Fixel-based analysis (FBA) has gained substantial interest for its ability to probe fibre-specific changes in the brain's white matter from diffusion-weighted imaging data. However, the reproducibility and reliability of fixel-based measures across different scanners remains largely unknown. In this work, we present TRAMFIX (TRavelling Across Melbourne for FIXel-based analysis): a multisite dataset of traveling participants (n = 10 healthy adults) scanned across four 3T MRI scanners using a harmonised multi-shell diffusion-weighted imaging (DWI) protocol. DWI data were processed using two pipelines that can be adopted when performing multi-site FBA studies (site-specific vs. pooled processing). We extracted fixel-based measures of fibre density (FD), fibre cross-section (FC), and fibre density and cross-section (FDC) from the harmonized protocol. While the primary goal was to assess reproducibility and reliability of FBA metrics, we additionally computed diffusion tensor imaging (DTI)-based fractional anisotropy (FA) and mean diffusivity (MD) for the purposes of comparison with previous studies. Within-subject coefficients of variation (CVws) and intraclass correlation coefficients (ICC) of FBA and DTI measures were computed at multiple resolutions of computation and analysis: (i) the whole-brain averaged level, (ii) tract-level, and (iii) fixel- or voxel-level. Fixel-based metrics demonstrated high reproducibility and reliability at the whole-brain level (CVws ranging between 0.51% to 1.57% and ICC between 0.782 and 0.994). While reproducibility and reliability remained high for tract-averaged FBA measures (particularly the FC and FDC metrics), some tracts exhibited lower ICC values < 0.8 for the FD measure. When examining fixel-level reliability and reproducibility, clear spatial patterns emerged, with lower ICC across subcortical and cerebellar regions, and higher CVws at the cortical boundaries. FBA metrics demonstrated comparable, if not slightly better, reliability than tensor-based metrics derived from a subset of the same data. Our findings provide support for the reproducibility and reliability of fixel-based measures, highlighting their potential for use in multi-site FBA studies. Future work examining protocol-related differences, as well as appropriate harmonization strategies when pooling data across sites and scanners, will be valuable. To facilitate this, we provide the TRAMFIX dataset as a resource for investigating reproducibility, reliability, and harmonization of fixel-based analysis measures.

Mounting evidence suggests that amygdalar nuclei receive visual information via both a well-characterized cortical pathway through the inferior temporal cortex and a subcortical route through the superior colliculus and pulvinar. This subcortical pathway may facilitate rapid responses to salient visual stimuli and could explain phenomena such as blindsight. However, controversies remain about the organization of the subcortical pathway, its role in visual processing, and how the cortical and subcortical pathways mature across development. To address these questions we used longitudinal, minimally processed multi-shell High Angular Resolution Diffusion Imaging (HARDI) data from 4361 participants in the Adolescent Brain Cognitive Development (ABCD) study, reconstructing every major segment of the cortical and subcortical amygdala pathways. We tested the existence of the subcortical pathway against null tractography models, characterized cortical and subcortical pathways development across early adolescence, and investigated their association with visual processing speed. Diffusion MRI data provide support for the existence of bilateral pulvinar-amygdala pathways against a null model (all p < 0.001, corrected). While cortical tracts involving the primary and extrastriate visual cortex and the inferior-temporal cortex strengthened with chronological age and over pubertal development, we demonstrate that subcortical pulvinar-amygdala connectivity decreased over pubertal development. Greater connectivity strength of the right pulvinar-amygdala tract was associated with faster responses on a visual task for both emotional face and place stimuli, a relationship also seen for cortical tracts. This study provides supporting evidence for the existence of pulvinar to amygdala tracts in the largest sample of adolescent participants studied to date. Greater connectivity in both cortical and subcortical tracts was associated with faster reaction time on a visual task, but further work will be needed to investigate the specificity of this association in terms of both task and tract. In line with the hypothesized importance of the subcortical pathway in early development, we show that the developmental trajectories of cortical and subcortical pathways diverge and highlight the influence of pubertal development, with cortical pathways generally strengthening and subcortical pathways weakening across early adolescence.

Resective neurosurgery is a cornerstone treatment for many neurological conditions. Although traditionally viewed as a localized procedure, increasing evidence from advanced MRI shows that non-resected anatomy can degenerate following surgery. The relationship between local tissue removal and these postoperative changes remains speculative. Here, we investigate the hypothesis that degenerative changes to surgically preserved grey and white matter are mediated by transneuronal degeneration, a deterioration of intact neuronal populations due to lost axonal input. Using a robust diffusion-weighted and T1-weighted MRI framework specifically tailored for longitudinal analysis of surgical image data, we evaluated evidence to support this mechanism in a series of patients undergoing resective surgery for epilepsy; namely, anterior temporal lobectomy (ATL, n = 31) or selective amygdalohippocampectomy (SAHE, n = 28). We mapped three key aspects of transneuronal degeneration for anatomical regions: (i) loss of surgically resected white matter; (ii) longitudinal change in cortical thickness; and (iii) longitudinal atrophy of non-resected white matter. Using mixed-effects models, we explored the evidence in support of a sequential progression of degeneration, where the loss of resected white matter leads to downstream atrophy of connected grey matter and the white matter connections thereof. Both ATL and SAHE resulted in extensive resection-related white matter losses predominantly connecting to ipsilateral regions close to the resection. We also found pronounced decreases in cortical thickness in these regions, as well as extensive white matter atrophy across the ipsilateral hemisphere. These postsurgical alterations were closely associated with resection-related white matter losses, with every 10-fold loss of connections leading to a 3.4% decrease in cortical thickness and a 7.2% decrease in density of downstream pathways. Beyond degenerative effects, we also demonstrate how failure to properly tailor longitudinal image processing to such data can yield misleading evidence for extensive structural network reorganization, with our more robust approach indicating limited capacity for macroscale plasticity post-resection.

Most diffusion MRI studies of white matter are interpreted in terms of anatomically defined fibre bundles, yet current statistical frameworks fail to simultaneously provide high-resolution pathway- level localisation, whole-brain coverage, and good statistical power with family-wise error control. We introduce Streamline-Based Analysis (SBA), a novel framework that performs statistical inference directly on individual tractography streamlines. SBA flexibly accommodates any imaging- derived, streamline-wise quantitative metric leveraging streamline similarity to perform streamline- wise data smoothing and statistical enhancement within a non-parametric permutation testing framework that controls family-wise error. Applied to healthy ageing, SBA recapitulated established patterns observed with Fixel-Based Analysis and uncovered previously unreported effects, while yielding more spatially coherent, pathway-level effects with improved anatomical interpretability. By achieving pathway-level specificity, whole-brain coverage, and family-wise error control, SBA fills a key analytical gap between voxel-, fixel-, and tract-level methods, providing a flexible framework for detecting white matter effects across diverse diffusion MRI studies.

This paper juxtaposes major historical developments in public engagement in Japan and the UK to unsettle the idea that the UK experience of how "publics" have been accounted for in science policy might be taken as a common or representative story. In comparison to the UK and European moves "from deficit to dialogue," recent events in Japan are much less well-known internationally, and do not follow the same pattern. Rather than being either "ahead" or "behind," Japan's experience has connected similar concepts or approaches in different ways. We highlight these contrasts to challenge and open up dominant Western narratives, to create space for considering how movements for public engagement with science-or what has been called "the participatory turn"-may unfold in different ways across different contexts. Simultaneously, we point to idiosyncrasies in the UK experience that may not always be observed as such. In so doing, we demonstrate that connections-or, as it may be, the absence of connections-between public engagement, science policy, and social responsibility cannot be taken for granted and must be addressed in specific contexts.

OBJECTIVE:Detection of epilepsy-causing structural brain lesions on magnetic resonance imaging (MRI) is critical for diagnosis, prognosis, and treatment planning in people with epilepsy. We aimed to establish an epilepsy-directed multisite harmonized 3-T MRI acquisition protocol for the Australian Epilepsy Project (AEP) and describe the clinical structural brain findings in the initial participant cohort. METHODS:The AEP MRI protocol was designed to meet clinical diagnostic and research needs. It includes HARNESS-MRI sequences plus targeted views (temporal pole), susceptibility and diffusion imaging, quantitative measurements (T1, T2), and selected functional MRI tasks. A total of 1330 adults were enrolled and completed imaging assessment at seven sites. Diagnoses at referral were 146 people with first unprovoked seizure only (FUS), 325 with newly diagnosed epilepsy (NDE), 468 with drug-resistant focal epilepsy (DRE), and 391 healthy controls. Imaging data were curated via a centralized platform, relevant sequences were clinically reported by subspecialist epilepsy neuroradiologists, and the report findings were classified. Findings were compared to prior reports from standard clinical MRI. RESULTS:A structural brain lesion considered to be epileptogenic was detected in 12% in FUS, 18% in NDE, and 39% in DRE, compared with 2% in healthy controls. The most common epileptogenic lesions included hippocampal sclerosis, acquired cortical injury, malformations of cortical development, long-term epilepsy associated tumors, and vascular malformations. Potentially epileptogenic lesions included temporal pole encephaloceles, which were detected in 12% of epilepsy participants versus 7% of healthy controls. In 682 participants with an available prior clinical MRI report, 62 (9.1%) had a new epileptogenic lesion detected on the AEP scan, indicating greater sensitivity of the AEP imaging and reporting process for detection of epileptogenic lesions over standard care (p < .001). SIGNIFICANCE:The AEP 3-T MRI research protocol is feasible at multisite national scale, and sensitive for detection of subtle lesions when read by neuroradiologists with expertise in epilepsy. When added to current Australian clinical practice, this strategy yields significantly more epileptogenic findings.

Harmful algal blooms caused by cyanobacteria threaten aquatic ecosystems, the economy, and human health. Previous work has tried to identify the mechanisms that allow blooms to form, focusing on the role of nutrients. However, little is known about how introduced nutrients influence gene expression in situ. To address this knowledge gap, we used in situ mesocosms initiated with water experiencing a Microcystis bloom. We added pulses of nutrients that are commonly associated with anthropogenic sources to the mesocosms for 72 hours and collected samples for metatranscriptomics to examine how the physiological function of Microcystis and bloom status changed. The addition of nitrogen (N) as urea, but not the addition of PO4, resulted in conspicuous bloom persistence for at least 9 days after the final introduction of nutrients. The addition of urea initially resulted in the upregulation of photosynthesis machinery, as well as phosphate, carbon, and N transport and metabolism. Once Microcystis presumably became N-replete, upregulation of amino acid metabolism, microcystin biosynthesis, and other processes associated with biomass generation occurred. These capacities coincided with the upregulation of toxin-antitoxin systems, CRISPR-cas genes, and transposases suggesting that phage defense and genome rearrangement are critical in bloom persistence. Overall, our results show the stepwise transcriptional response of a Microcystis bloom to the introduction of nutrients, specifically urea, as it is sustained in a natural setting. The transcriptomic shifts observed herein may serve as markers of the longevity of blooms while providing insight into why Microcystis blooms over other cyanobacteria.IMPORTANCEHarmful algal blooms represent a threat to human health and ecosystems. Understanding why blooms persist may help us develop warning indicators of bloom persistence and create novel mitigation strategies. Using mesocosm experiments initiated with water with an active bloom, we measured the stepwise transcription changes of the toxin-producing cyanobacterium Microcystis in response to the addition of nutrients that are important in causing blooms. We found that nitrogen (N), but not phosphorus, promoted bloom longevity. The initial introduction of N resulted in the upregulation of genes involved in photosynthesis and N import. At later times in the bloom, upregulation of genes involved in biomass generation, phage protection, genomic rearrangement, and toxin production was observed. Our results suggest that Microcystis first fulfills nutritional requirements before investing energy in pathways associated with growth and protection against competitors, which allowed bloom persistence more than a week after the final addition of nutrients.

The relationship between bacterial metabolism and antibiotic treatment is complex. On the one hand, antibiotics leverage cell metabolism to function. On the other hand, increasing research has highlighted that the metabolic state of the cell also impacts all aspects of antibiotic biology, from drug efficacy to the evolution of antimicrobial resistance (AMR). Given that AMR is a growing threat to the current global antibiotic arsenal and ability to treat infectious diseases, understanding these relationships is key to improving both public and human health. However, quantifying the contribution of metabolism to antibiotic activity and subsequent bacterial evolution has often proven challenging. In this Review, we discuss the complex and often bidirectional relationships between metabolism and the various facets of antibiotic treatment and response. We first summarize how antibiotics leverage metabolism for their function. We then focus on the converse of this relationship by specifically delineating the unique contribution of metabolism to three distinct but related arms of antibiotic biology: antibiotic efficacy, AMR evolution and AMR mechanisms. Finally, we note the relevance of metabolism in clinical contexts and explore the future of metabolic-based strategies for personalized antimicrobial therapies. A deeper understanding of these connections is crucial for the broader scientific community to address the growing crisis of AMR and develop future effective therapeutics. In this Review, Ahmad et al. examine how antibiotics influence bacterial metabolism and how metabolism, in turn, affects drug efficacy and the emergence and evolution of antimicrobial resistance. They also explore the role of bacterial metabolism in clinical contexts and the potential for metabolic-based therapies to improve antibacterial treatment.

Obesity and smoking prevalence by state from 2019 to 2021.

Connectional neuroanatomical maps can be generated in vivo by using diffusion-weighted magnetic resonance imaging (dMRI) data, and their representation as structural connectome (SC) atlases adopts network-based brain analysis methods. We explain the generation of high-quality SCs of brain connectivity by using recent advances for reconstructing long-range white matter connections such as local fiber orientation estimation on multi-shell dMRI data with constrained spherical deconvolution, which yields both increased sensitivity to detecting crossing fibers compared with competing methods and the ability to separate signal contributions from different macroscopic tissues, and improvements to streamline tractography such as anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms, which have increased the biological accuracy of SC creation. Here, we provide step-by-step instructions to creating SCs by using these methods. In addition, intermediate steps of our procedure can be adapted for related analyses, including region of interest-based tractography and quantification of local white matter properties. The associated software MRtrix3 implements the relevant tools for easy application of the protocol, with specific processing tasks deferred to components of the FSL software. The protocol is suitable for users with expertise in dMRI and neuroscience and requires between 2 h and 13 h to complete, depending on the available computational system. This is a comprehensive protocol for the analysis of white matter diffusion-weighted magnetic resonance imaging data, which enables the representation of neuroanatomical atlases by using network-based brain analysis methods.

BACKGROUND:Interindividual variability in the neurobiological and clinical characteristics of mental illnesses are often overlooked by classical group-mean case-control studies. Studies using normative modeling to infer person-specific deviations of gray matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. METHODS:We applied warped Bayesian linear regression normative models to T1-weighted magnetic resonance imaging data and mapped interindividual variability in person-specific white matter volume (WMV) deviations in 1294 cases (58% male) diagnosed with one of 6 disorders (attention-deficit/hyperactivity disorder, autism, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and schizophrenia) and 1465 matched control participants (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity on multiple spatial scales from individual voxels through interregional connections, specific brain regions, and spatially extended brain networks. RESULTS:The specific locations of WMV deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions, and large-scale networks in up to 69% of individuals. CONCLUSIONS:The prevalence of WMV deviations was lower than previously observed in gray matter, and the specific location of these deviations was highly heterogeneous when considering voxelwise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not in other disorders.

Multi-echo functional Magnetic Resonance Imaging (fMRI) data are acquired by recording image volumes at multiple echo times and can be used to improve the separation of neural activity from noise. TE-Dependent ANAlysis (tedana) is an open-source software tailored to denoising of multi-echo fMRI data. The efficacy of denoising can however be inconsistent, often necessitating manual inspection that precludes its application in large-scale studies where processing is ideally fully automated. Here, we introduce Robust-tedana, an optimised denoising pipeline that achieves adequate results at both single-subject and group level. Robust-tedana incorporates Marchenko-Pastur Principal Component Analysis (MPPCA) for effective thermal noise reduction, robust independent component analysis for stabilised signal decomposition, and a modified component classification process. We evaluated its performance on Multi-Band Multi-Echo (MBME) language-task fMRI data from the Australian Epilepsy Project (AEP) using objective measures, comparing to conventional fMRI analysis with and without multi-echo-based denoising. Experts' manual evaluation was undertaken on a subset of these data to validate the objective measures. The proposed pipeline both mitigates the prevalence of erroneous attenuation of genuine task activation due to instability of single-subject analysis, and increases the magnitude of group-wise effects. Robust-tedana therefore facilitates advanced analysis of MBME fMRI data in an automated pipeline, including for clinical research assessment of individuals.

Structural deficits in white matter fibre have been linked to psychosis. However, it remains unclear whether these aberrations are present in individuals that experience non-clinical psychotic-like experiences, predating illness onset. While previous research demonstrates that alterations in white matter in schizotypy are consistent with those in clinical psychosis, these studies often dichotomise healthy samples into high and low schizotypy, which may reduce statistical sensitivity. Previous research is also confounded by the investigation of diffusion MRI parameters that fail to account for complex crossing fibre populations. In this work, we treat psychotic-like experiences as a continuous variable, and applied Fixel-Based Analysis (FBA), a framework for investigating microstructural and morphological effects in brain white matter using diffusion-weighted imaging data. Across two independent cohorts of healthy participants with varied psychotic-like experiences including data from the IMAGEN consortium (Study 1 n = 41; Study 2 n = 1098), we hypothesized that greater psychotic-like experiences would be associated with FBA metrics sensitive to microstructural fibre density and/or cross-sectional morphological effects. Contrary to our hypothesis, we did not find significant correlations between psychotic-like experiences and FBA metrics across either dataset (FWE p < 0.05). Bayesian analysis of tract-aggregated data showed substantial evidence of no association (Bayes factor < 1/3) between psychotic-like experiences and fibre density, nor cross-sectional morphology, across several white matter tracts of interest, pre-defined from prior neuroimaging literature. These findings suggest that the relationship between non-clinical psychotic-like experiences and white matter microstructure may not be as robust as previously thought. This raises the possibility that white matter alterations across the psychosis spectrum echo clinical diagnostic thresholding, with observable effects in clinical but not sub-clinical presentations. Our findings show no association between whole-brain fibre-specific properties of white matter microstructure and sub-clinical psychotic-like experiences. Further, we show evidence for the lack of an association within tract-aggregated fibre-specific metrics. Future research should integrate longitudinal designs to explore whether fibre-specific white matter attributes provide clinically meaningful insight into the risk of psychosis onset.

As STS researchers working closely with scientists and engineers, we have been invited into, created, reluctantly entered, and stumbled upon many kinds of spaces, some of which might be considered spaces for responsible research and innovation. Here we discuss a selection of these spaces, drawing on our work in synthetic biology in the UK and molecular robotics in Japan. They include a multidisciplinary workshop, a public engagement event, an art performance, and several panels at scientific conferences on the ethical, legal, and social issues raised by our fields of study. Many of the spaces were owned and controlled by scientists and engineers, and our participation in them was constrained by pre-existing frames. But even in those spaces we designed ourselves it was difficult to challenge institutionalized practices and dominant understandings of the relationship between the natural and social sciences. We often found that the spaces that were most conducive to provocative, productive and critical cross-disciplinary dialogue were at the peripheries of formal events. We therefore explore the importance of peripheral spaces for RRI, drawing on the examples of interactions that took place at a soba restaurant and an oyster bar.

Resective neurosurgery is a cornerstone treatment for many neurological conditions. Although traditionally viewed as localised procedure, increasing evidence from advanced magnetic resonance imaging (MRI) shows that also non-resected anatomy can degenerate following surgery. The relationship between local tissue removal and these postoperative changes remains thus far speculative. Here, we investigate the hypothesis that degenerative changes to surgically preserved grey and white matter are mediated by transneuronal degeneration, a deterioration of intact neuronal populations due to lost axonal input. Using a robust structural and diffusion MRI framework, we first identify widespread postoperative atrophy: pronounced cortical thickness decreases near the resection, and extensive white matter impairments across the ipsilateral hemisphere. Importantly, we then link these alterations to surgical white matter disruption, revealing a sequential network atrophy following neurosurgery. Beyond degenerative effects, we also demonstrate often reported structural network reorganisations as an artefact of image processing, indicating limited capacity for macroscale plasticity post-resection. ### Competing Interest Statement The authors have declared no competing interest.

Eigenmodes can be derived from various structural brain properties, including cortical surface geometry[1][1] and interareal axonal connections comprising an organism’s connectome[2][2]. Pang and colleagues map geometric and connectome eigenmodes to spatial patterns of human brain activity, assessing whether brain connectivity or geometry provide greater explanatory power of brain function[3][3]. The authors find that geometric eigenmodes are superior predictors of cortical activity compared to connectome eigenmodes. They conclude that this supports the predictions of neural field theory (NFT)[4][4], in that “brain activity is best represented in terms of eigenmodes derived directly from the shape of the cortex, thus emphasizing a fundamental role of geometry in constraining dynamics”. The experimental comparisons favoring geometric eigenmodes over connectome eigenmodes, in conjunction with specific statements regarding the relative efficacy of geometry in representing brain activity, have been widely interpreted to mean that geometry imposes stronger constraints on cortical dynamics than connectivity[5][5]–[9][6]. Here, we reconsider the comparative experimental evidence focusing on the impact of connectome mapping methodology. Utilizing established methods to mitigate connectome construction limitations, we map new connectomes for the same dataset, finding that eigenmodes derived from these connectomes reach comparable accuracy in explaining brain activity to that of geometric eigenmodes. We conclude that the evidence presented to support the comparative proposition that “eigenmodes derived from brain geometry represent a more fundamental anatomical constraint on dynamics than the connectome” may require reconsideration in light of our findings. Pang and colleagues present compelling evidence for the important role of geometric constraints on brain function, but their findings should not be interpreted to mean that geometry has superior explanatory power over the connectome. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-9

Purpose: Diffusion weighted imaging (DWI) has revealed microstructural changes in lower limb nerves in people with diabetic neuropathy. Microstructural changes in upper limb nerves using DWI in people with diabetes have not yet been explored. Methods: This cross-sectional study aimed to quantify and compare the microstructure of the median and ulnar nerve in people without diabetes (n = 10), people with diabetes without distal symmetrical polyneuropathy (DSPN; n = 10), people with DSPN in the lower limbs only (DSPN (FEET ONLY); n = 12), and people with DSPN in the upper and lower limbs (DSPN (HANDS & FEET); n = 9). DSPN diagnosis included electrodiagnosis and corneal confocal microscopy. Tensor metrics, such as fractional anisotropy, radial diffusivity and axial diffusivity, and constrained spherical deconvolution metrics, such as dispersion and complexity, were calculated. Linear mixed-models were used to quantify DWI metrics from multiple models in median and ulnar nerves across the groups, and to evaluate potential differences in metrics at the wrist and elbow based on the principle of a distal-to-proximal disease progression. Results: Tensor metrics revealed microstructural abnormalities in the median and ulnar nerve in people with DSPN (HANDS & FEET), and also already in DSPN (FEET ONLY). There were significant negative correlations between electrodiagnostic parameters and tensor metrics. A distal-to-proximal pattern was more pronounced in the median nerve. Non-tensor metrics showed early microstructural changes in people with diabetes without DSPN. Conclusion: Compared to people without diabetes, microstructural changes in upper limb nerves can be identified in people with diabetes with and without DSPN, even before symptoms occur.

This chapter reproduces the second horizon scan for bioengineering conducted by the Centre for the Study of Existential Risk in 2020, one of the most significant pieces of horizon scanning that has been undertaken in the field to-date. Identifying the top 20 emergent issues, the authors group them according to a likely timeline for their realisation, and discuss each throughout the chapter. This allows for the most notable issues that may impact the planet and humanity to be tracked, and the most pressing issues to be identified. The early identification of such issues is relevant for researchers, policy-makers, and the general public, providing an opportunity to consider what anticipatory or future action might need to be taken.