Showing papers by "Philip Cooke published in 2023"

Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography

Abstract: White matter hyperintensity (WMH) lesions on T2 fluid‐attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal‐appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP‐Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age‐matched normative data.

Dynamic structural remodeling of the human visual system prompted by bilateral retinal gene therapy

Abstract: The impact of changes in visual input on neuronal circuitry is complex and much of our knowledge on human brain plasticity of the visual systems comes from animal studies. Reinstating vision in a group of patients with low vision through retinal gene therapy creates a unique opportunity to dynamically study the underlying process responsible for brain plasticity. Historically, increases in the axonal myelination of the visual pathway has been the biomarker for brain plasticity. Here, we demonstrate that to reach the long-term effects of myelination increase, the human brain may undergo demyelination as part of a plasticity process. The maximum change in dendritic arborization of the primary visual cortex and the neurite density along the geniculostriate tracks occurred at three months (3MO) post intervention, in line with timing for the peak changes in postnatal synaptogenesis within the visual cortex reported in animal studies. The maximum change at 3MO for both the gray and white matter significantly correlated with patients' clinical responses to light stimulations called full field sensitivity threshold (FST). Our results shed a new light on the underlying process of brain plasticity by challenging the concept of increase myelination being the hallmark of brain plasticity and instead reinforcing the idea of signal speed optimization as a dynamic process for brain plasticity.

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Abstract: Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Comparison of Anatomical and Diffusion MRI for detecting Parkinson’s Disease using Deep Convolutional Neural Network

Abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer’s disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification. Clinical Relevance This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson’s disease.