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Welton T, Hartono S, Shih YC, Schwarz ST, Xing Y, Tan EK, Auer DP, Harel N, Chan LL. Ultra-high-field 7T MRI in Parkinson's disease: ready for clinical use?-a narrative review. Quant Imaging Med Surg 2023; 13:7607-7620. [PMID: 37969629 PMCID: PMC10644128 DOI: 10.21037/qims-23-509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/15/2023] [Indexed: 11/17/2023]
Abstract
Background and Objective The maturation of ultra-high-field magnetic resonance imaging (MRI) [≥7 Tesla (7T)] has improved our capability to depict and characterise brain structures efficiently, with better signal-to-noise ratio (SNR) and spatial resolution. We evaluated whether these improvements benefit the clinical detection and management of Parkinson's disease (PD). Methods We performed a literature search in March 2023 in PubMed (MEDLINE), EMBASE and Google Scholar for articles on "7T MRI" AND "Parkinson*", written in English, published between inception and 1st March, 2023, which we synthesised in narrative form. Key Content and Findings In deep-brain stimulation (DBS) surgical planning, early studies show that 7T MRI can distinguish anatomical substructures, and that this results in reduced adverse effects. In other areas, while there is strong evidence for improved accuracy and precision of 7T MRI-based measurements for PD, there is limited evidence for meaningful clinical translation. In particular, neuromelanin-iron complex quantification and visualisation in midbrain nuclei is enhanced, enabling depiction of nigrosomes 1-5, improved morphometry and vastly improved radiological assessments; however, studies on the related clinical outcomes, diagnosis, subtyping, differentiation of atypical parkinsonisms, and monitoring of treatment response using 7T MRI are lacking. Moreover, improvements in clinical utility must be great enough to justify the additional costs. Conclusions Together, current evidence supports feasible future clinical implementation of 7T MRI for PD. Future impacts to clinical decision making for diagnosis, differentiation, and monitoring of progression or treatment response are likely; however, to achieve this, further longitudinal studies using 7T MRI are needed in prodromal, early-stage PD and parkinsonism cohorts focusing on clinical translational potential.
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Affiliation(s)
- Thomas Welton
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Septian Hartono
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Yao-Chia Shih
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
- Graduate Institute of Medicine, Yuan Ze University and National Taiwan University, Taipei
| | - Stefan T. Schwarz
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Radiology, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Yue Xing
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Eng-King Tan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Dorothee P. Auer
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Ling-Ling Chan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
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2
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Kidd AC, Anderson O, Cowell GW, Weir AJ, Voisey JP, Evison M, Tsim S, Goatman KA, Blyth KG. Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria. Thorax 2022; 77:1251-1259. [PMID: 35110367 PMCID: PMC9685726 DOI: 10.1136/thoraxjnl-2021-217808] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/20/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy. METHODS In a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen's kappa. Survival was assessed using Kaplan-Meier methodology. RESULTS Human and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3 (p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival. CONCLUSION We have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.
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Affiliation(s)
- Andrew C Kidd
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
| | - Owen Anderson
- School of Computing Science, University of Glasgow, Glasgow, UK
- Canon Medical Research Europe Ltd, Edinburgh, UK
| | - Gordon W Cowell
- Department of Imaging, Queen Elizabeth University Hospital, Glasgow, UK
| | | | | | - Matthew Evison
- Department of Respiratory Medicine, University Hospital of South Manchester, Manchester, UK
| | - Selina Tsim
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | - Kevin G Blyth
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- Beatson Institute for Cancer Research, Glasgow, UK
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3
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Baxter JSH, Jannin P. Combining simple interactivity and machine learning: a separable deep learning approach to subthalamic nucleus localization and segmentation in MRI for deep brain stimulation surgical planning. J Med Imaging (Bellingham) 2022; 9:045001. [PMID: 35836671 DOI: 10.1117/1.jmi.9.4.045001] [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: 11/30/2021] [Accepted: 06/16/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Deep brain stimulation (DBS) is an interventional treatment for some neurological and neurodegenerative diseases. For example, in Parkinson's disease, DBS electrodes are positioned at particular locations within the basal ganglia to alleviate the patient's motor symptoms. These interventions depend greatly on a preoperative planning stage in which potential targets and electrode trajectories are identified in a preoperative MRI. Due to the small size and low contrast of targets such as the subthalamic nucleus (STN), their segmentation is a difficult task. Machine learning provides a potential avenue for development, but it has difficulty in segmenting such small structures in volumetric images due to additional problems such as segmentation class imbalance. Approach: We present a two-stage separable learning workflow for STN segmentation consisting of a localization step that detects the STN and crops the image to a small region and a segmentation step that delineates the structure within that region. The goal of this decoupling is to improve accuracy and efficiency and to provide an intermediate representation that can be easily corrected by a clinical user. This correction capability was then studied through a human-computer interaction experiment with seven novice participants and one expert neurosurgeon. Results: Our two-step segmentation significantly outperforms the comparative registration-based method currently used in clinic and approaches the fundamental limit on variability due to the image resolution. In addition, the human-computer interaction experiment shows that the additional interaction mechanism allowed by separating STN segmentation into two steps significantly improves the users' ability to correct errors and further improves performance. Conclusions: Our method shows that separable learning not only is feasible for fully automatic STN segmentation but also leads to improved interactivity that can ease its translation into clinical use.
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Affiliation(s)
- John S H Baxter
- Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image (INSERM UMR 1099), Rennes, France
| | - Pierre Jannin
- Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image (INSERM UMR 1099), Rennes, France
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4
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Jin Z, Wang Y, Jokar M, Li Y, Cheng Z, Liu Y, Tang R, Shi X, Zhang Y, Min J, Liu F, He N, Yan F, Haacke EM. Automatic detection of neuromelanin and iron in the midbrain nuclei using a
magnetic resonance imaging
‐based brain template. Hum Brain Mapp 2022; 43:2011-2025. [PMID: 35072301 PMCID: PMC8933249 DOI: 10.1002/hbm.25770] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 12/17/2022] Open
Abstract
Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.
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Affiliation(s)
- Zhijia Jin
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Ying Wang
- SpinTech MRI, Inc. Detroit Michigan USA
- Department of Radiology Wayne State University Detroit Michigan USA
| | | | - Yan Li
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Rongbiao Tang
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Xiaofeng Shi
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jihua Min
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Fangtao Liu
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Naying He
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
- SpinTech MRI, Inc. Detroit Michigan USA
- Department of Radiology Wayne State University Detroit Michigan USA
- Department of Biomedical Engineering Wayne State University Detroit Michigan USA
- Department of Neurology Wayne State University Detroit Michigan USA
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5
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Beliveau V, Nørgaard M, Birkl C, Seppi K, Scherfler C. Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging. Hum Brain Mapp 2021; 42:4809-4822. [PMID: 34322940 PMCID: PMC8449109 DOI: 10.1002/hbm.25604] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 01/10/2023] Open
Abstract
The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron-rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state-of-the-art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi-atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi-atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images.
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Affiliation(s)
- Vincent Beliveau
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University of InnsbruckInnsbruckAustria
| | - Martin Nørgaard
- Neurobiology Research Unit & CIMBICopenhagen University HospitalCopenhagenDenmark
- Center for Reproducible Neuroscience, Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Christoph Birkl
- Department of NeuroradiologyMedical University of InnsbruckInnsbruckAustria
| | - Klaus Seppi
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University of InnsbruckInnsbruckAustria
| | - Christoph Scherfler
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University of InnsbruckInnsbruckAustria
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6
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Poston KL, Ua Cruadhlaoich MAI, Santoso LF, Bernstein JD, Liu T, Wang Y, Rutt B, Kerchner GA, Zeineh MM. Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinson's Disease: A 7 Tesla Imaging Study. JOURNAL OF PARKINSONS DISEASE 2021; 10:591-604. [PMID: 32250317 PMCID: PMC7242837 DOI: 10.3233/jpd-191890] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background: In postmortem analysis of late stage Parkinson’s disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor. Objective: To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. Methods: We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty). Results: We found that smaller SN correlated with longer disease duration (r = –0.49, p = 0.004), more severe MDS-UPDRS motor score (r = –0.42, p = 0.016), and more severe bradykinesia-rigidity subscore (r = –0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume. Conclusions: In mild-to-moderate PD, SN volume relates to motor manifestations in a motor domain-specific and laterality-dependent manner. Non-invasive in vivo 7.0 Tesla QSM may serve as a biomarker in longitudinal studies of SN atrophy and in studies of people at risk for developing PD.
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Affiliation(s)
- Kathleen L Poston
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew A I Ua Cruadhlaoich
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura F Santoso
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.,School of Medicine, University of Massachusetts, Worcester, MA, USA.,California Institute of Technology, Pasadena, CA, USA
| | - Jeffrey D Bernstein
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.,School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Tian Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Brian Rutt
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Geoffrey A Kerchner
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael M Zeineh
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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7
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Younce JR, Campbell MC, Hershey T, Tanenbaum AB, Milchenko M, Ushe M, Karimi M, Tabbal SD, Kim AE, Snyder AZ, Perlmutter JS, Norris SA. Resting-State Functional Connectivity Predicts STN DBS Clinical Response. Mov Disord 2021; 36:662-671. [PMID: 33211330 PMCID: PMC7987812 DOI: 10.1002/mds.28376] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/23/2020] [Accepted: 10/19/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Deep brain stimulation of the subthalamic nucleus is a widely used adjunctive therapy for motor symptoms of Parkinson's disease, but with variable motor response. Predicting motor response remains difficult, and novel approaches may improve surgical outcomes as well as the understanding of pathophysiological mechanisms. The objective of this study was to determine whether preoperative resting-state functional connectivity MRI predicts motor response from deep brain stimulation of the subthalamic nucleus. METHODS We collected preoperative resting-state functional MRI from 70 participants undergoing subthalamic nucleus deep brain stimulation. For this cohort, we analyzed the strength of STN functional connectivity with seeds determined by stimulation-induced (ON/OFF) 15 O H2 O PET regional cerebral blood flow differences in a partially overlapping group (n = 42). We correlated STN-seed functional connectivity strength with postoperative motor outcomes and applied linear regression to predict motor outcomes. RESULTS Preoperative functional connectivity between the left subthalamic nucleus and the ipsilateral internal globus pallidus correlated with postsurgical motor outcomes (r = -0.39, P = 0.0007), with stronger preoperative functional connectivity relating to greater improvement. Left pallidal-subthalamic nucleus connectivity also predicted motor response to DBS after controlling for covariates. DISCUSSION Preoperative pallidal-subthalamic nucleus resting-state functional connectivity predicts motor benefit from deep brain stimulation, although this should be validated prospectively before clinical application. These observations suggest that integrity of pallidal-subthalamic nucleus circuits may be critical to motor benefits from deep brain stimulation. © 2020 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- John R Younce
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Meghan C Campbell
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tamara Hershey
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aaron B Tanenbaum
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Mikhail Milchenko
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Mwiza Ushe
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Morvarid Karimi
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Samer D Tabbal
- Department of Neurology, American University of Beirut, Beirut, Lebanon
| | - Albert E Kim
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Joel S Perlmutter
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri, USA
- Program in Physical Therapy, Washington University in St. Louis, St. Louis, Missouri, USA
- Program in Occupational Therapy, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Scott A Norris
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
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8
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IE-Map: a novel in-vivo atlas and template of the human inner ear. Sci Rep 2021; 11:3293. [PMID: 33558581 PMCID: PMC7870663 DOI: 10.1038/s41598-021-82716-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 12/17/2020] [Indexed: 12/26/2022] Open
Abstract
Brain atlases and templates are core tools in scientific research with increasing importance also in clinical applications. Advances in neuroimaging now allowed us to expand the atlas domain to the vestibular and auditory organ, the inner ear. In this study, we present IE-Map, an in-vivo template and atlas of the human labyrinth derived from multi-modal high-resolution magnetic resonance imaging (MRI) data, in a fully non-invasive manner without any contrast agent or radiation. We reconstructed a common template from 126 inner ears (63 normal subjects) and annotated it with 94 established landmarks and semi-automatic segmentations of all relevant macroscopic vestibular and auditory substructures. We validated the atlas by comparing MRI templates to a novel CT/micro-CT atlas, which we reconstructed from 21 publicly available post-mortem images of the bony labyrinth. Templates in MRI and micro-CT have a high overlap, and several key anatomical measures of the bony labyrinth in IE-Map are in line with micro-CT literature of the inner ear. A quantitative substructural analysis based on the new template, revealed a correlation of labyrinth parameters with total intracranial volume. No effects of gender or laterality were found. We provide the validated templates, atlas segmentations, surface meshes and landmark annotations as open-access material, to provide neuroscience researchers and clinicians in neurology, neurosurgery, and otorhinolaryngology with a widely applicable tool for computational neuro-otology.
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9
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Shan YZ, Wang ZM, Fan XT, Zhang HQ, Ren LK, Wei PH, Zhao GG. Automatic labeling of the fanning and curving shape of Meyer's loop for epilepsy surgery: an atlas extracted from high-definition fiber tractography. BMC Neurol 2019; 19:302. [PMID: 31779601 PMCID: PMC6882219 DOI: 10.1186/s12883-019-1537-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/19/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Visual field defects caused by injury to Meyer's loop (ML) are common in patients undergoing anterior temporal lobectomy during epilepsy surgery. Evaluation of the anatomical shapes of the curving, fanning and sharp angles of ML to guide surgeries is important but still challenging for diffusion tensor imaging. We present an advanced diffusion data-based ML atlas and labeling protocol to reproduce anatomical features in individuals within a short time. METHODS Thirty Massachusetts General Hospital-Human Connectome Project (MGH-HCP) diffusion datasets (ultra-high magnetic gradient & 512 directions) were warped to standard space. The resulting fibers were projected together to create an atlas. The anatomical features and the tractography correspondence rates were evaluated in 30 MGH-HCP individuals and local diffusion spectrum imaging data (eight healthy subjects and six hippocampal sclerosis patients). RESULTS In the atlas, features of curves, sharp angles and fanning shapes were adequately reproduced. The distances from the anterior tip of the temporal lobe to the anterior ridge of Meyer's loop were 23.1 mm and 26.41 mm on the left and right sides, respectively. The upper and lower divisions of the ML were revealed to be twisting. Eighty-eight labeled sides were achieved, and the correspondence rates were 87.44% ± 6.92, 80.81 ± 10.62 and 72.83% ± 14.03% for MGH-HCP individuals, DSI-healthy individuals and DSI-patients, respectively. CONCLUSION Atlas-labeled ML is comparable to high angular resolution tractography in healthy or hippocampal sclerosis patients. Therefore, rapid identification of the ML location with a single modality of T1 is practical. This protocol would facilitate functional studies and visual field protection during neurosurgery.
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Affiliation(s)
- Yong-Zhi Shan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing, 100053, China
| | - Zhen-Ming Wang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Xiao-Tong Fan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing, 100053, China
| | - Hua-Qiang Zhang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing, 100053, China
| | - Lian-Kun Ren
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing, 100053, China.
| | - Guo-Guang Zhao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing, 100053, China.
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10
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Kim J, Duchin Y, Shamir RR, Patriat R, Vitek J, Harel N, Sapiro G. Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation. Hum Brain Mapp 2019; 40:679-698. [PMID: 30379376 PMCID: PMC6519731 DOI: 10.1002/hbm.24404] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 09/04/2018] [Accepted: 09/07/2018] [Indexed: 12/20/2022] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2 W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.
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Affiliation(s)
- Jinyoung Kim
- Surgical Information Sciences, Inc.MinneapolisMinnesota
| | - Yuval Duchin
- Surgical Information Sciences, Inc.MinneapolisMinnesota
| | | | - Remi Patriat
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesota
| | - Jerrold Vitek
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesota
| | - Noam Harel
- Surgical Information Sciences, Inc.MinneapolisMinnesota
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesota
- Department of NeurosurgeryUniversity of MinnesotaMinneapolisMinnesota
| | - Guillermo Sapiro
- Surgical Information Sciences, Inc.MinneapolisMinnesota
- Department of Electrical and Computer EngineeringDuke UniversityDurhamNorth Carolina
- Department of Biomedical EngineeringDuke UniversityDurhamNorth Carolina
- Department of Computer ScienceDuke UniversityDurhamNorth Carolina
- Department of MathematicsDuke UniversityDurhamNorth Carolina
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Milchenko M, Snyder AZ, Campbell MC, Dowling JL, Rich KM, Brier LM, Perlmutter JS, Norris SA. ESM-CT: a precise method for localization of DBS electrodes in CT images. J Neurosci Methods 2018; 308:366-376. [PMID: 30201271 PMCID: PMC6205293 DOI: 10.1016/j.jneumeth.2018.09.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 09/05/2018] [Accepted: 09/05/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus produces variable effects in Parkinson disease. Variation may result from different electrode positions relative to target. Thus, precise electrode localization is crucial when investigating DBS effects. NEW METHOD We developed a semi-automated method, Electrode Shaft Modeling in CT images (ESM-CT) to reconstruct DBS lead trajectories and contact locations. We evaluated methodological sensitivity to operator-dependent steps, robustness to image resampling, and test-retest replicability. ESM-CT was applied in 56 patients to study electrode position change (and relation to time between scans, postoperative subdural air volume, and head tilt during acquisition) between images acquired immediately post-implantation (DBS-CT) and months later (DEL-CT). RESULTS Electrode tip localization was robust to image resampling and replicable to within ∼ 0.2 mm on test-retest comparisons. Systematic electrode displacement occurred rostral-ventral-lateral between DBS-CT and DEL-CT scans. Head angle was a major explanatory factor (p < 0.001,Pearson's r = 0.46, both sides) and volume of subdural air weakly predicted electrode displacement (p = 0.02,r = 0.29:p = 0.1,r = 0.25 for left:right). Modeled shaft curvature was slightly greater in DEL-CT. Magnitude of displacement and degree of curvature were independent of elapsed time between scans. COMPARISON WITH EXISTING METHODS Comparison of ESM-CT against two existing methods revealed systematic differences in one coordinate (1 ± 0.3 mm,p < 0.001) for one method and in three coordinates for another method (x:0.1 ± 0.1 mm, y:0.4 ± 0.2 mm, z:0.4 ± 0.2 mm, p < 10-10). Within-method coordinate variability across participants is similar. CONCLUSION We describe a robust and precise method for CT DBS contact localization. Application revealed that acquisition head angle significantly impacts electrode position. DBS localization schemes should account for head angle.
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Affiliation(s)
- Mikhail Milchenko
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Meghan C Campbell
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Joshua L Dowling
- Department of Neurosurgical Surgery, Washington University School of Medicine, (CB 8057), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Keith M Rich
- Department of Neurosurgical Surgery, Washington University School of Medicine, (CB 8057), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Lindsey M Brier
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Joel S Perlmutter
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurosurgical Surgery, Washington University School of Medicine, (CB 8057), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neuroscience, Washington University School of Medicine, (CB 8108), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Occupational Therapy, CB 8505, 4444 Forest Park Ave, St. Louis, MO 63108, USA; Department of Physical Therapy, CB 8502, 4444 Forest Park Ave, St. Louis, MO, 63108, USA
| | - Scott A Norris
- Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA.
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