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Mueller C, Nenert R, Catiul C, Pilkington J, Szaflarski JP, Amara AW. Relationship between sleep, physical fitness, brain microstructure, and cognition in healthy older adults: A pilot study. Brain Res 2024; 1839:149016. [PMID: 38768934 DOI: 10.1016/j.brainres.2024.149016] [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: 02/22/2024] [Revised: 05/01/2024] [Accepted: 05/17/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND There is a critical need for neuroimaging markers of brain integrity to monitor effects of modifiable lifestyle factors on brain health. This observational, cross-sectional study assessed relationships between brain microstructure and sleep, physical fitness, and cognition in healthy older adults. METHODS Twenty-three adults aged 60 and older underwent whole-brain multi-shell diffusion imaging, comprehensive cognitive testing, polysomnography, and exercise testing. Neurite Orientation Dispersion and Density Imaging (NODDI) was used to quantify neurite density (NDI) and orientation dispersion (ODI). Diffusion tensor imaging (DTI) was used to quantify axial diffusivity (AxD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Relationships between sleep efficiency (SE), time and percent in N3 sleep, cognitive function, physical fitness (VO2 peak) and the diffusion metrics in regions of interest and the whole brain were evaluated. RESULTS Higher NDI in bilateral white and gray matter was associated with better executive functioning. NDI in the right anterior cingulate and adjacent white matter was positively associated with language skills. Higher NDI in the left posterior corona radiata was associated with faster processing speed. Physical fitness was positively associated with NDI in the left precentral gyrus and corticospinal tract. N3 % was positively associated with NDI in the left caudate and right pre- and postcentral gyri. Higher ODI in the left putamen and adjacent white matter was associated with better executive function. CONCLUSION NDI and ODI derived from NODDI are potential neuroimaging markers for associations between brain microstructure and modifiable risk factors in aging. If these associations are observable in clinical samples, NODDI could be incorporated into clinical trials assessing the effects of modifiable risk factors on brain integrity in aging and neurodegenerative diseases.
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Affiliation(s)
- Christina Mueller
- University of Alabama at Birmingham, Department of Neurology, 1719 6(th) Ave S, Birmingham, AL 35233, United States.
| | - Rodolphe Nenert
- University of Alabama at Birmingham, Department of Neurology, 1719 6(th) Ave S, Birmingham, AL 35233, United States
| | - Corina Catiul
- University of Alabama at Birmingham, Department of Neurology, 1719 6(th) Ave S, Birmingham, AL 35233, United States
| | - Jennifer Pilkington
- University of Alabama at Birmingham, Department of Neurology, 1719 6(th) Ave S, Birmingham, AL 35233, United States
| | - Jerzy P Szaflarski
- University of Alabama at Birmingham, Department of Neurology, 1719 6(th) Ave S, Birmingham, AL 35233, United States
| | - Amy W Amara
- University of Alabama at Birmingham, Department of Neurology, 1719 6(th) Ave S, Birmingham, AL 35233, United States; University of Colorado Anschutz Medical Campus, 1635 Aurora Ct, Aurora, CO 80045, United States
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Lindhardt TB, Skoven CS, Bordoni L, Østergaard L, Liang Z, Hansen B. Anesthesia-related brain microstructure modulations detected by diffusion magnetic resonance imaging. NMR IN BIOMEDICINE 2024; 37:e5033. [PMID: 37712335 DOI: 10.1002/nbm.5033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 07/06/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023]
Abstract
Recent studies have shown significant changes to brain microstructure during sleep and anesthesia. In vivo optical microscopy and magnetic resonance imaging (MRI) studies have attributed these changes to anesthesia and sleep-related modulation of the brain's extracellular space (ECS). Isoflurane anesthesia is widely used in preclinical diffusion MRI (dMRI) and it is therefore important to investigate if the brain's microstructure is affected by anesthesia to an extent detectable with dMRI. Here, we employ diffusion kurtosis imaging (DKI) to assess brain microstructure in the awake and anesthetized mouse brain (n = 22). We find both mean diffusivity (MD) and mean kurtosis (MK) to be significantly decreased in the anesthetized mouse brain compared with the awake state (p < 0.001 for both). This effect is observed in both gray matter and white matter. To further investigate the time course of these changes we introduce a method for time-resolved fast DKI. With this, we show the time course of the microstructural alterations in mice (n = 5) as they transition between states in an awake-anesthesia-awake paradigm. We find that the decrease in MD and MK occurs rapidly after delivery of gas isoflurane anesthesia and that values normalize only slowly when the animals return to the awake state. Finally, time-resolved fast DKI is employed in an experimental mouse model of brain edema (n = 4), where cell swelling causes the ECS volume to decrease. Our results show that isoflurane affects DKI parameters and metrics of brain microstructure and point to isoflurane causing a reduction in the ECS volume. The demonstrated DKI methods are suitable for in-bore perturbation studies, for example, for investigating microstructural modulations related to sleep/wake-dependent functions of the glymphatic system. Importantly, our study shows an effect of isoflurane anesthesia on rodent brain microstructure that has broad relevance to preclinical dMRI.
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Affiliation(s)
- Thomas Beck Lindhardt
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Sino-Danish Center for Education and Research, Aarhus, Denmark
- University of the Chinese Academy of Sciences, Beijing, China
| | - Christian Stald Skoven
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Luca Bordoni
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Letten Center, University of Oslo, Oslo, Norway
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Radiology, Neuroradiology Research Unit, Aarhus University Hospital, Aarhus, Denmark
| | - Zhifeng Liang
- CAS Center for Excellence in Brain Sciences and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Brian Hansen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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3
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Lee HH, Tian Q, Sheft M, Coronado-Leija R, Ramos-Llorden G, Abdollahzadeh A, Fieremans E, Novikov DS, Huang SY. The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5087. [PMID: 38168082 PMCID: PMC10942763 DOI: 10.1002/nbm.5087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/05/2024]
Abstract
The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.
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Affiliation(s)
- Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard–MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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4
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Anderson JA, Rashidi-Ranjbar N, Nazeri A, Chad JA, Zhukovsky P, Mulsant BH, Herrmann N, Mah L, Flint AJ, Fischer CE, Pollock BG, Rajji TK, Voineskos AN. Age-Related Alterations in Gray Matter Microstructure in Older People With Remitted Major Depression at Risk for Dementia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:374-384. [PMID: 38298786 PMCID: PMC10829634 DOI: 10.1016/j.bpsgos.2023.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/15/2023] [Accepted: 08/27/2023] [Indexed: 02/02/2024] Open
Abstract
Background Major depressive disorder (MDD) in late life is a risk factor for mild cognitive impairment (MCI) and Alzheimer's disease. However, studies of gray matter changes have produced varied estimates of which structures are implicated in MDD and dementia. Changes in gray matter volume and cortical thickness are macrostructural measures for the microstructural processes of free water accumulation and dendritic spine loss. Methods We conducted multishell diffusion imaging to assess gray matter microstructure in 244 older adults with remitted MDD (n = 44), MCI (n = 115), remitted MDD+MCI (n = 61), or without psychiatric disorders or cognitive impairment (healthy control participants; n = 24). We estimated measures related to neurite density, orientation dispersion, and free water (isotropic volume fraction) using a biophysically plausible model (neurite orientation dispersion and density imaging). Results Results showed that increasing age was correlated with an increase in isotropic volume fraction and a decrease in orientation dispersion index, which is consistent with neuropathology dendritic loss. In addition, this relationship between age and increased isotropic volume fraction was more disrupted in the MCI group than in the remitted MDD or healthy control groups. However, the association between age and orientation dispersion index was similar for all 3 groups. Conclusions The findings suggest that the neurite orientation dispersion and density imaging measures could be used to identify biological risk factors for Alzheimer's disease, signifying both conventional neurodegeneration observed with MCI and dendritic loss seen in MDD.
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Affiliation(s)
- John A.E. Anderson
- Department of Cognitive Science, Carleton University, Ottawa, Ontario, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Neda Rashidi-Ranjbar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
| | - Arash Nazeri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri (AN)
| | - Jordan A. Chad
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Baycrest Health Sciences, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Benoit H. Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Nathan Herrmann
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Linda Mah
- Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alastair J. Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Corinne E. Fischer
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Bruce G. Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K. Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - PACt-MD Study Group
- Department of Cognitive Science, Carleton University, Ottawa, Ontario, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri (AN)
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
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Schiavi S, Palombo M, Zacà D, Tazza F, Lapucci C, Castellan L, Costagli M, Inglese M. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp 2023; 44:4792-4811. [PMID: 37461286 PMCID: PMC10400787 DOI: 10.1002/hbm.26416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 05/02/2023] [Accepted: 06/23/2023] [Indexed: 08/05/2023] Open
Abstract
Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.
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Affiliation(s)
- Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Marco Palombo
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | | | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- HNSR, IRRCS Ospedale Policlinico San MartinoGenoaItaly
| | - Lucio Castellan
- Department of NeuroradiologyIRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- Laboratory of Medical Physics and Magnetic ResonanceIRCCS Stella MarisPisaItaly
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
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Maximov II, Westlye LT. Comparison of different neurite density metrics with brain asymmetry evaluation. Z Med Phys 2023:S0939-3889(23)00085-5. [PMID: 37562999 DOI: 10.1016/j.zemedi.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.
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Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jensen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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7
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Diao Y, Jelescu I. Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network. Magn Reson Med 2023; 89:1193-1206. [PMID: 36372982 DOI: 10.1002/mrm.29495] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE Biophysical modeling of the diffusion MRI (dMRI) signal provides estimates of specific microstructural tissue properties. Although non-linear least squares (NLLS) is the most widespread fitting method, it suffers from local minima and high computational cost. Deep learning approaches are steadily replacing NLLS, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. In this study, a novel fitting approach was proposed based on the encoder-decoder recurrent neural network (RNN) to accelerate model estimation with good generalization to various datasets. METHODS The white matter tract integrity (WMTI)-Watson model as an implementation of the Standard Model of diffusion in white matter derives its parameters indirectly from the diffusion and kurtosis tensors (DKI). The RNN-based solver, which estimates the WMTI-Watson model from DKI, is therefore more readily translatable to various data, irrespective of acquisition protocols as long as the DKI was pre-computed from the signal. An embedding approach was also used to render the model insensitive to potential differences in distributions between training data and experimental data. The analytical solution, NLLS, RNN-, and a multilayer perceptron (MLP)-based methods were evaluated on synthetic and in vivo datasets of rat and human brain. RESULTS The proposed RNN solver showed highly reduced computation time over the analytical solution and NLLS, with similar accuracy but improved robustness, and superior generalizability over MLP. CONCLUSION The RNN estimator can be easily applied to various datasets without retraining, which shows great potential for a widespread use.
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Affiliation(s)
- Yujian Diao
- Laboratory of Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
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