scholarly journals Modulative effects of COMT haplotype on age-related associations with brain morphology

2016 ◽  
Vol 37 (6) ◽  
pp. 2068-2082 ◽  
Author(s):  
Annie Lee ◽  
Anqi Qiu
Keyword(s):  
Brain Morphology ◽  
Age Related

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., 2017; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), the current paper is focused specifically on the implications of open data to brain morphology research.

scholarly journals Brain charts for the human lifespan

2021 ◽  
Author(s):  
Richard A.I. Bethlehem ◽  
Jakob Seidlitz ◽  
Simon R. White ◽  
Jacob W. Vogel ◽  
Kevin M. Anderson ◽  
...  
Keyword(s):  
Individual Differences ◽  
Clinical Studies ◽  
Collaborative Study ◽  
Basic Research ◽  
Brain Morphology ◽  
Longitudinal Assessment ◽  
Human Lifespan ◽  
Genetic Heritability ◽  
Age Related ◽  
Mri Study

Over the past 25 years, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, there are no reference standards against which to anchor measures of individual differences in brain morphology, in contrast to growth charts for traits such as height and weight. Here, we built an interactive online resource (www.brainchart.io) to quantify individual differences in brain structure from any current or future magnetic resonance imaging (MRI) study, against models of expected age-related trends. With the goal of basing these on the largest and most inclusive dataset, we aggregated MRI data spanning 115 days post-conception through 100 postnatal years, totaling 122,123 scans from 100,071 individuals in over 100 studies across 6 continents. When quantified as centile scores relative to the reference models, individual differences show high validity with non-MRI brain growth estimates and high stability across longitudinal assessment. Centile scores helped identify previously unreported brain developmental milestones and demonstrated increased genetic heritability compared to non-centiled MRI phenotypes. Crucially for the study of brain disorders, centile scores provide a standardised and interpretable measure of deviation that reveals new patterns of neuroanatomical differences across neurological and psychiatric disorders emerging during development and ageing. In sum, brain charts for the human lifespan are an essential first step towards robust, standardised quantification of individual variation and for characterizing deviation from age-related trends. Our global collaborative study provides such an anchorpoint for basic neuroimaging research and will facilitate implementation of research-based standards in clinical studies.

scholarly journals The Effects of Lutein and Zeaxanthin Supplementation on Brain Morphology in Older Adults: A Randomized, Controlled Trial

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Catherine M. Mewborn ◽  
Cutter A. Lindbergh ◽  
B. Randy Hammond ◽  
Lisa M. Renzi-Hammond ◽  
L. Stephen Miller
Keyword(s):  
Older Adults ◽  
Placebo Group ◽  
White Matter ◽  
Controlled Trial ◽  
Community Dwelling ◽  
Brain Morphology ◽  
Age Related ◽  
Supplement Group ◽  
One Year ◽  
Structural Brain

A growing literature emphasizes the importance of lifestyle factors such as nutrition in successful aging. The current study examined if one year of supplementation with lutein (L) and zeaxanthin (Z), two nutrients with known antioxidative properties and cognitive benefits, impacted structural brain outcomes in older adults using a double-blind, randomized, placebo-controlled trial design. Community-dwelling older adults (20 males and 27 females) aged 65–87 years (M = 71.8 years, SD = 6.04 years) were randomized into supplement (N = 33) and placebo groups (N = 14) using simple randomization. The supplement group received 10 mg L + 2 mg Z daily for 12 months while the placebo group received a visually identical, inert placebo. L and Z were measured via retinal concentrations (macular pigment optical density or MPOD). Structural brain outcomes, focusing on global and frontal-temporal lobe regions, were acquired using both T1-weighted and DTI MRI sequences. We hypothesized that the supplement group would increase, maintain, or show attenuated loss in hypothesized regions-of-interest (ROIs) while the placebo group would show age-related declines in brain structural integrity over the course of the trial. While results showed age-related declines for frontal and temporal gray and white matter volumes, as well as fornix white matter microstructure across both groups, only minimal differences were found between the supplement and placebo groups. However, exploratory analyses showed that individuals who responded better to supplementation (i.e., showed greater increases in MPOD) showed less decline in global and prefrontal gray matter volume than supplement “nonresponders.” While results suggest that one year of L and Z supplementation may have limited effects on structural brain outcomes overall, there may be a subsample of individuals for whom supplementation of L and Z provides greater benefits. ClinicalTrials.gov number, NCT02023645.

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., in press; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research.

scholarly journals Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults

2019 ◽  
Vol 29 (10) ◽  
pp. 4169-4193 ◽  
Author(s):  
Lu Zhao ◽  
William Matloff ◽  
Kaida Ning ◽  
Hosung Kim ◽  
Ivo D Dinov ◽  
...  
Keyword(s):  
Cognitive Functions ◽  
Fluid Intelligence ◽  
Genome Wide Association Study ◽  
Brain Aging ◽  
Break Point ◽  
Brain Morphology ◽  
Lifestyle Behaviors ◽  
Structural Morphology ◽  
Age Related ◽  
Morphological Differences

AbstractBrain structural morphology differs with age. This study examined age-differences in surface-based morphometric measures of cortical thickness, volume, and surface area in a well-defined sample of 8137 generally healthy UK Biobank participants aged 45–79 years. We illustrate that the complexity of age-related brain morphological differences may be related to the laminar organization and regional evolutionary history of the cortex, and age of about 60 is a break point for increasing negative associations between age and brain morphology in Alzheimer’s disease (AD)-prone areas. We also report novel relationships of age-related cortical differences with individual factors of sex, cognitive functions of fluid intelligence, reaction time and prospective memory, cigarette smoking, alcohol consumption, sleep disruption, genetic markers of apolipoprotein E, brain-derived neurotrophic factor, catechol-O-methyltransferase, and several genome-wide association study loci for AD and further reveal joint effects of cognitive functions, lifestyle behaviors, and education on age-related cortical differences. These findings provide one of the most extensive characterizations of age associations with major brain morphological measures and improve our understanding of normal structural brain aging and its potential modifiers.

Everyday memory: Self-perception and structural brain correlates in a healthy elderly population

2010 ◽  
Vol 16 (6) ◽  
pp. 1115-1126 ◽  
Author(s):  
ASTRID BJØRNEBEKK ◽  
LARS T. WESTLYE ◽  
KRISTINE B. WALHOVD ◽  
ANDERS M. FJELL
Keyword(s):  
Verbal Memory ◽  
Test Performance ◽  
Medial Temporal Lobe ◽  
Standardized Test ◽  
Daily Life ◽  
Neuropsychological Test ◽  
Brain Morphology ◽  
Everyday Memory ◽  
Healthy Elderly ◽  
Age Related

AbstractMapping the cerebral structural correlates of age-related cognitive decline is a growing area of research. The aim of the present study was to investigate the relationship between healthy elderly’s perceived memory functioning in daily life, neuropsychological test performance on a standardized test on verbal memory, and cortical thickness and subcortical volumes in brain regions implicated in memory networks, including the medial temporal lobe (MTL). Eighty-three healthy and cognitively well-functioning volunteers aged 60–85 years underwent MRI scans, Everyday Memory Questionnaire (EMQ), and neuropsychological assessment. Both self-perceived memory in daily life related to attention and executive functions and an objective measure of verbal recall (CVLT) were, independently, associated with thickness of the left MTL. The two cognitive variables were uncorrelated, and including both measures in the model nearly doubled the amount of explained variance on MTL thickness. This suggests that measures of perceived everyday memory might substantially inform and supplement studies investigating the relationships between neuropsychological test performance and brain morphology. The results are consistent with a bigger-is-better relationship in the MTL and suggest that EMQ and neuropsychological test performance have detectable and comparable structural correlates in a region critically involved in memory functions in the well-functioning elderly. (JINS, 2010, 16, 1115–1126.)

Obstetric complications and age-related changes in brain morphology in schizophrenia

1996 ◽  
Vol 40 (12) ◽  
pp. 1200-1208 ◽  
Author(s):  
Geoffrey N. Smith ◽  
G. William MacEwan ◽  
Siemion Altman ◽  
Barbara Meistrich ◽  
Jocelyne S. Lapointe ◽  
...  
Keyword(s):  
Obstetric Complications ◽  
Brain Morphology ◽  
Age Related ◽  
Age Related Changes

scholarly journals Aging Brain and Hearing: A Mini-Review

2022 ◽  
Vol 13 ◽  
Author(s):  
Yasue Uchida ◽  
Yukiko Nishita ◽  
Rei Otsuka ◽  
Saiko Sugiura ◽  
Michihiko Sone ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Brain Volume ◽  
Clinical Symptoms ◽  
Primary Auditory Cortex ◽  
Brain Morphology ◽  
Aging Brain ◽  
Brain Reserve ◽  
Morphological Studies ◽  
Age Related ◽  
Dementia Research

Brain reserve is a topic of great interest to researchers in aging medicine field. Some individuals retain well-preserved cognitive function until they fulfill their lives despite significant brain pathology. One concept that explains this paradox is the reserve hypothesis, including brain reserve that assumes a virtual ability to mitigate the effects of neuropathological changes and reduce the effects on clinical symptoms flexibly and efficiently by making complete use of the cognitive and compensatory processes. One of the surrogate measures of reserve capacity is brain volume. Evidence that dementia and hearing loss are interrelated has been steadily accumulating, and age-related hearing loss is one of the most promising modifiable risk factors of dementia. Research focused on the imaging analysis of the aged brain relative to auditory function has been gradually increasing. Several morphological studies have been conducted to understand the relationship between hearing loss and brain volume. In this mini review, we provide a brief overview of the concept of brain reserve, followed by a small review of studies addressing brain morphology and hearing loss/hearing compensation, including the findings obtained from our previous study that hearing loss after middle age could affect hippocampal and primary auditory cortex atrophy.

scholarly journals Predicting age from cortical structure across the lifespan

2018 ◽  
Author(s):  
Christopher R. Madan ◽  
Elizabeth A. Kensinger
Keyword(s):  
Fractal Dimensionality ◽  
Brain Morphology ◽  
Training Dataset ◽  
Anatomical Landmarks ◽  
Cortical Structure ◽  
Age Related ◽  
Median Error ◽  
Structural Measures ◽  
Cortical Morphology ◽  
Age Prediction

AbstractDespite inter-individual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. The present study assessed how accurately an individual’s age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification, and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from 1 region to 1000 regions. The age-prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated non-linear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.Graphical AbstractSeveral measures of cortical structure differ in relation to age. We examined the cortical granularity of these differences across seven parcellation approaches, from a 1 region (unparcellated cortical ribbon) to 1000 regions (patches with boundaries informed by anatomical landmarks), and three measures: thickness, gyrification, and fractal dimensionality. Rather than merely examining age-related relationships, we examined how these parcellations and measures can be used topredictage.

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