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Predictive and Populational model for Alzheimer´s disease using structural neuroimaging

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Predictive and Populational model for Alzheimer´s disease using structural neuroimaging. MEDICON 2013

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Predictive and Populational model for Alzheimer´s disease using structural neuroimaging

  1. 1. Predic've  and  Popula'onal  Model  for  Alzheimer's  Disease  using   Structural  Neuroimaging     ® Domingo  López  Rodríguez,  Antonio  García  Linares    Brain  Dynamics.  University  of  Málaga.  Málaga.   ObjecEves:  This  paper  aims  populaEonal  modeling  of  volumetric  degeneraEon  of  the  gray   maIer  due  to  Alzheimer's  disease,  to  establish  the  parameters  of  degeneraEon,  and  to   contrast  the  state  of  an  individual  with  respect  to  that  model.  In  this  way,  you  can  get  an   early  diagnosis  of  the  disease.       PopulaEonal  Model   Above:  Leh:  Different  evoluEons  of  parahippocampal  areas   in  controls  (red)  and  in  AD  paEents  (blue).  Right:  StaEsEcal   differences  of  gray  maIer  volume  loss  between  controls   and  AD.  Red  indicates  more  difference.     In  the  populaEonal  model,  the  regional  differences   between  healthy  and  AD  paEents  is  presented  (p-­‐value  of   the  gray  maIer  volume  difference)  in  the  table  aside.   STRUCTURE Paracentral)Lobe)(Right) Paracentral)Lobe)(Left) Postcentral)Gyrus)(Right) Postcentral)Gyrus)(Left) Precentral)Gyrus)(Right) Angular)Gyrus)(Right) Angular)Gyrus)(Left) Calcarine)Sulcus)(Left) Cuneus)(Left) Inferior)Occipital)Gyrus)(Right) 50 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 60 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.005 70 0.000 0.000 0.000 0.000 0.000 0.001 0.011 0.043 0.043 0.008 80 0.030 0.029 0.003 0.019 0.050 0.016 0.003 0.134 0.057 0.007 Classifier  and  PredicEve  System   Decision  Rule  Example:    IF  (AGE  >  55)  AND  (TEMPORAL  MIDDLE  GYRUS  THICKNESS  <=  2.448985)    THEN  CLASS  =  AD    [PROBABILITY  =  0.946]     Parameter Accuracy Sensitivity Specificity Value 91.48% 90.80% 92.30% Conclusions:  We  have  developed  a  system  capable  of  detecEng  early  AD  using  MRI  analysis   with  a  high  success  rate,  non-­‐invasive,  fast  and  objecEve.      

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