1
|
Merenstein JL, Zhao J, Madden DJ. Depthwise cortical iron relates to functional connectivity and fluid cognition in healthy aging. Neurobiol Aging 2025; 148:27-40. [PMID: 39893877 PMCID: PMC11867872 DOI: 10.1016/j.neurobiolaging.2025.01.006] [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: 06/27/2024] [Revised: 11/28/2024] [Accepted: 01/08/2025] [Indexed: 02/04/2025]
Abstract
Age-related differences in fluid cognition have been associated with both the merging of functional brain networks, defined from resting-state functional magnetic resonance imaging (rsfMRI), and with elevated cortical iron, assessed by quantitative susceptibility mapping (QSM). Limited information is available, however, regarding the depthwise profile of cortical iron and its potential relation to functional connectivity. Here, using an adult lifespan sample (n = 138; 18-80 years), we assessed relations among graph theoretical measures of functional connectivity, column-based depthwise measures of cortical iron, and fluid cognition (i.e., tests of memory, perceptual-motor speed, executive function). Increased age was related both to less segregated functional networks and to increased cortical iron, especially for superficial depths. Functional network segregation mediated age-related differences in memory, whereas depthwise iron mediated age-related differences in general fluid cognition. Lastly, higher mean parietal iron predicted lower network segregation for adults younger than 45 years of age. These findings suggest that functional connectivity and depthwise cortical iron have distinct, complementary roles in the relation between age and fluid cognition in healthy adults.
Collapse
Affiliation(s)
- Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA.
| | - Jiayi Zhao
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA
| |
Collapse
|
2
|
Essex CA, Overson DK, Merenstein JL, Truong TK, Madden DJ, Bedggood MJ, Morgan C, Murray HC, Holdsworth SJ, Stewart AW, Faull RLM, Hume P, Theadom A, Pedersen M. Mild traumatic brain injury increases cortical iron: evidence from individual susceptibility mapping. Brain Commun 2025; 7:fcaf110. [PMID: 40161218 PMCID: PMC11954555 DOI: 10.1093/braincomms/fcaf110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2024] [Revised: 02/18/2025] [Accepted: 03/10/2025] [Indexed: 04/02/2025] Open
Abstract
Quantitative susceptibility mapping has been applied to map brain iron distribution after mild traumatic brain injury to understand properties of neural tissue which may be related to cellular dyshomeostasis. However, this is a heterogeneous injury associated with microstructural brain changes, and 'traditional' group-wise statistical approaches may lead to a loss of clinically relevant information, as subtle alterations at the individual level can be obscured by averages and confounded by within-group variability. More precise and individualized approaches are needed to characterize mild traumatic brain injury better and elucidate potential cellular mechanisms to improve intervention and rehabilitation. To address this issue, we use quantitative MRI to build individualized profiles of regional positive (iron-related) magnetic susceptibility across 34 bilateral cortical ROIs following mild traumatic brain injury. Healthy population templates were constructed for each cortical area using standardized Z-scores derived from 25 age-matched male controls aged between 16 and 32 years (M = 21.10, SD = 4.35), serving as a reference against which Z-scores of 35 males with acute (<14 days) sports-related mild traumatic brain injury were compared [M = 21.60 years (range: 16-33), SD = 4.98]. Secondary analyses sensitive to cortical depth and curvature were also generated to approximate the location of iron accumulation in the cortical laminae and the effect of gyrification. Primary analyses indicated that approximately one-third (11/35; 31%) of injured participants exhibited elevated positive susceptibility indicative of abnormal iron profiles relative to the healthy population, a finding that was mainly concentrated in regions within the temporal lobe. Injury severity was significantly higher (P = 0.02) for these participants than their iron-normal counterparts, suggesting a link between injury severity, symptom burden, and elevated cortical iron. Secondary exploratory analyses of cortical depth and curvature profiles revealed abnormal iron accumulation in 83% (29/35) of mild traumatic brain injury participants, enabling better localization of injury-related changes in iron content to specific loci within each region and identifying effects that may be more subtle and lost in region-wise averaging. Our findings suggest that individualized approaches can further elucidate the clinical relevance of iron in mild head injury. Differences in injury severity between iron-normal and iron-abnormal mild traumatic brain injury participants identified in our primary analysis highlight not only why precise investigation is required to understand the link between objective changes in the brain and subjective symptomatology, but also identify iron as a candidate biomarker for tissue pathology after mild traumatic brain injury.
Collapse
Affiliation(s)
- Christi A Essex
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand
| | - Devon K Overson
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Trong-Kha Truong
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Mayan J Bedggood
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand
| | - Catherine Morgan
- Center for Advanced MRI, The University of Auckland, Auckland 1023, New Zealand
- School of Psychology, The University of Auckland, Auckland 1142, New Zealand
- Center for Brain Research, The University of Auckland, Auckland 1023, New Zealand
| | - Helen C Murray
- Center for Brain Research, The University of Auckland, Auckland 1023, New Zealand
| | - Samantha J Holdsworth
- Center for Brain Research, The University of Auckland, Auckland 1023, New Zealand
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1023, New Zealand
| | - Ashley W Stewart
- Center for Advanced Imaging, The University of Queensland, Queensland 4067, Australia
| | - Richard L M Faull
- Center for Brain Research, The University of Auckland, Auckland 1023, New Zealand
| | - Patria Hume
- School of Sport and Recreation, Faculty of Health and Environmental Science, Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland 0627, New Zealand
| | - Alice Theadom
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand
| | - Mangor Pedersen
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand
| |
Collapse
|
3
|
Essex CA, Merenstein JL, Overson DK, Truong TK, Madden DJ, Bedggood MJ, Murray H, Holdsworth SJ, Stewart AW, Morgan C, Faull RLM, Hume P, Theadom A, Pedersen M. Characterizing positive and negative quantitative susceptibility values in the cortex following mild traumatic brain injury: a depth- and curvature-based study. Cereb Cortex 2025; 35:bhaf059. [PMID: 40099836 PMCID: PMC11915090 DOI: 10.1093/cercor/bhaf059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2024] [Revised: 02/17/2025] [Accepted: 02/19/2025] [Indexed: 03/20/2025] Open
Abstract
Evidence has linked head trauma to increased risk factors for neuropathology, including mechanical deformation of the sulcal fundus and, later, perivascular accumulation of hyperphosphorylated tau adjacent to these spaces related to chronic traumatic encephalopathy. However, little is known about microstructural abnormalities and cellular dyshomeostasis in acute mild traumatic brain injury in humans, particularly in the cortex. To address this gap, we designed the first architectonically motivated quantitative susceptibility mapping study to assess regional patterns of net positive (iron-related) and net negative (myelin-, calcium-, and protein-related) magnetic susceptibility across 34 cortical regions of interest following mild traumatic brain injury. Bilateral, between-group analyses sensitive to cortical depth and curvature were conducted between 25 males with acute (<14 d) sports-related mild traumatic brain injury and 25 age-matched male controls. Results suggest a trauma-induced increase in net positive susceptibility focal to superficial, perivascular-adjacent spaces in the parahippocampal sulcus. Decreases in net negative susceptibility values in distinct voxel populations within the same region indicate a potential dual pathology of neural substrates. These mild traumatic brain injury-related patterns were distinct from age-related processes revealed by correlation analyses. Our findings suggest depth- and curvature-specific deposition of biological substrates in cortical tissue convergent with features of misfolded proteins in trauma-related neurodegeneration.
Collapse
Affiliation(s)
- Christi A Essex
- Department of Psychology and Neuroscience, Auckland University of Technology, 90 Akoranga Drive, Northcote, Auckland 0627, New Zealand
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, 40 Duke Medicine Cir #414, Durham, NC 27710, United States
| | - Devon K Overson
- Brain Imaging and Analysis Center, Duke University Medical Center, 40 Duke Medicine Cir #414, Durham, NC 27710, United States
| | - Trong-Kha Truong
- Brain Imaging and Analysis Center, Duke University Medical Center, 40 Duke Medicine Cir #414, Durham, NC 27710, United States
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, 40 Duke Medicine Cir #414, Durham, NC 27710, United States
| | - Mayan J Bedggood
- Department of Psychology and Neuroscience, Auckland University of Technology, 90 Akoranga Drive, Northcote, Auckland 0627, New Zealand
| | - Helen Murray
- Center for Brain Research, The University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Samantha J Holdsworth
- Mātai Medical Research Institute, 466 Childers Road, Te Hapara, Gisborne 4010, New Zealand
| | - Ashley W Stewart
- Center for Advanced Imaging, The University of Queensland, Building 57 of, University Dr, St Lucia QLD 4067, Australia
| | - Catherine Morgan
- Center for Advanced MRI, The University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Richard L M Faull
- Center for Brain Research, The University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Patria Hume
- Sports Performance Research Institute New Zealand, Auckland University of Technology, 17 Antares Place, Rosedale, Auckland 0632, New Zealand
| | - Alice Theadom
- Department of Psychology and Neuroscience, Auckland University of Technology, 90 Akoranga Drive, Northcote, Auckland 0627, New Zealand
| | - Mangor Pedersen
- Department of Psychology and Neuroscience, Auckland University of Technology, 90 Akoranga Drive, Northcote, Auckland 0627, New Zealand
| |
Collapse
|
4
|
Filippi M, Preziosa P, Barkhof F, Ciccarelli O, Cossarizza A, De Stefano N, Gasperini C, Geraldes R, Granziera C, Haider L, Lassmann H, Margoni M, Pontillo G, Ropele S, Rovira À, Sastre-Garriga J, Yousry TA, Rocca MA. The ageing central nervous system in multiple sclerosis: the imaging perspective. Brain 2024; 147:3665-3680. [PMID: 39045667 PMCID: PMC11531849 DOI: 10.1093/brain/awae251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/10/2024] [Accepted: 06/23/2024] [Indexed: 07/25/2024] Open
Abstract
The interaction between ageing and multiple sclerosis is complex and carries significant implications for patient care. Managing multiple sclerosis effectively requires an understanding of how ageing and multiple sclerosis impact brain structure and function. Ageing inherently induces brain changes, including reduced plasticity, diminished grey matter volume, and ischaemic lesion accumulation. When combined with multiple sclerosis pathology, these age-related alterations may worsen clinical disability. Ageing may also influence the response of multiple sclerosis patients to therapies and/or their side effects, highlighting the importance of adjusted treatment considerations. MRI is highly sensitive to age- and multiple sclerosis-related processes. Accordingly, MRI can provide insights into the relationship between ageing and multiple sclerosis, enabling a better understanding of their pathophysiological interplay and informing treatment selection. This review summarizes current knowledge on the immunopathological and MRI aspects of ageing in the CNS in the context of multiple sclerosis. Starting from immunosenescence, ageing-related pathological mechanisms and specific features like enlarged Virchow-Robin spaces, this review then explores clinical aspects, including late-onset multiple sclerosis, the influence of age on diagnostic criteria, and comorbidity effects on imaging features. The role of MRI in understanding neurodegeneration, iron dynamics and myelin changes influenced by ageing and how MRI can contribute to defining treatment effects in ageing multiple sclerosis patients, are also discussed.
Collapse
Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London WC1N 3BG, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, UCL Institute of Neurology, UCL, London WC1N 3BG, UK
- NIHR (National Institute for Health and Care Research) UCLH (University College London Hospitals) BRC (Biomedical Research Centre), London WC1N 3BG, UK
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, 42121 Modena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy
| | - Claudio Gasperini
- Department of Neurosciences, S Camillo Forlanini Hospital Rome, 00152 Rome, Italy
| | - Ruth Geraldes
- Clinical Neurology, John Radcliffe Hospital, Oxford University Foundation Trust, Oxford OX3 9DU, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Cristina Granziera
- Department of Neurology, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, 4031 Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
| | - Lukas Haider
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London WC1N 3BG, UK
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, 1090 Vienna, Austria
| | - Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Giuseppe Pontillo
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London WC1N 3BG, UK
- Department of Advanced Biomedical Sciences, University “Federico II”, 80138 Naples, Italy
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, 8010 Graz, Austria
| | - Àlex Rovira
- Neuroradiology Section, Department of Radiology, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Jaume Sastre-Garriga
- Neurology Department and Multiple Sclerosis Centre of Catalunya (Cemcat), Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Tarek A Yousry
- Lysholm Department of Neuroradiology, UCLH National Hospital for Neurology and Neurosurgery, Neuroradiological Academic Unit, UCL Institute of Neurology, London WC1N 3BG, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
| |
Collapse
|
5
|
Knoll C, Doehler J, Northall A, Schreiber S, Rotta J, Mattern H, Kuehn E. Age-related differences in human cortical microstructure depend on the distance to the nearest vein. Brain Commun 2024; 6:fcae321. [PMID: 39355004 PMCID: PMC11443451 DOI: 10.1093/braincomms/fcae321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 08/13/2024] [Accepted: 09/17/2024] [Indexed: 10/03/2024] Open
Abstract
Age-related differences in cortical microstructure are used to understand the neuronal mechanisms that underlie human brain ageing. The cerebral vasculature contributes to cortical ageing, but its precise interaction with cortical microstructure is poorly understood. In a cross-sectional study, we combine venous imaging with vessel distance mapping to investigate the interaction between venous distances and age-related differences in the microstructural architecture of the primary somatosensory cortex, the primary motor cortex and additional areas in the frontal cortex as non-sensorimotor control regions. We scanned 18 younger adults and 17 older adults using 7 Tesla MRI to measure age-related changes in longitudinal relaxation time (T1) and quantitative susceptibility mapping (QSM) values at 0.5 mm isotropic resolution. We modelled different cortical depths using an equi-volume approach and assessed the distance of each voxel to its nearest vein using vessel distance mapping. Our data reveal a dependence of cortical quantitative T1 values and positive QSM values on venous distance. In addition, there is an interaction between venous distance and age on quantitative T1 values, driven by lower quantitative T1 values in older compared to younger adults in voxels that are closer to a vein. Together, our data show that the local venous architecture explains a significant amount of variance in standard measures of cortical microstructure and should be considered in neurobiological models of human brain organisation and cortical ageing.
Collapse
Affiliation(s)
- Christoph Knoll
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
| | - Juliane Doehler
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
| | - Alicia Northall
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University Magdeburg, Magdeburg 39106, Germany
- Department of Neurology, Otto von Guericke University of Magdeburg, Magdeburg 39120, Germany
| | - Johanna Rotta
- Department of Neurology, Otto von Guericke University of Magdeburg, Magdeburg 39120, Germany
- Department of Neurology, Katharinenhospital, Klinikum Stuttgart, Stuttgart 70174, Germany
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University Magdeburg, Magdeburg 39106, Germany
- Department Biomedical Magnetic Resonance (BMMR), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
| | - Esther Kuehn
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- Hertie Institute for Clinical Brain Research (HIH), Tübingen 72076, Germany
- German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen 72076, Germany
| |
Collapse
|
6
|
Mueller SG. 7T MP2RAGE for cortical myelin segmentation: Impact of aging. PLoS One 2024; 19:e0299670. [PMID: 38626149 PMCID: PMC11020839 DOI: 10.1371/journal.pone.0299670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/14/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND Myelin and iron are major contributors to the cortical MR signal. The aim of this study was to investigate 1. Can MP2RAGE-derived contrasts at 7T in combination with k-means clustering be used to distinguish between heavily and sparsely myelinated layers in cortical gray matter (GM)? 2. Does this approach provide meaningful biological information? METHODS The following contrasts were generated from the 7T MP2RAGE images from 45 healthy controls (age: 19-75, f/m = 23/22) from the ATAG data repository: 1. T1 weighted image (UNI). 2. T1 relaxation image (T1map). 3. INVC/T1map ratio (RATIO). K-means clustering identified 6 clusters/tissue maps (csf, csf/gm-transition, wm, wm/gm transition, heavily myelinated cortical GM (dGM), sparsely myelinated cortical GM (sGM)). These tissue maps were then processed with SPM/DARTEL (volume-based analyses) and Freesurfer (surface-based analyses) and dGM and sGM volume/thickness of young adults (n = 27, 19-27 years) compared to those of older adults (n = 18, 42-75 years) at p<0.001 uncorrected. RESULTS The resulting maps showed good agreement with histological maps in the literature. Volume- and surface analyses found age-related dGM loss/thinning in the mid-posterior cingulate and parahippocampal/entorhinal gyrus and age-related sGM losses in lateral, mesial and orbitofrontal frontal, insular cortex and superior temporal gyrus. CONCLUSION The MP2RAGE derived UNI, T1map and RATIO contrasts can be used to identify dGM and sGM. Considering the close relationship between cortical myelo- and cytoarchitecture, the findings reported here indicate that this new technique might provide new insights into the nature of cortical GM loss in physiological and pathological conditions.
Collapse
Affiliation(s)
- Susanne G. Mueller
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA, United States of America
| |
Collapse
|
7
|
Northall A, Doehler J, Weber M, Tellez I, Petri S, Prudlo J, Vielhaber S, Schreiber S, Kuehn E. Multimodal layer modelling reveals in vivo pathology in amyotrophic lateral sclerosis. Brain 2024; 147:1087-1099. [PMID: 37815224 PMCID: PMC10907094 DOI: 10.1093/brain/awad351] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/01/2023] [Accepted: 09/24/2023] [Indexed: 10/11/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease characterized by the loss of motor control. Current understanding of ALS pathology is largely based on post-mortem investigations at advanced disease stages. A systematic in vivo description of the microstructural changes that characterize early stage ALS, and their subsequent development, is so far lacking. Recent advances in ultra-high field (7 T) MRI data modelling allow us to investigate cortical layers in vivo. Given the layer-specific and topographic signature of ALS pathology, we combined submillimetre structural 7 T MRI data (qT1, QSM), functional localizers of body parts (upper limb, lower limb, face) and layer modelling to systematically describe pathology in the primary motor cortex (M1), in 12 living ALS patients with reference to 12 matched controls. Longitudinal sampling was performed for a subset of patients. We calculated multimodal pathology maps for each layer (superficial layer, layer 5a, layer 5b, layer 6) of M1 to identify hot spots of demyelination, iron and calcium accumulation in different cortical fields. We show preserved mean cortical thickness and layer architecture of M1, despite significantly increased iron in layer 6 and significantly increased calcium in layer 5a and superficial layer, in patients compared to controls. The behaviourally first-affected cortical field shows significantly increased iron in L6 compared to other fields, while calcium accumulation is atopographic and significantly increased in the low myelin borders between cortical fields compared to the fields themselves. A subset of patients with longitudinal data shows that the low myelin borders are particularly disrupted and that calcium hot spots, but to a lesser extent iron hot spots, precede demyelination. Finally, we highlight that a very slow progressing patient (Patient P4) shows a distinct pathology profile compared to the other patients. Our data show that layer-specific markers of in vivo pathology can be identified in ALS patients with a single 7 T MRI measurement after first diagnosis, and that such data provide critical insights into the individual disease state. Our data highlight the non-topographic architecture of ALS disease spread and the role of calcium, rather than iron accumulation, in predicting future demyelination. We also highlight a potentially important role of low myelin borders, that are known to connect to multiple areas within the M1 architecture, in disease spread. Finally, the distinct pathology profile of a very-slow progressing patient (Patient P4) highlights a distinction between disease duration and progression. Our findings demonstrate the importance of in vivo histology imaging for the diagnosis and prognosis of neurodegenerative diseases such as ALS.
Collapse
Affiliation(s)
- Alicia Northall
- Institute for Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - Juliane Doehler
- Institute for Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
| | - Miriam Weber
- Department of Neurology, Otto-von-Guericke University Magdeburg (OVGU), Magdeburg 39120, Germany
| | - Igor Tellez
- Institute for Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
| | - Susanne Petri
- Department of Neurology, Hannover Medical School (MHH), Hanover 30625, Germany
| | - Johannes Prudlo
- Department of Neurology, Rostock University Medical Centre, Rostock 18147, Germany
- German Center for Neurodegenerative Diseases (DZNE), Rostock 18147, Germany
| | - Stefan Vielhaber
- Department of Neurology, Otto-von-Guericke University Magdeburg (OVGU), Magdeburg 39120, Germany
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany
- Department of Neurology, Otto-von-Guericke University Magdeburg (OVGU), Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS) Magdeburg, Magdeburg 39120, Germany
| | - Esther Kuehn
- Institute for Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University Magdeburg, Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS) Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen 72076, Germany
- Hertie Institute for Clinical Brain Research (HIH), Tübingen 72076, Germany
| |
Collapse
|
8
|
Merenstein JL, Zhao J, Overson DK, Truong TK, Johnson KG, Song AW, Madden DJ. Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease. Cereb Cortex 2024; 34:bhad525. [PMID: 38185996 PMCID: PMC10839848 DOI: 10.1093/cercor/bhad525] [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: 09/20/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/09/2024] Open
Abstract
In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.
Collapse
Affiliation(s)
- Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
| | - Jiayi Zhao
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
| | - Devon K Overson
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Medical Physics Graduate Program, Duke University, Durham, NC 27708, United States
| | - Trong-Kha Truong
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Medical Physics Graduate Program, Duke University, Durham, NC 27708, United States
| | - Kim G Johnson
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, United States
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Medical Physics Graduate Program, Duke University, Durham, NC 27708, United States
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, United States
- Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States
| |
Collapse
|