151
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Trinkle S, Foxley S, Wildenberg G, Kasthuri N, La Rivière P. The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections. Neuroimage 2021; 244:118576. [PMID: 34520833 PMCID: PMC8611903 DOI: 10.1016/j.neuroimage.2021.118576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.
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Affiliation(s)
- Scott Trinkle
- Department of Radiology, University of Chicago, Chicago, IL, USA.
| | - Sean Foxley
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Gregg Wildenberg
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
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152
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Bu X, Cao M, Huang X, He Y. The structural connectome in ADHD. PSYCHORADIOLOGY 2021; 1:257-271. [PMID: 38666220 PMCID: PMC10939332 DOI: 10.1093/psyrad/kkab021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 02/05/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) has been conceptualized as a brain dysconnectivity disorder. In the past decade, noninvasive diffusion magnetic resonance imaging (dMRI) studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome, and that these alterations are associated with core symptoms and cognitive deficits in patients. This review aims to summarize recent dMRI-based structural connectome studies in ADHD from voxel-, tractography-, and network-based perspectives. Voxel- and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts, the corpus callosum, the corticospinal tracts, and the cingulum bundle in patients with ADHD. Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes. The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification. These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation. However, given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations, these ADHD-related white matter alterations are still far from informing clinical practice. Future studies with larger and more homogeneous samples are needed to validate the consistency of current results; advanced dMRI techniques can help to generate much more precise estimation of white matter pathways and assure specific fiber configurations; and finally, dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.
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Affiliation(s)
- Xuan Bu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Miao Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
| | - Xiaoqi Huang
- Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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153
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Caffarra S, Karipidis II, Yablonski M, Yeatman JD. Anatomy and physiology of word-selective visual cortex: from visual features to lexical processing. Brain Struct Funct 2021; 226:3051-3065. [PMID: 34636985 PMCID: PMC8639194 DOI: 10.1007/s00429-021-02384-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/07/2021] [Indexed: 12/20/2022]
Abstract
Over the past 2 decades, researchers have tried to uncover how the human brain can extract linguistic information from a sequence of visual symbols. The description of how the brain's visual system processes words and enables reading has improved with the progressive refinement of experimental methodologies and neuroimaging techniques. This review provides a brief overview of this research journey. We start by describing classical models of object recognition in non-human primates, which represent the foundation for many of the early models of visual word recognition in humans. We then review functional neuroimaging studies investigating the word-selective regions in visual cortex. This research led to the differentiation of highly specialized areas, which are involved in the analysis of different aspects of written language. We then consider the corresponding anatomical measurements and provide a description of the main white matter pathways carrying neural signals crucial to word recognition. Finally, in an attempt to integrate structural, functional, and electrophysiological findings, we propose a view of visual word recognition, accounting for spatial and temporal facets of word-selective neural processes. This multi-modal perspective on the neural circuitry of literacy highlights the relevance of a posterior-anterior differentiation in ventral occipitotemporal cortex for visual processing of written language and lexical features. It also highlights unanswered questions that can guide us towards future research directions. Bridging measures of brain structure and function will help us reach a more precise understanding of the transformation from vision to language.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, Mikeletegi 69, 20009, San Sebastian, Spain
- University of Modena and Reggio Emilia, Via Campi 287, 41125, Modena, Italy
| | - Iliana I Karipidis
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, School of Medicine, Stanford University, 401 Quarry Road, Stanford, CA, 94305-5717, USA.
| | - Maya Yablonski
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
| | - Jason D Yeatman
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
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154
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Goghari VM, Kusi M, Shakeel MK, Beasley C, David S, Leemans A, De Luca A, Emsell L. Diffusion kurtosis imaging of white matter in bipolar disorder. Psychiatry Res Neuroimaging 2021; 317:111341. [PMID: 34411810 DOI: 10.1016/j.pscychresns.2021.111341] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 12/29/2022]
Abstract
White matter pathology likely contributes to the pathogenesis of bipolar disorder (BD). Most studies of white matter in BD have used diffusion tensor imaging (DTI), but the advent of more advanced multi-shell diffusion MRI imaging offers the possibility to investigate other aspects of white matter microstructure. Diffusion kurtosis imaging (DKI) extends the DTI model and provides additional measures related to diffusion restriction. Here, we investigated white matter in BD by applying whole-brain voxel-based analysis (VBA) and a network-based connectivity approach using constrained spherical deconvolution tractography to assess differences in DKI and DTI metrics between BD (n = 25) and controls (n = 24). The VBA showed lower mean kurtosis in the corona radiata and posterior association fibers in BD. Regional differences in connectivity were indicated by lower mean kurtosis and kurtosis anisotropy in streamlines traversing the temporal and occipital lobes, and lower mean axial kurtosis in the right cerebellar, thalamo-subcortical pathways in BD. Significant differences were not seen in DTI metrics following FDR-correction. The DKI findings indicate altered connectivity across cortical, subcortical and cerebellar areas in BD. DKI is sensitive to different microstructural properties and is a useful complementary technique to DTI to more fully investigate white matter in BD.
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Affiliation(s)
- Vina M Goghari
- Department of Psychology & Graduate Department of Psychological Clinical Science, University of Toronto, Toronto, Ontario, Canada.
| | - Mavis Kusi
- Department of Psychology & Graduate Department of Psychological Clinical Science, University of Toronto, Toronto, Ontario, Canada
| | - Mohammed K Shakeel
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Clare Beasley
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Szabolcs David
- Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands; Department of Radiation Oncology, UMC Utrecht, Utrecht, the Netherlands
| | | | - Alberto De Luca
- Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands; Neurology Department, Brain Center, UMC Utrecht, Utrecht, the Netherlands
| | - Louise Emsell
- Department of Geriatric Psychiatry, University Psychiatric Center, KU Leuven, Leuven, Belgium; Department of Imaging and Pathology and Department of Neurosciences, Translational MRI and Neuropsychiatry, Leuven Brain Institute, KU Leuven, Leuven, Belgium
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155
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Parcellation-Free prediction of task fMRI activations from dMRI tractography. Med Image Anal 2021; 76:102317. [PMID: 34871930 DOI: 10.1016/j.media.2021.102317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 11/22/2022]
Abstract
The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.
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156
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Schilling KG, Tax CM, Rheault F, Hansen C, Yang Q, Yeh FC, Cai L, Anderson AW, Landman BA. Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow. Neuroimage 2021; 242:118451. [PMID: 34358660 PMCID: PMC9933001 DOI: 10.1016/j.neuroimage.2021.118451] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 01/08/2023] Open
Abstract
When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.
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Affiliation(s)
- Kurt G. Schilling
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Chantal M.W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Colin Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, United States
| | - Leon Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Adam W. Anderson
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A. Landman
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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157
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Basile GA, Bertino S, Bramanti A, Ciurleo R, Anastasi GP, Milardi D, Cacciola A. Striatal topographical organization: Bridging the gap between molecules, connectivity and behavior. Eur J Histochem 2021; 65. [PMID: 34643358 PMCID: PMC8524362 DOI: 10.4081/ejh.2021.3284] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/07/2021] [Indexed: 12/22/2022] Open
Abstract
The striatum represents the major hub of the basal ganglia, receiving projections from the entire cerebral cortex and it is assumed to play a key role in a wide array of complex behavioral tasks. Despite being extensively investigated during the last decades, the topographical organization of the striatum is not well understood yet. Ongoing efforts in neuroscience are focused on analyzing striatal anatomy at different spatial scales, to understand how structure relates to function and how derangements of this organization are involved in various neuropsychiatric diseases. While being subdivided at the macroscale level into dorsal and ventral divisions, at a mesoscale level the striatum represents an anatomical continuum sharing the same cellular makeup. At the same time, it is now increasingly ascertained that different striatal compartments show subtle histochemical differences, and their neurons exhibit peculiar patterns of gene expression, supporting functional diversity across the whole basal ganglia circuitry. Such diversity is further supported by afferent connections which are heterogenous both anatomically, as they originate from distributed cortical areas and subcortical structures, and biochemically, as they involve a variety of neurotransmitters. Specifically, the cortico-striatal projection system is topographically organized delineating a functional organization which is maintained throughout the basal ganglia, subserving motor, cognitive and affective behavioral functions. While such functional heterogeneity has been firstly conceptualized as a tripartite organization, with sharply defined limbic, associative and sensorimotor territories within the striatum, it has been proposed that such territories are more likely to fade into one another, delineating a gradient-like organization along medio-lateral and ventro-dorsal axes. However, the molecular and cellular underpinnings of such organization are less understood, and their relations to behavior remains an open question, especially in humans. In this review we aimed at summarizing the available knowledge on striatal organization, especially focusing on how it links structure to function and its alterations in neuropsychiatric diseases. We examined studies conducted on different species, covering a wide array of different methodologies: from tract-tracing and immunohistochemistry to neuroimaging and transcriptomic experiments, aimed at bridging the gap between macroscopic and molecular levels.
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Affiliation(s)
- Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina.
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina.
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry "Medical School of Salerno", University of Salerno.
| | | | - Giuseppe Pio Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina.
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina.
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina.
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158
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Goddings AL, Roalf D, Lebel C, Tamnes CK. Development of white matter microstructure and executive functions during childhood and adolescence: a review of diffusion MRI studies. Dev Cogn Neurosci 2021; 51:101008. [PMID: 34492631 PMCID: PMC8424510 DOI: 10.1016/j.dcn.2021.101008] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/26/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) provides indirect measures of white matter microstructure that can be used to make inferences about structural connectivity within the brain. Over the last decade, a growing literature of cross-sectional and longitudinal studies have documented relationships between dMRI indices and cognitive development. In this review, we provide a brief overview of dMRI methods and how they can be used to study white matter and connectivity and review the extant literature examining the links between dMRI indices and executive functions during development. We explore the links between white matter microstructure and specific executive functions: inhibition, working memory and cognitive shifting, as well as performance on complex executive function tasks. Concordance in findings across studies are highlighted, and potential explanations for discrepancies between results, together with challenges with using dMRI in child and adolescent populations, are discussed. Finally, we explore future directions that are necessary to better understand the links between child and adolescent development of structural connectivity of the brain and executive functions.
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Affiliation(s)
- Anne-Lise Goddings
- UCL Great Ormond Street Institute of Child Health, University College London, UK.
| | - David Roalf
- Department of Psychiatry, University of Pennsylvania, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and the University of Pennsylvania, USA
| | - Catherine Lebel
- Department of Radiology, University of Calgary, Alberta, Canada
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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159
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Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-Weighted Imaging: Recent Advances and Applications. Semin Ultrasound CT MR 2021; 42:490-506. [PMID: 34537117 DOI: 10.1053/j.sult.2021.07.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.
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Affiliation(s)
- Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain.
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Center for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; E-health Center, Universitat Oberta de Catalunya. Barcelona. Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
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160
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Nozais V, Forkel SJ, Foulon C, Petit L, Thiebaut de Schotten M. Functionnectome as a framework to analyse the contribution of brain circuits to fMRI. Commun Biol 2021; 4:1035. [PMID: 34475518 PMCID: PMC8413369 DOI: 10.1038/s42003-021-02530-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/02/2021] [Indexed: 02/07/2023] Open
Abstract
In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain's functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits' anatomy to unlock and chart the first maps of functional white matter. To showcase this method's versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.
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Affiliation(s)
- Victor Nozais
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
| | - Stephanie J Forkel
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
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161
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Wu Y, Hong Y, Ahmad S, Yap PT. Active Cortex Tractography. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12907:467-476. [PMID: 35939282 PMCID: PMC9355463 DOI: 10.1007/978-3-030-87234-2_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Most existing diffusion tractography algorithms are affected by gyral bias, causing the termination of streamlines at gyral crowns instead of sulcal banks. In this paper, we propose a tractography technique, called active cortex tractography (ACT), to overcome gyral bias by enabling fiber streamlines to curve naturally into the cortex. We show that the cortex can play an active role in cortical tractography by providing anatomical information to overcome orientation ambiguities as the streamlines enter the superficial white matter in gyral blades and approach the cortex. This is achieved by devising a direction scouting mechanism that takes into account the white matter surface normal vectors. The scouting mechanism allows probing of directions further in space to prepare the streamlines to turn at appropriate angles. The surface normal vectors guide the streamlines to turn into the cortex, perpendicular to the white-gray matter interface. Evaluation using synthetic, macaque and human data with different streamline seeding schemes demonstrates that ACT improves cortical tractography.
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Affiliation(s)
- Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Yoonmi Hong
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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162
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Bertino S, Basile GA, Bramanti A, Ciurleo R, Tisano A, Anastasi GP, Milardi D, Cacciola A. Ventral intermediate nucleus structural connectivity-derived segmentation: anatomical reliability and variability. Neuroimage 2021; 243:118519. [PMID: 34461233 DOI: 10.1016/j.neuroimage.2021.118519] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/24/2021] [Accepted: 08/25/2021] [Indexed: 12/30/2022] Open
Abstract
The Ventral intermediate nucleus (Vim) of thalamus is the most targeted structure for the treatment of drug-refractory tremors. Since methodological differences across existing studies are remarkable and no gold-standard pipeline is available, in this study, we tested different parcellation pipelines for tractography-derived putative Vim identification. Thalamic parcellation was performed on a high quality, multi-shell dataset and a downsampled, clinical-like dataset using two different diffusion signal modeling techniques and two different voxel classification criteria, thus implementing a total of four parcellation pipelines. The most reliable pipeline in terms of inter-subject variability has been picked and parcels putatively corresponding to motor thalamic nuclei have been selected by calculating similarity with a histology-based mask of Vim. Then, spatial relations with optimal stimulation points for the treatment of essential tremor have been quantified. Finally, effect of data quality and parcellation pipelines on a volumetric index of connectivity clusters has been assessed. We found that the pipeline characterized by higher-order signal modeling and threshold-based voxel classification criteria was the most reliable in terms of inter-subject variability regardless data quality. The maps putatively corresponding to Vim were those derived by precentral and dentate nucleus-thalamic connectivity. However, tractography-derived functional targets showed remarkable differences in shape and sizes when compared to a ground truth model based on histochemical staining on seriate sections of human brain. Thalamic voxels connected to contralateral dentate nucleus resulted to be the closest to literature-derived stimulation points for essential tremor but at the same time showing the most remarkable inter-subject variability. Finally, the volume of connectivity parcels resulted to be significantly influenced by data quality and parcellation pipelines. Hence, caution is warranted when performing thalamic connectivity-based segmentation for stereotactic targeting.
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Affiliation(s)
- Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | | | | | - Adriana Tisano
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Giuseppe Pio Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.
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163
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He J, Zhang F, Xie G, Yao S, Feng Y, Bastos DCA, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI. Hum Brain Mapp 2021; 42:3887-3904. [PMID: 33978265 PMCID: PMC8288095 DOI: 10.1002/hbm.25472] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/24/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022] Open
Abstract
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
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Affiliation(s)
- Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Guoqiang Xie
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryNuclear Industry 215 Hospital of Shaanxi ProvinceXianyangChina
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Pituitary Tumor Surgery, Department of NeurosurgeryThe First Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouChina
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
| | - Dhiego C. A. Bastos
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry, Neurology and Radiology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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164
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Trinkle S, Foxley S, Kasthuri N, Rivière PL. Synchrotron X-ray micro-CT as a validation dataset for diffusion MRI in whole mouse brain. Magn Reson Med 2021; 86:1067-1076. [PMID: 33768633 PMCID: PMC8076078 DOI: 10.1002/mrm.28776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/26/2021] [Accepted: 02/28/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce synchrotron X-ray microcomputed tomography (microCT) and demonstrate its use as a natively isotropic, nondestructive, 3D validation modality for diffusion MRI in whole, fixed mouse brain. METHODS Postmortem diffusion MRI and microCT data were acquired of the same whole mouse brain. Diffusion data were processed using constrained spherical deconvolution. Synchrotron data were acquired at an isotropic voxel size of 1.17 μm. Structure tensor analysis was used to calculate fiber orientation distribution functions from the microCT data. A pipeline was developed to spatially register the 2 datasets in order to perform qualitative comparisons of fiber geometries represented by fiber orientation distribution functions. Fiber orientations from both modalities were used to perform whole-brain deterministic tractography to demonstrate validation of long-range white matter pathways. RESULTS Fiber orientation distribution functions were able to be extracted throughout the entire microCT dataset, with spatial registration to diffusion MRI simplified due to the whole brain extent of the microCT data. Fiber orientations and tract pathways showed good agreement between modalities. CONCLUSION Synchrotron microCT is a potentially valuable new tool for future multi-scale diffusion MRI validation studies, providing comparable value to optical histology validation methods while addressing some key limitations in data acquisition and ease of processing.
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Affiliation(s)
- Scott Trinkle
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Sean Foxley
- Department of Radiology, University of Chicago, Chicago, IL, USA
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165
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Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
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Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
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166
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Bedini M, Baldauf D. Structure, function and connectivity fingerprints of the frontal eye field versus the inferior frontal junction: A comprehensive comparison. Eur J Neurosci 2021; 54:5462-5506. [PMID: 34273134 PMCID: PMC9291791 DOI: 10.1111/ejn.15393] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 06/16/2021] [Accepted: 07/02/2021] [Indexed: 02/01/2023]
Abstract
The human prefrontal cortex contains two prominent areas, the frontal eye field and the inferior frontal junction, that are crucially involved in the orchestrating functions of attention, working memory and cognitive control. Motivated by comparative evidence in non-human primates, we review the human neuroimaging literature, suggesting that the functions of these regions can be clearly dissociated. We found remarkable differences in how these regions relate to sensory domains and visual topography, top-down and bottom-up spatial attention, spatial versus non-spatial (i.e., feature- and object-based) attention and working memory and, finally, the multiple-demand system. Functional magnetic resonance imaging (fMRI) studies using multivariate pattern analysis reveal the selectivity of the frontal eye field and inferior frontal junction to spatial and non-spatial information, respectively. The analysis of functional and effective connectivity provides evidence of the modulation of the activity in downstream visual areas from the frontal eye field and inferior frontal junction and sheds light on their reciprocal influences. We therefore suggest that future studies should aim at disentangling more explicitly the role of these regions in the control of spatial and non-spatial selection. We propose that the analysis of the structural and functional connectivity (i.e., the connectivity fingerprints) of the frontal eye field and inferior frontal junction may be used to further characterize their involvement in a spatial ('where') and a non-spatial ('what') network, respectively, highlighting segregated brain networks that allow biasing visual selection and working memory performance to support goal-driven behaviour.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
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167
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Lucena O, Vos SB, Vakharia V, Duncan J, Ashkan K, Sparks R, Ourselin S. Enhancing the estimation of fiber orientation distributions using convolutional neural networks. Comput Biol Med 2021; 135:104643. [PMID: 34280774 DOI: 10.1016/j.compbiomed.2021.104643] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/07/2021] [Accepted: 07/07/2021] [Indexed: 11/17/2022]
Abstract
Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and High-Resolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments.
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Affiliation(s)
- Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Department of Computer Sciences, University College London, London, UK; Neuroradiological Academic Unit, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Vejay Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, UK
| | - John Duncan
- Department of Clinical and Experimental Epilepsy, University College London, UK; National Hospital for Neurology and Neurosurgery, Queen Square, UK
| | | | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
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168
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Zhang J, Cortese R, De Stefano N, Giorgio A. Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis. Front Neurol 2021; 12:671894. [PMID: 34305785 PMCID: PMC8297166 DOI: 10.3389/fneur.2021.671894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/19/2021] [Indexed: 02/05/2023] Open
Abstract
Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.
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Affiliation(s)
- Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
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169
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The Complex Hodological Architecture of the Macaque Dorsal Intraparietal Areas as Emerging from Neural Tracers and DW-MRI Tractography. eNeuro 2021; 8:ENEURO.0102-21.2021. [PMID: 34039649 PMCID: PMC8266221 DOI: 10.1523/eneuro.0102-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/21/2021] [Accepted: 05/01/2021] [Indexed: 11/21/2022] Open
Abstract
In macaque monkeys, dorsal intraparietal areas are involved in several daily visuomotor actions. However, their border and sources of cortical afferents remain loosely defined. Combining retrograde histologic tracing and MRI diffusion-based tractography, we found a complex hodology of the dorsal bank of the intraparietal sulcus (db-IPS), which can be subdivided into a rostral intraparietal area PEip, projecting to the spinal cord, and a caudal medial intraparietal area MIP lacking such projections. Both include an anterior and a posterior sector, emerging from their ipsilateral, gradient-like connectivity profiles. As tractography estimations, we used the cross-sectional area of the white matter bundles connecting each area with other parietal and frontal regions, after selecting regions of interest (ROIs) corresponding to the injection sites of neural tracers. For most connections, we found a significant correlation between the proportions of cells projecting to all sectors of PEip and MIP along the continuum of the db-IPS and tractography. The latter also revealed “false positive” but plausible connections awaiting histologic validation.
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170
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Duffau H. Neural Connectivity: How to Reinforce the Bidirectional Synapse Between Basic Neuroscience and Routine Neurosurgical Practice? Front Neurol 2021; 12:705135. [PMID: 34354668 PMCID: PMC8336871 DOI: 10.3389/fneur.2021.705135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 05/10/2021] [Indexed: 11/29/2022] Open
Affiliation(s)
- Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors," National Institute for Health and Medical Research (INSERM), U1191 Laboratory, Institute of Functional Genomics, University of Montpellier, Montpellier, France
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171
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Barakovic M, Girard G, Schiavi S, Romascano D, Descoteaux M, Granziera C, Jones DK, Innocenti GM, Thiran JP, Daducci A. Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts. Front Neurosci 2021; 15:646034. [PMID: 34211362 PMCID: PMC8239216 DOI: 10.3389/fnins.2021.646034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/17/2021] [Indexed: 12/30/2022] Open
Abstract
In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.
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Affiliation(s)
- Muhamed Barakovic
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Gabriel Girard
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Simona Schiavi
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Computer Science, University of Verona, Verona, Italy
| | - David Romascano
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Giorgio M. Innocenti
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
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172
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Legarreta JH, Petit L, Rheault F, Theaud G, Lemaire C, Descoteaux M, Jodoin PM. Filtering in tractography using autoencoders (FINTA). Med Image Anal 2021; 72:102126. [PMID: 34161915 DOI: 10.1016/j.media.2021.102126] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 04/20/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tractograms to train the autoencoder, and to learn a robust representation of brain streamlines. Such an embedding is then used to filter undesired streamline samples using a nearest neighbor algorithm. Our experiments on both synthetic and in vivo human brain diffusion MRI tractography data obtain accuracy scores exceeding the 90% threshold on the test set. Results reveal that FINTA has a superior filtering performance compared to conventional, anatomy-based methods, and the RecoBundles state-of-the-art method. Additionally, we demonstrate that FINTA can be applied to partial tractograms without requiring changes to the framework. We also show that the proposed method generalizes well across different tracking methods and datasets, and shortens significantly the computation time for large (>1 M streamlines) tractograms. Together, this work brings forward a new deep learning framework in tractography based on autoencoders, which offers a flexible and powerful method for white matter filtering and bundling that could enhance tractometry and connectivity analyses.
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Affiliation(s)
- Jon Haitz Legarreta
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada; Videos & Images Theory and Analytics Laboratory (VITAL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada.
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, Bordeaux F-33000, France
| | - François Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Laboratory (VITAL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
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173
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Yeh CH, Jones DK, Liang X, Descoteaux M, Connelly A. Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities. J Magn Reson Imaging 2021; 53:1666-1682. [PMID: 32557893 PMCID: PMC7615246 DOI: 10.1002/jmri.27188] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.
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Affiliation(s)
- Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Xiaoyun Liang
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
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174
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Kringelbach ML, Deco G. Brain States and Transitions: Insights from Computational Neuroscience. Cell Rep 2021; 32:108128. [PMID: 32905760 DOI: 10.1016/j.celrep.2020.108128] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/22/2020] [Accepted: 08/19/2020] [Indexed: 11/25/2022] Open
Abstract
Within the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between the ability to accurately predict how and where best to perturb to force a transition from one brain state to another. The foremost challenge is a commonly agreed definition of a given brain state. Recent progress in computational neuroscience has made it possible to robustly define brain states and force transitions between them. Here, we review the state of the art and propose a framework for determining the functional hierarchical organization describing any given brain state. We describe the latest advances in creating sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems that can be studied fully in silico to predict and design novel pharmacological and electromagnetic interventions to rebalance them in disease.
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Affiliation(s)
- Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia.
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175
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Schlemm E, Ingwersen T, Königsberg A, Boutitie F, Ebinger M, Endres M, Fiebach JB, Fiehler J, Galinovic I, Lemmens R, Muir KW, Nighoghossian N, Pedraza S, Puig J, Simonsen CZ, Thijs V, Wouters A, Gerloff C, Thomalla G, Cheng B. Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke. Nat Commun 2021; 12:2590. [PMID: 33972513 PMCID: PMC8110812 DOI: 10.1038/s41467-021-22786-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 03/29/2021] [Indexed: 12/12/2022] Open
Abstract
Thrombolysis with recombinant tissue plasminogen activator in acute ischemic stroke aims to restore compromised blood flow and prevent further neuronal damage. Despite the proven clinical efficacy of this treatment, little is known about the short-term effects of systemic thrombolysis on structural brain connectivity. In this secondary analysis of the WAKE-UP trial, we used MRI-derived measures of infarct size and estimated structural network disruption to establish that thrombolysis is associated not only with less infarct growth, but also with reduced loss of large-scale connectivity between grey-matter areas after stroke. In a causal mediation analysis, infarct growth mediated a non-significant 8.3% (CI95% [-8.0, 32.6]%) of the clinical effect of thrombolysis on functional outcome. The proportion mediated jointly through infarct growth and change of structural connectivity, especially in the border zone around the infarct core, however, was as high as 33.4% (CI95% [8.8, 77.4]%). Preservation of structural connectivity is thus an important determinant of treatment success and favourable functional outcome in addition to lesion volume. It might, in the future, serve as an imaging endpoint in clinical trials or as a target for therapeutic interventions.
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Affiliation(s)
- Eckhard Schlemm
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Thies Ingwersen
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Königsberg
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florent Boutitie
- Hospices Civils de Lyon, Service de Biostatistique, Lyon, France
- Université Lyon 1, Villeurbanne, France
- CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, Villeurbanne, France
| | - Martin Ebinger
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Klinik für Neurologie, Medical Park Berlin Humboldtmühle, Berlin, Germany
| | - Matthias Endres
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Klinik und Hochschulambulanz für Neurologie, Charité-Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Berlin, Germany
- ExcellenceCluster NeuroCure, Berlin, Germany
| | - Jochen B Fiebach
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ivana Galinovic
- Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Robin Lemmens
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology, Leuven, Belgium
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, Campus Gasthuisberg, Leuven, Belgium
| | - Keith W Muir
- Institute of Neuroscience & Psychology, University of Glasgow, Glasgow, UK
| | - Norbert Nighoghossian
- Department of Stroke Medicine, Université Claude Bernard Lyon 1, CREATIS CNRS UMR 5220-INSERM U1206, INSA-Lyon; Hospices Civils de Lyon, Lyon, France
| | - Salvador Pedraza
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI), Parc Hospitalari Martí i Julià de Salt - Edifici M2, Salt, Girona, Spain
| | - Josep Puig
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomèdica de Girona (IDIBGI), Parc Hospitalari Martí i Julià de Salt - Edifici M2, Salt, Girona, Spain
| | - Claus Z Simonsen
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia
- Austin Health, Department of Neurology, Heidelberg, VIC, Australia
| | - Anke Wouters
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology, Leuven, Belgium
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, Campus Gasthuisberg, Leuven, Belgium
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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176
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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177
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Boshkovski T, Kocarev L, Cohen-Adad J, Mišić B, Lehéricy S, Stikov N, Mancini M. The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure. Netw Neurosci 2021; 5:358-372. [PMID: 34189369 PMCID: PMC8233108 DOI: 10.1162/netn_a_00179] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/11/2020] [Indexed: 11/05/2022] Open
Abstract
Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture. In the present work, we show that by using a myelin-sensitive measure we can complement the diffusion MRI-based connectivity and provide a different picture of the brain organization. We show that the R1-weighted average distribution does not follow the same trend as the number of streamlines strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions have higher R1-weighted average but fewer streamlines. This could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions rely on connections with higher myelination.
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Affiliation(s)
| | - Ljupco Kocarev
- Macedonian Academy of Sciences and Arts, Skopje, Macedonia
| | | | | | - Stéphane Lehéricy
- Paris Brain Institute (ICM), Centre for NeuroImaging Research (CENIR), Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013, Paris, France
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada
| | - Matteo Mancini
- NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada
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178
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Hodology of the superior longitudinal system of the human brain: a historical perspective, the current controversies, and a proposal. Brain Struct Funct 2021; 226:1363-1384. [PMID: 33881634 DOI: 10.1007/s00429-021-02265-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 03/23/2021] [Indexed: 02/07/2023]
Abstract
The description of human white matter pathways experienced a tremendous improvement, thanks to the advancement of neuroimaging and dissection techniques. The downside of this progress is the production of redundant and conflicting literature, bound by specific studies' methods and aims. The Superior Longitudinal System (SLS), encompassing the arcuate (AF) and the superior longitudinal fasciculi (SLF), becomes an illustrative example of this fundamental issue, being one of the most studied white matter association pathways of the brain. Herein, we provide a complete illustration of this white matter fiber system's current definition, from its early descriptions in the nineteenth century to its most recent characterizations. We propose a review of both in vivo diffusion magnetic resonance imaging-based tractography and anatomical dissection studies, enclosing all the information available up to date. Based on these findings, we reconstruct the wiring diagram of the SLS, highlighting a substantial variability in the description of its cortical sites of termination and the taxonomy and partonomy that characterize the system. We aim to level up discrepancies in the literature by proposing a parallel across the various nomenclature. Consistent with the topographical arrangement already documented for commissural and projection pathways, we suggest approaching the SLS organization as an orderly and continuous wiring diagram, respecting a medio-lateral palisading topography between the different frontal, parietal, occipital, and temporal gyri rather than in terms of individualized fascicles. A better and complete description of the fine organization of white matter association pathways' connectivity is fundamental for a better understanding of brain function and their clinical and neurosurgical applications.
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179
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Sedghi A, O'Donnell LJ, Kapur T, Learned-Miller E, Mousavi P, Wells WM. Image registration: Maximum likelihood, minimum entropy and deep learning. Med Image Anal 2021; 69:101939. [PMID: 33388458 PMCID: PMC8046343 DOI: 10.1016/j.media.2020.101939] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 11/28/2020] [Accepted: 12/07/2020] [Indexed: 01/12/2023]
Abstract
In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.
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Affiliation(s)
- Alireza Sedghi
- Medical Informatics Laboratory, Queen's University, Kingston, Canada.
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Erik Learned-Miller
- College of Information and Computer Sciences, University of Massachusetts, Amherst, USA
| | - Parvin Mousavi
- Medical Informatics Laboratory, Queen's University, Kingston, Canada
| | - William M Wells
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
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180
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Sierk A, Manthey A, Brakemeier EL, Walter H, Daniels JK. The dissociative subtype of posttraumatic stress disorder is associated with subcortical white matter network alterations. Brain Imaging Behav 2021; 15:643-655. [PMID: 32342260 PMCID: PMC8032639 DOI: 10.1007/s11682-020-00274-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Posttraumatic stress disorder (PTSD) is characterized by intrusions, avoidance, and hyperarousal while patients of the dissociative subtype (PTSD-D) experience additional dissociative symptoms. A neurobiological model proposes hyper-inhibition of limbic structures mediated by prefrontal cortices to underlie dissociation in PTSD. Here, we tested whether functional alterations in fronto-limbic circuits are underpinned by white matter network abnormalities on a network level. 23 women with PTSD-D and 19 women with classic PTSD participated. We employed deterministic diffusion tractography and graph theoretical analyses. Mean fractional anisotropy (FA) was chosen as a network weight and group differences assessed using network-based statistics. No significant white matter network alterations comprising both frontal and limbic structures in PTSD-D relative to classic PTSD were found. A subsequent whole brain exploratory analysis revealed relative FA alterations in PTSD-D in two subcortical networks, comprising connections between the left amygdala, hippocampus, and thalamus as well as links between the left ventral diencephalon, putamen, and pallidum, respectively. Dissociative symptom severity in the PTSD-D group correlated with FA values within both networks. Our findings suggest fronto-limbic inhibition in PTSD-D may present a dynamic neural process, which is not hard-wired via white matter tracts. Our exploratory results point towards altered fiber tract communication in a limbic-thalamic circuit, which may underlie (a) an initial strong emotional reaction to trauma reminders before conscious regulatory processes are enabled and (b) deficits in early sensory processing. In addition, aberrant structural connectivity in low-level motor regions may present neural correlates for dissociation as a passive threat-response.
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Affiliation(s)
- Anika Sierk
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, Berlin, Germany
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Antje Manthey
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, Berlin, Germany
| | - Eva-Lotta Brakemeier
- Department of Psychology & Marburg Center for Mind, Brain and Behavior (MCMBB), Philipps-Universität Marburg, Marburg, Germany
- Department of Clinical Psychology, University of Groningen, Groningen, Netherlands
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany, Berlin, Germany
| | - Judith K Daniels
- Department of Clinical Psychology, University of Groningen, Groningen, Netherlands.
- Psychologische Hochschule Berlin, Berlin, Germany.
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181
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Grazzini I, Venezia D, Cuneo GL. The role of diffusion tensor imaging in idiopathic normal pressure hydrocephalus: A literature review. Neuroradiol J 2021; 34:55-69. [PMID: 33263494 PMCID: PMC8041402 DOI: 10.1177/1971400920975153] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Idiopathic normal pressure hydrocephalus (iNPH) is a syndrome that comprises a triad of gait disturbance, dementia and urinary incontinence, associated with ventriculomegaly in the absence of elevated intraventricular cerebrospinal fluid (CSF) pressure. It is important to identify patients with iNPH because some of its clinical features may be reversed by the insertion of a CSF shunt. The diagnosis is based on clinical history, physical examination and brain imaging, especially magnetic resonance imaging (MRI). Recently, some papers have investigated the role of diffusion tensor imaging (DTI) in evaluating white matter alterations in patients with iNPH. DTI analysis in specific anatomical regions seems to be a promising MR biomarker of iNPH and could also be used in the differential diagnosis from other dementias. However, there is a substantial lack of structured reviews on this topic. Thus, we performed a literature search and analyzed the most recent and pivotal articles that investigated the role of DTI in iNPH in order to provide an up-to-date overview of the application of DTI in this setting. We reviewed studies published between January 2000 and June 2020. Thirty-eight studies and four reviews were included. Despite heterogeneity in analysis approaches, the majority of studies reported significant correlations between DTI and clinical symptoms in iNPH patients, as well as different DTI patterns in patients with iNPH compared to those with Alzheimer or Parkinson diseases. It remains to be determined whether DTI could predict the success after CSF shunting.
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Affiliation(s)
- Irene Grazzini
- Department of Radiology, Section of Neuroradiology, San Donato Hospital, Arezzo, Italy
| | - Duccio Venezia
- Department of Radiology, Section of Neuroradiology, San Donato Hospital, Arezzo, Italy
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182
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Bugain M, Dimech Y, Torzhenskaya N, Thiebaut de Schotten M, Caspers S, Muscat R, Bajada CJ. Occipital Intralobar fasciculi: a description, through tractography, of three forgotten tracts. Commun Biol 2021; 4:433. [PMID: 33785859 PMCID: PMC8010026 DOI: 10.1038/s42003-021-01935-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 03/03/2021] [Indexed: 02/01/2023] Open
Abstract
Diffusion MRI paired with tractography has facilitated a non-invasive exploration of many association, projection, and commissural fiber tracts. However, there is still a scarcity of research studies related to intralobar association fibers. The Dejerines' (two of the most notable neurologists of 19th century France) gave an in-depth description of the intralobar fibers of the occipital lobe. Unfortunately, their exquisite work has since been sparsely cited in the modern literature. This work gives a modern description of many of the occipital intralobar lobe fibers described by the Dejerines. We perform a virtual dissection and reconstruct the tracts using diffusion MRI tractography. The dissection is guided by the Dejerines' treatise, Anatomie des Centres Nerveux. As an accompaniment to this article, we provided a French-to-English translation of the treatise portion concerning five intra-occipital tracts, namely: the stratum calcarinum, the stratum proprium cunei, the vertical occipital fasciculus of Wernicke, the transverse fasciculus of the cuneus and the transverse fasciculus of the lingual lobule of Vialet. It was possible to reconstruct all but one of these tracts. For completeness, the recently described sledge runner fasciculus, although not one of the Dejerines' tracts, was identified and successfully reconstructed.
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Affiliation(s)
- Maeva Bugain
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, The University of Malta, Msida, Malta
| | - Yana Dimech
- Department of Cognitive Sciences, Faculty of Media and Knowledge Sciences, The University of Malta, Msida, Malta
| | - Natalia Torzhenskaya
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, The University of Malta, Msida, Malta
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives -UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Richard Muscat
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, The University of Malta, Msida, Malta
| | - Claude J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, The University of Malta, Msida, Malta.
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany.
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183
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Glomb K, Cabral J, Cattani A, Mazzoni A, Raj A, Franceschiello B. Computational Models in Electroencephalography. Brain Topogr 2021; 35:142-161. [PMID: 33779888 PMCID: PMC8813814 DOI: 10.1007/s10548-021-00828-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.
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Affiliation(s)
- Katharina Glomb
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Anna Cattani
- Department of Psychiatry, University of Wisconsin-Madison, Madison, USA.,Department of Biomedical and Clinical Sciences 'Luigi Sacco', University of Milan, Milan, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ashish Raj
- School of Medicine, UCSF, San Francisco, USA
| | - Benedetta Franceschiello
- Department of Ophthalmology, Hopital Ophthalmic Jules Gonin, FAA, Lausanne, Switzerland.,CIBM Centre for Biomedical Imaging, EEG Section CHUV-UNIL, Lausanne, Switzerland.,Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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184
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Brown BL, Zalla RM, Shepard CT, Howard RM, Kopechek JA, Magnuson DSK, Whittemore SR. Dual-Viral Transduction Utilizing Highly Efficient Retrograde Lentivirus Improves Labeling of Long Propriospinal Neurons. Front Neuroanat 2021; 15:635921. [PMID: 33828464 PMCID: PMC8019739 DOI: 10.3389/fnana.2021.635921] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/12/2021] [Indexed: 11/13/2022] Open
Abstract
The nervous system coordinates pathways and circuits to process sensory information and govern motor behaviors. Mapping these pathways is important to further understand the connectivity throughout the nervous system and is vital for developing treatments for neuronal diseases and disorders. We targeted long ascending propriospinal neurons (LAPNs) in the rat spinal cord utilizing Fluoro-Ruby (FR) [10kD rhodamine dextran amine (RDA)], and two dual-viral systems. Dual-viral tracing utilizing a retrograde adeno-associated virus (retroAAV), which confers robust labeling in the brain, resulted in a small number of LAPNs being labeled, but dual-viral tracing using a highly efficient retrograde (HiRet) lentivirus provided robust labeling similar to FR. Additionally, dual-viral tracing with HiRet lentivirus and tracing with FR may preferentially label different subpopulations of LAPNs. These data demonstrate that dual-viral tracing in the spinal cord employing a HiRet lentivirus provides robust and specific labeling of LAPNs and emphasizes the need to empirically optimize viral systems to target specific neuronal population(s).
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Affiliation(s)
- Brandon L Brown
- Interdisciplinary Program in Translational Neuroscience, University of Louisville, Louisville, KY, United States.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.,Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Rachel M Zalla
- Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.,Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, United States
| | - Courtney T Shepard
- Interdisciplinary Program in Translational Neuroscience, University of Louisville, Louisville, KY, United States.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.,Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Russell M Howard
- Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.,Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Jonathan A Kopechek
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, United States
| | - David S K Magnuson
- Interdisciplinary Program in Translational Neuroscience, University of Louisville, Louisville, KY, United States.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.,Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY, United States.,Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, United States.,Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Scott R Whittemore
- Interdisciplinary Program in Translational Neuroscience, University of Louisville, Louisville, KY, United States.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, United States.,Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY, United States.,Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY, United States
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185
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Chen Q, Lv X, Zhang S, Lin J, Song J, Cao B, Weng Y, Li L, Huang R. Altered properties of brain white matter structural networks in patients with nasopharyngeal carcinoma after radiotherapy. Brain Imaging Behav 2021; 14:2745-2761. [PMID: 31900892 DOI: 10.1007/s11682-019-00224-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Previous neuroimaging studies revealed radiation-induced brain injury in patients with nasopharyngeal carcinoma (NPC) in the years after radiotherapy (RT). These injuries may be associated with structural and functional alterations. However, differences in the brain structural connectivity of NPC patients at different times after RT, especially in the early-delayed period, remain unclear. We acquired diffusion tensor imaging (DTI) data from three groups of NPC patients, 25 in the pre-RT (before RT) group, 22 in the early-delayed (1-6 months) period (post-RT-ED) group, and 33 in the late-delayed (>6 months) period (post-RT-LD) group. Then, we constructed brain white matter (WM) structural networks and used graph theory to compare their between-group differences. The NPC patients in the post-RT-ED group showed decreased global properties when compared with the pre-RT group. We also detected the nodes with between-group differences in nodal parameters. The nodes that differed between the post-RT-ED and pre-RT groups were mainly located in the default mode (DMN) and central executive networks (CEN); those that differed between the post-RT-LD and pre-RT groups were located in the limbic system; and those that differed between the post-RT-LD and post-RT-ED groups were mainly in the DMN. These findings may indicate that radiation-induced brain injury begins in the early-delayed period and that a reorganization strategy begins in the late-delayed period. Our findings may provide new insight into the pathogenesis of radiation-induced brain injury in normal-appearing brain tissue from the network perspective.
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Affiliation(s)
- Qinyuan Chen
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Xiaofei Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Shufei Zhang
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Jiabao Lin
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Jie Song
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Bolin Cao
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Yihe Weng
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Li Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
| | - Ruiwang Huang
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, People's Republic of China.
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186
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Ulrichsen KM, Kolskår KK, Richard G, Alnæs D, Dørum ES, Sanders AM, Tornås S, Sánchez JM, Engvig A, Ihle-Hansen H, de Schotten MT, Nordvik JE, Westlye LT. Structural brain disconnectivity mapping of post-stroke fatigue. NEUROIMAGE-CLINICAL 2021; 30:102635. [PMID: 33799271 PMCID: PMC8044723 DOI: 10.1016/j.nicl.2021.102635] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/15/2021] [Accepted: 03/15/2021] [Indexed: 01/04/2023]
Abstract
We tested for associations between post stroke fatigue (PSF) and both lesion characteristics and brain structural disconnectome in 84 S patients. Results provided no evidence supporting a simple association between PSF severity and lesion characteristics or disconnectivity. PSF was strongly correlated with depression. Further studies including patients with more severe symptoms are needed to generalize the findings across a wider clinical spectrum.
Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remains unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a structural disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual structural brain disconnectome maps in 84 S survivors in the chronic phase (≥3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue among chronic stroke patients are not simply explained by lesion characteristics or the extent and distribution of structural brain disconnectome, and are discussed in light of methodological considerations.
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Affiliation(s)
- Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
| | - Knut K Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Erlend S Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | | | - Jennifer Monereo Sánchez
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Netherlands
| | - Andreas Engvig
- Department of Nephrology, Oslo University Hospital, Ullevål, Norway
| | | | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives- UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | | | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway.
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187
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Direct Structural Connections between Auditory and Visual Motion-Selective Regions in Humans. J Neurosci 2021; 41:2393-2405. [PMID: 33514674 DOI: 10.1523/jneurosci.1552-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 11/21/2022] Open
Abstract
In humans, the occipital middle-temporal region (hMT+/V5) specializes in the processing of visual motion, while the planum temporale (hPT) specializes in auditory motion processing. It has been hypothesized that these regions might communicate directly to achieve fast and optimal exchange of multisensory motion information. Here we investigated, for the first time in humans (male and female), the presence of direct white matter connections between visual and auditory motion-selective regions using a combined fMRI and diffusion MRI approach. We found evidence supporting the potential existence of direct white matter connections between individually and functionally defined hMT+/V5 and hPT. We show that projections between hMT+/V5 and hPT do not overlap with large white matter bundles, such as the inferior longitudinal fasciculus and the inferior frontal occipital fasciculus. Moreover, we did not find evidence suggesting the presence of projections between the fusiform face area and hPT, supporting the functional specificity of hMT+/V5-hPT connections. Finally, the potential presence of hMT+/V5-hPT connections was corroborated in a large sample of participants (n = 114) from the human connectome project. Together, this study provides a first indication for potential direct occipitotemporal projections between hMT+/V5 and hPT, which may support the exchange of motion information between functionally specialized auditory and visual regions.SIGNIFICANCE STATEMENT Perceiving and integrating moving signal across the senses is arguably one of the most important perceptual skills for the survival of living organisms. In order to create a unified representation of movement, the brain must therefore integrate motion information from separate senses. Our study provides support for the potential existence of direct connections between motion-selective regions in the occipital/visual (hMT+/V5) and temporal/auditory (hPT) cortices in humans. This connection could represent the structural scaffolding for the rapid and optimal exchange and integration of multisensory motion information. These findings suggest the existence of computationally specific pathways that allow information flow between areas that share a similar computational goal.
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188
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Zhang J, Wang L, Ding H, Fan K, Tian Q, Liang M, Sun Z, Shi D, Qin W. Abnormal large-scale structural rich club organization in Leber's hereditary optic neuropathy. NEUROIMAGE-CLINICAL 2021; 30:102619. [PMID: 33752075 PMCID: PMC8010853 DOI: 10.1016/j.nicl.2021.102619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 02/23/2021] [Accepted: 03/02/2021] [Indexed: 12/24/2022]
Abstract
LHON patients suffered large-scale structural network disruption. Non-rich club connections may be more vulnerable in the LHON. Both primary and secondary connectivity damage may coexist in the LHON.
Objective The purpose of this study was to investigate whether the large-scale structural rich club organization was abnormal in patients with Leber's hereditary optic neuropathy (LHON) using diffusion tensor imaging (DTI), and the associations among disrupted brain structural connectivity, disease duration, and neuro-ophthalmological impairment. Methods Nineteen acute, 34 chronic LHON patients, and 36 healthy controls (HC) underwent DTI and neuro-ophthalmological measurements. The brain structural network and rich club organization were constructed based on deterministic fiber tracking at the individual level. Then intergroup differences among the acute, chronic LHON patients and healthy controls (HC) in three types of structural connections, including rich club, feeder, and local ones, were compared. Network-based Statistics (NBS) was also used to test the intergroup connectivity differences for each fiber. Several linear and nonlinear curve fit models were applied to explore the associations among large-scale brain structural connectivity, disease duration, and neuro-ophthalmological metrics. Results Compared to the HC, both the acute and chronic LHON patients had consistently significantly lower fractional anisotropy (FA) and higher radial diffusion (RD) for feeder connections (p < 0.05, FDR correction). Acute LHON patients had significantly lower FA and higher RD for local connections (p < 0.05, FDR correction). There was no significant difference in large-scale brain structural connectivity between acute and chronic LHON (p > 0.05, FDR correction). NBS also identified reduced FA of three feeder connections and five local ones linking visual, auditory, and basal ganglia areas in LHON patients (p < 0.05, FDR correction). No structural connections showed linear or nonlinear association with either disease duration or neuro-ophthalmological indicators (p > 0.05, FDR correction). A significant negative correlation was shown between the retinal nerve fiber layer (RNFL) thickness and disease duration (p < 0.05, FDR correction). Conclusions Abnormal rich club organization of the structural network was identified in both the acute and chronic LHON. Furthermore, our findings suggest the coexistence of both primary and secondary connectivity damage in the LHON.
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Affiliation(s)
- Jiahui Zhang
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Ling Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Hao Ding
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Ke Fan
- Henan Eye Institute, Henan Eye Hospital, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Qin Tian
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Meng Liang
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Zhihua Sun
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| | - Dapeng Shi
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China.
| | - Wen Qin
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
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189
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Liang X, Su P, Patil SG, Elsaid NMH, Roys S, Stone M, Gullapalli RP, Prince JL, Zhuo J. Prospective motion detection and re-acquisition in diffusion MRI using a phase image-based method-Application to brain and tongue imaging. Magn Reson Med 2021; 86:725-737. [PMID: 33665929 DOI: 10.1002/mrm.28729] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 01/22/2021] [Accepted: 01/23/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop an image-based motion-robust diffusion MRI (dMRI) acquisition framework that is able to minimize motion artifacts caused by rigid and nonrigid motion, applicable to both brain and tongue dMRI. METHODS We developed a novel prospective motion-correction technique in dMRI using a phase image-based real-time motion-detection method (PITA-MDD) with re-acquisition of motion-corrupted images. The prospective PITA-MDD acquisition technique was tested in the brains and tongues of volunteers. The subjects were instructed to move their heads or swallow, to induce motion. Motion-detection efficacy was validated against visual inspection as the gold standard. The effect of the PITA-MDD technique on diffusion-parameter estimates was evaluated by comparing reconstructed fiber tracts using tractography with and without re-acquisition. RESULTS The prospective PITA-MDD technique was able to effectively and accurately detect motion-corrupted data as compared with visual inspection. Tractography results demonstrated that PITA-MDD motion detection followed by re-acquisition helps in recovering lost and misshaped fiber tracts in the brain and tongue that would otherwise be corrupted by motion and yield erroneous estimates of the diffusion tensor. CONCLUSION A prospective PITA-MDD technique was developed for dMRI acquisition, providing improved dMRI image quality and motion-robust diffusion estimation of the brain and tongue.
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Affiliation(s)
- Xiao Liang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Pan Su
- Siemens Medical Solutions USA Inc, Malvern, Pennsylvania, USA
| | - Sunil G Patil
- Siemens Medical Solutions USA Inc, Malvern, Pennsylvania, USA
| | - Nahla M H Elsaid
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Steven Roys
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Maureen Stone
- Department of Neural and Pain Sciences and Department of Orthodontics, University of Maryland School of Dentistry, Baltimore, Maryland, USA
| | - Rao P Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jiachen Zhuo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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190
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Nägele FL, Pasternak O, Bitzan LV, Mußmann M, Rauh J, Kubicki M, Leicht G, Shenton ME, Lyall AE, Mulert C. Cellular and extracellular white matter alterations indicate conversion to psychosis among individuals at clinical high-risk for psychosis. World J Biol Psychiatry 2021; 22:214-227. [PMID: 32643526 PMCID: PMC7798359 DOI: 10.1080/15622975.2020.1775890] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVES It is important to find biomarkers associated with transition to illness in individuals at clinical high-risk for psychosis (CHR). Here, we use free-water imaging, an advanced diffusion MRI technique, to identify white matter alterations in the brains of CHR subjects who subsequently develop psychosis (CHR-P) compared to those who do not (CHR-NP). METHODS Twenty-four healthy controls (HC) and 30 CHR individuals, 8 of whom converted to schizophrenia after a mean follow-up of 15.16 months, received baseline MRI scans. Maps of fractional anisotropy (FA), FA of cellular tissue (FAT), and extracellular free-water (FW) were extracted using tract-based spatial statistics after which voxel-wise non-parametric group statistics and correlations with symptom severity were performed. RESULTS There were no significant differences between HCs and the combined CHR group. However, prior to conversion, CHR-P showed widespread lower FA compared to CHR-NP (pFWE < 0.05). FA changes in CHR-P were associated with significantly lower FAT and higher FW, compared to CHR-NP. Positive symptoms correlated significantly with diffusion parameters in similar regions as those discriminating CHR-P from CHR-NP. CONCLUSIONS Our study suggests that cellular (FAT) and extracellular (FW) white matter alterations are associated with positive symptom severity and indicate an elevated illness risk among CHR individuals.
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Affiliation(s)
- Felix L. Nägele
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany;,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lisa V. Bitzan
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany;,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Marius Mußmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Jonas Rauh
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gregor Leicht
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;,VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany;,Centre for Psychiatry and Psychotherapy, Justus-Liebig-University, Giessen, Germany
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191
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Li C, Qiao K, Mu Y, Jiang L. Large-Scale Morphological Network Efficiency of Human Brain: Cognitive Intelligence and Emotional Intelligence. Front Aging Neurosci 2021; 13:605158. [PMID: 33732136 PMCID: PMC7959829 DOI: 10.3389/fnagi.2021.605158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/01/2021] [Indexed: 12/13/2022] Open
Abstract
Network efficiency characterizes how information flows within a network, and it has been used to study the neural basis of cognitive intelligence in adolescence, young adults, and elderly adults, in terms of the white matter in the human brain and functional connectivity networks. However, there were few studies investigating whether the human brain at different ages exhibited different underpins of cognitive and emotional intelligence (EI) from young adults to the middle-aged group, especially in terms of the morphological similarity networks in the human brain. In this study, we used 65 datasets (aging 18–64), including sMRI and behavioral measurements, to study the associations of network efficiency with cognitive intelligence and EI in young adults and the middle-aged group. We proposed a new method of defining the human brain morphological networks using the morphological distribution similarity (including cortical volume, surface area, and thickness). Our results showed inverted age × network efficiency interactions in the relationship of surface-area network efficiency with cognitive intelligence and EI: a negative age × global efficiency (nodal efficiency) interaction in cognitive intelligence, while a positive age × global efficiency (nodal efficiency) interaction in EI. In summary, this study not only proposed a new method of morphological similarity network but also emphasized the developmental effects on the brain mechanisms of intelligence from young adult to middle-aged groups and may promote mental health study on the middle-aged group in the future.
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Affiliation(s)
- Chunlin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Kaini Qiao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yan Mu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lili Jiang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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192
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Ocampo-Pineda M, Schiavi S, Rheault F, Girard G, Petit L, Descoteaux M, Daducci A. Hierarchical Microstructure Informed Tractography. Brain Connect 2021; 11:75-88. [PMID: 33512262 DOI: 10.1089/brain.2020.0907] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background: Tractography uses diffusion magnetic resonance imaging to noninvasively infer the macroscopic pathways of white matter fibers and it is the only available technique to probe in vivo the structural connectivity of the brain. However, despite this unique and compelling ability and its wide range of possible neurological applications, tractography is still limited, lacks anatomical precision, and suffers from a serious sensitivity/specificity trade-off. For this reason, in the past few years, tractography postprocessing techniques have emerged and proved effective for improving the quality of the reconstructions. Among them, the Convex Optimization Modeling for Microstructure Informed Tractography formulation allows incorporating the anatomical prior that fibers are naturally organized in fascicles, and has obtained exceptional results in increasing the accuracy of the estimated tractograms. Methods: We propose an extension to this idea and introduce a multilevel grouping of the streamlines to capture the white matter arrangement in fascicles and subfascicles. We tested our proposed formulation in synthetic and in vivo data. Results: Our experiments show that using multiple levels allows considering information about the white matter organization more adequately and helps to improve further the accuracy of the resulting tractograms. Conclusion: This new formulation represents a further important step toward a more accurate structural connectivity estimation.
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Affiliation(s)
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - François Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Gabriel Girard
- Center for BioMedical Imaging (CIBM), Lausanne, Switzerland.,University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle, Université de Bordeaux, CNRS, CEA, IMN, UMR 5293, Bordeaux, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Canada
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193
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Henderson F, Abdullah KG, Verma R, Brem S. Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus 2021; 48:E6. [PMID: 32006950 DOI: 10.3171/2019.11.focus19785] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 11/13/2019] [Indexed: 12/21/2022]
Abstract
The ability of diffusion tensor MRI to detect the preferential diffusion of water in cerebral white matter tracts enables neurosurgeons to noninvasively visualize the relationship of lesions to functional neural pathways. Although viewed as a research tool in its infancy, diffusion tractography has evolved into a neurosurgical tool with applications in glioma surgery that are enhanced by evolutions in crossing fiber visualization, edema correction, and automated tract identification. In this paper the current literature supporting the use of tractography in brain tumor surgery is summarized, highlighting important clinical studies on the application of diffusion tensor imaging (DTI) for preoperative planning of glioma resection, and risk assessment to analyze postoperative outcomes. The key methods of tractography in current practice and crucial white matter fiber bundles are summarized. After a review of the physical basis of DTI and post-DTI tractography, the authors discuss the methodologies with which to adapt DT image processing for surgical planning, as well as the potential of connectomic imaging to facilitate a network approach to oncofunctional optimization in glioma surgery.
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Affiliation(s)
- Fraser Henderson
- 1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania.,3Department of Neurosurgery, The Medical University of South Carolina, Charleston, South Carolina; and
| | - Kalil G Abdullah
- 4Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ragini Verma
- 1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania.,2DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Steven Brem
- 1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania
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194
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Kiselev VG, Körzdörfer G, Gall P. Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Invest Radiol 2021; 56:1-9. [PMID: 33186141 DOI: 10.1097/rli.0000000000000738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research fields are briefly reviewed as ways to respond to the challenge of quantitative MRI, both experiencing an exponential growth right now. Microstructure MRI strives to build physiology-based models from cells to signal and, given the signal, back to the cells again. Magnetic resonance fingerprinting aims at efficient simultaneous determination of multiple signal parameters. The synergy of these yet disjoined approaches promises truly quantitative MRI with specific target-oriented diagnostic tools rather than universal imaging methods.
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Affiliation(s)
- Valerij G Kiselev
- From the Medical Physics, Department of Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg
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195
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Towards linking diffusion MRI based macro- and microstructure measures with cortico-cortical transmission in brain tumor patients. Neuroimage 2020; 226:117567. [PMID: 33221443 DOI: 10.1016/j.neuroimage.2020.117567] [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: 08/20/2020] [Revised: 09/29/2020] [Accepted: 11/16/2020] [Indexed: 11/19/2022] Open
Abstract
We aimed to link macro- and microstructure measures of brain white matter obtained from diffusion MRI with effective connectivity measures based on a propagation of cortico-cortical evoked potentials induced with intrasurgical direct electrical stimulation. For this, we compared streamline lengths and log-transformed ratios of streamlines computed from presurgical diffusion-weighted images, and the delays and amplitudes of N1 peaks recorded intrasurgically with electrocorticography electrodes in a pilot study of 9 brain tumor patients. Our results showed positive correlation between these two modalities in the vicinity of the stimulation sites (Pearson coefficient 0.54±0.13 for N1 delays, and 0.47±0.23 for N1 amplitudes), which could correspond to the neural propagation via U-fibers. In addition, we reached high sensitivities (0.78±0.07) and very high specificities (0.93±0.03) in a binary variant of our comparison. Finally, we used the structural connectivity measures to predict the effective connectivity using a multiple linear regression model, and showed a significant role of brain microstructure-related indices in this relation.
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196
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Indovina I, Bosco G, Riccelli R, Maffei V, Lacquaniti F, Passamonti L, Toschi N. Structural connectome and connectivity lateralization of the multimodal vestibular cortical network. Neuroimage 2020; 222:117247. [PMID: 32798675 PMCID: PMC7779422 DOI: 10.1016/j.neuroimage.2020.117247] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 07/28/2020] [Accepted: 08/05/2020] [Indexed: 01/05/2023] Open
Abstract
Unlike other sensory systems, the structural connectivity patterns of the human vestibular cortex remain a matter of debate. Based on their functional properties and hypothesized centrality within the vestibular network, the ‘core’ cortical regions of this network are thought to be areas in the posterior peri-sylvian cortex, in particular the retro-insula (previously named the posterior insular cortex-PIC), and the subregion OP2 of the parietal operculum. To study the vestibular network, structural connectivity matrices from n=974 healthy individuals drawn from the public Human Connectome Project (HCP) repository were estimated using multi-shell diffusion-weighted data followed by probabilistic tractography and spherical-deconvolution informed filtering of tractograms in combination with subject-specific grey-matter parcellations. Weighted graph-theoretical measures, modularity, and ‘hubness’ of the multimodal vestibular network were then estimated, and a structural lateralization index was defined in order to assess the difference in fiber density of homonym regions in the right and left hemisphere. Differences in connectivity patterns between OP2 and PIC were also estimated. We found that the bilateral intraparietal sulcus, PIC, and to a lesser degree OP2, are key ‘hub’ regions within the multimodal vestibular network. PIC and OP2 structural connectivity patterns were lateralized to the left hemisphere, while structural connectivity patterns of the posterior peri-sylvian supramarginal and superior temporal gyri were lateralized to the right hemisphere. These lateralization patterns were independent of handedness. We also found that the structural connectivity pattern of PIC is consistent with a key role of PIC in visuo-vestibular processing and that the structural connectivity pattern of OP2 is consistent with integration of mainly vestibular somato-sensory and motor information. These results suggest an analogy between PIC and the simian visual posterior sylvian (VPS) area and OP2 and the simian parieto-insular vestibular cortex (PIVC). Overall, these findings may provide novel insights to the current models of vestibular function, as well as to the understanding of the complexity and lateralized signs of vestibular syndromes.
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Affiliation(s)
- Iole Indovina
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98125 Messina, Italy; Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, via Ardeatina 354, 00179 Rome, Italy.
| | - Gianfranco Bosco
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, via Ardeatina 354, 00179 Rome, Italy; Department of Systems Medicine and Centre of Space BioMedicine, University of Rome Tor Vergata, 00173 Rome, Italy
| | - Roberta Riccelli
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, via Ardeatina 354, 00179 Rome, Italy
| | - Vincenzo Maffei
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, via Ardeatina 354, 00179 Rome, Italy
| | - Francesco Lacquaniti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, via Ardeatina 354, 00179 Rome, Italy; Department of Systems Medicine and Centre of Space BioMedicine, University of Rome Tor Vergata, 00173 Rome, Italy
| | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, UK; Institute of Bioimaging & Molecular Physiology, National Research Council, Milano, Italy; IRCCS San Camillo Hospital, Venice, Italy.
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA
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197
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Morez J, Sijbers J, Vanhevel F, Jeurissen B. Constrained spherical deconvolution of nonspherically sampled diffusion MRI data. Hum Brain Mapp 2020; 42:521-538. [PMID: 33169880 PMCID: PMC7776001 DOI: 10.1002/hbm.25241] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 08/30/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022] Open
Abstract
Constrained spherical deconvolution (CSD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW-MRI signal on the sphere can be represented as the spherical convolution of a single-fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW-MRI data is sampled shell-wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b-values. On shell-wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell-wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.
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Affiliation(s)
- Jan Morez
- Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Floris Vanhevel
- Department of Radiology, University Hospital Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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198
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Wegmayr V, Buhmann JM. Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography. Int J Comput Vis 2020. [DOI: 10.1007/s11263-020-01384-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractWhite matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.
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199
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Howells H, Simone L, Borra E, Fornia L, Cerri G, Luppino G. Reproducing macaque lateral grasping and oculomotor networks using resting state functional connectivity and diffusion tractography. Brain Struct Funct 2020; 225:2533-2551. [PMID: 32936342 PMCID: PMC7544728 DOI: 10.1007/s00429-020-02142-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 09/02/2020] [Indexed: 12/31/2022]
Abstract
Cortico-cortical networks involved in motor control have been well defined in the macaque using a range of invasive techniques. The advent of neuroimaging has enabled non-invasive study of these large-scale functionally specialized networks in the human brain; however, assessing its accuracy in reproducing genuine anatomy is more challenging. We set out to assess the similarities and differences between connections of macaque motor control networks defined using axonal tracing and those reproduced using structural and functional connectivity techniques. We processed a cohort of macaques scanned in vivo that were made available by the open access PRIME-DE resource, to evaluate connectivity using diffusion imaging tractography and resting state functional connectivity (rs-FC). Sectors of the lateral grasping and exploratory oculomotor networks were defined anatomically on structural images, and connections were reproduced using different structural and functional approaches (probabilistic and deterministic whole-brain and seed-based tractography; group template and native space functional connectivity analysis). The results showed that parieto-frontal connections were best reproduced using both structural and functional connectivity techniques. Tractography showed lower sensitivity but better specificity in reproducing connections identified by tracer data. Functional connectivity analysis performed in native space had higher sensitivity but lower specificity and was better at identifying connections between intrasulcal ROIs than group-level analysis. Connections of AIP were most consistently reproduced, although those connected with prefrontal sectors were not identified. We finally compared diffusion MR modelling with histology based on an injection in AIP and speculate on anatomical bases for the observed false negatives. Our results highlight the utility of precise ex vivo techniques to support the accuracy of neuroimaging in reproducing connections, which is relevant also for human studies.
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Affiliation(s)
- Henrietta Howells
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy.
| | - Luciano Simone
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy.
| | - Elena Borra
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
| | - Luca Fornia
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Gabriella Cerri
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Giuseppe Luppino
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
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200
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Horn A, Fox MD. Opportunities of connectomic neuromodulation. Neuroimage 2020; 221:117180. [PMID: 32702488 PMCID: PMC7847552 DOI: 10.1016/j.neuroimage.2020.117180] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/12/2020] [Accepted: 07/16/2020] [Indexed: 12/14/2022] Open
Abstract
The process of altering neural activity - neuromodulation - has long been used to treat patients with brain disorders and answer scientific questions. Deep brain stimulation in particular has provided clinical benefit to over 150,000 patients. However, our understanding of how neuromodulation impacts the brain is evolving. Instead of focusing on the local impact at the stimulation site itself, we are considering the remote impact on brain regions connected to the stimulation site. Brain connectivity information derived from advanced magnetic resonance imaging data can be used to identify these connections and better understand clinical and behavioral effects of neuromodulation. In this article, we review studies combining neuromodulation and brain connectomics, highlighting opportunities where this approach may prove particularly valuable. We focus on deep brain stimulation, but show that the same principles can be applied to other forms of neuromodulation, such as transcranial magnetic stimulation and MRI-guided focused ultrasound. We outline future perspectives and provide testable hypotheses for future work.
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Affiliation(s)
- Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Sectio Charité - University Medicine Berlin,, Charitéplatz 1, D-10117 Berlin, Germany.
| | - Michael D Fox
- Berenson-Allen Center for Non-invasive Brain Stimulation, Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, United States; Martinos Center for Biomedical Imaging, Departments of Neurology and Radiology, Harvard Medical School and Massachusetts General Hospital, United States; Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, and Radiology, Harvard Medical School and Brigham and Women's Hospital, United States.
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