1
|
Wearn A, Tremblay SA, Tardif CL, Leppert IR, Gauthier CJ, Baracchini G, Hughes C, Hewan P, Tremblay-Mercier J, Rosa-Neto P, Poirier J, Villeneuve S, Schmitz TW, Turner GR, Spreng RN. Neuromodulatory subcortical nucleus integrity is associated with white matter microstructure, tauopathy and APOE status. Nat Commun 2024; 15:4706. [PMID: 38830849 PMCID: PMC11148077 DOI: 10.1038/s41467-024-48490-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/01/2024] [Indexed: 06/05/2024] Open
Abstract
The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer's disease (AD). They project broadly throughout the brain's white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development.
Collapse
Affiliation(s)
- Alfie Wearn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada.
| | - Stéfanie A Tremblay
- Department of Physics, Concordia University, Montreal, H4B 1R6, QC, Canada
- Montreal Heart Institute, Montreal, H1T 1C8, QC, Canada
- School of Health, Concordia University, Montreal, H4B 1R6, QC, Canada
| | - Christine L Tardif
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
- Department of Biomedical Engineering, McGill University, McGill, H3A 2B4, QC, Canada
| | - Ilana R Leppert
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
| | - Claudine J Gauthier
- Department of Physics, Concordia University, Montreal, H4B 1R6, QC, Canada
- Montreal Heart Institute, Montreal, H1T 1C8, QC, Canada
- School of Health, Concordia University, Montreal, H4B 1R6, QC, Canada
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
| | - Colleen Hughes
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
| | - Patrick Hewan
- Department of Psychology, York University, Toronto, M3J 1P3, ON, Canada
| | | | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada
| | - Judes Poirier
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada
- Department of Psychiatry, McGill University, Montreal, H3A 1A1, QC, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada
- Department of Psychiatry, McGill University, Montreal, H3A 1A1, QC, Canada
| | - Taylor W Schmitz
- Department of Physiology & Pharmacology, Western Institute for Neuroscience, Western University, London, N6A 5C1, ON, Canada
| | - Gary R Turner
- Department of Psychology, York University, Toronto, M3J 1P3, ON, Canada
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada.
- McConnell Brain Imaging Centre, McGill University, Montreal, H3A 2B4, QC, Canada.
- Douglas Mental Health University Institute-Research Center, Verdun, H4H 1R3, QC, Canada.
- Department of Psychiatry, McGill University, Montreal, H3A 1A1, QC, Canada.
| |
Collapse
|
2
|
Radunsky D, Solomon C, Stern N, Blumenfeld-Katzir T, Filo S, Mezer A, Karsa A, Shmueli K, Soustelle L, Duhamel G, Girard OM, Kepler G, Shrot S, Hoffmann C, Ben-Eliezer N. A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla. PLoS One 2024; 19:e0297244. [PMID: 38820354 PMCID: PMC11142522 DOI: 10.1371/journal.pone.0297244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 01/01/2024] [Indexed: 06/02/2024] Open
Abstract
Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.
Collapse
Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Shir Filo
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | | | | | - Gal Kepler
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States of America
| |
Collapse
|
3
|
Taubert M, Ziegler G, Lehmann N. Higher surface folding of the human premotor cortex is associated with better long-term learning capability. Commun Biol 2024; 7:635. [PMID: 38796622 PMCID: PMC11127997 DOI: 10.1038/s42003-024-06309-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 05/08/2024] [Indexed: 05/28/2024] Open
Abstract
The capacity to learn enabled the human species to adapt to various challenging environmental conditions and pass important achievements on to the next generation. A growing body of research suggests links between neocortical folding properties and numerous aspects of human behavior, but their impact on enhanced human learning capacity remains unexplored. Here we leverage three training cohorts to demonstrate that higher levels of premotor cortical folding reliably predict individual long-term learning gains in a challenging new motor task, above and beyond initial performance differences. Individual folding-related predisposition to motor learning was found to be independent of cortical thickness and intracortical microstructure, but dependent on larger cortical surface area in premotor regions. We further show that learning-relevant features of cortical folding occurred in close spatial proximity to practice-induced structural brain plasticity. Our results suggest a link between neocortical surface folding and human behavioral adaptability.
Collapse
Affiliation(s)
- Marco Taubert
- Department of Sport Science, Institute III, Faculty of Humanities, Otto von Guericke University, Zschokkestraße 32, 39104, Magdeburg, Germany.
- Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany.
- Collaborative Research Center 1436 Neural Resources of Cognition, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Gabriel Ziegler
- Collaborative Research Center 1436 Neural Resources of Cognition, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
- Germany German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Nico Lehmann
- Department of Sport Science, Institute III, Faculty of Humanities, Otto von Guericke University, Zschokkestraße 32, 39104, Magdeburg, Germany
- Collaborative Research Center 1436 Neural Resources of Cognition, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103, Leipzig, Germany
| |
Collapse
|
4
|
Lehmann N, Aye N, Kaufmann J, Heinze HJ, Düzel E, Ziegler G, Taubert M. Changes in Cortical Microstructure of the Human Brain Resulting from Long-Term Motor Learning. J Neurosci 2023; 43:8637-8648. [PMID: 37875377 PMCID: PMC10727185 DOI: 10.1523/jneurosci.0537-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/08/2023] [Accepted: 09/04/2023] [Indexed: 10/26/2023] Open
Abstract
The mechanisms subserving motor skill acquisition and learning in the intact human brain are not fully understood. Previous studies in animals have demonstrated a causal relationship between motor learning and structural rearrangements of synaptic connections, raising the question of whether neurite-specific changes are also observable in humans. Here, we use advanced diffusion magnetic resonance imaging (MRI), sensitive to dendritic and axonal processes, to investigate neuroplasticity in response to long-term motor learning. We recruited healthy male and female human participants (age range 19-29) who learned a challenging dynamic balancing task (DBT) over four consecutive weeks. Diffusion MRI signals were fitted using Neurite Orientation Dispersion and Density Imaging (NODDI), a theory-driven biophysical model of diffusion, yielding measures of tissue volume, neurite density and the organizational complexity of neurites. While NODDI indices were unchanged and reliable during the control period, neurite orientation dispersion increased significantly during the learning period mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor areas. Importantly, reorganization of cortical microstructure during the learning phase predicted concurrent behavioral changes, whereas there was no relationship between microstructural changes during the control phase and learning. Changes in neurite complexity were independent of alterations in tissue density, cortical thickness, and intracortical myelin. Our results are in line with the notion that structural modulation of neurites is a key mechanism supporting complex motor learning in humans.SIGNIFICANCE STATEMENT The structural correlates of motor learning in the human brain are not fully understood. Results from animal studies suggest that synaptic remodeling (e.g., reorganization of dendritic spines) in sensorimotor-related brain areas is a crucial mechanism for the formation of motor memory. Using state-of-the-art diffusion magnetic resonance imaging (MRI), we found a behaviorally relevant increase in the organizational complexity of neocortical microstructure, mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor regions, following training of a challenging dynamic balancing task (DBT). Follow-up analyses suggested structural modulation of synapses as a plausible mechanism driving this increase, while colocalized changes in cortical thickness, tissue density, and intracortical myelin could not be detected. These results advance our knowledge about the neurobiological basis of motor learning in humans.
Collapse
Affiliation(s)
- Nico Lehmann
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Magdeburg 39104, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Norman Aye
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Magdeburg 39104, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto von Guericke University, Magdeburg 39120, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto von Guericke University, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
- Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Magdeburg 39106, Germany
- Leibniz-Institute for Neurobiology (LIN), Magdeburg 39118, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
- Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Magdeburg 39106, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Magdeburg 39120, Germany
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, United Kingdom
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Magdeburg 39120, Germany
| | - Marco Taubert
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Magdeburg 39104, Germany
- Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Magdeburg 39106, Germany
| |
Collapse
|