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Noche JA, Radhakrishnan H, Ubele MF, Boaz K, Mefford JL, Jones ED, Perpich JA, McCarty K, Meacham B, Smiley J, Bailey SAB, Puskás LG, Powell DK, Sordo L, Phelan MJ, Norris CM, Head E, Stark CEL. Age-related brain atrophy and the positive effects of behavioral enrichment in middle-aged beagles. J Neurosci 2024:e2366232024. [PMID: 38561226 DOI: 10.1523/jneurosci.2366-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/08/2024] [Accepted: 02/28/2024] [Indexed: 04/04/2024] Open
Abstract
Aging dogs serve as a valuable preclinical model for Alzheimer's disease (AD) due to their natural age-related development of beta-amyloid (Aβ) plaques, human-like metabolism, and large brains that are ideal for studying structural brain aging trajectories from serial neuroimaging. Here we examined the effects of chronic treatment with the calcineurin inhibitor (CNI) tacrolimus or the nuclear factor of activated T cells (NFAT)-inhibiting compound Q134R on age-related canine brain atrophy from a longitudinal study in middle-aged beagles (36 females, 7 males) undergoing behavioral enrichment. Annual MRI was analyzed using modern, automated techniques for region-of-interest -based and voxel-based volumetric assessments. We found that the frontal lobe showed accelerated atrophy with age, while the caudate nucleus remained relatively stable. Remarkably, the hippocampus increased in volume in all dogs. None of these changes were influenced by tacrolimus or Q134R treatment. Our results suggest that behavioral enrichment can prevent atrophy and increase the volume of the hippocampus but does not prevent aging-associated prefrontal cortex atrophy.Significance Statement Aging canines naturally show significant neuropathological similarities to human aging and AD, making them valuable translational models for testing disease-modifying treatments. We applied modern, state-of-the-art longitudinal volumetric analysis approaches to evaluate treatment effects from structural MRI in a large cohort of middle-aged beagles treated with the FDA approved calcineurin inhibitor, tacrolimus, or the experimental NFAT inhibitor, Q134R, while undergoing extensive behavioral enrichment. We show increased hippocampal volumes across all dogs, even control placebo dogs, compelling evidence for a strong enrichment-related benefit on hippocampal structural integrity. Our findings are the first of its kind to demonstrate benefits of behavioral intervention on longitudinal structural brain changes in a higher mammalian model of aging and AD.
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Affiliation(s)
| | - Hamsanandini Radhakrishnan
- University of California, Irvine, Irvine, CA, USA, 92697
- University of Pennsylvania, Philadelphia, PA, USA, 19104
| | | | - Kathy Boaz
- University of Kentucky, Lexington, KY, USA, 40506
| | | | - Erin D Jones
- University of Kentucky, Lexington, KY, USA, 40506
| | | | | | | | | | | | | | | | - Lorena Sordo
- University of California, Irvine, Irvine, CA, USA, 92697
| | | | | | - Elizabeth Head
- University of California, Irvine, Irvine, CA, USA, 92697
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2
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair DA, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush‐Goebel S, Shinohara RT, Shou H, Tisdall MD, Vettel JM, Grafton ST, Cieslak M, Satterthwaite TD. A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging. Hum Brain Mapp 2024; 45:e26580. [PMID: 38520359 PMCID: PMC10960521 DOI: 10.1002/hbm.26580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Bioengineering, School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip A. Cook
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Damien A. Fair
- Masonic Institute for the Developing BrainUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Barry Giesbrecht
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sage Rush‐Goebel
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jean M. Vettel
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
- U.S. Army Research LaboratoryAberdeen Proving GroundAberdeenMarylandUSA
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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3
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Phillips JS, Adluru N, Chung MK, Radhakrishnan H, Olm CA, Cook PA, Gee JC, Cousins KAQ, Arezoumandan S, Wolk DA, McMillan CT, Grossman M, Irwin DJ. Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease. Front Neurosci 2024; 18:1353306. [PMID: 38567286 PMCID: PMC10986184 DOI: 10.3389/fnins.2024.1353306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.
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Affiliation(s)
- Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hamsanandini Radhakrishnan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip A. Cook
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - James C. Gee
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Katheryn A. Q. Cousins
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Memory Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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4
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Cieslak M, Cook PA, Shafiei G, Tapera TM, Radhakrishnan H, Elliott M, Roalf DR, Oathes DJ, Bassett DS, Tisdall MD, Rokem A, Grafton ST, Satterthwaite TD. Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme. Hum Brain Mapp 2024; 45:e26570. [PMID: 38339908 PMCID: PMC10826632 DOI: 10.1002/hbm.26570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 02/12/2024] Open
Abstract
Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.
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Affiliation(s)
- Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Philip A. Cook
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Tinashe M. Tapera
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Mark Elliott
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Desmond J. Oathes
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Dani S. Bassett
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of BioengineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Physics and AstronomyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Electrical and Systems EngineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Sante Fe InstituteSanta FeNew MexicoUnited States
| | - M. Dylan Tisdall
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Ariel Rokem
- Department of Psychology and the eScience InstituteUniversity of WashingtonSeattleWashingtonUnited States
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of California Santa BarbaraSanta BarbaraCaliforniaUnited States
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Penn‐CHOP Lifespan Brain InstitutePhiladelphiaPennsylvaniaUnited States
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5
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Bagautdinova J, Bourque J, Sydnor VJ, Cieslak M, Alexander-Bloch AF, Bertolero MA, Cook PA, Gur RE, Gur RC, Hu F, Larsen B, Moore TM, Radhakrishnan H, Roalf DR, Shinohara RT, Tapera TM, Zhao C, Sotiras A, Davatzikos C, Satterthwaite TD. Development of white matter fiber covariance networks supports executive function in youth. Cell Rep 2023; 42:113487. [PMID: 37995188 PMCID: PMC10795769 DOI: 10.1016/j.celrep.2023.113487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/05/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.
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Affiliation(s)
- Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamsanandini Radhakrishnan
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russel T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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6
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair D, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush-Goebel S, Shinohara R, Shou H, Tisdall MD, Vettel J, Grafton S, Cieslak M, Satterthwaite T. Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging. bioRxiv 2023:2023.02.22.529546. [PMID: 36865219 PMCID: PMC9980087 DOI: 10.1101/2023.02.22.529546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip A. Cook
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sage Rush-Goebel
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jean Vettel
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Scott Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Radhakrishnan H, Shabestari SK, Blurton-Jones M, Obenaus A, Stark CEL. Using Advanced Diffusion-Weighted Imaging to Predict Cell Counts in Gray Matter: Potential and Pitfalls. Front Neurosci 2022; 16:881713. [PMID: 35720733 PMCID: PMC9204138 DOI: 10.3389/fnins.2022.881713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/18/2022] [Indexed: 11/22/2022] Open
Abstract
Recent advances in diffusion imaging have given it the potential to non-invasively detect explicit neurobiological properties, beyond what was previously possible with conventional structural imaging. However, there is very little known about what cytoarchitectural properties these metrics, especially those derived from newer multi-shell models like Neurite Orientation Dispersion and Density Imaging (NODDI) correspond to. While these diffusion metrics do not promise any inherent cell type specificity, different brain cells have varying morphologies, which could influence the diffusion signal in distinct ways. This relationship is currently not well-characterized. Understanding the possible cytoarchitectural signatures of diffusion measures could allow them to estimate important neurobiological properties like cell counts, potentially resulting in a powerful clinical diagnostic tool. Here, using advanced diffusion imaging (NODDI) in the mouse brain, we demonstrate that different regions have unique relationships between cell counts and diffusion metrics. We take advantage of this exclusivity to introduce a framework to predict cell counts of different types of cells from the diffusion metrics alone, in a region-specific manner. We also outline the challenges of reliably developing such a model and discuss the precautions the field must take when trying to tie together medical imaging modalities and histology.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, CA, United States
| | - Sepideh Kiani Shabestari
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Mathew Blurton-Jones
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Andre Obenaus
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Craig E. L. Stark
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, CA, United States
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, United States
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Radhakrishnan H, Bennett IJ, Stark CE. Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition. Neuroimage 2022; 253:119063. [PMID: 35272021 PMCID: PMC10538083 DOI: 10.1016/j.neuroimage.2022.119063] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/02/2022] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Recent advances in diffusion-weighted imaging have enabled us to probe the microstructure of even gray matter non-invasively. However, these advanced multi-shell protocols are often not included in large-scale studies as they significantly increase scan time. In this study, we investigated whether one set of multi-shell diffusion metrics commonly used in gray matter (as derived from Neurite Orientation Dispersion and Density Imaging, NODDI) provide enough additional information over typical tensor and volume metrics to justify the increased acquisition time, using the cognitive aging framework in the human hippocampus as a testbed. We first demonstrated that NODDI metrics are robust and reliable by replicating previous findings from our lab in a larger population of 79 younger (20.41 ± 1.89 years, 46 females) and 75 older (73.56 ± 6.26 years, 45 females) adults, showing that these metrics in the hippocampal subfields are sensitive to age and memory performance. We then asked how these subfield specific hippocampal NODDI metrics compared with standard tensor metrics and volume in predicting age and memory ability. We discovered that both NODDI and tensor measures separately predicted age and cognition in comparable capacities. However, integrating these modalities together considerably increased the predictive power of our logistic models, indicating that NODDI and tensor measures may be capturing independent microstructural information. We use these findings to encourage neuroimaging data collection consortiums to include a multi-shell diffusion sequence in their protocols since existing NODDI measures (and potential future multi-shell measures) may be able to capture microstructural variance that is missed by traditional approaches, even in studies exclusively examining gray matter.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Mathematical, Computational and Systems Biology, University of California, Postal Address: 1400 Biological Sciences III, Irvine, CA 92697, United States
| | - Ilana J Bennett
- Department of Psychology, University of California Riverside, Riverside, California, United States
| | - Craig El Stark
- Mathematical, Computational and Systems Biology, University of California, Postal Address: 1400 Biological Sciences III, Irvine, CA 92697, United States; Department of Neurobiology and Behavior, University of California, Irvine, California 92697, United States.
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Radhakrishnan H, Ubele MF, Krumholz SM, Boaz K, Mefford JL, Jones ED, Meacham B, Smiley J, Puskás LG, Powell DK, Norris CM, Stark CEL, Head E. Tacrolimus Protects against Age-Associated Microstructural Changes in the Beagle Brain. J Neurosci 2021; 41:5124-5133. [PMID: 33952632 PMCID: PMC8197636 DOI: 10.1523/jneurosci.0361-21.2021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/09/2021] [Accepted: 04/16/2021] [Indexed: 11/21/2022] Open
Abstract
The overexpression of calcineurin leads to astrocyte hyperactivation, neuronal death, and inflammation, which are characteristics often associated with pathologic aging and Alzheimer's disease. In this study, we tested the hypothesis that tacrolimus, a calcineurin inhibitor, prevents age-associated microstructural atrophy, which we measured using higher-order diffusion MRI, in the middle-aged beagle brain (n = 30, male and female). We find that tacrolimus reduces hippocampal (p = 0.001) and parahippocampal (p = 0.002) neurite density index, as well as protects against an age-associated increase in the parahippocampal (p = 0.007) orientation dispersion index. Tacrolimus also protects against an age-related decrease in fractional anisotropy in the prefrontal cortex (p < 0.0001). We also show that these microstructural alterations precede cognitive decline and gross atrophy. These results support the idea that calcineurin inhibitors may have the potential to prevent aging-related pathology if administered at middle age.SIGNIFICANCE STATEMENT Hyperactive calcineurin signaling causes neuroinflammation and other neurobiological changes often associated with pathologic aging and Alzheimer's disease (AD). Controlling the expression of calcineurin before gross cognitive deficits are observable might serve as a promising avenue for preventing AD pathology. In this study, we show that the administration of the calcineurin inhibitor, tacrolimus, over 1 year prevents age- and AD-associated microstructural changes in the hippocampus, parahippocampal cortex, and prefrontal cortex of the middle-aged beagle brain, with no noticeable adverse effects. Tacrolimus is already approved by the Food and Drug Administration for use in humans to prevent solid organ transplant rejection, and our results bolster the promise of this drug to prevent AD and aging-related pathology.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, California 92697
| | - Margo F Ubele
- Sanders Brown Center on Aging, Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, Kentucky 40506
| | - Stephanie M Krumholz
- Sanders Brown Center on Aging, Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, Kentucky 40506
| | - Kathy Boaz
- Sanders Brown Center on Aging, Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, Kentucky 40506
| | - Jennifer L Mefford
- Division of Laboratory Animal Resources, University of Kentucky, Lexington, Kentucky 40506
| | - Erin Denhart Jones
- Division of Laboratory Animal Resources, University of Kentucky, Lexington, Kentucky 40506
| | - Beverly Meacham
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, Kentucky 40506
| | - Jeffrey Smiley
- Division of Laboratory Animal Resources, University of Kentucky, Lexington, Kentucky 40506
| | | | - David K Powell
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, Kentucky 40506
| | - Christopher M Norris
- Sanders Brown Center on Aging, Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, Kentucky 40506
| | - Craig E L Stark
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, California 92697
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California 92697
| | - Elizabeth Head
- Department of Pathology & Laboratory Medicine, University of California, Irvine, Irvine, California 92697
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Radhakrishnan H, Stark SM, Stark CEL. Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans. Front Aging Neurosci 2020; 12:94. [PMID: 32327992 PMCID: PMC7161377 DOI: 10.3389/fnagi.2020.00094] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/20/2020] [Indexed: 12/13/2022] Open
Abstract
Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, CA, United States
| | - Shauna M. Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States
| | - Craig E. L. Stark
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, CA, United States
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States
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Albets-Chico X, Radhakrishnan H, Kassinos SC. Full Magnetohydrodynamic Solution of Liquid-Metal Flow in an Insulating Pipe Subjected to a Strong Fringing Magnetic Field. Fusion Science and Technology 2017. [DOI: 10.13182/fst12-a13335] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- X. Albets-Chico
- University of Cyprus, Department of Mechanical and Manufacturing Engineering Computational Science Laboratory–UCY-Compsci, 75 Kallipoleos, Nicosia 1678, Cyprus
| | - H. Radhakrishnan
- University of Cyprus, Department of Mechanical and Manufacturing Engineering Computational Science Laboratory–UCY-Compsci, 75 Kallipoleos, Nicosia 1678, Cyprus
| | - S. C. Kassinos
- University of Cyprus, Department of Mechanical and Manufacturing Engineering Computational Science Laboratory–UCY-Compsci, 75 Kallipoleos, Nicosia 1678, Cyprus
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Abstract
Ammonium dichromate is an inorganic compound frequently used in screen and color printing. Being a strong oxidizing agent, it causes oxygen free radical injury resulting in organ failure. We report a 25-year-old female who presented with acute kidney injury after consumption of ammonium dichromate. She was managed successfully with hemodialysis and supportive measures. This case is reported to highlight the toxicity of ammonium dichromate.
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Affiliation(s)
- H Radhakrishnan
- Department of Nephrology, Mahatma Gandhi Medical College and Research Institute, Puducherry, India
| | - M Gopi
- Department of General Medicine, Mahatma Gandhi Medical College and Research Institute, Puducherry, India
| | - A Arumugam
- Department of General Medicine, Mahatma Gandhi Medical College and Research Institute, Puducherry, India
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Kutlay S, Kurultak I, Nergizoglu G, Erturk S, Karatan O, Azevedo P, Pinto CT, Pereira CM, Marinho A, Vanmassenhove J, Hoste E, Glorieux G, Dhondt A, Vanholder R, Van Biesen W, Rei S, Aleksandrova I, Kiselev V, Ilynskiy M, Berdnikov G, Marchenkova L, Vanmassenhove J, Hoste E, Glorieux G, Dhondt A, Vanholder R, Van Biesen W, Daher EF, Vieira APF, Souza JB, Falcao FS, Costa CR, Fernandes AACS, Mota RMS, Lima RSA, Silva Junior GB, Ulusal Okyay G, Erten Y, Er R, Aybar M, Inal S, Tekbudak M, Aygencel G, Onec K, Bali M, Sindel S, Soto K, Fidalgo P, Papoila AL, Vanmassenhove J, Hoste E, Glorieux G, Dhondt A, Vanholder R, Van Biesen W, Lentini P, Zanoli L, Granata A, Contestabile A, Basso A, Berlingo G, de Cal M, Pellanda V, Dell'Aquila R, Fortrie G, Stads S, van Bommel J, Zietse R, Betjes MG, Berrada A, Arias C, Riera M, Orfila MA, Rodriguez E, Barrios C, Peruzzi L, Chiale F, Camilla R, Martano C, Cresi F, Bertino E, Coppo R, Klimenko A, Villevalde S, Efremovtseva M, Kobalava Z, Pipili C, Ioannidou S, Kokkoris S, Poulaki S, Tripodaki ES, Parisi M, Papastylianou A, Nanas S, Wang YN, Cheng H, Chen YP, Wen Z, Li X, Shen P, Zou Y, Lu Y, Ma X, Chen Y, Ren H, Chen X, Chen N, Yue T, Cheng H, Chen YP, Elmamoun S, Wodeyar H, Goldsmith C, Abraham A, Wootton A, Ahmed S, Hill C, Curtis S, Miller A, Hine T, Stevens KK, Patel RK, Mark PB, Delles C, Jardine AG, Wilflingseder J, Heinzel A, Mayer P, Perco P, Kainz A, Mayer B, Oberbauer R, Huang TM, Wu VC, Park DJ, Bae EJ, Kang YJ, Cho HS, Chang SH, Lentini P, Zanoli L, Granata A, Contestabile A, Berlingo G, Basso A, Pellanda V, de Cal M, Stramana R, Cognolato D, Baiocchi M, Dell'Aquila R, Chiella BM, Pilla C, Balbinotto A, Antunes VH, Heglert A, Collares FM, Thome FS, Gjyzari A, Thereska N, Xhango O, Xue J, Chen MC, Wang L, Chen YJ, Sun XZ, An WS, Kim ES, Son YK, Kim SE, Kim KH, Oh YJ, Tsai HB, Ko WJ, Chao CT, Fortrie G, Stads S, Aarnoudse AJL, Zietse R, Betjes MG, Peride I, Radulescu D, Niculae A, Ciocalteu A, Checherita AI, Kao CC, Wang CY, Lai CF, Huang TM, Chen HH, Wu VC, Ko WJ, Wu KD, Klaus F, Goldani JC, Cantisani G, Zanotelli ML, Carvalho L, Klaus D, Garcia VD, Keitel E, Hussaini SM, Rao PN, Kul A, Ye N, Zhang Y, Cheng H, Chen YP, Baines R, Westacott R, Trew J, Kirtley J, Selby N, Carr S, Xu G, Steffgen J, Blaschke S, Brun-Schulte-Wissing N, Pagel P, Huber F, Mapes J, Jaehnige A, Pestel S, Deray G, Rouviere O, Bacigalupo L, Maes B, Hannedouche T, Vrtovsnik F, Rigothier C, Billiouw JM, Campioni P, Marti-Bonmati L, Gao YM, Li D, Cheng H, Chen YP, Woo S, Lee J, Noh H, Kwon SH, Han DC, Hetherington L, Valluri A, McQuarrie E, Fleming S, Geddes C, Bell S, MacKinnon B, Bell S, Patton A, Sneddon J, Donnan P, Vadiveloo T, Marwick C, Bennie M, Davey P, Yasuda H, Tsuji N, Tsuji T, Iwakura T, Ohashi N, Kato A, Fujigaki Y, Sasaki S, Kawarazaki H, Shibagaki Y, Kimura K, Lingaraju U, Rajanna S, Radhakrishnan H, Parekh A, Sreedhar CG, Sarvi R, Rainone F, Merlino L, Ritchie JP, Kalra PA, Daher EF, Vieira APF, Jacinto CN, Abreu KLS, Silva Junior GB, Neves M, Baptista JP, Rodrigues L, Pinho J, Teixeira L, Pimentel J, Gonzalez Sanchidrian S, Rangel Hidalgo G, Cebrian Andrada C, Deira Lorenzo J, Marin Alvarez J, Garcia-Bernalt Funes V, Gallego Dominguez S, Labrador Gomez P, Castellano Cervino I, Novillo Santana R, Gomez-Martino Arroyo J, Kim Y, Choi BS, Kim YO, Yoon SA, Lin MC, Wu VC, Ko WJ, Wu KD, Wang WJ, Melo MJ, Lopes JA, Raimundo M, Fragoso A, Antunes F, Martin-Moreno PL, Varo N, Restituto P, Sayon-Orea C, Garcia-Fernandez N, Leite Filho NCV, Souza LEO, Cavalcante RM, Silva Junior GB, Morais BM, Leite TT, Silva SL, Kubrusly M, Daher EF, Jung YS, Kim YN, Shin HS, Rim H, Bentall A, Al-Baaj F, Williamson S, Cheshire S, Jelakovic M, Ivkovic V, Laganovic M, Karanovic S, Pecin I, Premuzic V, Vukovic Lela I, Vrdoljak A, Fucek M, Cvitkovic A, Juric D, Bozina N, Bitunjac M, Leko N, Abramovic Baric M, Matijevic V, Jelakovic B, Ullah A, Exarchou K, Archer T, Anijeet H, Brown R, Ahmed S, Zhang Y, Ye N, Cheng H, Cheng YP, Rocha JCG, Gushiken da Silva T, de Castro PF, Kioroglo PS, Branco Martins JP, Tzanno-Martins C, Biesenbach P, Luf F, Fleischmann E, Grunberger T, Druml W, Gaipov A, Turkmen K, Toker A, Solak Y, Cicekler H, Ucar R, Kilicaslan A, Gormus N, Tonbul HZ, Yeksan M, Turk S, Monteburini T, Cenerelli S, Santarelli S, Boggi R, Tazza L, Bossola M, Ferraresi M, Merlo I, Giovinazzo G, Quercia AD, Gai M, Leonardi G, Anania P, Guarena C, Cantaluppi V, Pacitti A, Biancone L, Hissa PNG, Daher EDF, Liborio AB, Thereza BMF, Mendes CCP, Sousa ARO. AKI - human studies. Nephrol Dial Transplant 2013. [DOI: 10.1093/ndt/gft129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Scerbo M, Radhakrishnan H, Cotton B, Dua A, DelJunco D, Wade C, Holcomb J. Utilization of the Random Forest Algorithm to Predict Trauma Patient Disposition Based on Pre-hospital Variables. J Surg Res 2013. [DOI: 10.1016/j.jss.2012.10.525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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Albets-Chico X, Votyakov E, Radhakrishnan H, Kassinos S. Effects of the consistency of the fringing magnetic field on direct numerical simulations of liquid–metal flow. Fusion Engineering and Design 2011. [DOI: 10.1016/j.fusengdes.2010.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Abstract
Understanding transport and deposition of inhaled particles in the human airways plays a crucial role in the targeted therapy of pulmonary diseases, and the administration of inhaled medicines. Numerous researchers have studied the inhalation of particles using experiments or computer models. Even though experiments have shown that the airflow in the trachea and the upper branches of the lung is turbulent, the flow is taken to be laminar in most computer models. Only few recently published papers have looked at the turbulent transport of air in the human airways. Even fewer results have been published on the effect of the upper airway structures on the turbulent airflow in the lungs or on the effect of the turbulence on particle deposition. The previously published turbulent models have also mainly used RANS methods to predict the flow. To study the unsteady flow and particle deposition in a human lung, an LES model with a dynamic Smagorinsky sub-grid scale model was used. The model equations were solved to study steady inspirational flow at different flow rates for different particle sizes. Results indicate that the upper airway geometry produces turbulence in the flow and the deposition of particles is mainly affected by the particle size and Stokes number.
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Affiliation(s)
- H Radhakrishnan
- UCY-CompSci Computational Sciences Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
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Fang Q, Ou W, Radhakrishnan H, Huppert TJ, Hämäläinen M, Franceschini MA, Boas DA. A state-space approach for modeling simultaneous MEG and diffuse optical imaging measurements. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70584-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Radhakrishnan H, Charman WN. Authors? Reply. Ophthalmic Physiol Opt 2007. [DOI: 10.1111/j.1475-1313.2007.00515.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Calver R, Radhakrishnan H, Pardhan S, OLeary D. The effect of spherical aberration in myopic and non-myopic eyes: developing an optical model. J Vis 2002. [DOI: 10.1167/2.10.29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Bandyopadhyay A, Radhakrishnan H, Ramarao BV, Chatterjee SG. Moisture Sorption Response of Paper Subjected to Ramp Humidity Changes: Modeling and Experiments. Ind Eng Chem Res 2000. [DOI: 10.1021/ie991086b] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Bandyopadhyay A, Radhakrishnan H, Ramarao BV, Chatterjee SG. Moisture Sorption Response of Paper Subjected to Ramp Humidity Changes: Modeling and Experiments. Ind Eng Chem Res 1999. [DOI: 10.1021/ie990279w] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- A. Bandyopadhyay
- Empire State Paper Research Institute, SUNY College of Environmental Science and Forestry, Syracuse, New York 13210
| | - H. Radhakrishnan
- Empire State Paper Research Institute, SUNY College of Environmental Science and Forestry, Syracuse, New York 13210
| | - B. V. Ramarao
- Empire State Paper Research Institute, SUNY College of Environmental Science and Forestry, Syracuse, New York 13210
| | - S. G. Chatterjee
- Empire State Paper Research Institute, SUNY College of Environmental Science and Forestry, Syracuse, New York 13210
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