scholarly journals Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: a review

NeuroImage ◽  
2022 ◽  
pp. 118870
Author(s):  
Fan Zhang ◽  
Alessandro Daducci ◽  
Yong He ◽  
Simona Schiavi ◽  
Caio Seguin ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Structural Connectivity ◽  
Quantitative Mapping

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

scholarly journals The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

2020 ◽  
pp. 1-15
Author(s):  
Tommy Boshkovski ◽  
Ljupco Kocarev ◽  
Julien Cohen-Adad ◽  
Bratislav Mišić ◽  
Stéphane Lehéricy ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Added Value ◽  
Modular Organization ◽  
Sensitive Measure ◽  
Higher Order Functions ◽  
Insight Into

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

Comparison of structural connectivity in Parkinson's disease with depressive symptoms versus non-depressed: a diffusion MRI connectometry study

2018 ◽  
Vol 31 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Mina Ansari ◽  
Sahand Adib Moradi ◽  
Farzaneh Ghazi Sherbaf ◽  
Abozar Hedayatnia ◽  
Mohammad Hadi Aarabi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Depressive Symptoms ◽  
Diffusion Mri ◽  
Group Analysis ◽  
Structural Connectivity ◽  
Rem Sleep Behavior Disorder ◽  
Spin Distribution ◽  
Significant Difference ◽  
Left And Right

ABSTRACTObjective:Research on psychobiological markers of Parkinson's disease (PD) remains a hot topic. Non-motor symptoms such as depression and REM sleep behavior disorder (RBD) each attribute to a particular neurodegenerative cluster in PD, and might enlighten the way for early prediction/detection of PD. The neuropathology of mood disturbances remains unclear. In fact, a few studies have investigated depression using diffusion magnetic resonance imaging (diffusion MRI).Method:Diffusion MRI of PD patients without comorbid RBD was used to assess whether microstructural abnormalities are detectable in the brain of 40 PD patients with depression compared to 19 patients without depression. Diffusion MRI connectometry was used to carry out group analysis between age- and gender-matched PD patients with and without depressive symptoms. Diffusion MRI connectometry is based on spin distribution function, which quantifies the density of diffusing water and is a sensitive and specific analytical method to psychological differences between groups.Results:A significant difference (FDR = 0.016129) was observed in the left and right uncinate fasciculi, left and right inferior longitudinal fasciculi, left and right fornices, left inferior fronto-occipital fasciculus, right corticospinal tract, genu of corpus callosum, and middle cerebellar peduncle.Conclusion:These results suggest the prominent circuits involved in emotion recognition, particularly negative emotions, might be impaired in comorbid depressive symptoms in PD.

scholarly journals Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain

2019 ◽  
Vol 3 (4) ◽  
pp. 1038-1050 ◽  
Author(s):  
Céline Delettre ◽  
Arnaud Messé ◽  
Leigh-Anne Dell ◽  
Ophélie Foubet ◽  
Katja Heuer ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
State Of The Art ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Fiber Architecture ◽  
Tract Tracing ◽  
Central Focus ◽  
Highly Correlated ◽  
The Brain

The anatomical wiring of the brain is a central focus in network neuroscience. Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated. Here, we explored the ability of diffusion MRI tractography to match invasive anatomical tract-tracing connectivity data of the ferret brain. We also investigated the influence of several state-of-the-art tractography algorithms on this match to ground truth connectivity data. Tract-tracing connectivity data were obtained from retrograde tracer injections into the occipital, parietal, and temporal cortices of adult ferrets. We found that the relative densities of projections identified from the anatomical experiments were highly correlated with the estimates from all the studied diffusion tractography algorithms (Spearman’s rho ranging from 0.67 to 0.91), while only small, nonsignificant variations appeared across the tractography algorithms. These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results. Moreover, our results provide insights into the variations of sensitivity and specificity of the tractography algorithms, and hence into the influence of choosing one algorithm over another.

scholarly journals Assessment of Structural Connectivity in the Preterm Brain at Term Equivalent Age Using Diffusion MRI and T2 Relaxometry: A Network-Based Analysis

PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e68593 ◽  
Author(s):  
Kerstin Pannek ◽  
Xanthy Hatzigeorgiou ◽  
Paul B. Colditz ◽  
Stephen Rose
Keyword(s):  
Diffusion Mri ◽  
Structural Connectivity ◽  
T2 Relaxometry ◽  
Equivalent Age

scholarly journals Mapping Structural Connectivity Using Diffusion MRI : Challenges and Opportunities

2021 ◽  
Vol 53 (6) ◽  
Author(s):  
Chun‐Hung Yeh ◽  
Derek K. Jones ◽  
Xiaoyun Liang ◽  
Maxime Descoteaux ◽  
Alan Connelly
Keyword(s):  
Diffusion Mri ◽  
Structural Connectivity ◽  
Challenges And Opportunities

scholarly journals A functional-structural connectivity metric detects ipsilateral connections with distinct functional specialisation in each hemisphere

2020 ◽  
Author(s):  
Oren Civier ◽  
Marion Sourty ◽  
Fernando Calamante
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Measurement Errors ◽  
Diffusion Mri ◽  
Right Hemisphere ◽  
Structural Connectivity ◽  
Joint Analysis ◽  
Fibre Bundles ◽  
Human Connectome Project ◽  
The Brain

AbstractWe 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 StatementBased 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.

scholarly journals Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0239475
Author(s):  
Leon Weninger ◽  
Chuh-Hyoun Na ◽  
Kerstin Jütten ◽  
Dorit Merhof
Keyword(s):  
Deep Learning ◽  
Diffusion Mri ◽  
Structural Connectivity ◽  
Free Water ◽  
Water Modeling

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 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.

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