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

2020 ◽  
Author(s):  
Tommy Boshkovski ◽  
Ljupco Kocarev ◽  
Julien Cohen-Adad ◽  
Bratislav Mišić ◽  
Stéphane Lehéricy ◽  
...  
Keyword(s):  
White Matter ◽  
Neurological Diseases ◽  
Structural Connectivity ◽  
Weighted Average ◽  
Brain Regions ◽  
Strength Distribution ◽  
Quantitative Mri ◽  
Added Value ◽  
Modular Organization ◽  
Sensitive Measure

AbstractMyelin plays a crucial role in how well information travels between brain regions. Many neurological diseases affect the myelin in the white matter, making myelin-sensitive metrics derived from quantitative MRI of potential interest for early detection and prognosis of those conditions. 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 how the myeloarchitecture relates to brain function. In this work we weight the connectome by the longitudinal relaxation rate (R1) as 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 is able to separate different classes (unimodal and transmodal), following a functional gradient. Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.Author summaryIn the present work, we integrate a myelin sensitive MRI metric into the connectome and compare it with a connectome weighted with a standard diffusion-derived metric, number of streamlines (NOS). Our analysis shows that the R1-weighted connectome complements the NOS-weighted connectome. We show that the R1-weighted average distribution does not follow the same trend as the NOS strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions tend to have a higher R1-weighted average but fewer streamlines. In terms of network communication, this could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions take more myelinated, but fewer, connections for a reliable transfer of information.

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.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 871-890
Author(s):  
Arseny A. Sokolov ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Michael Erb ◽  
Philippe Ryvlin ◽  
...  
Keyword(s):  
White Matter ◽  
Dynamic Range ◽  
Diffusion Processes ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
Graph Laplacian ◽  
Brain Regions ◽  
High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

scholarly journals Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics

2021 ◽  
Vol 118 (46) ◽  
pp. e2109380118
Author(s):  
Maria Pope ◽  
Makoto Fukushima ◽  
Richard F. Betzel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Functional Neuroimaging ◽  
Empirical Studies ◽  
Structural Connectivity ◽  
Spatial Arrangement ◽  
High Amplitude ◽  
Brain Regions ◽  
Brain Dynamics ◽  
Modular Organization ◽  
Fine Scale

The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.

scholarly journals Using structural connectivity to augment community structure in EEG functional connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 761-787 ◽  
Author(s):  
Katharina Glomb ◽  
Emeline Mullier ◽  
Margherita Carboni ◽  
Maria Rubega ◽  
Giannarita Iannotti ◽  
...  
Keyword(s):  
Community Structure ◽  
White Matter ◽  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Source Analysis ◽  
Volume Conduction ◽  
Functional Relationships ◽  
The Impact

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

scholarly journals Apparent thinning of human visual cortex during childhood is associated with myelination

2019 ◽  
Vol 116 (41) ◽  
pp. 20750-20759 ◽  
Author(s):  
Vaidehi S. Natu ◽  
Jesse Gomez ◽  
Michael Barnett ◽  
Brianna Jeska ◽  
Evgeniya Kirilina ◽  
...  
Keyword(s):  
White Matter ◽  
Temporal Cortex ◽  
Mean Diffusivity ◽  
Brain Regions ◽  
Tissue Growth ◽  
Quantitative Mri ◽  
Childhood Development ◽  
Cortical Thinning ◽  
Ventral Temporal Cortex

Human cortex appears to thin during childhood development. However, the underlying microstructural mechanisms are unknown. Using functional magnetic resonance imaging (fMRI), quantitative MRI (qMRI), and diffusion MRI (dMRI) in children and adults, we tested what quantitative changes occur to gray and white matter in ventral temporal cortex (VTC) from childhood to adulthood, and how these changes relate to cortical thinning. T1 relaxation time from qMRI and mean diffusivity (MD) from dMRI provide independent and complementary measurements of microstructural properties of gray and white matter tissue. In face- and character-selective regions in lateral VTC, T1 and MD decreased from age 5 to adulthood in mid and deep cortex, as well as in their adjacent white matter. T1 reduction also occurred longitudinally in children’s brain regions. T1 and MD decreases 1) were consistent with tissue growth related to myelination, which we verified with adult histological myelin stains, and 2) were correlated with apparent cortical thinning. In contrast, in place-selective cortex in medial VTC, we found no development of T1 or MD after age 5, and thickness was related to cortical morphology. These findings suggest that lateral VTC likely becomes more myelinated from childhood to adulthood, affecting the contrast of MR images and, in turn, the apparent gray–white boundary. These findings are important because they suggest that VTC does not thin during childhood but instead gets more myelinated. Our data have broad ramifications for understanding both typical and atypical brain development using advanced in vivo quantitative measurements and clinical conditions implicating myelin.

scholarly journals Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis

2020 ◽  
Author(s):  
Md. Mamun Al-Amin ◽  
Joanes Grandjean ◽  
Jan Klohs ◽  
Jungsu Kim
Keyword(s):  
White Matter ◽  
Transgenic Mice ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Mice Model ◽  
Network Efficiency ◽  
Fiber Density ◽  
Neurofilament Light ◽  
Diffusion Weighted Images ◽  
Network Properties

AbstractAlthough amyloid beta (Aβ) deposition is one of the major causes of white matter (WM) alterations in Alzheimer’s disease (AD), little is known about the underlying basis of WM damage and its association with global structural connectivity and network topology. We aimed to dissect the contributions of WM microstructure to structural connectivity and network properties in the ArcAβ mice model of Aβ amyloidosis.We acquired diffusion-weighted images (DWI) of wild type (WT) and ArcAβ transgenic (TG) mice using a 9.4 T MRI scanner. Fixel-based analysis (FBA) was performed to measure fiber tract-specific properties. We also performed three complementary experiments; to identify the global differences in structural connectivity, to compute network properties and to measure cellular basis of white matter alterations.Transgenic mice displayed disrupted structural connectivity centered to the entorhinal cortex (EC) and a lower fiber density and fiber bundle cross-section. In addition, there was a reduced network efficiency and degree centrality in weighted structural connectivity in the transgenic mice. To further examine the underlying neuronal basis of connectivity and network deficits, we performed histology experiments. We found no alteration in myelination and an increased level of neurofilament light (NFL) in the brain regions with disrupted connectivity in the TG mice. Furthermore, TG mice had a reduced number of perineuronal nets (PNN) in the EC.The observed FDC reductions may indicate a decrease in axonal diameter or axon count which would explain the basis of connectivity deficits and reduced network efficiency in TG mice. The increase in NFL suggests a breakdown of axonal integrity, which would reduce WM fiber health. Considering the pivotal role of the EC in AD, Aβ deposition may primarily increase NFL release, damaging PNN in the entorhinal pathway, resulting in disrupted structural connectivity.

scholarly journals Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

2021 ◽  
Author(s):  
Ajay Peddada ◽  
Kevin Holly ◽  
Tejaswi D Sudhakar ◽  
Christina Ledbetter ◽  
Christopher E. Talbot ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Resting State ◽  
Classification Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Group Differences ◽  
Functional Connections ◽  
White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

scholarly journals Behavioral Effects of Chronic Gray and White Matter Stroke Lesions in a Functionally Defined Connectome for Naming

2018 ◽  
Vol 32 (6-7) ◽  
pp. 613-623 ◽  
Author(s):  
Shihui Xing ◽  
Ayan Mandal ◽  
Elizabeth H. Lacey ◽  
Laura M. Skipper-Kallal ◽  
Jinsheng Zeng ◽  
...  
Keyword(s):  
White Matter ◽  
Left Hemisphere ◽  
Phonological Processing ◽  
Semantic Processing ◽  
Structural Connectivity ◽  
Dorsal Stream ◽  
Naming Task ◽  
Brain Regions ◽  
Lesion Size ◽  
The Impact

Background. In functional magnetic resonance imaging studies, picture naming engages widely distributed brain regions in the parietal, frontal, and temporal cortices. However, it remains unknown whether those activated areas, along with white matter pathways between them, are actually crucial for naming. Objective. We aimed to identify nodes and pathways implicated in naming in healthy older adults and test the impact of lesions to the connectome on naming ability. Methods. We first identified 24 cortical nodes activated by a naming task and reconstructed anatomical connections between these nodes using probabilistic tractography in healthy adults. We then used structural scans and fractional anisotropy (FA) maps in 45 patients with left hemisphere stroke to assess the relationships of node and pathway integrity to naming, phonology, and nonverbal semantic ability. Results. We found that mean FA values in 13 left hemisphere white matter tracts within the dorsal and ventral streams and 1 interhemispheric tract significantly related to naming scores after controlling for lesion size and demographic factors. In contrast, lesion loads in the cortical nodes were not related to naming performance after controlling for the same variables. Among the identified tracts, the integrity of 4 left hemisphere ventral stream tracts related to nonverbal semantic processing and 1 left hemisphere dorsal stream tract related to phonological processing. Conclusions. Our findings reveal white matter structures vital for naming and its subprocesses. These findings demonstrate the value of multimodal methods that integrate functional imaging, structural connectivity, and lesion data to understand relationships between brain networks and behavior.

Diffusion Tensor Imaging

2014 ◽  
Author(s):  
Elisabeth A. Wilde ◽  
Kareem W. Ayoub ◽  
Asim F. Choudhri
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Reliability And Validity ◽  
Brain Regions ◽  
Functional Integrity ◽  
P Diffusion ◽  
Effects Of Aging ◽  
Normal White Matter

Diffusion tensor imaging (DTI) is a method of specifying and visualizing the functional integrity of white matter tracts that contribute to the functional and structural connectivity among different brain regions through the examination of water diffusion through tissue. It has gained rapid popularity in the past two decades, particularly for elucidating the process of normal white matter development and the effects of aging on it, as well as providing some insights into the possible neuroanatomical correlates of numerous psychiatric and neurologic disorders. This chapter outlines the instrumentation and the procedures employed in deriving estimates of the functional integrity of anatomical connections in the brain, and issues regarding the reliability and validity of the different DTI procedures are systematically addressed.

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