scholarly journals The ANTs Longitudinal Cortical Thickness Pipeline

2017 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Andrew J. Holbrook ◽  
Brian B. Avants ◽  
Jared M. Roberts ◽  
Philip A. Cook ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Cortical Thickness ◽  
Mixed Effects ◽  
Single Subject ◽  
Multiple Time ◽  
Supporting Evidence ◽  
Cross Sectional ◽  
Mixed Effects Modeling ◽  
Multiple Time Points ◽  
Modeling Strategy

AbstractLongitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. In this work, we introduce the open-source Advanced Normalization Tools (ANTs) registration-based cortical thickness longitudinal processing pipeline and its application to the first phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) comprising over 600 subjects with multiple time points from baseline to 36 months. We demonstrate in these data that the single-subject template construction and same orientation processing results in a simultaneous minimization of residual variability and maximization of between-subject variability immediately estimable from a longitudinal mixed-effects modeling strategy. It is known from the statistical literature that optimizing these dual criteria leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

scholarly journals Using longitudinal imaging to map the ‘relapse signature’ of schizophrenia and other psychoses

2014 ◽  
Vol 23 (3) ◽  
pp. 219-225 ◽  
Author(s):  
V. L. Cropley ◽  
C. Pantelis
Keyword(s):  
Longitudinal Studies ◽  
Clinical Picture ◽  
Detailed Examination ◽  
Multiple Time ◽  
Imaging Studies ◽  
Cross Sectional ◽  
Time Points ◽  
Effect Of Treatment ◽  
Longitudinal Imaging ◽  
Multiple Time Points

Brain imaging studies in schizophrenia have typically involved single assessment and cross-sectional designs, while longitudinal studies rarely incorporate more than two time points. While informative, these studies do not adequately capture potential trajectories of neurobiological change, particularly in the context of a changing clinical picture. We propose that the analysis of brain trajectories using multiple time points may inform our understanding of the illness and the effect of treatment. This paper makes the case for frequent serial neuroimaging across the course of schizophrenia psychoses and its application to active illness epsiodes to provide a detailed examination of psychosis relapse and remission.

Are “youth days” effective at motivating new sport participation? Evidence from a pre-post event research design

2019 ◽  
Vol 11 (1) ◽  
pp. 89-104
Author(s):  
Jordan Taylor Bakhsh ◽  
Luke R. Potwarka ◽  
Ryan Snelgrove
Keyword(s):  
Sport Participation ◽  
Control Group ◽  
Multiple Time ◽  
Elite Sport ◽  
Cross Sectional ◽  
Content Type ◽  
Sport Competition ◽  
Research Designs ◽  
Multiple Time Points ◽  
Motivation To Participate

Purpose The purpose of this paper is to explore the effects that exposure to a youth day event at an elite sport competition has on youth spectators’ motivations to participate in the sport on display. Design/methodology/approach The paper was underpinned by the theory of planned behavior (TPB). Pre- and post-event questionnaires were administered to local grade seven and eight students (n=318) as part of a youth day event at the 2016 Milton International Track Cycling Challenge in Ontario, Canada. Questionnaires assessed each TPB construct one week before the youth day and immediately following the event. Findings The paper provides empirical insights about the shifts from pre- to post-event behavioral antecedent measures. Results suggest youth day events can be effective at driving positive shifts in participation intention and subjective norm among youth populations. Research limitations/implications A control group was not possible as an ethical limitation was created from the school boards which did not allow for some students/classes within the study to not experience the event. Researchers are encouraged to develop a study which allows for a youth control group and assesses the shift in behavioral antecedents at multiple time points post-event. Practical implications The paper includes implications for how to leverage subjective norms as a means of motivating post-event participation. Originality/value The paper fulfils a methodological gap to move beyond cross-sectional data and employ pre-post event research designs to measure the effect spectating an elite sport competition can have on youth’s motivation to participate in the sport on display.

scholarly journals Ergodic Subspace Analysis

2020 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Timo von Oertzen ◽  
Florian Schmiedek ◽  
Manuel C. Voelkle
Keyword(s):  
Cognitive Abilities ◽  
Simulated Data ◽  
Population Level ◽  
Multiple Time ◽  
Subspace Analysis ◽  
Cross Sectional ◽  
Time Points ◽  
Single Person ◽  
The Difference ◽  
Multiple Time Points

Properties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the development of a single person over time. Recently, there has been an increased interest in the difference between covariance structures, expressed by covariance matrices, that evolve between persons and within a single person over multiple time points. If these structures are identical at the population level, the structure is called ergodic. However, recent data confirms that ergodicity is not generally given, particularly not for cognitive variables. For example, the g factor that is dominant for cognitive abilities between persons seems to explain far less variance when concentrating on a single person’s data. However, other subdimensions of cognitive abilities seem to appear both between and within persons; that is, there seems to be a lower-dimensional subspace of cognitive abilities in which cognitive abilities are in fact ergodic. In this article, we present ergodic subspace analysis (ESA), a mathematical method to identify, for a given set of variables, which subspace is most important within persons, which is most important between person, and which is ergodic. Similar to the common spatial patterns method, the ESA method first whitens a joint distribution from both the between and the within variance structure and then performs a principle component analysis (PCA) on the between distribution, which then automatically acts as an inverse PCA on the within distribution. The difference of the eigenvalues allows a separation of the rotated dimensions into the three subspaces corresponding to within, between, and ergodic substructures. We apply the method to simulated data and to data from the COGITO study to exemplify its usage.

Imputing missing height measures using a mixed-effects modeling strategy

2004 ◽  
Vol 34 (12) ◽  
pp. 2492-2500 ◽  
Author(s):  
Andrew P Robinson ◽  
William R Wykoff
Keyword(s):  
Mean Squared Error ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Tree Level ◽  
Root Mean Squared Error ◽  
Mixed Effects Modeling ◽  
Squared Error ◽  
Best Linear Unbiased ◽  
Modeling Strategy ◽  
Point Level

This paper proposes a method whereby height–diameter regression from an inventory can be incorporated into a height imputation algorithm. Point-level subsampling is often employed in forest inventory for efficiency. Some trees will be measured for diameter and species, while others will be measured for height and 10-year increment. Predictions of these missing measures would be useful for estimating volume and growth, respectively, so they are often imputed. We present and compare three imputation strategies: using a published model, using a localized version of a published model, and using best linear unbiased predictions from a mixed-effects model. The bases of our comparison are four-fold: minimum fitted root mean squared error and minimum predicted root mean squared error under a 2000-fold cross-validation for tree-level height and volume imputations. In each case the mixed-effects model proved superior. This result implies that substantial environmental variation existed in the height–diameter relationship for our data and that its representation in the model by means of random effects was profitable.

scholarly journals Design and protocol of a comprehensive multicentre biobank for abdominal aortic aneurysms

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e028858 ◽  
Author(s):  
Hamid Jalalzadeh ◽  
Reza Indrakusuma ◽  
Jan D. Blankensteijn ◽  
Willem Wisselink ◽  
Kak K Yeung ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Mixed Effects ◽  
Aortic Aneurysms ◽  
Mixed Effects Models ◽  
Multiple Time ◽  
Imaging Data ◽  
Time Points ◽  
Abdominal Aortic ◽  
Multiple Time Points

IntroductionThe pathophysiology and natural course of abdominal aortic aneurysms (AAAs) are insufficiently understood. In order to improve our understanding, it is imperative to carry out longitudinal research that combines biomarkers with clinical and imaging data measured over multiple time points. Therefore, a multicentre biobank, databank and imagebank has been established in the Netherlands: the ‘Pearl Abdominal Aortic Aneurysm’ (AAA bank).Methods and analysisThe AAA bank is a prospective multicentre observational biobank, databank and imagebank of patients with an AAA. It is embedded within the framework of the Parelsnoer Institute, which facilitates uniform biobanking in all university medical centres (UMCs) in the Netherlands. The AAA bank has been initiated by the two UMCs of Amsterdam UMC and by Leiden University Medical Center. Participants will be followed during AAA follow-up. Clinical data are collected every patient contact. Three types of biomaterials are collected at baseline and during follow-up: blood (including DNA and RNA), urine and AAA tissue if open surgical repair is performed. Imaging data that are obtained as part of clinical care are stored in the imagebank. All data and biomaterials are processed and stored in a standardised manner. AAA growth will be based on multiple measurements and will be analysed with a repeated measures analysis. Potential associations between AAA growth and risk factors that are also measured on multiple time points can be assessed with multivariable mixed-effects models, while potential associations between AAA rupture and risk factors can be tested with a conditional dynamic prediction model with landmarking or with joint models in which linear mixed-effects models are combined with Cox regression.Ethics and disseminationThe AAA bank is approved by the Medical Ethics Board of the Amsterdam UMC (University of Amsterdam).Trial registration numberNCT03320408.

scholarly journals Inequalities in Trends in Healthy and Disability-Free Life Expectancies: A Systematic Review

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 640-640
Author(s):  
Gemma Spiers ◽  
Fiona Beyer ◽  
Dawn Craig ◽  
Barbara Hanratty ◽  
Carol Jagger
Keyword(s):  
Life Expectancy ◽  
Multiple Time ◽  
Educational Inequalities ◽  
Active Life Expectancy ◽  
Cross Sectional ◽  
Healthy Life ◽  
Active Life ◽  
Disability Free Life Expectancy ◽  
Multiple Time Points ◽  
The Uk

Abstract To update previous reviews, we searched Medline, Embase, Scopus and the Office for National Statistics (ONS) website for studies and reports published after 2016 that describe trends in healthy life expectancy, active life expectancy or disability-free life expectancy (DFLE) in the UK and other OECD high-income countries. We focus here on studies reporting inequalities by socioeconomic position (SEP) in these trends. There was mixed evidence of educational and area-level deprivation inequalities in trends in DFLE, with four studies indicating that educational inequalities were widening in European countries. No studies were identified that examined inequalities in disability-free life expectancy trends in the UK. All studies were based on cross-sectional data from multiple time points or longitudinal panel studies. We discuss the size of inequalities in DFLE between SEP groups and the limitations of previous studies.

scholarly journals Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life

2020 ◽  
Author(s):  
Natalie Schaworonkow ◽  
Bradley Voytek
Keyword(s):  
Early Adulthood ◽  
Later Life ◽  
Multiple Time ◽  
Neuronal Oscillations ◽  
Healthy Human ◽  
Cross Sectional ◽  
Electrophysiological Recordings ◽  
Inherent Feature ◽  
Multiple Time Points ◽  
Aperiodic Signal

AbstractNeuronal oscillations emerge in early human development. These periodic oscillations are thought to rapidly change in infancy and stabilize during maturity. Given their numerous connections to physiological and cognitive processes, as well as their pathological divergence, understanding the trajectory of oscillatory development is important for understanding healthy human brain development. This understanding is complicated by recent evidence that assessment of periodic neuronal oscillations is confounded by aperiodic neuronal activity, which is an inherent feature of electrophysiological neuronal recordings. Recent cross-sectional evidence shows that this aperiodic signal progressively shifts from childhood through early adulthood, and from early adulthood into later life. None of these studies, however, have been performed in infants, nor have they been examined longitudinally. Here, we analyzed non-invasive EEG data from 22 typically developing infants, across multiple time points, ranging between 38 and 203 days old. We show that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first several months of life. These results highlight the importance of separating the periodic and aperiodic neuronal signals, because the aperiodic signal can bias measurement of neuronal oscillations. Given the infrequent, bursting nature of oscillations in infants, we recommend the use of quantitative time domain approaches that isolate bursts and uncover changes in waveform properties of oscillatory bursts.

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