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International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

BrainLes 2016: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 271–281Cite as

Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn Methods

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10154))

Abstract

Cognitive impairment has mainly two, non mutually exclusive, etiologies: structural or connectivity lesions. Analogously, we present here a methodology aimed at investigating magnetic resonance imaging (MRI) scans of subject after a traumatic brain injury (TBI) to detect the presence of these heterogeneous lesions and access the information content within. In particular, we use (i) complex network topological features to capture the effect of disease on connectivity and (ii) morphological brain measurements to describe anomalous patterns from a structural perspective. This integrated base of knowledge is then used to emphasize differences arising within a cohort including normal controls and patients labeled as category-I and category-II according to their outcome after TBI. Results suggest that topological measurements provide a suitable measurement to detect category-I subjects, while structural features are effective to distinguish controls from category-II subjects.

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Notes

  1. 1.

    freesurfer.nmr.mgh.harvard.edu

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Aknowledgement

Cortical reconstruction and volumetric segmentation were performed with the FreeSurfer image analysis suite, which is documented and freely available for download online. The technical details of these procedures are described in prior publications [6, 7, 9,10,11,12,13,14,15, 17, 19, 30]. Briefly, this processing includes motion correction and averaging [26] of multiple volumetric T1 weighted images (when more than one is available), removal of non-brain tissue using a hybrid watershed/surface deformation procedure [30], automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles) [12, 13] intensity normalization [32], tessellation of the gray matter white matter boundary, automated topology correction [11, 31], and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class [6, 7, 9]. Once the cortical models are complete, a number of deformable procedures can be performed for in further data processing and analysis including surface inflation [6], registration to a spherical atlas which utilized individual cortical folding patterns to match cortical geometry across subjects [15], parcellation of the cerebral cortex into units based on gyral and sulcal structure [8, 10], and creation of a variety of surface-based data including maps of curvature and sulcal depth. This method uses both intensity and continuity information from the entire three-dimensional MR volume in segmentation and deformation procedures to produce representations of cortical thickness, calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface [9]. The maps are created using spatial intensity gradients across tissue classes and are therefore not simply reliant on absolute signal intensity. The maps produced are not restricted to the voxel resolution of the original data thus are capable of detecting submillimeter differences between groups. Procedures for the measurement of cortical thickness have been validated against histological analysis [28] and manual measurements [21, 29]. Freesurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths [17, 27].

Author information

Authors and Affiliations

  1. Dipartimento Interateno di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy

    R. Bellotti, N. Amoroso & A. Tateo

  2. Sezione di Bari, Istituto Nazionale di Fisica Nucleare, Bari, Italy

    R. Bellotti, N. Amoroso, A. Tateo & S. Tangaro

  3. Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Bari, Italy

    A. Lombardi & C. Guaragnella

Authors
  1. R. Bellotti
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  2. A. Lombardi
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  3. C. Guaragnella
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  4. N. Amoroso
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  5. A. Tateo
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  6. S. Tangaro
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Corresponding author

Correspondence to S. Tangaro .

Editor information

Editors and Affiliations

  1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  2. TU München, Computer Science, Munich, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  6. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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Bellotti, R., Lombardi, A., Guaragnella, C., Amoroso, N., Tateo, A., Tangaro, S. (2016). Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn Methods. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_25

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_25

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