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.

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 Beyond the average brain: individual differences in social brain development are associated with friendship quality

2020 ◽  
Author(s):  
Andrik I Becht ◽  
Lara M Wierenga ◽  
Kathryn L Mills ◽  
Rosa Meuwese ◽  
Anna van Duijvenvoorde ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Development ◽  
Surface Area ◽  
Cortical Thickness ◽  
Friendship Quality ◽  
Brain Regions ◽  
Structural Development ◽  
Quality Development ◽  
Social Brain ◽  
Temporoparietal Junction

Abstract We tested whether adolescents differ from each other in the structural development of the social brain and whether individual differences in social brain development predicted variability in friendship quality development. Adolescents (N = 299, Mage T1 = 13.98 years) were followed across three biannual waves. We analysed self-reported friendship quality with the best friend at T1 and T3, and bilateral measures of surface area and cortical thickness of the medial prefrontal cortex (mPFC), posterior superior temporal sulcus (pSTS), temporoparietal junction (TPJ) and precuneus across all waves. At the group level, growth curve models confirmed non-linear decreases of surface area and cortical thickness in social brain regions. We identified substantial individual differences in levels and change rates of social brain regions, especially for surface area of the mPFC, pSTS and TPJ. Change rates of cortical thickness varied less between persons. Higher levels of mPFC surface area and cortical thickness predicted stronger increases in friendship quality over time. Moreover, faster cortical thinning of mPFC surface area predicted a stronger increase in friendship quality. Higher levels of TPJ cortical thickness predicted lower friendship quality. Together, our results indicate heterogeneity in social brain development and how this variability uniquely predicts friendship quality development.

scholarly journals Longitudinal ComBat: A Method for Harmonizing Longitudinal Multi-scanner Imaging Data

2019 ◽  
Author(s):  
Joanne C. Beer ◽  
Nicholas J. Tustison ◽  
Philip A. Cook ◽  
Christos Davatzikos ◽  
Yvette I. Sheline ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Statistical Power ◽  
Type I Error ◽  
Neurological Diseases ◽  
Brain Regions ◽  
Longitudinal Change ◽  
Type I ◽  
Imaging Data ◽  
Cross Sectional ◽  
Neuroimaging Data

AbstractWhile aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

scholarly journals Neural Correlates of Feedback Processing in Toddlers

2014 ◽  
Vol 26 (7) ◽  
pp. 1519-1527 ◽  
Author(s):  
Marlene Meyer ◽  
Harold Bekkering ◽  
Denise J. C. Janssen ◽  
Ellen R. A. de Bruijn ◽  
Sabine Hunnius
Keyword(s):  
Early Childhood ◽  
Brain Development ◽  
Brain Activity ◽  
Feedback System ◽  
Brain Regions ◽  
Adaptive Performance ◽  
Feedback Processing ◽  
Essential Information ◽  
Electrophysiological Studies ◽  
Neural Feedback

External feedback provides essential information for successful learning. Feedback is especially important for learning in early childhood, as toddlers strongly rely on external signals to determine the consequences of their actions. In adults, many electrophysiological studies have elucidated feedback processes using a neural marker called the feedback-related negativity (FRN). The neural generator of the FRN is assumed to be the ACC, located in medial frontal cortex. As frontal brain regions are the latest to mature during brain development, it is unclear when in early childhood a functional feedback system develops. Is feedback differentiated on a neural level in toddlers and in how far is neural feedback processing related to children's behavioral adjustment? In an EEG experiment, we addressed these questions by measuring the brain activity and behavioral performance of 2.5-year-old toddlers while they played a feedback-guided game on a touchscreen. Electrophysiological results show differential brain activity for feedback with a more negative deflection for incorrect than correct outcomes, resembling the adult FRN. This provides the first neural evidence for feedback processing in toddlers. Notably, FRN amplitudes were predictive of adaptive behavior: the stronger the differential brain activity for feedback, the better the toddlers' adaptive performance during the game. Thus, already in early childhood toddlers' feedback-guided performance directly relates to the functionality of their neural feedback processing. Implications for early feedback-based learning as well as structural and functional brain development are discussed.

scholarly journals 0873 Obstructive Sleep Apnea Impacts Brain Development in Obese Children and Adolescents: An MRI Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A333-A333
Author(s):  
F Sarzetto ◽  
T Naik ◽  
I Narang ◽  
A Kassner
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Brain Development ◽  
Cortical Thickness ◽  
Negative Impact ◽  
Combined Effect ◽  
Obese Adolescents ◽  
Obstructive Sleep ◽  
Brain Structure And Function ◽  
Mri Study

Abstract Introduction Obstructive sleep apnea (OSA) is a breathing disorder characterized by episodes of nocturnal hypoxia and chronic systemic inflammation, affecting more than 50% of obese youths. Both obesity and OSA independently have a negative impact on brain structure and function, but their combined effect on the developing brain is unknown. The purpose of this study was to assess MRI measurements of cortical thickness (CT) in obese youths with various degrees of OSA severity. We hypothesized that CT is abnormal in obese adolescents with OSA. Methods 55 obese subjects (26 females, 29 males, mean 14.3 ± 2.4 years) were included in the analysis. All subjects were assessed with polysomnography (PSG) to evaluate presence and severity of OSA. T1-weighted MPRAGE images were acquired using a 3T MRI scanner following PSG. CT was extracted using the CIVET 2.1.1 pipeline, and statistical analysis was performed on SurfStat to examine global and regional CT in relation to age using a general linear model. Results Based on PSG outcome, subjects were divided into 3 groups, no OSA (OAHI < 1.5 events/hr., n = 15), mild OSA (OAHI < 5, n = 14), and moderate/severe OSA (OAHI ≥ 5, n = 26). Cortical thickness analysis revealed a negative-trending correlation between global CT and age in no OSA (T = -0.49, P > 0.6), as seen in typical development. This correlation weakened in the presence of mild OSA (T = -0.20, P > 0.8) and became significantly positive in moderate/severe OSA (T = 3.87, P = 0.001), affecting several cortical areas. Conclusion These results indicate that brain development in obese adolescents with moderate/severe OSA significantly deviates from the typical trajectory of cortical thinning. This thickening could be due to exacerbated inflammation from the combined effect of both diseases, or a neurotrophic effect of leptin. More data is needed to validate these findings. Support None

scholarly journals Birth delivery mode alters perinatal cell death in the mouse brain

2018 ◽  
Vol 115 (46) ◽  
pp. 11826-11831 ◽  
Author(s):  
Alexandra Castillo-Ruiz ◽  
Morgan Mosley ◽  
Andrew J. Jacobs ◽  
Yarely C. Hoffiz ◽  
Nancy G. Forger
Keyword(s):  
Cell Death ◽  
Brain Development ◽  
Vaginal Delivery ◽  
Maternal Separation ◽  
Developmental Process ◽  
Neuronal Cell Death ◽  
Neuronal Cell ◽  
Brain Regions ◽  
Delivery Mode ◽  
Gestation Length

Labor and a vaginal delivery trigger changes in peripheral organs that prepare the mammalian fetus to survive ex utero. Surprisingly little attention has been given to whether birth also influences the brain, and to how alterations in birth mode affect neonatal brain development. These are important questions, given the high rates of cesarean section (C-section) delivery worldwide, many of which are elective. We examined the effect of birth mode on neuronal cell death, a widespread developmental process that occurs primarily during the first postnatal week in mice. Timed-pregnant dams were randomly assigned to C-section deliveries that were yoked to vaginal births to carefully match gestation length and circadian time of parturition. Compared with rates of cell death just before birth, vaginally-born offspring had an abrupt, transient decrease in cell death in many brain regions, suggesting that a vaginal delivery is neuroprotective. In contrast, cell death was either unchanged or increased in C-section–born mice. Effects of delivery mode on cell death were greatest for the paraventricular nucleus of the hypothalamus (PVN), which is central to the stress response and brain–immune interactions. The greater cell death in the PVN of C-section–delivered newborns was associated with a reduction in the number of PVN neurons expressing vasopressin at weaning. C-section–delivered mice also showed altered vocalizations in a maternal separation test and greater body mass at weaning. Our results suggest that vaginal birth acutely impacts brain development, and that alterations in birth mode may have lasting consequences.

scholarly journals Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

2020 ◽  
Author(s):  
Xin Niu ◽  
Alexei Taylor ◽  
Russell T. Shinohara ◽  
John Kounios ◽  
Fengqing Zhang
Keyword(s):  
Psychiatric Disorders ◽  
Robust Regression ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Regions ◽  
Developmental Trajectory ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain regions change in different ways and at different rates. This staggered developmental unfolding is determined by genetics and postnatal experience and is implicated in the progression of psychiatric and neurological disorders. Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, the unidimensional brain-age estimates provided by previous methods do not capture the divergent developmental trajectories of various brain structures. Here we propose and illustrate an analytic pipeline to compute an index of multidimensional brain-age that provides regional age predictions. First, using a database of 556 subjects that includes psychiatric and neurological patients as well as healthy controls we conducted robust regression to characterize the developmental trajectory of each MRI-based brain-imaging feature. We then utilized cluster analysis to identify subgroups of imaging features with a similar developmental trajectory. For each identified cluster, we obtained a brain-age prediction by applying machine-learning models with imaging features belonging to each cluster. Brain-age predictions from multiple clusters form a multidimensional brain-age index (MBAI). The MBAI is more sensitive to alterations in brain structures and captured distinct regional change patterns. In particular, the MBAI provided a more flexible analysis of brain age across brain regions that revealed changes in specific structures in psychiatric disorders that would otherwise have been combined in a unidimensional brain age prediction. More generally, brain-age prediction using a subset of homogeneous features circumvents the curse of dimensionality in neuroimaging data.

scholarly journals Physiology and Therapeutic Potential of SK, H, and M Medium AfterHyperPolarization Ion Channels

2021 ◽  
Vol 14 ◽  
Author(s):  
Deepanjali Dwivedi ◽  
Upinder S. Bhalla
Keyword(s):  
Ion Channels ◽  
Therapeutic Potential ◽  
Neurological Diseases ◽  
Brain Regions ◽  
Channel Activity ◽  
Current Review ◽  
Cellular Excitability ◽  
M Channels ◽  
Neuronal Functions

SK, HCN, and M channels are medium afterhyperpolarization (mAHP)-mediating ion channels. The three channels co-express in various brain regions, and their collective action strongly influences cellular excitability. However, significant diversity exists in the expression of channel isoforms in distinct brain regions and various subcellular compartments, which contributes to an equally diverse set of specific neuronal functions. The current review emphasizes the collective behavior of the three classes of mAHP channels and discusses how these channels function together although they play specialized roles. We discuss the biophysical properties of these channels, signaling pathways that influence the activity of the three mAHP channels, various chemical modulators that alter channel activity and their therapeutic potential in treating various neurological anomalies. Additionally, we discuss the role of mAHP channels in the pathophysiology of various neurological diseases and how their modulation can alleviate some of the symptoms.

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