Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults

Subject motion is a well-known confound in resting-state functional MRI (rs-fMRI) and the analysis of functional connectivity. Consequently, several clean-up strategies have been established to minimize the impact of subject motion. Physiological signals in response to cardiac activity and respiration are also known to alter the apparent rs-fMRI connectivity. Comprehensive comparisons of common noise regression techniques showed that the Independent Component Analysis based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) was a preferred pre-processing technique for teenagers and adults. However, motion and physiological noise characteristics may differ substantially for older adults. Here, we present a comprehensive comparison of noise-regression techniques for older adults from a large multi-site clinical trial of exercise and intensive pharmacological vascular risk factor reduction. The Risk Reduction for Alzheimer\u27s Disease (rrAD) trial included hypertensive older adults (60-84 years old) at elevated risk of developing Alzheimer\u27s Disease (AD). We compared the performance of censoring, censoring combined with global signal regression, non-aggressive and aggressive ICA-AROMA, as well as the Spatially Organized Component Klassifikator (SOCK) on the rs-fMRI baseline scans from 434 rrAD subjects. All techniques were rated based on network reproducibility, network identifiability, edge activity, spatial smoothness, and loss of temporal degrees of freedom (tDOF). We found that non-aggressive ICA-AROMA did not perform as well as the other four techniques, which performed table with marginal differences, demonstrating the validity of these techniques. Considering reproducibility as the most important factor for longitudinal studies, given low false-positive rates and a better preserved, more cohesive temporal structure, currently aggressive ICA-AROMA is likely the most suitable noise regression technique for rs-fMRI studies of older adults

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector \u27hubs\u27 are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed \u27variants\u27 in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability

A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity

It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Available online 6 March 2021.Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi- echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.This research was supported by the European Union’s Horizon 2020 research and innovation program ( Marie Sk ł odowska-Curie grant agreement No. 713673 ), a fellowship from La Caixa Foundation (ID 100010434 , fellowship code LCF/BQ/IN17/11620063 ), the Spanish Ministry of Economy and Competitiveness ( Ramon y Cajal Fellowship, RYC-2017- 21845 ), the Spanish State Research Agency (BCBL “Severo Ochoa ”excellence accreditation, SEV- 2015-490 ), the Basque Govern- ment ( BERC 2018-2021 and PIBA_2019_104 ), the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019-105520GB-100 and FJCI-2017-31814 ), and the Eunice Kennedy Shriver National Insti- tute of Child Health and Human Development of the National Institutes of Health under award number K12HD073945

Developing and Validating Open Source Tools for Advanced Neuroimaging Research

Almost all scientific research relies on software. This is particularly true for research that uses neuroimaging technologies, such as functional magnetic resonance imaging (fMRI). These technologies generate massive amounts of data per participant, which must be processed and analyzed using specialized software. A large portion of these tools are developed by teams of researchers, rather than trained software developers. In this kind of ecosystem, where the majority of software creators are scientists, rather than trained programmers, it becomes more important than ever to rely on community-based development, which may explain why most of this software is open source. It is in the development of this kind of research-oriented, open source software that I have focused much of my graduate training, as is reflected in this dissertation. One software package I have helped to develop and maintain is tedana, a Python library for denoising multi-echo fMRI data. In chapter 2, I describe this library in a short, published software paper. Another library I maintain as the primary developer is NiMARE, a Python library for performing neuroimaging meta-analyses and derivative analyses, such as automated annotation and functional decoding. In chapter 3, I present NiMARE in a hybrid software paper with embedded tutorial code exhibiting the functionality of the library. This paper is currently hosted as a Jupyter book that combines narrative content and code snippets that can be executed online. In addition to research software development, I have focused my graduate work on performing reproducible, open fMRI research. To that end, chapter 4 is a repli- cation and extension of a recent paper on multi-echo fMRI denoising methods Power et al. (2018a). This replication was organized as a registered report, in which the introduction and methods were submitted for peer review before the analyses were performed. Finally, chapter 5 is a conclusion to the dissertation, in which I reflect on the work I have done and the skills I have developed throughout my training

Into the Multiverse: Methods for Studying Developmental Neuroscience

One major challenge in developmental neuroscience research is the sheer number of choices researchers face when addressing even a single research question. Even once data collection is complete, the journey from raw data to interpretation of findings may depend on numerous decisions. To address this issue, this dissertation explores “multiverse” analysis techniques for following many analytical paths at once in the same dataset. In chapter 1, multiverses are used to examine which analyses of age-related change in amygdala-medial prefrontal cortex circuitry are robust versus sensitive to researcher decisions. Chapter 2 uses multiverse analysis to identify optimal solutions for mitigating breathing-induced artifacts in resting-state functional magnetic resonance imaging data. Chapter 3 uses a variety of model specifications to characterize simultaneous reward learning strategies in youth contingent on both visual task cues and spatial-motor information. Despite varied approaches and goals, each of the three studies highlight the benefits of conducting multiple parallel analyses for both addressing questions in developmental neuroscience and deepening understanding of the methods used to address them