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<meta name="DC.Title" content="Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results" />
<meta name="DC.Identifier" content="10.3174/ajnr.A3126" />
<meta name="DC.Date" content="2012-12-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.

MATERIALS AND METHODS: We included 40 consecutive patients with Parkinsonism suggestive of PD who had DTI at 3T, brain 123I ioflupane SPECT (DaTSCAN), and extensive neurologic testing including follow-up (17 PD: age range, 67.8 ± 6.7 years; 9 women; 23 Other: consisting of atypical forms of Parkinsonism; age range, 67.2 ± 9.7 years; 7 women). Data analysis included group-level TBSS and individual-level SVM classification.

RESULTS: At the group level, patients with PD versus Other had spatially consistent increase in FA and decrease in RD and MD in a bilateral network, predominantly in the right frontal white matter. At the individual level, SVM correctly classified patients with PD at the individual level with accuracies up to 97%.

CONCLUSIONS: Support vector machine–based pattern recognition of DTI data provides highly accurate detection of patients with PD among those with suspected PD at an individual level, which is potentially clinically applicable. Because most suspected subjects with PD undergo brain MR imaging, already existing MR imaging data may be reused; this practice is very cost-efficient.

FA
:   fractional anisotropy
123I
:   iodine 123
LD
:   longitudinal diffusivity
MD
:   mean diffusivity
MSA
:   multisystem atrophy
PD
:   Parkinson disease
PSP
:   progressive supranuclear palsy
RD
:   radial diffusivity
SVM
:   support vector machine
TBSS
:   tract-based spatial statistics
VBM
:   voxel-based morphometry" />
<meta name="DC.Contributor" content="S. Haller" />
<meta name="DC.Contributor" content="S. Badoud" />
<meta name="DC.Contributor" content="D. Nguyen" />
<meta name="DC.Contributor" content="V. Garibotto" />
<meta name="DC.Contributor" content="K.O. Lovblad" />
<meta name="DC.Contributor" content="P.R Burkhard" />
<meta name="article:published_time" content="2012-12-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;We included 40 consecutive patients with Parkinsonism suggestive of PD who had DTI at 3T, brain &lt;sup&gt;123&lt;/sup&gt;I ioflupane SPECT (DaTSCAN), and extensive neurologic testing including follow-up (17 PD: age range, 67.8 ± 6.7 years; 9 women; 23 Other: consisting of atypical forms of Parkinsonism; age range, 67.2 ± 9.7 years; 7 women). Data analysis included group-level TBSS and individual-level SVM classification.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;At the group level, patients with PD versus Other had spatially consistent increase in FA and decrease in RD and MD in a bilateral network, predominantly in the right frontal white matter. At the individual level, SVM correctly classified patients with PD at the individual level with accuracies up to 97%.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Support vector machine–based pattern recognition of DTI data provides highly accurate detection of patients with PD among those with suspected PD at an individual level, which is potentially clinically applicable. Because most suspected subjects with PD undergo brain MR imaging, already existing MR imaging data may be reused; this practice is very cost-efficient.&lt;/p&gt;" />
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<meta name="citation_issn" content="0195-6108" />
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<meta name="citation_author" content="S. Haller" />
<meta name="citation_author_institution" content="aFrom the Service Neuro-Diagnostique et Neuro-Interventionnel (S.H., K.O.L.), Département de l'Imagerie et Science de l'Information Médical, University Hospitals of Geneva, Geneva, Switzerland" />
<meta name="citation_author" content="S. Badoud" />
<meta name="citation_author_institution" content="bDepartment of Neurology (S.B., P.R.B.), Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland" />
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<meta name="twitter:title" content="Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.

MATERIALS AND METHODS: We included 40 consecutive patients with Parkinsonism suggestive of PD who had DTI at 3T, brain 123I ioflupane SPECT (DaTSCAN), and extensive neurologic testing including follow-up (17 PD: age range, 67.8 ± 6.7 years; 9 women; 23 Other: consisting of atypical forms of Parkinsonism; age range, 67.2 ± 9.7 years; 7 women). Data analysis included group-level TBSS and individual-level SVM classification.

RESULTS: At the group level, patients with PD versus Other had spatially consistent increase in FA and decrease in RD and MD in a bilateral network, predominantly in the right frontal white matter. At the individual level, SVM correctly classified patients with PD at the individual level with accuracies up to 97%.

CONCLUSIONS: Support vector machine–based pattern recognition of DTI data provides highly accurate detection of patients with PD among those with suspected PD at an individual level, which is potentially clinically applicable. Because most suspected subjects with PD undergo brain MR imaging, already existing MR imaging data may be reused; this practice is very cost-efficient.

FA
:   fractional anisotropy
123I
:   iodine 123
LD
:   longitudinal diffusivity
MD
:   mean diffusivity
MSA
:   multisystem atrophy
PD
:   Parkinson disease
PSP
:   progressive supranuclear palsy
RD
:   radial diffusivity
SVM
:   support vector machine
TBSS
:   tract-based spatial statistics
VBM
:   voxel-based morphometry" />
<meta name="og-title" property="og:title" content="Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/33/11/2123" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.

MATERIALS AND METHODS: We included 40 consecutive patients with Parkinsonism suggestive of PD who had DTI at 3T, brain 123I ioflupane SPECT (DaTSCAN), and extensive neurologic testing including follow-up (17 PD: age range, 67.8 ± 6.7 years; 9 women; 23 Other: consisting of atypical forms of Parkinsonism; age range, 67.2 ± 9.7 years; 7 women). Data analysis included group-level TBSS and individual-level SVM classification.

RESULTS: At the group level, patients with PD versus Other had spatially consistent increase in FA and decrease in RD and MD in a bilateral network, predominantly in the right frontal white matter. At the individual level, SVM correctly classified patients with PD at the individual level with accuracies up to 97%.

CONCLUSIONS: Support vector machine–based pattern recognition of DTI data provides highly accurate detection of patients with PD among those with suspected PD at an individual level, which is potentially clinically applicable. Because most suspected subjects with PD undergo brain MR imaging, already existing MR imaging data may be reused; this practice is very cost-efficient.

FA
:   fractional anisotropy
123I
:   iodine 123
LD
:   longitudinal diffusivity
MD
:   mean diffusivity
MSA
:   multisystem atrophy
PD
:   Parkinson disease
PSP
:   progressive supranuclear palsy
RD
:   radial diffusivity
SVM
:   support vector machine
TBSS
:   tract-based spatial statistics
VBM
:   voxel-based morphometry" />
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      <h1 class="highwire-cite-title" id="page-title">Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">S. Haller</span>, <span class="highwire-citation-author" data-delta="1">S. Badoud</span>, <span class="highwire-citation-author" data-delta="2">D. Nguyen</span>, <span class="highwire-citation-author" data-delta="3">V. Garibotto</span>, <span class="highwire-citation-author" data-delta="4">K.O. Lovblad</span> and <span class="highwire-citation-author" data-delta="5">P.R Burkhard</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">December 2012,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">33 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2123-2128; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A3126 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-12351--61938930973" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">S. Haller </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Service Neuro-Diagnostique et Neuro-Interventionnel (S.H., K.O.L.), Département de l'Imagerie et Science de l'Information Médical, University Hospitals of Geneva, Geneva, Switzerland</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=S.%2BHaller%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Haller%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AS.%2BHaller%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">S. Badoud </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology (S.B., P.R.B.), Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=S.%2BBadoud%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Badoud%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AS.%2BBadoud%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">D. Nguyen </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Centre Diagnostic Radiologique Carouge (D.N.), Carouge, Switzerland</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=D.%2BNguyen%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Nguyen%20D&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AD.%2BNguyen%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">V. Garibotto </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Nuclear Medicine and Molecular Imaging Unit (V.G.), Département de l'Imagerie et Science de l'Information Médical, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Switzerland.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=V.%2BGaribotto%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Garibotto%20V&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AV.%2BGaribotto%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">K.O. Lovblad </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Service Neuro-Diagnostique et Neuro-Interventionnel (S.H., K.O.L.), Département de l'Imagerie et Science de l'Information Médical, University Hospitals of Geneva, Geneva, Switzerland</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=K.O.%2BLovblad%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Lovblad%20K&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AK.O.%2BLovblad%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">P.R Burkhard </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurology (S.B., P.R.B.), Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=P.R%2BBurkhard%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Burkhard%20P&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AP.R%2BBurkhard%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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<p id="p-1"><strong>BACKGROUND AND PURPOSE:</strong> Brain MR imaging is routinely performed in the work-up of suspected PD, yet its role is essentially limited to the exclusion of other pathologies. We performed a pattern-recognition analysis based on DTI data to detect subjects with PD at the individual level.</p></div>
<div id="sec-2" class="subsection"> 
<p id="p-2"><strong>MATERIALS AND METHODS:</strong> We included 40 consecutive patients with Parkinsonism suggestive of PD who had DTI at 3T, brain <sup>123</sup>I ioflupane SPECT (DaTSCAN), and extensive neurologic testing including follow-up (17 PD: age range, 67.8 ± 6.7 years; 9 women; 23 Other: consisting of atypical forms of Parkinsonism; age range, 67.2 ± 9.7 years; 7 women). Data analysis included group-level TBSS and individual-level SVM classification.</p></div>
<div id="sec-3" class="subsection"> 
<p id="p-3"><strong>RESULTS:</strong> At the group level, patients with PD versus Other had spatially consistent increase in FA and decrease in RD and MD in a bilateral network, predominantly in the right frontal white matter. At the individual level, SVM correctly classified patients with PD at the individual level with accuracies up to 97%.</p></div>
<div id="sec-4" class="subsection"> 
<p id="p-4"><strong>CONCLUSIONS:</strong> Support vector machine–based pattern recognition of DTI data provides highly accurate detection of patients with PD among those with suspected PD at an individual level, which is potentially clinically applicable. Because most suspected subjects with PD undergo brain MR imaging, already existing MR imaging data may be reused; this practice is very cost-efficient.</p></div>
</div><div class="section glossary" id="glossary-1">
<h2>ABBREVIATIONS:</h2>
<dl id="def-list-1"><dt id="def-item-1">FA<span class="def-item-dt-sep"></span></dt><dd>fractional anisotropy<span class="def-item-dd-sep"></span></dd><dt id="def-item-2"><sup>123</sup>I<span class="def-item-dt-sep"></span></dt><dd>iodine 123<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">LD<span class="def-item-dt-sep"></span></dt><dd>longitudinal diffusivity<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">MD<span class="def-item-dt-sep"></span></dt><dd>mean diffusivity<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">MSA<span class="def-item-dt-sep"></span></dt><dd>multisystem atrophy<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">PD<span class="def-item-dt-sep"></span></dt><dd>Parkinson disease<span class="def-item-dd-sep"></span></dd><dt id="def-item-7">PSP<span class="def-item-dt-sep"></span></dt><dd>progressive supranuclear palsy<span class="def-item-dd-sep"></span></dd><dt id="def-item-8">RD<span class="def-item-dt-sep"></span></dt><dd>radial diffusivity<span class="def-item-dd-sep"></span></dd><dt id="def-item-9">SVM<span class="def-item-dt-sep"></span></dt><dd>support vector machine<span class="def-item-dd-sep"></span></dd><dt id="def-item-10">TBSS<span class="def-item-dt-sep"></span></dt><dd>tract-based spatial statistics<span class="def-item-dd-sep"></span></dd><dt id="def-item-11">VBM<span class="def-item-dt-sep"></span></dt><dd>voxel-based morphometry</dd></dl>
</div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2012 by American Journal of Neuroradiology</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/33/11/2123.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Haller</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Badoud</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Nguyen</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">V.</span> <span class="nlm-surname">Garibotto</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">K.O.</span> <span class="nlm-surname">Lovblad</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">P.R</span> <span class="nlm-surname">Burkhard</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2012, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">33 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2123-2128; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A3126 </span></div>
  
  
  
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