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Storey P, Novikov DS. Signatures of microstructure in gradient-echo and spin-echo signals. Magn Reson Med 2024; 92:269-288. [PMID: 38520259 DOI: 10.1002/mrm.30022] [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: 03/26/2023] [Revised: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE To determine whether the spatial scale and magnetic susceptibility of microstructure can be evaluated robustly from the decay of gradient-echo and spin-echo signals. THEORY AND METHODS Gradient-echo and spin-echo images were acquired from suspensions of spherical polystyrene microbeads of 10, 20, and 40 μm nominal diameter. The sizes of the beads and their magnetic susceptibility relative to the medium were estimated from the signal decay curves, using a lookup table generated from Monte Carlo simulations and an analytic model based on the Gaussian phase approximation. RESULTS Fitting Monte Carlo predictions to spin-echo data yielded acceptable estimates of microstructural parameters for the 20 and 40 μm microbeads. Using gradient-echo data, the Monte Carlo lookup table provided satisfactory parameter estimates for the 20 μm beads but unstable results for the diameter of the largest beads. Neither spin-echo nor gradient-echo data allowed accurate parameter estimation for the smallest beads. The analytic model performed poorly over all bead sizes. CONCLUSIONS Microstructural sources of magnetic susceptibility produce distinctive non-exponential signatures in the decay of gradient-echo and spin-echo signals. However, inverting the problem to extract microstructural parameters from the signals is nontrivial and, in certain regimes, ill-conditioned. For microstructure with small characteristic length scales, parameter estimation is hampered by the difficulty of acquiring accurate data at very short echo times. For microstructure with large characteristic lengths, the gradient-echo signal approaches the static-dephasing regime, where it becomes insensitive to size. Applicability of the analytic model was further limited by failure of the Gaussian phase approximation for all but the smallest beads.
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Affiliation(s)
- Pippa Storey
- Center for Biomedical Imaging, Department of Radiology, 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
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Chung S, Bacon T, Rath JF, Alivar A, Coelho S, Amorapanth P, Fieremans E, Novikov DS, Flanagan SR, Bacon JH, Lui YW. Callosal Interhemispheric Communication in Mild Traumatic Brain Injury: A Mediation Analysis on WM Microstructure Effects. AJNR Am J Neuroradiol 2024:ajnr.A8213. [PMID: 38637026 DOI: 10.3174/ajnr.a8213] [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: 05/26/2023] [Accepted: 01/27/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND PURPOSE Because the corpus callosum connects the left and right hemispheres and a variety of WM bundles across the brain in complex ways, damage to the neighboring WM microstructure may specifically disrupt interhemispheric communication through the corpus callosum following mild traumatic brain injury. Here we use a mediation framework to investigate how callosal interhemispheric communication is affected by WM microstructure in mild traumatic brain injury. MATERIALS AND METHODS Multishell diffusion MR imaging was performed on 23 patients with mild traumatic brain injury within 1 month of injury and 17 healthy controls, deriving 11 diffusion metrics, including DTI, diffusional kurtosis imaging, and compartment-specific standard model parameters. Interhemispheric processing speed was assessed using the interhemispheric speed of processing task (IHSPT) by measuring the latency between word presentation to the 2 hemivisual fields and oral word articulation. Mediation analysis was performed to assess the indirect effect of neighboring WM microstructures on the relationship between the corpus callosum and IHSPT performance. In addition, we conducted a univariate correlation analysis to investigate the direct association between callosal microstructures and IHSPT performance as well as a multivariate regression analysis to jointly evaluate both callosal and neighboring WM microstructures in association with IHSPT scores for each group. RESULTS Several significant mediators in the relationships between callosal microstructure and IHSPT performance were found in healthy controls. However, patients with mild traumatic brain injury appeared to lose such normal associations when microstructural changes occurred compared with healthy controls. CONCLUSIONS This study investigates the effects of neighboring WM microstructure on callosal interhemispheric communication in healthy controls and patients with mild traumatic brain injury, highlighting that neighboring noncallosal WM microstructures are involved in callosal interhemispheric communication and information transfer. Further longitudinal studies may provide insight into the temporal dynamics of interhemispheric recovery following mild traumatic brain injury.
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Affiliation(s)
- Sohae Chung
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Tamar Bacon
- Department of Neurology (T.B., J.H.B.), NY University Grossman School of Medicine, New York, New York
| | - Joseph F Rath
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University Grossman School of Medicine, New York, New York
| | - Alaleh Alivar
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Santiago Coelho
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Prin Amorapanth
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University Grossman School of Medicine, New York, New York
| | - Els Fieremans
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Dmitry S Novikov
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Steven R Flanagan
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University Grossman School of Medicine, New York, New York
| | - Joshua H Bacon
- Department of Neurology (T.B., J.H.B.), NY University Grossman School of Medicine, New York, New York
| | - Yvonne W Lui
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
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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 Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Chen J, Ades-Aron B, Lee HH, Mehrin S, Pang M, Novikov DS, Veraart J, Fieremans E. Optimization and Validation of the DESIGNER dMRI preprocessing pipeline in white matter aging. ArXiv 2024:arXiv:2305.14445v2. [PMID: 37292482 PMCID: PMC10246085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline in a large clinical dMRI dataset and using ground truth phantoms. DESIGNER has been modified to improve denoising and target Gibbs ringing for partial Fourier acquisitions. We compared the revisited DESIGNER (Dv2) (including denoising, Gibbs removal, correction for motion, EPI distortion, and eddy currents) against the original DESIGNER (Dv1) pipeline, minimal preprocessing (including correction for motion, EPI distortion, and eddy currents only), and no preprocessing on a large clinical dMRI dataset of 524 control subjects with ages between 25 and 75 years old. We evaluated the effect of specific processing steps on age correlations in white matter with DTI and DKI metrics. We also evaluated the added effect of minimal Gaussian smoothing to deal with noise and to reduce outliers in parameter maps compared to DESIGNER (Dv2)'s noise removal method. Moreover, DESIGNER (Dv2)'s updated noise and Gibbs removal methods were assessed using ground truth dMRI phantom to evaluate accuracy. Results show age correlation in white matter with DTI and DKI metrics were affected by the preprocessing pipeline, causing systematic differences in absolute parameter values and loss or gain of statistical significance. Both in clinical dMRI and ground truth phantoms, DESIGNER (Dv2) pipeline resulted in the smallest number of outlier voxels and improved accuracy in DTI and DKI metrics as noise was reduced and Gibbs removal was improved. Thus, DESIGNER (Dv2) provides more accurate and robust DTI and DKI parameter maps as compared to no preprocessing or minimal preprocessing.
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Coelho S, Liao Y, Szczepankiewicz F, Veraart J, Chung S, Lui YW, Novikov DS, Fieremans E. Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry. ArXiv 2024:arXiv:2402.17175v1. [PMID: 38463511 PMCID: PMC10925389] [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] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T 2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.
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Affiliation(s)
- Santiago Coelho
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Ying Liao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Sohae Chung
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Coronado-Leija R, Abdollahzadeh A, Lee HH, Coelho S, Ades-Aron B, Liao Y, Salo RA, Tohka J, Sierra A, Novikov DS, Fieremans E. Volume electron microscopy in injured rat brain validates white matter microstructure metrics from diffusion MRI. ArXiv 2024:arXiv:2310.04608v2. [PMID: 38259346 PMCID: PMC10802677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and extra-axonal water fractions and diffusivities. However, for SM to be fully applicable and correctly interpreted, it needs to be carefully evaluated using histology. Here, we perform a comprehensive histological validation of the SM parameters, by characterizing WM microstructure in sham and injured rat brains using volume (3d) electron microscopy (EM) and ex vivo dMRI. Sensitivity is evaluated by how close each SM metric is to its histological counterpart, and specificity by how independent it is from other, non-corresponding histological features. This comparison reveals that SM is sensitive and specific to microscopic properties, clearing the way for the clinical adoption of in vivo dMRI derived SM parameters as biomarkers for neurological disorders.
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Liao Y, Coelho S, Chen J, Ades-Aron B, Pang M, Osorio R, Shepherd T, Lui YW, Novikov DS, Fieremans E. Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI. ArXiv 2023:arXiv:2307.16386v2. [PMID: 37576119 PMCID: PMC10418545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.
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Chen J, Chung S, Li T, Fieremans E, Novikov DS, Wang Y, Lui YW. Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes. Neuroradiol J 2023; 36:693-701. [PMID: 37212469 PMCID: PMC10649530 DOI: 10.1177/19714009231177396] [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] [Indexed: 05/23/2023] Open
Abstract
PURPOSE Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI. METHODS 36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics. RESULTS Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81). CONCLUSION Feature selection and classification identifies diffusion metrics that characterize subconcussive RHI. Linear classifiers yield the best performance and mean diffusion, tissue microstructure complexity, and radial extra-axonal compartment diffusion (MD, MK, De,⊥) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.
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Affiliation(s)
- Junbo Chen
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Sohae Chung
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Tianhao Li
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Lee HH, Keerthivasan MB, Lemberskiy G, Zhang J, Fieremans E, Novikov DS. Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI. ArXiv 2023:arXiv:2311.16316v1. [PMID: 38076512 PMCID: PMC10705579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Random matrix theory (RMT) combined with principal component analysis has resulted in a widely used MPPCA noise mapping and denoising algorithm, that utilizes the redundancy in multiple acquisitions and in local image patches. RMT-based denoising relies on the uncorrelated identically distributed noise. This assumption breaks down after regridding of non-Cartesian sampling. Here we propose a Universal Sampling Denoising (USD) pipeline to homogenize the noise level and decorrelate the noise in non-Cartesian sampled k-space data after resampling to a Cartesian grid. In this way, the RMT approaches become applicable to MRI of any non-Cartesian k-space sampling. We demonstrate the denoising pipeline on MRI data acquired using radial trajectories, including diffusion MRI of a numerical phantom and ex vivo mouse brains, as well as in vivo $T_2$ MRI of a healthy subject. The proposed pipeline robustly estimates noise level, performs noise removal, and corrects bias in parametric maps, such as diffusivity and kurtosis metrics, and $T_2$ relaxation time. USD stabilizes the variance, decorrelates the noise, and thereby enables the application of RMT-based denoising approaches to MR images reconstructed from any non-Cartesian data. In addition to MRI, USD may also apply to other medical imaging techniques involving non-Cartesian acquisition, such as PET, CT, and SPECT.
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Chung S, Fieremans E, Novikov DS, Lui YW. Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction. Res Sq 2023:rs.3.rs-3645723. [PMID: 38045398 PMCID: PMC10690318 DOI: 10.21203/rs.3.rs-3645723/v1] [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: 12/05/2023]
Abstract
The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.
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Affiliation(s)
- Sohae Chung
- New York University Grossman School of Medicine
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Lemberskiy G, Chandarana H, Bruno M, Ginocchio LA, Huang C, Tong A, Keerthivasan MB, Fieremans E, Novikov DS. Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising. Invest Radiol 2023; 58:720-729. [PMID: 37222526 PMCID: PMC10527232 DOI: 10.1097/rli.0000000000000979] [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] [Indexed: 05/25/2023]
Abstract
INTRODUCTION Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils. METHODS Twenty-one volunteers and 2 prostate cancer patients were imaged with a 6-channel pelvic surface array coil and an 18-channel spine array on a prototype 0.55 T system created by ramping down a commercial magnetic resonance imaging system (1.5 T MAGNETOM Aera Siemens Healthcare) with 45 mT/m gradients and 200 T/m/s slew rate. Diffusion-weighted images were acquired with 4 non-collinear directions, for which b = 50 s/mm 2 was used with 8 averages and b = 1000 s/mm 2 with 40 averages; 2 extra b = 50 s/mm 2 were used as part of the dynamic field correction. Standard and RMT-based reconstructions were applied on DWI over different ranges of averages. Accuracy/precision was evaluated using the apparent diffusion coefficient (ADC), and image quality was evaluated over 5 separate reconstructions by 3 radiologists with a 5-point Likert scale. For the 2 patients, we compare image quality and lesion visibility of the RMT reconstruction versus the standard one on 0.55 T and on clinical 3.0 T. RESULTS The RMT-based reconstruction in this study reduces the noise floor by a factor of 5.8, thereby alleviating the bias on prostate ADC. Moreover, the precision of the ADC in prostate tissue after RMT increases over a range of 30%-130%, with the increase in both signal-to-noise ratio and precision being more prominent for a low number of averages. Raters found that the images were consistently of moderate to good overall quality (3-4 on the Likert scale). Moreover, they determined that b = 1000 s/mm 2 images from a 1:55-minute scan with the RMT-based reconstruction were on par with the corresponding images from a 14:20-minute scan with standard reconstruction. Prostate cancer was visible on ADC and calculated b = 1500 images even with the abbreviated 1:55-minute scan reconstructed with RMT. CONCLUSIONS Prostate imaging using DWI is feasible at low field and can be performed more rapidly with noninferior image quality compared with standard reconstruction.
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Affiliation(s)
- Gregory Lemberskiy
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Hersh Chandarana
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Mary Bruno
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Luke A. Ginocchio
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Chenchan Huang
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Angela Tong
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | | | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
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12
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Wu D, Turnbill V, Lee HH, Wang X, Ba R, Walczak P, Martin LJ, Fieremans E, Novikov DS, Northington FJ, Zhang J. In vivo Mapping of Cellular Resolution Neuropathology in Brain Ischemia by Diffusion MRI. bioRxiv 2023:2023.08.08.552374. [PMID: 37609182 PMCID: PMC10441332 DOI: 10.1101/2023.08.08.552374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Non-invasive mapping of cellular pathology can provide critical diagnostic and prognostic information. Recent developments in diffusion MRI have produced new tools for examining tissue microstructure at a level well below the imaging resolution. Here, we report the use of diffusion time ( t )-dependent diffusion kurtosis imaging ( t DKI) to simultaneously assess the morphology and transmembrane permeability of cells and their processes in the context of pathological changes in hypoxic-ischemic brain (HI) injury. Through Monte Carlo simulations and cell culture organoid imaging, we demonstrate feasibility in measuring effective size and permeability changes based on the peak and tail of t DKI curves. In a mouse model of HI, in vivo imaging at 11.7T detects a marked shift of the t DKI peak to longer t in brain edema, suggesting swelling and beading associated with the astrocytic processes and neuronal neurites. Furthermore, we observed a faster decrease of the t DKI tail in injured brain regions, reflecting increased membrane permeability that was associated with upregulated water exchange upon astrocyte activation at acute stage as well as necrosis with disrupted membrane integrity at subacute stage. Such information, unavailable with conventional diffusion MRI at a single t, can predict salvageable tissues. For a proof-of-concept, t DKI at 3T on an ischemic stroke patient suggested increased membrane permeability in the stroke region. This work therefore demonstrates the potential of t DKI for in vivo detection of the pathological changes in microstructural morphology and transmembrane permeability after ischemic injury using a clinically translatable protocol.
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Ruh A, Emerich P, Scherer H, Novikov DS, Kiselev VG. Observation of magnetic structural universality and jamming transition with NMR. J Magn Reson 2023; 353:107476. [PMID: 37392588 DOI: 10.1016/j.jmr.2023.107476] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/07/2023] [Accepted: 05/08/2023] [Indexed: 07/03/2023]
Abstract
Nuclear magnetic resonance (NMR) has been instrumental in deciphering the structure of proteins. Here we show that transverse NMR relaxation, through its time-dependent relaxation rate, is distinctly sensitive to the structure of complex materials or biological tissues at the mesoscopic scale, from micrometers to tens of micrometers. Based on the ideas of universality, we show analytically and numerically that the time-dependent transverse relaxation rate approaches its long-time limit in a power-law fashion, with the dynamical exponent reflecting the universality class of mesoscopic magnetic structure. The spectral line shape acquires the corresponding non-analytic power law singularity at zero frequency. We experimentally detect the change in the dynamical exponent as a result of the transition into maximally random jammed state characterized by hyperuniform correlations. The relation between relaxational dynamics and magnetic structure opens the way for noninvasive characterization of porous media, complex materials and biological tissues.
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Affiliation(s)
- Alexander Ruh
- Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp Emerich
- Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Harald Scherer
- Institute of Inorganic and Analytical Chemistry, University of Freiburg, Freiburg, Germany
| | - Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Valerij G Kiselev
- Medical Physics, Department of Radiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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14
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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 influence of axonal beading and undulation on axonal diameter mapping. bioRxiv 2023:2023.04.19.537494. [PMID: 37131702 PMCID: PMC10153226 DOI: 10.1101/2023.04.19.537494] [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: 05/04/2023]
Abstract
We consider the effect of non-cylindrical axonal shape on axonal diameter mapping with diffusion MRI. Practical sensitivity to axon diameter is attained at strong diffusion weightings b , where the deviation from the 1 / b scaling yields the finite transverse diffusivity, which is then translated into axon diameter. While axons are usually modeled as perfectly straight, impermeable cylinders, the local variations in diameter (caliber variation or beading) and direction (undulation) have been observed in microscopy data of human axons. Here we quantify the influence of cellular-level features such as caliber variation and undulation on axon diameter estimation. For that, we simulate the diffusion MRI signal in realistic axons segmented from 3-dimensional electron microscopy of a human brain sample. We then create artificial fibers with the same features and tune the amplitude of their caliber variations and undulations. Numerical simulations of diffusion in fibers with such tunable features show that caliber variations and undulations result in under- and over-estimation of axon diameters, correspondingly; this bias can be as large as 100%. 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, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
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15
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Li C, Fieremans E, Novikov DS, Ge Y, Zhang J. Measuring water exchange on a preclinical MRI system using filter exchange and diffusion time dependent kurtosis imaging. Magn Reson Med 2023; 89:1441-1455. [PMID: 36404493 PMCID: PMC9892228 DOI: 10.1002/mrm.29536] [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: 12/19/2021] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE Filter exchange imaging (FEXI) and diffusion time (t)-dependent diffusion kurtosis imaging (DKI(t)) are both sensitive to water exchange between tissue compartments. The restrictive effects of tissue microstructure, however, introduce bias to the exchange rate obtained by these two methods, as their interpretation conventionally rely on the Kärger model of barrier limited exchange between Gaussian compartments. Here, we investigated whether FEXI and DKI(t) can provide comparable exchange rates in ex vivo mouse brains. THEORY AND METHODS FEXI and DKI(t) data were acquired from ex vivo mouse brains on a preclinical MRI system. Phase cycling and negative slice prewinder gradients were used to minimize the interferences from imaging gradients. RESULTS In the corpus callosum, apparent exchange rate (AXR) from FEXI correlated with the exchange rate (the inverse of exchange time, 1/τex ) from DKI(t) along the radial direction. In comparison, discrepancies between FEXI and DKI(t) were found in the cortex due to low filter efficiency and confounding effects from tissue microstructure. CONCLUSION The results suggest that FEXI and DKI(t) are sensitive to the same exchange processes in white matter when separated from restrictive effects of microstructure. The complex microstructure in gray matter, with potential exchange among multiple compartments and confounding effects of microstructure, still pose a challenge for FEXI and DKI(t).
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Affiliation(s)
- Chenyang Li
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA
| | - Els Fieremans
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
| | - Dmitry S. Novikov
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
| | - Yulin Ge
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
| | - Jiangyang Zhang
- Department of Radiology, Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA
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16
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Jelescu IO, de Skowronski A, Geffroy F, Palombo M, Novikov DS. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. Neuroimage 2022; 256:119277. [PMID: 35523369 PMCID: PMC10363376 DOI: 10.1016/j.neuroimage.2022.119277] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [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: 01/21/2022] [Revised: 04/26/2022] [Accepted: 05/01/2022] [Indexed: 01/18/2023] Open
Abstract
Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.
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Affiliation(s)
- Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland; School of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Alexandre de Skowronski
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Marco Palombo
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK; School of Computer Science and Informatics, Cardiff University, Cardiff, UK; Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK
| | - Dmitry S Novikov
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
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17
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Huang SY, Witzel T, Keil B, Scholz A, Davids M, Dietz P, Rummert E, Ramb R, Kirsch JE, Yendiki A, Fan Q, Tian Q, Ramos-Llordén G, Lee HH, Nummenmaa A, Bilgic B, Setsompop K, Wang F, Avram AV, Komlosh M, Benjamini D, Magdoom KN, Pathak S, Schneider W, Novikov DS, Fieremans E, Tounekti S, Mekkaoui C, Augustinack J, Berger D, Shapson-Coe A, Lichtman J, Basser PJ, Wald LL, Rosen BR. Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome. Neuroimage 2021; 243:118530. [PMID: 34464739 PMCID: PMC8863543 DOI: 10.1016/j.neuroimage.2021.118530] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 08/27/2021] [Indexed: 11/26/2022] Open
Abstract
The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.
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Affiliation(s)
- Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | | | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kawin Setsompop
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandru V Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Michal Komlosh
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Dan Benjamini
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Kulam Najmudeen Magdoom
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sudhir Pathak
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Walter Schneider
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Slimane Tounekti
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Berger
- Department of Molecular and Cell Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Alexander Shapson-Coe
- Department of Molecular and Cell Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Jeff Lichtman
- Department of Molecular and Cell Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Lee HH, Novikov DS, Fieremans E. Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI. Magn Reson Med 2021; 86:2733-2750. [PMID: 34227142 DOI: 10.1002/mrm.28830] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/29/2021] [Accepted: 04/16/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To investigate and remove Gibbs-ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data. THEORY AND METHODS Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k-space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state-of-the-art. However, the asymmetric truncation of k-space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF-induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network-based approach. RESULTS For PF = 7/8 and 6/8, Gibbs-ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel. CONCLUSIONS RPG corrects Gibbs-ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
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Lemberskiy G, Feiweier T, Gyftopoulos S, Axel L, Novikov DS, Fieremans E. Assessment of myofiber microstructure changes due to atrophy and recovery with time-dependent diffusion MRI. NMR Biomed 2021; 34:e4534. [PMID: 34002901 DOI: 10.1002/nbm.4534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 03/24/2021] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
Current clinical MRI evaluation of musculature largely focuses on nonquantitative assessments (including T1-, T2- and PD-weighted images), which may vary greatly between imaging systems and readers. This work aims to determine the efficacy of a quantitative approach to study the microstructure of muscles at the cellular level with the random permeable barrier model (RPBM) applied to time-dependent diffusion tensor imaging (DTI) for varying diffusion time. Patients (N = 15, eight males and seven females) with atrophied calf muscles due to immobilization of one leg in a nonweight-bearing cast, were enrolled after providing informed consent. Their calf muscles were imaged with stimulated echo diffusion for DTI, T1-mapping and RPBM modeling. Specifically, After cast removal, both calf muscles (atrophied and contralateral control leg) were imaged with MRI for all patients, with follow-up scans to monitor recovery of the atrophied leg for six patients after 4 and 8 weeks. We compare RPBM-derived microstructural metrics: myofiber diameter, a, and sarcolemma permeability, κ, along with macroscopic anatomical parameters (muscle cross-sectional area, fiber orientation, <θ>, and T1 relaxation). ROC analysis was used to compare parameters between control and atrophied muscle, while the Friedman test was used to evaluate the atrophied muscle longitudinally. We found that the RPBM framework enables measurement of microstructural parameters from diffusion time-dependent DTI, of which the myofiber diameter is a stronger predictor of intramuscular morphological changes than either macroscopic (anatomical) measurements or empirical diffusion parameters. This work demonstrates the potential of RPBM to assess pathological changes in musculature that seem undetectable with standard diffusion and anatomical MRI.
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Affiliation(s)
- Gregory Lemberskiy
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | | | - Soterios Gyftopoulos
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Leon Axel
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
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Zhang J, Lemberskiy G, Moy L, Fieremans E, Novikov DS, Kim SG. Measurement of cellular-interstitial water exchange time in tumors based on diffusion-time-dependent diffusional kurtosis imaging. NMR Biomed 2021; 34:e4496. [PMID: 33634508 PMCID: PMC8170918 DOI: 10.1002/nbm.4496] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 02/08/2021] [Indexed: 05/10/2023]
Abstract
PURPOSE To assess the feasibility of using diffusion-time-dependent diffusional kurtosis imaging (tDKI) to measure cellular-interstitial water exchange time (τex ) in tumors, both in animals and in humans. METHODS Preclinical tDKI studies at 7 T were performed with the GL261 glioma model and the 4T1 mammary tumor model injected into the mouse brain. Clinical studies were performed at 3 T with women who had biopsy-proven invasive ductal carcinoma. tDKI measurement was conducted using a diffusion-weighted STEAM pulse sequence with multiple diffusion times (20-800 ms) at a fixed echo time, while keeping the b-values the same (0-3000 s/mm2 ) by adjusting the diffusion gradient strength. The tDKI data at each diffusion time t were used for a weighted linear least-squares fit method to estimate the diffusion-time-dependent diffusivity, D(t), and diffusional kurtosis, K(t). RESULTS Both preclinical and clinical studies showed that, when diffusion time t ≥ 200 ms, D(t) did not have a noticeable change while K(t) decreased monotonically with increasing diffusion time in tumors and t ≥ 100 ms for the cortical ribbon of the mouse brain. The estimated τex averaged median and interquartile range (IQR) of GL261 and 4T1 tumors were 93 (IQR = 89) ms and 68 (78) ms, respectively. For the cortical ribbon, the estimated τex averaged median and IQR were 41 (34) ms for C57BL/6 and 30 (17) ms for BALB/c. For invasive ductal carcinoma, the estimated τex median and IQR of the two breast cancers were 70 (94) and 106 (92) ms. CONCLUSION The results of this proof-of-concept study substantiate the feasibility of using tDKI to measure cellular-interstitial water exchange time without using an exogenous contrast agent.
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Affiliation(s)
- Jin Zhang
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Gregory Lemberskiy
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Sungheon Gene Kim
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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21
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Muckley MJ, Ades-Aron B, Papaioannou A, Lemberskiy G, Solomon E, Lui YW, Sodickson DK, Fieremans E, Novikov DS, Knoll F. Training a neural network for Gibbs and noise removal in diffusion MRI. Magn Reson Med 2021; 85:413-428. [PMID: 32662910 PMCID: PMC7722184 DOI: 10.1002/mrm.28395] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [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: 11/22/2019] [Revised: 05/29/2020] [Accepted: 06/03/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. RESULTS Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. CONCLUSIONS The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
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Affiliation(s)
- Matthew J. Muckley
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Benjamin Ades-Aron
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Antonios Papaioannou
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Gregory Lemberskiy
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Eddy Solomon
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Yvonne W. Lui
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S. Novikov
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Florian Knoll
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
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Ades-Aron B, Lemberskiy G, Veraart J, Golfinos J, Fieremans E, Novikov DS, Shepherd T. Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising. Radiology 2020; 298:365-373. [PMID: 33289611 DOI: 10.1148/radiol.2020200822] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Benjamin Ades-Aron
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Gregory Lemberskiy
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Jelle Veraart
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - John Golfinos
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Els Fieremans
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Dmitry S Novikov
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Timothy Shepherd
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
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Lee HH, Fieremans E, Novikov DS. Realistic Microstructure Simulator (RMS): Monte Carlo simulations of diffusion in three-dimensional cell segmentations of microscopy images. J Neurosci Methods 2020; 350:109018. [PMID: 33279478 DOI: 10.1016/j.jneumeth.2020.109018] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/16/2020] [Accepted: 11/29/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Monte Carlo simulations of diffusion are commonly used as a model validation tool as they are especially suitable for generating the diffusion MRI signal in complicated tissue microgeometries. NEW METHOD Here we describe the details of implementing Monte Carlo simulations in three-dimensional (3d) voxelized segmentations of cells in microscopy images. Using the concept of the corner reflector, we largely reduce the computational load of simulating diffusion within and exchange between multiple cells. Precision is further achieved by GPU-based parallel computations. RESULTS Our simulation of diffusion in white matter axons segmented from a mouse brain demonstrates its value in validating biophysical models. Furthermore, we provide the theoretical background for implementing a discretized diffusion process, and consider the finite-step effects of the particle-membrane reflection and permeation events, needed for efficient simulation of interactions with irregular boundaries, spatially variable diffusion coefficient, and exchange. COMPARISON WITH EXISTING METHODS To our knowledge, this is the first Monte Carlo pipeline for MR signal simulations in a substrate composed of numerous realistic cells, accounting for their permeable and irregularly-shaped membranes. CONCLUSIONS The proposed RMS pipeline makes it possible to achieve fast and accurate simulations of diffusion in realistic tissue microgeometry, as well as the interplay with other MR contrasts. Presently, RMS focuses on simulations of diffusion, exchange, and T1 and T2 NMR relaxation in static tissues, with a possibility to straightforwardly account for susceptibility-induced T2* effects and flow.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA.
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
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Lee HH, Papaioannou A, Novikov DS, Fieremans E. In vivo observation and biophysical interpretation of time-dependent diffusion in human cortical gray matter. Neuroimage 2020; 222:117054. [PMID: 32585341 PMCID: PMC7736473 DOI: 10.1016/j.neuroimage.2020.117054] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 12/25/2022] Open
Abstract
The dependence of the diffusion MRI signal on the diffusion time t is a hallmark of tissue microstructure at the scale of the diffusion length. Here we measure the time-dependence of the mean diffusivity D(t) and mean kurtosis K(t) in cortical gray matter and in 25 gray matter sub-regions, in 10 healthy subjects. Significant diffusivity and kurtosis time-dependence is observed for t=21.2-100 ms, and is characterized by a power-law tail ∼t-ϑ with dynamical exponent ϑ. To interpret our measurements, we systematize the relevant scenarios and mechanisms for diffusion time-dependence in the brain. Using the effective medium theory formalism, we derive an exact relation between the power-law tails in D(t) and K(t). The estimated dynamical exponent ϑ≃1/2 in both D(t) and K(t) is consistent with one-dimensional diffusion in the presence of randomly positioned restrictions along neurites. We analyze the short-range disordered statistics of synapses on axon collaterals in the cortex, and perform one-dimensional Monte Carlo simulations of diffusion restricted by permeable barriers with a similar randomness in their placement, to confirm the ϑ=1/2 exponent. In contrast, the Kärger model of exchange is less consistent with the data since it does not capture the diffusivity time-dependence, and the estimated exchange time from K(t) falls below our measured t-range. Although we cannot exclude exchange as a contributing factor, we argue that structural disorder along neurites is mainly responsible for the observed time-dependence of diffusivity and kurtosis. Our observation and theoretical interpretation of the t-1/2 tail in D(t) and K(t) altogether establish the sensitivity of a macroscopic MRI signal to micrometer-scale structural heterogeneities along neurites in human gray matter in vivo.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA.
| | - Antonios Papaioannou
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
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25
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Novikov DS. The present and the future of microstructure MRI: From a paradigm shift to normal science. J Neurosci Methods 2020; 351:108947. [PMID: 33096152 DOI: 10.1016/j.jneumeth.2020.108947] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/29/2020] [Accepted: 09/10/2020] [Indexed: 12/29/2022]
Abstract
The aspiration of imaging tissue microstructure with MRI is to uncover micrometer-scale tissue features within millimeter-scale imaging voxels, in vivo. This kind of super-resolution has fueled a paradigm shift within the biomedical imaging community. However, what feels like an ongoing revolution in MRI, has been conceptually experienced in physics decades ago; from this point of view, our current developments can be seen as Thomas Kuhn's "normal science" stage of progress. While the concept of model-based quantification below the nominal imaging resolution is not new, its possibilities in neuroscience and neuroradiology are only beginning to be widely appreciated. This disconnect calls for communicating the progress of tissue microstructure MR imaging to its potential users. Here, a number of recent research developments are outlined in terms of the overarching concept of coarse-graining the tissue structure over an increasing diffusion length. A variety of diffusion models and phenomena are summarized on the phase diagram of diffusion MRI, with the unresolved problems and future directions corresponding to its unexplored domains.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
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26
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Grussu F, Battiston M, Veraart J, Schneider T, Cohen-Adad J, Shepherd TM, Alexander DC, Fieremans E, Novikov DS, Gandini Wheeler-Kingshott CAM. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising. Neuroimage 2020; 217:116884. [PMID: 32360689 PMCID: PMC7378937 DOI: 10.1016/j.neuroimage.2020.116884] [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] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/18/2020] [Accepted: 04/23/2020] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
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Affiliation(s)
- Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Marco Battiston
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Canada
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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27
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Lee HH, Jespersen SN, Fieremans E, Novikov DS. The impact of realistic axonal shape on axon diameter estimation using diffusion MRI. Neuroimage 2020; 223:117228. [PMID: 32798676 PMCID: PMC7806404 DOI: 10.1016/j.neuroimage.2020.117228] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 07/29/2020] [Indexed: 11/24/2022] Open
Abstract
To study axonal microstructure with diffusion MRI, axons are typically modeled as straight impermeable cylinders, whereby the transverse diffusion MRI signal can be made sensitive to the cylinder’s inner diameter. However, the shape of a real axon varies along the axon direction, which couples the longitudinal and transverse diffusion of the overall axon direction. Here we develop a theory of the intra-axonal diffusion MRI signal based on coarse-graining of the axonal shape by 3-dimensional diffusion. We demonstrate how the estimate of the inner diameter is confounded by the diameter variations (beading), and by the local variations in direction (undulations) along the axon. We analytically relate diffusion MRI metrics, such as time-dependent radial diffusivity D⊥(t) and kurtosis K⊥(t), to the axonal shape, and validate our theory using Monte Carlo simulations in synthetic undulating axons with randomly positioned beads, and in realistic axons reconstructed from electron microscopy images of mouse brain white matter. We show that (i) In the narrow pulse limit, the inner diameter from D⊥(t) is overestimated by about twofold due to a combination of axon caliber variations and undulations (each contributing a comparable effect size); (ii) The narrow-pulse kurtosis K⊥∣t→∞ deviates from that in an ideal cylinder due to caliber variations; we also numerically calculate the fourth-order cumulant for an ideal cylinder in the wide pulse limit, which is relevant for inner diameter overestimation; (iii) In the wide pulse limit, the axon diameter overestimation is mainly due to undulations at low diffusion weightings b; and (iv) The effect of undulations can be considerably reduced by directional averaging of high-b signals, with the apparent inner diameter given by a combination of the axon caliber (dominated by the thickest axons), caliber variations, and the residual contribution of undulations.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA.
| | - Sune N Jespersen
- CFIN/MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
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Lee HH, Papaioannou A, Kim SL, Novikov DS, Fieremans E. A time-dependent diffusion MRI signature of axon caliber variations and beading. Commun Biol 2020; 3:354. [PMID: 32636463 PMCID: PMC7341838 DOI: 10.1038/s42003-020-1050-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 06/04/2020] [Indexed: 01/08/2023] Open
Abstract
MRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here, we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter and identify its origins by Monte Carlo simulations in realistic substrates from 3-dimensional electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependence originates from axon caliber variation, rather than from mitochondria or axonal undulations. We report a decreased amplitude of time-dependence in multiple sclerosis lesions, illustrating the potential sensitivity of our method to axonal beading in a plethora of neurodegenerative disorders. This specificity to microstructure offers an exciting possibility of bridging across scales to image cellular-level pathology with a clinically feasible MRI technique.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA.
| | - Antonios Papaioannou
- Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Sung-Lyoung Kim
- Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Els Fieremans
- Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
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29
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Dong JW, Jelescu IO, Ades-Aron B, Novikov DS, Friedman K, Babb JS, Osorio RS, Galvin JE, Shepherd TM, Fieremans E. Diffusion MRI biomarkers of white matter microstructure vary nonmonotonically with increasing cerebral amyloid deposition. Neurobiol Aging 2020; 89:118-128. [PMID: 32111392 PMCID: PMC7314576 DOI: 10.1016/j.neurobiolaging.2020.01.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [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: 11/23/2018] [Revised: 12/14/2019] [Accepted: 01/14/2020] [Indexed: 01/27/2023]
Abstract
Beta amyloid (Aβ) accumulation is the earliest pathological marker of Alzheimer's disease (AD), but early AD pathology also affects white matter (WM) integrity. We performed a cross-sectional study including 44 subjects (23 healthy controls and 21 mild cognitive impairment or early AD patients) who underwent simultaneous PET-MR using 18F-Florbetapir, and were categorized into 3 groups based on Aβ burden: Aβ- [mean mSUVr ≤1.00], Aβi [1.00 < mSUVr <1.17], Aβ+ [mSUVr ≥1.17]. Intergroup comparisons of diffusion MRI metrics revealed significant differences across multiple WM tracts. Aβi group displayed more restricted diffusion (higher fractional anisotropy, radial kurtosis, axonal water fraction, and lower radial diffusivity) than both Aβ- and Aβ+ groups. This nonmonotonic trend was confirmed by significant continuous correlations between mSUVr and diffusion metrics going in opposite direction for 2 cohorts: pooled Aβ-/Aβi and pooled Aβi/Aβ+. The transient period of increased diffusion restriction may be due to inflammation that accompanies rising Aβ burden. In the later stages of Aβ accumulation, neurodegeneration is the predominant factor affecting diffusion.
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Affiliation(s)
- Jian W Dong
- Department of Radiology, New York University School of Medicine, New York, NY, USA; Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ileana O Jelescu
- Department of Radiology, New York University School of Medicine, New York, NY, USA; Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Benjamin Ades-Aron
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Kent Friedman
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - James S Babb
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ricardo S Osorio
- Center for Sleep and Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, NY, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca-Raton, FL, USA
| | - Timothy M Shepherd
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Department of Radiology, New York University School of Medicine, New York, NY, USA.
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30
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Veraart J, Nunes D, Rudrapatna U, Fieremans E, Jones DK, Novikov DS, Shemesh N. Nonivasive quantification of axon radii using diffusion MRI. eLife 2020; 9:e49855. [PMID: 32048987 PMCID: PMC7015669 DOI: 10.7554/elife.49855] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/07/2020] [Indexed: 12/13/2022] Open
Abstract
Axon caliber plays a crucial role in determining conduction velocity and, consequently, in the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. Until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how - when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated - heavily diffusion-weighted MRI signals become sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric, the effective radius, representing the entire axon radius distribution within a voxel that emphasizes the larger axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified.
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Affiliation(s)
- Jelle Veraart
- Champalimaud ResearchChampalimaud Centre for the UnknownLisbonPortugal
- Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkUnited States
- imec-Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
| | - Daniel Nunes
- Champalimaud ResearchChampalimaud Centre for the UnknownLisbonPortugal
| | - Umesh Rudrapatna
- CUBRIC, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Els Fieremans
- Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkUnited States
| | - Derek K Jones
- CUBRIC, School of PsychologyCardiff UniversityCardiffUnited Kingdom
- Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneAustralia
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkUnited States
| | - Noam Shemesh
- Champalimaud ResearchChampalimaud Centre for the UnknownLisbonPortugal
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31
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Adhikari BM, Jahanshad N, Shukla D, Turner J, Grotegerd D, Dannlowski U, Kugel H, Engelen J, Dietsche B, Krug A, Kircher T, Fieremans E, Veraart J, Novikov DS, Boedhoe PSW, van der Werf YD, van den Heuvel OA, Ipser J, Uhlmann A, Stein DJ, Dickie E, Voineskos AN, Malhotra AK, Pizzagalli F, Calhoun VD, Waller L, Veer IM, Walter H, Buchanan RW, Glahn DC, Hong LE, Thompson PM, Kochunov P. A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. Brain Imaging Behav 2020; 13:1453-1467. [PMID: 30191514 DOI: 10.1007/s11682-018-9941-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Dinesh Shukla
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | | | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Harald Kugel
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Jennifer Engelen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Bruno Dietsche
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Premika S W Boedhoe
- Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands
| | - Ysbrand D van der Werf
- Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands
| | - Jonathan Ipser
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Erin Dickie
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Anil K Malhotra
- Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, New York, NY, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Vince D Calhoun
- The Mind Research Network & The University of New Mexico, Albuquerque, NM, USA
| | - Lea Waller
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany
| | - Ilja M Veer
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany
| | - Hernik Walter
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - David C Glahn
- Department of Psychiatry, Yale University, School of Medicine, New Haven, CT, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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32
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Georgiadis M, Schroeter A, Gao Z, Guizar-Sicairos M, Novikov DS, Fieremans E, Rudin M. Retrieving neuronal orientations using 3D scanning SAXS and comparison with diffusion MRI. Neuroimage 2019; 204:116214. [PMID: 31568873 DOI: 10.1016/j.neuroimage.2019.116214] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [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: 07/06/2019] [Revised: 09/06/2019] [Accepted: 09/18/2019] [Indexed: 01/08/2023] Open
Abstract
While diffusion MRI (dMRI) is currently the method of choice to non-invasively probe tissue microstructure and study structural connectivity in the brain, its spatial resolution is limited and its results need structural validation. Current ex vivo methods employed to provide 3D fiber orientations have limitations, including tissue-distorting sample preparation, small field of view or inability to quantify 3D fiber orientation distributions. 3D fiber orientation in tissue sections can be obtained from 3D scanning small-angle X-ray scattering (3D sSAXS) by analyzing the anisotropy of scattering signals. Here we adapt the 3D sSAXS method for use in brain tissue, exploiting the high sensitivity of the SAXS signal to the ordered molecular structure of myelin. We extend the characterization of anisotropy from vectors to tensors, employ the Funk-Radon-Transform for converting scattering information to real space fiber orientations, and demonstrate the feasibility of the method in thin sections of mouse brain with minimal sample preparation. We obtain a second rank tensor representing the fiber orientation distribution function (fODF) for every voxel, thereby generating fODF maps. Finally, we illustrate the potential of 3D sSAXS by comparing the result with diffusion MRI fiber orientations in the same mouse brain. We show a remarkably good correspondence, considering the orthogonality of the two methods, i.e. the different physical processes underlying the two signals. 3D sSAXS can serve as validation method for microstructural MRI, and can provide novel microstructural insights for the nervous system, given the method's orthogonality to dMRI, high sensitivity to myelin sheath's orientation and abundance, and the possibility to extract myelin-specific signal and to perform micrometer-resolution scanning.
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Affiliation(s)
- Marios Georgiadis
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland; Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, USA; Department of Radiology, Stanford Medicine, USA.
| | - Aileen Schroeter
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland
| | - Zirui Gao
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland; Paul Scherrer Institute, Villigen, Switzerland
| | | | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, USA
| | - Markus Rudin
- Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
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33
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Chung S, Fieremans E, Wang X, Kucukboyaci NE, Morton CJ, Babb J, Amorapanth P, Foo FYA, Novikov DS, Flanagan SR, Rath JF, Lui YW. White Matter Tract Integrity: An Indicator of Axonal Pathology after Mild Traumatic Brain Injury. J Neurotrauma 2019; 35:1015-1020. [PMID: 29239261 DOI: 10.1089/neu.2017.5320] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (mTBI) using multi-shell diffusion magnetic resonance imaging, deriving compartment-specific white matter tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of white matter (WM) changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with mTBI within 30 days of injury and 21 age- and sex-matched controls were imaged on a 3 Tesla magnetic resonance scanner. Multi-shell diffusion acquisition was performed with five b-values (250-2500 sec/mm2) along 6-60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrated focally lower intra-axonal diffusivity (Daxon) in mTBI patients in the splenium of the corpus callosum (sCC; p < 0.05, FWE-corrected). The area under the curve value for Daxon was 0.76 with a low sensitivity of 46.9% but 100% specificity. These results indicate that Daxon may be a useful imaging biomarker highly specific for mTBI-related WM injury. The observed decrease in Daxon suggests restriction of the diffusion along the axons occurring shortly after injury.
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Affiliation(s)
- Sohae Chung
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Els Fieremans
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Xiuyuan Wang
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Nuri E Kucukboyaci
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Charles J Morton
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - James Babb
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Prin Amorapanth
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Farng-Yang A Foo
- 4 Department of Neurology, New York University Langone Health , New York, New York
| | - Dmitry S Novikov
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Steven R Flanagan
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Joseph F Rath
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Yvonne W Lui
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
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34
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Chung S, Wang X, Fieremans E, Rath JF, Amorapanth P, Foo FYA, Morton CJ, Novikov DS, Flanagan SR, Lui YW. Altered Relationship between Working Memory and Brain Microstructure after Mild Traumatic Brain Injury. AJNR Am J Neuroradiol 2019; 40:1438-1444. [PMID: 31371359 DOI: 10.3174/ajnr.a6146] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 06/19/2019] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND PURPOSE Working memory impairment is one of the most troubling and persistent symptoms after mild traumatic brain injury (MTBI). Here we investigate how working memory deficits relate to detectable WM microstructural injuries to discover robust biomarkers that allow early identification of patients with MTBI at the highest risk of working memory impairment. MATERIALS AND METHODS Multi-shell diffusion MR imaging was performed on a 3T scanner with 5 b-values. Diffusion metrics of fractional anisotropy, diffusivity and kurtosis (mean, radial, axial), and WM tract integrity were calculated. Auditory-verbal working memory was assessed using the Wechsler Adult Intelligence Scale, 4th ed, subtests: 1) Digit Span including Forward, Backward, and Sequencing; and 2) Letter-Number Sequencing. We studied 19 patients with MTBI within 4 weeks of injury and 20 healthy controls. Tract-Based Spatial Statistics and ROI analyses were performed to reveal possible correlations between diffusion metrics and working memory performance, with age and sex as covariates. RESULTS ROI analysis found a significant positive correlation between axial kurtosis and Digit Span Backward in MTBI (Pearson r = 0.69, corrected P = .04), mainly present in the right superior longitudinal fasciculus, which was not observed in healthy controls. Patients with MTBI also appeared to lose the normal associations typically seen in fractional anisotropy and axonal water fraction with Letter-Number Sequencing. Tract-Based Spatial Statistics results also support our findings. CONCLUSIONS Differences between patients with MTBI and healthy controls with regard to the relationship between microstructure measures and working memory performance may relate to known axonal perturbations occurring after injury.
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Affiliation(s)
- S Chung
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - X Wang
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - E Fieremans
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - J F Rath
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University School of Medicine, New York, New York
| | - P Amorapanth
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University School of Medicine, New York, New York
| | - F-Y A Foo
- Department of Neurology (F.-Y.A.F.), New York University Langone Health, New York, New York
| | - C J Morton
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - D S Novikov
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - S R Flanagan
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University School of Medicine, New York, New York
| | - Y W Lui
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
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35
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Assländer J, Novikov DS, Lattanzi R, Sodickson DK, Cloos MA. Hybrid-state free precession in nuclear magnetic resonance. Commun Phys 2019; 2:73. [PMID: 31328174 PMCID: PMC6641569 DOI: 10.1038/s42005-019-0174-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 05/30/2019] [Indexed: 05/27/2023]
Abstract
The dynamics of large spin-1/2 ensembles are commonly described by the Bloch equation, which is characterized by the magnetization's non-linear response to the driving magnetic field. Consequently, most magnetic field variations result in non-intuitive spin dynamics, which are sensitive to small calibration errors. Although simplistic field variations result in robust spin dynamics, they do not explore the richness of the system's phase space. Here, we identify adiabaticity conditions that span a large experiment design space with tractable dynamics. All dynamics are trapped in a one-dimensional subspace, namely in the magnetization's absolute value, which is in a transient state, while its direction adiabatically follows the steady state. In this hybrid state, the polar angle is the effective drive of the spin dynamics. As an example, we optimize this drive for robust and efficient quantification of spin relaxation times and utilize it for magnetic resonance imaging of the human brain.
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Affiliation(s)
- Jakob Assländer
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
| | - Riccardo Lattanzi
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Daniel K Sodickson
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Martijn A Cloos
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
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36
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Novikov DS, Fieremans E, Jespersen SN, Kiselev VG. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR Biomed 2019; 32:e3998. [PMID: 30321478 PMCID: PMC6481929 DOI: 10.1002/nbm.3998] [Citation(s) in RCA: 240] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 06/11/2018] [Accepted: 06/28/2018] [Indexed: 05/18/2023]
Abstract
We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along three major avenues. The first avenue focusses on transient, or time-dependent, effects in diffusion. These effects signify the gradual coarse-graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that transient effects contain information about the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, as well as the degree of structural disorder along the neurites. The second avenue corresponds to the long-time limit, when the observed signal can be approximated as a sum of multiple nonexchanging anisotropic Gaussian components. Here, the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on future directions that could open exciting possibilities for designing quantitative markers of tissue physiology and pathology, based on methods of studying mesoscopic transport in disordered systems.
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Affiliation(s)
- Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Sune N. Jespersen
- CFIN/MINDLab, Department of Clinical Medicine and Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Valerij G. Kiselev
- Medical Physics, Deptartment of Radiology, Faculty of Medicine, University of Freiburg, Germany
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Kochunov P, Donohue B, Mitchell BD, Ganjgahi H, Adhikari B, Ryan M, Medland SE, Jahanshad N, Thompson PM, Blangero J, Fieremans E, Novikov DS, Marcus D, Van Essen DC, Glahn DC, Elliot Hong L, Nichols TE. Genomic kinship construction to enhance genetic analyses in the human connectome project data. Hum Brain Mapp 2018; 40:1677-1688. [PMID: 30496643 DOI: 10.1002/hbm.24479] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 11/06/2018] [Accepted: 11/07/2018] [Indexed: 12/24/2022] Open
Abstract
Imaging genetic analyses quantify genetic control over quantitative measurements of brain structure and function using coefficients of relationship (CR) that code the degree of shared genetics between subjects. CR can be inferred through self-reported relatedness or calculated empirically using genome-wide SNP scans. We hypothesized that empirical CR provides a more accurate assessment of shared genetics than self-reported relatedness. We tested this in 1,046 participants of the Human Connectome Project (HCP) (480 M/566 F) recruited from the Missouri twin registry. We calculated the heritability for 17 quantitative traits drawn from four categories (brain diffusion and structure, cognition, and body physiology) documented by the HCP. We compared the heritability and genetic correlation estimates calculated using self-reported and empirical CR methods Kinship-based INference for GWAS (KING) and weighted allelic correlation (WAC). The polygenetic nature of traits was assessed by calculating the empirical CR from chromosomal SNP sets. The heritability estimates based on whole-genome empirical CR were higher but remained significantly correlated (r ∼0.9) with those obtained using self-reported values. Population stratification in the HCP sample has likely influenced the empirical CR calculations and biased heritability estimates. Heritability values calculated using empirical CR for chromosomal SNP sets were significantly correlated with the chromosomal length (r 0.7) suggesting a polygenic nature for these traits. The chromosomal heritability patterns were correlated among traits from the same knowledge domains; among traits with significant genetic correlations; and among traits sharing biological processes, without being genetically related. The pedigree structures generated in our analyses are available online as a web-based calculator (www.solar-eclipse-genetics.org/HCP).
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Brian Donohue
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland.,Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland
| | - Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Bhim Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Meghann Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Herston, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - John Blangero
- University of Texas Rio Grand Valley, Harlingen, Texas
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Daniel Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - David C Van Essen
- Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri
| | - David C Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Thomas E Nichols
- Big Data Science Institute, Department of Statistics, University of Oxford, Oxford, United Kingdom
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38
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Veraart J, Novikov DS, Fieremans E. TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T 2 relaxation times. Neuroimage 2018; 182:360-369. [PMID: 28935239 PMCID: PMC5858973 DOI: 10.1016/j.neuroimage.2017.09.030] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [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: 04/22/2017] [Revised: 09/12/2017] [Accepted: 09/15/2017] [Indexed: 11/24/2022] Open
Abstract
Biophysical modeling of macroscopic diffusion-weighted MRI signal in terms of microscopic cellular parameters holds the promise of quantifying the integrity of white matter. Unfortunately, even fairly simple multi-compartment models of proton diffusion in the white matter do not provide a unique, biophysically plausible solution. Here we report a nontrivial diffusion MRI signal dependence on echo time (TE) in human white matter in vivo. We demonstrate that such TE dependence originates from compartment-specific T2 values and that it is a promising "orthogonal measure" able to break the degeneracy in parameter estimation, and to yield important relaxation metrics robustly. We thereby enable the precise estimation of the intra- and extra-axonal water T2 relaxation times, which is precluded by a limited signal-to-noise ratio when using multi-echo relaxometry alone.
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Affiliation(s)
- Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA.
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
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Abstract
Transverse NMR relaxation is a fundamental physical phenomenon underpinning a wide range of MRI-based techniques, essential for non-invasive studies in biology, physiology and neuroscience, as well as in diagnostic imaging. Biophysically, transverse relaxation originates from a number of distinct scales - molecular (nanometers), cellular (micrometers), and macroscopic (millimeter-level MRI resolution). Here we review the contributions to the observed relaxation from each of these scales, with the main focus on the cellular level of tissue organization, commensurate with the diffusion length of spin-carrying molecules. We discuss how the interplay between diffusion and spin dephasing in a spatially heterogeneous tissue environment leads to a non-monoexponential time-dependent transverse relaxation signal that contains important biophysical information about tissue microstructure.
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Affiliation(s)
- Valerij G Kiselev
- Medical Physics, Department of Diagnostic Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
| | - Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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40
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Lee HH, Fieremans E, Novikov DS. What dominates the time dependence of diffusion transverse to axons: Intra- or extra-axonal water? Neuroimage 2018; 182:500-510. [PMID: 29253652 PMCID: PMC6004237 DOI: 10.1016/j.neuroimage.2017.12.038] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [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: 07/22/2017] [Revised: 11/18/2017] [Accepted: 12/13/2017] [Indexed: 12/31/2022] Open
Abstract
Brownian motion of water molecules provides an essential length scale, the diffusion length, commensurate with cell dimensions in biological tissues. Measuring the diffusion coefficient as a function of diffusion time makes in vivo diffusion MRI uniquely sensitive to the cellular features about three orders of magnitude below imaging resolution. However, there is a longstanding debate, regarding which contribution - intra- or extra-cellular - is more relevant in the overall time-dependence of the MRI-derived diffusion metrics. Here we resolve this debate in the human brain white matter. By varying not just the diffusion time, but also the gradient pulse duration of a standard diffusion MRI sequence, we identify a functional form of the measured time-dependent diffusion coefficient transverse to white matter tracts in 10 healthy volunteers. This specific functional form is shown to originate from the extra-axonal space, and provides estimates of the fiber packing correlation length for axons in a bundle. Our results offer a metric for the outer axonal diameter, a promising candidate marker for demyelination in neurodegenerative diseases. From the methodological perspective, our analysis demonstrates how competing models, which describe different physics yet interpolate standard measurements equally well, can be distinguished based on their prediction for an independent "orthogonal" measurement.
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Affiliation(s)
- Hong-Hsi Lee
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY 10016, United States.
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY 10016, United States
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY 10016, United States
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41
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Lemberskiy G, Fieremans E, Veraart J, Deng FM, Rosenkrantz AB, Novikov DS. Characterization of prostate microstructure using water diffusion and NMR relaxation. Front Phys 2018; 6:91. [PMID: 30568939 PMCID: PMC6296484 DOI: 10.3389/fphy.2018.00091] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
For many pathologies, early structural tissue changes occur at the cellular level, on the scale of micrometers or tens of micrometers. Magnetic resonance imaging (MRI) is a powerful non-invasive imaging tool used for medical diagnosis, but its clinical hardware is incapable of reaching the cellular length scale directly. In spite of this limitation, microscopic tissue changes in pathology can potentially be captured indirectly, from macroscopic imaging characteristics, by studying water diffusion. Here we focus on water diffusion and NMR relaxation in the human prostate, a highly heterogeneous organ at the cellular level. We present a physical picture of water diffusion and NMR relaxation in the prostate tissue, that is comprised of a densely-packed cellular compartment (composed of stroma and epithelium), and a luminal compartment with almost unrestricted water diffusion. Transverse NMR relaxation is used to identify fast and slow T 2 components, corresponding to these tissue compartments, and to disentangle the luminal and cellular compartment contributions to the temporal evolution of the overall water diffusion coefficient. Diffusion in the luminal compartment falls into the short-time surface-to-volume (S/V) limit, indicating that only a small fraction of water molecules has time to encounter the luminal walls of healthy tissue; from the S/V ratio, the average lumen diameter averaged over three young healthy subjects is measured to be 217.7±188.7 μm. Conversely, the diffusion in the cellular compartment is highly restricted and anisotropic, consistent with the fibrous character of the stromal tissue. Diffusion transverse to these fibers is well described by the random permeable barrier model (RPBM), as confirmed by the dynamical exponent ϑ = 1/2 for approaching the long-time limit of diffusion, and the corresponding structural exponent p = -1 in histology. The RPBM-derived fiber diameter and membrane permeability were 19.8±8.1 μm and 0.044±0.045 μm/ms, respectively, in agreement with known values from tissue histology and membrane biophysics. Lastly, we revisited 38 prostate cancer cases from a recently published study, and found the same dynamical exponent ϑ = 1/2 of diffusion in tumors and benign regions. Our results suggest that a multi-parametric MRI acquisition combined with biophysical modeling may be a powerful non-invasive complement to prostate cancer grading, potentially foregoing biopsies.
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Affiliation(s)
- Gregory Lemberskiy
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA; Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA,
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA,
| | - Fang-Ming Deng
- Department of Pathology, New York University Langone Medical Center, New York, NY New York, NY, USA;
| | - Andrew B Rosenkrantz
- Department of Radiology, New York University Langone Medical Center, New York, NY New York, NY, USA;
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA,
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42
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Ades-Aron B, Veraart J, Kochunov P, McGuire S, Sherman P, Kellner E, Novikov DS, Fieremans E. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. Neuroimage 2018; 183:532-543. [PMID: 30077743 DOI: 10.1016/j.neuroimage.2018.07.066] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 07/11/2018] [Accepted: 07/30/2018] [Indexed: 01/09/2023] Open
Abstract
This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods.
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Affiliation(s)
- Benjamin Ades-Aron
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA.
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA.
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, MD, USA
| | - Stephen McGuire
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH, 45433-7913, USA
| | - Paul Sherman
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH, 45433-7913, USA
| | - Elias Kellner
- Department of Diagnostic Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
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43
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Novikov DS, Reisert M, Kiselev VG. Effects of mesoscopic susceptibility and transverse relaxation on diffusion NMR. J Magn Reson 2018; 293:134-144. [PMID: 30012279 DOI: 10.1016/j.jmr.2018.06.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 06/04/2018] [Accepted: 06/07/2018] [Indexed: 06/08/2023]
Abstract
Measuring molecular diffusion is based on the spatial encoding of spin-carrying molecules using external Larmor frequency gradients. Intrinsic variations of the Larmor frequency and of the local relaxation rate, commonly present in structurally complex samples, interfere with the external gradients, confounding the NMR-measured diffusion propagator. Here we consider, analytically and numerically, the effects of the mesoscopic magnetic structure (local susceptibility and transverse relaxation rate) on the NMR-measured "apparent" diffusion coefficient (ADC). We show that in the fast diffusion regime, when molecules spread past the correlation length of the magnetic structure, the deviation of ADC from the genuine diffusion coefficient increases as a power law of diffusion time. The effect of mesoscopically varying transverse relaxation rate is sequence-independent and always leads to the decrease of ADC with time, whereas the effect sign for the mesoscopic Larmor frequency variations depends on the presence of refocussing pulses in the diffusion sequence. We connect this unexpectedly diverging with time ADC discrepancy to the spatial statistics of the mesocopic magnetic structure. Our results establish a novel kind of NMR contrast tied to the microstructural complexity, and can be applied to discern the mesoscopic effects of hindrances to molecular diffusion, susceptibility variations, and varying local relaxation rate, on the measured diffusion propagator. In particular, we numerically show that the susceptibility effect of a microvascular network is sufficient to explain the observed ADC decrease due to superparamagnetic iron-oxide contrast injection in monkeys.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
| | - Marco Reisert
- Medical Physics, Department of Diagnostic Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Valerij G Kiselev
- Medical Physics, Department of Diagnostic Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
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44
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Adhikari BM, Jahanshad N, Shukla D, Glahn DC, Blangero J, Fox PT, Reynolds RC, Cox RW, Fieremans E, Veraart J, Novikov DS, Nichols TE, Hong LE, Thompson PM, Kochunov P. Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline. Hum Brain Mapp 2018; 39:4893-4902. [PMID: 30052318 DOI: 10.1002/hbm.24331] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 06/01/2018] [Accepted: 07/12/2018] [Indexed: 12/20/2022] Open
Abstract
We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the genetics of brain structure (GOBS) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age = 47.9 ± 13.2 years) from 29 extended pedigrees (average family size = 9 people; range 5-32). The GOBS rsfMRI data was collected using a 7.5-min acquisition sequence (spatial resolution = 1.72 × 1.72 × 3 mm3 ). The HCP cohort consisted of 518 twins and family members (240M/278F; average age = 28.7 ± 3.7 years). rsfMRI data was collected using 28.8-min sequence (spatial resolution = 2 × 2 × 2 mm3 ). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. We observed significant heritability (h2 = 0.2-0.4, p < .05) for functional connections from seven networks across both cohorts, with a significant positive correlation between heritability estimates across two cohorts. The similarity in heritability estimates for resting state connectivity measurements suggests that the additive genetic contribution to functional connectivity is robustly detectable across populations and imaging acquisition parameters. The overarching genetic influence, and means to consistently detect it, provides an opportunity to define a common genetic search space for future gene discovery studies.
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Affiliation(s)
- Bhim M Adhikari
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, California
| | - Dinesh Shukla
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - David C Glahn
- Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut
| | - John Blangero
- Genomics Computing Center, University of Texas at Rio Grande Valley, Edinburg, Texas
| | - Peter T Fox
- University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | | | - Robert W Cox
- National Institute of Mental Health, Bethesda, Maryland
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, California
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
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45
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Abstract
Mapping tissue microstructure with MRI holds great promise as a noninvasive window into tissue organization at the cellular level. Having originated within the realm of diffusion NMR in the late 1970s, this field is experiencing an exponential growth in the number of publications. At the same time, model-based approaches are also increasingly incorporated into advanced MRI acquisition and reconstruction techniques. However, after about two decades of intellectual and financial investment, microstructural mapping has yet to find a single commonly accepted clinical application. Here, we suggest that slow progress in clinical translation may signify unresolved fundamental problems. We outline such problems and related practical pitfalls, as well as review strategies for developing and validating tissue microstructure models, to provoke a discussion on how to bridge the gap between our scientific aspirations and the clinical reality. We argue for recalibrating the efforts of our community toward a more systematic focus on fundamental research aimed at identifying relevant degrees of freedom affecting the measured MR signal. Such a focus is essential for realizing the truly revolutionary potential of noninvasive three-dimensional in vivo microstructural mapping.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Valerij G Kiselev
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Sune N Jespersen
- CFIN/MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
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46
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Winters KV, Reynaud O, Novikov DS, Fieremans E, Kim SG. Quantifying myofiber integrity using diffusion MRI and random permeable barrier modeling in skeletal muscle growth and Duchenne muscular dystrophy model in mice. Magn Reson Med 2018; 80:2094-2108. [PMID: 29577406 DOI: 10.1002/mrm.27188] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/27/2018] [Accepted: 03/02/2018] [Indexed: 02/06/2023]
Abstract
PURPOSE To measure the microstructural changes during skeletal muscle growth and progressive pathologies using the random permeable model with diffusion MRI, and compare findings to conventional imaging modalities such as three-point Dixon and T2 imaging. METHODS In vivo and ex vivo DTI experiments with multiple diffusion times (20-700 ms) were completed on wild-type (n = 22) and muscle-dystrophic mdx mice (n = 8) at various developmental time points. The DTI data were analyzed with the random permeable model framework that provides estimates of the unrestricted diffusion coefficient (D0 ), membrane surface-to-volume ratio (S/V), and membrane permeability (κ). In addition, the MRI experiments included conventional measures, such as tissue fat fractions and T2 relaxation. RESULTS During normal muscle growth between week 4 and week 13, the in vivo S/V, fractional anisotropy, and fat fraction correlated positively with age (ρ = 0.638, 0.664, and 0.686, respectively), whereas T2 correlated negatively (ρ = -0.847). In mdx mice, all DTI random permeable model parameters and fat fraction had significant positive correlation with age, whereas fractional anisotropy and T2 did not have significant correlation with age. Histological measurements of the perimeter-to-area ratio served as a proxy for the model-derived S/V in the cylindrical myofiber geometry, and had a significant correlation with the ex vivo S/V (r = 0.71) as well as the in vivo S/V (r = 0.56). CONCLUSION The present study demonstrates that DTI at multiple diffusion times with the random permeable model analysis allows for noninvasively quantifying muscle fiber microstructural changes during both normal muscle growth and disease progression. Future studies can apply our technique to evaluate current and potential treatments to muscle myopathies.
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Affiliation(s)
- Kerryanne V Winters
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Olivier Reynaud
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Dmitry S Novikov
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Sungheon Gene Kim
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
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47
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Novikov DS, Veraart J, Jelescu IO, Fieremans E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. Neuroimage 2018; 174:518-538. [PMID: 29544816 DOI: 10.1016/j.neuroimage.2018.03.006] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 02/22/2018] [Accepted: 03/03/2018] [Indexed: 10/17/2022] Open
Abstract
We develop a general analytical and numerical framework for estimating intra- and extra-neurite water fractions and diffusion coefficients, as well as neurite orientational dispersion, in each imaging voxel. By employing a set of rotational invariants and their expansion in the powers of diffusion weighting, we analytically uncover the nontrivial topology of the parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice varies across the brain. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ileana O Jelescu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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Chung S, Fieremans E, Kucukboyaci NE, Wang X, Morton CJ, Novikov DS, Rath JF, Lui YW. Working Memory And Brain Tissue Microstructure: White Matter Tract Integrity Based On Multi-Shell Diffusion MRI. Sci Rep 2018; 8:3175. [PMID: 29453439 PMCID: PMC5816650 DOI: 10.1038/s41598-018-21428-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 02/05/2018] [Indexed: 11/30/2022] Open
Abstract
Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.
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Affiliation(s)
- Sohae Chung
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Els Fieremans
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | | | - Xiuyuan Wang
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Charles J Morton
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Dmitry S Novikov
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Joseph F Rath
- Department of Rehabilitation Medicine, New York University School of Medicine, New York, NY, 10016, USA
| | - Yvonne W Lui
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA.
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA.
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Ryan MC, Sherman P, Rowland LM, Wijtenburg SA, Acheson A, Fieremans E, Veraart J, Novikov DS, Hong LE, Sladky J, Peralta PD, Kochunov P, McGuire SA. Miniature pig model of human adolescent brain white matter development. J Neurosci Methods 2018; 296:99-108. [PMID: 29277719 PMCID: PMC5817010 DOI: 10.1016/j.jneumeth.2017.12.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [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: 11/14/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND Neuroscience research in brain development and disorders can benefit from an in vivo animal model that portrays normal white matter (WM) development trajectories and has a sufficiently large cerebrum for imaging with human MRI scanners and protocols. NEW METHOD Twelve three-month-old Sinclair™ miniature pigs (Sus scrofa domestica) were longitudinally evaluated during adolescent development using advanced diffusion weighted imaging (DWI) focused on cerebral WM. Animals had three MRI scans every 23.95 ± 3.73 days using a 3-T scanner. The DWI imaging protocol closely modeled advanced human structural protocols and consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32-directions/shell. DWI data were analyzed using diffusion kurtosis and bi-exponential modeling that provided measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axial kurtosis, tortuosity, and permeability-diffusivity index (PDI). RESULTS Significant longitudinal effects of brain development were observed for whole-brain average FA, KA, and PDI (all p < 0.001). There were expected regional differences in trends, with corpus callosum fibers showing the highest rate of change. COMPARISON WITH EXISTING METHOD(S) Pigs have a large, gyrencephalic brain that can be studied using clinical MRI scanners/protocols. Pigs are less complex than non-human primates thus satisfying the "replacement" principle of animal research. CONCLUSIONS Longitudinal effects were observed for whole-brain and regional diffusion measurements. The changes in diffusion measurements were interepreted as evidence for ongoing myelination and maturation of cerebral WM. Corpus callosum and superficial cortical WM showed the expected higher rates of change, mirroring results in humans.
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Affiliation(s)
- Meghann C Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - Paul Sherman
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH 45433-7913, United States
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - S Andrea Wijtenburg
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - Ashley Acheson
- Department of Psychiatry, University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, United States
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, 660 1st Avenue, New York, NY 10016, United States
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, 660 1st Avenue, New York, NY 10016, United States
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, 660 1st Avenue, New York, NY 10016, United States
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - John Sladky
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH 45433-7913, United States; Department of Neurology, 59th Medical Wing, 2200 Bergquist Drive, Suite 1, Joint Base San Antonio-Lackland AFB, TX 78236, United States
| | - P Dana Peralta
- Department of Neurology, 59th Medical Wing, 2200 Bergquist Drive, Suite 1, Joint Base San Antonio-Lackland AFB, TX 78236, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States.
| | - Stephen A McGuire
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH 45433-7913, United States; Department of Neurology, 59th Medical Wing, 2200 Bergquist Drive, Suite 1, Joint Base San Antonio-Lackland AFB, TX 78236, United States
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50
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Adhikari BM, Jahanshad N, Shukla D, Glahn DC, Blangero J, Reynolds RC, Cox RW, Fieremans E, Veraart J, Novikov DS, Nichols TE, Hong LE, Thompson PM, Kochunov P. Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline. Pac Symp Biocomput 2018; 23:307-318. [PMID: 29218892 PMCID: PMC5728672] [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] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on modelfree Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20-40% were observed across both cohorts.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatry Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,
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