Causal effects of motor control on gait kinematics after orthopedic surgery in cerebral palsy: a machine-learning approach

Background Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control effects movement and treatment outcomes is important, but challenging due to complex interactions between impairments. While regression can be used to examine associations between impairments and gait, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility. The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS). Methods We evaluated the impact of SEMLS on change in Gait Deviation Index (GDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, change in GDI, baseline GDI (GDIpre), baseline neurologic and orthopedic impairments (Imppre), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on change in GDI and the impact of Imppre on change in GDI and GDIpre. We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects. Results We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N=54, 35 males, age: 10.5+/-3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N=55, 30 males, age: 10.0+/-3.4 years). There was a small positive causal effect of SEMLS on change in GDI (1.68 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDIpre, but minimal effects on change in GDI. Spasticity and orthopedic impairments had minimal effects on GDIpre or change in GDI. Conclusions Altered motor control and other baseline impairments did have a strong effect on GDIpre, indicating that these impairments do have a causal effect on a child's gait pattern but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP.

Altered Effective Brain Connectivity During Habituation in First Episode Schizophrenia With Auditory Verbal Hallucinations: A Dichotic Listening EEG Study

Background: Habituation is considered to have protective and filtering mechanisms. The present study is aim to find the casual relationship and mechanisms of excitatory–inhibitory (E/I) dysfunctions in schizophrenia (SCZ) via habituation.Methods: A dichotic listening paradigm was performed with simultaneous EEG recording on 22 schizophrenia patients and 22 gender- and age-matched healthy controls. Source reconstruction and dynamic causal modeling (DCM) analysis were performed to estimate the effective connectivity and casual relationship between frontal and temporal regions before and after habituation.Results: The schizophrenia patients expressed later habituation onset (p < 0.01) and hyper-activity in both lateral frontal–temporal cortices than controls (p = 0.001). The patients also showed decreased top-down and bottom-up connectivity in bilateral frontal–temporal regions (p < 0.01). The contralateral frontal–frontal and temporal–temporal connectivity showed a left to right decreasing (p < 0.01) and right to left strengthening (p < 0.01).Conclusions: The results give causal evidence for E/I imbalance in schizophrenia during dichotic auditory processing. The altered effective connectivity in frontal–temporal circuit could represent the trait bio-marker of schizophrenia with auditory hallucinations.

Prenatal Metal Exposures and Infants’ Developmental Outcomes in a Navajo Population

Early-life exposure to environmental toxicants can have detrimental effects on children’s neurodevelopment. In the current study, we employed a causal modeling framework to examine the direct effect of specific maternal prenatal exposures on infants’ neurodevelopment in the context of co-occurring metals. Maternal metal exposure and select micronutrients’ concentrations were assessed using samples collected at the time of delivery from mothers living across Navajo Nation with community exposure to metal mixtures originating from abandoned uranium mines. Infants’ development across five domains was measured at ages 10 to 13 months using the Ages and Stages Questionnaire Inventory (ASQ:I), an early developmental screener. After adjusting for effects of other confounding metals and demographic variables, prenatal exposure to lead, arsenic, antimony, barium, copper, and molybdenum predicted deficits in at least one of the ASQ:I domain scores. Strontium, tungsten, and thallium were positively associated with several aspects of infants’ development. Mothers with lower socioeconomic status (SES) had higher lead, cesium, and thallium exposures compared to mothers from high SES backgrounds. These mothers also had infants with lower scores across various developmental domains. The current study has many strengths including its focus on neurodevelopmental outcomes during infancy, an understudied developmental period, and the use of a novel analytical method to control for the effects of co-occurring metals while examining the effect of each metal on neurodevelopmental outcomes. Yet, future examination of how the effects of prenatal exposure on neurodevelopmental outcomes unfold over time while considering all potential interactions among metals and micronutrients is warranted.

Effective connectivity of LSD-induced ego dissolution

Classic psychedelic-induced ego dissolution involves a shift in the sense of self and blurring of boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated to the balance of anticorrelated activity between the default mode network (DMN) – which directs attention inwards – and the salience network (SN) – which recruits the dorsal attention network (DAN) to direct attention outward. To test whether change in anticorrelated networks underlie the peak effects of LSD, we applied dynamic causal modeling to infer effective connectivity of resting state functional MRI scans from a study of 25 healthy adults who were administered 100mg of LSD, or placebo. We found that change in inhibitory effective connectivity from the SN to DMN became excitatory, and inhibitory effective connectivity from DMN to DAN decreased under the peak effect of LSD. These changes in connectivity reflect diminution of the anticorrelation between resting state networks that may be a key neural mechanism of LSD-induced ego dissolution. Our findings suggest the hierarchically organised balance of resting state networks is a central feature in the construct of self.SignificanceThe findings can inform the parallel between the maintenance of subject-object boundary and changes to anticorrelated canonical resting state brain networks. Effective connectivity informs the hierarchical organisation of brain networks underlying modes of perception. Moreover, the anticorrelation of brain networks is an important measure of mental function. Understanding the neural mechanisms of anticorrelation change under psychedelics help identify its relationship to psychosis and its association to psychedelic assisted therapeutic outcomes.

Could Causal Discovery in Proteogenomics Assist in Understanding Gene–Protein Relations? A Perennial Fruit Tree Case Study Using Sweet Cherry as a Model

Genome-wide transcriptome analysis is a method that produces important data on plant biology at a systemic level. The lack of understanding of the relationships between proteins and genes in plants necessitates a further thorough analysis at the proteogenomic level. Recently, our group generated a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues represented by 29,247 genes and 7584 proteins. The aim of the current study was to perform a targeted analysis at the gene/protein level to assess the structure of their relation, and the biological implications. Weighted correlation network analysis and causal modeling were employed to, respectively, cluster the gene/protein pairs, and reveal their cause–effect relations, aiming to assess the associated biological functions. To the best of our knowledge, this is the first time that causal modeling has been employed within the proteogenomics concept in plants. The analysis revealed the complex nature of causal relations among genes/proteins that are important for traits of interest in perennial fruit trees, particularly regarding the fruit softening and ripening process in sweet cherry. Causal discovery could be used to highlight persistent relations at the gene/protein level, stimulating biological interpretation and facilitating further study of the proteogenomic atlas in plants.

Alteration of Effective Connectivity in the Default Mode Network of Autism After an Intervention

Neuroimaging has revealed numerous atypical functional connectivity of default mode network (DMN) dedicated to social communications (SC) in autism spectrum disorder (ASD), yet their nature and directionality remain unclear. Here, preschoolers with autism received physical intervention from a 12-week mini-basketball training program (12W-MBTP). Therefore, the directionality and nature of regional interactions within the DMN after the intervention are evaluated while assessing the impact of an intervention on SC. Based on the results of independent component analysis (ICA), we applied spectral dynamic causal modeling (DCM) for participants aged 3–6 years (experimental group, N = 17, control group, N = 14) to characterize the longitudinal changes following intervention in intrinsic and extrinsic effective connectivity (EC) between core regions of the DMN. Then, we analyzed the correlation between the changes in EC and SRS-2 scores to establish symptom-based validation. We found that after the 12W-MBTP intervention, the SRS-2 score of preschoolers with ASD in the experimental group was decreased. Concurrently, the inhibitory directional connections were observed between the core regions of the DMN, including increased self-inhibition in the medial prefrontal cortex (mPFC), and the changes of EC in mPFC were significantly correlated with change in the social responsiveness scale-2 (SRS-2) score. These new findings shed light on DMN as a potential intervention target, as the inhibitory information transmission between its core regions may play a positive role in improving SC behavior in preschoolers with ASD, which may be a reliable neuroimaging biomarker for future studies.Clinical Trial Registration: This study registered with the Chinese Clinical Trial Registry (ChiCTR1900024973) on August 05, 2019.

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as “dynamic causal modeling” (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

Neuroimaging studies suggest that the human brain consists of intrinsically organized large-scale neural networks. Among those networks, the interplay among default-mode network (DMN), salience network (SN), and central-executive network (CEN)has been widely employed to understand the functional interaction patterns in health and diseases. This triple network model suggests that SN causally controls DMN and CEN in healthy individuals. This interaction is often referred to as the dynamic controlling mechanism of SN. However, such interactions are not well understood in individuals with schizophrenia. In this study, we leveraged resting state functional magnetic resonance imaging (fMRI) data of schizophrenia (n = 67) and healthy controls (n = 81) to evaluate the functional interactions among DMN, SN, and CEN using dynamical causal modeling. In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10−8). In schizophrenia, however, our analyses revealed the disrupted SN-based controlling mechanism on DMN and CEN (Mann-Whitney U test; p < 10−16). These results indicate that the disrupted controlling mechanism of SN on two other neural networks may be a candidate neuroimaging phenotype in schizophrenia.

Looking from the top: enhanced top-down sensorimotor processing in somatic anxiety

BackgroundFunctional neuroimaging research on anxiety has traditionally focused on brain networks associated with the complex psychological aspects of anxiety. In this study, instead, we target the somatic aspects of anxiety. Motivated by the growing recognition that top-down cortical processing plays crucial roles in perception and action, we investigate effective connectivity among hierarchically organized sensorimotor regions and its association with (trait) anxiety.MethodsWe selected 164 participants from the Human Connectome Project based on psychometric measures. We used their resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess effective connectivity within and between key regions in the exteroceptive, interoceptive, and motor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor networks and investigated its association with fear somatic arousal (FSA) and fear affect (FA) scores. To probe the robustness of our results, we implemented a leave-one-out cross validation analysis.ResultsAt the group level, the top-down connections in exteroceptive cortices were inhibitory in nature whereas in interoceptive and motor cortices they were excitatory. With increasing FSA scores, the pattern of top-down effective connectivity was enhanced in all three networks: an observation that corroborates well with anxiety phenomenology. Anxiety associated changes in effective connectivity were of effect size sufficiently large to predict whether somebody has mild or severe somatic anxiety. Interestingly, the enhancement in top-down processing in sensorimotor cortices were associated with FSA but not FA scores, thus establishing the (relative) dissociation between somatic and cognitive dimensions of anxiety.ConclusionsOverall, enhanced top-down effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of trait somatic anxiety. These results pave the way for a novel approach into investigating the neural underpinnings of anxiety based on the recognition of anxiety as an embodied phenomenon and the emerging interest in top-down cortical processing.