scholarly journals Collegiate athlete brain data for white matter mapping and network neuroscience

2020 ◽  
Author(s):  
Bradley Caron ◽  
Ricardo Stuck ◽  
Brent McPherson ◽  
Daniel Bullock ◽  
Lindsey Kitchell ◽  
...  
Keyword(s):  
White Matter ◽  
Structural Connectivity ◽  
Collegiate Athletics ◽  
Collegiate Athlete ◽  
Head Impacts ◽  
Weighted Magnetic Resonance Imaging ◽  
Brain Data ◽  
Athlete Group ◽  
Data Processing Methods

AbstractWe describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

scholarly journals Collegiate athlete brain data for white matter mapping and network neuroscience

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Bradley Caron ◽  
Ricardo Stuck ◽  
Brent McPherson ◽  
Daniel Bullock ◽  
Lindsey Kitchell ◽  
...  
Keyword(s):  
White Matter ◽  
Structural Connectivity ◽  
Collegiate Athletics ◽  
Collegiate Athlete ◽  
Head Impacts ◽  
Weighted Magnetic Resonance Imaging ◽  
Brain Data ◽  
Athlete Group ◽  
Data Processing Methods

AbstractWe describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

scholarly journals Listening to Mom in the NICU: effects of increased maternal speech exposure on language outcomes and white matter development in infants born very preterm

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Edith Brignoni-Pérez ◽  
Maya Chan Morales ◽  
Virginia A. Marchman ◽  
Melissa Scala ◽  
Heidi M. Feldman ◽  
...  
Keyword(s):  
White Matter ◽  
Structural Connectivity ◽  
Receptive Language ◽  
Secondary Outcome ◽  
Language Outcomes ◽  
Theoretical Understanding ◽  
Very Preterm ◽  
White Matter Development ◽  
Maternal Speech

Abstract Background Infants born very preterm (< 32 weeks gestational age (GA)) are at risk for developmental language delays. Poor language outcomes in children born preterm have been linked to neurobiological factors, including impaired development of the brain’s structural connectivity (white matter), and environmental factors, including decreased exposure to maternal speech in the neonatal intensive care unit (NICU). Interventions that enhance preterm infants’ exposure to maternal speech show promise as potential strategies for improving short-term health outcomes. Intervention studies have yet to establish whether increased exposure to maternal speech in the NICU offers benefits beyond the newborn period for brain and language outcomes. Methods This randomized controlled trial assesses the long-term effects of increased maternal speech exposure on structural connectivity at 12 months of age (age adjusted for prematurity (AA)) and language outcomes between 12 and 18 months of age AA. Study participants (N = 42) will include infants born very preterm (24–31 weeks 6/7 days GA). Newborns are randomly assigned to the treatment (n = 21) or standard medical care (n = 21) group. Treatment consists of increased maternal speech exposure, accomplished by playing audio recordings of each baby’s own mother reading a children’s book via an iPod placed in their crib/incubator. Infants in the control group have the identical iPod setup but are not played recordings. The primary outcome will be measures of expressive and receptive language skills, obtained from a parent questionnaire collected at 12–18 months AA. The secondary outcome will be measures of white matter development, including the mean diffusivity and fractional anisotropy derived from diffusion magnetic resonance imaging scans performed at around 36 weeks postmenstrual age during the infants’ routine brain imaging session before hospital discharge and 12 months AA. Discussion The proposed study is expected to establish the potential impact of increased maternal speech exposure on long-term language outcomes and white matter development in infants born very preterm. If successful, the findings of this study may help to guide NICU clinical practice for promoting language and brain development. This clinical trial has the potential to advance theoretical understanding of how early language exposure directly changes brain structure for later language learning. Trial registration NIH Clinical Trials (ClinicalTrials.gov) NCT04193579. Retrospectively registered on 10 December 2019.

scholarly journals White matter microstructure of the extended limbic system in male and female youth with conduct disorder

2019 ◽  
Vol 50 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Karen González-Madruga ◽  
Jack Rogers ◽  
Nicola Toschi ◽  
Roberta Riccelli ◽  
Areti Smaragdi ◽  
...  
Keyword(s):  
Sex Differences ◽  
White Matter ◽  
Conduct Disorder ◽  
Limbic System ◽  
Structural Connectivity ◽  
Imaging Data ◽  
Spherical Deconvolution ◽  
Weighted Magnetic Resonance Imaging ◽  
The Right ◽  
And Control

AbstractBackgroundPrevious studies of conduct disorder (CD) have reported structural and functional alterations in the limbic system. However, the white matter tracts that connect limbic regions have not been comprehensively studied. The uncinate fasciculus (UF), a tract connecting limbic to prefrontal regions, has been implicated in CD. However, CD-related alterations in other limbic tracts, such as the cingulum and the fornix, have not been investigated. Furthermore, few studies have examined the influence of sex and none have been adequately powered to test whether the relationship between CD and structural connectivity differs by sex. We examined whether adolescent males and females with CD exhibit differences in structural connectivity compared with typically developing controls.MethodsWe acquired diffusion-weighted magnetic resonance imaging data from 101 adolescents with CD (52 females) and 99 controls (50 females). Data were processed for deterministic spherical deconvolution tractography. Virtual dissections of the UF, the three subdivisions of the cingulum [retrosplenial cingulum (RSC), parahippocampal and subgenual cingulum], and the fornix were performed and measures of fractional anisotropy (FA) and hindrance-modulated orientational anisotropy (HMOA) were analysed.ResultsThe CD group had lower FA and HMOA in the right RSC tract relative to controls. Importantly, these effects were moderated by sex – males with CD significantly lower FA compared to male controls, whereas CD and control females did not differ.ConclusionsOur results highlight the importance of considering sex when studying the neurobiological basis of CD. Sex differences in RSC connectivity may contribute to sex differences in the clinical presentation of CD.

scholarly journals Persistent, Long-term Cerebral White Matter Changes after Sports-Related Repetitive Head Impacts

PLoS ONE ◽  
2014 ◽  
Vol 9 (4) ◽  
pp. e94734 ◽  
Author(s):  
Jeffrey J. Bazarian ◽  
Tong Zhu ◽  
Jianhui Zhong ◽  
Damir Janigro ◽  
Eric Rozen ◽  
...  
Keyword(s):  
White Matter ◽  
Cerebral White Matter ◽  
White Matter Changes ◽  
Head Impacts ◽  
Repetitive Head Impacts

scholarly journals Hemoglobin and anemia in relation to dementia risk and accompanying changes on brain MRI

Neurology ◽  
2019 ◽  
Vol 93 (9) ◽  
pp. e917-e926 ◽  
Author(s):  
Frank J. Wolters ◽  
Hazel I. Zonneveld ◽  
Silvan Licher ◽  
Lotte G.M. Cremers ◽  
M. Kamran Ikram ◽  
...  
Keyword(s):  
White Matter ◽  
Cerebral Perfusion ◽  
Brain Mri ◽  
Structural Connectivity ◽  
Mean Diffusivity ◽  
Population Based ◽  
White Matter Hyperintensity ◽  
Dementia Risk ◽  
Increased Risk

ObjectiveTo determine the long-term association of hemoglobin levels and anemia with risk of dementia, and explore underlying substrates on brain MRI in the general population.MethodsSerum hemoglobin was measured in 12,305 participants without dementia of the population-based Rotterdam Study (mean age 64.6 years, 57.7% women). We determined risk of dementia and Alzheimer disease (AD) (until 2016) in relation to hemoglobin and anemia. Among 5,267 participants without dementia with brain MRI, we assessed hemoglobin in relation to vascular brain disease, structural connectivity, and global cerebral perfusion.ResultsDuring a mean follow-up of 12.1 years, 1,520 individuals developed dementia, 1,194 of whom had AD. We observed a U-shaped association between hemoglobin levels and dementia (p = 0.005), such that both low and high hemoglobin levels were associated with increased dementia risk (hazard ratio [95% confidence interval (CI)], lowest vs middle quintile 1.29 [1.09–1.52]; highest vs middle quintile 1.20 [1.00–1.44]). Overall prevalence of anemia was 6.1%, and anemia was associated with a 34% increased risk of dementia (95% CI 11%–62%) and 41% (15%–74%) for AD. Among individuals without dementia with brain MRI, similar U-shaped associations were seen of hemoglobin with white matter hyperintensity volume (p = 0.03), and structural connectivity (for mean diffusivity, p < 0.0001), but not with presence of cortical and lacunar infarcts. Cerebral microbleeds were more common with anemia. Hemoglobin levels inversely correlated to cerebral perfusion (p < 0.0001).ConclusionLow and high levels of hemoglobin are associated with an increased risk of dementia, including AD, which may relate to differences in white matter integrity and cerebral perfusion.

scholarly journals Processing the diffusion-weighted magnetic resonance imaging of the PING dataset

2020 ◽  
Author(s):  
Noor B Al-Sharif ◽  
Etienne St-Onge ◽  
Guillaume Theaud ◽  
Alan C Evans ◽  
Maxime Descoteaux
Keyword(s):  
Magnetic Resonance Imaging ◽  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Fine Tuning ◽  
Resonance Imaging ◽  
Diffusion Weighted ◽  
Weighted Magnetic Resonance Imaging ◽  
The Brain

AbstractDiffusion-weighted magnetic resonance imaging (dMRI) allows for the in-vivo assessment of anatomical white matter in the brain, thus allowing the depiction of structural connectivity. Using structural processing techniques and related methods, a growing body of literature has illustrated that connectomics is a crucial aspect to assessing the brain in health and disease. The Pediatric Imaging Neurocognition and Genetics (PING) dataset was collected and released openly to contribute to the assessment of typical brain development in a pediatric sample. This current work details the processing of diffusion-weighted images from the PING dataset, including rigorous quality assessment and fine-tuning of parameters at every step, to increase the accessibility of these data for connectomic analysis. This processing provides state-of-the-art diffusion measures, both classical diffusion tensor imaging (DTI) and more advanced HARDI-based metrics, enabling the evaluation not only of structural white matter but also of integrated multimodal analyses, i.e. combining structural information from dMRI with functional or gray matter analyses.

scholarly journals Advanced mapping of the human white matter microstructure better separates elite sports participation

2020 ◽  
Author(s):  
Bradley Caron ◽  
Daniel Bullock ◽  
Lindsey Kitchell ◽  
Brent McPherson ◽  
Derek Alexander Kellar ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Tissue ◽  
Sports Participation ◽  
Tissue Structure ◽  
Football Players ◽  
Model Parameters ◽  
Head Impacts ◽  
Repetitive Head Impacts ◽  
Brain White Matter

Collision-sport athletes, especially football players, are exposed to a higher number of repetitive head impacts. Little is known, however, regarding the effects of long-term exposure to repetitive head impacts on brain tissue structure and the locations (i.e. superficial or deep tissue structures) affected. On top of this, little is known about the effects of highly competitive, strenuous, long-term athletics on brain tissue structure. We investigated this relationship using advanced microstructural mapping techniques. Specifically, we examined the baseline differences in collegiate athletic participants by using two models of the diffusion-weighted magnetic resonance imaging signal (the Diffusion Tensor and NODDI model). DTI and NODDI parameters were mapped in both cortical and subcortical structures, as well as in the major white matter tracts. Three groups of young adults participated in our study; IU football players, cross country runners, and non-athlete students. For both models, athletes were found to have consistently higher measures of microstructure than controls. The NODDI model parameters showed stronger results indicating that it might be more sensitive to capturing differences in brain white matter tissue microstructure than the DTI model. This was the first investigation into the effects of repetitive head impacts to use an open-source data processing platform brainlife.io. Data and analyses for this study are available at https://doi.org/10.25663/brainlife.pub.14.

Individual differences in white and grey matter structure associated with verbal habits of thought

2019 ◽  
Author(s):  
Justin C. Hayes ◽  
Katherine L Alfred ◽  
Rachel Pizzie ◽  
Joshua S. Cetron ◽  
David J. M. Kraemer
Keyword(s):  
Individual Differences ◽  
White Matter ◽  
Cognitive Processing ◽  
Grey Matter ◽  
Structural Changes ◽  
Structural Integrity ◽  
Diffusion Tensor ◽  
Self Report ◽  
White Matter Tracts

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.

scholarly journals Brain Metabolic Correlates of Persistent Olfactory Dysfunction after SARS-Cov2 Infection

Biomedicines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 287
Author(s):  
Maria Isabella Donegani ◽  
Alberto Miceli ◽  
Matteo Pardini ◽  
Matteo Bauckneht ◽  
Silvia Chiola ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Structural Connectivity ◽  
Statistical Parametric Mapping ◽  
Brain Regions ◽  
Olfactory Dysfunction ◽  
Whole Body ◽  
Mapping Software ◽  
18F Fdg ◽  
Left Insula

We aimed to evaluate the brain hypometabolic signature of persistent isolated olfactory dysfunction after SARS-CoV-2 infection. Twenty-two patients underwent whole-body [18F]-FDG PET, including a dedicated brain acquisition at our institution between May and December 2020 following their recovery after SARS-Cov2 infection. Fourteen of these patients presented isolated persistent hyposmia (smell diskettes olfaction test was used). A voxel-wise analysis (using Statistical Parametric Mapping software version 8 (SPM8)) was performed to identify brain regions of relative hypometabolism in patients with hyposmia with respect to controls. Structural connectivity of these regions was assessed (BCB toolkit). Relative hypometabolism was demonstrated in bilateral parahippocampal and fusiform gyri and in left insula in patients with respect to controls. Structural connectivity maps highlighted the involvement of bilateral longitudinal fasciculi. This study provides evidence of cortical hypometabolism in patients with isolated persistent hyposmia after SARS-Cov2 infection. [18F]-FDG PET may play a role in the identification of long-term brain functional sequelae of COVID-19.

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.

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