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•The fixel-based analysis framework was proposed for fibre-specific statistical analysis of diffusion MRI data.•A “fixel” represents an individual fibre population in a voxel, allowing for increased specificity over voxel-wise measures.•A state-of-the-art fixel-based analysis pipeline consists of several bespoke steps, but is conceptually similar to a voxel-based analysis.•Fixel-based analysis has seen increased adoption recently, with 75 published studies to date.•The framework has unique benefits and future opportunities, but specific challenges and limitations exist as well. Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple “crossing” fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the “Fixel-Based Analysis” (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities. [Display omitted]

Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like ‘navigation efficiency’ have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan–Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.

Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases ( = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome. Single-subject profiling captures heterogeneity of the structural connectome to characterize brain network alterations in patients with traumatic brain injury (TBI). We profile individual patients based on their unique brain graphs in comparison to healthy controls, to provide insights into brain network disruption otherwise obscured by group-level approaches. Our implementation extends current methods by analyzing TBI structural profiles when automatic sub/cortical segmentation or parcellation of MRIs fail in the presence of lesions. Our approach highlights the translational potential for single-subject network analyses in the study of brain injury. Personalized connectome profiling provides a novel user-friendly framework for leveraging graph metrics to benefit the individual patient, by characterizing network-level brain alterations with potential prognostic relevance.

Traumatic brain injury (TBI) is associated with cognitive and motor deficits, and poses a significant personal, societal, and economic burden. One mechanism by which TBI is thought to affect cognition and behavior is through changes in functional connectivity. Graph theory is a powerful framework for quantifying topological features of neuroimaging-derived functional networks. The objective of this paper is to review studies examining functional connectivity in TBI with an emphasis on graph theoretical analysis that is proving to be valuable in uncovering network abnormalities in this condition. We review studies that have examined TBI-related alterations in different properties of the functional brain network, including global integration, segregation, centrality and resilience. We focus on functional data using task-related fMRI or resting-state fMRI in patients with TBI of different severity and recovery phase, and consider how graph metrics may inform rehabilitation and enhance efficacy. Moreover, we outline some methodological challenges associated with the examination of functional connectivity in patients with brain injury, including the sample size, parcellation scheme used, node definition and subgroup analyses. The findings suggest that TBI is associated with hyperconnectivity and a suboptimal global integration, characterized by increased connectivity degree and strength and reduced efficiency of functional networks. This altered functional connectivity, also evident in other clinical populations, is attributable to diffuse white matter pathology and reductions in gray and white matter volume. These functional alterations are implicated in post-concussional symptoms, posttraumatic stress and neurocognitive dysfunction after TBI. Finally, the effects of focal lesions have been found to depend critically on topological position and their role in the network. Graph theory is a unique and powerful tool for exploring functional connectivity in brain-injured patients. One limitation is that its results do not provide specific measures about the biophysical mechanism underlying TBI. Continued work in this field will hopefully see graph metrics used as biomarkers to provide more accurate diagnosis and help guide treatment at the individual patient level. •We review studies that examine TBI-Related alterations using graph theory.•Graph theory is a powerful tool when exploring brain-injured patients.•Graph metrics relate to behavioural impairments in brain-injured patients.•Graph metrics also relate to cognitive training responses.•Future research is needed before graph metrics can be used as clinical biomarkers.

Cannabis use disorder (CUD) affects ~22-million people globally and is characterised by difficulties in cutting down and quitting use, but the underlying neurobiology remains unclear. We examined resting-state functional connectivity (rsFC) between regions of interest (ROIs) of the addiction neurocircuitry and the rest of the brain in 65 individuals with moderate-to-severe CUD who reported attempts to cut down or quit, compared to 42 controls, and explored the association between rsFC and cannabis exposure and related problems, to elucidate potential drivers of rsFC alterations. The CUD group showed greater rsFC than controls between ROIs implicated in reward processing and habitual substance use (i.e., nucleus accumbens, putamen and pallidum) and occipito/parietal areas implicated in salience processing and disinhibition. Putamen-occipital rsFC correlated with levels of problematic cannabis use and depression symptoms. CUD appears to show neuroadaptations of the addiction neurocircuitry, previously demonstrated in other substance use disorders.

The rhg1-b allele of soybean is widely used for resistance against soybean cyst nematode (SCN), the most economically damaging pathogen of soybeans in the United States. Gene silencing showed that genes in a 31-kilobase segment at rhg1-b, encoding an amino add transporter, an α-SNAP protein, and a WI12 (wound-inducible domain) protein, each contribute to resistance. There is one copy of the 31-kilobase segment per haploid genome in susceptible varieties, but 10 tandem copies are present in an rhg1-b haplotype. Overexpression of the individual genes in roots was ineffective, but overexpression of the genes together conferred enhanced SCN resistance. Hence, SCN resistance mediated by the soybean quantitative trait locus Rhg1 is conferred by copy number variation that increases the expression of a set of dissimilar genes in a repeated multigene segment.

The purpose of this study was to analyze the longitudinal effects of participation in different categories of sports on the stability, locomotor, and manipulative motor competence domains of children. This study used a prospective cohort design involving 124 participants, including 68 boys and 56 girls, all 6 years old. The study spanned 6 months, with assessments conducted at three time points: baseline, 3 months, and 6 months. The assessments were conducted using the Motor Competence Assessment (MCA) battery, which includes six tests designed to evaluate motor competence across three domains: stability, locomotor, and manipulative skills. Participants were categorized as a cohort based on their regular extracurricular physical activity, specifically in sports classified into four categories: target games, striking/fielding games, net/wall games, and invasion games. A mixed ANOVA was conducted to compare the groups across the three assessment time points for statistical analysis. No significant differences were observed at baseline between groups in the locomotor ( p  = 0.917; ES = 0.008) or manipulative ( p  = 0.914; ES = 0.008) domains, but significant differences were found after 3-months ( p  = 0.045; ES = 0.078), and after 6-months ( p  < 0.001; ES = 0.209) respectively. No significant differences were noted in the stability domain at any time period ( p  > 0.05). In conclusion, children engaged in striking/fielding and invasion games demonstrated significant improvements in manipulative and locomotor skills over six months. Specifically, invasion games enhanced locomotor skills, while striking/fielding games improved manipulative skills. These findings highlight the positive and specific impact of diverse sports experiences on motor skill development.

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to \"classic\" open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, \"classic\" open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of \"classic\" open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by \"classic\" open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME's uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME's OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

The aim of this study was to compare the acute effects of compression contrast therapy (CT) and dry needling therapy (DN) on muscle tension (MT), muscle strength (Fmax), pressure pain threshold (PPT), and perfusion (PU) following fatigue of forearm muscles (e.g., flexor carpi radialis) in combat sports athletes. A single-blind randomized controlled trial was employed. Participants first underwent muscle fatigue induction, which involved sustaining an isometric handgrip at 60% of their maximum voluntary contraction in 5-second cycles. This was followed by exposure to one of the regenerative therapies. Forty-five participants were randomly assigned to one of three groups: CT/DN (n = 15), CT/ShDN (n = 15), and ShCT/DN (n = 15). The sham condition (Sh) involved a simulated version of the technique. Measurements were taken at four time points: (i) at rest; (ii) immediately after exercise that led to a state of fatigue; (iii) 5 minutes after therapy (PostTh5min); and (iv) 24 hours after therapy (PostTh24h). Each participant was exposed to one experimental condition and one control condition, thereby undergoing evaluation in two sessions. Significant differences between groups were found in MT during the PostTh5min (p = 0.005), as well as in PU during the PostTh5min (p < 0.001) and PU during the PostTh24h (p < 0.001). All groups showed significant improvements at 5 minutes post-therapy compared to immediately post-muscle fatigue. As conclusions, CT/DN seems to be significantly better for enhancing MT and PU after 5 minutes of muscle fatigue induction. Using either CT, DN, or both combined is recommended to enhance the recovery of muscle functionality and properties, favoring recovery and potentially speeding up performance enhancement.

The aims of this scoping review were (i) to characterize the main methodological approaches to assessing individualized running speed thresholds in team sports players; (ii) to assess the use of traditional arbitrary (absolute) thresholds compared to individualized running speed thresholds in team sports players; (iii) to provide an evidence gap map (EGM) about the approaches and study designs employed in investigations in team sports and (iv) to provide directions for future research and practical applications for the strength and conditioning field. Methods studies were searched for in the following databases: (i) PubMed; (ii) Scopus; (iii) SPORTDiscus and (iv) Web of Science. The search was conducted on 15/07/2022. Risk of bias was assessed using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). From 3,195 potentially relevant articles, 36 were eligible for inclusion in this review. Of the 36 included articles, 27 (75%) focused on the use of arbitrary and individualized running speed thresholds to describe the locomotor demands (e.g., high intensity running) of players. Thirty-four articles used individualized speed running thresholds based on physical fitness assessments (e.g., 40-m linear sprint) or physical performance (e.g., maximal acceleration). This scoping review supported the need for a greater focus to be placed on improving the methodological aspects of using individualized speed running thresholds in team sports. More than just creating alternatives to arbitrary thresholds, it is essential to increase the replicability of methodological conditions whilst ensuring that research comparing the most adequate measures and approaches to individualization takes into consideration the population and context of each study.