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4. Structural brain network degeneration and functional up-regulation in Huntington's disease

Author

McColgan, Peter

Subjects
612.8
Abstract

Huntington’s disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene on chromosome 4. In recent years there have been significant advances in understanding both the cellular pathology and the macrostructural changes that occur in the striatum and cortical structures as the disease proceeds. However, it remains unclear how abnormalities at the cellular level lead to characteristic patterns of macrostructural change in the brains of HD patients. In this thesis I aim to link structural and functional brain network abnormalities with regional changes at the cellular level. Using diffusion tractography and resting state functional MRI in well characterised HD cohorts I examine the relationship between structural and functional brain network organisation. I link these changes in structure and function to the neuropsychiatric symptoms prevalent in HD, occurring years before the manifestation of motor symptoms. By characterising changes in white matter brain networks I reveal how the brain network breaks down as HD progresses and show how this network deterioration leads to the emergence of clinical deficits. Using characteristics of the healthy white matter brain network I demonstrate how it is possible to predict the atrophy of specific brain connections in HD over time. In doing so I highlight a hierarchy of white matter connection vulnerability showing cortico-striatal connections are the first to be affected. In order to link these macrostructural white matter changes to cellular level abnormalities I utilise data from the Allen Institute transcription atlas and show how differences in regional gene expression in the healthy brain can account for the selective vulnerability of specific white matter connections in HD. The work presented in this thesis demonstrates how linking systems and cellular pathobiology in HD can inform us about disease mechanisms that drive brain atrophy and ultimately lead to clinical deficits.

Published
2018

6. Biological and clinical characteristics of gene carriers far from predicted onset in the Huntington's disease Young Adult Study (HD-YAS): a cross-sectional analysis

Author

Scahill, Rachael I, Zeun, Paul, Osborne-Crowley, Katherine, Johnson, Eileanoir B, Gregory, Sarah, Parker, Christopher, Lowe, Jessica, Nair, Akshay, O'Callaghan, Claire, Langley, Christelle, Papoutsi, Marina, McColgan, Peter, Estevez-Fraga, Carlos, Fayer, Kate, Wellington, Henny, Rodrigues, Filipe B, Byrne, Lauren M, Heselgrave, Amanda, Hyare, Harpreet, Sampaio, Cristina, Zetterberg, Henrik, Zhang, Hui, Wild, Edward J, Rees, Geraint, Robbins, Trevor W, Sahakian, Barbara J, Langbehn, Douglas, and Tabrizi, Sarah J

Published
2020
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7. Plasma and CSF neurofilament light chain are stabilized in response to mutant huntingtin lowering in the brain of Huntington disease mice

Author

Caron, Nicholas S., primary, Byrne, Lauren M., additional, Lemarié, Fanny L., additional, Bone, Jeffrey N., additional, Aly, Amirah E.-E., additional, Ko, Seunghyun, additional, Anderson, Christine, additional, Casal, Lorenzo, additional, Hill, Austin M., additional, Hawellek, David J., additional, McColgan, Peter, additional, Wild, Edward J., additional, Leavitt, Blair R., additional, and Hayden, Michael R, additional

Published
2024
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13. NfL concentration in CSF is a quantitative marker of the rate of neurodegeneration in aging and Huntington's disease: a semi-mechanistic model-based analysis.

Author

Machacek, Matthias, Garcia-Montoya, Elena, McColgan, Peter, Sanwald-Ducray, Patricia, and Mazer, Norman Alan

Subjects
HUNTINGTON disease, NEURODEGENERATION, PRESBYCUSIS, CEREBROSPINAL fluid shunts, AGING, ALZHEIMER'S disease, SPINAL muscular atrophy
Abstract

The article discusses the development of a semi-mechanistic model that relates the concentration of neurofilament light chain (NfL) in cerebrospinal fluid (CSF) to neurodegeneration in aging and Huntington's disease (HD). The model successfully predicts NfL concentration in CSF and suggests that it reflects the rate of neurodegeneration. The text also discusses the potential application of the model to other neurodegenerative diseases. The document is a list of references that cover various studies and papers related to NfL and its role in neurological disorders, providing a comprehensive source for further research. [Extracted from the article]

Published
2024
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15. Brain Regions Showing White Matter Loss in Huntington’s Disease Are Enriched for Synaptic and Metabolic Genes

Author

Coleman, A., Decolongon, J., Fan, M., Petkau, T., Jauffret, C., Justo, D., Lehericy, S., Nigaud, K., Valabrègue, R., Schoonderbeek, A., ‘t Hart, E.P., Moss, D. J. Hensman, Ghosh, R., Crawford, H., Papoutsi, M., Berna, C., Mahaleskshmi, D., Reilmann, R., Weber, N., Labuschagne, I., Stout, J., Landwehrmeyer, B., Orth, M., Mayer, I., Johnson, H., Crawfurd, D., McColgan, Peter, Gregory, Sarah, Seunarine, Kiran K., Razi, Adeel, Papoutsi, Marina, Johnson, Eileanoir, Durr, Alexandra, Roos, Raymund A.C., Leavitt, Blair R., Holmans, Peter, Scahill, Rachael I., Clark, Chris A., Rees, Geraint, and Tabrizi, Sarah J.

Published
2018
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16. Tominersen in Adults with Manifest Huntington’s Disease

Author

McColgan, Peter, primary, Thobhani, Alpa, additional, Boak, Lauren, additional, Schobel, Scott A., additional, Nicotra, Alessia, additional, Palermo, Giuseppe, additional, Trundell, Dylan, additional, Zhou, Julian, additional, Schlegel, Valerie, additional, Sanwald Ducray, Patricia, additional, Hawellek, David J., additional, Dorn, Jonas, additional, Simillion, Cedric, additional, Lindemann, Michael, additional, Wheelock, Vicki, additional, Durr, Alexandra, additional, Anderson, Karen E., additional, Long, Jeffrey D., additional, Wild, Edward J., additional, Landwehrmeyer, G. Bernhard, additional, Leavitt, Blair R., additional, Tabrizi, Sarah J., additional, and Doody, Rachelle, additional

Published
2023
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20. Genetic topography and cortical cell loss in Huntington's disease link development and neurodegeneration

Author

Estevez-Fraga, Carlos, primary, Altmann, Andre, additional, Parker, Christopher S, additional, Scahill, Rachael I, additional, Costa, Beatrice, additional, Chen, Zhongbo, additional, Manzoni, Claudia, additional, Zarkali, Angeliki, additional, Durr, Alexandra, additional, Roos, Raymund A C, additional, Landwehrmeyer, Bernhard, additional, Leavitt, Blair R, additional, Rees, Geraint, additional, Tabrizi, Sarah J, additional, and McColgan, Peter, additional

Published
2023
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29. J03 Evaluating tominersen effect on self-reported quality of life (QoL) with high-frequency longitudinal, digital euroqol 5-dimensions 5-level (EQ-5D-5L) survey in generation HD1, a phase III trial of tominersen in adult individuals with manifest Huntington’s disease (HD)

Author

Berchtold, Doris, primary, Rennig, Johannes, additional, Simillion, Cedric, additional, Lipsmeier, Florian, additional, McColgan, Peter, additional, Lindemann, Michael, additional, and Dorn, Jonas, additional

Published
2022
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31. Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis

Author

Edwards, Luke J., McColgan, Peter, Helbling, Saskia, Zarkali, Angeliki, Vaculčiaková, Lenka, Pine, Kerrin J., Dick, Fred, Weiskopf, Nikolaus, Edwards, Luke J., McColgan, Peter, Helbling, Saskia, Zarkali, Angeliki, Vaculčiaková, Lenka, Pine, Kerrin J., Dick, Fred, and Weiskopf, Nikolaus

Abstract

Quantitative magnetic resonance imaging (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (equation ImEquation1), effective transverse relaxation rate (equation ImEquation2), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (equation ImEquation3 at 3T, equation ImEquation4 at 7T), endothelial cells (equation ImEquation5 and MTsat at 3T), microglia (equation ImEquation6 and MTsat at 3T, equation ImEquation7 at 7T), and oligodendrocytes and oligodendrocyte precursor cells (equation ImEquation8 at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types.

Published
2022

32. Neurofilament light-associated connectivity in young-adult Huntington’s disease is related to neuronal genes

Author

McColgan, Peter, primary, Gregory, Sarah, additional, Zeun, Paul, additional, Zarkali, Angeliki, additional, Johnson, Eileanoir B, additional, Parker, Christopher, additional, Fayer, Kate, additional, Lowe, Jessica, additional, Nair, Akshay, additional, Estevez-Fraga, Carlos, additional, Papoutsi, Marina, additional, Zhang, Hui, additional, Scahill, Rachael I, additional, Tabrizi, Sarah J, additional, and Rees, Geraint, additional

Published
2022
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42. F05 Biological and clinical characteristics of gene carriers far from predicted onset in the hd-yas study: a cross-sectional analysis

Author

Scahill, Rachael, primary, Zeun, Paul, additional, Osborne-Crowley, Katherine, additional, Johnson, Eileanoir, additional, Gregory, Sarah, additional, Parker, Christopher, additional, Lowe, Jessica, additional, Nair, Akshay, additional, O’Callaghan, Claire, additional, Langley, Christelle, additional, Papoutsi, Marina, additional, McColgan, Peter, additional, Estevez Fraga, Carlos, additional, Fayer, Kate, additional, Wellington, Henny, additional, Brogueira Rodrigues, Filipe, additional, Byrne, Lauren, additional, Heslegrave, Amanda, additional, Hyare, Harpreet, additional, Sampaio, Cristina, additional, Zetterberg, Henrik, additional, Zhang, Hui, additional, Wild, Edward, additional, Rees, Geraint, additional, Robbins, Trevor, additional, Sahakian, Barbara, additional, Langbehn, Douglas, additional, and Tabrizi, Sarah, additional

Published
2021
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43. Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics

Author

McColgan, Peter, primary, Helbling, Saskia, additional, Vaculčiaková, Lenka, additional, Pine, Kerrin, additional, Wagstyl, Konrad, additional, Attar, Fakhereh Movahedian, additional, Edwards, Luke, additional, Papoutsi, Marina, additional, Wei, Yongbin, additional, Van den Heuvel, Martijn Pieter, additional, Tabrizi, Sarah J, additional, Rees, Geraint, additional, and Weiskopf, Nikolaus, additional

Published
2021
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44. Can neuroimaging predict dementia in Parkinson’s disease?

Author

Lanskey, Juliette H, McColgan, Peter, Schrag, Anette E, Acosta-Cabronero, Julio, Rees, Geraint, Morris, Huw R, and Weil, Rimona S

Subjects
Dopaminergic Neurons, imaging, Brain, Neuroimaging, Parkinson Disease, Review Article, Prognosis, Magnetic Resonance Imaging, Cholinergic Neurons, nervous system diseases, Cognition, Risk Factors, mental disorders, Parkinson’s disease, Disease Progression, Humans, Cognitive Dysfunction, Dementia, MRI
Abstract

Dementia is common in Parkinson’s disease but there is wide variation in severity and timing, and identifying at-risk individuals has proved challenging. Lanskey et al. provide an overview of conventional and emerging neuroimaging techniques with the potential to predict dementia in patients with Parkinson’s disease., Dementia in Parkinson’s disease affects 50% of patients within 10 years of diagnosis but there is wide variation in severity and timing. Thus, robust neuroimaging prediction of cognitive involvement in Parkinson’s disease is important: (i) to identify at-risk individuals for clinical trials of potential new treatments; (ii) to provide reliable prognostic information for individuals and populations; and (iii) to shed light on the pathophysiological processes underpinning Parkinson’s disease dementia. To date, neuroimaging has not made major contributions to predicting cognitive involvement in Parkinson’s disease. This is perhaps unsurprising considering conventional methods rely on macroscopic measures of topographically distributed neurodegeneration, a relatively late event in Parkinson’s dementia. However, new technologies are now emerging that could provide important insights through detection of other potentially relevant processes. For example, novel MRI approaches can quantify magnetic susceptibility as a surrogate for tissue iron content, and increasingly powerful mathematical approaches can characterize the topology of brain networks at the systems level. Here, we present an up-to-date overview of the growing role of neuroimaging in predicting dementia in Parkinson’s disease. We discuss the most relevant findings to date, and consider the potential of emerging technologies to detect the earliest signs of cognitive involvement in Parkinson’s disease.

Published
2018

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