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Quach M, Ali I, Shultz SR, Casillas-Espinosa PM, Hudson MR, Jones NC, Silva JC, Yamakawa GR, Braine EL, Immonen R, Staba RJ, Tohka J, Harris NG, Gröhn O, O'Brien TJ, Wright DK. ComBating inter-site differences in field strength: harmonizing preclinical traumatic brain injury MRI data. NMR IN BIOMEDICINE 2024:e5142. [PMID: 38494895 DOI: 10.1002/nbm.5142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/09/2023] [Accepted: 02/15/2024] [Indexed: 03/19/2024]
Abstract
Integrating datasets from multiple sites and scanners can increase statistical power for neuroimaging studies but can also introduce significant inter-site confounds. We evaluated the effectiveness of ComBat, an empirical Bayes approach, to combine longitudinal preclinical MRI data acquired at 4.7 or 9.4 T at two different sites in Australia. Male Sprague Dawley rats underwent MRI on Days 2, 9, 28, and 150 following moderate/severe traumatic brain injury (TBI) or sham injury as part of Project 1 of the NIH/NINDS-funded Centre Without Walls EpiBioS4Rx project. Diffusion-weighted and multiple-gradient-echo images were acquired, and outcomes included QSM, FA, and ADC. Acute injury measures including apnea and self-righting reflex were consistent between sites. Mixed-effect analysis of ipsilateral and contralateral corpus callosum (CC) summary values revealed a significant effect of site on FA and ADC values, which was removed following ComBat harmonization. Bland-Altman plots for each metric showed reduced variability across sites following ComBat harmonization, including for QSM, despite appearing to be largely unaffected by inter-site differences and no effect of site observed. Following harmonization, the combined inter-site data revealed significant differences in the imaging metrics consistent with previously reported outcomes. TBI resulted in significantly reduced FA and increased susceptibility in the ipsilateral CC, and significantly reduced FA in the contralateral CC compared with sham-injured rats. Additionally, TBI rats also exhibited a reversal in ipsilateral CC ADC values over time with significantly reduced ADC at Day 9, followed by increased ADC 150 days after injury. Our findings demonstrate the need for harmonizing multi-site preclinical MRI data and show that this can be successfully achieved using ComBat while preserving phenotypical changes due to TBI.
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Affiliation(s)
- Mara Quach
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, Victoria, Australia
| | - Idrish Ali
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Sandy R Shultz
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
- Health Sciences, Vancouver Island University, Nanaimo, British Columbia, Canada
| | - Pablo M Casillas-Espinosa
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
- Department of Neurology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Matthew R Hudson
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Nigel C Jones
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Juliana C Silva
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Glenn R Yamakawa
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Emma L Braine
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Riikka Immonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California, USA
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Neil G Harris
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California, USA
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Terence J O'Brien
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
- Department of Neurology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - David K Wright
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
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2
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Diffusion Measures of Subcortical Structures Using High-Field MRI. Brain Sci 2023; 13:brainsci13030391. [PMID: 36979201 PMCID: PMC10046555 DOI: 10.3390/brainsci13030391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/26/2023] Open
Abstract
The pathology of Parkinson’s disease (PD) involves the death of dopaminergic neurons in the substantia nigra (SN), which slowly influences downstream basal ganglia pathways as dopamine transport diminishes. Diffusion magnetic resonance imaging (MRI) has been used to diagnose PD by assessing white matter connectivity in some brain areas. For this study, we applied Lead-DBS to human connectome project data to automatically segment 11 subcortical structures of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. The Lead-connectome pipeline, which utilizes DSI Studio to generate structural connectomes from each 3T and 7T diffusion image, was applied to 3T and 7T data to investigate possible differences in diffusion measures due to different acquisition protocols. Significantly higher fractional anisotropy (FA) values were found in the 3T left SN; significantly higher MD values were found in the 3T left SN and the right amygdala, SN, and subthalamic nucleus (STN); significantly higher AD values were found in the right RN and STN; and significantly higher RD values were found in the left RN and right amygdala. We illustrate a methodology for obtaining diffusion measures of basal ganglia and basal ganglia connectivity using diffusion images, as well as show possible differences in diffusion measures that can arise due to the differences in MRI acquisitions.
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Pušnik L, Serša I, Umek N, Cvetko E, Snoj Ž. Correlation between diffusion tensor indices and fascicular morphometric parameters of peripheral nerve. Front Physiol 2023; 14:1070227. [PMID: 36909220 PMCID: PMC9995878 DOI: 10.3389/fphys.2023.1070227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that measures the anisotropy of water diffusion. Clinical magnetic resonance imaging scanners enable visualization of the structural integrity of larger axonal bundles in the central nervous system and smaller structures like peripheral nerves; however, their resolution for the depiction of nerve fascicular morphology is limited. Accordingly, high-field strength MRI and strong magnetic field gradients are needed to depict the fascicular pattern. The study aimed to quantify diffusion tensor indices with high-field strength MRI within different anatomical compartments of the median nerve and determine if they correlate with nerve structure at the fascicular level. Methods: Three-dimensional pulsed gradient spin-echo (PGSE) imaging sequence in 19 different gradient directions and b value 1,150 s/mm2 was performed on a 9.4T wide-bore vertical superconducting magnet. Nine-millimeter-long segments of five median nerve samples were obtained from fresh cadavers and acquired in sixteen 0.625 mm thick slices. Each nerve sample had the fascicles, perineurium, and interfascicular epineurium segmented. The diffusion tensor was calculated from the region-average diffusion-weighted signals for all diffusion gradient directions. Subsequently, correlations between diffusion tensor indices of segmentations and nerve structure at the fascicular level (number of fascicles, fascicular ratio, and cross-sectional area of fascicles or nerve) were assessed. The acquired diffusion tensor imaging data was employed for display with trajectories and diffusion ellipsoids. Results: The nerve fascicles proved to be the most anisotropic nerve compartment with fractional anisotropy 0.44 ± 0.05. In the interfascicular epineurium, the diffusion was more prominent in orthogonal directions with fractional anisotropy 0.13 ± 0.02. Diffusion tensor indices within the fascicles and perineurium differed significantly between the subjects (p < 0.0001); however, there were no differences within the interfascicular epineurium (p ≥ 0.37). There were no correlations between diffusion tensor indices and nerve structure at the fascicular level (p ≥ 0.29). Conclusion: High-field strength MRI enabled the depiction of the anisotropic diffusion within the fascicles and perineurium. Diffusion tensor indices of the peripheral nerve did not correlate with nerve structure at the fascicular level. Future studies should investigate the relationship between diffusion tensor indices at the fascicular level and axon- and myelin-related parameters.
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Affiliation(s)
- Luka Pušnik
- Institute of Anatomy, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Igor Serša
- Jožef Stefan Institute, Ljubljana, Slovenia
| | - Nejc Umek
- Institute of Anatomy, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Erika Cvetko
- Institute of Anatomy, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Žiga Snoj
- Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.,Clinical Institute of Radiology, University Medical Centre Ljubljana, Ljubljana, Slovenia
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Christidi F, Tsiptsios D, Fotiadou A, Kitmeridou S, Karatzetzou S, Tsamakis K, Sousanidou A, Psatha EA, Karavasilis E, Seimenis I, Kokkotis C, Aggelousis N, Vadikolias K. Diffusion Tensor Imaging as a Prognostic Tool for Recovery in Acute and Hyperacute Stroke. Neurol Int 2022; 14:841-874. [PMID: 36278693 PMCID: PMC9589952 DOI: 10.3390/neurolint14040069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/17/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022] Open
Abstract
Stroke represents a major cause of mortality and long-term disability among adult populations, leaving a devastating socioeconomic impact globally. Clinical manifestation of stroke is characterized by great diversity, ranging from minor disability to considerable neurological impairment interfering with activities of daily living and even death. Prognostic ambiguity has stimulated the interest for implementing stroke recovery biomarkers, including those provided by structural neuroimaging techniques, i.e., diffusion tensor imaging (DTI) and tractography for the study of white matter (WM) integrity. Considering the necessity of prompt and accurate prognosis in stroke survivors along with the potential capacity of DTI as a relevant imaging biomarker, the purpose of our study was to review the pertinent literature published within the last decade regarding DTI as a prognostic tool for recovery in acute and hyperacute stroke. We conducted a thorough literature search in two databases (MEDLINE and Science Direct) in order to trace all relevant studies published between 1 January 2012 and 16 March 2022 using predefined terms as key words. Only full-text human studies published in the English language were included. Forty-four studies were identified and are included in this review. We present main findings and by describing several methodological issues, we highlight shortcomings and gaps in the current literature so that research priorities for future research can be outlined. Our review suggests that DTI can track longitudinal changes and identify prognostic correlates in acute and hyperacute stroke patients.
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Affiliation(s)
- Foteini Christidi
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Dimitrios Tsiptsios
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Aggeliki Fotiadou
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Sofia Kitmeridou
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Stella Karatzetzou
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Konstantinos Tsamakis
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London SE5 8AB, UK
| | - Anastasia Sousanidou
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Evlampia A. Psatha
- Department of Radiology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | | | - Ioannis Seimenis
- Medical Physics Laboratory, School of Medicine, National and Kapodistrian University, 11527 Athens, Greece
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Konstantinos Vadikolias
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
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5
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Sarwar T, Ramamohanarao K, Zalesky A. A critical review of connectome validation studies. NMR IN BIOMEDICINE 2021; 34:e4605. [PMID: 34516016 DOI: 10.1002/nbm.4605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/22/2021] [Accepted: 08/01/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
| | - Kotagiri Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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6
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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7
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A simulation study investigating potential diffusion-based MRI signatures of microstrokes. Sci Rep 2021; 11:14229. [PMID: 34244549 PMCID: PMC8271016 DOI: 10.1038/s41598-021-93503-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 06/22/2021] [Indexed: 02/06/2023] Open
Abstract
Recent studies suggested that cerebrovascular micro-occlusions, i.e. microstokes, could lead to ischemic tissue infarctions and cognitive deficits. Due to their small size, identifying measurable biomarkers of these microvascular lesions remains a major challenge. This work aims to simulate potential MRI signatures combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). Driving our hypothesis are recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially-oriented, and optical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n = 5) before and after inducing targeted photothrombosis, were analyzed. Computational vascular graphs combined with a 3D Monte-Carlo simulator were used to characterize the magnetic resonance (MR) response, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins. We quantified the minimal intravoxel signal loss ratio when applying multiple gradient directions, at varying sequence parameters with and without ASL. With ASL, our results demonstrate a significant difference (p < 0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p < 0.005) using angiograms measured at week 4. Without ASL, no reliable signal change was found. We found that higher ratios, and accordingly improved significance, were achieved at lower magnetic field strengths (e.g., B0 = 3T) and shorter echo time TE (< 16 ms). Our simulations suggest that microstrokes might be characterized through ASL-DWI sequence, providing necessary insights for posterior experimental validations, and ultimately, future translational trials.
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8
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Moyer D, Ver Steeg G, Tax CMW, Thompson PM. Scanner invariant representations for diffusion MRI harmonization. Magn Reson Med 2020; 84:2174-2189. [PMID: 32250475 PMCID: PMC7384065 DOI: 10.1002/mrm.28243] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 12/23/2022]
Abstract
Purpose In the present work, we describe the correction of diffusion‐weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi‐site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory‐based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto‐encoders (VAE) to construct scanner invariant encodings of the imaging data. Results To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusions As imaging studies continue to grow, the use of pooled multi‐site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.
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Affiliation(s)
- Daniel Moyer
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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9
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Muller J, Alizadeh M, Li L, Thalheimer S, Matias C, Tantawi M, Miao J, Silverman M, Zhang V, Yun G, Romo V, Mohamed FB, Wu C. Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator. NEUROIMAGE-CLINICAL 2019; 25:102135. [PMID: 31901789 PMCID: PMC6948366 DOI: 10.1016/j.nicl.2019.102135] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/27/2019] [Accepted: 12/13/2019] [Indexed: 01/03/2023]
Abstract
Deep brain stimulation (DBS) for Parkinson's disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS.
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Affiliation(s)
- J Muller
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States.
| | - M Alizadeh
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - L Li
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - S Thalheimer
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - C Matias
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - M Tantawi
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - J Miao
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - M Silverman
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - V Zhang
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - G Yun
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
| | - V Romo
- Department of Anesthesiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - F B Mohamed
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - C Wu
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States
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10
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Smith K, Bastin ME, Cox SR, Valdés Hernández MC, Wiseman S, Escudero J, Sudlow C. Hierarchical complexity of the adult human structural connectome. Neuroimage 2019; 191:205-215. [PMID: 30772400 PMCID: PMC6503942 DOI: 10.1016/j.neuroimage.2019.02.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 02/06/2019] [Accepted: 02/11/2019] [Indexed: 11/29/2022] Open
Abstract
The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.
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Affiliation(s)
- Keith Smith
- Usher Institute for Population Health Science and Informatics, Medical School, University of Edinburgh, Edinburgh, EH16 4UX, UK.
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, EH4 2XU, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Maria C Valdés Hernández
- Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, EH4 2XU, UK; Row Fogo Centre into Ageing and the Brain, Edinburgh Dementia Research Institute, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Stewart Wiseman
- Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh, EH9 3FB, UK
| | - Catherine Sudlow
- Usher Institute for Population Health Science and Informatics, Medical School, University of Edinburgh, Edinburgh, EH16 4UX, UK
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Nath V, Remedios S, Parvathaneni P, Hansen CB, Bayrak RG, Bermudez C, Blaber JA, Schilling KG, Janve VA, Gao Y, Huo Y, Lyu I, Williams O, Resnick S, Beason-Held L, Rogers BP, Stepniewska I, Anderson AW, Landman BA. Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10949:10.1117/12.2512902. [PMID: 32089583 PMCID: PMC7034942 DOI: 10.1117/12.2512902] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI biomarkers across field strengths (including different scanners, hardware performance, and sequence design considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN) architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512 independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm2, voxel resolution at 2.2mm, 30-32 gradient volumes) with an average interval of 4 years (standard deviation 1.3 years). After image registration, we used paired white matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and device-independent manner.
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Affiliation(s)
- Vishwesh Nath
- Computer Science, Vanderbilt University, Nashville, TN
| | - Samuel Remedios
- Dept. of Computer Science, Middle Tennessee State University
| | | | | | - Roza G Bayrak
- Computer Science, Vanderbilt University, Nashville, TN
| | - Camilo Bermudez
- Biomedical Engineering, Vanderbilt University, Nashville, TN
| | | | | | - Vaibhav A Janve
- Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Yuankai Huo
- Computer Science, Vanderbilt University, Nashville, TN
| | - Ilwoo Lyu
- Computer Science, Vanderbilt University, Nashville, TN
| | - Owen Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN
| | | | - Adam W Anderson
- Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Bennett A Landman
- Computer Science, Vanderbilt University, Nashville, TN
- Biomedical Engineering, Vanderbilt University, Nashville, TN
- Electrical Engineering, Vanderbilt University, Nashville, TN
- Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN
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12
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13
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Choi CH, Yi KS, Lee SR, Lee Y, Jeon CY, Hwang J, Lee C, Choi SS, Lee HJ, Cha SH. A novel voxel-wise lesion segmentation technique on 3.0-T diffusion MRI of hyperacute focal cerebral ischemia at 1 h after permanent MCAO in rats. J Cereb Blood Flow Metab 2018; 38:1371-1383. [PMID: 28598225 PMCID: PMC6092770 DOI: 10.1177/0271678x17714179] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To assess hyperacute focal cerebral ischemia in rats on 3.0-Tesla diffusion-weighted imaging (DWI), we developed a novel voxel-wise lesion segmentation technique that overcomes intra- and inter-subject variation in apparent diffusion coefficient (ADC) distribution. Our novel technique involves the following: (1) intensity normalization including determination of the optimal type of region of interest (ROI) and its intra- and inter-subject validation, (2) verification of focal cerebral ischemic lesions at 1 h with gross and high-magnification light microscopy of hematoxylin-eosin (H&E) pathology, (3) voxel-wise segmentation on ADC with various thresholds, and (4) calculation of dice indices (DIs) to compare focal cerebral ischemic lesions at 1 h defined by ADC and matching H&E pathology. The best coefficient of variation was the mode of the left hemisphere after normalization using whole left hemispheric ROI, which showed lower intra- (2.54 ± 0.72%) and inter-subject (2.67 ± 0.70%) values than the original. Focal ischemic lesion at 1 h after middle cerebral artery occlusion (MCAO) was confirmed on both gross and microscopic H&E pathology. The 83 relative threshold of normalized ADC showed the highest mean DI (DI = 0.820 ± 0.075). We could evaluate hyperacute ischemic lesions at 1 h more reliably on 3-Tesla DWI in rat brains.
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Affiliation(s)
- Chi-Hoon Choi
- 1 Department of Radiology, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Kyung Sik Yi
- 1 Department of Radiology, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Sang-Rae Lee
- 2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Youngjeon Lee
- 2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Chang-Yeop Jeon
- 2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Jinwoo Hwang
- 3 Clinical Science, Philips Healthcare, Seoul, Republic of Korea
| | - Chulhyun Lee
- 4 Bioimaging Research Team, Korea Basic Science Institute, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Sung Sik Choi
- 5 Medical Research Institute, Chung-Ang University, Seoul, Republic of Korea
| | - Hong Jun Lee
- 5 Medical Research Institute, Chung-Ang University, Seoul, Republic of Korea
| | - Sang-Hoon Cha
- 1 Department of Radiology, Chungbuk National University Hospital, Cheongju-si, Chungcheongbuk-do, Republic of Korea.,6 College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju-si, Chungcheongbuk-do, Republic of Korea
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14
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Hafzalla GW, Ragothaman A, Faskowitz J, Jahanshad N, McMahon KL, de Zubicaray GI, Wright MJ, Braskie MN, Prasad G, Thompson PM. A COMPARISON OF NETWORK DEFINITIONS FOR DETECTING SEX DIFFERENCES IN BRAIN CONNECTIVITY USING SUPPORT VECTOR MACHINES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2017; 2017:961-965. [PMID: 29201283 DOI: 10.1109/isbi.2017.7950675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Human brain connectomics is a rapidly evolving area of research, using various methods to define connections or interactions between pairs of regions. Here we evaluate how the choice of (1) regions of interest, (2) definitions of a connection, and (3) normalization of connection weights to total brain connectivity and region size, affect our calculation of the structural connectome. Sex differences in the structural connectome have been established previously. We study how choices in reconstruction of the connectome affect our ability to classify subjects by sex using a support vector machine (SVM) classifier. The use of cluster-based regions led to higher accuracy in sex classification, compared to atlas-based regions. Sex classification was more accurate when based on finer cortical partitions and when using dilations of regions of interest prior to computing brain networks.
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Affiliation(s)
- George W Hafzalla
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
| | - Anjanibhargavi Ragothaman
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
| | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD 4072, Australia
| | - Greig I de Zubicaray
- Faculty of Health and Institute of Health Biomedical Innovation (IHBI), Queensland University of Technology, Brisbane, QLD, 4072
| | - Margaret J Wright
- QIMR Berghofer Medical Research Institute Brisbane, QLD 4029, Australia
| | - Meredith N Braskie
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
| | - Gautam Prasad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey CA 90292, USA
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15
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van der Zwaag W, Schäfer A, Marques JP, Turner R, Trampel R. Recent applications of UHF-MRI in the study of human brain function and structure: a review. NMR IN BIOMEDICINE 2016; 29:1274-1288. [PMID: 25762497 DOI: 10.1002/nbm.3275] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/19/2014] [Accepted: 01/22/2015] [Indexed: 06/04/2023]
Abstract
The increased availability of ultra-high-field (UHF) MRI has led to its application in a wide range of neuroimaging studies, which are showing promise in transforming fundamental approaches to human neuroscience. This review presents recent work on structural and functional brain imaging, at 7 T and higher field strengths. After a short outline of the effects of high field strength on MR images, the rapidly expanding literature on UHF applications of blood-oxygenation-level-dependent-based functional MRI is reviewed. Structural imaging is then discussed, divided into sections on imaging weighted by relaxation time, including quantitative relaxation time mapping, phase imaging and quantitative susceptibility mapping, angiography, diffusion-weighted imaging, and finally magnetization-transfer imaging. The final section discusses studies using the high spatial resolution available at UHF to identify explicit links between structure and function. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Wietske van der Zwaag
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Switzerland
| | - Andreas Schäfer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - José P Marques
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Robert Turner
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Spinoza Centre, University of Amsterdam, The Netherlands
- SPMMRC, School of Physics and Astronomy, University of Nottingham, UK
| | - Robert Trampel
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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16
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Wang T, Shi F, Jin Y, Yap PT, Wee CY, Zhang J, Yang C, Li X, Xiao S, Shen D. Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer's Disease: A Diffusion MRI Study with DTI and HARDI Models. Neural Plast 2016; 2016:2947136. [PMID: 26881100 PMCID: PMC4737469 DOI: 10.1155/2016/2947136] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 11/22/2015] [Indexed: 01/27/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia in elderly people. It is an irreversible and progressive brain disease. In this paper, we utilized diffusion-weighted imaging (DWI) to detect abnormal topological organization of white matter (WM) structural networks. We compared the differences between WM connectivity characteristics at global, regional, and local levels in 26 patients with probable AD and 16 normal control (NC) elderly subjects, using connectivity networks constructed with the diffusion tensor imaging (DTI) model and the high angular resolution diffusion imaging (HARDI) model, respectively. At the global level, we found that the WM structural networks of both AD and NC groups had a small-world topology; however, the AD group showed a significant decrease in both global and local efficiency, but an increase in clustering coefficient and the average shortest path length. We further found that the AD patients had significantly decreased nodal efficiency at the regional level, as well as weaker connections in multiple local cortical and subcortical regions, such as precuneus, temporal lobe, hippocampus, and thalamus. The HARDI model was found to be more advantageous than the DTI model, as it was more sensitive to the deficiencies in AD at all of the three levels.
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Affiliation(s)
- Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Feng Shi
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yan Jin
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chong-Yaw Wee
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jianye Zhang
- Department of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Cece Yang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Xia Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Dinggang Shen
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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17
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Jin Y, Wee CY, Shi F, Thung KH, Ni D, Yap PT, Shen D. Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks. Hum Brain Mapp 2015; 36:4880-96. [PMID: 26368659 DOI: 10.1002/hbm.22957] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Revised: 07/27/2015] [Accepted: 08/20/2015] [Indexed: 12/26/2022] Open
Abstract
Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis.
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Affiliation(s)
- Yan Jin
- Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina
| | - Chong-Yaw Wee
- Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina
| | - Feng Shi
- Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina
| | - Kim-Han Thung
- Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina
| | - Dong Ni
- The Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Shenzhen University, China
| | - Pew-Thian Yap
- Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina
| | - Dinggang Shen
- Biomedical Research Imaging Center, Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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18
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Daianu M, Jahanshad N, Nir TM, Jack CR, Weiner MW, Bernstein MA, Thompson PM. Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network. Hum Brain Mapp 2015; 36:3087-103. [PMID: 26037224 PMCID: PMC4504816 DOI: 10.1002/hbm.22830] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 02/04/2015] [Accepted: 04/21/2015] [Indexed: 11/11/2022] Open
Abstract
Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative-50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the "rich club" - a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.
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Affiliation(s)
- Madelaine Daianu
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, California
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, California
| | - Talia M Nir
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, California
| | | | - Michael W Weiner
- Department of Radiology, Medicine, and Psychiatry, University of California San Francisco, California
- Department of Veterans Affairs Medical Center, San Francisco, California
| | | | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, California
- Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, University of Southern California, Los Angeles, California
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19
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Zhan L, Liu Y, Wang Y, Zhou J, Jahanshad N, Ye J, Thompson PM. Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci 2015; 9:257. [PMID: 26257601 PMCID: PMC4513242 DOI: 10.3389/fnins.2015.00257] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 07/10/2015] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
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Affiliation(s)
- Liang Zhan
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
| | - Yashu Liu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Jiayu Zhou
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, MI, USA ; Department of Electrical Engineering and Computer Science, University of Michigan Ann Arbor, MI, USA
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine, University of Southern California Marina del Rey, CA, USA
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20
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Zitella LM, Xiao Y, Teplitzky BA, Kastl DJ, Duchin Y, Baker KB, Vitek JL, Adriany G, Yacoub E, Harel N, Johnson MD. In Vivo 7T MRI of the Non-Human Primate Brainstem. PLoS One 2015; 10:e0127049. [PMID: 25965401 PMCID: PMC4428864 DOI: 10.1371/journal.pone.0127049] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 04/11/2015] [Indexed: 12/28/2022] Open
Abstract
Structural brain imaging provides a critical framework for performing stereotactic and intraoperative MRI-guided surgical procedures, with procedural efficacy often dependent upon visualization of the target with which to operate. Here, we describe tools for in vivo, subject-specific visualization and demarcation of regions within the brainstem. High-field 7T susceptibility-weighted imaging and diffusion-weighted imaging of the brain were collected using a customized head coil from eight rhesus macaques. Fiber tracts including the superior cerebellar peduncle, medial lemniscus, and lateral lemniscus were identified using high-resolution probabilistic diffusion tractography, which resulted in three-dimensional fiber tract reconstructions that were comparable to those extracted from sequential application of a two-dimensional nonlinear brain atlas warping algorithm. In the susceptibility-weighted imaging, white matter tracts within the brainstem were also identified as hypointense regions, and the degree of hypointensity was age-dependent. This combination of imaging modalities also enabled identifying the location and extent of several brainstem nuclei, including the periaqueductal gray, pedunculopontine nucleus, and inferior colliculus. These clinically-relevant high-field imaging approaches have potential to enable more accurate and comprehensive subject-specific visualization of the brainstem and to ultimately improve patient-specific neurosurgical targeting procedures, including deep brain stimulation lead implantation.
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Affiliation(s)
- Laura M. Zitella
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - YiZi Xiao
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Benjamin A. Teplitzky
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Daniel J. Kastl
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Yuval Duchin
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Kenneth B. Baker
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Gregor Adriany
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
- Institute for Translational Neuroscience, University of Minnesota, Minneapolis, Minnesota, United States of America
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21
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Zhan L, Zhou J, Wang Y, Jin Y, Jahanshad N, Prasad G, Nir TM, Leonardo CD, Ye J, Thompson PM, for the Alzheimer’s Disease Neuroimaging Initiative. Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease. Front Aging Neurosci 2015; 7:48. [PMID: 25926791 PMCID: PMC4396191 DOI: 10.3389/fnagi.2015.00048] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 03/25/2015] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.
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Affiliation(s)
- Liang Zhan
- Imaging Genetics Center, University of Southern California, Los AngelesCA, USA
- Department of Neurology, Psychiatry, Pediatrics, Engineering, Radiology, and Ophthalmology, Keck School of Medicine, University of Southern California, Los AngelesCA, USA
| | - Jiayu Zhou
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, TempeAZ, USA
- Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, TempeAZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, TempeAZ, USA
| | - Yan Jin
- Imaging Genetics Center, University of Southern California, Los AngelesCA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Los AngelesCA, USA
| | - Gautam Prasad
- Imaging Genetics Center, University of Southern California, Los AngelesCA, USA
| | - Talia M. Nir
- Imaging Genetics Center, University of Southern California, Los AngelesCA, USA
| | | | - Jieping Ye
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, TempeAZ, USA
- Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, TempeAZ, USA
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Los AngelesCA, USA
- Department of Neurology, Psychiatry, Pediatrics, Engineering, Radiology, and Ophthalmology, Keck School of Medicine, University of Southern California, Los AngelesCA, USA
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22
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Zhan L, Liu Y, Zhou J, Ye J, Thompson PM. Boosting Classification Accuracy of Diffusion MRI Derived Brain Networks for the Subtypes of Mild Cognitive Impairment Using Higher Order Singular Value Decomposition. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2015; 2015:131-135. [PMID: 26413202 PMCID: PMC4578228 DOI: 10.1109/isbi.2015.7163833] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying early versus late MCI.
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Affiliation(s)
- L Zhan
- Dept. of Neurology, University of California, Los Angeles, CA 90095, USA ; Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina Del Rey, CA 90292, USA
| | - Y Liu
- Dept. of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - J Zhou
- Dept. of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - J Ye
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48105, USA ; Dept. of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - P M Thompson
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina Del Rey, CA 90292, USA
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23
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Karimi M, Perlmutter JS. The role of dopamine and dopaminergic pathways in dystonia: insights from neuroimaging. TREMOR AND OTHER HYPERKINETIC MOVEMENTS (NEW YORK, N.Y.) 2015; 5:280. [PMID: 25713747 PMCID: PMC4314610 DOI: 10.7916/d8j101xv] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 01/03/2015] [Indexed: 12/14/2022]
Abstract
Background Dystonia constitutes a heterogeneous group of movement abnormalities, characterized by sustained or intermittent muscle contractions causing abnormal postures. Overwhelming data suggest involvement of basal ganglia and dopaminergic pathways in dystonia. In this review, we critically evaluate recent neuroimaging studies that investigate dopamine receptors, endogenous dopamine release, morphology of striatum, and structural or functional connectivity in cortico-basal ganglia-thalamo-cortical and related cerebellar circuits in dystonia. Method A PubMed search was conducted in August 2014. Results Positron emission tomography (PET) imaging offers strong evidence for altered D2/D3 receptor binding and dopaminergic release in many forms of idiopathic dystonia. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data reveal likely involvement of related cerebello-thalamo-cortical and sensory-motor networks in addition to basal ganglia. Discussion PET imaging of dopamine receptors or transmitter release remains an effective means to investigate dopaminergic pathways, yet may miss factors affecting dopamine homeostasis and related subcellular signaling cascades that could alter the function of these pathways. fMRI and DTI methods may reveal functional or anatomical changes associated with dysfunction of dopamine-mediated pathways. Each of these methods can be used to monitor target engagement for potential new treatments. PET imaging of striatal phosphodiesterase and development of new selective PET radiotracers for dopamine D3-specific receptors and Mechanistic target of rampamycin (mTOR) are crucial to further investigate dopaminergic pathways. A multimodal approach may have the greatest potential, using PET to identify the sites of molecular pathology and magnetic resonance methods to determine their downstream effects.
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Affiliation(s)
- Morvarid Karimi
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Joel S Perlmutter
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA ; Department of Radiology, Neurobiology, Physical Therapy and Occupational Therapy, Washington University in St. Louis, St. Louis, MO, USA
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24
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Nir TM, Jahanshad N, Toga AW, Bernstein MA, Jack CR, Weiner MW, Thompson PM. Connectivity network measures predict volumetric atrophy in mild cognitive impairment. Neurobiol Aging 2015; 36 Suppl 1:S113-20. [PMID: 25444606 PMCID: PMC4276308 DOI: 10.1016/j.neurobiolaging.2014.04.038] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 01/13/2014] [Accepted: 04/17/2014] [Indexed: 01/12/2023]
Abstract
Alzheimer's disease (AD) is characterized by cortical atrophy and disrupted anatomic connectivity, and leads to abnormal interactions between neural systems. Diffusion-weighted imaging (DWI) and graph theory can be used to evaluate major brain networks and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard magnetic resonance imaging (MRI), we assessed baseline white-matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI, mean age 71.8 ± 7.5 years, 18 males and 12 females) from the Alzheimer's Disease Neuroimaging Initiative. Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global "small-world" architecture measures, including mean clustering coefficient and characteristic path length. We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3-dimensional Jacobian "expansion factor maps" between baseline and 6-month follow-up anatomic scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Arthur W Toga
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, University of California-San Francisco School of Medicine, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA; Department of Neurology, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, University of Southern California, Los Angeles, CA, USA; Department of Radiology, University of Southern California, Los Angeles, CA, USA; Department of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, University of Southern California, Los Angeles, CA, USA.
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25
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Steininger SC, Liu X, Gietl A, Wyss M, Schreiner S, Gruber E, Treyer V, Kälin A, Leh S, Buck A, Nitsch RM, Prüssmann KP, Hock C, Unschuld PG. Cortical Amyloid Beta in Cognitively Normal Elderly Adults is Associated with Decreased Network Efficiency within the Cerebro-Cerebellar System. Front Aging Neurosci 2014; 6:52. [PMID: 24672483 PMCID: PMC3957491 DOI: 10.3389/fnagi.2014.00052] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2014] [Accepted: 03/03/2014] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Deposition of cortical amyloid beta (Aβ) is a correlate of aging and a risk factor for Alzheimer disease (AD). While several higher order cognitive processes involve functional interactions between cortex and cerebellum, this study aims to investigate effects of cortical Aβ deposition on coupling within the cerebro-cerebellar system. METHODS We included 15 healthy elderly subjects with normal cognitive performance as assessed by neuropsychological testing. Cortical Aβ was quantified using (11)carbon-labeled Pittsburgh compound B positron-emission-tomography late frame signals. Volumes of brain structures were assessed by applying an automated parcelation algorithm to three dimensional magnetization-prepared rapid gradient-echo T1-weighted images. Basal functional network activity within the cerebro-cerebellar system was assessed using blood-oxygen-level dependent resting state functional magnetic resonance imaging at the high field strength of 7 T for measuring coupling between cerebellar seeds and cerebral gray matter. A bivariate regression approach was applied for identification of brain regions with significant effects of individual cortical Aβ load on coupling. RESULTS Consistent with earlier reports, a significant degree of positive and negative coupling could be observed between cerebellar seeds and cerebral voxels. Significant positive effects of cortical Aβ load on cerebro-cerebellar coupling resulted for cerebral brain regions located in inferior temporal lobe, prefrontal cortex, hippocampus, parahippocampal gyrus, and thalamus. CONCLUSION Our findings indicate that brain amyloidosis in cognitively normal elderly subjects is associated with decreased network efficiency within the cerebro-cerebellar system. While the identified cerebral regions are consistent with established patterns of increased sensitivity for Aβ-associated neurodegeneration, additional studies are needed to elucidate the relationship between dysfunction of the cerebro-cerebellar system and risk for AD.
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Affiliation(s)
- Stefanie C Steininger
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Xinyang Liu
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School , Boston, MA , USA
| | - Anton Gietl
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Michael Wyss
- Institute for Biomedical Engineering, University of Zürich, ETH Zürich , Zürich , Switzerland
| | - Simon Schreiner
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Esmeralda Gruber
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Valerie Treyer
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland ; Division of Nuclear Medicine, University of Zürich , Zürich , Switzerland
| | - Andrea Kälin
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Sandra Leh
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Alfred Buck
- Division of Nuclear Medicine, University of Zürich , Zürich , Switzerland
| | - Roger M Nitsch
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Klaas P Prüssmann
- Institute for Biomedical Engineering, University of Zürich, ETH Zürich , Zürich , Switzerland
| | - Christoph Hock
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
| | - Paul G Unschuld
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zürich , Zürich , Switzerland
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26
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Uğurbil K, Xu J, Auerbach EJ, Moeller S, Vu AT, Duarte-Carvajalino JM, Lenglet C, Wu X, Schmitter S, Van de Moortele PF, Strupp J, Sapiro G, De Martino F, Wang D, Harel N, Garwood M, Chen L, Feinberg DA, Smith SM, Miller KL, Sotiropoulos SN, Jbabdi S, Andersson JLR, Behrens TEJ, Glasser MF, Van Essen DC, Yacoub E. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. Neuroimage 2013; 80:80-104. [PMID: 23702417 PMCID: PMC3740184 DOI: 10.1016/j.neuroimage.2013.05.012] [Citation(s) in RCA: 566] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Revised: 05/05/2013] [Accepted: 05/07/2013] [Indexed: 12/21/2022] Open
Abstract
The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce 'functional connectivity'; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 s for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.
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Affiliation(s)
- Kamil Uğurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
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27
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Thompson PM, Ge T, Glahn DC, Jahanshad N, Nichols TE. Genetics of the connectome. Neuroimage 2013; 80:475-88. [PMID: 23707675 PMCID: PMC3905600 DOI: 10.1016/j.neuroimage.2013.05.013] [Citation(s) in RCA: 132] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Revised: 05/05/2013] [Accepted: 05/08/2013] [Indexed: 11/24/2022] Open
Abstract
Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods--such as genome-wide association studies (GWAS), linkage and candidate gene studies--that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. Studies that emphasized the genetic influences on brain connectivity. Some of these analyses of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of the genomic and network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity.
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Affiliation(s)
- Paul M Thompson
- Imaging Genetics Center, Laboratory of NeuroImaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA.
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28
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Dennis EL, Jahanshad N, McMahon KL, de Zubicaray GI, Martin NG, Hickie IB, Toga AW, Wright MJ, Thompson PM. Development of insula connectivity between ages 12 and 30 revealed by high angular resolution diffusion imaging. Hum Brain Mapp 2013; 35:1790-800. [PMID: 23836455 DOI: 10.1002/hbm.22292] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Revised: 02/05/2013] [Accepted: 03/04/2013] [Indexed: 12/22/2022] Open
Abstract
The insula, hidden deep within the Sylvian fissures, has proven difficult to study from a connectivity perspective. Most of our current information on the anatomical connectivity of the insula comes from studies of nonhuman primates and post mortem human dissections. To date, only two neuroimaging studies have successfully examined the connectivity of the insula. Here we examine how the connectivity of the insula develops between ages 12 and 30, in 307 young adolescent and adult subjects scanned with 4-Tesla high angular resolution diffusion imaging (HARDI). The density of fiber connections between the insula and the frontal and parietal cortex decreased with age, but the connection density between the insula and the temporal cortex generally increased with age. This trajectory is in line with well-known patterns of cortical development in these regions. In addition, males and females showed different developmental trajectories for the connection between the left insula and the left precentral gyrus. The insula plays many different roles, some of them affected in neuropsychiatric disorders; this information on the insula's connectivity may help efforts to elucidate mechanisms of brain disorders in which it is implicated.
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Affiliation(s)
- Emily L Dennis
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
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29
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Daianu M, Dennis EL, Jahanshad N, Nir TM, Toga AW, Jack CR, Weiner MW, Thompson PM. Alzheimer's Disease Disrupts Rich Club Organization in Brain Connectivity Networks. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2013:266-269. [PMID: 24953139 DOI: 10.1109/isbi.2013.6556463] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Diffusion imaging and brain connectivity analyses can monitor white matter deterioration, revealing how neural pathways break down in aging and Alzheimer's disease (AD). Here we tested how AD disrupts the 'rich club' effect - a network property found in the normal brain - where high-degree nodes in the connectivity network are more heavily interconnected with each other than expected by chance. We analyzed 3-Tesla whole-brain diffusionweighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. As expected, AD patients had a lower nodal degree (average number of connections) in cortical regions implicated in the disease. Unexpectedly, the normalized rich club coefficient was higher in AD. AD disrupts cortical networks by removing connections; when these networks are thresholded, organizational properties are disrupted leading to additional new biomarkers of AD.
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Affiliation(s)
- Madelaine Daianu
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Emily L Dennis
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology, Medicine, and Psychiatry, University of California San Francisco, CA, USA ; Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
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30
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Toga AW, Thompson PM. Connectomics sheds new light on Alzheimer's disease. Biol Psychiatry 2013; 73:390-2. [PMID: 23399468 PMCID: PMC3661406 DOI: 10.1016/j.biopsych.2013.01.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Accepted: 01/07/2013] [Indexed: 11/30/2022]
Affiliation(s)
- Arthur W Toga
- Laboratory of Neuro Imaging, University of California, Los Angeles, School of Medicine, Los Angeles, California.
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31
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Daianu M, Jahanshad N, Nir TM, Toga AW, Jack CR, Weiner MW, Thompson PM. Breakdown of brain connectivity between normal aging and Alzheimer's disease: a structural k-core network analysis. Brain Connect 2013; 3:407-22. [PMID: 23701292 PMCID: PMC3749712 DOI: 10.1089/brain.2012.0137] [Citation(s) in RCA: 137] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone--the so-called "k-core"--of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.
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Affiliation(s)
- Madelaine Daianu
- Department of Neurology, Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
| | - Neda Jahanshad
- Department of Neurology, Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
| | - Talia M. Nir
- Department of Neurology, Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
| | - Arthur W. Toga
- Department of Neurology, Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
| | | | - Michael W. Weiner
- Department of Radiology, Medicine, and Psychiatry, University of California San Francisco, San Francisco, California
- San Francisco VA Medical Center, U.S. Department of Veteran Affairs, San Francisco, California
| | - Paul M. Thompson
- Department of Neurology, Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
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