title: Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability.
abstract: 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).
  
  
  
  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.affiliation: 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.
authors.forename: Long
authors.initials: L
authors.lastname: Xie
authors.affiliation: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
authors.forename: John B
authors.initials: JB
authors.lastname: Pluta
authors.affiliation: 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.
authors.forename: Sandhitsu R
authors.initials: SR
authors.lastname: Das
authors.affiliation: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Laura E M
authors.initials: LEM
authors.lastname: Wisse
authors.affiliation: IBM Almanden Research Center, San Jose, USA.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.affiliation: Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Lauren
authors.initials: L
authors.lastname: Mancuso
authors.affiliation: Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Dasha
authors.initials: D
authors.lastname: Kliot
authors.affiliation: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Brian B
authors.initials: BB
authors.lastname: Avants
authors.affiliation: Allen Institute for Brain Science, Seattle, USA; School of Basic Sciences, Guangzhou Medical University, Guangzhou, China.
authors.forename: Song-Lin
authors.initials: SL
authors.lastname: Ding
authors.affiliation: 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.
authors.forename: José V
authors.initials: JV
authors.lastname: Manjón
authors.affiliation: Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Wolk
authors.affiliation: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Yushkevich
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D000071039:Perirhinal Cortex / Q000033:anatomy & histology* / Q000000981:diagnostic imaging / Q000473:pathology
keywords: Alzheimer's disease; Anatomical variability; Cortical thickness analysis; Magnetic resonance imaging; Manifold learning; Perirhinal cortex