The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research

Goscinski, Wojtek; McIntosh, Paul; Felzmann, U.; Maksimenko, Anton; Hall, Christopher; Gureyev, T. E.; Thompson, Darren; Janke, Andrew L.; Galloway, Graham J.; Killeen, N. E. B.; Raniga, Parnesh; Kaluza, Owen; Ng, Amanda; Poudel, Govinda; Barnes, David G.; Nguyên, Toàn; Bonnington, C. Paul; Egan, Gary F.

doi:10.3389/fninf.2014.00030

Cited by 83 publications

(52 citation statements)

References 34 publications

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“…Cortical reconstruction and volumetric segmentation was performed with the Freesurfer image analysis suite (http://surfer.nmr.mgh.harvard.edu/). The analysis was performed with the use of the high performance computing infrastructure of Multi‐modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) (Goscinski, McIntosh, Felzmann, Maksimenko, Hall et al ., ). The technical details of Freesurfer are described in prior publications (Dale, Fischl & Sereno, ; Fischl, Sereno & Dale, ; Fischl, ).…”

Section: Methodsmentioning

confidence: 99%

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism

Caeyenberghs

Taymans

Wilson

et al. 2016

Developmental Science

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Children with autism spectrum disorders (ASD) often exhibit motor clumsiness (Developmental Coordination Disorder, DCD), i.e. they struggle with everyday tasks that require motor coordination like dressing, self-care, and participating in sport and leisure activities. Previous studies in these neurodevelopmental disorders have demonstrated functional abnormalities and alterations of white matter microstructural integrity in specific brain regions. These findings suggest that the global organization of brain networks is affected in DCD and ASD and support the hypothesis of a 'dys-connectivity syndrome' from a network perspective. No studies have compared the structural covariance networks between ASD and DCD in order to look for the signature of DCD independent of comorbid autism. Here, we aimed to address the question of whether abnormal connectivity in DCD overlaps that seen in autism or comorbid DCD-autism. Using graph theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 53 children: 8 ASD children with DCD (DCD+ASD), 15 ASD children without DCD (ASD), 11 with DCD only, and 19 typically developing (TD) children. We constructed separate structural correlation networks based on cortical thickness derived from Freesurfer. The children were assessed on the Movement-ABC and the Beery Test of Visual Motor Integration. Behavioral results demonstrated that the DCD group and DCD+ASD group scored on average poorer than the TD and ASD groups on various motor measures. Furthermore, although the brain networks of all groups exhibited small-world properties, the topological architecture of the networks was significantly altered in children with ASD compared with DCD and TD. ASD children showed increased normalized path length and higher values of clustering coefficient. Also, paralimbic regions exhibited nodal clustering coefficient alterations in singular disorders. These changes were disorder-specific, and included alterations in clustering coefficient in the isthmus of the right cingulate gyrus and the pars orbitalis of the right inferior frontal gyrus in ASD children, and DCD-related increases in the lateral orbitofrontal cortex. Children meeting criteria for both DCD and ASD exhibited topological changes that were more widespread from those seen in children with only DCD, i.e. children with DCD+ASD showed alterations of clustering coefficient in (para)limbic regions, primary areas, and association areas. The DCD+ASD group showed changes in clustering coefficient in the left association cortex relative to the ASD group. Finally, the DCD+ASD group shared ASD-specific abnormalities in the pars orbitalis of right inferior frontal gyrus, which was hypothesized to reflect atypical emotional-cognitive processing. Our results provide evidence that DCD and ASD are neurodevelopmental disorders with a low degree of overlap in abnormalities in connectivity. The co-occurrence of DCD+ASD was also associated with a distinct topological pattern, highlighting the...

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Section: Methodsmentioning

confidence: 99%

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism

Caeyenberghs

Taymans

Wilson

et al. 2016

Developmental Science

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“…For each file, only the longest tracks were analyzed (upper third of track length over time). PhagoSight was used in command line mode without user interaction to allow for automated processing, using the MASSIVE cluster [47].…”

Section: Methodsmentioning

confidence: 99%

β-glucan dependent shuttling of conidia from neutrophils to macrophages occurs during fungal infection establishment

Pazhakh

Ellett

O’Donnell

et al. 2019

Preprint

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41The initial host response to fungal pathogen invasion is critical to infection 42 establishment and outcome. However, the diversity of leukocyte-pathogen 43 interactions is only recently being appreciated. We describe a new form of inter-44 leukocyte conidial exchange called "shuttling". In Talaromyces marneffei and 45 Aspergillus fumigatus zebrafish in vivo infections, live imaging demonstrated 46 conidia initially phagocytosed by neutrophils were transferred to macrophages.47 Shuttling is unidirectional, not a chance event, involves alterations of phagocyte 48 mobility, inter-cellular tethering, and phagosome transfer. Shuttling kinetics 49 were fungal species-specific, implicating a fungal determinant. β-glucan serves as 50 a fungal-derived signal sufficient for shuttling. Murine phagocytes also shuttled 51 in vitro. The impact of shuttling for microbiological outcomes of in vivo infections 52 is difficult to specifically assess experimentally, but for these two pathogens, 53 shuttling augments initial conidial redistribution away from fungicidal 54 neutrophils into the favourable macrophage intracellular niche. Shuttling is a 55 frequent host/pathogen interaction contributing to fungal infection 56 establishment patterns. 57 58 59 150 words / 150 words allowed 60 61 Pazhakh et al: Fungal spore shuttling between phagocytes Page 3/58 62 Introduction63 In vertebrates, two phagocytic cell types have long been recognized as key 64 players in the initial host defense response to infection: neutrophil granulocytes 65 and macrophages [1]. Neutrophils and macrophages share many features: they 66 are both migratory cells, they phagocytose microorganisms on encountering 67 them, and they have intracellular mechanisms for killing microorganisms.68 However, although both phagocyte types engulf microorganisms, individual 69 microorganisms interact with neutrophils and macrophages with different 70 species-specific preferences, in different ways, and using different molecular 71 mechanisms [2]. Conversely, the host has evolved diverse cellular strategies for 72 these two different phagocytes to protect against the panoply of potentially 73 pathogenic microorganisms. 74 75 The exchange of cytoplasmic material through contact-dependent mechanisms 76 between adjacent cells is currently a topical field in cell biology. An example is 77 the contact-dependent exchange of cytoplasm from macrophage to tumor cells as 78 a metastasis-promoting mechanism [3], distinct from the cytoplasmic exchange 79 between macrophages and tumor cells that occur via extracellular vesicles and 80 nanotubes [4-6].81 82 During infections, neutrophils and macrophages also engage in intercellular 83 exchanges. Some microorganisms have evolved mechanisms that exploit these to 84 enhance their pathogenicity and promote their spread between phagocytes. For 85 example, Yersinia pestis and Leishmania promastigotes induce apoptosis in host 86 neutrophils to then exploit efferocytosis, whereby clearance of dead neutrophils Pazhakh et al: Fungal spore shuttling...

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“…We note that other software packages are available (e.g., DTIstudio, ExploreDTI), and these may entail other preprocessing choices which we do not examine here. Data processing took was conducted using the Multi-modal Australian ScienceS Imaging and Visualisation Environment (MASSIVE) high performance computing infrastructure (Goscinski et al, 2014). Details of each of the steps and choices we consider in our analysis, and the data used, are described in the following sections.…”

Section: Overviewmentioning

confidence: 99%

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

Oldham

Arnatkevičiūtė

Smith

et al. 2020

Preprint

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Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.HighlightsWe assess how motion affects structural connectivity in 240 preprocessing pipelinesMotion contamination of structural connectivity depends on preprocessing choicesAdvanced motion correction tools reduce motion confoundsFA edge weighting is more susceptible to motion effects than streamline count

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The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research

Cited by 83 publications

References 34 publications

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism

β-glucan dependent shuttling of conidia from neutrophils to macrophages occurs during fungal infection establishment

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

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