected by reversal of disease-induced gene expression in blood

Background: Myotonic dystrophy type 1 (DM1) is an incurable multisystem disease caused by a CTG-repeat expansion in the DM1 protein kinase (DMPK) gene. The OPTIMISTIC clinical trial demonstrated positive and heterogenous effects of cognitive behavioral therapy (CBT) on the capacity for activity and social participations in DM1 patients. Through a process of reverse engineering, this study aims to identify druggable molecular biomarkers associated with the clinical improvement in the OPTIMISTIC cohort. Methods: Based on full blood samples collected during OPTIMISTIC, we performed paired mRNA sequencing for 27 patients before and after the CBT intervention. Linear mixed effect models were used to identify biomarkers associated with the disease-causing CTG expansion and the mean clinical improvement across all clinical outcome measures. Results: We identified 608 genes for which their expression was significantly associated with the CTG-repeat expansion, as well as 1176 genes significantly associated with the average clinical response towards the intervention. Remarkably, all 97 genes associated with both returned to more normal levels in patients who benefited the most from CBT. This main finding has been replicated based on an external dataset of mRNA data of DM1 patients and controls, singling these genes out as candidate biomarkers for therapy response. Among these candidate genes were DNAJB12, HDAC5, and TRIM8, each belonging to a protein family that is being studied in the context of neurological disorders or muscular dystrophies. Across the different gene sets, gene pathway enrichment analysis revealed disease-relevant impaired signaling in, among others, insulin-, metabolism-, and immune-related pathways. Furthermore, evidence for shared dysregulations with another neuromuscular disease, Duchenne muscular dystrophy, was found, suggesting a partial overlap in blood-based gene dysregulation. Conclusions: DM1-relevant disease signatures can be identified on a molecular level in peripheral blood, opening new avenues for drug discovery and therapy efficacy assessments.</p

Examining Individual Differences in Social Reward Valuation: a Person-Based Approach

Social reward refers to the motivational and pleasurable aspects of our interactions with other people. While some people experience social encounters as pleasurable, others experience them as aversive. However, the current knowledge on individual differences in social reward valuation in relation to pro- and antisocial personality characteristics is limited. The Social Reward Questionnaire (SRQ) was developed to assess individual differences in the value of different types of social rewards. First, the present study examined the validity and reliability of the Dutch version of the SRQ in a Dutch and Flemish community sample (N = 1892). Second, using latent profile analysis (LPA), it was investigated whether subgroups of participants existed with distinctive patterns of social reward valuation, and whether these subgroups differed in their level of psychopathic traits, aggression, and social anxiety. The results confirmed the original six-factor structure and showed good reliability and validity. The LPA identified four classes of individuals, labelled as: Low Social Interest, High Social Interest, Undifferentiated Social Reward-seekers, and Socially Cruel. These classes were further typified by distinct levels of psychopathy, reactive and proactive aggression, and social anxiety. The present findings contribute to our understanding of individual variability in the underlying motives of social behaviors

Identification of an age-dependent biomarker signature in children and adolescents with autism spectrum disorders

Background: Autism spectrum disorders (ASDs) are neurodevelopmental conditions with symptoms manifesting before the age of 3, generally persisting throughout life and affecting social development and com

COMPULS:Design of a multicenter phenotypic, cognitive, genetic, and magnetic resonance imaging study in children with compulsive syndromes

Background: Compulsivity, the closely linked trait impulsivity and addictive behaviour are associated with several neurodevelopmental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive compulsive disorder (OCD). All three disorders show impaired fronto-striatal functioning, which may be related to altered glutamatergic signalling. Genetic factors are also thought to play an important role in the aetiology of compulsivity-related disorders. Methods: The COMPULS study is a multi-center study designed to investigate the relationship between the traits compulsivity, impulsivity, and, to a lesser extent, addictive behaviour within and across the neurodevelopmental disorders ADHD, ASD, and OCD. This will be done at the phenotypic, cognitive, neural, and genetic level. In total, 240 participants will take part in COMPULS across four different sites in Europe. Data collection will include diagnostic interviews, behavioural questionnaires, cognitive measures, structural, functional and spectral neuroimaging, and genome-wide genetic information. Discussion: The COMPULS study will offer the unique opportunity to investigate several key aspects of compulsivity across a large cohort of ADHD, ASD and OCD patients

Memantine treatment does not affect compulsive behavior or frontostriatal connectivity in an adolescent rat model for quinpirole-induced compulsive checking behavior

RATIONALE: Compulsivity often develops during childhood and is associated with elevated glutamate levels within the frontostriatal system. This suggests that anti-glutamatergic drugs, like memantine, may be an effective treatment. OBJECTIVE: Our goal was to characterize the acute and chronic effect of memantine treatment on compulsive behavior and frontostriatal network structure and function in an adolescent rat model of compulsivity. METHODS: Juvenile Sprague-Dawley rats received repeated quinpirole, resulting in compulsive checking behavior (n = 32; compulsive) or saline injections (n = 32; control). Eight compulsive and control rats received chronic memantine treatment, and eight compulsive and control rats received saline treatment for seven consecutive days between the 10th and 12th quinpirole/saline injection. Compulsive checking behavior was assessed, and structural and functional brain connectivity was measured with diffusion MRI and resting-state fMRI before and after treatment. The other rats received an acute single memantine (compulsive: n = 12; control: n = 12) or saline injection (compulsive: n = 4; control: n = 4) during pharmacological MRI after the 12th quinpirole/saline injection. An additional group of rats received a single memantine injection after a single quinpirole injection (n = 8). RESULTS: Memantine treatment did not affect compulsive checking nor frontostriatal structural and functional connectivity in the quinpirole-induced adolescent rat model. While memantine activated the frontal cortex in control rats, no significant activation responses were measured after single or repeated quinpirole injections. CONCLUSIONS: The lack of a memantine treatment effect in quinpirole-induced compulsive adolescent rats may be partly explained by the interaction between glutamatergic and dopaminergic receptors in the brain, which can be evaluated with functional MRI

Converging evidence points towards a role of insulin signaling in regulating compulsive behavior.

Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder with childhood onset, and is characterized by intrusive thoughts and fears (obsessions) that lead to repetitive behaviors (compulsions). Previously, we identified insulin signaling being associated with OCD and here, we aim to further investigate this link in vivo. We studied TALLYHO/JngJ (TH) mice, a model of type 2 diabetes mellitus, to (1) assess compulsive and anxious behaviors, (2) determine neuro-metabolite levels by 1 H magnetic resonance spectroscopy (MRS) and brain structural connectivity by diffusion tensor imaging (DTI), and (3) investigate plasma and brain protein levels for molecules previously associated with OCD (insulin, Igf1, Kcnq1, and Bdnf) in these subjects. TH mice showed increased compulsivity-like behavior (reduced spontaneous alternation in the Y-maze) and more anxiety (less time spent in the open arms of the elevated plus maze). In parallel, their brains differed in the white matter microstructure measures fractional anisotropy (FA) and mean diffusivity (MD) in the midline corpus callosum (increased FA and decreased MD), in myelinated fibers of the dorsomedial striatum (decreased FA and MD), and superior cerebellar peduncles (decreased FA and MD). MRS revealed increased glucose levels in the dorsomedial striatum and increased glutathione levels in the anterior cingulate cortex in the TH mice relative to their controls. Igf1 expression was reduced in the cerebellum of TH mice but increased in the plasma. In conclusion, our data indicates a role of (abnormal) insulin signaling in compulsivity-like behavior

A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)

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