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Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies

Periodic Reporting for period 3 - BUNGEE-TOOLS (Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies)

Reporting period: 2019-09-01 to 2021-02-28

Recent advances in magnetic resonance imaging (MRI) scanners are providing us with images of the human brain of increasing detail. While these images hold promise to greatly increase our understanding of the human brain works, researchers still analyze the images with old software tools that are over a decade old, and were designed to work with older, less detailed images. In particular, these tools do not consider smaller brain regions that are visible in present-day scans, but not in older MRI images. This inability to capitalize on the vast improvement of MRI is slowing down progress in brain research.

In this project, we propose to build next-generation software tools that will enable researchers to take full advantage of the increasing image quality of modern MRI scanners. The core of these tools will be a high-resolution map (“atlas”) of the human brain, which we are building using brains from dead donors. Such brains have the advantage that the can be stained with different dyes and looked under the microscope, allowing us to build maps of superior detail. The atlas will be used to analyze MRI scans of living people, enabling researchers to extract more detailed information and more subtle changes due to e.g. aging or disease. We will make the developed tools freely available to the scientific community, and therefore expect them to have a tremendous impact on the quest to understand the human brain (in health and in disease), and ultimately on public health and the economy.
The main aspects of the project are the following:

1. Obtaining the images that are necessary to build our atlas: We obtained the brain samples through the UCL Queen Square Brain Bank (QSBB). We have designed a protocol for MRI scanning of the samples, in collaboration with our colleagues at the UCL Institute of Neurology, where the scanner is located. We have a technician who works full time at QSBB, and another one who works part time, both employed by this project. These technicians routinely section, stain, and digitize the tissue, with help from our collaborators at QSBB. The follow a pipeline that we have developed in this project, and which facilitates solving the “jigsaw puzzle” of figuring out where in the brain is each section that we have cut (a problem know as “3D histology reconstruction”). The acquisition pipeline is in place and constantly producing the data that we need to build the atlas.

2. Manually tracing the boundaries of brain structures on the stained sections: we have recently hired a research technician who is in charge of this. Tracing these boundaries is necessary in order to build the maps of the brain, as we need to know where we are at every image location. Manual delineation is a very repetitive, time-consuming process, so we have developed computer programs to assist the technician and speed up the process. For example, she can delineate one section every 3 or 4, and let the program fill in the boundaries in between.

3. Designing computer algorithms to solve the 3D histology reconstruction problem: This is a very difficult problem, particularly if a computer must solve it without any sort of help from a human (who could for example specify pairs of matching points to help solve the puzzle, but this is difficult and extremely time consuming). We have made significant progress in developing computer programs that can perform this reconstruction without any human intervention.

4. Building the atlas: We will not be able to do this until all the images have been acquired and label.

5. Building programs that apply the atlas to the automated analysis of MRI scans of living people: Even though the atlas is not ready yet, we have been using our previous atlases to start developing these programs, which will be partly based on Dr. Iglesias’s postdoctoral work. Even though these older atlases are limited to smaller parts of the brain, and are not as detailed as the one we want to build, they still allow us to work on these programs without having to wait for the final atlas. We have made significant improvements to the methods and programs built by Dr. Iglesias in the past.

6. Using the programs from Point 5 above to shed light on the different forms of frontotemporal dementia: Our colleagues from the UCL Dementia Research Centre are using our software to try to improve our understanding of the different variants of this disease – which will hopefully lead to improved diagnosis and treatment of the disease. Moreover, colleagues at the UCL Medical Physics department have used our software to better understand the role of the hippocampus (a brain structure in charge of spatial navigation and memory) in Alzheimer’s disease.

7. Scientific output: The project has so far yielded a large number of publications, which have been made freely available (“open access”). A list can be found in the project website. In addition, we have: organized workshops and symposia (3D histology reconstruction, joint meetings between imaging laboratories from UCL and Imperial College London); given seminars on how to apply for European grants (ERC and Marie Curie programs); been part of the MICCAI mentorship program (“Connecting early-career researchers with experienced peers who share their experiences in academia, industry or entrepreneurship, to help develop the next generation of entrepreneurs, and innovators”); and co-organized the summer school of our laboratory at UCL. Finally, our first Ph.D. student working on this project (Ms. Alessia Atzeni) has successfully upgraded to Ph.D. candidate, and our postdoctoral researcher Dr. Matteo Mancini has obtained a highly prestigious fellowship from the Wellcome Trust.

8. Public engagement: We have taken dissemination very seriously. Engagement activities have included: participation in the In2ScienceUK program (hosting two high-school students from disadvantaged backgrounds for two weeks); articles in the newsletters of the UCL Medical Physics department and of the UCL QS Brain Bank; live demonstrations at schools in the London area as part of the STEM Ambassador Program; and live demonstrations to schoolchildren at the University College Hospital open day
We have written a number of articles that describe progress beyond the state of the art. Some of these are published; some are under revision; and some are in preparation (overviews of them have been or are to be presented in conferences).

- We have shown that Fluorinert, a liquid that is frequently use to acquire images of dead human brains with MRI scanners, does not affect the microscopic properties of the brain tissue, at least if immersed for less than a week. This is important because Fluorinert is used routinely without knowing the exact impact it may have on the subsequent analyses.

- We have designed and implemented a novel pipeline for MRI-informed 3D histology of the human brain.

- We have applied early prototypes of our tools to build atlases of the human thalamus and amygdala.

- We have developed a number of novel image analysis methods for:
* Filling in manual delineations, as explained in Point 2 of the previous sections.
* Histology reconstruction.
* Improved spatial alignment of histological and MRI images of the same tissue.
* Exploiting information from diffusion MRI (a type of MRI used to study the connectivity of brain regions) in order to improve the automated labelling (“segmentation”) of brain MRI scans of living subjects.

- We have collaborated with colleagues at the UCL Dementia Research Centre and the UCL Medical Physics department, who have used our tools to:
* better understand the connection between genetics, frontotemporal dementia, and different regions of the human brain.
* better understand the connection between genetics, Alzheimer’s disease, and the human hippocampus.

- We have also published a number of papers in areas that are related to BUNGEE-TOOLS in terms of mathematical methods, mostly led by colleagues at UCL and other international universities:
* Building atlases of the infant brain, with collaborators from Harvard Medical School (Boston, USA).
* Improved method for image registration (spatially aligning images), with collaborators from Harvard Medical School.
* Applying state-of-the-art AI techniques to the early diagnosis of breast cancer (UCL).
* Building atlases of the fruit fly (INRA, France).
* Improving mosaicking methods for fetoscopy (UCL).
* Building atlases of the rabbit brain (UCL).

Expected results until the end of the project include:
- Hardening of the tools that we have already built, making sure they are easily usable by other researchers.
- Obtaining the manual delineations that are necessary to build the atlas.
- Building the atlas.
- Developing tools to apply the atlas to the automated analysis (segmentation) of scans of living subjects.
- Sharing the final tools and datasets.