Conor Wild

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Optimizing stimulation and analysis protocols for neonatal fMRI.

The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF.

Adult-like processing of naturalistic sounds in auditory cortex by 3- and 9-month old infants

&NA; Functional neuroimaging has been used to show that the developing auditory cortex of very young human infants responds, in some way, to sound. However, impoverished stimuli and uncontrolled designs have made it difficult to attribute brain responses to specific auditory features, and thus made it difficult to assess the maturity of feature tuning in auditory cortex. To address this, we used functional magnetic resonance imaging (fMRI) to measure the brain activity evoked by naturalistic sounds (a series of sung lullabies) in two groups of infants (3 and 9 months) and adults. We developed a novel analysis method – inter‐subject regression (ISR) – to quantify the similarity of cortical responses between infants and adults, and to decompose components of the response due to different auditory features. We found that the temporal pattern of activity in infant auditory cortex shared similarity with adults. Some of this shared response could be attributed to simple acoustic features, such as frequency, pitch, envelope, but other parts were not, suggesting that even more complex adult‐like features are represented in auditory cortex in early infancy. HighlightsComplex brain responses to naturalistic sounds were observed in 3‐month old infants.A novel method based on inter‐subject synchrony was used to tease these apart.Infant responses in auditory cortex were quantifiably similar to adult responses.Low‐level acoustic features could explain only a part of this common response.This suggests that complex adult‐like auditory processing is present at 3 months.

Auditory Structural Connectivity in Preterm and Healthy Term Infants During the First Postnatal Year

Assessing language development in the first postnatal year is difficult, as receptive and expressive skills are rudimentary. Although outward manifestations of change are limited, the auditory language system is thought to undergo critical development at this age, as the foundations are laid for the rapid onset of spoken language in the second and third years. We recruited 11 infants, 7 healthy controls (gestational ageu2009=u200940.69u2009±u20090.56; range from 40 to 41.43) and preterm babies (gestational ageu2009=u200928.04u2009±u20090.95; range from 27.43 to 29.43) who underwent a Magnetic Resonance Imaging study during the first postnatal year (age at scanu2009=u2009194.18u2009±u200997.98). We assessed white matter tracts using diffusion-weighted magnetic resonance imaging with probabilistic tractography. Fractional anisotropy was found to be largely mature even at one month, although there was a little further increase during the first postnatal year in both the acoustic radiation and the direct brainstem-Heschls pathway.

Differences in the spatial and temporal patterns of head motion during MRI of adults and infants

Aim Head motion has a profound effect on MRI, and will contaminate comparisons of function or structure between groups that move differently. This work compares adults and infants. Infants might move differently for physical, physiological and cognitive reasons, but so far these differences have not been quantified. Methods The spatial modes and total magnitude of motion in the MRI scanner were measured (N=211). The effects of group (infant vs. adult) and stimulation paradigm (auditory vs. visual) were evaluated. Results Spatial modes of motion were found to be distinct between infant and adult groups. Infants had less anterior-posterior translational motion, but greater motion in other dimensions, often with complex multi-axis patterns. In magnitude distribution, sleeping infants often remained more still than adults, but when movement did occur it was more extreme and abrupt. Two groups of adults presented with different stimulation showed similar shapes of motion. Conclusion The spatial modes and magnitude distribution of motion differed substantially between groups, and must be considered carefully as a confound in comparisons of structure or function. The abruptness and magnitude of movement suggests that for infants relative to adults post-processing strategies such as de-noising are likely to be more effective than prospective motion correction. Key notes Quantified the spatial and temporal distribution of motion during MRI in 211 adults and neonates The different spatial modes in adults and infants were visualized and statistically contrasted The magnitude of motion had “heavier tails” in infants, with more still periods, and more large movements, than adults.

Using Functional Magnetic Resonance Imaging to Detect Preserved Function in a Preterm Infant with Brain Injury

&NA; We studied developmental plasticity using functional magnetic resonance imaging (fMRI) in a preterm infant with brain injury on structural MRI. fMRI showed preserved brain function and subsequent neurodevelopment was within the normal range. Multimodal neuroimaging including fMRI can improve understanding of neural plasticity after preterm birth and brain injury.

Dissociable effects of self-reported daily sleep duration on high-level cognitive abilities

Abstract Most people will at some point experience not getting enough sleep over a period of days, weeks, or months. However, the effects of this kind of everyday sleep restriction on high-level cognitive abilities—such as the ability to store and recall information in memory, solve problems, and communicate—remain poorly understood. In a global sample of over 10000 people, we demonstrated that cognitive performance, measured using a set of 12 well-established tests, is impaired in people who reported typically sleeping less, or more, than 7–8 hours per night—which was roughly half the sample. Crucially, performance was not impaired evenly across all cognitive domains. Typical sleep duration had no bearing on short-term memory performance, unlike reasoning and verbal skills, which were impaired by too little, or too much, sleep. In terms of overall cognition, a self-reported typical sleep duration of 4 hours per night was equivalent to aging 8 years. Also, sleeping more than usual the night before testing (closer to the optimal amount) was associated with better performance, suggesting that a single night’s sleep can benefit cognition. The relationship between sleep and cognition was invariant with respect to age, suggesting that the optimal amount of sleep is similar for all adult age groups, and that sleep-related impairments in cognition affect all ages equally. These findings have significant real-world implications, because many people, including those in positions of responsibility, operate on very little sleep and may suffer from impaired reasoning, problem-solving, and communications skills on a daily basis.

Disruption to functional networks in neonates with perinatal brain injury predicts motor skills at 8 months

Objective Functional connectivity magnetic resonance imaging (fcMRI) of neonates with perinatal brain injury could improve prediction of motor impairment before symptoms manifest, and establish how early brain organization relates to subsequent development. This cohort study is the first to describe and quantitatively assess functional brain networks and their relation to later motor skills in neonates with a diverse range of perinatal brain injuries. Methods Infants (nu202f=u202f65, included in final analyses: nu202f=u202f53) were recruited from the neonatal intensive care unit (NICU) and were stratified based on their age at birth (premature vs. term), and on whether neuropathology was diagnosed from structural MRI. Functional brain networks and a measure of disruption to functional connectivity were obtained from 14u202fmin of fcMRI acquired during natural sleep at term-equivalent age. Results Disruption to connectivity of the somatomotor and frontoparietal executive networks predicted motor impairment at 4 and 8u202fmonths. This disruption in functional connectivity was not found to be driven by differences between clinical groups, or by any of the specific measures we captured to describe the clinical course. Conclusion fcMRI was predictive over and above other clinical measures available at discharge from the NICU, including structural MRI. Motor learning was affected by disruption to somatomotor networks, but also frontoparietal executive networks, which supports the functional importance of these networks in early development. Disruption to these two networks might be best addressed by distinct intervention strategies.

Why does language not emerge until the second year

ABSTRACT From their second year, infants typically begin to show rapid acquisition of receptive and expressive language. Here, we ask why these language skills do not begin to develop earlier. One evolutionary hypothesis is that infants are born when many brains systems are immature and not yet functioning, including those critical to language, because human infants have large have a large head and their mothers pelvis size is limited, necessitating an early birth. An alternative proposal, inspired by discoveries in machine learning, is that the language systems are mature enough to function but need auditory experience to develop effective representations of speech, before the language functions that manifest in behaviour can emerge. Growing evidence, in particular from neuroimaging, is supporting this latter hypothesis. We have previously shown with magnetic resonance imaging (MRI) that the acoustic radiation, carrying rich information to auditory cortex, is largely mature by 1 month, and using functional MRI (fMRI) that auditory cortex is processing many complex features of natural sounds by 3 months. However, speech perception relies upon a network of regions beyond auditory cortex, and it is not established if this network is mature. Here we measure the maturity of the speech network using functional connectivity with fMRI in infants at 3 months (N = 6) and 9 months (N = 7), and in an adult comparison group (N = 15). We find that functional connectivity in speech networks is mature at 3 months, suggesting that the delay in the onset of language is not due to brain immaturity but rather to the time needed to develop representations through experience. Future avenues for the study of language development are proposed, and the implications for clinical care and infant education are discussed. HighlightsWe examined the maturity of the speech network in infants in their first year using functional connectivity fMRI.At 3 and 9‐months old, we found the pattern of connectivity of the network to be similar to adults.Supports growing evidence that broader language system is functioning from early in the first year.Long period of helplessness in human infants after birth is not because they are born with immature brains.Instead, the delay in language may be due to “silent learning”, analogous to the pre‐training in deep‐neural networks.

Brainstem shape is affected by clinical course in the neonatal intensive care unit

The brainstem, critical for motor function, autonomic regulation, and many neurocognitive functions, undergoes rapid development from the third trimester. Accordingly, we hypothesized it would be vulnerable to insult during this period, and that a difficult clinical course in the neonatal intensive care unit (NICU) would affect development, and be reflected through atypical shape. Our study population consisted of 66 neonates – all inpatients from the NICU at Victoria Hospital, London Health Sciences Centre, ON, Canada, of which 45 entered the final analysis. The cohort varied in gestational age (GA) and ranged from neurologically healthy to severely brain-injured. Structural MRI was used to quantify brainstem shape at term-equivalent age. From these images, brainstems were semi-automatically segmented and co-registered across subjects. The anterior-posterior dimensions on a sagittal maximum intensity projection were used as the basis for shape comparison. Factor analysis was used to summarize variation in shape and in clinical course to determine three shape factors and three clinical factors, and their relationship assessed using correlation. A factor driven by low GA and associated complications correlated with alterations in the posterior medulla, while a factor driven by complications independent of GA correlated with alterations in the midbrain. Additionally, single clinical measures most representative of their respective clinical factor (days in NICU; days on ventilation) predicted the changes. Thus, different clinical courses in the NICU may have different effects on the shape of the brainstem, and may mediate some of the distinct neurodevelopmental profiles observed in premature and brain-injured neonates.