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Tanner J, Faskowitz J, Teixeira AS, Seguin C, Coletta L, Gozzi A, Mišić B, Betzel RF. A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity. Nat Commun 2024; 15:5865. [PMID: 38997282 PMCID: PMC11245624 DOI: 10.1038/s41467-024-50248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
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Affiliation(s)
- Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andreia Sofia Teixeira
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, USA.
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, USA.
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2
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Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | | | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Boston Children's Hospital, Harvard Medical School
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3
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Mendoza C, Román C, Mangin JF, Hernández C, Guevara P. Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter. Front Neurosci 2024; 18:1394681. [PMID: 38737100 PMCID: PMC11088237 DOI: 10.3389/fnins.2024.1394681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/14/2024] Open
Abstract
In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.
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Affiliation(s)
- Cristóbal Mendoza
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
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4
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Ragone E, Tanner J, Jo Y, Zamani Esfahlani F, Faskowitz J, Pope M, Coletta L, Gozzi A, Betzel R. Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains. Commun Biol 2024; 7:126. [PMID: 38267534 PMCID: PMC10810083 DOI: 10.1038/s42003-024-05766-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.
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Affiliation(s)
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
| | - Youngheun Jo
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Farnaz Zamani Esfahlani
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Maria Pope
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Richard Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA.
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5
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Pope M, Seguin C, Varley TF, Faskowitz J, Sporns O. Co-evolving dynamics and topology in a coupled oscillator model of resting brain function. Neuroimage 2023; 277:120266. [PMID: 37414231 DOI: 10.1016/j.neuroimage.2023.120266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/24/2023] [Accepted: 07/04/2023] [Indexed: 07/08/2023] Open
Abstract
Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.
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Affiliation(s)
- Maria Pope
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States.
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Thomas F Varley
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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6
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Wang Z, He M, Lv Y, Ge E, Zhang S, Qiang N, Liu T, Zhang F, Li X, Ge B. Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism. Cereb Cortex 2023:7133663. [PMID: 37083279 DOI: 10.1093/cercor/bhad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/22/2023] Open
Abstract
Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines' shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.
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Affiliation(s)
- Zhenwei Wang
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Mengshen He
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Yifan Lv
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Enjie Ge
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Ning Qiang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Bao Ge
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an, China
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
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7
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Zamani Esfahlani F, Faskowitz J, Slack J, Mišić B, Betzel RF. Local structure-function relationships in human brain networks across the lifespan. Nat Commun 2022; 13:2053. [PMID: 35440659 PMCID: PMC9018911 DOI: 10.1038/s41467-022-29770-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 03/29/2022] [Indexed: 12/13/2022] Open
Abstract
A growing number of studies have used stylized network models of communication to predict brain function from structure. Most have focused on a small set of models applied globally. Here, we compare a large number of models at both global and regional levels. We find that globally most predictors perform poorly. At the regional level, performance improves but heterogeneously, both in terms of variance explained and the optimal model. Next, we expose synergies among predictors by using pairs to jointly predict FC. Finally, we assess age-related differences in global and regional coupling across the human lifespan. We find global decreases in the magnitude of structure-function coupling with age. We find that these decreases are driven by reduced coupling in sensorimotor regions, while higher-order cognitive systems preserve local coupling with age. Our results describe patterns of structure-function coupling across the cortex and how this may change with age.
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Affiliation(s)
- Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Jonah Slack
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA.
- Cognitive Science Program, Indiana University, Bloomington, IN, 47405, USA.
- Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
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8
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Pope M, Fukushima M, Betzel RF, Sporns O. Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics. Proc Natl Acad Sci U S A 2021; 118:e2109380118. [PMID: 34750261 PMCID: PMC8609635 DOI: 10.1073/pnas.2109380118] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2021] [Indexed: 11/22/2022] Open
Abstract
The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.
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Affiliation(s)
- Maria Pope
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405
| | - Makoto Fukushima
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan
- Data Science Center, Nara Institute of Science and Technology, Nara 630-0192, Japan
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka 565-0871, Japan
| | - Richard F Betzel
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
- Network Science Institute, Indiana University, Bloomington, IN 47405
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN 47405;
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
- Cognitive Science Program, Indiana University, Bloomington, IN 47405
- Network Science Institute, Indiana University, Bloomington, IN 47405
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9
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Mendoza C, Roman C, Vazquez A, Poupon C, Mangin JF, Hernandez C, Guevara P. Enhanced Automatic Segmentation for Superficial White Matter Fiber Bundles for Probabilistic Tractography Datasets. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3654-3658. [PMID: 34892029 DOI: 10.1109/embc46164.2021.9630529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper presents an enhanced algorithm for automatic segmentation of superficial white matter (SWM) bundles from probabilistic dMRI tractography datasets, based on a multi-subject bundle atlas. Previous segmentation methods use the maximum Euclidean distance between corresponding points of the subject fibers and the atlas centroids. However, this scheme might include noisy fibers. Here, we propose a three step approach to discard noisy fibers improving the identification of fibers. The first step applies a fiber clustering and the segmentation is performed between the centroids of the clusters and the atlas centroids. This step removes outliers and enables a better identification of fibers with similar shapes. The second step applies a fiber filter based on two different fiber similarities. One is the Symmetrized Segment-Path Distance (SSPD) over 2D ISOMAP and the other is an adapted version of SSPD for 3D space. The last step eliminates noisy fibers by removing those that connect regions that are far from the main atlas bundle connections. We perform an experimental evaluation using ten subjects of the Human Connectome (HCP) database. The evaluation only considers the bundles connecting precentral and postcentral gyri, with a total of seven bundles per hemisphere. For comparison, the bundles of the ten subjects were manually segmented. Bundles segmented with our method were evaluated in terms of similarity to manually segmented bundles and the final number of fibers. The results show that our approach obtains bundles with a higher similarity score than the state-of-the-art method and maintains a similar number of fibers.Clinical relevance-Many brain pathologies or disorders can occur in specific regions of the SWM automatic segmentation of reliable SWM bundles would help applications to clinical research.
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10
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Girard G, Caminiti R, Battaglia-Mayer A, St-Onge E, Ambrosen KS, Eskildsen SF, Krug K, Dyrby TB, Descoteaux M, Thiran JP, Innocenti GM. On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data. Neuroimage 2020; 221:117201. [PMID: 32739552 DOI: 10.1016/j.neuroimage.2020.117201] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/17/2020] [Accepted: 07/22/2020] [Indexed: 12/22/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.
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Affiliation(s)
- Gabriel Girard
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Roberto Caminiti
- Neuroscience and Behavior Laboratory, Istituto Italiano di Tecnologia, Rome, Italy
| | | | - Etienne St-Onge
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Jean-Philippe Thiran
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Giorgio M Innocenti
- Signal Processing Lab (LTS5), 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
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11
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Groupwise track filtering via iterative message passing and pruning. Neuroimage 2020; 221:117147. [PMID: 32673747 PMCID: PMC7780547 DOI: 10.1016/j.neuroimage.2020.117147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 06/13/2020] [Accepted: 07/06/2020] [Indexed: 02/07/2023] Open
Abstract
Tractography is an important tool for the in vivo analysis of brain connectivity based on diffusion MRI data, but it also has well-known limitations in false positives and negatives for the faithful reconstruction of neuroanatomy. These problems persist even in the presence of strong anatomical priors in the form of multiple region of interests (ROIs) to constrain the trajectories of fiber tractography. In this work, we propose a novel track filtering method by leveraging the groupwise consistency of fiber bundles that naturally exists across subjects. We first formalize our groupwise concept with a flexible definition that characterizes the consistency of a track with respect to other group members based on three important aspects: degree, affinity, and proximity. An iterative algorithm is then developed to dynamically update the localized consistency measure of all streamlines via message passing from a reference set, which then informs the pruning of outlier points from each streamline. In our experiments, we successfully applied our method to diffusion imaging data of varying resolutions from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Human Connectome Project (HCP) for the consistent reconstruction of three important fiber bundles in human brain: the fornix, locus coeruleus pathways, and corticospinal tract. Both qualitative evaluations and quantitative comparisons showed that our method achieved significant improvement in enhancing the anatomical fidelity of fiber bundles.
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Jordan KM, Keshavan A, Caverzasi E, Osorio J, Papinutto N, Amirbekian B, Berger MS, Henry RG. Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline. J Neuroimaging 2020; 30:443-457. [PMID: 32436352 DOI: 10.1111/jon.12713] [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: 12/16/2019] [Accepted: 03/27/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded. METHODS We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. RESULTS The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure. CONCLUSIONS Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.
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Affiliation(s)
- Kesshi M Jordan
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Anisha Keshavan
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, CA
| | - Joseph Osorio
- Division of Neurosurgery, Department of Surgery, University of California, San Diego, CA
| | - Nico Papinutto
- Department of Neurology, University of California, San Francisco, CA
| | - Bagrat Amirbekian
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA
| | - Mitchel S Berger
- Department of Neurosurgery, University of California, San Francisco, CA
| | - Roland G Henry
- UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.,Department of Neurology, University of California, San Francisco, CA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
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Jordan K, Morin O, Wahl M, Amirbekian B, Chapman C, Owen J, Mukherjee P, Braunstein S, Henry R. An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography. Front Oncol 2019; 9:810. [PMID: 31544062 PMCID: PMC6730482 DOI: 10.3389/fonc.2019.00810] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/08/2019] [Indexed: 12/04/2022] Open
Abstract
There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.
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Affiliation(s)
- Kesshi Jordan
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Michael Wahl
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States.,Department of Radiation Oncology, Samaritan Pastega Regional Cancer Center, Corvallis, OR, United States
| | - Bagrat Amirbekian
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States
| | - Christopher Chapman
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Julia Owen
- Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology, University of Washington, Seattle, WA, United States
| | - Pratik Mukherjee
- Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,Center for Imaging of Neurodegenerative Disease, San Francisco VA Medical Center, San Francisco, CA, United States
| | - Steve Braunstein
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Roland Henry
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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Keshavan A, Yeatman JD, Rokem A. Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging. Front Neuroinform 2019; 13:29. [PMID: 31139070 PMCID: PMC6517786 DOI: 10.3389/fninf.2019.00029] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 04/01/2019] [Indexed: 01/02/2023] Open
Abstract
Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination. We propose to (1) start with expertly labeled data, (2) amplify labels through web applications that engage citizen scientists, and (3) train machine learning on amplified labels, to emulate the experts. Demonstrating this, we developed a system to quality control brain magnetic resonance images. Expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99). Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist.
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Affiliation(s)
- Anisha Keshavan
- eScience Institute, University of Washington, Seattle, WA, United States
- Institute for Neuroengineering, University of Washington, Seattle, WA, United States
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing, University of Washington, Seattle, WA, United States
| | - Jason D. Yeatman
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing, University of Washington, Seattle, WA, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States
- Institute for Neuroengineering, University of Washington, Seattle, WA, United States
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