scholarly journals The Relation Between White Matter Microstructure and Network Complexity: Implications for Processing Efficienc

2018 ◽  
Author(s):  
Ian M. McDonough ◽  
Jonathan T. Siegel
Keyword(s):  
White Matter ◽  
Time Scales ◽  
Brain Structure ◽  
Brain Networks ◽  
Simulation Models ◽  
Multiscale Entropy ◽  
Fast Time ◽  
Bold Signal ◽  
White Matter Microstructure ◽  
Human Connectome Project

AbstractBrain structure has been proposed to facilitate as well as constrain functional interactions within brain networks. Simulation models suggest that integrity of white matter (WM) microstructure should be positively related to the complexity of BOLD signal—a measure of network interactions. Using 121 young adults from the Human Connectome Project, we empirically tested whether greater WM integrity would be associated with greater complexity of the BOLD signal during rest via multiscale entropy. Multiscale entropy measures the lack of predictability within a given time series across varying time scales, thus being able to estimate fluctuating signal dynamics within brain networks. Using multivariate analysis techniques (Partial Least Squares), we found that greater WM integrity was associated with greater network complexity at fast time scales, but less network complexity at slower time scales. These findings implicate two separate pathways through which WM integrity affects brain function in the prefrontal cortex—an executive-prefrontal pathway and a perceptuo-occipital pathway. In two additional samples, the main patterns of WM and network complexity were replicated. These findings support simulation models of WM integrity and network complexity and provide new insights into brain structure-function relationships.

scholarly journals White matter microstructure predicts foreign language learning in army interpreters

2020 ◽  
Vol 23 (4) ◽  
pp. 763-771 ◽  
Author(s):  
Johan Mårtensson ◽  
Johan Eriksson ◽  
Nils Christian Bodammer ◽  
Magnus Lindgren ◽  
Mikael Johansson ◽  
...  
Keyword(s):  
White Matter ◽  
Foreign Language ◽  
Language Learning ◽  
Language Proficiency ◽  
Brain Structure ◽  
Foreign Language Learning ◽  
Control Group ◽  
Final Test ◽  
Foreign Language Acquisition ◽  
White Matter Microstructure

AbstractAdult foreign language acquisition is challenging, and the degree of success varies among individuals. Anatomical differences in brain structure prior to training can partly explain why some learn more than others. We followed a sample of conscript interpreters undergoing intense language training to study learning-related changes in white-matter microstructure (FA, MD, RD and AD) and associations between differences in brain structure prior to training with acquired language proficiency. No evidence for changes in white matter microstructure relative to a control group was found. Starting values of RD, AD and MD were positively related to final test scores of language proficiency, corroborating earlier findings in the field and highlighting the need for further study of how initial brain structure influences and interacts with learning outcomes.

scholarly journals Cerebral Autoregulation and Brain Networks in Occlusive Processes of the Internal Carotid Artery

2014 ◽  
Vol 35 (2) ◽  
pp. 240-247 ◽  
Author(s):  
Keren Avirame ◽  
Anne Lesemann ◽  
Jonathan List ◽  
Anja Veronica Witte ◽  
Stephan Joachim Schreiber ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Carotid Artery ◽  
Internal Carotid Artery ◽  
Cerebral Autoregulation ◽  
Internal Carotid ◽  
Brain Networks ◽  
Transcranial Doppler Sonography ◽  
Vasomotor Reactivity ◽  
White Matter Microstructure

Patients with unilateral occlusive processes of the internal carotid artery (ICA) show subtle cognitive deficits. Decline in cerebral autoregulation and in functional and structural integrity of brain networks have previously been reported in the affected hemisphere (AH). However, the association between cerebral autoregulation, brain networks, and cognition remains to be elucidated. Fourteen neurologically asymptomatic patients (65±11 years) with either ICA occlusion or high-grade ICA stenosis and 11 age-matched healthy controls (HC) (67±6 years) received neuropsychologic testing, transcranial Doppler sonography to assess cerebral autoregulation using vasomotor reactivity (VMR), and magnetic resonance imaging to probe white matter microstructure and resting-state functional connectivity (RSFC). Patients performed worse on memory and executive tasks when compared with controls. Vasomotor reactivity, white matter microstructure, and RSFC were lower in the AH of the patients when compared with the unaffected hemisphere and with controls. Lower VMR of the AH was associated with several ipsilateral clusters of lower white matter microstructure and lower bilateral RSFC in patients. No correlations were found between VMR and cognitive scores. In sum, impaired cerebral autoregulation was associated with reduced structural and functional connectivity in cerebral networks, indicating possible mechanisms by which severe unilateral occlusive processes of the ICA lead to cognitive decline.

scholarly journals Factors affecting brain structure in smoking-related diseases: Chronic Obstructive Pulmonary Disease (COPD) and coronary artery disease

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259375
Author(s):  
Catherine A. Spilling ◽  
Mohani-Preet K. Dhillon ◽  
Daniel R. Burrage ◽  
Sachelle Ruickbie ◽  
Emma H. Baker ◽  
...  
Keyword(s):  
Blood Pressure ◽  
White Matter ◽  
Brain Structure ◽  
Retinal Vessel ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
White Matter Microstructure ◽  
Organ Systems ◽  
Artery Disease ◽  
Multivariable Regression Models

Background Changes in brain structure and cognitive decline occur in Chronic Obstructive Pulmonary Disease (COPD). They also occur with smoking and coronary artery disease (CAD), but it is unclear whether a common mechanism is responsible. Methods Brain MRI markers of brain structure were tested for association with disease markers in other organs. Where possible, principal component analysis (PCA) was used to group markers within organ systems into composite markers. Univariate relationships between brain structure and the disease markers were explored using hierarchical regression and then entered into multivariable regression models. Results 100 participants were studied (53 COPD, 47 CAD). PCA identified two brain components: brain tissue volumes and white matter microstructure, and six components from other organ systems: respiratory function, plasma lipids, blood pressure, glucose dysregulation, retinal vessel calibre and retinal vessel tortuosity. Several markers could not be grouped into components and were analysed as single variables, these included brain white matter hyperintense lesion (WMH) volume. Multivariable regression models showed that less well organised white matter microstructure was associated with lower respiratory function (p = 0.028); WMH volume was associated with higher blood pressure (p = 0.036) and higher C-Reactive Protein (p = 0.011) and lower brain tissue volume was associated with lower cerebral blood flow (p<0.001) and higher blood pressure (p = 0.001). Smoking history was not an independent correlate of any brain marker. Conclusions Measures of brain structure were associated with a range of markers of disease, some of which appeared to be common to both COPD and CAD. No single common pathway was identified, but the findings suggest that brain changes associated with smoking-related diseases may be due to vascular, respiratory, and inflammatory changes.

scholarly journals The Impact of Graph Construction Scheme and Community Detection Algorithm on the Reliability of Community and Hub Identification in Structural Brain Networks

2020 ◽  
Author(s):  
Stavros I. Dimitriadis ◽  
Eirini Messaritaki ◽  
Derek K. Jones
Keyword(s):  
White Matter ◽  
Community Detection ◽  
Brain Networks ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Construction Scheme ◽  
Human Connectome Project ◽  
Community Detection Algorithm ◽  
The Impact ◽  
The Brain

AbstractThe human brain is a complex network of volumes of tissue (nodes) that are interconnected by white matter tracts (edges). It can be represented as a graph to allow us to use graph theory to gain insight into normal human development and brain disorders. Most graph theoretical metrics measure either whole-network (global) or node-specific (local) properties of the network. However, a critical question in network neuroscience is how nodes cluster together to form communities, each of which possibly plays a specific functional role. Community partition analysis allows us to investigate the mesoscale organization of the brain. Various algorithms have been proposed in the literature, that allow the identification of such communities, with each algorithm resulting in different communities for the network. Those communities also depend on the method used to weigh the edges of the graphs representing the brain networks. In this work, we use the test-retest data from the Human Connectome Project to compare 32 such community detection algorithms, each paired with 7 graph construction schemes, and assess the reproducibility of the resulting community partitions.The reproducibility of community partition depended heavily on both the graph construction scheme and the community detection algorithm. Hard community detection algorithms, via which each node is assigned to only one community, outperformed soft ones, via which each node can be a part of more than one community. The best reproducibility was observed for the graph construction scheme that combines 9 white matter tract metrics paired with the greedy stability optimization algorithm, with either discrete or continuous Markovian chain. This graph-construction scheme / community detection algorithm pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs were better reproduced than provincial hubs.

scholarly journals M155. RECIPROCAL CHANGES IN WHITE MATTER MICROSTRUCTURE IN 22Q11.2 DELETION AND DUPLICATION SYNDROME

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S194-S195
Author(s):  
Johanna Seitz ◽  
Monica Lyons ◽  
Leila Kushan ◽  
Kang Ik Kevin Cho ◽  
Tashrif Billah ◽  
...  
Keyword(s):  
White Matter ◽  
Symptom Score ◽  
Negative Symptom ◽  
Free Water ◽  
22Q11.2 Deletion Syndrome ◽  
Deletion Syndrome ◽  
Psychotic Symptoms ◽  
22Q11.2 Deletion ◽  
White Matter Microstructure ◽  
Human Connectome Project

Abstract Background The 22q11.2 deletion syndrome is a neurogenetic disorder that is associated with both physical anomalies and neurocognitive impairments. Deletion carriers have a greatly elevated risk of developing schizophrenia (SCZ); as such, it offers a compelling ‘high-penetrance’ model to explore the neuropathology of SCZ risk. Indeed, widespread structural alterations of both gray and white matter have been reported for 22q11.2 deletion carriers. Interestingly, there are also cases of duplications at the same gene locus. While less is known about the phenotype associated with 22q11.2 duplication, carriers also present physical and neurodevelopmental abnormalities, although they may have reduced risk of developing SCZ compared to the general population. The only study to date which looked at brain structure in duplication carriers found reciprocal effects of 22q11.2 deletion and duplication on cortical thickness and surface measurements. In the present study, we apply diffusion magnetic resonance imaging (MRI) to examine the white matter microstructure in both 22q11.2 deletion and duplication carriers. Methods Multi-shell diffusion-weighted images were acquired on a 3 Tesla MRI scanner from 13 healthy control individuals (HC), 25 deletion carriers, and 18 22q11.2 duplication carriers. Images were preprocessed utilizing the Human Connectome Project (HCP) Minimal Preprocessing Pipeline v4.0.0. Free Water imaging was applied, which differentiates the diffusion signal into a free-water compartment and a tissue compartment. The output parameters are the free-water fractional volume (FW) and a free-water corrected diffusion tensor from which fractional anisotropy of the tissue (FAT) is calculated. We compared FAT and FW maps between 1) HC and 22q11.2 deletion carriers and 2) HC and 22q11.2 duplication carriers using Tract-Based Spatial Statistics (TBSS) and voxel-wise, non-parametric statistics (5000 permutations, threshold-free cluster enhancement, corrected for age and sex). Lastly, white matter clusters that displayed significant differences between 22q11.2 deletion or duplication and HC were extracted. We averaged FAT and FW values over these significant clusters for each individual and correlated with the scores of the Structured Interview for Prodromal Syndromes (SIPS). Results 22q11.2 deletion carriers showed significant (p&lt;0.05) FW reductions (72% of white matter skeleton) and FAT increase (8%) when compared to HC. In contrast, 22q11.2 duplication carriers displayed the opposite effect, with significant (p&lt;0.05) widespread FW increase (51%) and FAT decrease (50%) when compared to HC. Both 22q11.2 deletion and duplication carriers scored higher on the SIPS than HC, with negative symptom score differences being the most pronounced (mean for HC= 1.36, mean for 22q11.2 duplication = 7.0, mean for 22q11.2 deletion =9.96, F=6.68, df=2, p&lt;.003). FAT and FW were not associated with SIPS scores in 22q11.2 deletion syndrome. However, FAT was negatively correlated with the negative symptom score in 22q11.2 duplication carriers (Spearman rho=-.61, p&lt;.009). Discussion We observed opposing effects of gene-dosage on FAT and FW. While we did not see an association between WM measurements and psychotic symptoms in 22q11.2 deletion, there was an association of WM structure with negative symptoms in 22q11.2 duplication carriers. These findings highlight the importance of studying the influence of reciprocal chromosomal imbalance on white matter architecture. Ongoing longitudinal studies may help advance understanding of the role of microstructural white matter abnormalities in the emergence of neuropsychiatric symptoms.

scholarly journals Inter-subject phase synchronization and the dynamics of human cognition

2017 ◽  
Author(s):  
Taylor Bolt ◽  
Jason S. Nomi ◽  
Shruti G. Vij ◽  
Catie Chang ◽  
Lucina Q. Uddin
Keyword(s):  
Cognitive Processes ◽  
Phase Synchronization ◽  
Brain Activity ◽  
Methodological Approach ◽  
Brain Networks ◽  
Human Cognition ◽  
Bold Signal ◽  
Top Down ◽  
Whole Brain ◽  
Human Connectome Project

AbstractMassive whole-brain blood-oxygen-level dependent (BOLD) signal modulation (up to 95% of brain voxels) in response to task stimuli has recently been reported in functional MRI investigations. These findings have two implications. First, they highlight inability of a conventional ‘top-down’ general linear model approach to capture all forms of task-driven brain activity. Second, as opposed to a static ‘active’ or ‘non-active’ localization theory of the neural implementation of cognitive processes, functional neuroimaging should develop and pursue dynamical theories of cognition involving the dynamic interactions of all brain networks, in line with psychological constructionist theories of cognition. In this study, we describe a novel exploratory, bottom-up approach that directly estimates task-driven brain activity regardless of whether it follows an a priori reference function. Leveraging the property that task-driven brain activity is associated with reductions in BOLD signal variability, we combine the tools of instantaneous phase synchronization and independent component analysis to characterize whole-brain task-driven activity in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this novel framework to task fMRI data from a motor, theory of mind and working memory task provided through the Human Connectome Project. We discovered a large number of brain networks that dynamically synchronized to various features of the task scan, some overlapping with areas identified as ‘active’ in the top-down GLM approach. Using the results provided through this novel approach, we provide a more comprehensive description of cognitive processes whereby task-related brain activity is not restricted to dichotomous ‘active’ or ‘non-active’ inferences, but is characterized by the temporal dynamics of brain networks across time.Significance StatementThis study describes the results of a novel exploratory methodological approach that allows for direct estimation of task-driven brain activity in terms of group-wise similarity in temporal signal dynamics, as opposed to the conventional approach of identifying task-driven brain activity with a hypothesized temporal pattern. This approach applied to three different task paradigms yielded novel insights into the brain activity associated with these tasks in terms of time-varying, low-frequency dynamics of replicable synchronization networks. We suggest that this exploratory methodological approach provides a framework in which the complexity and dynamics of the neural mechanisms underlying cognitive processes can be captured more comprehensively.

scholarly journals Prenatal maternal and childhood bisphenol a exposure and brain structure and behavior of young children

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Melody N. Grohs ◽  
◽  
Jess E. Reynolds ◽  
Jiaying Liu ◽  
Jonathan W. Martin ◽  
...  
Keyword(s):  
White Matter ◽  
Bisphenol A ◽  
Child Behavior ◽  
Brain Structure ◽  
Child Behavior Checklist ◽  
Mean Diffusivity ◽  
Internalizing Behavior ◽  
Spot Urine ◽  
White Matter Microstructure ◽  
The Relationship

Abstract Background Bisphenol A (BPA) is commonly used in the manufacture of plastics and epoxy resins. In North America, over 90% of the population has detectable levels of urinary BPA. Human epidemiological studies have reported adverse behavioral outcomes with BPA exposure in children, however, corresponding effects on children’s brain structure have not yet been investigated. The current study examined the association between prenatal maternal and childhood BPA exposure and white matter microstructure in children aged 2 to 5 years, and investigated whether brain structure mediated the association between BPA exposure and child behavior. Methods Participants were 98 mother-child pairs who were recruited between January 2009 and December 2012. Total BPA concentrations in spot urine samples obtained from mothers in the second trimester of pregnancy and from children at 3–4 years of age were analyzed. Children participated in a diffusion magnetic resonance imaging (MRI) scan at age 2–5 years (3.7 ± 0.8 years). Associations between prenatal maternal and childhood BPA and children’s fractional anisotropy and mean diffusivity of 10 isolated white matter tracts were investigated, controlling for urinary creatinine, child sex, and age at the time of MRI. Post-hoc analyses examined if alterations in white matter mediated the relationship of BPA and children’s scores on the Child Behavior Checklist (CBCL). Results Prenatal maternal urinary BPA was significantly associated with child mean diffusivity in the splenium and right inferior longitudinal fasciculus. Splenium diffusivity mediated the relationship between maternal prenatal BPA levels and children’s internalizing behavior (indirect effect: β = 0.213, CI [0.0167, 0.564]). No significant associations were found between childhood BPA and white matter microstructure. Conclusions This study provides preliminary evidence for the neural correlates of BPA exposure in humans. Our findings suggest that prenatal maternal exposure to BPA may lead to alterations in white matter microstructure in preschool aged children, and that such alterations mediate the relationship between early life exposure to BPA and internalizing problems.

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