Controlled Antenatal Thyroid Screening Study III: Effects of gestational thyroid status on adolescent brain morphology.

CONTEXT: Children born to mothers with gestational hypo- or hyperthyroidism may have increased risk of adverse neurodevelopmental outcomes. However, the effects of maternal thyroid status on offspring brain development are unclear. OBJECTIVE: To establish whether adolescent brain morphology is affected by suboptimal gestational thyroid function (SGTF). DESIGN AND SETTING: The Controlled Antenatal Thyroid Screening (CATS) study randomized mothers with SGTF to levothyroxine or no supplementation from ∼12 weeks' gestation. At age 9, children born to mothers who were over-treated with levothyroxine had a higher risk of conduct and hyperactivity traits. For the current CATS III study, children underwent neuroimaging studies, including T1-weighted structural magnetic resonance imaging (MRI). PARTICIPANTS: A total of 85 children aged 11-16 years had usable T1-weighted MRI data (exposed to untreated SGTF (n=21), normal GTF (n=24), or treated SGTF (optimally-treated (n=21), over-treated (n=20)). MAIN OUTCOME MEASURES: Primary outcome: to examine the association of SGTF and its treatment with global brain volumes. Secondary and exploratory outcomes: to investigate the association of maternal TSH and free T4 levels with global and subregional brain volumes. Results were adjusted for age, sex and pubertal scores. RESULTS: There were no significant differences in global brain volumetric measures between groups, including total gray matter volume (p=0.373). Weak positive correlations were found between maternal TSH, but not FT4, levels and several brain volumes, but these did not survive testing for multiple comparisons. CONCLUSIONS: We found no evidence that SGTF was associated with differences in adolescent brain morphology, and no impact of levothyroxine supplementation.

Inter-rater reliability of functional MRI data quality control assessments: A standardised protocol and practical guide using pyfMRIqc.

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either "include," "uncertain," or "exclude." There was moderate to substantial agreement between raters for "include" and "exclude," but little to no agreement for "uncertain." In most cases only a single rater used the "uncertain" classification for a given participant's data, with the remaining raters showing agreement for "include"/"exclude" decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for "uncertain" cases, aiding classification consistency.

Moral Decision-Making During COVID-19: Moral Judgements, Moralisation, and Everyday Behaviour.

The COVID-19 pandemic continues to pose significant health, economic, and social challenges. Given that many of these challenges have moral relevance, the present studies investigate whether the COVID-19 pandemic is influencing moral decision-making and whether moralisation of behaviours specific to the crisis predict adherence to government-recommended behaviours. Whilst we find no evidence that utilitarian endorsements have changed during the pandemic at two separate timepoints, individuals have moralised non-compliant behaviours associated with the pandemic such as failing to physically distance themselves from others. Importantly, our findings show that this moralisation predicts sustained individual compliance with government-recommended behaviours.

Aberrant white matter microstructure in treatment-resistant schizophrenia✰.

Treatment response in schizophrenia divides into three subcategories: treatment-responsive (first-line responders; FLR), treatment-resistant (TRS), and ultra-treatment-resistant schizophrenia (UTRS). White matter abnormalities could drive antipsychotic resistance but little work has investigated differences between TRS and UTRS. The current study aimed to establish whether differences in white matter structure are present across both treatment-resistant subtypes or if UTRS is distinct from TRS. Diffusion-weighted images were acquired for 18 individuals with TRS, 14 with UTRS, 18 FLR and 20 healthy controls. Measures of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) were obtained using tract-based spatial statistics. Analysis of variance and post-hoc t-tests were conducted for each measure. Those with TRS had lower FA than healthy controls in superior longitudinal fasciculus, corpus callosum, thalamic radiation, corticospinal tract, internal capsule, corona radiata and fronto-occipital fasciculus (p<.05 FWE-corrected). Lower FA was also observed in TRS compared with UTRS in the superior longitudinal fasciculus (p<.05 FWE-corrected). No post-hoc tests survived corrections for multiple comparisons and no differences in MD, AD or RD were observed. These data suggest that microstructural deficits in white matter could contribute to TRS but suggest that other mechanisms may be more relevant for UTRS.

Integrating Space, Time, and Orientation in Spiking Neural Networks: A Case Study on Multimodal Brain Data Modeling.

Recent progress in a noninvasive brain data sampling technology has facilitated simultaneous sampling of multiple modalities of brain data, such as functional magnetic resonance imaging, electroencephalography, diffusion tensor imaging, and so on. In spite of the potential benefits from integrating predictive modeling of multiple modality brain data, this area of research remains mostly unexplored due to a lack of methodological advancements. The difficulty in fusing multiple modalities of brain data within a single model lies in the heterogeneous temporal and spatial characteristics of the data sources. Recent advances in spiking neural network systems, however, provide the flexibility to incorporate multidimensional information within the model. This paper proposes a novel, unsupervised learning algorithm for fusing temporal, spatial, and orientation information in a spiking neural network architecture that could potentially be used to understand and perform predictive modeling using multimodal data. The proposed algorithm is evaluated both qualitatively and quantitatively using synthetically generated data to characterize its behavior and its ability to utilize spatial, temporal, and orientation information within the model. This leads to improved pattern recognition capabilities and performance along with robust interpretability of the brain data. Furthermore, a case study is presented, which aims to build a computational model that discriminates between people with schizophrenia who respond or do not respond to monotherapy with the antipsychotic clozapine.

Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia.

Schizophrenia may develop from disruptions in functional connectivity regulated by neurotransmitters such as dopamine and acetylcholine. The modulatory effects of these neurotransmitters might explain how antipsychotics attenuate symptoms of schizophrenia and account for the variable response to antipsychotics observed in clinical practice. Based on the putative mechanisms of antipsychotics and evidence of disrupted connectivity in schizophrenia, we hypothesised that functional network connectivity, as assessed using network-based statistics, would exhibit differences between treatment response subtypes of schizophrenia and healthy controls. Resting-state functional MRI data were obtained from 17 healthy controls as well as individuals with schizophrenia who responded well to first-line atypical antipsychotics (first-line responders; FLR, n=18), had failed at least two trials of antipsychotics but responded to clozapine (treatment-resistant schizophrenia; TRS, n=18), or failed at least two trials of antipsychotics and a trial of clozapine (ultra-treatment-resistant schizophrenia; UTRS, n=16). Data were pre-processed using the Advanced Normalization Toolkit and BrainWavelet Toolbox. Network connectivity was assessed using the Network-Based Statistics toolbox in Matlab. ANOVA revealed a significant difference in functional connectivity between groups that extended between cerebellar and parietal regions to the frontal cortex (p<0.05). Post-hoc t-tests revealed weaker network connectivity in individuals with UTRS compared with healthy controls but no other differences between groups. Results demonstrated distinct differences in functional connectivity between individuals with UTRS and healthy controls. Future work must determine whether these changes occur prior to the onset of treatment and if they can be used to predict resistance to antipsychotics during first-episode psychosis.

Treatment-Resistant Schizophrenia: Treatment Response and Resistance in Psychosis (TRRIP) Working Group Consensus Guidelines on Diagnosis and Terminology.

OBJECTIVE: Research and clinical translation in schizophrenia is limited by inconsistent definitions of treatment resistance and response. To address this issue, the authors evaluated current approaches and then developed consensus criteria and guidelines. METHOD: A systematic review of randomized antipsychotic clinical trials in treatment-resistant schizophrenia was performed, and definitions of treatment resistance were extracted. Subsequently, consensus operationalized criteria were developed through 1) a multiphase, mixed methods approach, 2) identification of key criteria via an online survey, and 3) meetings to achieve consensus. RESULTS: Of 2,808 studies identified, 42 met inclusion criteria. Of these, 21 studies (50%) did not provide operationalized criteria. In the remaining studies, criteria varied considerably, particularly regarding symptom severity, prior treatment duration, and antipsychotic dosage thresholds; only two studies (5%) utilized the same criteria. The consensus group identified minimum and optimal criteria, employing the following principles: 1) current symptoms of a minimum duration and severity determined by a standardized rating scale; 2) moderate or worse functional impairment; 3) prior treatment consisting of at least two different antipsychotic trials, each for a minimum duration and dosage; 4) systematic monitoring of adherence and meeting of minimum adherence criteria; 5) ideally at least one prospective treatment trial; and 6) criteria that clearly separate responsive from treatment-resistant patients. CONCLUSIONS: There is considerable variation in current approaches to defining treatment resistance in schizophrenia. The authors present consensus guidelines that operationalize criteria for determining and reporting treatment resistance, adequate treatment, and treatment response, providing a benchmark for research and clinical translation.

Single chemical entity legal highs: assessing the risk for long term harm.

A recent and dramatic increase in the emergence of novel psychoactive substances ('legal highs') has left many governments unable to provide a timely response to an increasing number of potentially harmful drugs now available to the public. In response to this rapid increase in lawful drug use, the UK government intends to implement temporary class drug orders, whereby substances with a potential for misuse and harm can be regulated for a 12 month period. During this period an investigation of the potential for harms induced by these drugs will take place. However, the short time-frame in which information must be gathered, and the paucity of data available on novel psychoactive substances, means that robust pharmacological and toxicological analyses may be replaced by extrapolating data from illegal drugs with similar chemical structures. This review explores the potential pharmacology and toxicology of past and present 'legal highs' and discusses the risks of failing to carry out in-depth scientific research on individual substances.