Getting the Nod: Characterizing pediatric head motion in movie- and resting-state fMRI.

Head motion continues to be a major problem in fMRI research, particularly in developmental studies where an inverse relationship exists between head motion and age. Despite multifaceted and costly efforts to mitigate motion and motion-related signal artifact, few studies have characterized in-scanner head motion itself. This study leverages a large public dataset (N=1388, age 5-21y, The Healthy Brain Network Biobank) to characterize pediatric head motion in space, frequency, and time. We focus on practical aspects of head motion that could impact future study design, including comparing motion across groups (low, medium, and high movers), across conditions (movie-watching and rest), and between males and females. Analyses showed that in all conditions, high movers exhibited a different pattern of motion than low and medium movers that was dominated by x-rotation, and z- and y-translation. High motion spikes (>0.3mm) from all participants also showed this pitch-z-y pattern. Problematic head motion is thus composed of a single type of biomechanical motion, which we infer to be a nodding movement, providing a focused target for motion reduction strategies. A second type of motion was evident via spectral analysis of raw displacement data. This was observed in low and medium movers and was consistent with respiration rates. We consider this to be a baseline of motion best targeted in data preprocessing. Further, we found that males moved more than, but not differently from, females. Significant cross-condition differences in head motion were found. Movies had lower mean motion, and especially in high movers, movie-watching reduced within-run linear increases in head motion (i.e., temporal drift). Finally, we used intersubject correlations of framewise displacement (FD-ISCs) to assess for stimulus-correlated motion trends. Subject motion was more correlated in movie than rest and stimulus-correlated stillness occurred more often than stimulus-correlated motion. Possible reasons and future implications of these findings are discussed.

Download Full-text

The impact of customized head molds on motion and motion-related artifacts from structural and functional MRI scans in children

10.1101/2021.03.24.21253213 ◽

2021 ◽

Author(s):

Timothy Weng ◽

Ruben Vela ◽

Wade Weber ◽

Manwitha Dodla ◽

Anibal Solon Heinsfeld ◽

...

Keyword(s):

Young Children ◽

Data Quality ◽

Resting State Fmri ◽

Head Motion ◽

Developmental Studies ◽

Motion Data ◽

Neuroimaging Data ◽

Aging Populations ◽

Mri Scans ◽

The Impact

Although neuroimaging provides powerful tools for assessing brain structure and function, their utility for elucidating mechanisms underlying neuropsychiatric disorders is limited by their sensitivity to head motion. Several publications have shown that standard retrospective motion correction and arduous quality assessment are insufficient to fully remove the deleterious impacts of motion on functional (fMRI) and structural (sMRI) neuroimaging data. These residual errors tend to be correlated with age and clinical diagnosis, resulting in artifactual findings in studies of clinical, developmental, and aging populations. As such there is a continued need to explore and evaluate novel methods for reducing head motion, and their applicability in these populations. Recently, a custom-fitted styrofoam head mold was reported to reduce motion across a range of ages, mostly adolescents, during a resting state fMRI scan. In the present study, we tested the efficacy of these head molds in a sample exclusively of young children (N = 19; mean age = 7.9 years) including those with ADHD (N = 6). We evaluated the head mold's impact on head motion, data quality, and analysis results derived from the data. Importantly, we also evaluated whether the head molds were tolerated by our population. We also assessed the extent to which the head mold's efficacy was related to anxiety levels and ADHD symptoms. In addition to fMRI, we examined the head mold's impact on sMRI by using a specialized sequence with embedded volumetric navigators (vNAV) to determine head motion during sMRI. We evaluated the head mold's impact on head motion, data quality, and analysis results derived from the data. Additionally, we conducted acoustic measurements and analyses to determine the extent to which the head mold reduced the noise dosage from the scanner. We found that some individuals benefited while others did not improve significantly. One individual's sMRI motion was made worse by the head mold. We were unable to identify predictors of the head mold response due to the smaller sample size. The head molds were tolerated well by young children, including those with ADHD, and they provided ample hearing protection. Although the head mold was not a universal solution for reducing head motion and improving data quality, we believe the time and cost required for using the head mold may outweigh the potential loss of data from excessive head motion for developmental studies.

Download Full-text

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies

Cerebral Cortex ◽

10.1093/cercor/bhaa139 ◽

2020 ◽

Vol 30 (10) ◽

pp. 5544-5559 ◽

Cited By ~ 5

Author(s):

Jonathan D Power ◽

Charles J Lynch ◽

Babatunde Adeyemo ◽

Steven E Petersen

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Critical Event ◽

Head Motion ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Decay Properties ◽

Signal Decay ◽

Biological Variables ◽

Related Approach

Abstract This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like “Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties”. Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

Download Full-text

Aging effect on head motion: A Machine Learning study on resting state fMRI data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109084 ◽

2021 ◽

Vol 352 ◽

pp. 109084

Author(s):

Valeria Saccà ◽

Alessia Sarica ◽

Andrea Quattrone ◽

Federico Rocca ◽

Aldo Quattrone ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Aging Effect ◽

Resting State Fmri ◽

Fmri Data ◽

Head Motion ◽

Learning Study

Download Full-text

Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

10.1101/344473 ◽

2018 ◽

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Temporal Structure ◽

Structural Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Network ◽

Dynamical Analysis ◽

Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

Download Full-text

Multimodal multilayer network centrality relates to executive functioning

10.1101/2021.06.28.450180 ◽

2021 ◽

Author(s):

Lucas C. Breedt ◽

Fernando A.N. Santos ◽

Arjan Hillebrand ◽

Liesbeth Reneman ◽

Anne-Fleur van Rootselaar ◽

...

Keyword(s):

Executive Functioning ◽

Positive Association ◽

Resting State Fmri ◽

Brain Network ◽

Network Organization ◽

Eigenvector Centrality ◽

Multilayer Network ◽

Network Analyses ◽

Frontoparietal Network ◽

Structural Networks

Executive functioning is a higher-order cognitive process that is thought to depend on a brain network organization facilitating network integration across specialized subnetworks. The frontoparietal network (FPN), a subnetwork that has diverse connections to other brain modules, seems pivotal to this integration, and a more central role of regions in the FPN has been related to better executive functioning. Brain networks can be constructed using different modalities: diffusion MRI (dMRI) can be used to reconstruct structural networks, while resting-state fMRI (rsfMRI) and magnetoencephalography (MEG) yield functional networks. These networks are often studied in a unimodal way, which cannot capture potential complementary or synergistic modal information. The multilayer framework is a relatively new approach that allows for the integration of different modalities into one 'network of networks'. It has already yielded promising results in the field of neuroscience, having been related to e.g. cognitive dysfunction in Alzheimer's disease. Multilayer analyses thus have the potential to help us better understand the relation between brain network organization and executive functioning. Here, we hypothesized a positive association between centrality of the FPN and executive functioning, and we expected that multimodal multilayer centrality would supersede unilayer centrality in explaining executive functioning. We used dMRI, rsfMRI, MEG, and neuropsychological data obtained from 33 healthy adults (age range 22-70 years) to construct eight modality-specific unilayer networks (dMRI, fMRI, and six MEG frequency bands), as well as a multilayer network comprising all unilayer networks. Interlayer links in the multilayer network were present only between a node's counterpart across layers. We then computed and averaged eigenvector centrality of the nodes within the FPN for every uni- and multilayer network and used multiple regression models to examine the relation between uni- or multilayer centrality and executive functioning. We found that higher multilayer FPN centrality, but not unilayer FPN centrality, was related to better executive functioning. To further validate multilayer FPN centrality as a relevant measure, we assessed its relation with age. Network organization has been shown to change across the life span, becoming increasingly efficient up to middle age and regressing to a more segregated topology at higher age. Indeed, the relation between age and multilayer centrality followed an inverted-U shape. These results show the importance of FPN integration for executive functioning as well as the value of a multilayer framework in network analyses of the brain. Multilayer network analysis may particularly advance our understanding of the interplay between different brain network aspects in clinical populations, where network alterations differ across modalities.

Download Full-text

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

Scientific Reports ◽

10.1038/s41598-020-60527-z ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 5

Author(s):

Naho Ichikawa ◽

Giuseppe Lisi ◽

Noriaki Yahata ◽

Go Okada ◽

Masahiro Takamura ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Learning Algorithm ◽

Inferior Frontal Gyrus ◽

Resting State Fmri ◽

Posterior Cingulate Cortex ◽

Brain Network ◽

Major Depressive ◽

Functional Connections ◽

Limited Efficacy

Abstract The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second ‘most important’ FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Download Full-text

Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

Scientific Data ◽

10.1038/s41597-020-00699-5 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Sharna D. Jamadar ◽

Phillip G. D. Ward ◽

Thomas G. Close ◽

Alex Fornito ◽

Malin Premaratne ◽

...

Keyword(s):

Fdg Pet ◽

Imaging Modality ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Haemodynamic Response ◽

Constant Infusion ◽

High Temporal Resolution ◽

Bold Fmri ◽

Positron Emission

Abstract Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain – cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18–20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.

Download Full-text

Altered brain network organization in romantic love as measured with resting-state fMRI and graph theory

Brain Imaging and Behavior ◽

10.1007/s11682-019-00226-0 ◽

2020 ◽

Vol 14 (6) ◽

pp. 2771-2784 ◽

Cited By ~ 1

Author(s):

Chuan Wang ◽

Sensen Song ◽

Federico d’Oleire Uquillas ◽

Anna Zilverstand ◽

Hongwen Song ◽

...

Keyword(s):

Graph Theory ◽

Resting State ◽

Romantic Love ◽

Resting State Fmri ◽

Brain Network ◽

Network Organization

Download Full-text

Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33012556 ◽

2019 ◽

Vol 33 ◽

pp. 2556-2563 ◽

Cited By ~ 5

Author(s):

Abhay M S Aradhya ◽

Aditya Joglekar ◽

Sundaram Suresh ◽

M. Pratama

Keyword(s):

Resting State ◽

Cognitive Disorders ◽

Feature Space ◽

Resting State Fmri ◽

Transformation Method ◽

Brain Network ◽

Fmri Data ◽

Functional Activities ◽

Temporal Correlations ◽

Spatio Temporal

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

Download Full-text