Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

Assessment of cortical activity, functional connectivity, and neuroplasticity in cerebral palsy using functional near-infrared spectroscopy: A scoping review

AIM

To map and critically appraise the literature on the feasibility and current use of functional near-infrared spectroscopy (fNIRS) to assess cortical activity, functional connectivity, and neuroplasticity in individuals with cerebral palsy (CP).

METHOD

A scoping review methodology was prospectively registered and reported following Preferred Reporting Items for Systematic review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted in four databases. Empirical studies using fNIRS to assess neural activity, functional connectivity, or neuroplasticity in individuals with CP aged 3 years or older were included.

RESULTS

Sixteen studies met the inclusion criteria. Individuals with CP (age range = 3-43 years; 70% unilateral CP) underwent fNIRS-based assessment for task-evoked activity (studies [n] = 15) and/or resting-state functional connectivity (n = 3). Preliminary observations suggest greater magnitude, extent, and ipsilateral hemispheric lateralization of sensorimotor cortex activity in CP, while magnitude and patterns of prefrontal cortex activity in CP appear dependent on task demands. Normalization of fNIRS-based activity metrics observed postintervention (n = 3) paralleled improvements in functional outcomes, highlighting their potential as promising biomarkers for functional gains in CP.

INTERPRETATION

This review details the use of fNIRS in CP, highlights research gaps and technical limitations, and offers recommendations to support fNIRS implementation for ecologically valid functional neuroimaging in individuals with CP.

The role of anxiety in modulating temporal processing and sensory hyperresponsiveness in autism spectrum disorder: an fMRI study

The atypical sensory features and high comorbidity of anxiety disorders in individuals with autism spectrum disorder (ASD) are attracting increasing attention. Among individuals with ASD, those who exhibit heightened sensory hyperresponsiveness tend to show enhanced temporal processing of sensory stimuli, despite no observed differences in stimulus detection thresholds. A previous study reported the role of anxiety in modulating emotion-cued changes of visual temporal resolution in ASD. Building on this, we hypothesized that elevated anxiety might contribute to increased activation of neural circuits for timing perception and sensory hyperresponsiveness. This study included 25 individuals with ASD and 25 typically developed (TD) participants. Using functional magnetic resonance imaging (fMRI), we examined neural activity during a visual temporal order judgment task pre-cued by facial emotions. In the TD group, but not the ASD group, the presence of fearful facial expressions enhanced temporal processing. However, a correlation of anxiety levels with emotion-cued task performance and sensory hyperresponsiveness, respectively, was evident in the ASD group. In the TD group, neuroimaging revealed greater activation of the right caudate compared with that in the ASD group and a functional connectivity between the amygdala and left supramarginal gyrus. Individuals with ASD showed a relationship between anxiety level and activation of the right angular gyrus. Moreover, anxiety mediated the link between right angular gyrus activation and sensory hyperresponsiveness in the ASD group. These findings suggest that enhancement of temporal processing by fear-related cues-reflecting an emotion-timing neural circuit-may be disrupted in individuals with ASD. Heightened anxiety and sensory hyperresponsiveness in ASD may be mediated by brain regions involved in timing perception.

Differences in Head Motion During Functional Magnetic Resonance Imaging Across Pediatric Neuropsychiatric Disorders

Background

Robust correction for head motion during functional magnetic resonance imaging is critical to avoid artifact-driven findings. Despite head motion differences across neuropsychiatric disorders, pediatric head motion across a range of diagnoses and covariates has not yet been evaluated. We tested 4 preregistered hypotheses: 1) externalizing disorder diagnoses will associate with more head motion during scanning; 2) internalizing disorder diagnoses will associate with less motion; 3) among children without attention-deficit/hyperactivity disorder, externalizing disorders will associate with more motion; and 4) among children with attention-deficit/hyperactivity disorder, comorbid internalizing disorders will associate with less motion.

Methods

Healthy Brain Network data releases 1.0-7.0 (n = 971) were analyzed in a discovery phase, and additional data released by February 29, 2024 (n = 437) were used in confirmatory analyses. Linear mixed-effects models were fitted with in-scanner head motion as the dependent variable. Binary independent variables of interest assessed for the presence or absence of externalizing or internalizing disorders.

Results

The confirmatory sample did not show significant associations between head motion and externalizing or internalizing disorders or support for the preregistered hypotheses. Across samples, there was a consistent interaction between age and neurodevelopmental diagnoses such that age-related decreases in head motion were attenuated in children with neurodevelopmental disorders.

Conclusions

Head motion remains an important confound in pediatric neuroimaging that may be associated with many factors, including neuropsychiatric symptoms, age, cognitive and physical attributes, and interactions among these variables. This work takes a step toward parsing these complex associations, focusing on neuropsychiatric diagnoses, age, and their interaction.

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning. However, data augmentation strategies employed in these approaches may damage the original blood-oxygen-level-dependent (BOLD) signals, thus hindering subsequent fMRI feature extraction. In this paper, we propose a self-supervised graph contrastive learning framework with diffusion augmentation (GCDA) for functional MRI analysis. The GCDA consists of a pretext model and a task-specific model. In the pretext model, we first augment each brain functional connectivity network derived from fMRI through a graph diffusion augmentation (GDA) module, and then use two graph isomorphism networks with shared parameters to extract features in a self-supervised contrastive learning manner. The pretext model can be optimized without the need for labeled training data, while the GDA focuses on perturbing graph edges and nodes, thus preserving the integrity of original BOLD signals. The task-specific model involves fine-tuning the trained pretext model to adapt to downstream tasks. Experimental results on two rs-fMRI cohorts with a total of 1230 subjects demonstrate the effectiveness of our method compared with several state-of-the-arts.

Representing Brain-Behavior Associations by Retaining High-Motion Minoritized Youth

BACKGROUND

Population neuroscience datasets provide an opportunity for researchers to estimate reproducible effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals.

METHODS

We used motion-ordering and motion-ordering+resampling (bagging) to test whether these methods preserve functional magnetic resonance imaging (fMRI) data in the Adolescent Brain Cognitive Development (ABCD) Study (N = 5733). For the 2 methods, brain-behavior associations were computed as the partial Spearman's rank correlations (Rs) between functional connectivity and cognitive performance (NIH Cognition Toolbox) as well as externalizing and internalizing psychopathology (Child Behavior Checklist) while adjusting for participant sex assigned at birth and head motion.

RESULTS

Black and Hispanic youth exhibited excess head motion relative to data collected from White youth and were discarded disproportionately when conventional approaches were used. Motion-ordering and bagging methods retained more than 99% of Black and Hispanic youth. Both methods produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data.

CONCLUSIONS

The motion-ordering and bagging methods are 2 feasible approaches that can enhance sample representation for testing brain-behavior associations and that result in reproducible effect sizes in diverse populations.

Transdiagnostic Symptom Domains are Associated with Head Motion During Multimodal Imaging in Children

Background

Head motion is a challenge for neuroimaging research in developmental populations. However, it is unclear how transdiagnostic symptom domains including attention, disruptive behavior (e.g., externalizing behavior), and internalizing problems are linked to scanner motion in children, particularly across structural and functional MRI. The current study examined whether transdiagnostic domains of attention, disruptive behavior, and internalizing symptoms are associated with scanner motion in children during multimodal imaging.

Methods

In a sample of 9,045 children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control metrics and mean framewise displacement for the following: T1-weighted structural, resting-state fMRI, diffusion MRI, Stop-Signal Task, Monetary Incentive Delay task, and Emotional n-Back task. The Child Behavior Checklist was used as a continuous measure of symptom severity.

Results

Greater attention and disruptive behavior problem severity was associated with a lower likelihood of passing motion quality control across several imaging modalities. In contrast, increased internalizing severity was associated with a higher likelihood of passing motion quality control. Increased attention and disruptive behavior problem severity was also associated with increased mean motion, whereas increased internalizing problem severity was associated with decreased mean motion.

Conclusion

Transdiagnostic domains emerged as predictors of motion in youths. These findings have implications for advancing development of generalizable and robust brain-based biomarkers, computational approaches for mitigating motion effects, and enhancing accessibility of imaging protocols for children with varying symptom severities.

Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism

Independent component analysis (ICA) is widely used to estimate spatial resting-state networks and their time courses in neuroimaging studies. It is thought that independent components correspond to sparse patterns of co-activating brain locations. Previous approaches for introducing sparsity to ICA replace the non-smooth objective function with smooth approximations, resulting in components that do not achieve exact zeros. We propose a novel Sparse ICA method that enables sparse estimation of independent source components by solving a non-smooth non-convex optimization problem via the relax-and-split framework. The proposed Sparse ICA method balances statistical independence and sparsity simultaneously and is computationally fast. In simulations, we demonstrate improved estimation accuracy of both source signals and signal time courses compared to existing approaches. We apply our Sparse ICA to cortical surface resting-state fMRI in school-aged autistic children. Our analysis reveals differences in brain activity between certain regions in autistic children compared to children without autism. Sparse ICA selects coactivating locations, which we argue is more interpretable than dense components from popular approaches. Sparse ICA is fast and easy to apply to big data.

Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study

OBJECTIVE

How abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology.

METHODS

A total of 83 ASD and typically developing (TD) individuals participated in this study. Participants' interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio.

RESULTS

ASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017).

CONCLUSIONS

Differential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation.

SIGNIFICANCE

Brain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations

Population neuroscience datasets allow researchers to estimate reliable effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals. We employ motion-ordering and motion-ordering+resampling (bagging) to test if these methods preserve functional MRI (fMRI) data in the Adolescent Brain Cognitive Development Study ( N = 5,733 ). Black and Hispanic youth exhibited excess head motion relative to data collected from White youth, and were discarded disproportionately when using conventional approaches. Both methods retained more than 99% of Black and Hispanic youth. They produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data. The motion-ordering and bagging methods are two feasible approaches that can enhance sample representation for testing brain-behavior associations and fulfill the promise of consortia datasets to produce generalizable effect sizes across diverse populations.

Heterogeneity Analysis on Multi-state Brain Functional Connectivity and Adolescent Neurocognition

Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concentrate on brain activities under a single cognitive state, and fail to consider heterogeneity when attempting to characterize brain-to-behavior relationships. In this work, we study the complex impact of multi-state functional connectivity on behaviors by analyzing the data from a recent landmark brain development and child health study. We propose a nonparametric, Bayesian supervised heterogeneity analysis to uncover neurodevelopmental subtypes with distinct effect mechanisms. We impose stochastic block structures to identify network-based functional phenotypes and develop a variational expectation-maximization algorithm to facilitate an efficient posterior computation. Through integrating resting-state and task-related functional connectomes, we dissect heterogeneous effect mechanisms on children's fluid intelligence from the functional network phenotypes including Fronto-parietal Network and Default Mode Network under different cognitive states. Based on extensive simulations, we further confirm the superior performance of our method on uncovering brain-to-behavior relationships.

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

PURPOSE

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.

METHODS

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results.

RESULTS

Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE.

CONCLUSIONS

There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

Disrupted Brain Functional Status in Patients with Reversible Cerebral Vasoconstriction Syndrome

OBJECTIVES

The aim of this study was to investigate the functional networks in subjects with reversible cerebral vasoconstriction syndrome (RCVS) using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We prospectively recruited patients with RCVS and healthy controls (HCs) between February 2017 and April 2021. The rs-fMRI data were analyzed using graph theory methods. We compared node-based global and regional topological metrics (Bundle 1) and network-based intranetwork and internetwork connectivity (Bundle 2) between RCVS patients and HCs. We also explored the associations of clinical and vascular (ie, the Lindegaard index, LI) parameters with significant rs-fMRI metrics.

RESULTS

A total of 104 RCVS patients and 93 HCs were included in the final analysis. We identified significantly decreased local efficiency of the left dorsal anterior insula (dAI; p = 0.0005) in RCVS patients within 30 days after disease onset as compared to HCs, which improved 1 month later. RCVS patients also had increased global efficiency (p = 0.009) and decreased average degree centrality (p = 0.045), clustering coefficient (p = 0.033), and assortativity values (p = 0.003) in node-based analysis. In addition, patients with RCVS had increased internetwork connectivity of the default mode network (DMN) with the salience (p = 0.027) and dorsal attention (p = 0.016) networks. Significant correlations between LI and regional local efficiency in left dAI (rs  = -0.418, p = 0.042) was demonstrated.

INTERPRETATION

The significantly lower local efficiency of the left dAI, suggestive of impaired central autonomic modulation, was negatively correlated with vasoconstriction severity, which is highly plausible for the pathogenesis of RCVS. ANN NEUROL 2023;94:772-784.

Brief mock-scan training reduces head motion during real scanning for children: A growth curve study

Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.

Heart Rate Variability Covaries with Amygdala Functional Connectivity During Voluntary Emotion Regulation

The Neurovisceral Integration Model posits that shared neural networks support the effective regulation of emotions and heart rate, with heart rate variability (HRV) serving as an objective, peripheral index of prefrontal inhibitory control. Prior neuroimaging studies have predominantly examined both HRV and associated neural functional connectivity at rest, as opposed to contexts that require active emotion regulation. The present study sought to extend upon previous resting-state functional connectivity findings, examining task-related HRV and corresponding amygdala functional connectivity during a cognitive reappraisal task. Seventy adults (52 older and 18 younger adults, 18-84 years, 51% male) received instructions to cognitively reappraise negative affective images during functional MRI scanning. HRV measures were derived from a finger pulse signal throughout the scan. During the task, younger adults exhibited a significant inverse association between HRV and amygdala-medial prefrontal cortex (mPFC) functional connectivity, in which higher task-related HRV was correlated with weaker amygdala-mPFC coupling, whereas older adults displayed a slight positive, albeit non-significant correlation. Furthermore, voxelwise whole-brain functional connectivity analyses showed that higher task-based HRV was linked to weaker right amygdala-posterior cingulate cortex connectivity across older and younger adults, and in older adults, higher task-related HRV correlated positively with stronger right amygdala-right ventrolateral prefrontal cortex connectivity. Collectively, these findings highlight the importance of assessing HRV and neural functional connectivity during active regulatory contexts to further identify neural concomitants of HRV and adaptive emotion regulation.

Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate

Reliability and robustness of resting state functional connectivity MRI (rs-fcMRI) relies, in part, on minimizing the influence of head motion on measured brain signals. The confounding effects of head motion on functional connectivity have been extensively studied in adults, but its impact on newborn brain connectivity remains unexplored. Here, using a large newborn data set consisting of 159 rs-fcMRI scans acquired in the Developing Brain Institute at Children's National Hospital and 416 scans from The Developing Human Connectome Project (dHCP), we systematically investigated associations between head motion and rs-fcMRI. Head motion during the scan significantly affected connectivity at sensory-related networks and default mode networks, and at the whole brain scale; the direction of motion effects varied across the whole brain. Comparing high- versus low-head motion groups suggested that head motion can impact connectivity estimates across the whole brain. Censoring of high-motion volumes using frame-wise displacement significantly reduced the confounding effects of head motion on neonatal rs-fcMRI. Lastly, in the dHCP data set, we demonstrated similar persistent associations between head motion and network connectivity despite implementing a standard denoising strategy. Collectively, our results highlight the importance of using rigorous head motion correction in preprocessing neonatal rs-fcMRI to yield reliable estimates of brain activity.

Demonstrating quality control procedures for fMRI in DPABI

Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now.

A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB

Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.

Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD

Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.

Recent Developments in Autism Genetic Research: A Scientometric Review from 2018 to 2022

Genetic research in Autism Spectrum Disorder (ASD) has progressed tremendously in recent decades. Dozens of genetic loci and hundreds of alterations in the genetic sequence, expression, epigenetic transformation, and interactions with other physiological and environmental systems have been found to increase the likelihood of developing ASD. There is therefore a need to represent this wide-ranging yet voluminous body of literature in a systematic manner so that this information can be synthesised and understood at a macro level. Therefore, this study made use of scientometric methods, particularly document co-citation analysis (DCA), to systematically review literature on ASD genetic research from 2018 to 2022. A total of 14,818 articles were extracted from Scopus and analyzed with CiteSpace. An optimized DCA analysis revealed that recent literature on ASD genetic research can be broadly organised into 12 major clusters representing various sub-topics. These clusters are briefly described in the manuscript and potential applications of this study are discussed.