scholarly journals Variations in structural MRI quality significantly impact commonly used measures of brain anatomy

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alysha D. Gilmore ◽  
Nicholas J. Buser ◽  
Jamie L. Hanson
Keyword(s):  
Image Quality ◽  
Surface Area ◽  
Cortical Thickness ◽  
Structural Mri ◽  
Public Access ◽  
Brain Anatomy ◽  
Cortical Surface ◽  
Cortical Surface Area ◽  
Multiple Regions ◽  
Subcortical Volumes

AbstractSubject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: (1) relate to rigorous quality-control checks visually completed by human raters; (2) are associated with sociodemographic variables of interest; (3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (N = 388; ages 5–21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain (~ 23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

scholarly journals Variations in Structural MRI Quality Significantly Impact Commonly-Used Measures of Brain Anatomy

2020 ◽  
Author(s):  
Alysha Gilmore ◽  
Nicholas Buser ◽  
Jamie Hanson
Keyword(s):  
Image Quality ◽  
Cortical Thickness ◽  
Structural Mri ◽  
Public Access ◽  
Brain Anatomy ◽  
Indirect Measures ◽  
Neuroimaging Data ◽  
Multiple Regions ◽  
Structural Image ◽  
Subcortical Volumes

Abstract Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy and indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to directly measure image quality from T1-weighted images to understand if these measures of image quality: 1) relate to rigorous quality-control checks visually completed by human raters; 2) are associated with sociodemographic variables of interest; 3) influence regional estimates of cortical thickness and subcortical volumes from the commonly-used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range ( n=388; ages 5-21 ). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical thickness and subcortical volumes from multiple regions across the brain (~44% of the regions investigated). We believe these results our important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using direct measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

scholarly journals Variations in Structural MRI Quality Significantly Impact Commonly-Used Measures of Brain Anatomy

2019 ◽  
Author(s):  
Alysha Gilmore ◽  
Nicholas Buser ◽  
Jamie L. Hanson
Keyword(s):  
Image Quality ◽  
Cortical Thickness ◽  
Structural Mri ◽  
Public Access ◽  
Brain Anatomy ◽  
Indirect Measures ◽  
Neuroimaging Data ◽  
Multiple Regions ◽  
Structural Image ◽  
Subcortical Volumes

AbstractSubject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy and indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to directly measure image quality from T1-weighted images to understand if these measures of image quality: 1) relate to rigorous quality-control checks visually completed by human raters; 2) are associated with sociodemographic variables of interest; 3) influence regional estimates of cortical thickness and subcortical volumes from the commonly-used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (n=388; ages 5-21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical thickness and subcortical volumes from multiple regions across the brain (∼44% of the regions investigated). We believe these results our important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using direct measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

Cortical thickness, cortical surface area and subcortical volumes in schizophrenia and bipolar disorder patients with cannabis use

2018 ◽  
Vol 28 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Cecilie Bhandari Hartberg ◽  
Elisabeth H. Lange ◽  
Trine Vik Lagerberg ◽  
Unn K. Haukvik ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Surface Area ◽  
Cortical Thickness ◽  
Cannabis Use ◽  
Cortical Surface ◽  
Cortical Surface Area ◽  
Subcortical Volumes

Cortical surface area and cortical thickness in the precuneus of adult humans

Neuroscience ◽  
2015 ◽  
Vol 286 ◽  
pp. 345-352 ◽  
Author(s):  
E. Bruner ◽  
F.J. Román ◽  
J.M. de la Cuétara ◽  
M. Martin-Loeches ◽  
R. Colom
Keyword(s):  
Surface Area ◽  
Cortical Thickness ◽  
Cortical Surface ◽  
Cortical Surface Area ◽  
Adult Humans

scholarly journals Cortical thickness and surface area relate to specific symptoms in early relapsing–remitting multiple sclerosis

2014 ◽  
Vol 21 (4) ◽  
pp. 402-414 ◽  
Author(s):  
Gro O Nygaard ◽  
Kristine B Walhovd ◽  
Piotr Sowa ◽  
Joy-Loi Chepkoech ◽  
Atle Bjørnerud ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Surface Area ◽  
Cortical Thickness ◽  
Clinical Symptoms ◽  
Cortical Surface ◽  
Healthy Controls ◽  
Neurological Disability ◽  
Cortical Surface Area ◽  
Relapsing Remitting ◽  
Relapsing Remitting Multiple Sclerosis

Background: Cortical atrophy is common in early relapsing–remitting multiple sclerosis (RRMS). Whether this atrophy is caused by changes in cortical thickness or cortical surface area is not known, nor is their separate contributions to clinical symptoms. Objectives: To investigate the difference in cortical surface area, thickness and volume between early RRMS patients and healthy controls; and the relationship between these measures and neurological disability, cognitive decline, fatigue and depression. Methods: RRMS patients ( n = 61) underwent magnetic resonance imaging (MRI), neurological and neuropsychological examinations. We estimated cortical surface area, thickness and volume and compared them with matched healthy controls ( n = 61). We estimated the correlations between clinical symptoms and cortical measures within the patient group. Results: We found no differences in cortical surface area, but widespread differences in cortical thickness and volume between the groups. Neurological disability was related to regionally smaller cortical thickness and volume. Better verbal memory was related to regionally larger surface area; and better visuo-spatial memory, to regionally larger cortical volume. Higher depression scores and fatigue were associated with regionally smaller cortical surface area and volume. Conclusions: We found that cortical thickness, but not cortical surface area, is affected in early RRMS. We identified specific structural correlates to the main clinical symptoms in early RRMS.

scholarly journals Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume

2017 ◽  
Author(s):  
Stuart J. Ritchie ◽  
David Alexander Dickie ◽  
Simon R. Cox ◽  
Maria del C. Valdés Hernández ◽  
Alison Pattie ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Ability ◽  
Surface Area ◽  
Cortical Thickness ◽  
White Matter Hyperintensity ◽  
Socioeconomic Background ◽  
Intracranial Volume ◽  
Cortical Surface ◽  
Age Cohorts ◽  
Cortical Surface Area

AbstractFully characterizing age differences in the brain is a key task for combatting ageing-related cognitive decline. Using propensity score matching on two independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age 92 years (n = 42) to very similar participants at mean age 73 (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in grey and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural ageing, in which brains from the eighth and tenth decades of life differ widely despite the same cognitive, socio-economic, and brain-volumetric starting points.

Quantification of Cortical Thickness and Cortical Surface Area Abnormalities in Patients Affected by Migraine (S16.005)

Neurology ◽  
2012 ◽  
Vol 78 (Meeting Abstracts 1) ◽  
pp. S16.005-S16.005
Author(s):  
R. Messina ◽  
M. Rocca ◽  
P. Valsasina ◽  
B. Colombo ◽  
A. Falini ◽  
...  
Keyword(s):  
Surface Area ◽  
Cortical Thickness ◽  
Cortical Surface ◽  
Cortical Surface Area

Cortical folding scales universally with surface area and thickness, not number of neurons

Science ◽  
2015 ◽  
Vol 349 (6243) ◽  
pp. 74-77 ◽  
Author(s):  
Bruno Mota ◽  
Suzana Herculano-Houzel
Keyword(s):  
Free Energy ◽  
Physical Model ◽  
Surface Area ◽  
Cortical Thickness ◽  
Cortical Surface ◽  
Square Root ◽  
Cortical Folding ◽  
Axonal Elongation ◽  
Cortical Surface Area ◽  
Simple Physical Model

Larger brains tend to have more folded cortices, but what makes the cortex fold has remained unknown. We show that the degree of cortical folding scales uniformly across lissencephalic and gyrencephalic species, across individuals, and within individual cortices as a function of the product of cortical surface area and the square root of cortical thickness. This relation is derived from the minimization of the effective free energy associated with cortical shape according to a simple physical model, based on known mechanisms of axonal elongation. This model also explains the scaling of the folding index of crumpled paper balls. We discuss the implications of this finding for the evolutionary and developmental origin of folding, including the newfound continuum between lissencephaly and gyrencephaly, and for pathologies such as human lissencephaly.

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