Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations

In Vivo Microwave-Induced Thermoacoustic Endoscopy for Colorectal Tumor Detection in Deep Tissue

Optical endoscopy, as one of the common clinical diagnostic modalities, provides irreplaceable advantages in the diagnosis and treatment of internal organs. However, the approach is limited to the characterization of superficial tissues due to the strong optical scattering properties of tissue. In this work, a microwave-induced thermoacoustic (TA) endoscope (MTAE) was developed and evaluated. The MTAE system integrated a homemade monopole sleeve antenna (diameter = 7 mm) for providing homogenized pulsed microwave irradiation to induce a TA signal in the colorectal cavity and a side-viewing focus ultrasonic transducer (diameter = 3 mm) for detecting the TA signal in the ultrasonic spectrum to construct the image. Our MTAE, system combined microwave excitation and acoustic detection; produced images with dielectric contrast and high spatial resolution at several centimeters deep in soft tissues, overcome the current limitations of the imaging depth of optical endoscopy and mechanical wave-based imaging contrast of ultrasound endoscopy, and had the ability to extract complete features for deep location tumors that could be infiltrating and invading adjacent structures. The practical feasibility of the MTAE system was evaluated i n vivo with rabbits having colorectal tumors. The results demonstrated that colorectal tumor progression could be visualized from the changes in electromagnetic parameters of the tissue via MTAE, showing its potential clinical application.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Concurrent and prospective associations between infant frontoparietal and default mode network connectivity and negative affectivity

Emotion dysregulation is linked to differences in frontoparietal (FPN) and default mode (DMN) brain network functioning. These differences may be identifiable early in development. Temperamental negative affectivity has been identified as a precursor to later emotion dysregulation, though the underlying neurodevelopmental mechanism is unknown. The present study explores concurrent and prospective associations between FPN and DMN connectivity in infants and measures of negative affectivity. 72 infants underwent 5.03-13.28 min of resting state fMRI during natural sleep (M±SD age=4.90 ± 0.84 weeks; 54% male; usable data=9.92 ± 2.15 min). FPN and DMN intra- and internetwork connectivity were computed using adult network assignments. Crying was obtained from both parent-report and day-long audio recordings. Temperamental negative affectivity was obtained from a parent-report questionnaire. In this preregistered study, based on analyses conducted with a subset of this data (N = 32), we hypothesized that greater functional connectivity within and between FPN and DMN would be associated with greater negative affectivity. In the full sample we did not find support for these hypotheses. Instead, greater DMN intranetwork connectivity at age one month was associated with lower concurrent parent-reported crying and temperamental negative affectivity at age six months (ßs>-0.35, ps<.025), but not crying at age six months. DMN intranetwork connectivity was also negatively associated with internalizing symptoms at age eighteen-months (ß=-0.58, p = .012). FPN intra- and internetwork connectivity was not associated with negative affectivity measures after accounting for covariates. This work furthers a neurodevelopmental model of emotion dysregulation by suggesting that infant functional connectivity at rest is associated with later emotional functioning.

Prenatal environment is associated with the pace of cortical network development over the first three years of life

Environmental influences on brain structure and function during early development have been well-characterized. In pre-registered analyses, we test the theory that socioeconomic status (SES) is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal SES is associated with developmental increases in cortical network segregation, with neonates and toddlers from lower-SES backgrounds showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between SES and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. SES-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results yield key insight into the timing and directionality of associations between the early environment and trajectories of cortical development.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.

Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing

Functional MRI (fMRI) data may be contaminated by artifacts arising from a myriad of sources, including subject head motion, respiration, heartbeat, scanner drift, and thermal noise. These artifacts cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data-all of which can have negative consequences for the accuracy and power of downstream statistical analysis. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts and generally comes in two flavors. Motion scrubbing based on subject head motion-derived measures is popular but suffers from a number of drawbacks, among them the need to choose a threshold, a lack of generalizability to multiband acquisitions, and high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods like DVARS are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we propose "projection scrubbing", a novel data-driven scrubbing method based on a statistical outlier detection framework and strategic dimension reduction, including independent component analysis (ICA), to isolate artifactual variation. We undertake a comprehensive comparison of motion scrubbing with data-driven projection scrubbing and DVARS. We argue that an appropriate metric for the success of scrubbing is maximal data retention subject to reasonable performance on typical benchmarks such as the validity, reliability, and identifiability of functional connectivity. We find that stringent motion scrubbing yields worsened validity, worsened reliability, and produced small improvements to fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater improvements to fingerprinting while not generally worsening validity or reliability. Importantly, however, data-driven scrubbing excludes a fraction of the number of volumes or entire sessions compared to motion scrubbing. The ability of data-driven fMRI scrubbing to improve data retention without negatively impacting the quality of downstream analysis has major implications for sample sizes in population neuroscience research.

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.

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Network-specific selectivity of functional connections in the neonatal brain

The adult human brain is organized into functional brain networks, groups of functionally connected segregated brain regions. A key feature of adult functional networks is long-range selectivity, the property that spatially distant regions from the same network have higher functional connectivity than spatially distant regions from different networks. Although it is critical to establish the status of functional networks and long-range selectivity during the neonatal period as a foundation for typical and atypical brain development, prior work in this area has been mixed. Although some studies report distributed adult-like networks, other studies suggest that neonatal networks are immature and consist primarily of spatially isolated regions. Using a large sample of neonates (n = 262), we demonstrate that neonates have long-range selective functional connections for the default mode, fronto-parietal, and dorsal attention networks. An adult-like pattern of functional brain networks is evident in neonates when network-detection algorithms are tuned to these long-range connections, when using surface-based registration (versus volume-based registration), and as per-subject data quantity increases. These results help clarify factors that have led to prior mixed results, establish that key adult-like functional network features are evident in neonates, and provide a foundation for studies of typical and atypical brain development.