scholarly journals Comparing Brain Asymmetries Independently of Brain Size

2021 ◽  
Author(s):  
Camille Michele WILLIAMS ◽  
Hugo Peyre ◽  
Roberto Toro ◽  
Franck Ramus
Keyword(s):  
Sex Differences ◽  
Large Scale ◽  
Brain Size ◽  
Asymmetry Index ◽  
Age Effects ◽  
Group Differences ◽  
Total Brain Volume ◽  
Cerebral Asymmetries ◽  
Total Brain ◽  
Global Brain

Studies examining cerebral asymmetries typically divide the L-R Measure (e.g., Left-Right Volume) by the L+R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L+R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries. We analyzed the extent to which the L+R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and L-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L+R Measure, the TCM, and the L-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13-42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.

scholarly journals Neuroanatomical Norms in the UK Biobank: The Impact of Allometric Scaling, Sex, and Age.

2020 ◽  
Author(s):  
Camille WILLIAMS ◽  
Hugo Peyre ◽  
Roberto Toro ◽  
Franck Ramus
Keyword(s):  
Sex Differences ◽  
Large Scale ◽  
Common Property ◽  
Brain Size ◽  
Uk Biobank ◽  
Aging Effects ◽  
Total Brain Volume ◽  
Brain Volumes ◽  
The Uk ◽  
The Impact

Few neuroimaging studies are sufficiently large to adequately describe population-wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40 028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry - the non-linear scaling relationship between a region and brain size (e.g., Total Brain Volume) across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |std. beta|= 0.13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. This large scale-study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.

Abstract WP308: Global Cerebral Edema Among Good Grade Patients with Intracerebral Hemorrhage. Results from the Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH) Study

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Shahram Majidi ◽  
Waqas I Gilani ◽  
Nauman Tariq ◽  
Haitham M Hussein ◽  
Yuko Y Palesch ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Ct Scan ◽  
Brain Edema ◽  
Cerebral Edema ◽  
Brain Volume ◽  
Prognostic Significance ◽  
Ct Scans ◽  
Total Brain Volume ◽  
Total Brain ◽  
Global Brain

INTRODUCTION: There is some evidence that injury and blood brain barrier disruption can be seen in regions distant from the hematoma in patients with intracerebral hemorrhage (ICH). Objective: To ascertain the occurrence of global brain edema in patients with ICH and to explore the relationship between patient characteristics and three month outcomes. Design: A post-hoc analysis of a traditional Phase I dose escalation multicenter prospective study recruited patients with ICH, elevated SBP≥170 mmHg, and Glasgow Coma Scale score ≥8, who presented within 6 hours of symptom onset. Computed tomographic (CT) scans at baseline, 24 hours, and any performed at later intervals were submitted to a core image laboratory. We were able to ascertain the presence and magnitude of global brain edema in 41 of 60 subjects with adequate CT scan resolution. Settings: Emergency departments and intensive care units. Primary Outcomes: We determined the total brain, hematoma, and perihematoma edema volumes from baseline, 24 hour, and 48 hour (if available) CT scans using image analysis software. The global brain edema volume was determined by subtracting the hematoma and perihematomal edema volumes from the total brain volume. Results: A total of 18 (44%) of 41 patients had global cerebral edema that developed between initial CT scan and 24 hour CT scan. The median increase in brain volume among the 18 subjects was 35 cc ranging from 0.12 cc to 296 cc. The baseline GCS score (median 15 versus 15) and hematoma volume (mean±SD; 11.5±10.3 versus 13.9±17) were similar between subjects who experienced global cerebral edema and those who did not. The initial serum glucose was higher among subjects with global cerebral edema (150.5±74.3 mg/dl verus 119.7±34.6 mg/dl). Of the 18 patients who underwent a CT scan at 48 hours, 5 had either new or worsening global cerebral edema. Three of the 18 patients with global cerebral edema underwent neurological deterioration and 1 patient died during hospitalization. Conclusions: Global cerebral edema can occur even in subjects with mild ICH. The pathophysiological basis and prognostic significance needs to be studied in future trials.

scholarly journals Carriage of Supernumerary Sex Chromosomes Decreases the Volume and Alters the Shape of Limbic Structures

2018 ◽  
Author(s):  
Ajay Nadig ◽  
Paul K. Reardon ◽  
Jakob Seidlitz ◽  
Cassidy L. McDermott ◽  
Jonathan D. Blumenthal ◽  
...  
Keyword(s):  
Sex Chromosomes ◽  
Sex Chromosome ◽  
Brain Volume ◽  
Brain Size ◽  
Chromosome Complement ◽  
Autism Spectrum ◽  
Psychiatric Disease ◽  
Typically Developing ◽  
Total Brain Volume ◽  
Total Brain

AbstractSex chromosome aneuploidy (SCA) enhances risk for several psychiatric disorders associated with the limbic system, including mood and autism spectrum disorders. These patients provide a powerful genetics-first model for understanding the biological basis of psychopathology. Additionally, these disorders are frequently sex-biased in prevalence, further suggesting an etiological role for sex chromosomes. To clarify how limbic anatomy varies across sex and sex chromosome complement, we characterized amygdala and hippocampus structure in a uniquely large sample of patients carrying supernumerary sex chromosomes (n = 132) and typically developing controls (n=166). After correction for sex-differences in brain size, karyotypically normal males (XY) and females (XX) did not differ in volume or shape of either structure. In contrast, all SCAs were associated with lowered amygdala volume relative to gonadally-matched controls. This effect was robust to three different methods for total brain volume correction, including an allometric analysis that derived normative scaling rules for these structures in a separate, typically developing population (n = 79). Hippocampal volume was insensitive to SCA after correction for total brain volume. However, surface-based analysis revealed that SCA, regardless of specific karyotype, was consistently associated with a spatially specific pattern of shape change in both amygdala and hippocampus. In particular, SCA was accompanied by contraction around the basomedial nucleus of the amygdala and an area within the hippocampal surface that cuts across hippocampal subfields. These results demonstrate the power of SCA as a model to understand how copy number variation can precipitate changes in brain systems relevant to psychiatric disease.

scholarly journals Are Bigger Brains Smarter? Evidence From a Large-Scale Preregistered Study

2018 ◽  
Vol 30 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Gideon Nave ◽  
Wi Hoon Jung ◽  
Richard Karlsson Linnér ◽  
Joseph W. Kable ◽  
Philipp D. Koellinger
Keyword(s):  
Population Structure ◽  
Publication Bias ◽  
Large Scale ◽  
Positive Relationship ◽  
Fluid Intelligence ◽  
Brain Volume ◽  
Empirical Studies ◽  
Age Groups ◽  
Total Brain Volume ◽  
Total Brain

A positive relationship between brain volume and intelligence has been suspected since the 19th century, and empirical studies seem to support this hypothesis. However, this claim is controversial because of concerns about publication bias and the lack of systematic control for critical confounding factors (e.g., height, population structure). We conducted a preregistered study of the relationship between brain volume and cognitive performance using a new sample of adults from the United Kingdom that is about 70% larger than the combined samples of all previous investigations on this subject ( N = 13,608). Our analyses systematically controlled for sex, age, height, socioeconomic status, and population structure, and our analyses were free of publication bias. We found a robust association between total brain volume and fluid intelligence ( r = .19), which is consistent with previous findings in the literature after controlling for measurement quality of intelligence in our data. We also found a positive relationship between total brain volume and educational attainment ( r = .12). These relationships were mainly driven by gray matter (rather than white matter or fluid volume), and effect sizes were similar for both sexes and across age groups.

scholarly journals White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE Study

2019 ◽  
Author(s):  
Markus D. Schirmer ◽  
Adrian V. Dalca ◽  
Ramesh Sridharan ◽  
Anne-Katrin Giese ◽  
Kathleen L. Donahue ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Acute Ischemic Stroke ◽  
Large Scale ◽  
Brain Volume ◽  
Expert Knowledge ◽  
White Matter Hyperintensity ◽  
Total Brain Volume ◽  
Brain Extraction ◽  
Total Brain

AbstractWhite matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery.In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95) and Pearson correlation of total brain volume (r=0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N=2783) and identify a decrease in total brain volume of -2.4cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing.Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2,533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

scholarly journals Sex differences in brain development in fetuses and infants who are at low or high likelihood for autism

2021 ◽  
Author(s):  
Luca M Villa ◽  
Sarah Hampton ◽  
Ezra Aydin ◽  
Roger Tait ◽  
Matthew J Leming ◽  
...  
Keyword(s):  
Sex Differences ◽  
Functional Connectivity ◽  
Sexual Differentiation ◽  
Brain Structure ◽  
In Utero ◽  
High Likelihood ◽  
Resting State Functional Connectivity ◽  
Total Brain Volume ◽  
Total Brain ◽  
Brain Structure And Function

Background: It is unknown whether the neural underpinnings of autism are present in utero. In addition, it is unclear whether typical neural sexual differentiation, which is associated with the development of autism, is evident in utero. We longitudinally investigated fetal and infant sex differences in brain structure and function, and differences in brain development in those at low and high likelihood for autism. Here, we use the term "typical" interchangeably with the term "low-autism likelihood". Methods: Participants were longitudinally studied in utero first at 30-33 weeks of gestation, and then as infants 8-12 weeks after birth. We compared total brain volumes and resting-state functional connectivity between 15 female and 15 male low-autism likelihood fetuses (defined as having no first degree autistic relative). We also compared the brain structure and function of these 30 fetuses to a rare group of 11 fetuses (5 females and 6 males) who had an autistic mother or sibling, and therefore a higher likelihood of developing autism. Although a small sample, the high-autism likelihood group are reported as they are challenging to recruit. Additionally, we correlated sex differences in functional connectivity with autism likelihood group differences across the fetal and infant brains. Results: There was a group-by-sex interaction in fetal total brain volume. Typical males, on average, showed faster total brain volume growth in the perinatal period than typical females. The high-autism likelihood group showed lower resting-state functional connectivity at both time-points compared to the typical group, and regions indicating sex differences overlapped with those associated with high-autism likelihood group differences in functional connectivity. Conclusions: In utero sexual differentiation of brain structure was more pronounced in fetuses with a high likelihood for autism. Moreover, sexual differentiation of the fetal and infant brain may overlap with the neural development of autism.

scholarly journals Association of Visual Impairment with Brain Structure

2021 ◽  
Author(s):  
Zhuoting Zhu ◽  
Wenyi Hu ◽  
Huan Liao ◽  
Danli Shi ◽  
Zachary Tan ◽  
...  
Keyword(s):  
Visual Impairment ◽  
Brain Structures ◽  
Brain Regions ◽  
Uk Biobank ◽  
Total Brain Volume ◽  
Brain Volumes ◽  
Total Brain ◽  
Magnetic Resonance Imaging Mri ◽  
The Uk ◽  
Global Brain

AbstractObjectiveTo investigate the association of visual impairment (VI) with brain structures in the UK Biobank Study.MethodsThe UK Biobank Study is a large prospective study that recruited more than 500,000 participants aged 40-69 from 2006 to 2010 across the UK. Visual acuity (VA) of worse than 0.3 LogMAR units (Snellen 20/40) was defined as VI. Structural magnetic resonance imaging (MRI) data were obtained using a 3.0-T MRI imager. Volumetric measures of five global brain volumes (total brain volume, total grey matter, total white matter, cerebrospinal fluid (CSF), brain stem) and the volumes of seven specific brain region (thalamus, caudate nucleus, basal ganglia, pallidum, hippocampus, amygdala and nucleus accumbens) were included in the present analysis. Multivariable linear regression was used to investigate the association of VI with global and specific brain volumes.ResultsA total of 8976 participants free of neurological disorders at baseline assessment were included for the present analysis. The prevalence of VI was 0.02% (n=181). After adjusting for a range of cofounding factors, VI was significantly associated with decreased volumes of the total brain (β = -0.12, 95% confidence interval (CI) -0.23 to 0.00, P = 0.049), thalamus (β = -0.16, 95% CI -0.18 to -0.04, P = 0.010), caudatenucleus (β = -0.14, 95% CI -0.27 to 0.00, P = 0.046), pallidum (β = -0.15, 95% CI-0.27 to -0.02, P = 0.028) and amygdala (β = -0.18, 95% CI -0.31 to -0.04, P = 0.012).InterpretationWe found that VI is associated with a decrease in total brain volumes and the volumes of specific brain regions implicated in neurodegenerative diseases.

scholarly journals Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

SLEEP ◽  
2021 ◽  
Author(s):  
Dorothee Fischer ◽  
Elizabeth B Klerman ◽  
Andrew J K Phillips
Keyword(s):  
Large Scale ◽  
Research Question ◽  
Group Differences ◽  
Regularity Index ◽  
Wake Patterns ◽  
Health Related ◽  
Phase Deviation ◽  
Unbiased Estimates ◽  
Large Scale Simulations ◽  
Larger Sample

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

scholarly journals Pyruvate to Lactate Metabolic Changes during Neurodevelopment Measured Dynamically Using Hyperpolarized 13C Imaging in Juvenile Murine Brain

2015 ◽  
Vol 38 (1) ◽  
pp. 34-40 ◽  
Author(s):  
Yiran Chen ◽  
Hosung Kim ◽  
Robert Bok ◽  
Subramaniam Sukumar ◽  
Xin Mu ◽  
...  
Keyword(s):  
Body Weight ◽  
Magnetic Resonance ◽  
Mouse Brain ◽  
Brain Volume ◽  
Metabolic Changes ◽  
Brain Maturation ◽  
Multiple Time ◽  
Total Brain Volume ◽  
Hyperpolarized 13C ◽  
Total Brain

Hyperpolarized 13C magnetic resonance imaging has recently been used to dynamically image metabolism in vivo. This technique provides the capability to investigate metabolic changes in mouse brain development over multiple time points. In this study, we used 13C magnetic resonance spectroscopic imaging and hyperpolarized 13C-1-labeled pyruvate to analyze its conversion into lactate. We also applied T2-weighted anatomical imaging to examine brain volume changes starting from postnatal day 18 (P18). We combined these results with body weight measurements for a comprehensive interpretation of mouse brain maturation. Both the produced lactate level and pyruvate to lactate conversion rate decreased with increasing age in a linear manner. Total brain volume remained the same after P18, even though body weight continued to grow exponentially. Our results have shown that the rate of metabolism of 13C-1 pyruvate to lactate in brain is high in the young mouse and decreases with age. The brain at P18 is still relatively immature and continues to develop even as the total brain volume remains the same.

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