High Throughput Neuro-Imaging Informatics
This paper describes neuroinformatics technologies at 1 mm anatomical scale based on high throughput 3D functional and structural imaging technologies of the human brain. The core is an abstract pipeline for converting functional and structural imagery into their high dimensional neuroinformatic r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00031/full |
| _version_ | 1811326681019842560 |
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| author | Michael I Miller Michael I Miller Michael I Miller Andreia V Faria Kenichi eOishi Susumu eMori |
| author_facet | Michael I Miller Michael I Miller Michael I Miller Andreia V Faria Kenichi eOishi Susumu eMori |
| author_sort | Michael I Miller |
| collection | DOAJ |
| description | This paper describes neuroinformatics technologies at 1 mm anatomical scale based on high throughput 3D functional and structural imaging technologies of the human brain. The core is an abstract pipeline for converting functional and structural imagery into their high dimensional neuroinformatic representations index containing O(E3-E4) discriminating dimensions. The pipeline is based on advanced image analysis coupled to digital knowledge representations in the form of dense atlases of the human brain at gross anatomical scale. We demonstrate the integration of these high-dimensional representations with machine learning methods, which have become the mainstay of other fields of science including genomics as well as social networks. Such high throughput facilities have the potential to alter the way medical images are stored and utilized in radiological workflows. The neuroinformatics pipeline is used to examine cross-sectional and personalized analyses of neuropsychiatric illnesses in clinical applications as well as longitudinal studies. We demonstrate the use of high throughput machine learning methods for supporting (i) cross-sectional image analysis to evaluate the health status of individual subjects with respect to the population data, (ii) integration of image and non-image information for diagnosis and prognosis. |
| first_indexed | 2024-04-13T14:53:02Z |
| format | Article |
| id | doaj.art-030aa71b97664af39d2577c249a957d5 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-13T14:53:02Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-030aa71b97664af39d2577c249a957d52022-12-22T02:42:31ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962013-12-01710.3389/fninf.2013.0003168872High Throughput Neuro-Imaging InformaticsMichael I Miller0Michael I Miller1Michael I Miller2Andreia V Faria3Kenichi eOishi4Susumu eMori5The Johns Hopkins UniversityThe Johns Hopkins UniversityThe Johns Hopkins UniversityThe Johns Hopkins University School of MedicineThe Johns Hopkins University School of MedicineThe Johns Hopkins University School of MedicineThis paper describes neuroinformatics technologies at 1 mm anatomical scale based on high throughput 3D functional and structural imaging technologies of the human brain. The core is an abstract pipeline for converting functional and structural imagery into their high dimensional neuroinformatic representations index containing O(E3-E4) discriminating dimensions. The pipeline is based on advanced image analysis coupled to digital knowledge representations in the form of dense atlases of the human brain at gross anatomical scale. We demonstrate the integration of these high-dimensional representations with machine learning methods, which have become the mainstay of other fields of science including genomics as well as social networks. Such high throughput facilities have the potential to alter the way medical images are stored and utilized in radiological workflows. The neuroinformatics pipeline is used to examine cross-sectional and personalized analyses of neuropsychiatric illnesses in clinical applications as well as longitudinal studies. We demonstrate the use of high throughput machine learning methods for supporting (i) cross-sectional image analysis to evaluate the health status of individual subjects with respect to the population data, (ii) integration of image and non-image information for diagnosis and prognosis.http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00031/fullfunctional imagingneuroinformaticsNeuro-imagingcomputational anatomystructural imaging |
| spellingShingle | Michael I Miller Michael I Miller Michael I Miller Andreia V Faria Kenichi eOishi Susumu eMori High Throughput Neuro-Imaging Informatics Frontiers in Neuroinformatics functional imaging neuroinformatics Neuro-imaging computational anatomy structural imaging |
| title | High Throughput Neuro-Imaging Informatics |
| title_full | High Throughput Neuro-Imaging Informatics |
| title_fullStr | High Throughput Neuro-Imaging Informatics |
| title_full_unstemmed | High Throughput Neuro-Imaging Informatics |
| title_short | High Throughput Neuro-Imaging Informatics |
| title_sort | high throughput neuro imaging informatics |
| topic | functional imaging neuroinformatics Neuro-imaging computational anatomy structural imaging |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00031/full |
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