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Paté-Cornell E. Preferences in AI algorithms: The need for relevant risk attitudes in automated decisions under uncertainties. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024. [PMID: 38184297 DOI: 10.1111/risa.14268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/21/2023] [Accepted: 12/21/2023] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a prescriptive AI algorithm, some problems may occur, first and clearly, if the AI information is wrong or incomplete. But the main point of this article is that under uncertainties, the decision algorithm, rational or not, includes, in one way or another, a risk attitude in addition to deterministic preferences. That risk attitude implemented in the software is chosen by the analysts, the organization that they serve, the experts who inform them, and more generally by the process of identifying possible options. The problem is that it may or may not represent, as it should, the preferences of the actual decision maker (the risk manager) and of the people subjected to his/her decisions. This article briefly describes the sometimes-serious problem of that discrepancy between the preferences of the risk managers who use an AI output, and the risk attitude embedded in the AI system. The recommendation is to make these AI factors as accessible and transparent as possible and to allow for preference adjustments in the model if needed. The formulation of two simplified examples is described, that of a medical doctor and his/her patient when using an AI system to decide of a treatment option, and that of a skipper in a sailing race such as the America's Cup, receiving AI-processed sensor signals about the sailing conditions on different possible courses.
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Affiliation(s)
- Elisabeth Paté-Cornell
- Department of Management Science and Engineering, Stanford University, Stanford, California, USA
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2
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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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3
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Cohesive parcellation of the human brain using resting-state fMRI. J Neurosci Methods 2022; 377:109629. [PMID: 35618164 DOI: 10.1016/j.jneumeth.2022.109629] [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: 08/17/2021] [Revised: 04/14/2022] [Accepted: 05/19/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.
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4
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Zhi D, King M, Hernandez-Castillo CR, Diedrichsen J. Evaluating brain parcellations using the distance-controlled boundary coefficient. Hum Brain Mapp 2022; 43:3706-3720. [PMID: 35451538 PMCID: PMC9294308 DOI: 10.1002/hbm.25878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/13/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
Abstract
One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within‐parcel to between‐parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance‐controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting‐state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task‐based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting‐state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi‐modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct.
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Affiliation(s)
- Da Zhi
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada
| | - Maedbh King
- Department of Psychology, University of California, Berkeley, California, USA
| | | | - Jörn Diedrichsen
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada.,Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
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5
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Albers KJ, Liptrot MG, Ambrosen KS, Røge R, Herlau T, Andersen KW, Siebner HR, Hansen LK, Dyrby TB, Madsen KH, Schmidt MN, Mørup M. Uncovering Cortical Units of Processing From Multi-Layered Connectomes. Front Neurosci 2022; 16:836259. [PMID: 35360166 PMCID: PMC8960198 DOI: 10.3389/fnins.2022.836259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.
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Affiliation(s)
- Kristoffer Jon Albers
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Matthew G. Liptrot
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Karen Sandø Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Rasmus Røge
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Tue Herlau
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Kasper Winther Andersen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R. Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Tim B. Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Kristoffer H. Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Mikkel N. Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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6
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Hahn S, Owens MM, Yuan D, Juliano AC, Potter A, Garavan H, Allgaier N. Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study. Cereb Cortex 2022; 33:176-194. [PMID: 35238352 PMCID: PMC9758581 DOI: 10.1093/cercor/bhac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 11/13/2022] Open
Abstract
The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.
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Affiliation(s)
- Sage Hahn
- Corresponding author: Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, 100 South Prospect Street Burlington, Vermont 05401, United States.
| | - Max M Owens
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - DeKang Yuan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Anthony C Juliano
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Alexandra Potter
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Hugh Garavan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Nicholas Allgaier
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
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7
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Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
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8
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The human mediodorsal thalamus: Organization, connectivity, and function. Neuroimage 2022; 249:118876. [PMID: 34998970 DOI: 10.1016/j.neuroimage.2022.118876] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/06/2021] [Accepted: 01/04/2022] [Indexed: 01/10/2023] Open
Abstract
The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition.
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9
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Eschenburg KM, Grabowski TJ, Haynor DR. Learning Cortical Parcellations Using Graph Neural Networks. Front Neurosci 2021; 15:797500. [PMID: 35002611 PMCID: PMC8739886 DOI: 10.3389/fnins.2021.797500] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.
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Affiliation(s)
- Kristian M. Eschenburg
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, United States
| | - Thomas J. Grabowski
- Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, United States
- Department of Radiology, University of Washington Medical Center, Seattle, WA, United States
- Department of Neurology, University of Washington Medical Center, Seattle, WA, United States
| | - David R. Haynor
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, United States
- Department of Radiology, University of Washington Medical Center, Seattle, WA, United States
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10
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Baghaarabani L, Goliaei S, Foroughmand-Araabi MH, Shariatpanahi SP, Goliaei B. Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data. BMC Bioinformatics 2021; 22:416. [PMID: 34461827 PMCID: PMC8404257 DOI: 10.1186/s12859-021-04338-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 08/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. RESULT In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. CONCLUSIONS In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.
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Affiliation(s)
- Leila Baghaarabani
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sama Goliaei
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | | | | | - Bahram Goliaei
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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11
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Bryce NV, Flournoy JC, Guassi Moreira JF, Rosen ML, Sambook KA, Mair P, McLaughlin KA. Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity. Neuroimage 2021; 243:118487. [PMID: 34419594 PMCID: PMC8629133 DOI: 10.1016/j.neuroimage.2021.118487] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 12/16/2022] Open
Abstract
Over the past decade extensive research has examined the segregation of the human brain into large-scale functional networks. The resulting network maps, i.e. parcellations, are now commonly used for the a priori identification of functional networks. However, the use of these parcellations, particularly in developmental and clinical samples, hinges on four fundamental assumptions: (1) the various parcellations are equally able to recover the networks of interest; (2) adult-derived parcellations well represent the networks in children’s brains; (3) network properties, such as within-network connectivity, are reliably measured across parcellations; and (4) parcellation selection does not impact the results with regard to individual differences in given network properties. In the present study we examined these assumptions using eight common parcellation schemes in two independent developmental samples. We found that the parcellations are equally able to capture networks of interest in both children and adults. However, networks bearing the same name across parcellations (e.g., default network) do not produce reliable within-network measures of functional connectivity. Critically, parcellation selection significantly impacted the magnitude of associations of functional connectivity with age, poverty, and cognitive ability, producing meaningful differences in interpretation of individual differences in functional connectivity based on parcellation choice. Our findings suggest that work employing parcellations may benefit from the use of multiple schemes to confirm the robustness and generalizability of results. Furthermore, researchers looking to gain insight into functional networks may benefit from employing more nuanced network identification approaches such as using densely-sampled data to produce individual-derived network parcellations. A transition towards precision neuroscience will provide new avenues in the characterization of functional brain organization across development and within clinical populations.
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Affiliation(s)
- Nessa V Bryce
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States.
| | - John C Flournoy
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - João F Guassi Moreira
- Department of Psychology, University of California, Los Angeles, CA 90095, United States
| | - Maya L Rosen
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - Kelly A Sambook
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - Patrick Mair
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - Katie A McLaughlin
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
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12
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Albers KJ, Ambrosen KS, Liptrot MG, Dyrby TB, Schmidt MN, Mørup M. Using connectomics for predictive assessment of brain parcellations. Neuroimage 2021; 238:118170. [PMID: 34087365 DOI: 10.1016/j.neuroimage.2021.118170] [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: 02/05/2021] [Revised: 04/19/2021] [Accepted: 05/10/2021] [Indexed: 12/29/2022] Open
Abstract
The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard. We propose a framework for quantitative evaluation of brain parcellations via statistical prediction of connectomics data. Specifically, we evaluate the extent in which the network representation at the level of cortical units (defined as parcels) accounts for high-resolution brain connectivity. Herein, we assess the pertinence and comparative ranking of ten existing parcellation atlases to account for functional (FC) and structural connectivity (SC) data based on data from the Human Connectome Project (HCP), and compare them to data-driven as well as spatially-homogeneous geometric parcellations including geodesic parcellations with similar size distributions as the atlases. We find substantial discrepancy in parcellation structures that well characterize FC and SC and differences in what well represents an individual's functional connectome when compared against the FC structure that is preserved across individuals. Surprisingly, simple spatial homogenous parcellations generally provide good representations of both FC and SC, but are inferior when their within-parcellation distribution of individual parcel sizes is matched to that of a valid atlas. This suggests that the choice of fine grained and coarse representations used by existing atlases are important. However, we find that resolution is more critical than the exact border location of parcels.
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Affiliation(s)
- Kristoffer J Albers
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark
| | - Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance,Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Matthew G Liptrot
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark
| | - Tim B Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance,Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Mikkel N Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark.
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13
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Tan KM, Lu J, Zhang T, Liu H. Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks. Biometrics 2021; 77:379-390. [PMID: 32413154 PMCID: PMC10473899 DOI: 10.1111/biom.13297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 04/20/2020] [Indexed: 11/26/2022]
Abstract
Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surroundings. To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story. The main challenge with this approach is that the measured signal consists of both the stimulus-induced signal, as well as intrinsic-neural and nonneuronal signals. By exploiting the experimental design, we propose to estimate stimulus-locked brain networks by treating nonstimulus-induced signals as nuisance parameters. In many neuroscience applications, it is often important to identify brain regions that are connected to many other brain regions during cognitive process. We propose an inferential method to test whether the maximum degree of the estimated network is larger than a prespecific number. We prove that the type I error can be controlled and that the power increases to one asymptotically. Simulation studies are conducted to assess the performance of our method. Finally, we analyze a functional magnetic resonance imaging dataset obtained under the Sherlock Holmes movie stimuli.
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Affiliation(s)
- Kean Ming Tan
- Department of Statistics, University of Michigan, Ann Arbor, Michigan
| | - Junwei Lu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Tong Zhang
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Han Liu
- Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois
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14
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Luo Z, Zeng LL, Qin J, Hou C, Shen H, Hu D. Functional Parcellation of Human Brain Precuneus Using Density-Based Clustering. Cereb Cortex 2021; 30:269-282. [PMID: 31044223 DOI: 10.1093/cercor/bhz086] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 03/12/2019] [Accepted: 03/29/2019] [Indexed: 12/22/2022] Open
Abstract
The human precuneus is involved in many high-level cognitive functions, which strongly suggests the existence of biologically meaningful subdivisions. However, the functional parcellation of the precuneus needs much to be investigated. In this study, we developed an eigen clustering (EIC) approach for the parcellation using precuneus-cortical functional connectivity from fMRI data of the Human Connectome Project. The EIC approach is robust to noise and can automatically determine the cluster number. It is consistently demonstrated that the human precuneus can be subdivided into six symmetrical and connected parcels. The anterior and posterior precuneus participate in sensorimotor and visual functions, respectively. The central precuneus with four subregions indicates a media role in the interaction of the default mode, dorsal attention, and frontoparietal control networks. The EIC-based functional parcellation is free of the spatial distance constraint and is more functionally coherent than parcellation using typical clustering algorithms. The precuneus subregions had high accordance with cortical morphology and revealed good functional segregation and integration characteristics in functional task-evoked activations. This study may shed new light on the human precuneus function at a delicate level and offer an alternative scheme for human brain parcellation.
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Affiliation(s)
- Zhiguo Luo
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Ling-Li Zeng
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Jian Qin
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha, Hunan, China
| | - Hui Shen
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
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15
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Abstract
Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between brain regions under natural stimuli. We propose a modeling framework for ISA that is based on Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of a partial Gaussian graphical model. The main statistical challenge is that we do not impose sparsity constraints on the whole precision matrix and we only assume the inter-subject part is sparse. For estimation, we propose to estimate an alternative parameter to get around the nonsparse issue and it can achieve asymptotic consistency even if the intra-subject dependency is dense. For inference, we propose an "untangle and chord" procedure to de-bias our estimator. It is valid without the sparsity assumption on the inverse Hessian of the log-likelihood function. This inferential method is general and can be applied to many other statistical problems, thus it is of independent theoretical interest. Numerical experiments on both simulated and brain imaging data validate our methods and theory. Supplementary materials for this article are available online.
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Affiliation(s)
- Cong Ma
- Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ
| | - Junwei Lu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Han Liu
- Department of Computer Science and Department of Statistics, Northwestern University, Evanston, IL
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16
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Regev M, Simony E, Lee K, Tan KM, Chen J, Hasson U. Propagation of Information Along the Cortical Hierarchy as a Function of Attention While Reading and Listening to Stories. Cereb Cortex 2020; 29:4017-4034. [PMID: 30395174 DOI: 10.1093/cercor/bhy282] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 08/21/2018] [Accepted: 10/17/2018] [Indexed: 11/12/2022] Open
Abstract
How does attention route information from sensory to high-order areas as a function of task, within the relatively fixed topology of the brain? In this study, participants were simultaneously presented with 2 unrelated stories-one spoken and one written-and asked to attend one while ignoring the other. We used fMRI and a novel intersubject correlation analysis to track the spread of information along the processing hierarchy as a function of task. Processing the unattended spoken (written) information was confined to auditory (visual) cortices. In contrast, attending to the spoken (written) story enhanced the stimulus-selective responses in sensory regions and allowed it to spread into higher-order areas. Surprisingly, we found that the story-specific spoken (written) responses for the attended story also reached secondary visual (auditory) regions of the unattended sensory modality. These results demonstrate how attention enhances the processing of attended input and allows it to propagate across brain areas.
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Affiliation(s)
- Mor Regev
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA.,Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Erez Simony
- Faculty of Electrical Engineering, Holon Institute of Technology, Holon, Israel.,Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Katherine Lee
- Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, USA
| | - Kean Ming Tan
- School of Statistics, University of Minnesota, Minneapolis, MN, USA
| | - Janice Chen
- Department of Psychology & Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
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17
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Hunt D, Dighe M, Gatenby C, Studholme C. Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:601-610. [PMID: 31395540 PMCID: PMC7189742 DOI: 10.1109/tmi.2019.2932681] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Reconstruction of white matter connectivity in the fetal brain from in utero diffusion-weighted magnetic resonance imaging (MRI) faces many challenges, including subject motion, small anatomical scale, and limited image resolution and signal. These issues are compounded by the need to track significant changes in structural connectivity throughout development. We present an automated method for improved reliability and completeness of tract extraction across a wide range of gestational ages, based on the geometry of coherent patterns in streamline tractography, and apply it to the reconstruction of the corpus callosum. This method, focused specifically at addressing the challenges of fetal brain imaging, avoids depending on a tractography atlas, and handles variations in size, shape, and tissue properties of developing brains, both between subjects and across ages. Although tractography from in utero MRI generally suffers from a significant number of misleading and missing pathways, we demonstrate the feasibility of extracting the coherent bundle of the corpus callosum while avoiding inappropriate diversions into other tracts.
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18
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Kurashige H, Kaneko J, Yamashita Y, Osu R, Otaka Y, Hanakawa T, Honda M, Kawabata H. Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database. Front Hum Neurosci 2020; 13:457. [PMID: 31998102 PMCID: PMC6965330 DOI: 10.3389/fnhum.2019.00457] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/12/2019] [Indexed: 11/13/2022] Open
Abstract
To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.
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Affiliation(s)
- Hiroki Kurashige
- Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan.,National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Jun Kaneko
- Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan
| | - Yuichi Yamashita
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Rieko Osu
- Faculty of Human Sciences, Waseda University, Tokyo, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Aichi, Japan.,Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Manabu Honda
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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19
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Challenges and Opportunities in Connectome Construction and Quantification in the Developing Human Fetal Brain. Top Magn Reson Imaging 2020; 28:265-273. [PMID: 31592993 DOI: 10.1097/rmr.0000000000000212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The white matter structure of the human brain undergoes critical developmental milestones in utero, which we can observe noninvasively using diffusion-weighted magnetic resonance imaging. In order to understand this fascinating developmental process, we must establish the variability inherent in such a challenging imaging environment and how measurable quantities can be transformed into meaningful connectomes. We review techniques for reconstructing and studying the brain connectome and explore promising opportunities for in utero studies that could lead to more accurate measurement of structural properties and allow for more refined and insightful analyses of the fetal brain. Opportunities for more sophisticated analyses of the properties of the brain and its dynamic changes have emerged in recent years, based on the development of iterative techniques to reconstruct motion-corrupted diffusion-weighted data. Although reconstruction quality is greatly improved, the treatment of fundamental quantities like edge strength requires careful treatment because of the specific challenges of imaging in utero. There are intriguing challenges to overcome, from those in analysis due to both imaging limitations and the significant changes in structural connectivity, to further image processing to address the specific properties of the target anatomy and quantification into a developmental connectome.
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20
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Ambrosen KS, Eskildsen SF, Hinne M, Krug K, Lundell H, Schmidt MN, van Gerven MAJ, Mørup M, Dyrby TB. Validation of structural brain connectivity networks: The impact of scanning parameters. Neuroimage 2019; 204:116207. [PMID: 31539592 DOI: 10.1016/j.neuroimage.2019.116207] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 08/20/2019] [Accepted: 09/17/2019] [Indexed: 12/20/2022] Open
Abstract
Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1.03 mm3) are in better agreement with the tracer network, than those derived from high image resolution (0.53 mm3) or at an even lower image resolution (2.03 mm3). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60-64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70-75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.
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Affiliation(s)
- Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. 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 (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Max Hinne
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Mikkel N Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Marcel A J van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - 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, Kgs. Lyngby, Denmark.
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21
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Arfa R, Yusof R, Shabanzadeh P. Novel trajectory clustering method based on distance dependent Chinese restaurant process. PeerJ Comput Sci 2019; 5:e206. [PMID: 33816859 PMCID: PMC7924552 DOI: 10.7717/peerj-cs.206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/24/2019] [Indexed: 06/12/2023]
Abstract
Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers' behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling.
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Affiliation(s)
- Reza Arfa
- Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
- Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Rubiyah Yusof
- Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
- Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Parvaneh Shabanzadeh
- Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
- Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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22
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Xia X, Fan L, Hou B, Zhang B, Zhang D, Cheng C, Deng H, Dong Y, Zhao X, Li H, Jiang T. Fine-Grained Parcellation of the Macaque Nucleus Accumbens by High-Resolution Diffusion Tensor Tractography. Front Neurosci 2019; 13:709. [PMID: 31354418 PMCID: PMC6635473 DOI: 10.3389/fnins.2019.00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 06/24/2019] [Indexed: 11/13/2022] Open
Abstract
Limited in part by the spatial resolution of typical in vivo magnetic resonance imaging (MRI) data, recent neuroimaging studies have only identified a connectivity-based shell-core-like partitioning of the nucleus accumbens (Acb) in humans. This has hindered the process of making a more refined description of the Acb using non-invasive neuroimaging technologies and approaches. In this study, high-resolution ex vivo macaque brain diffusion MRI data were acquired to investigate the tractography-based parcellation of the Acb. Our results identified a shell-core-like partitioning in macaques that is similar to that in humans as well as an alternative solution that subdivided the Acb into four parcels, the medial shell, the lateral shell, the ventral core, and the dorsal core. Furthermore, we characterized the specific anatomical and functional connectivity profiles of these Acb subregions and generalized their specialized functions to establish a fine-grained macaque Acb brainnetome atlas. This atlas should be helpful in neuroimaging, stereotactic surgery, and comparative neuroimaging studies to reveal the neurophysiological substrates of various diseases and cognitive functions associated with the Acb.
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Affiliation(s)
- Xiaoluan Xia
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Bing Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, China
| | - Chen Cheng
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hongxia Deng
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
| | - Yunyun Dong
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
| | - Xudong Zhao
- State Key Laboratory of Brain and Cognitive Sciences, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Haifang Li
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
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23
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Fountalis I, Dovrolis C, Bracco A, Dilkina B, Keilholz S. δ-MAPS: from spatio-temporal data to a weighted and lagged network between functional domains. APPLIED NETWORK SCIENCE 2018; 3:21. [PMID: 30839838 PMCID: PMC6214317 DOI: 10.1007/s41109-018-0078-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 07/03/2018] [Indexed: 06/09/2023]
Abstract
In real physical systems the underlying spatial components might not have crisp boundaries and their interactions might not be instantaneous. To this end, we propose δ-MAPS; a method that identifies spatially contiguous and possibly overlapping components referred to as domains, and identifies the lagged functional relationships between them. Informally, a domain is a spatially contiguous region that somehow participates in the same dynamic effect or function. The latter will result in highly correlated temporal activity between grid cells of the same domain. δ-MAPS first identifies the epicenters of activity of a domain. Next, it identifies a domain as the maximum possible set of spatially contiguous grid cells that include the detected epicenters and satisfy a homogeneity constraint. After identifying the domains, δ-MAPS infers a functional network between them. The proposed network inference method examines the statistical significance of each lagged correlation between two domains, applies a multiple-testing process to control the rate of false positives, infers a range of potential lag values for each edge, and assigns a weight to each edge reflecting the magnitude of interaction between two domains. δ-MAPS is related to clustering, multivariate statistical techniques and network community detection. However, as we discuss and also show with synthetic data, it is also significantly different, avoiding many of the known limitations of these methods. We illustrate the application of δ-MAPS on data from two domains: climate science and neuroscience. First, the sea-surface temperature climate network identifies some well-known teleconnections (such as the lagged connection between the El Nin õ Southern Oscillation and the Indian Ocean). Second, the analysis of resting state fMRI cortical data confirms the presence of known functional resting state networks (default mode, occipital, motor/somatosensory and auditory), and shows that the cortical network includes a backbone of relatively few regions that are densely interconnected.
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Affiliation(s)
| | | | - Annalisa Bracco
- School of Earth and Atmospheric Sciences, Georgia Tech, Atlanta, USA
| | - Bistra Dilkina
- Viterbi School of Engineering, University of Southern California, Los Angeles, USA
| | - Shella Keilholz
- Dept. of Biomedical Engr., Georgia Tech and Emory, Atlanta, USA
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24
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Pandya S, Kuceyeski A, Raj A. The Brain's Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer's Disease. J Alzheimers Dis 2018; 55:1639-1657. [PMID: 27911289 DOI: 10.3233/jad-160090] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alzheimer's disease (AD), one of the most common causes of dementia in adults, is a progressive neurodegenerative disorder exhibiting well-defined neuropathological hallmarks. It is known that disease pathology involves misfolded amyloid-β (Aβ) and tau proteins, and exhibits a relatively stereotyped progression over decades. The relationship between AD neuropathological hallmarks (Aβ, hypometabolism, and tau proteins) and imaging biomarkers (MRI, AV-45/FDG-PET) is not fully understood. In addition, biomarker pathologies are oftentimes discordant, wherein it may show varying levels of abnormality across brain regions. Evidence based on recent elucidation of trans-neuronal "prion-like" transmission and other available data already suggests that disease spread follows the brain's fiber connectivity network. Thereby, the brain's connectome information can be used to predict the process of disease spread in AD. A recently established mathematical model of AD pathology spread using a connectome-based network diffusion model was successful in encapsulating neurodegenerative progression. Motivated by these network-based findings, the current study explores whether and how network connectivity mediates the interactions between various AD biomarkers. We hypothesized that the structural connectivity matrix will mediate the cross-sectional association between regional AD-associated hypometabolism and Aβ deposition. Given recent reports of inherent or lifetime activity of brain regions as strong predictors of Aβ deposition in patients, we also tested whether healthy metabolism exerts a network-mediated effect on Aβ deposition and hypometabolism in AD patients. We found that regional Aβ deposition is best predicted by a linear combination of both regional healthy local metabolism and connectome-mediated regional healthy metabolism.
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25
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Betzel RF, Medaglia JD, Bassett DS. Diversity of meso-scale architecture in human and non-human connectomes. Nat Commun 2018; 9:346. [PMID: 29367627 PMCID: PMC5783945 DOI: 10.1038/s41467-017-02681-z] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 12/19/2017] [Indexed: 12/21/2022] Open
Abstract
Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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A flexible graphical model for multi-modal parcellation of the cortex. Neuroimage 2017; 162:226-248. [PMID: 28889005 DOI: 10.1016/j.neuroimage.2017.09.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 08/16/2017] [Accepted: 09/03/2017] [Indexed: 01/12/2023] Open
Abstract
Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.
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Xia X, Fan L, Cheng C, Eickhoff SB, Chen J, Li H, Jiang T. Multimodal connectivity-based parcellation reveals a shell-core dichotomy of the human nucleus accumbens. Hum Brain Mapp 2017; 38:3878-3898. [PMID: 28548226 DOI: 10.1002/hbm.23636] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 04/14/2017] [Accepted: 04/21/2017] [Indexed: 12/21/2022] Open
Abstract
The subdifferentiation of the nucleus accumbens (NAc) has been extensively studied using neuroanatomy and histochemistry, yielding a well-accepted dichotomic shell/core architecture that reflects dissociable roles, such as in reward and aversion, respectively. However, in vivo parcellation of these structures in humans has been rare, potentially impairing future research into the structural and functional characteristics and alterations of putative NAc subregions. Here, we used three complementary parcellation schemes based on tractography, task-independent functional connectivity, and task-dependent co-activation to investigate the regional differentiation within the NAc. We found that a 2-cluster solution with shell-like and core-like subdivisions provided the best description of the data and was consistent with the earlier anatomical shell/core architecture. The consensus clusters from this optimal solution, which was based on the three schemes, were used as the final parcels for the subsequent connection analyses. The resulting connectivity patterns presented inter-hemispheric symmetry, convergence and divergence across the modalities, and, most importantly, clearly distinct patterns between the two subregions. This convergent connectivity patterns also confirmed the connections in animal models, supporting views that the two subregions could have antagonistic roles in some circumstances. Finally, the identified parcels should be helpful in further neuroimaging studies of the NAc. Hum Brain Mapp 38:3878-3898, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiaoluan Xia
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Chen Cheng
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52425 Juelich, Germany.,Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225, Germany
| | - Junjie Chen
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Haifang Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
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Baldassano C, Beck DM, Fei-Fei L. Human-Object Interactions Are More than the Sum of Their Parts. Cereb Cortex 2017; 27:2276-2288. [PMID: 27073216 DOI: 10.1093/cercor/bhw077] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Understanding human-object interactions is critical for extracting meaning from everyday visual scenes and requires integrating complex relationships between human pose and object identity into a new percept. To understand how the brain builds these representations, we conducted 2 fMRI experiments in which subjects viewed humans interacting with objects, noninteracting human-object pairs, and isolated humans and objects. A number of visual regions process features of human-object interactions, including object identity information in the lateral occipital complex (LOC) and parahippocampal place area (PPA), and human pose information in the extrastriate body area (EBA) and posterior superior temporal sulcus (pSTS). Representations of human-object interactions in some regions, such as the posterior PPA (retinotopic maps PHC1 and PHC2) are well predicted by a simple linear combination of the response to object and pose information. Other regions, however, especially pSTS, exhibit representations for human-object interaction categories that are not predicted by their individual components, indicating that they encode human-object interactions as more than the sum of their parts. These results reveal the distributed networks underlying the emergent representation of human-object interactions necessary for social perception.
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Affiliation(s)
| | - Diane M Beck
- Department of Psychology and Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Li Fei-Fei
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
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Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 2017; 170:5-30. [PMID: 28412442 DOI: 10.1016/j.neuroimage.2017.04.014] [Citation(s) in RCA: 198] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 03/15/2017] [Accepted: 04/05/2017] [Indexed: 11/21/2022] Open
Abstract
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
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Janssen RJ, Jylänki P, van Gerven MAJ. Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data. PLoS One 2016; 11:e0164703. [PMID: 27935937 PMCID: PMC5147788 DOI: 10.1371/journal.pone.0164703] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 09/29/2016] [Indexed: 11/18/2022] Open
Abstract
We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.
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Affiliation(s)
- Ronald J. Janssen
- Radboud University, Donders Centre for Brain Cognition and Behaviour, Nijmegen, the Netherlands
- * E-mail:
| | - Pasi Jylänki
- Radboud University, Donders Centre for Brain Cognition and Behaviour, Nijmegen, the Netherlands
| | - Marcel A. J. van Gerven
- Radboud University, Donders Centre for Brain Cognition and Behaviour, Nijmegen, the Netherlands
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Honnorat N, Satterthwaite TD, Gur RE, Gur RC, Davatzikos C. sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain. J Neurosci Methods 2016; 277:1-20. [PMID: 27913211 DOI: 10.1016/j.jneumeth.2016.11.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 10/27/2016] [Accepted: 11/24/2016] [Indexed: 10/20/2022]
Abstract
BACKGROUND Resting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for. NEW METHOD In this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually. RESULTS Several cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population. COMPARISON WITH EXISTING METHODS We compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data. CONCLUSIONS Our parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.
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Affiliation(s)
- N Honnorat
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - T D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - R E Gur
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - R C Gur
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - C Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Nie L, Matthews PM, Guo Y. Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex. IEEE Trans Biomed Eng 2016; 63:2505-2517. [PMID: 27875122 DOI: 10.1109/tbme.2016.2571221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual-or state-dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. METHODS We propose a method called "joint K-means" to robustly parcellate the cerebral cortex using functional magnetic resonance imaging (fMRI) data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. RESULTS Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are nonuniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal, and occipito-parietal cortices. This pattern is reproducible across sessions, between groups, and using different numbers of parcels. CONCLUSION The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. SIGNIFICANCE The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences.
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Two Distinct Scene-Processing Networks Connecting Vision and Memory. eNeuro 2016; 3:eN-NWR-0178-16. [PMID: 27822493 PMCID: PMC5075944 DOI: 10.1523/eneuro.0178-16.2016] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 09/02/2016] [Accepted: 09/30/2016] [Indexed: 11/21/2022] Open
Abstract
A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.
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