Long Xie1, John B Pluta2, Sandhitsu R Das3, Laura E M Wisse2, Hongzhi Wang4, Lauren Mancuso5, Dasha Kliot5, Brian B Avants2, Song-Lin Ding6, José V Manjón7, David A Wolk5, Paul A Yushkevich2. 1. Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: lxie@seas.upenn.edu. 2. Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA. 3. Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA. 4. IBM Almanden Research Center, San Jose, USA. 5. Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA. 6. Allen Institute for Brain Science, Seattle, USA; School of Basic Sciences, Guangzhou Medical University, Guangzhou, China. 7. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain.
Abstract
RATIONAL: The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants METHODS: A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). RESULTS: The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.
RATIONAL: The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants METHODS: A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). RESULTS: The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.
Authors: Stephen M Smith; Mark Jenkinson; Mark W Woolrich; Christian F Beckmann; Timothy E J Behrens; Heidi Johansen-Berg; Peter R Bannister; Marilena De Luca; Ivana Drobnjak; David E Flitney; Rami K Niazy; James Saunders; John Vickers; Yongyue Zhang; Nicola De Stefano; J Michael Brady; Paul M Matthews Journal: Neuroimage Date: 2004 Impact factor: 6.556
Authors: P Aljabar; R Wolz; L Srinivasan; S J Counsell; M A Rutherford; A D Edwards; J V Hajnal; D Rueckert Journal: IEEE Trans Med Imaging Date: 2011-07-22 Impact factor: 10.048
Authors: Hongzhi Wang; Sandhitsu R Das; Jung Wook Suh; Murat Altinay; John Pluta; Caryne Craige; Brian Avants; Paul A Yushkevich Journal: Neuroimage Date: 2011-01-13 Impact factor: 6.556
Authors: Jean C Augustinack; Kristen E Huber; Allison A Stevens; Michelle Roy; Matthew P Frosch; André J W van der Kouwe; Lawrence L Wald; Koen Van Leemput; Ann C McKee; Bruce Fischl Journal: Neuroimage Date: 2012-08-30 Impact factor: 6.556
Authors: Alison R Preston; Aaron M Bornstein; J Benjamin Hutchinson; Meghan E Gaare; Gary H Glover; Anthony D Wagner Journal: J Cogn Neurosci Date: 2010-01 Impact factor: 3.225
Authors: Karolina M Lempert; Dawn J Mechanic-Hamilton; Long Xie; Laura E M Wisse; Robin de Flores; Jieqiong Wang; Sandhitsu R Das; Paul A Yushkevich; David A Wolk; Joseph W Kable Journal: Neuropsychologia Date: 2020-07-02 Impact factor: 3.139
Authors: Long Xie; Sandhitsu R Das; Laura E M Wisse; Ranjit Ittyerah; Paul A Yushkevich; David A Wolk Journal: J Alzheimers Dis Date: 2018 Impact factor: 4.472
Authors: Long Xie; Laura E M Wisse; John Pluta; Robin de Flores; Virgine Piskin; Jose V Manjón; Hongzhi Wang; Sandhitsu R Das; Song-Lin Ding; David A Wolk; Paul A Yushkevich Journal: Hum Brain Mapp Date: 2019-04-29 Impact factor: 5.038
Authors: Sandhitsu R Das; Long Xie; Laura E M Wisse; Nicolas Vergnet; Ranjit Ittyerah; Salena Cui; Paul A Yushkevich; David A Wolk Journal: Alzheimers Dement Date: 2019-09-06 Impact factor: 21.566
Authors: Robin de Flores; Laura E M Wisse; Sandhitsu R Das; Long Xie; Corey T McMillan; John Q Trojanowski; John L Robinson; Murray Grossman; Edward Lee; David J Irwin; Paul A Yushkevich; David A Wolk Journal: Alzheimers Dement Date: 2020-04-22 Impact factor: 21.566
Authors: D Berron; P Vieweg; A Hochkeppler; J B Pluta; S-L Ding; A Maass; A Luther; L Xie; S R Das; D A Wolk; T Wolbers; P A Yushkevich; E Düzel; L E M Wisse Journal: Neuroimage Clin Date: 2017-05-26 Impact factor: 4.881
Authors: Long Xie; Laura E M Wisse; Sandhitsu R Das; Nicolas Vergnet; Mengjin Dong; Ranjit Ittyerah; Robin de Flores; Paul A Yushkevich; David A Wolk Journal: Hum Brain Mapp Date: 2020-08-26 Impact factor: 5.038