Role of deep learning in infant brain MRI analysis

Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them

Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants

Age-Related Changes in Human Anatomical and Functional Brain Networks

Thesis (Ph.D.) - Indiana University, Psychological and Brain Sciences, 2015i) The first component characterizes age-related changes in specific connections. We find that functional connections within and between intrinsic connectivity networks (ICNs) follow distinct lifespan trajectories. We further characterize these changes in terms of each ICN’s “modularity” and find that most ICNs become less modular (i.e. less segregated) with age. In anatomical networks we find that hub regions are disproportionately affected by age and become less efficiently connected to the rest of the brain. Finally, we find that, with age stronger functional connections are supported by longer (multi-step) anatomical pathways for communication. ii) The second component is concerned with characterizing age-related changes in the boundaries of ICNs. To this end we used a multi-layer variant of modularity maximization to decompose networks into modules at different organizational scales, which we find exhibit scale-specific trends with age. At coarse scales, for example, we find that modules become more segregated whereas modules defined at finer scales become less segregated. We also find that module composition changes with age, and specific areas associated with memory change their module allegiance with age. iii) In the final component we use generative models to uncover wiring rules for the anatomical brain networks. Modeling network growth as a spatial penalty combined with homophily, we find that we can generate synthetic networks with many of the same properties as real-world brain networks. Fitting this model to individuals, we show that the parameter governing the severity of the spatial penalty weakens monotonically with age and that the overall ability to reproduce realistic connectomes for older individuals suffers. These results suggest that, with age, additional constraints may play an important role in shaping the topology of brain structural networks

ENCODING OF SALTATORY TACTILE VELOCITY IN THE ADULT OROFACIAL SOMATOSENSORY SYSTEM

Processing dynamic tactile inputs is a key function of somatosensory systems. Spatial velocity encoding mechanisms by the nervous system are important for skilled movement production and may play a role in recovery of motor function following neurological insult. Little is known about tactile velocity encoding in trigeminal networks associated with mechanosensory inputs to the face, or the consequences of movement. High resolution functional magnetic resonance imaging (fMRI) was used to investigate the neural substrates of velocity encoding in the human orofacial somatosensory system during unilateral saltatory pneumotactile inputs to perioral hairy skin in 20 healthy adults. A custom multichannel, scalable pneumotactile array consisting of 7 TAC-Cells was used to present 5 stimulus conditions: 5 cm/s, 25 cm/s, 65 cm/s, ALL-ON synchronous activation, and ALL-OFF. The spatial organization of cerebral and cerebellar blood oxygen level-dependent (BOLD) response as a function of stimulus velocity was analyzed using general linear modeling (GLM) of pooled group fMRI signal data. The sequential saltatory inputs to the lower face produced localized, predominantly contralateral BOLD responses in primary somatosensory (SI), secondary somatosensory (SII), primary motor (MI), supplemental motor area (SMA), posterior parietal cortices (PPC), and insula, whose spatial organization and intensity were highly dependent on velocity. Additionally, ipsilateral sensorimotor, insular and cerebellar BOLD responses were prominent during the lowest velocity (5 cm/s). Advisor: Steven M. Barlo

ENCODING OF SALTATORY TACTILE VELOCITY IN THE ADULT OROFACIAL SOMATOSENSORY SYSTEM

Processing dynamic tactile inputs is a key function of somatosensory systems. Spatial velocity encoding mechanisms by the nervous system are important for skilled movement production and may play a role in recovery of motor function following neurological insult. Little is known about tactile velocity encoding in trigeminal networks associated with mechanosensory inputs to the face, or the consequences of movement. High resolution functional magnetic resonance imaging (fMRI) was used to investigate the neural substrates of velocity encoding in the human orofacial somatosensory system during unilateral saltatory pneumotactile inputs to perioral hairy skin in 20 healthy adults. A custom multichannel, scalable pneumotactile array consisting of 7 TAC-Cells was used to present 5 stimulus conditions: 5 cm/s, 25 cm/s, 65 cm/s, ALL-ON synchronous activation, and ALL-OFF. The spatial organization of cerebral and cerebellar blood oxygen level-dependent (BOLD) response as a function of stimulus velocity was analyzed using general linear modeling (GLM) of pooled group fMRI signal data. The sequential saltatory inputs to the lower face produced localized, predominantly contralateral BOLD responses in primary somatosensory (SI), secondary somatosensory (SII), primary motor (MI), supplemental motor area (SMA), posterior parietal cortices (PPC), and insula, whose spatial organization and intensity were highly dependent on velocity. Additionally, ipsilateral sensorimotor, insular and cerebellar BOLD responses were prominent during the lowest velocity (5 cm/s). Advisor: Steven M. Barlo