Increased Extra-axial Cerebrospinal Fluid in High-Risk Infants Who Later Develop Autism

Background We previously reported that infants who developed autism spectrum disorder (ASD) had increased cerebrospinal fluid (CSF) in the subarachnoid space (i.e., extra-axial CSF) from 6 to 24 months of age. We attempted to confirm and extend this finding in a larger independent sample. Methods A longitudinal magnetic resonance imaging study of infants at risk for ASD was carried out on 343 infants, who underwent neuroimaging at 6, 12, and 24 months. Of these infants, 221 were at high risk for ASD because of an older sibling with ASD, and 122 were at low risk with no family history of ASD. A total of 47 infants were diagnosed with ASD at 24 months and were compared with 174 high-risk and 122 low-risk infants without ASD. Results Infants who developed ASD had significantly greater extra-axial CSF volume at 6 months compared with both comparison groups without ASD (18% greater than high-risk infants without ASD; Cohen's d = 0.54). Extra-axial CSF volume remained elevated through 24 months (d = 0.46). Infants with more severe autism symptoms had an even greater volume of extra-axial CSF from 6 to 24 months (24% greater at 6 months, d = 0.70; 15% greater at 24 months, d = 0.70). Extra-axial CSF volume at 6 months predicted which high-risk infants would be diagnosed with ASD at 24 months with an overall accuracy of 69% and corresponding 66% sensitivity and 68% specificity, which was fully cross-validated in a separate sample. Conclusions This study confirms and extends previous findings that increased extra-axial CSF is detectable at 6 months in high-risk infants who develop ASD. Future studies will address whether this anomaly is a contributing factor to the etiology of ASD or an early risk marker for ASD

What volume increase is needed for the management of raised intracranial pressure in children with craniosynostosis?

Craniosynostosis describes a fusion of one or more sutures in the skull. It can occur in isolation or as part of a syndrome. In either setting, it is a condition which may lead to raised intracranial pressure. The exact cause of raised intracranial pressure in craniosynostosis is unknown. It may be due to; a volume mismatch between the intracranial contents and their containing cavity, venous hypertension, hydrocephalus or airway obstruction, which is often a sequela of an associated syndrome. At Great Ormond Street Hospital, after hydrocephalus and airway obstruction have been treated, the next surgical treatment of choice is cranial vault expansion. This expansion has been shown to reduce intracranial pressure, interestingly despite its success, the reasons behind its benefits are not fully understood. Using reconstructed 3-dimensional imaging, accurate measurement of cranial volumes can now be achieved. The aim of this project is to use the advances in 3-dimensional imaging and image processing to provide novel information on the volume changes that occur following cranial vault expansion. This information will be combined with clinical metrics to create a greater understanding of the causes of raised intracranial pressure in craniosynostosis, why cranial vault expansion treats them and whether there is an optimal volume expansion

Multimodal MRI analysis using deep learning methods

Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures. In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts. We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production

An Information Theory Model for Optimizing Quantitative Magnetic Resonance Imaging Acquisitions

Quantitative magnetic resonance imaging (qMRI) is a powerful group of imaging techniques with a growing number of clinical applications, including synthetic image generation in post-processing, automatic segmentation, and diagnosis of disease from quantitative parameter values. Currently, acquisition parameter selection is performed empirically for quantitative MRI. Tuning parameters for different scan times, tissues, and resolutions requires some measure of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to maximize image quality and the reliability of the previously mentioned methods which follow image acquisition. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for synthetic MRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of the acquisition parameters and allowing informed parameter selection. The information theory framework was tested on synthetic data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The application of this information theory framework resulted in successful parameter optimization with respect to mutual information. Both the phantom and in vivo measurements showed that higher mutual information calculated by the model correlated with smaller standard deviation in the reconstructed parametric maps. With this framework, optimal acquisition parameters can be selected to improve image quality, image repeatability, or scan time. This method could reduce the time and labor necessary to achieve images of the desired quality. Making an informed acquisition parameter selection reduces uncertainty in the imaging output and optimizes information gain within the bounds of clinical constraints

Investigating Glymphatic Function In Alzheimer’s Disease Pathology

Alzheimer’s disease is fast becoming the greatest healthcare challenge of our time, with no known cure to-date. Brought about by the toxic formation of plaques of amyloid-β and tangles of tau in the brain, much is still unknown about the precise mechanisms that initiate these protein accumulations, thought to occur decades before clinical manifestation of symptoms. One theory is that an imbalance between the production of these proteins and their removal from the brain promotes retention that eventually aggregates into entities that devastate molecular and cellular machinery. Thus, targeting waste clearance mechanisms responsible for removing cerebral metabolites, including amyloid-β and tau, present novel, enthralling research targets. The glymphatic system is one such pathway that has been recently characterised. Considered a surrogate for lymphatics which are largely lacking in the brain, this fluid network relies on the water channel aquaporin-4, expressed highly on glia, thus being named “glymphatics”. In this work, first, a surgical protocol was established in the mouse brain to facilitate the delivery of tracer molecules into the cerebrospinal fluid. Direct, single time-point, histological assessment of fluorescent tracer distribution was performed to check consistency with previous characterisation of glymphatics in the mouse brain. Glymphatics were then visualised dynamically across the whole brain using magnetic resonance imaging. Glymphatic patterns were investigated in real-time by imaging fluid dynamics in the brain alongside a potent blocker of aquaporin-4. Next, imaging was used to characterise glymphatic changes and aquaporin-4 profiles in mouse models of Alzheimer’s pathology. This revealed a time-dependant relationship between glymphatics and tau accumulation. Finally, the findings were extrapolated onto humans by studying aquaporin-4 modifications in subjects with and without cognitive deficits. Here, the crucial relationship between aquaporin-4 and pathological aggregates of tau and amyloid-β was determined. Furthermore, dystrobrevin, a membrane protein linked to aquaporin-4, was also profiled in the setting of aging and amyloid-β pathology. The work presented herein elucidates the role of glymphatic perturbances in the context of Alzheimer’s disease and clarifies the implications of aquaporin-4 mediated clearance in neurodegeneration