Multi-contrast unbiased MRI atlas of a Parkinson’s disease population
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Parkinson’s disease (PD) is the second leading neurodegenerative disease after Alzheimer’s disease. In PD research and its surgical treatment, such as deep brain stimulation (DBS), anatomical structural identification and references for spatial normalization are essential, and human brain atlases/templates are proven highly instrumental. However, two shortcomings affect current templates used for PD. First, many templates are derived from a single healthy subject that is not sufficiently representative of the PD-population anatomy. This may result in suboptimal surgical plans for DBS surgery and biased analysis for morphological studies. Second, commonly used mono-contrast templates lack sufficient image contrast for some subcortical structures (i.e., subthalamic nucleus) and biochemical information (i.e., iron content), a valuable feature in current PD research.
We employed a novel T1–T2* fusion MRI that visualizes both cortical and subcortical structures to drive groupwise registration to create co-registered multi-contrast (T1w, T2*w, T1–T2* fusion, phase, and an R2* map) unbiased templates from 15 PD patients, and a high-resolution histology-derived 3D atlas is co-registered. For validation, these templates are compared against the Colin27 template for landmark registration and midbrain nuclei segmentation.
While the T1w, T2*w, and T1–T2* fusion templates provide excellent anatomical details for both cortical and subcortical structures, the phase and R2* map contain the biochemical features. By one-way ANOVA tests, our templates significantly (\(p<0.05\)) outperform the Colin27 template in the registration-based tasks.
The proposed unbiased templates are more representative of the population of interest and can benefit both the surgical planning and research of PD.
KeywordsParkinson’s disease Multi-echo FLASH Unbiased brain atlas Multi-contrast Susceptibility-weighted imaging
Conflict of interest
Yiming Xiao, Vladimir Fonov, Silvain Bériault, Fahd Al Subaie, M. Mallar Chakravarty, Abbas F. Sadikot, G. Bruce Pike, and D. Louis Collins declare that they have no conflict of interest.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study.
- 4.Schaltenbrand G, Wahren W (1977) Atlas for stereotaxy of the human brain. 2nd, rev. and enl. edn. Year Book Medical Publishers, ChicagoGoogle Scholar
- 5.Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging. G. Thieme; New YorkGoogle Scholar
- 7.Bardinet E, Bhattacharjee M, Dormont D, Pidoux B, Malandain G, Schupbach M, Ayache N, Cornu P, Agid Y, Yelnik J (2009) A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease. J Neurosurg 110(2):208–219CrossRefPubMedGoogle Scholar
- 9.Haegelen C, Coupe P, Fonov V, Guizard N, Jannin P, Morandi X, Collins DL (2013) Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease. Int J Comput Assist Radiol Surg 8(1):99–110Google Scholar
- 14.Montgomery EB (2010) Deep brain stimulation programming: principles and practice. Oxford University Press, OxfordGoogle Scholar
- 15.Chakravarty MM, Sadikot AF, Germann J, Hellier P, Bertrand G, Collins DL (2009) Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications. Hum Brain Mapp 30(11):3574–3595CrossRefPubMedGoogle Scholar
- 21.Sudhyadhom A, Haq IU, Foote KD, Okun MS, Bova FJ (2009) A high resolution and high contrast MRI for differentiation of subcortical structures for DBS targeting: the fast gray matter acquisition T1 inversion recovery (FGATIR). Neuroimage 47(Suppl 2):T44–T52Google Scholar
- 22.Xiao Y, Bailey L, Chakravarty MM, Beriault S, Sadikot AF, Pike GB, Collins DL (2012) Atlas-based segmentation of the subthalamic nucleus, red nucleus, and substantia nigra for deep brain stimulation by incorporating multiple MRI contrasts. In: Information processing in computer-assisted interventions (IPCAI), pp 135–145Google Scholar
- 23.Yelnik J, Bardinet E, Dormont D, Malandain G, Ourselin S, Tande D, Karachi C, Ayache N, Cornu P, Agid Y (2007) A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. Neuroimage 34(2):618–638CrossRefPubMedGoogle Scholar
- 34.O’Gorman RL, Shmueli K, Ashkan K, Samuel M, Lythgoe DJ, Shahidiani A, Wastling SJ, Footman M, Selway RP, Jarosz J (2011) Optimal MRI methods for direct stereotactic targeting of the subthalamic nucleus and globus pallidus. Eur Radiol 21(1):130–136Google Scholar
- 39.Peran P, Hagberg G, Luccichenti G, Cherubini A, Brainovich V, Celsis P, Caltagirone C, Sabatini U (2007) Voxel-based analysis of R2* maps in the healthy human brain. J Magn Reson Imaging 26(6):1413–1420 Google Scholar
- 52.Guizzard N, Fonov V, Collins DL (2009) Symmetric optimization scheme versus constrained symmetrization for non-linear registrations. In: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis (validation and methodological issues)”Google Scholar