scholarly journals On the Reproducibility and Interpretability of Group-Level Task-fMRI Results

2020 ◽  
Author(s):  
Alex Bowring
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Effect Size ◽  
Fmri Data ◽  
Group Level ◽  
Confidence Sets ◽  
Cohen’S D ◽  
3D Monte Carlo ◽  
Cohen's D ◽  
The Brain

In this thesis, we aim to address two topical issues at the forefront of task-based functional magnetic resonance imaging (fMRI). The first of these is a growing apprehension within the field about the reproducibility of findings that make up the neuroimaging literature. To confront this, we assess how the choice of software package for analyzing fMRI data can impact the final group-level results of a neuroimaging study. We reanalyze data from three published task-fMRI studies within the three most widely-used neuroimaging software packages -- AFNI, FSL, and SPM -- and then apply a range of comparison methods to gauge the scale of variability across the results. While qualitatively we find similarities, our quantitative assessment methods discover considerable differences between the final statistical images obtained with each package. Ultimately, we conclude that exceedingly weak effects may not generalize across fMRI analysis software. In the second part of this work we shift our attention to the analytical methods applied for fMRI inference. Here, we seek to overcome limitations with the traditional statistical approach, where for sufficiently large data sizes current methods determine universal activation across the brain, rendering the results as uninterpretable. We extend on a method proposed by (Sommerfeld et al., 2018; SSS) to develop spatial Confidence Sets (CSs) on clusters found in thresholded raw blood-oxygen-level-dependent (BOLD) effect size maps. The CSs give statements on the locations where raw effect sizes exceed, and fall short of, a purposeful non-zero threshold. We propose several theoretical and practical implementation advancements to the original method formulated in SSS, delivering a procedure with superior performance in sample sizes as low as N = 60. We validate the method with 3D Monte Carlo simulations that resemble fMRI data. We then compute CSs for the Human Connectome Project (HCP) working memory task contrast images, illustrating the brain regions that show a reliable %BOLD for a given %BOLD threshold. In the final part of this thesis, we develop the CSs to operate on standardized Cohen's d effect size images. We derive the statistical properties of the Cohen's d estimator to motivate three algorithms for computing Cohen's d CSs, including a novel method based on normalizing the distribution of Cohen's d. With intensive 3D Monte Carlo simulations, we find that two of these methods can be effectively applied to fMRI data. We compute Cohen's d CSs on the HCP data, and by comparing the CSs with results obtained from a standard testing procedure, exemplify the improved localization of effects that can be gained by using the Confidence Sets.

scholarly journals Confidence Sets for Cohen’s d effect size images

NeuroImage ◽  
2021 ◽  
Vol 226 ◽  
pp. 117477
Author(s):  
Alexander Bowring ◽  
Fabian J.E. Telschow ◽  
Armin Schwartzman ◽  
Thomas E. Nichols
Keyword(s):  
Effect Size ◽  
Confidence Sets ◽  
Cohen’S D ◽  
Cohen's D

Tetrahedral elements in self-consistent parallel 3D Monte Carlo simulations of MOSFETs

2008 ◽  
Vol 7 (3) ◽  
pp. 201-204 ◽  
Author(s):  
M. Aldegunde ◽  
A. J. García-Loureiro ◽  
A. Martinez ◽  
K. Kalna
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Tetrahedral Elements ◽  
Self Consistent ◽  
3D Monte Carlo

scholarly journals Assessment of the frequency-domain multi-distance method to evaluate the brain optical properties: Monte Carlo simulations from neonate to adult

2011 ◽  
Vol 2 (3) ◽  
pp. 552 ◽  
Author(s):  
Mathieu Dehaes ◽  
P. Ellen Grant ◽  
Danielle D. Sliva ◽  
Nadège Roche-Labarbe ◽  
Rudolph Pienaar ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Optical Properties ◽  
Monte Carlo Simulations ◽  
Frequency Domain ◽  
Distance Method ◽  
The Brain

A non-parametric effect size measure capturing changes in central tendency and shape of data distributions more flexibly than Cohen’s d

2020 ◽  
Author(s):  
Jörn Lötsch ◽  
Alfred Ultsch
Keyword(s):  
Effect Size ◽  
Data Science ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Central Tendency ◽  
Effect Size Measure ◽  
Specific Data ◽  
Cohen’S D ◽  
Size Measure ◽  
Cohen's D

Abstract Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. "Impact” is a novel nonparametric measure of effect size obtained as the sum of two separate components and includes (i) the change in the central tendency of the group-specific data, normalized to the overall variability, and (ii) the difference in the probability density of the group-specific data. Results obtained on artificial data and empirical biomedical data showed that impact outperforms Cohen's d by this additional component. It is shown that in a multivariate setting, while standard statistical analyses and Cohen’s d are not able to identify effects that lead to changes in the form of data distribution, “Impact” correctly captures them. The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. It is numerically stable even for degenerate distributions consisting of singular values. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from (big) and heterogeneous data.

Estimating Subgroup Differences in Staffing Research When the Selection Mechanism Is Unknown: A Response to Li’s Case IV Correction

2018 ◽  
Vol 23 (2) ◽  
pp. 367-384 ◽  
Author(s):  
Dustin A. Fife ◽  
Jorge Mendoza ◽  
Eric Day ◽  
Robert Terry
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Group Membership ◽  
Group Differences ◽  
Selection Mechanism ◽  
Range Restriction ◽  
Subgroup Differences ◽  
Cohen’S D ◽  
Selection Decisions ◽  
Cohen's D

When estimating subgroup differences on incumbents, range restriction may bias estimates. Bobko, Roth, and Bobko recognized this problem and developed a Case II and Case III correction for Cohen’s d. Subsequently, Li developed a Case IV correction, which seeks to estimate group differences on a predictor using only incumbent data but must assume that group membership (e.g., ethnicity) plays no role in selection decisions. In this paper, we extend Li’s correction and relax this assumption. In addition, this new correction allows for the estimation of subgroup differences on both the criterion and predictor. Using Monte Carlo simulation, we study the performance of both estimators under situations where Li’s assumptions are violated and demonstrate that this new procedure almost always outperforms Li’s Case IV correction and does so with greater precision. We also provide R code to assist applied researchers in using these corrections.

scholarly journals 0636 Using AI To Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A243-A243
Author(s):  
W Hevener ◽  
B Beine ◽  
J Woodruff ◽  
D Munafo ◽  
C Fernandez ◽  
...  
Keyword(s):  
Effect Size ◽  
Statistical Significance ◽  
Post Intervention ◽  
Cpap Adherence ◽  
Cohen’S D ◽  
Usage Patterns ◽  
Patient Outreach ◽  
Cohen's D ◽  
The Impact ◽  
Usage Data

Abstract Introduction Clinical management of CPAP adherence remains an ongoing challenge. Behavioral and technical interventions such as patient outreach, coaching, troubleshooting, and resupply may be deployed to positively impact adherence. Previous authors have described adherence phenotypes that retrospectively categorize patients by discrete usage patterns. We design an AI model that predictively categorizes patients into previously studied adherence phenotypes and analyzes the statistical significance and effect size of several types of interventions on subsequent CPAP adherence. Methods We collected a cross-sectional cohort of subjects (N = 13,917) with 455 days of daily CPAP usage data acquired. Patient outreach notes and resupply data were temporally synchronized with daily CPAP usage. Each 30-days of usage was categorized into one of four adherence phenotypes as defined by Aloia et al. (2008) including Good Users, Variable Users, Occasional Attempters, and Non-Users. Cross-validation was used to train and evaluate a Recurrent Neural Network model for predicting future adherence phenotypes based on the dynamics of prior usage patterns. Two-sided 95% bootstrap confidence intervals and Cohen’s d statistic were used to analyze the significance and effect size of changes in usage behavior 30-days before and after administration of several resupply interventions. Results The AI model predicted the next 30-day adherence phenotype with an average of 90% sensitivity, 96% specificity, 95% accuracy, and 0.83 Cohen’s Kappa. The AI model predicted the number of days of CPAP non-use, use under 4-hours, and use over 4-hours for the next 30-days with OLS Regression R-squared values of 0.94, 0.88, and 0.95 compared to ground truth. Ten resupply interventions were associated with statistically significant increases in adherence, and ranked by adherence effect size using Cohen’s d. The most impactful were new cushions or masks, with a mean post-intervention CPAP adherence increase of 7-14% observed in Variable User, Occasional Attempter, and Non-User groups. Conclusion The AI model applied past CPAP usage data to predict future adherence phenotypes and usage with high sensitivity and specificity. We identified resupply interventions that were associated with significant increases in adherence for struggling patients. This work demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence. Support  

scholarly journals The Unforgettables: a chorus for people with dementia with their family members and friends

2018 ◽  
Vol 30 (6) ◽  
pp. 779-789 ◽  
Author(s):  
Mary Sherman Mittelman ◽  
Panayiota Maria Papayannopoulou
Keyword(s):  
Quality Of Life ◽  
Social Support ◽  
Pilot Study ◽  
Effect Size ◽  
Family Members ◽  
Positive Effects ◽  
Cohen’S D ◽  
People With Dementia ◽  
Cohen's D

Summary/AbstractOur experience evaluating a museum program for people with dementia together with their family members demonstrated benefits for all participants. We hypothesized that participation in a chorus would also have positive effects, giving them an opportunity to share a stimulating and social activity that could improve their quality of life. We inaugurated a chorus for people with dementia and their family caregivers in 2011, which rehearses and performs regularly. Each person with dementia must be accompanied by a friend or family member and must commit to attending all rehearsals and the concert that ensues. A pilot study included a structured assessment, take home questionnaires and focus groups. Analyses of pre-post scores were conducted; effect size was quantified using Cohen's d. Results showed that quality of life and communication with the other member of the dyad improved (Effect size: Cohen's d between 0.32 and 0.72) for people with dementia; quality of life, social support, communication and self-esteem improved (d between 0.29 and 0.68) for caregivers. Most participants stated that benefits included belonging to a group, having a normal activity together and learning new skills. Participants attended rehearsals in spite of harsh weather conditions. The chorus has been rehearsing and performing together for more than 6 years and contributing to its costs. Results of this pilot study suggest that people in the early to middle stage of dementia and their family members and friends can enjoy and learn from rehearsing and performing in concerts that also engage the wider community. It is essential to conduct additional larger studies of the benefits of participating in a chorus, which may include improved quality of life and social support for all, and reduced cognitive decline among people with dementia.

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