scholarly journals The Digital Brain Bank: an open access platform for post-mortem datasets

2021 ◽  
Author(s):  
Benjamin C. Tendler ◽  
Taylor Hanayik ◽  
Olaf Ansorge ◽  
Sarah Bangerter-Christensen ◽  
Gregory S. Berns ◽  
...  
Keyword(s):  
Open Access ◽  
Diffusion Mri ◽  
Structural Connectivity ◽  
Primate Species ◽  
Post Mortem ◽  
Imaging Studies ◽  
Whole Brain ◽  
Brain Bank ◽  
Interactive Data ◽  
Human Primate

Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), an interactive data discovery and release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes - Digital Neuroanatomist: data for neuroanatomical investigations; Digital Brain Zoo: data for comparative neuroanatomy; Digital Pathologist: data for neuropathology investigations. The first Digital Brain Bank release includes fourteen distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in seven non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. Taken together, the Digital Brain Bank provides a cross-scale, cross-species investigation framework facilitating the incorporation of post-mortem data into neuroimaging studies.

scholarly journals Diffusion MRI data, sulcal anatomy, and tractography for eight species from the Primate Brain Bank

2021 ◽  
Author(s):  
Katherine L. Bryant ◽  
Dirk Jan Ardesch ◽  
Lea Roumazeilles ◽  
Lianne H. Scholtens ◽  
Alexandre A. Khrapitchev ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Large Scale ◽  
Structural Mri ◽  
Relative Volume ◽  
Primate Species ◽  
Brain Bank ◽  
Cingulum Bundle ◽  
Primate Brain ◽  
Multiple Levels ◽  
Comparative Neuroscience

AbstractLarge-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal–frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.

scholarly journals Impact of long- and short-range fiber depletion on the cognitive deficits of fronto-temporal dementia

2020 ◽  
Author(s):  
Melissa Savard ◽  
Tharick A. Pascoal ◽  
Thijs Dhollander ◽  
Yasser Iturria-Medina ◽  
Paolo Vitali ◽  
...  
Keyword(s):  
Risk Factors ◽  
White Matter ◽  
Diffusion Mri ◽  
Short Range ◽  
Large Scale ◽  
Structural Connectivity ◽  
Executive Dysfunction ◽  
Cognitive Symptoms ◽  
Whole Brain ◽  
Relative Contribution

AbstractFronto-temporal dementia (FTD) is a neurodegenerative disease characterized by focal atrophy of the gray matter (GM), especially in the frontal and temporal regions. Recent studies suggest a framework where white matter (WM) atrophy plays an important role in FTD pathophysiology. However, these studies often overlook the fact that WM tracts bridging different brain regions may have different vulnerabilities to the disease and the relative contribution of GM atrophy to this WM model, resulting in a less comprehensive understanding of the relationship between clinical symptoms and pathology. Here, by leveraging the sensitivity of advanced diffusion MRI modelling and metrics to precise white matter microstructural properties, we aim to clarify the relative contributions of WM fibers and GM atrophy to the cognitive symptoms typically found in FTD. A total of 155 participant from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) were analysed, including 68 normal elderly controls (CN), 28 behavioral variants (BV), 26 sematic variants (SV) and 30 progressive non fluent aphasia variants (PNFA) of FTD. Diffusion MRI analysis was performed using two complementary techniques: whole brain fixel-based analysis (FBA) and structural connectivity based on probabilistic tractography. Whole brain GM atrophy was assessed using voxel-based morphometry (VBM). Using a common factor analysis to extract a semantic and an executive factor, we aim to test the relative contribution of WM and GM of specific tracts in predicting cognition. We found that semantic symptoms were mainly dependent on short-range WM fiber disruption, while damage to long-range WM fibers was preferentially associated to executive dysfunction with the GM contribution to cognition being predominant for local processing. Our results support the importance of the disruption of specific WM tracts to the core cognitive symptoms associated with FTD. As large-scale WM tracts, which are particularly vulnerable to vascular disease, were highly associated with executive dysfunction, our findings highlight the importance of controlling for risk factors associated with deep white matter disease, such as vascular risk factors, in patients with FTD in order not to potentiate underlying executive dysfunction.

scholarly journals Integrated resource for functional and structural connectivity of the marmoset brain

2021 ◽  
Author(s):  
Xiaoguang Tian ◽  
Yuyuan Chen ◽  
Piotr Majka ◽  
Diego Szczupak ◽  
Yonatan Sanz-Perl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Mri ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Cellular Level ◽  
Structural Basis ◽  
Neuronal Tracing ◽  
Brain Functions ◽  
Topographical Organization ◽  
Human Primate

The comprehensive integration of structural and functional connectivity data is required for the accurate modeling of brain functions. While resources for studying structural connectivity of non-human primate marmoset brains already exist, their integration with functional connectivity data has remained unavailable. Therefore, we present a comprehensive resource for marmoset brain mapping, which integrates the largest awake resting-state fMRI dataset to date (39 marmosets, 709 runs, and 12053 mins), cellular-level neuronal-tracing dataset (52 marmosets and 143 injections), and multi-resolution diffusion MRI dataset. The combination of these data into the same MRI space allowed us to 1). map the fine-detailed functional networks and cortical parcellations; 2). develop a deep-learning-based parcellation generator to preserve the topographical organization of functional connectivity and reflect individual variabilities; 3). investigate the structural basis underlying functional connectivity by computational modeling. Our resource will broadly model the marmoset brain architecture and facilitate future comparative and translational studies of primate brains.

Post mortem high resolution diffusion MRI for large specimen imaging at 11.7 T with 3D segmented echo-planar imaging

2019 ◽  
Vol 311 ◽  
pp. 222-234
Author(s):  
Sophie Bernadette Sébille ◽  
Anne-Sophie Rolland ◽  
Marie-Laure Welter ◽  
Eric Bardinet ◽  
Mathieu David Santin
Keyword(s):  
High Resolution ◽  
Diffusion Mri ◽  
Echo Planar Imaging ◽  
Post Mortem ◽  
Planar Imaging ◽  
Large Specimen ◽  
Echo Planar

scholarly journals Evaluating cellularity and structural connectivity on whole brain slides using a custom-made digital pathology pipeline

2019 ◽  
Vol 311 ◽  
pp. 215-221 ◽  
Author(s):  
Thomas Roetzer ◽  
Konrad Leskovar ◽  
Nadine Peter ◽  
Julia Furtner ◽  
Martina Muck ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
Digital Pathology ◽  
Whole Brain ◽  
Custom Made

scholarly journals White-matter free-water diffusion MRI in schizophrenia: a systematic review and meta-analysis

2022 ◽  
Author(s):  
Inês Carreira Figueiredo ◽  
Faith Borgan ◽  
Ofer Pasternak ◽  
Federico E. Turkheimer ◽  
Oliver D. Howes
Keyword(s):  
White Matter ◽  
Healthy Volunteers ◽  
Diffusion Mri ◽  
Clinical Symptoms ◽  
Free Water ◽  
Brain Regions ◽  
Water Levels ◽  
First Episode ◽  
Whole Brain ◽  
Illness Duration

AbstractWhite-matter abnormalities, including increases in extracellular free-water, are implicated in the pathophysiology of schizophrenia. Recent advances in diffusion magnetic resonance imaging (MRI) enable free-water levels to be indexed. However, the brain levels in patients with schizophrenia have not yet been systematically investigated. We aimed to meta-analyse white-matter free-water levels in patients with schizophrenia compared to healthy volunteers. We performed a literature search in EMBASE, MEDLINE, and PsycINFO databases. Diffusion MRI studies reporting free-water in patients with schizophrenia compared to healthy controls were included. We investigated the effect of demographic variables, illness duration, chlorpromazine equivalents of antipsychotic medication, type of scanner, and clinical symptoms severity on free-water measures. Ten studies, including five of first episode of psychosis have investigated free-water levels in schizophrenia, with significantly higher levels reported in whole-brain and specific brain regions (including corona radiata, internal capsule, superior and inferior longitudinal fasciculus, cingulum bundle, and corpus callosum). Six studies, including a total of 614 participants met the inclusion criteria for quantitative analysis. Whole-brain free-water levels were significantly higher in patients relative to healthy volunteers (Hedge’s g = 0.38, 95% confidence interval (CI) 0.07–0.69, p = 0.02). Sex moderated this effect, such that smaller effects were seen in samples with more females (z = −2.54, p < 0.05), but antipsychotic dose, illness duration and symptom severity did not. Patients with schizophrenia have increased free-water compared to healthy volunteers. Future studies are necessary to determine the pathological sources of increased free-water, and its relationship with illness duration and severity.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

2020 ◽  
Author(s):  
Kyesam Jung ◽  
Simon B. Eickhoff ◽  
Oleksandr V. Popovych
Keyword(s):  
Data Processing ◽  
Resting State ◽  
Optimal Parameter ◽  
Structural Connectivity ◽  
Model Performance ◽  
Brain Dynamics ◽  
Dynamical Modeling ◽  
Whole Brain ◽  
Brain Connectome ◽  
Network Properties

AbstractDynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

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

2020 ◽  
Author(s):  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
Robert E. Smith ◽  
Jeggan Tiego ◽  
Mark A. Bellgrove ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Motion Correction ◽  
Healthy Adult ◽  
Structural Connectivity ◽  
Head Motion ◽  
List Type ◽  
Edge Weighting ◽  
Quantitative Metrics ◽  
The Impact ◽  
Connectivity Strength

AbstractHead 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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