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Kebiri H, Gholipour A, Lin R, Vasung L, Calixto C, Krsnik Ž, Karimi D, Bach Cuadra M. Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study. Med Image Anal 2024; 95:103186. [PMID: 38701657 DOI: 10.1016/j.media.2024.103186] [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: 07/28/2023] [Revised: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rizhong Lin
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Stewart BW, Keaser ML, Lee H, Margerison SM, Cormie MA, Moayedi M, Lindquist MA, Chen S, Mathur BN, Seminowicz DA. Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain. Curr Biol 2024; 34:1953-1966.e6. [PMID: 38614082 DOI: 10.1016/j.cub.2024.03.021] [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: 01/17/2024] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 04/15/2024]
Abstract
Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.
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Affiliation(s)
- Brent W Stewart
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Michael L Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Hwiyoung Lee
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Sarah M Margerison
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Penn Street, Baltimore, MD 21201, USA
| | - Matthew A Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada; Department of Dentistry, Mount Sinai Hospital, University Avenue, Toronto, ON M5G 1X5, Canada; Division of Clinical & Computational Neuroscience, Krembil Brain Institute, University Health Network, Nassau Street, Toronto, ON M5T 1M8, Canada
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, N Wolfe Street, Baltimore, MD 21205, USA
| | - Shuo Chen
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Brian N Mathur
- Department of Pharmacology, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA; Department of Psychiatry, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA.
| | - David A Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, Richmond Street, London, ON N6A 5C1, Canada.
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Liang Q, Ma J, Chen X, Lin Q, Shu N, Dai Z, Lin Y. A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1895-1909. [PMID: 38194401 DOI: 10.1109/tmi.2024.3351907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.
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Gerussi T, Graïc JM, Cozzi B, Schlaffke L, Güntürkün O, Behroozi M. Constrained spherical deconvolution on diffusion-weighted images of dolphin brains. Magn Reson Imaging 2024; 108:104-110. [PMID: 38336113 DOI: 10.1016/j.mri.2024.02.002] [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: 03/07/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
Invasive neuronal tract-tracing is not permitted in very large or endangered animals. This is especially the case in marine mammals like dolphins. Diffusion-weighted imaging of fiber tracts could be an alternative if feasible even in brains that have been fixed in formalin for a long time. This currently is a problem, especially for detecting crossing fibers. We applied a state-of-the-art algorithm of Diffusion-weighted imaging called Constrained Spherical Deconvolution on diffusion data of three fixed brains of bottlenose dolphins using clinical human MRI parameters and were able to identify complex fiber patterns within a voxel. Our findings indicate that in order to maintain the structural integrity of the tissue, short-term post-mortem fixation is necessary. Furthermore, pre-processing steps are essential to remove the classical Diffusion-weighted imaging artifacts from images: however, the algorithm is still able to resolve fiber tracking in regions with various signal intensities. The described imaging technique reveals complex fiber patterns in cetacean brains that have been preserved in formalin for extended periods of time and thus opens a new window into our understanding of cetacean neuroanatomy.
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Affiliation(s)
- Tommaso Gerussi
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy.
| | - Jean-Marie Graïc
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy
| | - Bruno Cozzi
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy
| | - Lara Schlaffke
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789 Bochum, Germany; Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Onur Güntürkün
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801 Bochum, Germany; Research Center One Health Ruhr, Research Alliance Ruhr, Ruhr-University Bochum, Bochum, Germany
| | - Mehdi Behroozi
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801 Bochum, Germany.
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Park TY, Franke L, Pieper S, Haehn D, Ning L. A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation. Biomed Eng Lett 2024; 14:393-405. [PMID: 38645587 PMCID: PMC11026361 DOI: 10.1007/s13534-024-00373-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/27/2024] [Accepted: 03/04/2024] [Indexed: 04/23/2024] Open
Abstract
Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models.
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Affiliation(s)
- Tae Young Park
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea
- Division of Biomedical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, 02792 Republic of Korea
- Brigham and Women’s Hospital, Boston, MA 02115 USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA 02125 USA
| | | | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Lipeng Ning
- Brigham and Women’s Hospital, Boston, MA 02115 USA
- Harvard Medical School, Boston, MA 02115 USA
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Monchi O, Pinilla-Monsalve GD, Almgren H, Ghahremani M, Kibreab M, Maarouf N, Kathol I, Boré A, Rheault F, Descoteaux M, Ismail Z. White Matter Microstructural Underpinnings of Mild Behavioral Impairment in Parkinson's Disease. Mov Disord 2024. [PMID: 38661496 DOI: 10.1002/mds.29804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/13/2024] [Accepted: 03/18/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early- to mid-stage PD remain poorly understood. OBJECTIVE The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early- to mid-stage PD using diffusion magnetic resonance imaging (dMRI). METHODS A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment-Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel-based apparent fiber density (AFD). RESULTS The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD-MBI) to PD-non-MBI, as evidenced by increased RDt (η2 = 0.09, P = 0.004) and both decreased AFD (η2 = 0.05, P = 0.048) and FAt (η2 = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η2 = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η2 = 0.14, P = 0.008), and increased RDt (η2 = 0.25, P = 0.028) with decreased AFD (η2 = 0.10, P = 0.046) between the right OrG and the caudate nucleus. CONCLUSION MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD-MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Oury Monchi
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Département de radiologie, radio-oncologie et médicine nucléaire, Université de Montréal, Montreal, Quebec, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Gabriel D Pinilla-Monsalve
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Département de radiologie, radio-oncologie et médicine nucléaire, Université de Montréal, Montreal, Quebec, Canada
| | - Hannes Almgren
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Maryam Ghahremani
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Psychiatry and Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Mekale Kibreab
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Nadia Maarouf
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Iris Kathol
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Arnaud Boré
- Département d'informatique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - François Rheault
- Département d'informatique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Maxime Descoteaux
- Département d'informatique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Psychiatry and Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
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Freire IS, Lopes TS, Afonso SG, Pereira DJ. From images to insights: a neuroradiologist's practical guide on white matter fiber tract anatomy and DTI patterns for pre-surgical planning. Neuroradiology 2024:10.1007/s00234-024-03362-7. [PMID: 38635028 DOI: 10.1007/s00234-024-03362-7] [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: 01/02/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
INTRODUCTION Diffusion tensor imaging (DTI) is a valuable non-invasive imaging modality for mapping white matter tracts and assessing microstructural integrity, and can be used as a "biomarker" in diagnosis, differentiation, and therapeutic monitoring. Although it has gained clinical importance as a marker of neuropathology, limitations in its interpretation underscore the need for caution. METHODS This review provides an overview of the principles and clinical applicability of DTI. We focus on major white matter fiber bundles, detailing their normal anatomy and pathological DTI patterns, with emphasis on tracts routinely requested in our neurosurgical department in the preoperative context (uncinate fasciculus, arcuate fasciculus, pyramidal pathway, optic radiation, and dentatorubrothalamic tract). RESULTS We guide neuroradiologists and neurosurgeons in defining volumes of interest for mapping individual tracts and demonstrating their 3D reconstructions. The intricate trajectories of white matter tracts pose a challenge for accurate fiber orientation recording, with each bundle exhibiting specific characteristics. Tracts adjacent to brain lesions are categorized as displaced, edematous, infiltrated, or disrupted, illustrated with clinical cases of brain neoplasms. To improve structured reporting, we propose a checklist of topics for inclusion in imaging evaluations and MRI reports. CONCLUSION DTI is emerging as a powerful tool for assessing microstructural changes in brain disorders, despite some challenges in standardization and interpretation. This review serves an educational purpose by providing guidance for fiber monitoring and interpretation of pathological patterns observed in clinical cases, highlighting the importance and potential pitfalls of DTI in neuroradiology and surgical planning.
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Affiliation(s)
- Inês S Freire
- Department of Neuroradiology - Centro Hospitalar Universitário de Lisboa Central (CHULC), Rua José António Serrano, 1150-199, Lisbon, Portugal.
- NOVA Medical School, Universidade Nova de Lisboa, Lisbon, Portugal.
| | - Tânia S Lopes
- Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Coimbra, Portugal
| | - Sónia G Afonso
- Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Coimbra, Portugal
| | - Daniela J Pereira
- Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Coimbra, Portugal
- Functional Unit of Neuroradiology - Centro Hospitalar e Universitário de Coimbra (CHUC), Coimbra, Portugal
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Falcó-Roget J, Cacciola A, Sambataro F, Crimi A. Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations. Commun Biol 2024; 7:419. [PMID: 38582867 PMCID: PMC10998892 DOI: 10.1038/s42003-024-06119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.
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Affiliation(s)
- Joan Falcó-Roget
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | - Fabio Sambataro
- Department of Neuroscience, University of Padova, Padua, Italy
| | - Alessandro Crimi
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
- Faculty of Computer Science, AGH University of Krakow, Kraków, Poland.
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Prieto ML, Maduke M. Towards an ion-channel-centric approach to ultrasound neuromodulation. Curr Opin Behav Sci 2024; 56:101355. [PMID: 38505510 PMCID: PMC10947167 DOI: 10.1016/j.cobeha.2024.101355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Ultrasound neuromodulation is a promising technology that could revolutionize study and treatment of brain conditions ranging from mood disorders to Alzheimer's disease and stroke. An understanding of how ultrasound directly modulates specific ion channels could provide a roadmap for targeting specific neurological circuits and achieving desired neurophysiological outcomes. Although experimental challenges make it difficult to unambiguously identify which ion channels are sensitive to ultrasound in vivo, recent progress indicates that there are likely several different ion channels involved, including members of the K2P, Piezo, and TRP channel families. A recent result linking TRPM2 channels in the hypothalamus to induction of torpor by ultrasound in rodents demonstrates the feasibility of targeting a specific ion channel in a specific population of neurons.
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Affiliation(s)
- Martin Loynaz Prieto
- Department of Molecular and Cellular Physiology, Stanford University, 279 Campus Drive West, B151 Beckman Center, Stanford, CA 94305
| | - Merritt Maduke
- Department of Molecular and Cellular Physiology, Stanford University, 279 Campus Drive West, B155 Beckman Center, Stanford, CA 94305
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Gabusi I, Battocchio M, Bosticardo S, Schiavi S, Daducci A. Blurred streamlines: A novel representation to reduce redundancy in tractography. Med Image Anal 2024; 93:103101. [PMID: 38325156 DOI: 10.1016/j.media.2024.103101] [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: 10/20/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical - but usually underrated - parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called "blurred streamlines", which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.
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Affiliation(s)
- Ilaria Gabusi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy.
| | - Matteo Battocchio
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; ASG Superconductors S.p.A., Genova, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
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Martins B, Baba MY, Dimateo EM, Costa LF, Camara AS, Lukasova K, Nucci MP. Investigating Dyslexia through Diffusion Tensor Imaging across Ages: A Systematic Review. Brain Sci 2024; 14:349. [PMID: 38672001 PMCID: PMC11047980 DOI: 10.3390/brainsci14040349] [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: 02/29/2024] [Revised: 03/17/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Dyslexia is a neurodevelopmental disorder that presents a deficit in accuracy and/or fluency while reading or spelling that is not expected given the level of cognitive functioning. Research indicates brain structural changes mainly in the left hemisphere, comprising arcuate fasciculus (AF) and corona radiata (CR). The purpose of this systematic review is to better understand the possible methods for analyzing Diffusion Tensor Imaging (DTI) data while accounting for the characteristics of dyslexia in the last decade of the literature. Among 124 articles screened from PubMed and Scopus, 49 met inclusion criteria, focusing on dyslexia without neurological or psychiatric comorbidities. Article selection involved paired evaluation, with a third reviewer resolving discrepancies. The selected articles were analyzed using two topics: (1) a demographic and cognitive assessment of the sample and (2) DTI acquisition and analysis. Predominantly, studies centered on English-speaking children with reading difficulties, with preserved non-verbal intelligence, attention, and memory, and deficits in reading tests, rapid automatic naming, and phonological awareness. Structural differences were found mainly in the left AF in all ages and in the bilateral superior longitudinal fasciculus for readers-children and adults. A better understanding of structural brain changes of dyslexia and neuroadaptations can be a guide for future interventions.
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Affiliation(s)
- Bruce Martins
- Laboratório de Investigação Médica em Neurorradiologia—LIM44—Hospital das Clínicas da Faculdade Medicina, Universidade de São Paulo, São Paulo 05403-000, Brazil; (B.M.); (M.Y.B.); (E.M.D.)
| | - Mariana Yumi Baba
- Laboratório de Investigação Médica em Neurorradiologia—LIM44—Hospital das Clínicas da Faculdade Medicina, Universidade de São Paulo, São Paulo 05403-000, Brazil; (B.M.); (M.Y.B.); (E.M.D.)
| | - Elisa Monteiro Dimateo
- Laboratório de Investigação Médica em Neurorradiologia—LIM44—Hospital das Clínicas da Faculdade Medicina, Universidade de São Paulo, São Paulo 05403-000, Brazil; (B.M.); (M.Y.B.); (E.M.D.)
| | - Leticia Fruchi Costa
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC, Santo André 09210-580, Brazil; (L.F.C.); (A.S.C.); (K.L.)
| | - Aila Silveira Camara
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC, Santo André 09210-580, Brazil; (L.F.C.); (A.S.C.); (K.L.)
| | - Katerina Lukasova
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC, Santo André 09210-580, Brazil; (L.F.C.); (A.S.C.); (K.L.)
| | - Mariana Penteado Nucci
- Laboratório de Investigação Médica em Neurorradiologia—LIM44—Hospital das Clínicas da Faculdade Medicina, Universidade de São Paulo, São Paulo 05403-000, Brazil; (B.M.); (M.Y.B.); (E.M.D.)
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12
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Balestrino M, Adriano E, Alì PA, Pardini M. Selective Alteration of the Left Arcuate Fasciculus in Two Patients Affected by Creatine Transporter Deficiency. Brain Sci 2024; 14:337. [PMID: 38671990 PMCID: PMC11048612 DOI: 10.3390/brainsci14040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/28/2024] Open
Abstract
(1) Background: In hereditary creatine transporter deficiency (CTD), there is an absence of creatine in the brain and neurological symptoms are present, including severe language impairment. However, the pathological changes caused by creatine deficiency that generate neuropsychological symptoms have been poorly studied. (2) Aims: To investigate if the language impairment in CTD is underpinned by possible pathological changes. (3) Methods: We used MRI tractography to investigate the trophism of the left arcuate fasciculus, a white matter bundle connecting Wernicke's and Broca's language areas that is specifically relevant for language establishment and maintenance, in two patients (28 and 18 y.o.). (4) Results: The T1 and T2 MRI imaging results were unremarkable, but the left arcuate fasciculus showed a marked decrease in mean fractional anisotropy (FA) compared to healthy controls. In contrast, the FA values in the corticospinal tract were similar to those of healthy controls. Although white matter atrophy has been reported in CTD, this is the first report to show a selective abnormality of the language-relevant arcuate fasciculus, suggesting a possible region-specific impact of creatine deficiency.
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Affiliation(s)
- Maurizio Balestrino
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI), University of Genoa, 16132 Genoa, Italy; (E.A.); (P.A.A.); (M.P.)
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Enrico Adriano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI), University of Genoa, 16132 Genoa, Italy; (E.A.); (P.A.A.); (M.P.)
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Paolo Alessandro Alì
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI), University of Genoa, 16132 Genoa, Italy; (E.A.); (P.A.A.); (M.P.)
| | - Matteo Pardini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI), University of Genoa, 16132 Genoa, Italy; (E.A.); (P.A.A.); (M.P.)
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
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13
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Georgiadis M, auf der Heiden F, Abbasi H, Ettema L, Nirschl J, Taghavi HM, Wakatsuki M, Liu A, Ho WHD, Carlson M, Doukas M, Koppes SA, Keereweer S, Sobel RA, Setsompop K, Liao C, Amunts K, Axer M, Zeineh M, Menzel M. Uncovering microstructural architecture from histology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586745. [PMID: 38585744 PMCID: PMC10996646 DOI: 10.1101/2024.03.26.586745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Microstructural tissue organization underlies the complex connectivity of the brain and controls properties of connective, muscle, and epithelial tissue. However, discerning microstructural architecture with high resolution for large fields of view remains prohibitive. We address this challenge with computational scattered light imaging (ComSLI), which exploits the anisotropic light scattering of aligned structures. Using a rotating lightsource and a high-resolution camera, ComSLI determines fiber architecture with micrometer resolution from histological sections across preparation and staining protocols. We show complex fiber architecture in brain and non-brain sections, including histological paraffin-embedded sections with various stains, and demonstrate its applicability on animal and human tissue, including disease cases with altered microstructure. ComSLI opens new avenues for investigating fiber architecture in new and archived sections across organisms, tissues, and diseases.
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Affiliation(s)
| | - Franca auf der Heiden
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH; 52425 Jülich, Germany
| | - Hamed Abbasi
- Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology; 2628 CJ Delft, the Netherlands
- Department of Otorhinolaryngology and Head and Neck Surgery, Erasmus MC, University Medical Center Rotterdam; 3015 CN Rotterdam, the Netherlands
| | - Loes Ettema
- Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology; 2628 CJ Delft, the Netherlands
| | - Jeffrey Nirschl
- Department of Pathology, Stanford University; Stanford, 94305, USA
| | | | - Moe Wakatsuki
- Department of Radiology, Stanford University; Stanford, 94305, USA
| | - Andy Liu
- Department of Radiology, Stanford University; Stanford, 94305, USA
| | | | - Mackenzie Carlson
- Department of Radiology, Stanford University; Stanford, 94305, USA
- Department of Neurology and Neurological Sciences, Stanford University; Stanford, 94305, USA
| | - Michail Doukas
- Department of Pathology, Erasmus MC, University Medical Center Rotterdam; 3015 CN Rotterdam, the Netherlands
| | - Sjors A. Koppes
- Department of Pathology, Erasmus MC, University Medical Center Rotterdam; 3015 CN Rotterdam, the Netherlands
| | - Stijn Keereweer
- Department of Otorhinolaryngology and Head and Neck Surgery, Erasmus MC, University Medical Center Rotterdam; 3015 CN Rotterdam, the Netherlands
| | - Raymond A. Sobel
- Department of Pathology, Stanford University; Stanford, 94305, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University; Stanford, 94305, USA
| | - Congyu Liao
- Department of Radiology, Stanford University; Stanford, 94305, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH; 52425 Jülich, Germany
- C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Medical Faculty, University Düsseldorf, Germany
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH; 52425 Jülich, Germany
- Department of Physics, School of Mathematics and Natural Sciences, University of Wuppertal; 52119 Wuppertal, Germany
| | - Michael Zeineh
- Department of Radiology, Stanford University; Stanford, 94305, USA
| | - Miriam Menzel
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH; 52425 Jülich, Germany
- Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology; 2628 CJ Delft, the Netherlands
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14
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Joshi A, Li H, Parikh NA, He L. A systematic review of automated methods to perform white matter tract segmentation. Front Neurosci 2024; 18:1376570. [PMID: 38567281 PMCID: PMC10985163 DOI: 10.3389/fnins.2024.1376570] [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: 01/25/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.
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Affiliation(s)
- Ankita Joshi
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Computer Science, Biomedical Informatics, and Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
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15
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Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ. Genetic architecture of the structural connectome. Nat Commun 2024; 15:1962. [PMID: 38438384 PMCID: PMC10912129 DOI: 10.1038/s41467-024-46023-2] [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: 09/13/2022] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.
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Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Natalie J Forde
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Salim Mansour
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Isabel Kerrebijn
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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16
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Zhu Y, Wang Y. Brain fiber structure estimation based on principal component analysis and RINLM filter. Med Biol Eng Comput 2024; 62:751-771. [PMID: 37996628 DOI: 10.1007/s11517-023-02972-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023]
Abstract
Diffusion magnetic resonance imaging is a technique for non-invasive detection of microstructure in the white matter of the human brain, which is widely used in neuroscience research of the brain. However, diffusion-weighted images(DWI) are sensitive to noise, which affects the subsequent reconstruction of fiber orientation direction, microstructural parameter estimation and fiber tracking. In order to better eliminate the noise in diffusion-weighted images, this study proposes a noise reduction method combining Marchenko-Pastur principal component analysis(MPPCA) and rotation-invariant non-local means filter(RINLM) to further remove residual noise and preserve the image texture detail information. In this study, the algorithm is applied to the fiber structure and the prevailing microstructural models within the human brain voxels based on simulated and real human brain datasets. Experimental comparisons between the proposed method and the state-of-the-art methods are performed in single-fiber, multi-fiber, crossed and curved-fiber regions as well as in different microstructure estimation models. Results demonstrated the superior performance of the proposed method in denoising DWI data, which can reduce the angular error in fiber orientation reconstruction to obtain more valid fiber structure estimation and enable more complete fiber tracking trajectories with higher coverage. Meanwhile, the method reduces the estimation errors of various white matter microstructural parameters and verifies the performance of the method in white matter microstructure estimation.
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Affiliation(s)
- Yuemin Zhu
- Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuanjun Wang
- Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
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17
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Filipiak P, Sajitha TA, Shepherd TM, Clarke K, Goldman H, Placantonakis DG, Zhang J, Chan KC, Boada FE, Baete SH. Improved reconstruction of crossing fibers in the mouse optic pathways with orientation distribution function fingerprinting. Magn Reson Med 2024; 91:1075-1086. [PMID: 37927121 DOI: 10.1002/mrm.29911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/10/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra- and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. METHODS Common approaches based on Orientation Distribution Function (ODF) peak finding or statistical inference were compared qualitatively and quantitatively to ODF Fingerprinting (ODF-FP) for reconstruction of crossing fibers within the optic chiasm using in vivo diffusion MRI (n = 18 $$ n=18 $$ healthy C57BL/6 mice). Manganese-Enhanced MRI (MEMRI) was obtained after intravitreal injection of manganese chloride and used as a reference standard for the optic pathway anatomy. RESULTS ODF-FP outperformed by over 100% all the tested methods in terms of the ratios between the contra- and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. CONCLUSION In this challenging model system, ODF-Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles.
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Affiliation(s)
- Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | | | - Timothy M Shepherd
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Kamri Clarke
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Hannah Goldman
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Dimitris G Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, New York, USA
| | - Jiangyang Zhang
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Kevin C Chan
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
- Department of Ophthalmology, NYU Langone Health, New York, New York, USA
| | - Fernando E Boada
- Radiological Sciences Laboratory and Molecular Imaging Program at Stanford, Department of Radiology, Stanford University, Stanford, California, USA
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, New York, USA
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18
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Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
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Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
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19
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Young F, Aquilina K, Seunarine KK, Mancini L, Clark CA, Clayden JD. Fibre orientation atlas guided rapid segmentation of white matter tracts. Hum Brain Mapp 2024; 45:e26578. [PMID: 38339907 PMCID: PMC10826637 DOI: 10.1002/hbm.26578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/12/2024] Open
Abstract
Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.
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Affiliation(s)
- Fiona Young
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Kristian Aquilina
- Department of NeurosurgeryGreat Ormond Street Hospital for ChildrenLondonUK
| | - Kiran K. Seunarine
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Department of RadiologyGreat Ormond Street Hospital for ChildrenLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Chris A. Clark
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Jonathan D. Clayden
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
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20
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Gatto RG, Martin PR, Utianski RL, Duffy JR, Clark HM, Botha H, Machulda MM, Josephs KA, Whitwell JL. Diffusion tensor imaging-based multi-fiber tracking reconstructions can regionally differentiate phonetic versus prosodic subtypes of progressive apraxia of speech. Cortex 2024; 171:272-286. [PMID: 38061209 PMCID: PMC10922200 DOI: 10.1016/j.cortex.2023.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/30/2023] [Accepted: 08/09/2023] [Indexed: 02/12/2024]
Abstract
Two subtypes of progressive apraxia of speech (PAOS) have been recognized: phonetic PAOS (PAOS_ph) where speech output is dominated by distorted sound substitutions and prosodic PAOS (PAOS_pr) which is dominated by segmented speech. We investigate whether these PAOS subtypes have different white matter microstructural abnormalities measured by diffusion tensor tractography. Thirty-three patients with PAOS (21 PAOS_ph and 12 PAOS_pr) and 19 healthy controls were recruited by the Neurodegenerative Research Group (NRG) and underwent diffusion MRI. Using a whole-brain tractography approach, fractional anisotropy (FA) and mean diffusivity (MD) were extracted for cortico-cortical, cortico-subcortical, cortical-projection, and cerebello-cortical white matter tracts. A hierarchical linear model was applied to assess tract-level FA and MD across groups. Both PAOS_ph and PAOS_pr showed degeneration of cortico-cortical, cortico-subcortical, cortical-projection, and cerebello-cortical white matter tracts compared to controls. However, degeneration of the body of corpus callosum, superior thalamic radiation, and superior cerebellar peduncle was greater in PAOS_pr compared to PAOS_ph, and degeneration of the inferior segment of the superior longitudinal fasciculus (SLF) was greater in PAOS_ph compared to PAOS_pr. Worse parkinsonism correlated with greater degeneration of cortico-cortical and cortico-subcortical tracts in PAOS_ph. Apraxia of speech articulatory error score correlated with degeneration of the superior cerebellar peduncle tracts in PAOS_pr. Phonetic and prosodic PAOS involve the compromise of a similar network of tracts, although there are connectivity differences between types. Whereas clinical parameters are the current gold standard to distinguish PAOS subtypes, our results allege the use of DTI-based tractography as a supplementary method to investigate such variants.
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Affiliation(s)
| | - Peter R Martin
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Joseph R Duffy
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
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21
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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22
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Neher P, Hirjak D, Maier-Hein K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 2024; 15:303. [PMID: 38182594 PMCID: PMC10770385 DOI: 10.1038/s41467-023-44591-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024] Open
Abstract
Tract-specific microstructural analysis of the brain's white matter (WM) using diffusion MRI has been a driver for neuroscientific discovery with a wide range of applications. Tractometry enables localized tissue analysis along tracts but relies on bare summary statistics and reduces complex image information along a tract to few scalar values, and so may miss valuable information. This hampers the applicability of tractometry for predictive modelling. Radiomics is a promising method based on the analysis of numerous quantitative image features beyond what can be visually perceived, but has not yet been used for tract-specific analysis of white matter. Here we introduce radiomic tractometry (RadTract) and show that introducing rich radiomics-based feature sets into the world of tractometry enables improved predictive modelling while retaining the localization capability of tractometry. We demonstrate its value in a series of clinical populations, showcasing its performance in diagnosing disease subgroups in different datasets, as well as estimation of demographic and clinical parameters. We propose that RadTract could spark the establishment of a new generation of tract-specific imaging biomarkers with benefits for a range of applications from basic neuroscience to medical research.
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Affiliation(s)
- Peter Neher
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159, Mannheim, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), partner site Heidelberg, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and the university medical center Heidelberg, Heidelberg, Germany
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23
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Bosticardo S, Schiavi S, Schaedelin S, Battocchio M, Barakovic M, Lu PJ, Weigel M, Melie-Garcia L, Granziera C, Daducci A. Evaluation of tractography-based myelin-weighted connectivity across the lifespan. Front Neurosci 2024; 17:1228952. [PMID: 38239829 PMCID: PMC10794573 DOI: 10.3389/fnins.2023.1228952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 12/04/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction Recent studies showed that the myelin of the brain changes in the life span, and demyelination contributes to the loss of brain plasticity during normal aging. Diffusion-weighted magnetic resonance imaging (dMRI) allows studying brain connectivity in vivo by mapping axons in white matter with tractography algorithms. However, dMRI does not provide insight into myelin; thus, combining tractography with myelin-sensitive maps is necessary to investigate myelin-weighted brain connectivity. Tractometry is designated for this purpose, but it suffers from some serious limitations. Our study assessed the effectiveness of the recently proposed Myelin Streamlines Decomposition (MySD) method in estimating myelin-weighted connectomes and its capacity to detect changes in myelin network architecture during the process of normal aging. This approach opens up new possibilities compared to traditional Tractometry. Methods In a group of 85 healthy controls aged between 18 and 68 years, we estimated myelin-weighted connectomes using Tractometry and MySD, and compared their modulation with age by means of three well-known global network metrics. Results Following the literature, our results show that myelin development continues until brain maturation (40 years old), after which degeneration begins. In particular, mean connectivity strength and efficiency show an increasing trend up to 40 years, after which the process reverses. Both Tractometry and MySD are sensitive to these changes, but MySD turned out to be more accurate. Conclusion After regressing the known predictors, MySD results in lower residual error, indicating that MySD provides more accurate estimates of myelin-weighted connectivity than Tractometry.
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Affiliation(s)
- Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
| | - Simona Schiavi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
- ASG Superconductors S.p.A., Genoa, Italy
| | - Sabine Schaedelin
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
| | - Matteo Battocchio
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d’Informatique, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
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24
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Bielanin JP, Metwally SAH, Paruchuri SS, Sun D. An overview of mild traumatic brain injuries and emerging therapeutic targets. Neurochem Int 2024; 172:105655. [PMID: 38072207 DOI: 10.1016/j.neuint.2023.105655] [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: 10/31/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 01/01/2024]
Abstract
The majority of traumatic brain injuries (TBIs), approximately 90%, are classified as mild (mTBIs). Globally, an estimated 4 million injuries occur each year from concussions or mTBIs, highlighting their significance as a public health crisis. TBIs can lead to substantial long-term health consequences, including an increased risk of developing Alzheimer's Disease, Parkinson's Disease (PD), chronic traumatic encephalopathy (CTE), and nearly doubling one's risk of suicide. However, the current management of mTBIs in clinical practice and the available treatment options are limited. There exists an unmet need for effective therapy. This review addresses various aspects of mTBIs based on the most up-to-date literature review, with the goal of stimulating translational research to identify new therapeutic targets and improve our understanding of pathogenic mechanisms. First, we provide a summary of mTBI symptomatology and current diagnostic parameters such as the Glasgow Coma Scale (GCS) for classifying mTBIs or concussions, as well as the utility of alternative diagnostic parameters, including imaging techniques like MRI with diffusion tensor imaging (DTI) and serum biomarkers such as S100B, NSE, GFAP, UCH-L1, NFL, and t-tau. Our review highlights several pre-clinical concussion models employed in the study of mTBIs and the underlying cellular mechanisms involved in mTBI-related pathogenesis, including axonal damage, demyelination, inflammation, and oxidative stress. Finally, we examine a selection of new therapeutic targets currently under investigation in pre-clinical models. These targets may hold promise for clinical translation and address the pressing need for more effective treatments for mTBIs.
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Affiliation(s)
- John P Bielanin
- University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Pittsburgh Institute for Neurodegenerative Disorders, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Shamseldin A H Metwally
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Pittsburgh Institute for Neurodegenerative Disorders, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Satya S Paruchuri
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Pittsburgh Institute for Neurodegenerative Disorders, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Dandan Sun
- University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Pittsburgh Institute for Neurodegenerative Disorders, University of Pittsburgh, Pittsburgh, PA, 15213, USA; Veterans Affairs Pittsburgh Health Care System, Pittsburgh, PA, 15213, USA.
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25
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Ruiz-Rizzo AL, Finke K, Archila-Meléndez ME. Diffusion Tensor Imaging in Alzheimer's Studies. Methods Mol Biol 2024; 2785:105-113. [PMID: 38427191 DOI: 10.1007/978-1-0716-3774-6_8] [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] [Indexed: 03/02/2024]
Abstract
In this chapter, we describe the use of quantitative metrics of white matter obtained from the diffusion tensor model based on diffusion-weighted imaging in Alzheimer's disease (AD). Our description synthesizes insights not only from patient populations with AD dementia but also from participants at risk for AD dementia (e.g., amnestic mild cognitive impairment, subjective cognitive decline, or familial AD mutation carriers). A reference to studies examining correlations with behavioral variables is also included. Our main message is to caution against the overinterpretation of diffusion metrics and to favor analyses that focus on regions of interest or major white matter tracts for biomarker studies in AD.
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Affiliation(s)
| | - Kathrin Finke
- Department of Neurology, Jena University Hospital, Jena, Germany
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26
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Kamagata K, Andica C, Uchida W, Takabayashi K, Saito Y, Lukies M, Hagiwara A, Fujita S, Akashi T, Wada A, Hori M, Kamiya K, Zalesky A, Aoki S. Advancements in Diffusion MRI Tractography for Neurosurgery. Invest Radiol 2024; 59:13-25. [PMID: 37707839 DOI: 10.1097/rli.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
ABSTRACT Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.
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Affiliation(s)
- Koji Kamagata
- From the Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan (K.K., C.A., W.U., K.T., Y.S., A.H., S.F., T.A., A.W., S.A.); Faculty of Health Data Science, Juntendo University, Chiba, Japan (C.A., S.A.); Department of Radiology, Alfred Health, Melbourne, Victoria, Australia (M.L.); Department of Radiology, University of Tokyo, Tokyo, Japan (S.F.); Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan (M.H., K.K.); Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia (A.Z.); and Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia (A.Z.)
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27
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Gajwani M, Oldham S, Pang JC, Arnatkevičiūtė A, Tiego J, Bellgrove MA, Fornito A. Can hubs of the human connectome be identified consistently with diffusion MRI? Netw Neurosci 2023; 7:1326-1350. [PMID: 38144690 PMCID: PMC10631793 DOI: 10.1162/netn_a_00324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/17/2023] [Indexed: 12/26/2023] Open
Abstract
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
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Affiliation(s)
- Mehul Gajwani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - James C. Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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28
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Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JSW, Schiavi S, Jin H, Tavakol S, Vos de Wael R, Rodriguez-Cruces R, Pike GB, Bernhardt BC, Daducci A, Misic B, Tardif CL. The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Netw Neurosci 2023; 7:1363-1388. [PMID: 38144691 PMCID: PMC10697181 DOI: 10.1162/netn_a_00330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/19/2023] [Indexed: 12/26/2023] Open
Abstract
A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.
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Affiliation(s)
- Mark C. Nelson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jessica Royer
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Wen Da Lu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ilana R. Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jennifer S. W. Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Hyerang Jin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Shahin Tavakol
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Raul Rodriguez-Cruces
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - G. Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Boris C. Bernhardt
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | | | - Bratislav Misic
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Christine L. Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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29
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Baruzzi V, Lodi M, Sorrentino F, Storace M. Bridging functional and anatomical neural connectivity through cluster synchronization. Sci Rep 2023; 13:22430. [PMID: 38104227 PMCID: PMC10725511 DOI: 10.1038/s41598-023-49746-2] [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: 06/01/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023] Open
Abstract
The dynamics of the brain results from the complex interplay of several neural populations and is affected by both the individual dynamics of these areas and their connection structure. Hence, a fundamental challenge is to derive models of the brain that reproduce both structural and functional features measured experimentally. Our work combines neuroimaging data, such as dMRI, which provides information on the structure of the anatomical connectomes, and fMRI, which detects patterns of approximate synchronous activity between brain areas. We employ cluster synchronization as a tool to integrate the imaging data of a subject into a coherent model, which reconciles structural and dynamic information. By using data-driven and model-based approaches, we refine the structural connectivity matrix in agreement with experimentally observed clusters of brain areas that display coherent activity. The proposed approach leverages the assumption of homogeneous brain areas; we show the robustness of this approach when heterogeneity between the brain areas is introduced in the form of noise, parameter mismatches, and connection delays. As a proof of concept, we apply this approach to MRI data of a healthy adult at resting state.
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Affiliation(s)
| | - Matteo Lodi
- DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Marco Storace
- DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy.
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30
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Lippa SM, Yeh PH, Kennedy JE, Bailie JM, Ollinger J, Brickell TA, French LM, Lange RT. Lifetime Blast Exposure Is Not Related to White Matter Integrity in Service Members and Veterans With and Without Uncomplicated Mild Traumatic Brain Injury. Neurotrauma Rep 2023; 4:827-837. [PMID: 38156076 PMCID: PMC10754347 DOI: 10.1089/neur.2023.0043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023] Open
Abstract
This study examines the impact of lifetime blast exposure on white matter integrity in service members and veterans (SMVs). Participants were 227 SMVs, including those with a history of mild traumatic brain injury (mTBI; n = 124), orthopedic injury controls (n = 58), and non-injured controls (n = 45), prospectively enrolled in a Defense and Veterans Brain Injury Center (DVBIC)/Traumatic Brain Injury Center of Excellence (TBICoE) study. Participants were divided into three groups based on number of self-reported lifetime blast exposures: none (n = 53); low (i.e., 1-9 blasts; n = 81); and high (i.e., ≥10 blasts; n = 93). All participants underwent diffusion tensor imaging (DTI) at least 11 months post-injury. Tract-of-interest (TOI) analysis was applied to investigate fractional anisotropy and mean, radial, and axial diffusivity (AD) in left and right total cerebral white matter as well as 24 tracts. Benjamini-Hochberg false discovery rate (FDR) correction was used. Regressions investigating blast exposure and mTBI on white matter integrity, controlling for age, revealed that the presence of mTBI history was associated with lower AD in the bilateral superior longitudinal fasciculus and arcuate fasciculus and left cingulum (βs = -0.255 to -0.174; ps < 0.01); however, when non-injured controls were removed from the sample (but orthopedic injury controls remained), these relationships were attenuated and did not survive FDR correction. Regression models were rerun with modified post-traumatic stress disorder (PTSD) diagnosis added as a predictor. After FDR correction, PTSD was not significantly associated with white matter integrity in any of the models. Overall, there was no relationship between white matter integrity and self-reported lifetime blast exposure or PTSD.
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Affiliation(s)
- Sara M. Lippa
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
- National Intrepid Center of Excellence, Bethesda, Maryland, USA
- Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Ping-Hong Yeh
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
- National Intrepid Center of Excellence, Bethesda, Maryland, USA
| | - Jan E. Kennedy
- Traumatic Brain Injury Center of Excellence, Silver Spring, Maryland, USA
- Contractor, General Dynamics Information Technology, Silver Spring, Maryland, USA
- Brooke Army Medical Center, Joint Base, San Antonio, Texas, USA
| | - Jason M. Bailie
- Traumatic Brain Injury Center of Excellence, Silver Spring, Maryland, USA
- Contractor, General Dynamics Information Technology, Silver Spring, Maryland, USA
- 33 Area Branch Clinic, Camp Pendleton, California, USA
| | - John Ollinger
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
- National Intrepid Center of Excellence, Bethesda, Maryland, USA
| | - Tracey A. Brickell
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
- National Intrepid Center of Excellence, Bethesda, Maryland, USA
- Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Traumatic Brain Injury Center of Excellence, Silver Spring, Maryland, USA
- Contractor, General Dynamics Information Technology, Silver Spring, Maryland, USA
| | - Louis M. French
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
- National Intrepid Center of Excellence, Bethesda, Maryland, USA
- Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Rael T. Lange
- Walter Reed National Military Medical Center, Bethesda, Maryland, USA
- National Intrepid Center of Excellence, Bethesda, Maryland, USA
- Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
- Traumatic Brain Injury Center of Excellence, Silver Spring, Maryland, USA
- Contractor, General Dynamics Information Technology, Silver Spring, Maryland, USA
- University of British Columbia, Vancouver, British Columbia, USA
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31
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Liu W, Zhuo Z, Liu Y, Ye C. One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer. Med Image Anal 2023; 90:102968. [PMID: 37729793 DOI: 10.1016/j.media.2023.102968] [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: 01/04/2023] [Revised: 07/24/2023] [Accepted: 09/11/2023] [Indexed: 09/22/2023]
Abstract
The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.
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Affiliation(s)
- Wan Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
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32
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Rostampour M, Gharaylou Z, Rostampour A, Shahbodaghy F, Zarei M, Fadaei R, Khazaie H. Study of structural network connectivity using DTI tractography in insomnia disorder. Psychiatry Res Neuroimaging 2023; 336:111730. [PMID: 37944426 DOI: 10.1016/j.pscychresns.2023.111730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/01/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
Most of tractography studies on insomnia disorder (ID) have reported decreased structural connectivity between cortical and subcortical structures. Tractography based on standard diffusion tensor imaging (DTI) can generate high number of false-positive streamlines connections between gray matter regions. In the present study, we employed the convex optimization modeling for microstructure informed tractography-2 (COMMIT2) to improve the accuracy of the reconstructed whole-brain connectome and filter implausible brain connections in 28 patients with ID and compared with 27 healthy controls. Then, we used NBS-predict (a prediction-based extension to the network-based statistic method) in the COMMIT2-weighted connectome. Our results revealed decreased structural connectivity between subregions of the left somatomotor, ventral attention, frontoparietal, dorsal attention and default mode networks in the insomnia group. Moreover, there is a negative correlation between sleep efficiency and structural connectivity within the left frontoparietal, visual, default mode network, limbic, dorsal attention, right dorsal attention as well as right default mode networks. By comparing with standard connectivity analysis, we showed that by removing of false-positive streamlines connections after COMMIT2 filtering, abnormal structural connectivity was reduced in patients with ID compared to controls. Our results demonstrate the importance of improving the accuracy of tractography for understanding structural connectivity networks in ID.
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Affiliation(s)
- Masoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | - Ali Rostampour
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
| | - Fatemeh Shahbodaghy
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Mojtaba Zarei
- Department of Neurology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Reza Fadaei
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
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33
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Zuo C, Suo X, Lan H, Pan N, Wang S, Kemp GJ, Gong Q. Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis. Neuropsychol Rev 2023; 33:783-802. [PMID: 36125651 PMCID: PMC10770271 DOI: 10.1007/s11065-022-09559-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 06/14/2022] [Indexed: 10/14/2022]
Abstract
Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.
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Affiliation(s)
- Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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He J, Zhang F, Pan Y, Feng Y, Rushmore J, Torio E, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Hum Brain Mapp 2023; 44:6055-6073. [PMID: 37792280 PMCID: PMC10619402 DOI: 10.1002/hbm.26497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/09/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.
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Affiliation(s)
- Jianzhong He
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Fan Zhang
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- University of Electronic Science and Technology of ChinaChengduSichuanChina
| | - Yiang Pan
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Yuanjing Feng
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Jarrett Rushmore
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Erickson Torio
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Nozais V, Theaud G, Descoteaux M, Thiebaut de Schotten M, Petit L. Improved Functionnectome by dissociating the contributions of white matter fiber classes to functional activation. Brain Struct Funct 2023; 228:2165-2177. [PMID: 37804431 DOI: 10.1007/s00429-023-02714-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/19/2023] [Indexed: 10/09/2023]
Abstract
Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks. In that regard, we recently introduced the Functionnectome. This structural-functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to map cognitive processes onto the white matter. However, this multimodal integration faces three significant challenges: (1) the necessarily limited overlap between tractography streamlines and the grey matter, which may reduce the amount of functional signal associated with the related structural connectivity; (2) the scrambling effect of crossing fibers on functional signal, as a single voxel in such regions can be structurally connected to several cognitive networks with heterogeneous functional signals; and (3) the difficulty of interpretation of the resulting cognitive maps, as crossing and overlapping white matter tracts can obscure the organization of the studied network. In the present study, we tackled these problems by developing a streamline-extension procedure and dividing the white matter anatomical priors between association, commissural, and projection fibers. This approach significantly improved the characterization of the white matter involvement in the studied cognitive processes. The new Functionnectome priors produced are now readily available, and the analysis workflow highlighted here should also be generalizable to other structural-functional approaches. We improved the Functionnectome approach to better study the involvement of white matter in brain function by separating the analysis of the three classes of white matter fibers (association, commissural, and projection fibers). This step successfully clarified the activation maps and increased their statistical significance.
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Affiliation(s)
- Victor Nozais
- Groupe d'Imagerie Neurofonctionnelle - Institut des Maladies Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA, Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case 28, 33076, Bordeaux Cedex, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle - Institut des Maladies Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA, Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case 28, 33076, Bordeaux Cedex, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle - Institut des Maladies Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA, Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case 28, 33076, Bordeaux Cedex, France.
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36
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Kim CW, Kim Y, Kim HH, Choi JY. The aspect of structural connectivity in relation to age-related gait performance. PSYCHORADIOLOGY 2023; 3:kkad028. [PMID: 38666123 PMCID: PMC10917373 DOI: 10.1093/psyrad/kkad028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/05/2023] [Accepted: 11/24/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Cheol-Woon Kim
- Department of Physical Education, Korea University,, 02841, Seoul, Republic of Korea
| | - Yechan Kim
- Department of Biomedical Engineering, Yonsei University,, 26493, Wonju, Republic of Korea
| | - Hyun-Ho Kim
- Department of Biomedical Engineering, Yonsei University,, 26493, Wonju, Republic of Korea
| | - Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University,, 26493, Wonju, Republic of Korea
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37
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Modo M, Sparling K, Novotny J, Perry N, Foley LM, Hitchens TK. Mapping mesoscale connectivity within the human hippocampus. Neuroimage 2023; 282:120406. [PMID: 37827206 PMCID: PMC10623761 DOI: 10.1016/j.neuroimage.2023.120406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 10/14/2023] Open
Abstract
The connectivity of the hippocampus is essential to its functions. To gain a whole system view of intrahippocampal connectivity, ex vivo mesoscale (100 μm isotropic resolution) multi-shell diffusion MRI (11.7T) and tractography were performed on entire post-mortem human right hippocampi. Volumetric measurements indicated that the head region was largest followed by the body and tail regions. A unique anatomical organization in the head region reflected a complex organization of the granule cell layer (GCL) of the dentate gyrus. Tractography revealed the volumetric distribution of the perforant path, including both the tri-synaptic and temporoammonic pathways, as well as other well-established canonical connections, such as Schaffer collaterals. Visualization of the perforant path provided a means to verify the borders between the pro-subiculum and CA1, as well as between CA1/CA2. A specific angularity of different layers of fibers in the alveus was evident across the whole sample and allowed a separation of afferent and efferent connections based on their origin (i.e. entorhinal cortex) or destination (i.e. fimbria) using a cluster analysis of streamlines. Non-canonical translamellar connections running along the anterior-posterior axis were also discerned in the hilus. In line with "dentations" of the GCL, mossy fibers were bunching together in the sagittal plane revealing a unique lamellar organization and connections between these. In the head region, mossy fibers projected to the origin of the fimbria, which was distinct from the body and tail region. Mesoscale tractography provides an unprecedented systems view of intrahippocampal connections that underpin cognitive and emotional processing.
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Affiliation(s)
- Michel Modo
- Department of Radiology; Department of BioEngineering; McGowan Institute for Regenerative Medicine; Centre for Neuroscience University of Pittsburgh (CNUP); Centre for the Neural Basis of Cognition (CNBC).
| | | | | | | | | | - T Kevin Hitchens
- Small Animal Imaging Center; Departmnet of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15203, USA
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Stewart BW, Keaser ML, Lee H, Margerison SM, Cormie MA, Moayedi M, Lindquist MA, Chen S, Mathur BN, Seminowicz DA. Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.564054. [PMID: 37961503 PMCID: PMC10635040 DOI: 10.1101/2023.11.01.564054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a lateral aspect of the right dorsolateral prefrontal cortex (latDLPFC) in migraine patients. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this latDLPFC region, and diffusion weighted imaging (DWI) verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.
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Affiliation(s)
- Brent W. Stewart
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
| | - Michael L. Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
| | - Hwiyoung Lee
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Catonsville, MD, USA
| | - Sarah M. Margerison
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
- Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Matthew A. Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, ON, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, ON, Canada
- Department of Dentistry, Mount Sinai Hospital, Toronto, ON, Canada
- Division of Clinical & Computational Neuroscience, Krembil Brain Institute, University Health Network
| | | | - Shuo Chen
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Catonsville, MD, USA
| | - Brian N. Mathur
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - David A. Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada
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39
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Gerussi T, Graïc JM, Peruffo A, Behroozi M, Schlaffke L, Huggenberger S, Güntürkün O, Cozzi B. The prefrontal cortex of the bottlenose dolphin (Tursiops truncatus Montagu, 1821): a tractography study and comparison with the human. Brain Struct Funct 2023; 228:1963-1976. [PMID: 37660322 PMCID: PMC10517040 DOI: 10.1007/s00429-023-02699-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023]
Abstract
Cetaceans are well known for their remarkable cognitive abilities including self-recognition, sound imitation and decision making. In other mammals, the prefrontal cortex (PFC) takes a key role in such cognitive feats. In cetaceans, however, a PFC could up to now not be discerned based on its usual topography. Classical in vivo methods like tract tracing are legally not possible to perform in Cetacea, leaving diffusion-weighted imaging (DWI) as the most viable alternative. This is the first investigation focussed on the identification of the cetacean PFC homologue. In our study, we applied the constrained spherical deconvolution (CSD) algorithm on 3 T DWI scans of three formalin-fixed brains of bottlenose dolphins (Tursiops truncatus) and compared the obtained results to human brains, using the same methodology. We first identified fibres related to the medio-dorsal thalamic nuclei (MD) and then seeded the obtained putative PFC in the dolphin as well as the known PFC in humans. Our results outlined the dolphin PFC in areas not previously studied, in the cranio-lateral, ectolateral and opercular gyri, and furthermore demonstrated a similar connectivity pattern between the human and dolphin PFC. The antero-lateral rotation of the PFC, like in other areas, might be the result of the telescoping process which occurred in these animals during evolution.
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Affiliation(s)
- Tommaso Gerussi
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy.
| | - Jean-Marie Graïc
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy
| | - Antonella Peruffo
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy
| | - Mehdi Behroozi
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Lara Schlaffke
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-La-Camp-Platz 1, 44789, Bochum, Germany
| | - Stefan Huggenberger
- Institute of Anatomy and Clinical Morphology, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, 58448, Witten, Germany
| | - Onur Güntürkün
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany
- Research Center One Health Ruhr, Research Alliance Ruhr, Ruhr-University Bochum, Bochum, Germany
| | - Bruno Cozzi
- Department of Comparative Biomedicine and Food Science (BCA), University of Padua, Legnaro, Italy
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40
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Sarwar T, Ramamohanarao K, Daducci A, Schiavi S, Smith RE, Zalesky A. Evaluation of tractogram filtering methods using human-like connectome phantoms. Neuroimage 2023; 281:120376. [PMID: 37714389 DOI: 10.1016/j.neuroimage.2023.120376] [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: 07/19/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023] Open
Abstract
Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic- unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic- unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic- unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic- unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Victoria, 3000, Australia.
| | | | | | - Simona Schiavi
- Department of Computer Science, University of Verona, 37129, Italy
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, 3084, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 2010, Australia
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Huang X, Jiang R, Peng S, Wei Y, Hu X, Chen J, Lian W. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI. Open Life Sci 2023; 18:20220725. [PMID: 37941782 PMCID: PMC10628570 DOI: 10.1515/biol-2022-0725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/10/2023] [Accepted: 08/19/2023] [Indexed: 11/10/2023] Open
Abstract
The purpose of this study was to explore the value of resting-state magnetic resonance imaging (MRI) based on the brain extraction tool (BET) algorithm in evaluating the cranial nerve function of patients with delirium in intensive care unit (ICU). A total of 100 patients with delirium in hospital were studied, and 20 healthy volunteers were used as control. All the subjects were examined by MRI, and the images were analyzed by the BET algorithm, and the convolution neural network (CNN) algorithm was introduced for comparison. The application effects of the two algorithms were analyzed, and the differences of brain nerve function between delirium patients and normal people were explored. The results showed that the root mean square error, high frequency error norm, and structural similarity of the BET algorithm were 70.4%, 71.5%, and 0.92, respectively, which were significantly higher than those of the CNN algorithm (P < 0.05). Compared with normal people, the ReHo values of pontine, hippocampus (right), cerebellum (left), midbrain, and basal ganglia in delirium patients were significantly higher. ReHo values of frontal gyrus, middle frontal gyrus, left inferior frontal gyrus, parietal lobe, and temporal lobe and anisotropy scores (FA) of cerebellums (left), frontal lobe, temporal lobe (left), corpus callosum, and hippocampus (left) decreased significantly. The average diffusivity (MD) of medial frontal lobe, superior temporal gyrus (right), the first half of cingulate gyrus, bilateral insula, and caudate nucleus (left) increased significantly (P < 0.05). MRI based on the deep learning algorithm can effectively improve the image quality, which is valuable in evaluating the brain nerve function of delirium patients. Abnormal brain structure damage and abnormal function can be used to help diagnose delirium.
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Affiliation(s)
- Xiaocheng Huang
- Department of Respiratory and Critical Care Medicine, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
| | - Ruilai Jiang
- Department of Respiratory and Critical Care Medicine, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
| | - Shushan Peng
- Department of Psychiatry, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
| | - Yanbin Wei
- Department of Respiratory and Critical Care Medicine, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
| | - Xiaogang Hu
- Department of Respiratory and Critical Care Medicine, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
| | - Jian Chen
- Department of Psychiatry, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
| | - Weibin Lian
- Department of Psychiatry, Lishui Second People’s Hospital, Lishui, 323000, Zhejiang, China
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Li D, Nguyen P, Zhang Z, Dunson D. Tree representations of brain structural connectivity via persistent homology. Front Neurosci 2023; 17:1200373. [PMID: 37901431 PMCID: PMC10603366 DOI: 10.3389/fnins.2023.1200373] [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: 04/04/2023] [Accepted: 09/05/2023] [Indexed: 10/31/2023] Open
Abstract
The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. There has been abundant interest in studying how the structural connectome varies across individuals in relation to their traits, ranging from age and gender to neuropsychiatric outcomes. After applying tractography to dMRI to get white matter fiber bundles, a key question is how to represent the brain connectome to facilitate statistical analyses relating connectomes to traits. The current standard divides the brain into regions of interest (ROIs), and then relies on an adjacency matrix (AM) representation. Each cell in the AM is a measure of connectivity, e.g., number of fiber curves, between a pair of ROIs. Although the AM representation is intuitive, a disadvantage is the high-dimensionality due to the large number of cells in the matrix. This article proposes a simpler tree representation of the brain connectome, which is motivated by ideas in computational topology and takes topological and biological information on the cortical surface into consideration. We demonstrate that our tree representation preserves useful information and interpretability, while reducing dimensionality to improve statistical and computational efficiency. Applications to data from the Human Connectome Project (HCP) are considered and code is provided for reproducing our analyses.
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Affiliation(s)
- Didong Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Phuc Nguyen
- Department of Statistical Science, Duke University, Durham, NC, United States
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - David Dunson
- Department of Statistical Science, Duke University, Durham, NC, United States
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Liang R, Schwendner M, Grziwotz M, Wiestler B, Wostrack M, Meyer B, Krieg SM, Ille S. Improving tractography in brainstem cavernoma patients by distortion correction. BRAIN & SPINE 2023; 3:102685. [PMID: 38021010 PMCID: PMC10668098 DOI: 10.1016/j.bas.2023.102685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/16/2023] [Accepted: 10/04/2023] [Indexed: 12/01/2023]
Abstract
Introduction The resection of brainstem cerebral cavernous malformations (CCM) harbors the risk of damaging the corticospinal tract (CST) and other major tracts. Hence, visualization of eloquent fiber tracts supports pre- and intraoperative planning. However, diffusion tensor imaging fiber tracking at brainstem level suffers from distortion due to field inhomogeneities and eddy currents by steep diffusion gradients. Research question This study aims to analyze the effect of distortion correction for CST tractography in brainstem CCM patients. Material and methods 25 patients who underwent resection of brainstem CCM were enrolled, 24 suffered from hemorrhage. We performed an anatomically based tractography of the CST with a mean minimal fractional anisotropy of 0.22 ± 0.04 before and after cranial distortion correction (CDC). Accuracy was measured by anatomical plausibility and aberrant fibers. Results CDC led to a more precise CST tractography, further approximating its assumed anatomical localization in all cases. CDC resulted in a significantly more ventral location of the CST of 1.5 ± 0.6 mm (6.1 ± 2.7 mm before CDC vs. 4.6 ± 2.1 mm after CDC; p < .0001) as measured by the distance to the basilar artery and of 1.7 ± 0.6 mm (8.9 ± 2.7 mm vs. 7.2 ± 2.1 mm; p < .0001) in relation to the clivus. Aberrant fibers were reduced by CDC in 44% of cases. We found a mean difference in CST volume of 0.6 ± 0.8 ccm. We could not detect motor deficits after resection of irregular fibers. Discussion and conclusion CDC effectively corrects tractography for distortion at brainstem level, especially in patients suffering from brainstem CCM, further approximating its actual anatomical localization.
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Affiliation(s)
- Raimunde Liang
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Maximilian Schwendner
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Marc Grziwotz
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Maria Wostrack
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Sandro M. Krieg
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Sebastian Ille
- Department of Neurosurgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
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Rajesh A, Seider NA, Newbold DJ, Adeyemo B, Marek S, Greene DJ, Snyder AZ, Shimony JS, Laumann TO, Dosenbach NUF, Gordon EM. Structure-Function Coupling in Highly Sampled Individual Brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.04.560909. [PMID: 37873167 PMCID: PMC10592963 DOI: 10.1101/2023.10.04.560909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Structural connections (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to structural connections may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 minutes of diffusion-weighted MRI for SC and 360 minutes of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas. SIGNIFICANCE STATEMENT Structural connections between distant regions of the human brain support networked function that enables cognition and behavior. Improving our understanding of how structure enables function could allow better insight into how brain disconnection injuries impair brain function.Previous work using neuroimaging suggested that structure-function relationships vary systematically across the brain, with structure better explaining function in basic visual/motor areas than in higher-order areas. However, this work was conducted in group-averaged data, which may obscure details of individual-specific structure-function relationships.Using individual-specific densely sampled neuroimaging data, we found that in addition to visual/motor regions, structure strongly predicts function in specific circuits of the higher-order cingulate gyrus. The cingulate's structure-function relationship suggests that its organization may be unique among higher-order cortical regions.
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Alushaj E, Hemachandra D, Kuurstra A, Menon RS, Ganjavi H, Sharma M, Kashgari A, Barr J, Reisman W, Khan AR, MacDonald PA. Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder. Neuroimage Clin 2023; 40:103519. [PMID: 37797434 PMCID: PMC10568416 DOI: 10.1016/j.nicl.2023.103519] [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: 07/12/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Dimuthu Hemachandra
- Robarts Research Institute, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alia Kashgari
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Jennifer Barr
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - William Reisman
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
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Fritz FJ, Mordhorst L, Ashtarayeh M, Periquito J, Pohlmann A, Morawski M, Jaeger C, Niendorf T, Pine KJ, Callaghan MF, Weiskopf N, Mohammadi S. Fiber-orientation independent component of R 2* obtained from single-orientation MRI measurements in simulations and a post-mortem human optic chiasm. Front Neurosci 2023; 17:1133086. [PMID: 37694109 PMCID: PMC10491021 DOI: 10.3389/fnins.2023.1133086] [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: 12/28/2022] [Accepted: 08/04/2023] [Indexed: 09/12/2023] Open
Abstract
The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the fibre-orientation dependence of R2* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R2,iso*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter, β1, can be biophysically related to R2,iso* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted β1 can estimate R2,iso* using meGRE with long maximum-echo time (TEmax ≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for β1 that reduced the error to 12% for ex vivo compartmental R2 values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the ex vivo compartmental R2 values but not for the in vivo values. M2 and the HCFM-based simulations failed to explain the measured R2*-orientation-dependence around the magic angle for a typical in vivo meGRE protocol (with TEmax ≈ 18 ms). In conclusion, further validation and the development of movement-robust in vivo meGRE protocols with TEmax ≈ 54 ms are required before M2 can be used to estimate R2,iso* in subjects.
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Affiliation(s)
- Francisco J. Fritz
- Department of Systems Neurosciences, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Laurin Mordhorst
- Department of Systems Neurosciences, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mohammad Ashtarayeh
- Department of Systems Neurosciences, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Joao Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Markus Morawski
- Paul Flechsig Institute – Center for Neuropathology and Brain Research, University of Leipzig, Leipzig, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Carsten Jaeger
- Paul Flechsig Institute – Center for Neuropathology and Brain Research, University of Leipzig, Leipzig, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kerrin J. Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Martina F. Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Siawoosh Mohammadi
- Department of Systems Neurosciences, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
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Cormie MA, Kaya B, Hadjis GE, Mouseli P, Moayedi M. Insula-cingulate structural and functional connectivity: an ultra-high field MRI study. Cereb Cortex 2023; 33:9787-9801. [PMID: 37429832 PMCID: PMC10656949 DOI: 10.1093/cercor/bhad244] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 07/12/2023] Open
Abstract
The insula and the cingulate are key brain regions with many heterogenous functions. Both regions are consistently shown to play integral roles in the processing of affective, cognitive, and interoceptive stimuli. The anterior insula (aINS) and the anterior mid-cingulate cortex (aMCC) are two key hubs of the salience network (SN). Beyond the aINS and aMCC, previous 3 Tesla (T) magnetic resonance imaging studies have suggested both structural connectivity (SC) and functional connectivity (FC) between other insular and cingulate subregions. Here, we investigate the SC and FC between insula and cingulate subregions using ultra-high field 7T diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI). DTI revealed strong SC between posterior INS (pINS) and posterior MCC (pMCC), and rs-fMRI revealed strong FC between the aINS and aMCC that was not supported by SC, indicating the likelihood of a mediating structure. Finally, the insular pole had the strongest SC to all cingulate subregions, with a slight preference for the pMCC, indicative of a potential relay node of the insula. Together these finding shed new light on the understanding of insula-cingulate functioning, both within the SN and other cortical processes, through a lens of its SC and FC.
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Affiliation(s)
- Matthew A Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Batu Kaya
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Georgia E Hadjis
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Pedram Mouseli
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
- University of Toronto Centre for the Study of Pain, Toronto, ON, Canada
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Dentistry, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
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Mahmoodi AL, Landers MJF, Rutten GJM, Brouwers HB. Characterization and Classification of Spatial White Matter Tract Alteration Patterns in Glioma Patients Using Magnetic Resonance Tractography: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:3631. [PMID: 37509291 PMCID: PMC10377290 DOI: 10.3390/cancers15143631] [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: 04/27/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION Magnetic resonance (MR) tractography can be used to study the spatial relations between gliomas and white matter (WM) tracts. Various spatial patterns of WM tract alterations have been described in the literature. We reviewed classification systems of these patterns, and investigated whether low-grade gliomas (LGGs) and high-grade gliomas (HGGs) demonstrate distinct spatial WM tract alteration patterns. METHODS We conducted a systematic review and meta-analysis to summarize the evidence regarding MR tractography studies that investigated spatial WM tract alteration patterns in glioma patients. RESULTS Eleven studies were included. Overall, four spatial WM tract alteration patterns were reported in the current literature: displacement, infiltration, disruption/destruction and edematous. There was a considerable heterogeneity in the operational definitions of these terms. In a subset of studies, sufficient homogeneity in the classification systems was found to analyze pooled results for the displacement and infiltration patterns. Our meta-analyses suggested that LGGs displaced WM tracts significantly more often than HGGs (n = 259 patients, RR: 1.79, 95% CI [1.14, 2.79], I2 = 51%). No significant differences between LGGs and HGGs were found for WM tract infiltration (n = 196 patients, RR: 1.19, 95% CI [0.95, 1.50], I2 = 4%). CONCLUSIONS The low number of included studies and their considerable methodological heterogeneity emphasize the need for a more uniform classification system to study spatial WM tract alteration patterns using MR tractography. This review provides a first step towards such a classification system, by showing that the current literature is inconclusive and that the ability of fractional anisotropy (FA) to define spatial WM tract alteration patterns should be critically evaluated. We found variations in spatial WM tract alteration patterns between LGGs and HGGs, when specifically examining displacement and infiltration in a subset of the included studies.
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Affiliation(s)
- Arash L Mahmoodi
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
| | - Maud J F Landers
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
| | - Geert-Jan M Rutten
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
| | - H Bart Brouwers
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
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Nozais V, Forkel SJ, Petit L, Talozzi L, Corbetta M, Thiebaut de Schotten M, Joliot M. Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain. Commun Biol 2023; 6:726. [PMID: 37452124 PMCID: PMC10349117 DOI: 10.1038/s42003-023-05107-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.
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Affiliation(s)
- Victor Nozais
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Donders Institute for Brain Cognition Behaviour, Radboud University, Nijmegen, the Netherlands
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Departments of Neurosurgery, Technical University of Munich School of Medicine, Munich, Germany
| | - Laurent Petit
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France
| | - Lia Talozzi
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine and Padova Neuroscience Center, University of Padua, Padova, PD, 32122, Italy
| | - Michel Thiebaut de Schotten
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Marc Joliot
- Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, GIN, F-33000, Bordeaux, France.
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Smits AR, van Zandvoort MJE, Ramsey NF, de Haan EHF, Raemaekers M. Reliability and validity of DTI-based indirect disconnection measures. Neuroimage Clin 2023; 39:103470. [PMID: 37459698 PMCID: PMC10368919 DOI: 10.1016/j.nicl.2023.103470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023]
Abstract
White matter connections enable the interaction within and between brain networks. Brain lesions can cause structural disconnections that disrupt networks and thereby cognitive functions supported by them. In recent years, novel methods have been developed to quantify the extent of structural disconnection after focal lesions, using tractography data from healthy controls. These methods, however, are indirect and their reliability and validity have yet to be fully established. In this study, we present our implementation of this approach, in a tool supplemented by uncertainty metrics for the predictions overall and at voxel-level. These metrics give an indication of the reliability and are used to compare predictions with direct measures from patients' diffusion tensor imaging (DTI) data in a sample of 95 first-ever stroke patients. Results show that, except for small lesions, the tool can predict fiber loss with high reliability and compares well to direct patient DTI estimates. Clinical utility of the method was demonstrated using lesion data from a subset of patients suffering from hemianopia. Both tract-based measures outperformed lesion localization in mapping visual field defects and showed a network consistent with the known anatomy of the visual system. This study offers an important contribution to the validation of structural disconnection mapping. We show that indirect measures of structural disconnection can be a reliable and valid substitute for direct estimations of fiber loss after focal lesions. Moreover, based on these results, we argue that indirect structural disconnection measures may even be preferable to lower-quality single subject diffusion MRI when based on high-quality healthy control datasets.
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Affiliation(s)
- A R Smits
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands.
| | - M J E van Zandvoort
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, the Netherlands; Department of Experimental Psychology, Helmholtz Institute, Utrecht University, the Netherlands
| | - N F Ramsey
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, the Netherlands
| | - E H F de Haan
- Department of Psychology, University of Amsterdam, the Netherlands; St. Hugh's College, Oxford University, United Kingdom
| | - M Raemaekers
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, the Netherlands
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