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Bridgen P, Tomi-Tricot R, Uus A, Cromb D, Quirke M, Almalbis J, Bonse B, De la Fuente Botella M, Maggioni A, Cio PD, Cawley P, Casella C, Dokumaci AS, Thomson AR, Willers Moore J, Bridglal D, Saravia J, Finck T, Price AN, Pickles E, Cordero-Grande L, Egloff A, O’Muircheartaigh J, Counsell SJ, Giles SL, Deprez M, De Vita E, Rutherford MA, Edwards AD, Hajnal JV, Malik SJ, Arichi T. High resolution and contrast 7 tesla MR brain imaging of the neonate. FRONTIERS IN RADIOLOGY 2024; 3:1327075. [PMID: 38304343 PMCID: PMC10830693 DOI: 10.3389/fradi.2023.1327075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/29/2023] [Indexed: 02/03/2024]
Abstract
Introduction Ultra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system. Methods Images were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM). Results There was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy. Discussion We demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life.
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Affiliation(s)
- Philippa Bridgen
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Raphael Tomi-Tricot
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Megan Quirke
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jennifer Almalbis
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Beya Bonse
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Miguel De la Fuente Botella
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alessandra Maggioni
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Pierluigi Di Cio
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Paul Cawley
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Chiara Casella
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Ayse Sila Dokumaci
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alice R. Thomson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Jucha Willers Moore
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Devi Bridglal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joao Saravia
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Thomas Finck
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Elisabeth Pickles
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, ISCIII, Madrid, Spain
| | - Alexia Egloff
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sharon L. Giles
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Enrico De Vita
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Physics, Radiology Department, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Mary A. Rutherford
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - A. David Edwards
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Shaihan J. Malik
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Tomoki Arichi
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
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Malaga KA, Houshmand L, Costello JT, Chandrasekaran J, Chou KL, Patil PG. Thalamic Segmentation and Neural Activation Modeling Based on Individual Tissue Microstructure in Deep Brain Stimulation for Essential Tremor. Neuromodulation 2023; 26:1689-1698. [PMID: 36470728 DOI: 10.1016/j.neurom.2022.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/08/2022] [Accepted: 09/13/2022] [Indexed: 12/05/2022]
Abstract
OBJECTIVE Thalamic deep brain stimulation (DBS) is the primary surgical therapy for essential tremor (ET). Thalamic DBS traditionally uses an atlas-based targeting approach, which, although nominally accurate, may obscure individual anatomic differences from population norms. The objective of this study was to compare this traditional atlas-based approach with a novel quantitative modeling methodology grounded in individual tissue microstructure (N-of-1 approach). MATERIALS AND METHODS The N-of-1 approach uses individual patient diffusion tensor imaging (DTI) data to perform thalamic segmentation and volume of tissue activation (VTA) modeling. For each patient, the thalamus was individually segmented into 13 nuclei using DTI-based k-means clustering. DBS-induced VTAs associated with tremor suppression and side effects were then computed for each patient with finite-element electric-field models incorporating DTI microstructural data. Results from N-of-1 and traditional atlas-based modeling were compared for a large cohort of patients with ET treated with thalamic DBS. RESULTS The size and shape of individual N-of-1 thalamic nuclei and VTAs varied considerably across patients (N = 22). For both methods, tremor-improving therapeutic VTAs showed similar overlap with motor thalamic nuclei and greater motor than sensory nucleus overlap. For VTAs producing undesirable sustained paresthesia, 94% of VTAs overlapped with N-of-1 sensory thalamus estimates, whereas 74% of atlas-based segmentations overlapped. For VTAs producing dysarthria/motor contraction, the N-of-1 approach predicted greater spread beyond the thalamus into the internal capsule and adjacent structures than the atlas-based method. CONCLUSIONS Thalamic segmentation and VTA modeling based on individual tissue microstructure explain therapeutic stimulation equally well and side effects better than a traditional atlas-based method in DBS for ET. The N-of-1 approach may be useful in DBS targeting and programming, particularly when patient neuroanatomy deviates from population norms.
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Affiliation(s)
- Karlo A Malaga
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Layla Houshmand
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Joseph T Costello
- Department of Electrical Engineering, University of Michigan, Ann Arbor, MI, USA
| | | | - Kelvin L Chou
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
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Ikramuddin SS, Brinda AK, Butler RD, Hill ME, Dharnipragada R, Aman JE, Schrock LE, Cooper SE, Palnitkar T, Patriat R, Harel N, Vitek JL, Johnson MD. Active contact proximity to the cerebellothalamic tract predicts initial therapeutic current requirement with DBS for ET: an application of 7T MRI. Front Neurol 2023; 14:1258895. [PMID: 38020603 PMCID: PMC10666159 DOI: 10.3389/fneur.2023.1258895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Objective To characterize how the proximity of deep brain stimulation (DBS) active contact locations relative to the cerebellothalamic tract (CTT) affect clinical outcomes in patients with essential tremor (ET). Background DBS is an effective treatment for refractory ET. However, the role of the CTT in mediating the effect of DBS for ET is not well characterized. 7-Tesla (T) MRI-derived tractography provides a means to measure the distance between the active contact and the CTT more precisely. Methods A retrospective review was conducted of 12 brain hemispheres in 7 patients at a single center who underwent 7T MRI prior to ventral intermediate nucleus (VIM) DBS lead placement for ET following failed medical management. 7T-derived diffusion tractography imaging was used to identify the CTT and was merged with the post-operative CT to calculate the Euclidean distance from the active contact to the CTT. We collected optimized stimulation parameters at initial programing, 1- and 2-year follow up, as well as a baseline and postoperative Fahn-Tolosa-Marin (FTM) scores. Results The therapeutic DBS current mean (SD) across implants was 1.8 mA (1.8) at initial programming, 2.5 mA (0.6) at 1 year, and 2.9 mA (1.1) at 2-year follow up. Proximity of the clinically-optimized active contact to the CTT was 3.1 mm (1.2), which correlated with lower current requirements at the time of initial programming (R2 = 0.458, p = 0.009), but not at the 1- and 2-year follow up visits. Subjects achieved mean (SD) improvement in tremor control of 77.9% (14.5) at mean follow-up time of 22.2 (18.9) months. Active contact distance to the CTT did not predict post-operative tremor control at the time of the longer term clinical follow up (R2 = -0.073, p = 0.58). Conclusion Active DBS contact proximity to the CTT was associated with lower therapeutic current requirement following DBS surgery for ET, but therapeutic current was increased over time. Distance to CTT did not predict the need for increased current over time, or longer term post-operative tremor control in this cohort. Further study is needed to characterize the role of the CTT in long-term DBS outcomes.
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Affiliation(s)
- Salman S. Ikramuddin
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Annemarie K. Brinda
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Rebecca D. Butler
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Meghan E. Hill
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | | | - Joshua E. Aman
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Lauren E. Schrock
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Scott E. Cooper
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Tara Palnitkar
- CMRR, University of Minnesota, Minneapolis, MN, United States
| | - Rémi Patriat
- CMRR, University of Minnesota, Minneapolis, MN, United States
| | - Noam Harel
- CMRR, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
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Saleem KS, Avram AV, Yen CCC, Magdoom KN, Schram V, Basser PJ. Multimodal anatomical mapping of subcortical regions in marmoset monkeys using high-resolution MRI and matched histology with multiple stains. Neuroimage 2023; 281:120311. [PMID: 37634884 DOI: 10.1016/j.neuroimage.2023.120311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/05/2023] [Accepted: 08/04/2023] [Indexed: 08/29/2023] Open
Abstract
Subcortical nuclei and other deep brain structures play essential roles in regulating the central and peripheral nervous systems. However, many of these nuclei and their subregions are challenging to identify and delineate in conventional MRI due to their small size, hidden location, and often subtle contrasts compared to neighboring regions. To address these limitations, we scanned the whole brain of the marmoset monkeys in ex vivo using a clinically feasible diffusion MRI method, called the mean apparent propagator (MAP)-MRI, along with T2W and MTR (T1-like contrast) images acquired at 7 Tesla. Additionally, we registered these multimodal MRI volumes to the high-resolution images of matched whole-brain histology sections with seven different stains obtained from the same brain specimens. At high spatial resolution, the microstructural parameters and fiber orientation distribution functions derived with MAP-MRI can distinguish the subregions of many subcortical and deep brain structures, including fiber tracts of different sizes and orientations. The good correlation with multiple but distinct histological stains from the same brain serves as a thorough validation of the structures identified with MAP-MRI and other MRI parameters. Moreover, the anatomical details of deep brain structures found in the volumes of MAP-MRI parameters are not visible in conventional T1W or T2W images. The high-resolution mapping using novel MRI contrasts, combined and correlated with histology, can elucidate structures that were previously invisible radiologically. Thus, this multimodal approach offers a roadmap toward identifying salient brain areas in vivo in future neuroradiological studies. It also provides a useful anatomical standard reference for the region definition of subcortical targets and the generation of a 3D digital template atlas for the marmoset brain research (Saleem et al., 2023). Additionally, we conducted a cross-species comparison between marmoset and macaque monkeys using results from our previous studies (Saleem et al., 2021). We found that the two species had distinct patterns of iron distribution in subregions of the basal ganglia, red nucleus, and deep cerebellar nuclei, confirmed with T2W MRI and histology.
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Affiliation(s)
- Kadharbatcha S Saleem
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892, United States; Center for Neuroscience and Regenerative Medicine (CNRM), Henry M. Jackson Foundation (HJF) for the Advancement of Military Medicine, Bethesda, MD 20817, United States.
| | - Alexandru V Avram
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892, United States; Center for Neuroscience and Regenerative Medicine (CNRM), Henry M. Jackson Foundation (HJF) for the Advancement of Military Medicine, Bethesda, MD 20817, United States
| | - Cecil Chern-Chyi Yen
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, United States
| | - Kulam Najmudeen Magdoom
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892, United States; Center for Neuroscience and Regenerative Medicine (CNRM), Henry M. Jackson Foundation (HJF) for the Advancement of Military Medicine, Bethesda, MD 20817, United States
| | - Vincent Schram
- Microscopy and Imaging Core (MIC), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892, United States
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892, United States
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Saleem KS, Avram AV, Yen CCC, Magdoom KN, Schram V, Basser PJ. Multimodal anatomical mapping of subcortical regions in Marmoset monkeys using high-resolution MRI and matched histology with multiple stains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.30.534950. [PMID: 37034636 PMCID: PMC10081239 DOI: 10.1101/2023.03.30.534950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Subcortical nuclei and other deep brain structures play essential roles in regulating the central and peripheral nervous systems. However, many of these nuclei and their subregions are challenging to identify and delineate in conventional MRI due to their small size, hidden location, and often subtle contrasts compared to neighboring regions. To address these limitations, we scanned the whole brain of the marmoset monkeys in ex vivo using a clinically feasible diffusion MRI method, called the mean apparent propagator (MAP)-MRI, along with T2W and MTR (T1-like contrast) images acquired at 7 Tesla. Additionally, we registered these multimodal MRI volumes to the high-resolution images of matched whole-brain histology sections with seven different stains obtained from the same brain specimens. At high spatial resolution, the microstructural parameters and fiber orientation distribution functions derived with MAP-MRI can distinguish the subregions of many subcortical and deep brain structures, including fiber tracts of different sizes and orientations. The good correlation with multiple but distinct histological stains from the same brain serves as a thorough validation of the structures identified with MAP-MRI and other MRI parameters. Moreover, the anatomical details of deep brain structures found in the volumes of MAP-MRI parameters are not visible in conventional T1W or T2W images. The high-resolution mapping using novel MRI contrasts, combined and correlated with histology, can elucidate structures that were previously invisible radiologically. Thus, this multimodal approach offers a roadmap toward identifying salient brain areas in vivo in future neuroradiological studies. It also provides a useful anatomical standard reference for the region definition of subcortical targets and the generation of a 3D digital template atlas for the marmoset brain research (Saleem et al., 2023). Additionally, we conducted a cross-species comparison between marmoset and macaque monkeys using results from our previous studies (Saleem et al., 2021). We found that the two species had distinct patterns of iron distribution in subregions of the basal ganglia, red nucleus, and deep cerebellar nuclei, confirmed with T2W MRI and histology.
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Grier MD, Yacoub E, Adriany G, Lagore RL, Harel N, Zhang RY, Lenglet C, Uğurbil K, Zimmermann J, Heilbronner SR. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain. Neuroimage 2022; 255:119200. [PMID: 35427769 PMCID: PMC9446284 DOI: 10.1016/j.neuroimage.2022.119200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/08/2022] [Accepted: 04/07/2022] [Indexed: 11/26/2022] Open
Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Ru-Yuan Zhang
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States.
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Okada T, Fujimoto K, Fushimi Y, Akasaka T, Thuy DHD, Shima A, Sawamoto N, Oishi N, Zhang Z, Funaki T, Nakamoto Y, Murai T, Miyamoto S, Takahashi R, Isa T. Neuroimaging at 7 Tesla: a pictorial narrative review. Quant Imaging Med Surg 2022; 12:3406-3435. [PMID: 35655840 PMCID: PMC9131333 DOI: 10.21037/qims-21-969] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/05/2022] [Indexed: 01/26/2024]
Abstract
Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.
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Affiliation(s)
- Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koji Fujimoto
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Thai Akasaka
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Dinh H. D. Thuy
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Atsushi Shima
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobukatsu Sawamoto
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Medial Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Zhilin Zhang
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takeshi Funaki
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Susumu Miyamoto
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tadashi Isa
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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8
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High-resolution mapping and digital atlas of subcortical regions in the macaque monkey based on matched MAP-MRI and histology. Neuroimage 2021; 245:118759. [PMID: 34838750 DOI: 10.1016/j.neuroimage.2021.118759] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/21/2021] [Accepted: 11/23/2021] [Indexed: 12/21/2022] Open
Abstract
Subcortical nuclei and other deep brain structures are known to play an important role in the regulation of the central and peripheral nervous systems. It can be difficult to identify and delineate many of these nuclei and their finer subdivisions in conventional MRI due to their small size, buried location, and often subtle contrast compared to neighboring tissue. To address this problem, we applied a multi-modal approach in ex vivo non-human primate (NHP) brain that includes high-resolution mean apparent propagator (MAP)-MRI and five different histological stains imaged with high-resolution microscopy in the brain of the same subject. By registering these high-dimensional MRI data to high-resolution histology data, we can map the location, boundaries, subdivisions, and micro-architectural features of subcortical gray matter regions in the macaque monkey brain. At high spatial resolution, diffusion MRI in general, and MAP-MRI in particular, can distinguish a large number of deep brain structures, including the larger and smaller white matter fiber tracts as well as architectonic features within various nuclei. Correlation with histology from the same brain enables a thorough validation of the structures identified with MAP-MRI. Moreover, anatomical details that are evident in images of MAP-MRI parameters are not visible in conventional T1-weighted images. We also derived subcortical template "SC21" from segmented MRI slices in three-dimensions and registered this volume to a previously published anatomical template with cortical parcellation (Reveley et al., 2017; Saleem and Logothetis, 2012), thereby integrating the 3D segmentation of both cortical and subcortical regions into the same volume. This newly updated three-dimensional D99 digital brain atlas (V2.0) is intended for use as a reference standard for macaque neuroanatomical, functional, and connectional imaging studies, involving both cortical and subcortical targets. The SC21 and D99 digital templates are available as volumes and surfaces in standard NIFTI and GIFTI formats.
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9
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Deelchand DK, Ho ML, Nestrasil I. Ultra-High-Field Imaging of the Pediatric Brain and Spinal Cord. Magn Reson Imaging Clin N Am 2021; 29:643-653. [PMID: 34717851 DOI: 10.1016/j.mric.2021.06.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Neuroimaging with ultra-high field magnets (≥7T) provides superior signal-to-noise, spatial resolution and tissue contrast; but also greater safety concerns, longer scanning times, and increased distortion and field inhomogeneity. Brain and spinal cord anatomic microstructure and function imaged in greater detail offers improved lesion detection, delineation, and characterization. The ongoing development of novel imaging contrasts and translation of cutting-edge sequences will aid more accurate, sensitive, and precise diagnosis, interventional planning, and follow-up for a variety of pathologic conditions.
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Affiliation(s)
- Dinesh Kumar Deelchand
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street Southeast, Minneapolis, MN 55455, USA
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive, Columbus, OH 43205, USA
| | - Igor Nestrasil
- Masonic Institute for the Developing Brain, Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, 2025 East River Parkway, Minneapolis, MN 55414, USA.
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10
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Yedavalli V, DiGiacomo P, Tong E, Zeineh M. High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping. Magn Reson Imaging Clin N Am 2021; 29:13-39. [PMID: 33237013 DOI: 10.1016/j.mric.2020.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.
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Affiliation(s)
- Vivek Yedavalli
- Department of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA 94305-5105, USA; Division of Neuroradiology, Johns Hopkins University, 600 N. Wolfe St. B-112 D, Baltimore, MD 21287, USA
| | - Phillip DiGiacomo
- Department of Bioengineering, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA
| | - Elizabeth Tong
- Department of Radiology, 300 Pasteur Drive, Room S031, Stanford, CA 94305-5105, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA.
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11
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Steuber ER, Seligowski AV, Roeckner AR, Reda M, Lebois LAM, van Rooij SJH, Murty VP, Ely TD, Bruce SE, House SL, Beaudoin FL, An X, Zeng D, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Rauch SL, Lewandowski C, Sheikh S, Jones CW, Punches BE, Swor RA, McGrath ME, Hudak LA, Pascual JL, Chang AM, Pearson C, Peak DA, Domeier RM, O'Neil BJ, Rathlev NK, Sanchez LD, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Elliott JM, Kessler RC, Koenen KC, McLean SA, Ressler KJ, Jovanovic T, Harnett NG, Stevens JS. Thalamic volume and fear extinction interact to predict acute posttraumatic stress severity. J Psychiatr Res 2021; 141:325-332. [PMID: 34304036 PMCID: PMC8513112 DOI: 10.1016/j.jpsychires.2021.07.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 07/04/2021] [Accepted: 07/13/2021] [Indexed: 11/26/2022]
Abstract
Posttraumatic stress disorder (PTSD) is associated with lower gray matter volume (GMV) in brain regions critical for extinction of learned threat. However, relationships among volume, extinction learning, and PTSD symptom development remain unclear. We investigated subcortical brain volumes in regions supporting extinction learning and fear-potentiated startle (FPS) to understand brain-behavior interactions that may impact PTSD symptom development in recently traumatized individuals. Participants (N = 99) completed magnetic resonance imaging and threat conditioning two weeks following trauma exposure as part of a multisite observational study to understand the neuropsychiatric effects of trauma (AURORA Study). Participants completed self-assessments of PTSD (PTSD Checklist for DSM-5; PCL-5), dissociation, and depression symptoms two- and eight-weeks post-trauma. We completed multiple regressions to investigate relationships between FPS during late extinction, GMV, and PTSD symptom development. The interaction between thalamic GMV and FPS during late extinction at two weeks post-trauma predicted PCL-5 scores eight weeks (t (75) = 2.49, β = 0.28, p = 0.015) post-trauma. Higher FPS predicted higher PCL-5 scores in the setting of increased thalamic GMV. Meanwhile, lower FPS also predicted higher PCL-5 scores in the setting of decreased thalamic GMV. Thalamic GMV and FPS interactions also predicted posttraumatic dissociative and depressive symptoms. Amygdala and hippocampus GMV by FPS interactions were not associated with posttraumatic symptom development. Taken together, thalamic GMV and FPS during late extinction interact to contribute to adverse posttraumatic neuropsychiatric outcomes. Multimodal assessments soon after trauma have the potential to distinguish key phenotypes vulnerable to posttraumatic neuropsychiatric outcomes.
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Affiliation(s)
| | - Antonia V Seligowski
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Alyssa R Roeckner
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Mariam Reda
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Vishnu P Murty
- Department of Psychology, College of Liberal Arts, Temple University, Philadelphia, PA, USA
| | - Timothy D Ely
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca L Beaudoin
- Department of Emergency Medicine & Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA
| | - Xinming An
- Department of Anesthesiology, Institute of Trauma Recovery, UNC School of Medicine, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas C Neylan
- San Francisco VA Healthcare System and Departments of Psychiatry and Neurology, University of California, San Francisco, CA, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine and Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sarah D Linnstaedt
- Department of Anesthesiology, Institute of Trauma Recovery, UNC School of Medicine, Chapel Hill, NC, USA
| | - Laura T Germine
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; The Many Brains Project, Acton, MA, USA
| | - Scott L Rauch
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | | | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E Punches
- Department of Emergency Medicine, University of Cincinnati College of Medicine & University of Cincinnati College of Nursing, Cincinnati, OH, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester Hills, MI, USA
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, MA, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L Pascual
- Department of Surgery and Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ann Arbor, MI, USA
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Robert H Pietrzak
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA & Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, College of Arts & Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | | | - James M Elliott
- The Kolling Institute of Medical Research, Northern Clinical School, University of Sydney, St Leonards, New South Wales, Australia; Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia; Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Samuel A McLean
- Department of Anesthesiology, Institute of Trauma Recovery, UNC School of Medicine, Chapel Hill, NC, USA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA
| | - Nathaniel G Harnett
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA.
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA.
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12
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Lehman VT, Lee KH, Klassen BT, Blezek DJ, Goyal A, Shah BR, Gorny KR, Huston J, Kaufmann TJ. MRI and tractography techniques to localize the ventral intermediate nucleus and dentatorubrothalamic tract for deep brain stimulation and MR-guided focused ultrasound: a narrative review and update. Neurosurg Focus 2021; 49:E8. [PMID: 32610293 DOI: 10.3171/2020.4.focus20170] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 04/07/2020] [Indexed: 11/06/2022]
Abstract
The thalamic ventral intermediate nucleus (VIM) can be targeted for treatment of tremor by several procedures, including deep brain stimulation (DBS) and, more recently, MR-guided focused ultrasound (MRgFUS). To date, such targeting has relied predominantly on coordinate-based or atlas-based techniques rather than directly targeting the VIM based on imaging features. While general regional differences of features within the thalamus and some related white matter tracts can be distinguished with conventional imaging techniques, internal nuclei such as the VIM are not discretely visualized. Advanced imaging methods such as quantitative susceptibility mapping (QSM) and fast gray matter acquisition T1 inversion recovery (FGATIR) MRI and high-field MRI pulse sequences that improve the ability to image the VIM region are emerging but have not yet been shown to have reliability and accuracy to serve as the primary method of VIM targeting. Currently, the most promising imaging approach to directly identify the VIM region for clinical purposes is MR diffusion tractography.In this review and update, the capabilities and limitations of conventional and emerging advanced methods for evaluation of internal thalamic anatomy are briefly reviewed. The basic principles of tractography most relevant to VIM targeting are provided for familiarization. Next, the key literature to date addressing applications of DTI and tractography for DBS and MRgFUS is summarized, emphasizing use of direct targeting. This literature includes 1-tract (dentatorubrothalamic tract [DRT]), 2-tract (pyramidal and somatosensory), and 3-tract (DRT, pyramidal, and somatosensory) approaches to VIM region localization through tractography.The authors introduce a 3-tract technique used at their institution, illustrating the oblique curved course of the DRT within the inferior thalamus as well as the orientation and relationship of the white matter tracts in the axial plane. The utility of this 3-tract tractography approach to facilitate VIM localization is illustrated with case examples of variable VIM location, targeting superior to the anterior commissure-posterior commissure plane, and treatment in the setting of pathologic derangement of thalamic anatomy. Finally, concepts demonstrated with these case examples and from the prior literature are synthesized to highlight several potential advantages of tractography for VIM region targeting.
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Affiliation(s)
| | | | | | | | - Abhinav Goyal
- 4Mayo Clinic College of Medicine, Rochester, Minnesota; and
| | - Bhavya R Shah
- 5Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
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13
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Schneider TM, Ma J, Wagner P, Behl N, Nagel AM, Ladd ME, Heiland S, Bendszus M, Straub S. Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. Front Neurosci 2021; 15:661504. [PMID: 34234639 PMCID: PMC8255625 DOI: 10.3389/fnins.2021.661504] [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: 01/30/2021] [Accepted: 05/17/2021] [Indexed: 12/02/2022] Open
Abstract
Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging. Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures. Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei. Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.
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Affiliation(s)
- Till M Schneider
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Jackie Ma
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Patrick Wagner
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Nicolas Behl
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Armin M Nagel
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mark E Ladd
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Faculty of Physics and Astronomy and Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
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14
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Bello EM, Agnesi F, Xiao Y, Dao J, Johnson MD. Frequency-dependent spike-pattern changes in motor cortex during thalamic deep brain stimulation. J Neurophysiol 2020; 124:1518-1529. [PMID: 32965147 DOI: 10.1152/jn.00198.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The cerebellar-receiving area of the motor thalamus is the primary anatomical target for treating essential tremor with deep brain stimulation (DBS). Although neuroimaging studies have shown that higher stimulation frequencies in this target correlate with increased cortical metabolic activity, less is known about the cellular-level functional changes that occur in the primary motor cortex (M1) with thalamic stimulation and how these changes depend on the frequency of DBS. In this study, we used a preclinical animal model of DBS to collect single-unit spike recordings in M1 before, during, and after DBS targeting the cerebellar-receiving area of the motor thalamus (VPLo, nucleus ventralis posterior lateralis pars oralis). The effects of VPLo-DBS on M1 spike rates, interspike interval entropy, and peristimulus phase-locking were compared across stimulus pulse train frequencies ranging from 10 to 130 Hz. Although VPLo-DBS modulated the spike rates of 20-50% of individual M1 cells in a frequency-dependent manner, the population-level average spike rate only weakly depended on stimulation frequency. In contrast, the population-level entropy measure showed a pronounced decrease with high-frequency stimulation, caused by a subpopulation of cells that exhibited strong phase-locking and general spike-pattern regularization. Contrarily, low-frequency stimulation induced an entropy increase (spike-pattern disordering) in a relatively large portion of the recorded population, which diminished with higher stimulation frequencies. These results also suggest that changes in phase-locking and spike-pattern entropy are not necessarily equivalent pattern phenomena, but rather that they should both be weighed when quantifying stimulation-induced spike-pattern changes.NEW & NOTEWORTHY The network mechanisms of thalamic deep brain stimulation (DBS) are not well understood at the cellular level. This study investigated the neuronal firing rate and pattern changes in the motor cortex resulting from stimulation of the cerebellar-receiving area of the motor thalamus. We showed that there is a nonintuitive relationship between general entropy-based spike-pattern measures and phase-locked regularization to DBS.
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Affiliation(s)
- Edward M Bello
- Department of Biomedical Engineering, University of Minnesota, Minneapolis
| | - Filippo Agnesi
- Department of Biomedical Engineering, University of Minnesota, Minneapolis
| | - Yizi Xiao
- Department of Biomedical Engineering, University of Minnesota, Minneapolis
| | - Joan Dao
- Department of Biomedical Engineering, University of Minnesota, Minneapolis
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis.,Institute for Translational Neuroscience, University of Minnesota, Minneapolis
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15
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Majdi MS, Keerthivasan MB, Rutt BK, Zahr NM, Rodriguez JJ, Saranathan M. Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks. Magn Reson Imaging 2020; 73:45-54. [PMID: 32828985 DOI: 10.1016/j.mri.2020.08.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/25/2020] [Accepted: 08/17/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei. METHODS A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3 T and 7 T field strengths. WMn-MPRAGE images were manually delineated by a trained neuroradiologist using the Morel histological atlas as a guide to generate reference ground truth labels. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performance. Clinical utility was demonstrated by applying this method to study the effect of MS on thalamic nuclei atrophy. RESULTS Segmentation of each thalamus into twelve nuclei was achieved in under a minute. For 7 T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0.05-0.18) and VSI for four nuclei (increase in the range of 0.05-0.19), while performing comparably for healthy and MS subjects. Dice and VSI achieved using 7 T WMn-MPRAGE data are comparable to those using 3 T WMn-MPRAGE data. For conventional MPRAGE, the proposed method shows a statistically significant Dice improvement in the range of 0.14-0.63 over FreeSurfer for all nuclei and disease types. Effect of noise on network performance shows robustness to images with SNR as low as half the baseline SNR. Atrophy of four thalamic nuclei and whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for the effect of parallel imaging, intracranial volume, gender, and age (p < 0.004). CONCLUSION The proposed segmentation method is fast, accurate, performs well across disease types and field strengths, and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases.
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Affiliation(s)
- Mohammad S Majdi
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States of America
| | - Mahesh B Keerthivasan
- Department of Medical Imaging, University of Arizona, Tucson, AZ, United States of America; Siemens Healthcare, Tucson, AZ, USA
| | - Brian K Rutt
- Department of Radiology, Stanford University, Stanford, CA, United States of America
| | - Natalie M Zahr
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States of America
| | - Jeffrey J Rodriguez
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States of America
| | - Manojkumar Saranathan
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States of America; Department of Medical Imaging, University of Arizona, Tucson, AZ, United States of America.
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16
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Jorge J, Gretsch F, Najdenovska E, Tuleasca C, Levivier M, Maeder P, Gallichan D, Marques JP, Bach Cuadra M. Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T. Magn Reson Med 2020; 84:1218-1234. [PMID: 32052486 DOI: 10.1002/mrm.28197] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 01/10/2020] [Accepted: 01/13/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging. METHODS High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B0 perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases. RESULTS Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization. CONCLUSION Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.
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Affiliation(s)
- João Jorge
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland.,Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Frédéric Gretsch
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elena Najdenovska
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Constantin Tuleasca
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Marc Levivier
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe Maeder
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Daniel Gallichan
- Cardiff University Brain Research Imaging Centre, School of Engineering, Cardiff University, Cardiff, UK
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Meritxell Bach Cuadra
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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17
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Hoch MJ, Bruno MT, Faustin A, Cruz N, Mogilner AY, Crandall L, Wisniewski T, Devinsky O, Shepherd TM. 3T MRI Whole-Brain Microscopy Discrimination of Subcortical Anatomy, Part 2: Basal Forebrain. AJNR Am J Neuroradiol 2019; 40:1095-1105. [PMID: 31196861 DOI: 10.3174/ajnr.a6088] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 04/22/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE The basal forebrain contains multiple structures of great interest to emerging functional neurosurgery applications, yet many neuroradiologists are unfamiliar with this neuroanatomy because it is not resolved with current clinical MR imaging. MATERIALS AND METHODS We applied an optimized TSE T2 sequence to washed whole postmortem brain samples (n = 13) to demonstrate and characterize the detailed anatomy of the basal forebrain using a clinical 3T MR imaging scanner. We measured the size of selected internal myelinated pathways and measured subthalamic nucleus size, oblique orientation, and position relative to the intercommissural point. RESULTS We identified most basal ganglia and diencephalon structures using serial axial, coronal, and sagittal planes relative to the intercommissural plane. Specific oblique image orientations demonstrated the positions and anatomic relationships for selected structures of interest to functional neurosurgery. We observed only 0.2- to 0.3-mm right-left differences in the anteroposterior and superoinferior length of the subthalamic nucleus (P = .084 and .047, respectively). Individual variability for the subthalamic nucleus was greatest for angulation within the sagittal plane (range, 15°-37°), transverse dimension (range, 2-6.7 mm), and most inferior border (range, 4-7 mm below the intercommissural plane). CONCLUSIONS Direct identification of basal forebrain structures in multiple planes using the TSE T2 sequence makes this challenging neuroanatomy more accessible to practicing neuroradiologists. This protocol can be used to better define individual variations relevant to functional neurosurgical targeting and validate/complement advanced MR imaging methods being developed for direct visualization of these structures in living patients.
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Affiliation(s)
- M J Hoch
- From the Department of Radiology and Imaging Sciences, (M.J.H.), Emory University, Atlanta, Georgia
| | - M T Bruno
- Departments of Radiology (M.T.B., N.C., T.M.S.)
| | | | - N Cruz
- Departments of Radiology (M.T.B., N.C., T.M.S.)
| | | | - L Crandall
- Neurology (L.C., T.W., O.D.).,SUDC Foundation (L.C., O.D.), New York, New York
| | - T Wisniewski
- Pathology (A.F., T.W.).,Neurology (L.C., T.W., O.D.).,Psychiatry (T.W.), New York University, New York, New York
| | - O Devinsky
- Neurology (L.C., T.W., O.D.).,SUDC Foundation (L.C., O.D.), New York, New York
| | - T M Shepherd
- Departments of Radiology (M.T.B., N.C., T.M.S.) .,Center for Advanced Imaging Innovation and Research (T.M.S.), New York, New York
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18
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Su JH, Thomas FT, Kasoff WS, Tourdias T, Choi EY, Rutt BK, Saranathan M. Thalamus Optimized Multi Atlas Segmentation (THOMAS): fast, fully automated segmentation of thalamic nuclei from structural MRI. Neuroimage 2019; 194:272-282. [PMID: 30894331 DOI: 10.1016/j.neuroimage.2019.03.021] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 02/10/2019] [Accepted: 03/10/2019] [Indexed: 12/21/2022] Open
Abstract
The thalamus and its nuclei are largely indistinguishable on standard T1 or T2 weighted MRI. While diffusion tensor imaging based methods have been proposed to segment the thalamic nuclei based on the angular orientation of the principal diffusion tensor, these are based on echo planar imaging which is inherently limited in spatial resolution and suffers from distortion. We present a multi-atlas segmentation technique based on white-matter-nulled MP-RAGE imaging that segments the thalamus into 12 nuclei with computation times on the order of 10 min on a desktop PC; we call this method THOMAS (THalamus Optimized Multi Atlas Segmentation). THOMAS was rigorously evaluated on 7T MRI data acquired from healthy volunteers and patients with multiple sclerosis by comparing against manual segmentations delineated by a neuroradiologist, guided by the Morel atlas. Segmentation accuracy was very high, with uniformly high Dice indices: at least 0.85 for large nuclei like the pulvinar and mediodorsal nuclei and at least 0.7 even for small structures such as the habenular, centromedian, and lateral and medial geniculate nuclei. Volume similarity indices ranged from 0.82 for the smaller nuclei to 0.97 for the larger nuclei. Volumetry revealed that the volumes of the right anteroventral, right ventral posterior lateral, and both right and left pulvinar nuclei were significantly lower in MS patients compared to controls, after adjusting for age, sex and intracranial volume. Lastly, we evaluated the potential of this method for targeting the Vim nucleus for deep brain surgery and focused ultrasound thalamotomy by overlaying the Vim nucleus segmented from pre-operative data on post-operative data. The locations of the ablated region and active DBS contact corresponded well with the segmented Vim nucleus. Our fast, direct structural MRI based segmentation method opens the door for MRI guided intra-operative procedures like thalamotomy and asleep DBS electrode placement as well as for accurate quantification of thalamic nuclear volumes to follow progression of neurological disorders.
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Affiliation(s)
- Jason H Su
- Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Francis T Thomas
- Electrical & Computer Engineering, University of Arizona, Tucson, AZ, USA
| | - Willard S Kasoff
- Division of Neurosurgery, University of Arizona, Tucson, AZ, USA
| | - Thomas Tourdias
- Service de Neuroimagerie Diagnostique et Thérapeutique, Université de Bordeaux, Bordeaux, France
| | | | - Brian K Rutt
- Radiology, Stanford University, Stanford, CA, USA
| | - Manojkumar Saranathan
- Electrical & Computer Engineering, University of Arizona, Tucson, AZ, USA; Medical Imaging, University of Arizona, Tucson, AZ, USA.
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19
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Sinnecker T, Granziera C, Wuerfel J, Schlaeger R. Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS. Curr Treat Options Neurol 2018; 20:17. [PMID: 29679165 DOI: 10.1007/s11940-018-0504-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE OF REVIEW Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS. RECENT FINDINGS In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.
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Affiliation(s)
- Tim Sinnecker
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Regina Schlaeger
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
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20
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Zhang S, Connolly AT, Madden LR, Vitek JL, Johnson MD. High-resolution local field potentials measured with deep brain stimulation arrays. J Neural Eng 2018; 15:046019. [PMID: 29651998 DOI: 10.1088/1741-2552/aabdf5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Local field potential (LFP) recordings along a deep brain stimulation (DBS) lead can provide useful feedback for titrating DBS therapy. However, conventional DBS leads with four cylindrical macroelectrodes likely undersample the spatial distribution of sinks and sources in a given brain region. In this study, we investigated the spectral power and spatial feature sizes of LFP activity in non-human primate subthalamic nucleus and globus pallidus using chronically implanted 32-channel directional DBS arrays. APPROACH Subthalamic nucleus and globus pallidus LFP signals were recorded from directional DBS arrays in the resting state and during a reach-and-retrieval task in two non-human primates in naïve and parkinsonian conditions. LFP recordings were compared amongst bipolar pairs of electrodes using individual and grouped electrode configurations, with the latter mimicking the cylindrical macroelectrode configurations used in current clinical LFP recordings. MAIN RESULTS Recordings from these DBS arrays showed that (1) beta oscillations have spatial 'fingerprints' in the subthalamic nucleus and globus pallidus, and (2) that these oscillations were muted when grouping electrode contacts together to create cylindrical macroelectrodes similar in relative dimension to those used clinically. Further, these maps depended on parkinsonian condition and whether the subject was resting or performing a motor task. SIGNIFICANCE Development of future closed-loop DBS therapies that rely on LFP feedback will benefit from implanting DBS arrays with electrode sizes and spacings that are more consistent with the dimensions of oscillatory sinks and sources within the brain.
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Affiliation(s)
- Simeng Zhang
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
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21
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Deep brain stimulation induces sparse distributions of locally modulated neuronal activity. Sci Rep 2018; 8:2062. [PMID: 29391468 PMCID: PMC5794783 DOI: 10.1038/s41598-018-20428-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 01/18/2018] [Indexed: 12/17/2022] Open
Abstract
Deep brain stimulation (DBS) therapy is a potent tool for treating a range of brain disorders. High frequency stimulation (HFS) patterns used in DBS therapy are known to modulate neuronal spike rates and patterns in the stimulated nucleus; however, the spatial distribution of these modulated responses are not well understood. Computational models suggest that HFS modulates a volume of tissue spatially concentrated around the active electrode. Here, we tested this theory by investigating modulation of spike rates and patterns in non-human primate motor thalamus while stimulating the cerebellar-receiving area of motor thalamus, the primary DBS target for treating Essential Tremor. HFS inhibited spike activity in the majority of recorded cells, but increasing stimulation amplitude also shifted the response to a greater degree of spike pattern modulation. Modulated responses in both categories exhibited a sparse and long-range spatial distribution within motor thalamus, suggesting that stimulation preferentially affects afferent and efferent axonal processes traversing near the active electrode and that the resulting modulated volume strongly depends on the local connectome of these axonal processes. Such findings have important implications for current clinical efforts building predictive computational models of DBS therapy, developing directional DBS lead technology, and formulating closed-loop DBS strategies.
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22
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Peña E, Zhang S, Deyo S, Xiao Y, Johnson MD. Particle swarm optimization for programming deep brain stimulation arrays. J Neural Eng 2017; 14:016014. [PMID: 28068291 DOI: 10.1088/1741-2552/aa52d1] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. APPROACH Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. MAIN RESULTS The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. SIGNIFICANCE The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
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