Using High Angular Resolution Diffusion Imaging Data to Discriminate Cortical Regions
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{"title"=>"Using High Angular Resolution Diffusion Imaging Data to Discriminate Cortical Regions", "type"=>"journal", "authors"=>[{"first_name"=>"Zoltan", "last_name"=>"Nagy", "scopus_author_id"=>"56307775100"}, {"first_name"=>"Daniel C.", "last_name"=>"Alexander", "scopus_author_id"=>"7402830766"}, {"first_name"=>"David L.", "last_name"=>"Thomas", "scopus_author_id"=>"55485914900"}, {"first_name"=>"Nikolaus", "last_name"=>"Weiskopf", "scopus_author_id"=>"6602786222"}, {"first_name"=>"Martin I.", "last_name"=>"Sereno", "scopus_author_id"=>"7005050489"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84877856125", "pmid"=>"23691102", "pui"=>"368937307", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84877856125", "doi"=>"10.1371/journal.pone.0063842", "issn"=>"19326203"}, "id"=>"5dfef6c4-36a5-315c-9520-b07f58e15c14", "abstract"=>"Brodmann's 100-year-old summary map has been widely used for cortical localization in neuroscience. There is a pressing need to update this map using non-invasive, high-resolution and reproducible data, in a way that captures individual variability. We demonstrate here that standard HARDI data has sufficiently diverse directional variation among grey matter regions to inform parcellation into distinct functional regions, and that this variation is reproducible across scans. This characterization of the signal variation as non-random and reproducible is the critical condition for successful cortical parcellation using HARDI data. This paper is a first step towards an individual cortex-wide map of grey matter microstructure, The gray/white matter and pial boundaries were identified on the high-resolution structural MRI images. Two HARDI data sets were collected from each individual and aligned with the corresponding structural image. At each vertex point on the surface tessellation, the diffusion-weighted signal was extracted from each image in the HARDI data set at a point, half way between gray/white matter and pial boundaries. We then derived several features of the HARDI profile with respect to the local cortical normal direction, as well as several fully orientationally invariant features. These features were taken as a fingerprint of the underlying grey matter tissue, and used to distinguish separate cortical areas. A support-vector machine classifier, trained on three distinct areas in repeat 1 achieved 80-82% correct classification of the same three areas in the unseen data from repeat 2 in three volunteers. Though gray matter anisotropy has been mostly overlooked hitherto, this approach may eventually form the foundation of a new cortical parcellation method in living humans. Our approach allows for further studies on the consistency of HARDI based parcellation across subjects and comparison with independent microstructural measures such as ex-vivo histology.", "link"=>"http://www.mendeley.com/research/using-high-angular-resolution-diffusion-imaging-data-discriminate-cortical-regions", "reader_count"=>58, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>19, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>19, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>6, "Medicine and Dentistry"=>12, "Agricultural and Biological Sciences"=>7, "Neuroscience"=>10, "Arts and Humanities"=>1, "Physics and Astronomy"=>5, "Psychology"=>8, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>12}, "Neuroscience"=>{"Neuroscience"=>10}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>8}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "United States"=>3, "Italy"=>1, "United Kingdom"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1548605"], "description"=>"*<p>B1 Map uses two echoes, one spin echo and one stimulated echo. The flip angles were varied between 270°–130° in steps of 10° for the spin echo and between 135°–65° in steps of 5° for the stimulated echo <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063842#pone.0063842-Lutti3\" target=\"_blank\">[43]</a>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Nervous system components", "neuroanatomy", "neurology", "Autonomic nervous system", "neuroimaging", "radiology", "Medical physics"], "article_id"=>1068963, "categories"=>["Physics", "Biological Sciences", "Medicine"], "users"=>["Zoltan Nagy", "Daniel C. Alexander", "David L. Thomas", "Nikolaus Weiskopf", "Martin I. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0063842.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MRI_data_acquisition_parameters_/1068963", "title"=>"MRI data acquisition parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-17 07:50:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1548590"], "description"=>"<p>Due to the convoluted nature of the cortical sheet even small, functionally and histologically homogeneous regions will have varied spatial orientation. Three of the areas investigated in this study are depicted. The background color indicated local cortical curvature (not gyrification). The mesh edges connect surface vertices.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Nervous system components", "neuroanatomy", "neurology", "Autonomic nervous system", "neuroimaging", "radiology", "Medical physics", "rois", "convoluted", "cortical"], "article_id"=>1068948, "categories"=>["Physics", "Biological Sciences", "Medicine"], "users"=>["Zoltan Nagy", "Daniel C. Alexander", "David L. Thomas", "Nikolaus Weiskopf", "Martin I. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0063842.g001", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depiction_of_selected_ROIs_on_the_convoluted_cortical_surface_/1068948", "title"=>"Depiction of selected ROIs on the convoluted cortical surface.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-17 07:50:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1548604"], "description"=>"<p>The feature vectors for all vertices in a single hemisphere were fed to a k–means clustering algorithm to produce 40 distinct clusters. The color bar runs from 1 (red) to 40 (green) where clusters with similar feature vectors are colored similarly. Clusters were sorted in such a way as to minimize the sum of Euclidean distances between consecutive cluster centres. The reproducibility of results between the two repeats is striking. Data from Subject 1 are displayed.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Nervous system components", "neuroanatomy", "neurology", "Autonomic nervous system", "neuroimaging", "radiology", "Medical physics", "clustering"], "article_id"=>1068962, "categories"=>["Physics", "Biological Sciences", "Medicine"], "users"=>["Zoltan Nagy", "Daniel C. Alexander", "David L. Thomas", "Nikolaus Weiskopf", "Martin I. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0063842.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unsupervised_clustering_using_k_8211_means_/1068962", "title"=>"Unsupervised clustering using k–means.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-17 07:50:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1548601"], "description"=>"<p>(Top) Classification results displayed on the map of cortical curvature. After being trained on data from ROIs of the 1<sup>st</sup> acquisition data from the same ROIs of the 2<sup>nd</sup> acquisition are classified as MT+ (blue), Ang (green) and STS ROI (red). The reliability of the classification process is supported by the fact that data from each cortical area is classified correctly in a large connected set of vertices and only the edges are classified erroneously as one of the other tissue types. (Inset) When the area just anterior to MT+, which was unseen while training the classifier is, is included in the classification an approximate border can be identified where success of the classifier drops sharply. (Middle) The ability of the support vector machine (SVM) to distinguish pairwise data from 12 distinct cortical regions. The color code indicates percent of correct classification of data in repeat 2 after the SVM was trained on data from repeat 1. Most regions are classifiable above chance though the SVM struggles with classifying V1 (primary visual cortex) correctly. Another internal check shows that the IPS1 and IPS2,3 (IPS = lateral intraparietal sulcus), which are neighboring, functionally related parietal visual areas, are hard to distinguish. For the definitions of the abbreviations please see Methods. (Bottom) Depiction of all regions where data were extracted from one or all three of the subjects. The short descriptive names are defined in the text. M-I was used only for Subjects 2 and 3, hence it is designated (not used) to indicate that it is not included in the 12×12 matrix of Subject 1 (middle).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Central nervous system", "Nervous system components", "neuroanatomy", "neurology", "Autonomic nervous system", "neuroimaging", "radiology", "Medical physics"], "article_id"=>1068959, "categories"=>["Physics", "Biological Sciences", "Medicine"], "users"=>["Zoltan Nagy", "Daniel C. Alexander", "David L. Thomas", "Nikolaus Weiskopf", "Martin I. Sereno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0063842.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_results_of_the_test_re_8211_test_data_/1068959", "title"=>"Classification results of the test/re–test data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-17 07:50:58"}

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Relative Metric

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