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Recent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs’ weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graph-theoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest ∼70–90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies. •We evaluate removal of weak connections from diffusion MRI dense weighted connectomes.•We calculate graph-theoretical metrics after enforcing various connectome densities.•Removal of the weakest connections is inconsequential for graph-theoretical analysis.•Removing larger extent of weak connections has ramifications to statistical analyses.•We advocate against removal of weak connections from dMRI dense weighted connectomes.

•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]

We introduce a novel connectomics method, MFCSC, that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from functional MRI, at individual subject level. The MFCSC method is based on the fact that SC only broadly predicts FC, and for each connection in the brain, the method calculates a value that quantifies the mismatch that often still exists between the two modalities. To capture underlying physiological properties, MFCSC minimises biases in SC and addresses challenges with the multimodal analysis, including by using a data-driven normalisation approach. We ran MFCSC on data from the Human Connectome Project and used the output to detect pairs of left and right unilateral connections that have distinct relationship between structure and function in each hemisphere; we suggest that this reflects cases of hemispheric functional specialisation. In conclusion, the MFCSC method provides new information on brain organisation that may not be inferred from an analysis that considers SC and FC separately.

The frontal aslant tract (FAT) is a pathway that connects the inferior frontal gyrus with the supplementary motor area (SMA) and pre-SMA. The FAT was recently identified and introduced as part of a “motor stream” that plays an important role in speech production. In this study, we use diffusion imaging to examine the hypothesis that the FAT underlies speech fluency, by studying its properties in individuals with persistent developmental stuttering, a speech disorder that disrupts the production of fluent speech. We use tractography to quantify the volume and diffusion properties of the FAT in a group of adults who stutter (AWS) and fluent controls. Additionally, we use tractography to extract these measures from the corticospinal tract (CST), a well-known component of the motor system. We compute diffusion measures in multiple points along the tracts, and examine the correlation between these diffusion measures and behavioral measures of speech fluency. Our data show increased mean diffusivity in bilateral FAT of AWS compared with controls. In addition, the results show regions within the left FAT and the left CST where diffusivity values are increased in AWS compared with controls. Last, we report that in AWS, diffusivity values measured within sub-regions of the left FAT negatively correlate with speech fluency. Our findings are the first to relate the FAT with fluent speech production in stuttering, thus adding to the current knowledge of the functional role that this tract plays in speech production and to the literature of the etiology of persistent developmental stuttering.

IntroductionAltered neuromuscular control of the scapula and humeral head is a typical feature of multidirectional instability (MDI) of the glenohumeral joint, suggesting a central component to this condition. A previous randomised controlled trial showed MDI patients participating in the Watson Instability Program 1 (WIP1) had significantly improved clinical outcomes compared with a general shoulder strength programme. The aim of this paper is to outline a multimodal MRI protocol to identify potential ameliorative effects of the WIP1 on the brain.Methods and analysisThirty female participants aged 18–35 years with right-sided atraumatic MDI and 30 matched controls will be recruited. MDI patients will participate in 24 weeks of the WIP1, involving prescription and progression of a home exercise programme. Multimodal MRI scans will be collected from both groups at baseline and in MDI patients at follow-up. Potential brain changes (primary outcome 1) in MDI patients will be probed using region-of-interest (ROI) and whole-brain approaches. ROIs will depict areas of functional alteration in MDI patients during executed and imagined shoulder movements (MDI vs controls at baseline), then examining the effects of the 24-week WIP1 intervention (baseline vs follow-up in MDI patients only). Whole-brain analyses will examine baseline versus follow-up voxel-wise measures in MDI patients only. Outcome measures used to assess WIP1 efficacy will include the Western Ontario Shoulder Index and the Melbourne Instability Shoulder Score (primary outcomes 2 and 3). Secondary outcomes will include the Tampa Scale for Kinesiophobia, Short Form Orebro, Global Rating of Change Score, muscle strength, scapular upward rotation, programme compliance and adverse events.DiscussionThis trial will establish if the WIP1 is associated with brain changes in MDI.Ethics and disseminationParticipant confidentiality will be maintained with publication of results. Swinburne Human Research Ethics Committee (Ref: 20202806-5692).Trial registration numberAustralian New Zealand Clinical Trial Registry (ACTRN12621001207808).

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud. Neurodesk is a platform for analyzing human neuroimaging data, which provides numerous tools in a containerized form, thereby ensuring reproducibility and portability.

Stuttering is a speech motor control disorder of unknown etiology whose hallmark is part-syllable repetition. The principal aim of this dissertation is to understand the neural mechanisms underlying stuttering through computational modeling with DIVA and GODIVA, neural network models of speech acquisition and production. The first part of the dissertation investigates the hypothesis that stuttering may result in part from impaired readout of feedforward commands for speech, which forces persons who stutter (PWS) to produce speech with a motor strategy that is weighted too much toward auditory feedback control. Over-reliance on feedback control leads to sensory errors which, if they grow large enough, can cause the motor system to “reset” and repeat the current syllable. This hypothesis is investigated by impairing the feedforward control subsystem of the DIVA model. The model's outputs are compared to published acoustic data from PWS' fluent speech, and to combined acoustic and articulatory-movement data collected from the dysfluent speech of one PWS. The simulations mimic the errors observed in the PWS subject's speech, as well as the repairs of these errors. Additional simulations were able to account for enhancements of fluency gained by slowed/prolonged speech and masking noise. The second part of the dissertation explores the role of the basal ganglia (BG)—left ventral premotor cortex (vPMC) loop in the impaired readout of feedforward control. Two hypotheses are put to test: (1) due to structural abnormality in the corticostriatal projections carrying corollary discharge of motor commands, the BG fail to detect the context for initiating the next syllable, and (2) due to increased dopamine binding in the striatum leading to a ceiling effect, the BG are unable to bias cortical competition in favor of the correct syllable. Simulations of a neurally impaired version of the extended GODIVA model show that both hypotheses can explain dysfluent speech and associated abnormal brain activations. Further simulations account for the alleviation of stuttering with D2 antagonists. Together these results support the hypothesis that many dysfluencies in stuttering are due to abnormalities interfering with normal BG-vPMC loop operation, which ultimately biases the system away from feedforward control and toward feedback control.

Abstract We introduce a connectomics metric that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from resting-state functional MRI, at individual subject level. The metric is based on the ability of SC to broadly predict FC using a simple linear predictive model; for each connection in the brain, the metric quantifies the deviation from that model. For the metric to capture underlying physiological properties, we minimise systematic measurement errors and processing biases in both SC and FC, and address several challenges with the joint analysis. This also includes a data-driven normalisation approach. The combined metric may provide new information by indirectly assessing white matter structural properties that cannot be inferred from diffusion MRI alone, and/or complex interregional neural interactions that cannot be inferred from functional MRI alone. To demonstrate the utility of the metric, we used young adult data from the Human Connectome Project to examine all bilateral pairs of ipsilateral connections, i.e. each left-hemisphere connection in the brain was paired with its right-hemisphere homologue. We detected a minority of bilateral pairs where the metric value is significantly different across hemispheres, which we suggest reflects cases of ipsilateral connections that have distinct functional specialisation in each hemisphere. The pairs with significant effects spanned all cortical lobes, and also included several cortico-subcortical connections. Our findings highlight the potential in a joint analysis of structural and functional measures of connectivity, both for clinical applications and to help in the interpretation of results from standard functional connectivity analysis. Significance Statement Based on the notion that structure predicts function, the scientific community sought to demonstrate that structural information on fibre bundles that connect brain regions is sufficient to estimate the strength of interregional interactions. However, an accurate prediction using MRI has proved elusive. This paper posits that the failure to predict function from structure originates from limitations in measurement or interpretation of either diffusion MRI (to assess fibre bundles), fMRI (to assess functional interactions), or both. We show that these limitations can be nevertheless beneficial, as the extent of divergence between the two modalities may reflect hard-to-measure properties of interregional connections, such as their functional role in the brain. This provides many insights, including into the division of labour between hemispheres. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵* Oren Civier. Email: orenciv{at}gmail.com

Recent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs' weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graph-theoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest ~70-90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies.