1
|
Barsoum S, Latimer CS, Nolan AL, Barrett A, Chang K, Troncoso J, Keene CD, Benjamini D. Resiliency to Alzheimer's disease neuropathology can be distinguished from dementia using cortical astrogliosis imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592719. [PMID: 38766087 PMCID: PMC11100587 DOI: 10.1101/2024.05.06.592719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Despite the presence of significant Alzheimer's disease (AD) pathology, characterized by amyloid β (Aβ) plaques and phosphorylated tau (pTau) tangles, some cognitively normal elderly individuals do not inevitably develop dementia. These findings give rise to the notion of cognitive 'resilience', suggesting maintained cognitive function despite the presence of AD neuropathology, highlighting the influence of factors beyond classical pathology. Cortical astroglial inflammation, a ubiquitous feature of symptomatic AD, shows a strong correlation with cognitive impairment severity, potentially contributing to the diversity of clinical presentations. However, noninvasively imaging neuroinflammation, particularly astrogliosis, using MRI remains a significant challenge. Here we sought to address this challenge and to leverage multidimensional (MD) MRI, a powerful approach that combines relaxation with diffusion MR contrasts, to map cortical astrogliosis in the human brain by accessing sub-voxel information. Our goal was to test whether MD-MRI can map astroglial pathology in the cerebral cortex, and if so, whether it can distinguish cognitive resiliency from dementia in the presence of hallmark AD neuropathological changes. We adopted a multimodal approach by integrating histological and MRI analyses using human postmortem brain samples. Ex vivo cerebral cortical tissue specimens derived from three groups comprised of non-demented individuals with significant AD pathology postmortem, individuals with both AD pathology and dementia, and non-demented individuals with minimal AD pathology postmortem as controls, underwent MRI at 7 T. We acquired and processed MD-MRI, diffusion tensor, and quantitative T 1 and T 2 MRI data, followed by histopathological processing on slices from the same tissue. By carefully co-registering MRI and microscopy data, we performed quantitative multimodal analyses, leveraging targeted immunostaining to assess MD-MRI sensitivity and specificity towards Aβ, pTau, and glial fibrillary acidic protein (GFAP), a marker for astrogliosis. Our findings reveal a distinct MD-MRI signature of cortical astrogliosis, enabling the creation of predictive maps for cognitive resilience amid AD neuropathological changes. Multiple linear regression linked histological values to MRI changes, revealing that the MD-MRI cortical astrogliosis biomarker was significantly associated with GFAP burden (standardized β=0.658, pFDR<0.0001), but not with Aβ (standardized β=0.009, p FDR =0.913) or pTau (standardized β=-0.196, p FDR =0.051). Conversely, none of the conventional MRI parameters showed significant associations with GFAP burden in the cortex. While the extent to which pathological glial activation contributes to neuronal damage and cognitive impairment in AD is uncertain, developing a noninvasive imaging method to see its affects holds promise from a mechanistic perspective and as a potential predictor of cognitive outcomes.
Collapse
|
2
|
Singh K, Barsoum S, Schilling KG, An Y, Ferrucci L, Benjamini D. Neuronal microstructural changes in the human brain are associated with neurocognitive aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575206. [PMID: 38260525 PMCID: PMC10802615 DOI: 10.1101/2024.01.11.575206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Gray matter (GM) alterations play a role in aging-related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age-related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project-Aging cohort that included 707 unimpaired participants (394 female; median age = 58, range = 36-90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro- and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age-related functional decline.
Collapse
Affiliation(s)
- Kavita Singh
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Stephanie Barsoum
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yang An
- Brain Aging and Behavior Section, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| |
Collapse
|
3
|
Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [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: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
Collapse
Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
| |
Collapse
|
4
|
Kundu S, Barsoum S, Ariza J, Nolan AL, Latimer CS, Keene CD, Basser PJ, Benjamini D. Mapping the individual human cortex using multidimensional MRI and unsupervised learning. Brain Commun 2023; 5:fcad258. [PMID: 37953850 PMCID: PMC10638106 DOI: 10.1093/braincomms/fcad258] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/31/2023] [Accepted: 10/05/2023] [Indexed: 11/14/2023] Open
Abstract
Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion-relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion-relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion-relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.
Collapse
Affiliation(s)
- Shinjini Kundu
- Department of Radiology, The Johns Hopkins Hospital, Baltimore, MD 21287, USA
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA
| | - Stephanie Barsoum
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Jeanelle Ariza
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Amber L Nolan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Caitlin S Latimer
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| |
Collapse
|
5
|
Bell RP, Meade CS, Gadde S, Towe SL, Hall SA, Chen NK. Principal component analysis denoising improves sensitivity of MR diffusion to detect white matter injury in neuroHIV. J Neuroimaging 2022; 32:544-553. [PMID: 35023234 PMCID: PMC9090947 DOI: 10.1111/jon.12965] [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: 07/23/2021] [Revised: 12/27/2021] [Accepted: 12/27/2021] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND AND PURPOSE Diffusion-weighted imaging is able to capture important information about cerebral white matter (WM) structure. However, diffusion data can suffer from MRI and biological noise that degrades the quality of the images and makes finding important features difficult. We investigated how effectively local and nonlocal denoising increased the sensitivity to detect differences in cerebral WM in neuroHIV. METHODS We utilized principal component analysis (PCA) denoising to detect WM differences using fractional anisotropy. Local and nonlocal PCA denoising paradigms were implemented that varied in search area and number of components. We examined different-sized WM tracts that consistently show differences between people living with Human Immunodeficiency Virus (HIV) (PWH) and HIV-negative individuals (corpus callosum, forceps minor, and right uncinate fasciculus), and size-matched tracts not typically associated with HIV-related differences (spinothalamic, right medial lemniscus, and left occipitopontine). We first conducted a full sample comparison of WM differences between groups, and then randomly reduced the sample to the point where we still found differences in WM. RESULTS Nonlocal PCA denoising allowed us to detect differences after a sample reduction of 35% in the forceps minor, 17% in the right uncinate fasciculus, and 6% in the corpus callosum. CONCLUSIONS PCA denoising had a beneficial effect on detecting significant differences in PWH after sample size reduction. The smaller forceps minor tract and right uncinate fasciculus showed greater sensitivity to PCA denoising than the larger corpus callosum. These results show the importance of identifying the most effective PCA denoising strategy when investigating WM in PWH.
Collapse
Affiliation(s)
- Ryan P Bell
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christina S Meade
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, USA
| | - Syam Gadde
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, USA
| | - Sheri L Towe
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Shana A Hall
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
| |
Collapse
|
6
|
Huang S, Huang C, Li M, Zhang H, Liu J. White Matter Abnormalities and Cognitive Deficit After Mild Traumatic Brain Injury: Comparing DTI, DKI, and NODDI. Front Neurol 2022; 13:803066. [PMID: 35359646 PMCID: PMC8960262 DOI: 10.3389/fneur.2022.803066] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/24/2022] [Indexed: 12/29/2022] Open
Abstract
White matter (WM) disruption is an important determinant of cognitive impairment after mild traumatic brain injury (mTBI), but traditional diffusion tensor imaging (DTI) shows some limitations in assessing WM damage. Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) show advantages over DTI in this respect. Therefore, we used these three diffusion models to investigate complex WM changes in the acute stage after mTBI. From 32 mTBI patients and 31 age-, sex-, and education-matched healthy controls, we calculated eight diffusion metrics based on DTI (fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity), DKI (mean kurtosis), and NODDI (orientation dispersion index, volume fraction of intracellular water (Vic), and volume fraction of the isotropic diffusion compartment). We used tract-based spatial statistics to identify group differences at the voxel level, and we then assessed the correlation between diffusion metrics and cognitive function. We also performed subgroup comparisons based on loss of consciousness. Patients showed WM abnormalities and cognitive deficit. And these two changes showed positive correlation. The correlation between Vic of the splenium of the corpus callosum and Digit Symbol Substitution Test scores showed the smallest p-value (p = 0.000, r = 0.481). We concluded that WM changes, especially in the splenium of the corpus callosum, correlate to cognitive deficit in this study. Furthermore, the high voxel count of NODDI results and the consistency of mean kurtosis and the volume fraction of intracellular water in previous studies and our study showed the functional complementarity of DKI and NODDI to DTI.
Collapse
Affiliation(s)
- Sihong Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chuxin Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Mengjun Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
- Department of Radiology Quality Control Center, Changsha, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China
- *Correspondence: Jun Liu
| |
Collapse
|