Test-retest reliability of structural brain networks from diffusion MRI

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability

Structural brain networks from diffusion MRI: methods and application

Structural brain networks can be constructed at a macroscopic scale using diffusion magnetic resonance imaging (dMRI) and whole-brain tractography. Under this approach, grey matter regions, such as Brodmann areas, form the nodes of a network and tractography is used to construct a set of white matter fibre tracts which form the connections. Graph-theoretic measures may then be used to characterise patterns of connectivity. In this study, we measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. High resolution T1-weighted brains were parcellated into regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, constraints on anatomical plausibility and three alternative network weightings. Test-retest performance was found to improve when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography, rather than deterministic. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is most representative of the underlying axonal connections. These findings were then used to inform network construction for two further cohorts: a casecontrol analysis of 30 patients with amyotrophic lateral sclerosis (ALS) compared with 30 age-matched healthy controls; and a cross-sectional analysis of 80 healthy volunteers aged 25– 64 years. In both cases, networks were constructed using a weighting reflecting tract-averaged fractional anisotropy (FA). A mass-univariate statistical technique called network-based statistics, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls. Reduced FA for three of the impaired network connections, which involved fibres of the cortico-spinal tract, were significantly correlated with the rate of disease progression. Cross-sectional analysis of the 80 healthy volunteers was intended to provide supporting evidence for the widely reported age-related decline in white matter integrity. However, no meaningful relationships were found between increasing age and impaired connectivity based on global, lobar and nodal network properties – findings which were confirmed with a conventional voxel-based analysis of the dMRI data. In conclusion, whilst current acquisition protocols and methods can produce networks capable of characterising the genuine between-subject differences in connectivity, it is challenging to measure subtle white matter changes, for example, due to normal ageing. We conclude that future work should be undertaken to address these concerns

Mitochondrial Dysfunction and Infection Generate Immunity–Fecundity Tradeoffs in \u3ci\u3eDrosophila\u3c/i\u3e

Physiological responses to short-term environmental stressors, such as infection, can have long-term consequences for fitness, particularly if the responses are inappropriate or nutrient resources are limited. Genetic variation affecting energy acquisition, storage, and usage can limit cellular energy availability and may influence resourceallocation tradeoffs even when environmental nutrients are plentiful. Here, we utilized Drosophila mitochondrial– nuclear genotypes to test whether disrupted mitochondrial function interferes with nutrient-sensing pathways, and whether this disruption has consequences for tradeoffs between immunity and fecundity. We found that an energetically-compromised genotype was relatively resistant to rapamycin—a drug that targets nutrient-sensing pathways and mimics resource limitation. Dietary resource limitation decreased survival of energetically-compromised flies. Furthermore, survival of infection with a natural pathogen was decreased in this genotype, and females of this genotype experienced immunity–fecundity tradeoffs that were not evident in genotypic controls with normal energy metabolism. Together, these results suggest that this genotype may have little excess energetic capacity and fewer cellular nutrients, even when environmental nutrients are not limiting. Genetic variation in energy metabolism may therefore act to limit the resources available for allocation to life-history traits in ways that generate tradeoffs even when environmental resources are not limiting

Pipeline comparisons of convolutional neural networks for structural connectomes: predicting sex across 3,152 participants

With several initiatives well underway towards amassing large and high-quality population-based neuroimaging datasets, deep learning is set to push the boundaries of what is possible in classification and prediction in neuroimaging studies. This includes those that derive increasingly popular structural connectomes, which map out the connections (and their relative strengths) between brain regions. Here, we test different Convolutional Neural Network (CNN) models in a benchmark sex prediction task in a large sample of N=3,152 structural connectomes acquired from the UK Biobank, and compare results across different connectome processing choices. The best results (76.5% test accuracy) were achieved using Fractional Anisotropy (FA) weighted connectomes, without sparsification, and with a simple weight normalisation through division by the maximum FA value. We also confirm that for structural connectomes, a Graph CNN approach, the recently proposed BrainNetCNN, outperforms an image-based CNN

Allostatic load and ageing; linking the microbiome and nutrition with age related health

Ageing is a process of decline in physiological function and capability over time. It is an anticipated major burden on societal health-care costs due to an increasingly aged global population. Accelerated biological ageing is a feature of age-related morbidities, which also appear to share common underpinning features, including low-grade persistent inflammation, phosphate toxicity, diminished Nrf2 activity, a depleted metabolic capability, depressed mitochondrial biogenesis and a low diversity gut microbiome. Social, psychological, lifestyle and nutritional risk factors can all influence the trajectory of age-related health, as part of an individual's exposome, which reflects the interplay between the genome and the environment. This is manifest as allostatic (over)load reflecting the burden of lifestyle/disease at both a physiological and molecular level. In particular, age-related genomic methylation levels and inflammatory status reflect exposome differences. These features may be mediated by changes in microbial diversity. This can drive the generation of pro-inflammatory factors, such as TMAO, implicated in the ‘diseasome’ of ageing. Additionally, it can be influenced by the ‘foodome’, via nutritional differences affecting the availability of methyl donors required for maintenance of the epigenome and by the provision of nutritionally derived Nrf2 agonists. Both these factors influence age-related physiological resilience and health. This offers novel insights into possible interventions to improve health span, including a rage of emerging senotherapies and simple modifications of the nutritional and environmental exposome. In essence, the emerging strategy is to treat ageing processes common to the diseasome of ageing itself and thus preempt the development or progression of a range of age-related morbidities

Inventing and implementing future-ready archival education

The Archival / Preservation Education SIG panel engages with community-responsive master's-level archival education. Seven ten-minute individual presentations and audience discussion traverse the decision points in managing curricular change; presenters bring perspectives from multiple states. "Audio Preservation as Metacognitive Archival Education" by Sarah Buchanan discusses how audiovisual archiving experiences support the continual development of students' metacognitive skills during their graduate program. Based on community collaboration, the activity progressions provide students with digital experiences, faculty with curricular guidance, and online audiences with more representative primary sources. "LIS Students Contributing to Building a Sustainable Digital Community Archive" by Krystyna Matusiak describes a community-based two-year project aimed at preserving and promoting the Park County Local History Archive in rural Colorado, now available at https://pclha.cvlcollections.org/. The presentation illustrates students' many contributions: organizing materials and assessing their copyright status, digitizing photographs, converting oral histories, creating metadata records, building exhibits, and showcasing community resilience. "Changing Horses Midstream: Revising Curriculum and Student Engagement to Ensure a Resilient Future" by Edward Benoit, III and Amanda Lima discusses the revision process for transitioning two programs to LSU Online, compares assessments from the traditional and LSU Online programs, and reflects on completing the first year. Additionally, the presenters will highlight the use of student-run Slack channels and virtual coffee hours as online student community building tools for the new LSU Online students, and discuss the school's future in the platform. "Producing Practical Professionals with Curriculum for Equity, Diversity, and Inclusion" by Aisha Johnson acknowledges that cultural heritage programs should address the need for cultural preservation and reflection, for archivists of Black, Indigenous, and Persons of Color (BIPOC) heritage. The presentation will review a reestablished Archives and Records Management concentration, with core archival and complementary knowledge curriculum, as a case study for exploring new approaches to pedagogy on the purpose, value, and importance of archives in society. "Learning from Experience: Lessons from a Virtual Service-Learning Experiment" by Colin Post discusses a service-learning project documenting an artist's performance as well as their artwork archives. While such projects place even greater pressure on the instructor as a project manager, they enhance connections between theory and practice in online courses. "Lessons Learned from the Digital Preservation Outreach and Education Network" by Anthony Cocciolo and Erin Barsan discusses the types of needs they have uncovered, the communities served, and the lessons learned over the course of a year running DPOE-N. The Network's response to the COVID-19 pandemic comprises microfunding for professional development and emergency hardware support for cultural heritage professionals. "National Forum Grant Project: Exploring New Frontiers in 21st Century Archival Education" by Alex Poole and Jane Zhang discusses the environmental scan, National Forum event, and final outputs of their year-long project. The presentation addresses motivation and need, historical and current context, research components, and intended results and impact. The moderator will facilitate Q&A within and across the presentations

Gaussian Process Regression models for the properties of micro-tearing modes in spherical tokamak

Spherical tokamaks (STs) have many desirable features that make them an attractive choice for a future fusion power plant. Power plant viability is intrinsically related to plasma heat and particle confinement and this is often determined by the level of micro-instability driven turbulence. Accurate calculation of the properties of turbulent micro-instabilities is therefore critical for tokamak design, however, the evaluation of these properties is computationally expensive. The considerable number of geometric and thermodynamic parameters and the high resolutions required to accurately resolve these instabilities makes repeated use of direct numerical simulations in integrated modelling workflows extremely computationally challenging and creates the need for fast, accurate, reduced-order models. This paper outlines the development of a data-driven reduced-order model, often termed a {\it surrogate model} for the properties of micro-tearing modes (MTMs) across a spherical tokamak reactor-relevant parameter space utilising Gaussian Process Regression (GPR) and classification; techniques from machine learning. These two components are used in an active learning loop to maximise the efficiency of data acquisition thus minimising computational cost. The high-fidelity gyrokinetic code GS2 is used to calculate the linear properties of the MTMs: the mode growth rate, frequency and normalised electron heat flux; core components of a quasi-linear transport model. Five-fold cross-validation and direct validation on unseen data is used to ascertain the performance of the resulting surrogate models

Lack of association between COMT gene and deficit/nondeficit schizophrenia

BACKGROUND: The dopamine dysregulation hypothesis of schizophrenia posits that positive, negative and cognitive symptoms correlate with cortical/subcortical imbalances in dopaminergic transmission. A functional polymorphism (Val(158)Met) in the catechol-O-methyltransferase (COMT) gene is implicated in the pathophysiology of schizophrenia by its effect on prefrontal dopamine transmission, and its unique impact on prefrontal cognitive and behavioral phenotypes. Cognitive impairments and negative symptoms in schizophrenia have been hypothesized to be associated with hypodopaminergic states. Schizophrenia patients with the deficit syndrome are characterized by primary enduring negative symptoms, impairment on neurocognitive tasks sensitive to frontal and parietal cortical functioning, and poorer functional outcome compared to non-deficit patients. METHODS: Eighty-six schizophrenia cases that met DSM-IV criteria for schizophrenia were recruited. Additional categorization into deficit and nondeficit syndrome was performed using the Schedule for the Deficit Syndrome (SDS). A healthy comparison group (n = 50) matched to cases on age and ethnicity was recruited. Allele and genotype frequencies of the Val(158)Met polymorphism were compared among healthy controls, and schizophrenia cases with the deficit (n = 21), and nondeficit syndrome (n = 65). RESULTS: There was a significant difference in Val/Val genotype frequencies between schizophrenia cases (combined deficit/nondeficit) and healthy controls (p = 0.004). No significant differences in allele or genotype frequencies were observed between deficit and nondeficit cases. CONCLUSION: Results from this preliminary analysis failed to show an effect of COMT gene on deficit schizophrenia