T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance

2017 ◽  
Author(s):  
John D Lewis ◽  
Alan C Evans ◽  
Jussi Tohka
Keyword(s):  
Brain Development ◽  
Cognitive Performance ◽  
Gray Matter ◽  
Brain Regions ◽  
Brain Maturation ◽  
Chronological Age ◽  
Brain Images ◽  
Cortical Gray Matter ◽  
The Brain ◽  
Age Prediction

The maturational schedule of human brain development appears to be narrowly confined. The chronological age of an individual can be predicted from brain images with considerable accuracy, and deviation from the typical pattern of brain maturation has been related to cognitive performance. Methods using multi-modal data, or complex measures derived from voxels throughout the brain have shown the greatest accuracy, but are difficult to interpret in terms of the biology. Measures based on the cortical surface(s) have yielded less accurate predictions, suggesting that perhaps developmental changes related to cortical gray matter are not strongly related to chronological age, and that perhaps development is more strongly related to changes in subcortical regions or in deep white matter. We show that a simple metric based on the white/gray contrast at the inner border of the cortical gray-matter is a comparably good predictor of chronological age, and our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are more strongly related to IQ than are those from cortical thickness, suggesting that this metric is more sensitive to aspects of brain development that reflect cognitive performance.

scholarly journals A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex

2020 ◽  
Author(s):  
A Erramuzpe ◽  
R Schurr ◽  
J D Yeatman ◽  
I H Gotlib ◽  
M D Sacchet ◽  
...  
Keyword(s):  
Brain Development ◽  
Cortical Thickness ◽  
Prediction Models ◽  
Neurological Diseases ◽  
Brain Regions ◽  
Disease States ◽  
Original Dataset ◽  
Cortical Gray Matter ◽  
Development And Aging ◽  
Age Prediction

Abstract Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6–81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.

Neurodevelopment in the first three years: implications for child development, professional practice and policy

2014 ◽  
Vol 9 (2) ◽  
pp. 154-164 ◽  
Author(s):  
Danya Glaser
Keyword(s):  
Brain Development ◽  
Brain Activity ◽  
Brain Maturation ◽  
Policy And Practice ◽  
Content Type ◽  
Key Issues ◽  
And Function ◽  
The Relationship ◽  
The Brain ◽  
Brain Connections

Purpose – The purpose of this paper is to outline brain structure and development, the relationship between environment and brain development and implications for practice. Design/methodology/approach – The paper is based on a selected review of the literature and clinical experience. Findings – While genetics determine the sequence of brain maturation, the nature of brain development and functioning is determined by the young child's caregiving environment, to which the developing brain constantly adapts. The absence of input during sensitive periods may lead to later reduced functioning. There is an undoubted immediate equivalence between every mind function – emotion, cognition, behaviour and brain activity, although the precise location of this in the brain is only very partially determinable, since brain connections and function are extremely complex. Originality/value – This paper provides an overview of key issues in neurodevelopment relating to the development of young children, and implications for policy and practice.

scholarly journals Chronic Trigeminal Ganglionectomy or Topical Capsaicin Application to Pial Vessels Attenuates Postocclusive Cortical Hyperemia but Does Not Influence Postischemic Hypoperfusion

1991 ◽  
Vol 11 (2) ◽  
pp. 261-271 ◽  
Author(s):  
Robert Macfarlane ◽  
Erol Tasdemiroglu ◽  
Michael A. Moskowitz ◽  
Yoshihiko Uemura ◽  
Enoch P. Wei ◽  
...  
Keyword(s):  
Gray Matter ◽  
Brain Regions ◽  
Nerve Fibers ◽  
Global Cerebral Ischemia ◽  
Vessel Occlusion ◽  
Axon Reflex ◽  
Capsaicin Application ◽  
Cortical Gray Matter ◽  
Topical Capsaicin ◽  
Sensory Fibers

Marked hyperemia accompanies reperfusion after ischemia in the brain, and may account for the propensity of cerebral hemorrhage to follow embolic stroke or carotid endarterectomy, and for the morbidity that follows head injury or the ligation of large arteriovenous malformations. To evaluate the contribution of trigeminal sensory fibers to the hyperemic response, CBF was determined in 12 symmetrical brain regions, using microspheres with up to five different isotopic labels, in four groups of cats. Measurements were made at 15-min intervals for up to 2 h of reperfusion after global cerebral ischemia induced by four-vessel occlusion combined with systemic hypotension of either 10- or 20-min duration. In normal animals, hyperemia in cortical gray matter 30 min after reperfusion was significantly greater after 20 min (n = 10) than after 10 min (n = 7) of ischemia (312 ml/100 g/min versus 245 ml/100 g/min; p < 0.01). CBF returned to preischemic levels ∼45 min after reperfusion and was reduced to ∼65% of basal CBF for the remaining 75 min. In cats subjected to chronic trigeminal ganglionectomy (n = 15), postocclusive hyperemia in cortical gray matter was attenuated by up to 48% on the denervated side (249 versus 150 ml/100 g/min; p < 0.01) after 10 min of ischemia. This effect was maximal in the middle cerebral artery (MCA) territory, and was confined to regions known to receive a trigeminal innervation. In these animals, substance P (SP) levels in the MCA were reduced by 64% (p < 0.01), and the density of nerve fibers containing calcitonin gene-related peptide (but not vasoactive intestinal polypeptide or neuropeptide Y) was decreased markedly on the lesioned side. Topical application of capsaicin (100 n M; 50 μl) to the middle or posterior temporal branch of the MCA 10–14 days before ischemia decreased SP levels by 36%. Postocclusive hyperemia in cortical gray matter was attenuated throughout the ipsilateral hemisphere by up to 58%, but the cerebral vascular response to hypercapnia (Paco2 = 60 mm Hg) was unimpaired. The duration of hyperemia and the severity of the delayed hypoperfusion were not influenced by trigeminalectomy, capsaicin application, or the intravenous administration of ATP. These data demonstrate the importance of neurogenic mechanisms in the development of postischemic hyperperfusion, and suggest the potential utility of strategies aimed at blocking axon reflex-like mechanisms to reduce severe cortical hyperemia.

scholarly journals MR Image Analysis of Neonatal Brain using Adaptive Mathematical Morphological Processing and Region based Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 4127-4132
Keyword(s):  
Gray Matter ◽  
Right Hemisphere ◽  
Morphological Processing ◽  
Adult Brain ◽  
Size Difference ◽  
Mr Images ◽  
Neonatal Brain ◽  
Cortical Gray Matter ◽  
Manual Interaction ◽  
The Brain

In the study of evaluation of infant brain development, the segmentation of obtained MR images is an important step. When compared with the MR images of adult brain it is difficult to identify the different regions in infant brains with that of the methods used for the analysis of adult brains. This is due to the size difference of the brain and the differences in the properties of the brain tissues. So, for analyzing these MR images it requires manual interaction with the images resulting in the bias of the results. For this problem we propose another approach for the segmentation of neonatal brain MR images. This method doesn’t require any manual interaction and produces unbiased results. Our algorithm segments the different layers (right hemisphere, left hemisphere, cerebellum, brain stem) and the different tissues like sub cortical gray matter, Militated & un mylinated gray matter and cerebrospinal fluid, resulting in the better understanding of the development of different parts of the brain. Our algorithm can be used for the analysis of MR images of infant brains of age as less as 3to6 months.

scholarly journals Thermodynamic Measures of Human Brain Development from Fetal Stage to Adulthood

2019 ◽  
Author(s):  
Edward A. Rietman ◽  
Sophie Taylor ◽  
Hava T. Siegelmann ◽  
Marco Cavaglia ◽  
Jack A. Tuszynski
Keyword(s):  
Free Energy ◽  
Brain Development ◽  
Gibbs Free Energy ◽  
Protein Interaction ◽  
Brain Regions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
The Brain

AbstractThis paper analyzes the data obtained from tissue samples of the human brains containing protein expression values. The data have been processed for their thermodynamic measure in terms of the Gibbs free energy of the corresponding protein-protein interaction networks. We have investigated the functional dependence of the Gibbs free energies on age and found consistent trends for most of the 16 main brain areas. The peak of the Gibbs energy values is found at birth with a trend toward plateauing at the age of maturity. We have also compared the data for males and females and uncovered functional differences for some of the brain regions.Significance StatementIn this paper we briefly outline the theoretical basis for a novel analysis of brain development in terms of a thermodynamic measure (Gibbs free energy) for the corresponding protein-protein interaction networks. We analyzed the overall developmental patterns for Gibbs free energy as a function of age across all brain regions. Of particular note was the significant upward trend in the fetal stages, which is generally followed by a sharp dip at birth and a plateau at maturity. We then compared the trends for female and male samples. A crossover pattern was observed for most of the brain regions, where the Gibbs free energy of the male samples were lower than the female samples at prenatal and neonatal ages, but higher at ages 8-40.

scholarly journals Individual variation underlying brain age estimates in typical development

2020 ◽  
Author(s):  
Gareth Ball ◽  
Claire E Kelly ◽  
Richard Beare ◽  
Marc L Seal
Keyword(s):  
Machine Learning ◽  
Brain Development ◽  
Machine Learning Algorithms ◽  
Brain Maturation ◽  
Childhood And Adolescence ◽  
Typically Developing ◽  
Feature Importance ◽  
Brain Age ◽  
Age Estimates ◽  
Age Prediction

AbstractTypical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed ‘brain age’, this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis.In a large cohort of n=768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to estimate sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regional brain development. However, while several regions are found to contribute to brain age prediction, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample.Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.

scholarly journals Impact on Brain due to Alcoholism using Improved Fuzzy C-Regression based Alcohol Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 1317-1325
Keyword(s):  
Risk Factors ◽  
Human Brain ◽  
Gray Matter ◽  
Membership Functions ◽  
Brain Images ◽  
Men And Women ◽  
Brain Image ◽  
Left Thalamus ◽  
Posterior Insula ◽  
The Brain

In general, two risk factors such as alcohol expectancy and impulsivity have been concerned with alcohol abuse Currently, many people have been addicted to alcoholism and have an Alcohol Use Disorder (AUD) that affects neurons behavior in the human brain. Still, how such risk factors interrelate to estimate the alcoholism. To solve this problem, Fuzzy C-Regression based Alcoholism Detection (FCRAD) method has been proposed that segments the Region-Of-Interests (ROIs) from the human brain image to predict the Gray Matter Volume (GMV) reduction in the right posterior insula in women and the left thalamus in both men and women efficiently. However, it requires the detection of GMV reduction in the other brain image regions. This multi-modality can decrease the fuzziness of the partition and the crisp membership degrees were not derived easily. Therefore in this article, the GMV reduction in other regions of the brain images including right posterior insula in women and left thalamus in both men and women has been detected, an Improved FCRAD (IFCRAD) method is proposed to simplify the segmentation of the brain images by considering the second regularization term in the objective function of the FCR to take into account the noisy data. Also, the Euclidean distance is replaced with the Voronoi distance for computing different fuzzy membership functions. Moreover, new error measure and reward function are used in the objective function of the FCR to reward nearly crisp membership functions and to obtain more crisp partition. So, the brain images are segmented into gray-matter images that derive the ROIs to analyze the GMV reduction with less complexity. Finally, the experimental results illustrate the proposed IFCRAD method achieves higher accuracy than the existing AD methods.

scholarly journals MRI of Capn15 knockout mice and analysis of Capn15 distribution reveal possible roles in brain development and plasticity

2019 ◽  
Author(s):  
Congyao Zha ◽  
Carole A Farah ◽  
Vladimir Fonov ◽  
David A. Rudko ◽  
Wayne S Sossin
Keyword(s):  
Brain Development ◽  
Knockout Mice ◽  
Brain Plasticity ◽  
Small Animal ◽  
Cell Types ◽  
Cysteine Proteases ◽  
Brain Regions ◽  
Conditional Knockout ◽  
Specific Cell ◽  
The Brain

AbstractPurposeThe non-classical Small Optic Lobe (SOL) family of calpains are intracellular cysteine proteases that are expressed in the nervous system and appear to play an important role in neuronal development in both Drosophila, where loss of this calpain leads to the eponymous small optic lobes, and in mouse and human, where loss of this calpain (Capn15) leads to eye anomalies. However, the brain regions where this calpain is expressed and the areas most affected by the loss of this calpain have not been carefully examined.ProceduresWe utilize an insert strain where lacZ is expressed under the control of the Capn15 promoter, together with immunocytochemistry with markers of specific cell types to address where Capn 15 is expressed in the brain. We use small animal MRI comparing WT, Capn15 knockout and Capn15 conditional knockout mice to address the brain regions that are affected when Capn 15 is not present, either in early development of the adult.ResultsCapn15 is expressed in diverse brain regions, many of them involved in plasticity such as the hippocampus, lateral amygdala and Purkinje neurons. Capn15 knockout mice have smaller brains, and present specific deficits in the thalamus and hippocampal regions. There are no deficits revealed by MRI in brain regions when Capn15 is knocked out after development.ConclusionsAreas where Capn15 is expressed in the adult are not good markers for the specific regions where the loss of Capn15 specifically affects brain development. Thus, it is likely that this calpain plays distinct roles in brain development and brain plasticity.

scholarly journals Fully automatic brain segmentation using model-guided level sets and skeleton-based models

2013 ◽  
Author(s):  
Chunliang Wang ◽  
Chun Wang ◽  
Örjan Smedby
Keyword(s):  
Level Set ◽  
Gray Matter ◽  
Brain Segmentation ◽  
Segmentation Method ◽  
Implicit Model ◽  
Segmentation Methods ◽  
Cortical Gray Matter ◽  
Fully Automatic ◽  
Level Set Framework ◽  
The Brain

A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

scholarly journals Investigating Associations Between Inflammatory Biomarkers, Gray Matter, Neurofilament Light and Cognitive Performance in Healthy Older Adults

2021 ◽  
Vol 13 ◽  
Author(s):  
Hollis C. Karoly ◽  
Carillon J. Skrzynski ◽  
Erin Moe ◽  
Angela D. Bryan ◽  
Kent E. Hutchison
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Cognitive Performance ◽  
Gray Matter ◽  
Regression Models ◽  
Normal Aging ◽  
Neurofilament Light ◽  
Healthy Older Adults ◽  
Analytic Models ◽  
The Brain

Background: Exploring biological variables that may serve as indicators of the development and progression of cognitive decline is currently a high-priority research area. Recent studies have demonstrated that during normal aging, individuals experience increased inflammation throughout the brain and body, which may be linked to cognitive impairment and reduced gray matter volume in the brain. Neurofilament light polypeptide (NfL), which is released into the circulation following neuronal damage, has been proposed as a biomarker for neurodegenerative diseases, and may also have utility in the context of normal aging. The present study tested associations between age, peripheral levels of the pro-inflammatory cytokine IL-6, peripheral NfL, brain volume, and cognitive performance in a sample of healthy adults over 60 years old.Methods: Of the 273 individuals who participated in this study, 173 had useable neuroimaging data, a subset of whom had useable blood data (used for quantifying IL-6 and NfL) and completed a cognitive task. Gray matter (GM) thickness values were extracted from brain areas of interest using Freesurfer. Regression models were used to test relationships between IL-6, NfL, GM, and cognitive performance. To test putative functional relationships between these variables, exploratory path analytic models were estimated, in which the relationship between age, IL-6, and working memory performance were linked via four different operationalizations of brain health: (1) a latent GM variable composed of several regions linked to cognitive impairment, (2) NfL alone, (3) NfL combined with the GM latent variable, and (4) the hippocampus alone.Results: Regression models showed that IL-6 and NfL were significantly negatively associated with GM volume and that GM was positively associated with cognitive performance. The path analytic models indicated that age and cognitive performance are linked by GM in the hippocampus as well as several other regions previously associated with cognitive impairment, but not by NfL alone. Peripheral IL-6 was not associated with age in any of the path models.Conclusions: Results suggest that among healthy older adults, there are several GM regions that link age and cognitive performance. Notably, NfL alone is not a sufficient marker of brain changes associated with aging, inflammation, and cognitive performance.

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