Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection

Perinatal neuroanatomical structure is incredibly intricate and, at time of birth, is undergoing continuous change due to interweaving developmental processes (growth, myelination and gyrification). While there is some small variability in structure and rates of development, all follow proscribed pathways with well documented milestones. Brain injury or other disruption of these processes can result in poor neurodevelopmental outcomes or mortality, making their early identification critical to estimate, and potentially forestall, negative effects. MRI is an increasingly used method of investigating suspected neonatal encephalopathies and injuries.Identification of these injuries and malformations is more challenging in neonates compared to adults due to the brain’s continuously evolving appearance. This makes radiological review of neonatal MRI an intensive and time-consuming task which, in an ideal setting, requires a team of highly skilled clinicians and radiologists with complementary training and extensive experience. To assist this review process, some localisation method that highlights areas likely to contain tissue abnormalities would be highly desirable, as it could quickly draw attention to these locations. In addition, identifying neonates whose MRI is likely to contain some form of pathology could allow for review prioritisation.In this thesis, I first investigated using normative models of neonatal tissue intensity for brain tissue abnormality detection. I applied voxel-wise Gaussian process (GP) regression to a training cohort of neonates with no obvious lesions, all born preterm (<37 weeks) but imaged between 28-55 weeks. Gestational age at birth (GA), postmenstrual age at scan (PMA) and sex were used as input variables and voxel intensity as the output variable. GPs output a mean value and its variance inferred from neonates within the training cohort whose demographic information most closely matched those of the prediction target. The voxel specific models were put together to form a synthesised typical image and standard deviation image derived from the variance outputs. Z-score abnormality maps were constructed by taking the difference between neonates actual MRI and GP-calculated synthetic image and scaling by their standard deviation map. Higher Z-score map values indicate voxels more likely to contain abnormal tissue intensity. Using manually delineated masks of common brain injuries seen in a subset of neonates, these abnormality Z-score maps demonstrated good detection performance using area under the curve of receiver operating characteristic scores, with the exception of small punctate lesions.The initial voxel-wise models had substantial false positives around the edges of the brain where there is large typical heterogeneity. I next investigated if incorporating local structural information into predictive models could improve their ability to accommodate typical anatomical heterogeneity seen across individual brains and improve the accuracy of synthetic images and abnormality detection. To achieve this, voxel intensity values in a patch surrounding the prediction target were appended to the design matrix, alongside GMA, PMA and sex. The patch-based synthetic images were able to match an individual’s brain structure more closely and had lower false positives in normal appearing tissue. However, a weakness was that the centre of some larger lesions was included in the predictions (thereby classified as ‘healthy’ tissue), having a deleterious effect on their coverage, increasing false negatives. This was offset by much better coverage of smaller, more subtle lesions, to the extent that overall performance was higher compared to that seen in the earlier model.I also investigated if the Z-score abnormality maps could be used to classify neonates with MRI positive brain injury from those with normal appearing brains. While many machine learning algorism see frequent use in neuroimaging classification tasks, I opted for a logistic regression model due to its high levels of interpretability and simple implementation. Using the histograms of the Z-score abnormality maps as inputs, the model demonstrated good performance, being able to correctly identify neonates with injuries, but not those with subtle lesions like punctate lesions, whilst minimising false identification of neonates with normal appearing brains.To ascertain if performance could be improved, I explored multiple classification methods. Specifically, the use of other more complex classifiers (random forest, support vector machines, GP classification) and the use of a regional abnormal voxel count, that allowed localisation of lesioned tissue rather than the more global detection ability of the Z-score histograms. Using these innovations, I investigated their application towards a specific pathology; hypoxic ischemic encephalopathy (HIE). This is a good test for the system, as HIE has high incidence rates, multiple associated lesion types and a time dependant appearance. Further, I wanted to know if, given a positive HIE diagnosis, the Z-score abnormality maps could be used to predict long-term outcomes (normal vs poor). Several models demonstrated an excellent ability to separate HIE and healthy control neonates achieving >90% accuracy, a statistically significant result even after false discovery rate (FDR) correction (p-value < 0.05). While the outcome prediction models achieved reasonable accuracy, >70% in multiple models, none of these were statistically significant after FDR correction.Overall, this work demonstrates how normative modelling can be used to create individual voxel-wise / image-wise estimation of tissue abnormality for neonatal MRI across a range of gestational ages. It further demonstrates that these abnormality maps can be utilised for additional tasks, in this instance, three increasingly challenging neurological classification problems. These include the separation of neonates with and without MRI positive lesions, identification of neonates with a specific pathological condition (HIE) and prediction of long-term functional outcome (normal vs poor). Within a radiological setting, these classifications task can be considered analogous to three radiological challenges, image triage, diagnostic detection and estimation of developmental prognosis, important for the clinical team but also infants and their families

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