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.

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 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 Retrospective analysis of hemispheric structural network change as a function of location and size of glioma

2020 ◽  
Author(s):  
Shawn D’Souza ◽  
Lisa Hirt ◽  
David R Ormond ◽  
John A Thompson
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Temporal Lobe ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Structural Network ◽  
Tumour Location ◽  
The Impact ◽  
Tractography Data ◽  
Diffusion Tensor Imaging Tractography

Abstract Gliomas are neoplasms that arise from glial cell origin and represent the largest fraction of primary malignant brain tumours (77%). These highly infiltrative malignant cell clusters modify brain structure and function through expansion, invasion and intratumoral modification. Depending on the growth rate of the tumour, location and degree of expansion, functional reorganization may not lead to overt changes in behaviour despite significant cerebral adaptation. Studies in simulated lesion models and in patients with stroke reveal both local and distal functional disturbances, using measures of anatomical brain networks. Investigations over the last two decades have sought to use diffusion tensor imaging tractography data in the context of intracranial tumours to improve surgical planning, intraoperative functional localization, and post-operative interpretation of functional change. In this study, we used diffusion tensor imaging tractography to assess the impact of tumour location on the white matter structural network. To better understand how various lobe localized gliomas impact the topology underlying efficiency of information transfer between brain regions, we identified the major alterations in brain network connectivity patterns between the ipsilesional versus contralesional hemispheres in patients with gliomas localized to the frontal, parietal or temporal lobe. Results were indicative of altered network efficiency and the role of specific brain regions unique to different lobe localized gliomas. This work draws attention to connections and brain regions which have shared structural susceptibility in frontal, parietal and temporal lobe glioma cases. This study also provides a preliminary anatomical basis for understanding which affected white matter pathways may contribute to preoperative patient symptomology.

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 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.

Abstract P474: Functional Outcomes and Regional Significance of Ischemic Lesions After Thrombectomy for Large Vessel Occlusion Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Robert W Regenhardt ◽  
Anna K Bonkhoff ◽  
Martin Bretzner ◽  
Mark R Etherton ◽  
Alvin S Das ◽  
...  
Keyword(s):  
White Matter ◽  
Stroke Care ◽  
Brain Regions ◽  
Lesion Size ◽  
Bayesian Regression ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion ◽  
White Matter Tracts ◽  
Subcortical Nuclei

Introduction: Endovascular thrombectomy (EVT) has revolutionized large vessel occlusion (LVO) stroke care. However, over half remain functionally disabled or die despite treatment. Understanding outcomes may influence EVT selection, novel therapies, and prognostication. We sought to identify associations between outcomes and brain regions involved in ischemic lesions. Methods: For consecutive LVO patients with post-EVT MRI, acute ischemic lesions were manually segmented from DWI and spatially normalized. Individual lesion volumes were automatically parcellated (atlas-defined 94 cortical regions, 14 subcortical nuclei, 20 white matter tracts) and then reduced to ten anatomically plausible lesion patterns using unsupervised dimensionality reduction techniques. Ninety-day modified Rankin Scale (mRS) was modeled via Bayesian regression, taking the ten lesion patterns as inputs and controlling for lesion size, age, sex, acute NIH Stroke Scale, alteplase, and TICI 2b-3 reperfusion. Results: We identified 153 LVO patients with mean age 68±15 years and 51% female. Median NIHSS was 16 (IQR 13-20), 56% received alteplase, and 84% achieved TICI2b-3. The lesion patterns predictive of 90-day mRS involved bilateral subcortical nuclei, pre- and postcentral gyri, insular and opercular cortex, as well as left-sided inferior frontal and angular gyri ( Figure 1A ). Lesions affecting white matter tracts had the highest relevance predicting 90-day mRS ( Figure 1B ). Conclusions: These data describe the significance for outcomes of specific brain regions involved in ischemic lesions on MRI after EVT. Future work in additional datasets is needed to confirm these granular findings.

Neuron–glia synapses in the brain: properties, diversity and functions of NG2 glia

e-Neuroforum ◽  
2015 ◽  
Vol 21 (3) ◽  
Author(s):  
Christian Steinhäuser ◽  
Dirk Dietrich
Keyword(s):  
White Matter ◽  
Glial Cells ◽  
Temporal Integration ◽  
Brain Regions ◽  
Gabaergic Neurons ◽  
Cell Type ◽  
Transmitter Receptors ◽  
Ng2 Proteoglycan ◽  
The Impact ◽  
The Brain

AbstractAlthough NG2 glial cells represent a frequent glial cell type in the brain, characterized by expression of the NG2 proteoglycan, the functional impact of these cells is still enigmatic. A large proportion of NG2 glia are proliferatively active throughout life. These cells express a plethora of ion channels and transmitter receptors, which enable them to detect neuronal activity. Intriguingly, NG2 glial cells receive synaptic input from glutamatergic and GABAergic neurons. Since these postsynaptic glial currents are very small, their spatial and temporal integration might play an important role. In white matter, most NG2 glial cells differentiate into oligodendrocytes and this process might be influenced through the activity of the aforementioned neuron-glia synapses. Increasing evidence suggests that the properties of NG2 glia vary across brain regions; however, the impact of this variability is not understood yet.

scholarly journals Triangulation of the neurocomputational architecture underpinning reading aloud

2015 ◽  
Vol 112 (28) ◽  
pp. E3719-E3728 ◽  
Author(s):  
Paul Hoffman ◽  
Matthew A. Lambon Ralph ◽  
Anna M. Woollams
Keyword(s):  
Individual Differences ◽  
Phonological Processing ◽  
Semantic Processing ◽  
Word Reading ◽  
Reading Aloud ◽  
Brain Regions ◽  
Semantic Knowledge ◽  
Cognitive Models ◽  
Causal Modeling ◽  
Neural Pathways

The goal of cognitive neuroscience is to integrate cognitive models with knowledge about underlying neural machinery. This significant challenge was explored in relation to word reading, where sophisticated computational-cognitive models exist but have made limited contact with neural data. Using distortion-corrected functional MRI and dynamic causal modeling, we investigated the interactions between brain regions dedicated to orthographic, semantic, and phonological processing while participants read words aloud. We found that the lateral anterior temporal lobe exhibited increased activation when participants read words with irregular spellings. This area is implicated in semantic processing but has not previously been considered part of the reading network. We also found meaningful individual differences in the activation of this region: Activity was predicted by an independent measure of the degree to which participants use semantic knowledge to read. These characteristics are predicted by the connectionist Triangle Model of reading and indicate a key role for semantic knowledge in reading aloud. Premotor regions associated with phonological processing displayed the reverse characteristics. Changes in the functional connectivity of the reading network during irregular word reading also were consistent with semantic recruitment. These data support the view that reading aloud is underpinned by the joint operation of two neural pathways. They reveal that (i) the ATL is an important element of the ventral semantic pathway and (ii) the division of labor between the two routes varies according to both the properties of the words being read and individual differences in the degree to which participants rely on each route.

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.

Sign in / Sign up

Export Citation Format

Share Document