38
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Voxel‐wise intermodal coupling analysis of two or more modalities using local covariance decomposition

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities – that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two‐modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two‐modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel‐wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi‐modal data continues to increase, principal‐component‐based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.

          Abstract

          Multi‐modal neuroimaging datasets contain information within modalities and between them. Here, we developed a method for studying relationships between more than two modalities at a local scale and found intermodal coupling of cerebral blood flow, resting‐state fluctuations, and local connectivity is spatially heterogeneous and varies throughout neurodevelopment in discrete regions with age and sex. This method reveal patterns unique to those in individual modalities alone and can enable future advancements in our understanding of the brain.

          Related collections

          Most cited references64

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Welcome to the Tidyverse

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

              An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
                Bookmark

                Author and article information

                Contributors
                fengling.hu@pennmedicine.upenn.edu
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                22 June 2022
                15 October 2022
                : 43
                : 15 ( doiID: 10.1002/hbm.v43.15 )
                : 4650-4663
                Affiliations
                [ 1 ] Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
                [ 2 ] Department of Psychiatry Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
                [ 3 ] The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
                [ 4 ] National Institute of Mental Health, Intramural Research Program National Institute of Health Bethesda Maryland USA
                [ 5 ] Department of Radiology Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
                [ 6 ] Department of Neurology Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
                [ 7 ] Department of Biostatistics Vanderbilt University Nashville Tennessee USA
                [ 8 ] Center for Biomedical Image Computing and Analytics (CBICA) Perelman School of Medicine Philadelphia Pennsylvania USA
                Author notes
                [*] [* ] Correspondence

                Fengling Hu, Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, USA.

                Email: fengling.hu@ 123456pennmedicine.upenn.edu

                Author information
                https://orcid.org/0000-0003-1081-5038
                https://orcid.org/0000-0003-3152-6286
                https://orcid.org/0000-0002-7987-3773
                https://orcid.org/0000-0002-0869-1948
                https://orcid.org/0000-0001-9049-0135
                https://orcid.org/0000-0002-5622-1190
                https://orcid.org/0000-0002-1728-9782
                https://orcid.org/0000-0002-9657-1996
                https://orcid.org/0000-0002-7457-9073
                https://orcid.org/0000-0002-8115-6343
                https://orcid.org/0000-0001-6554-1893
                https://orcid.org/0000-0001-7072-9399
                https://orcid.org/0000-0001-8627-8203
                Article
                HBM25980
                10.1002/hbm.25980
                9491276
                35730989
                1cad6c91-283e-4789-8095-54b0d76f8132
                © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 May 2022
                : 25 February 2022
                : 31 May 2022
                Page count
                Figures: 8, Tables: 0, Pages: 14, Words: 10594
                Funding
                Funded by: National Institute of Health Medical Scientist Training Program
                Award ID: T32 GM07170
                Funded by: National Institute of Mental Health , doi 10.13039/100000025;
                Award ID: 2T32MH019112‐29A1
                Award ID: R01MH107235
                Award ID: R01MH112847
                Award ID: R01MH113550
                Award ID: R01MH119185
                Award ID: R01MH120174
                Award ID: R01MH120482
                Award ID: R01MH123550
                Award ID: R56AG066656
                Award ID: RC2MH089924
                Award ID: RC2MH089983
                Award ID: MH089924
                Award ID: MH089983
                Award ID: MH123550
                Funded by: National Science Foundation , doi 10.13039/501100008982;
                Funded by: National Institute of Health
                Award ID: GM07170
                Award ID: T32
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                October 15, 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.8 mode:remove_FC converted:21.09.2022

                Neurology
                asl,connectivity,coupling,fmri,intermodal,mri,neurodevelopment
                Neurology
                asl, connectivity, coupling, fmri, intermodal, mri, neurodevelopment

                Comments

                Comment on this article