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FSL-BASED HYBRID ATLAS PROMOTES ACTIVATION WEIGHTED VECTOR ANALYSIS IN FUNCTIONAL NEURORADIOLOGY

Alberto A. Vergani1*, Renzo Minotto2, Sabina Strocchi3 and Elisabetta Binaghi1
  • 1 University of Insubria, Department of Theoretical and Applied Science, Italy
  • 2 Ospedale di Circolo e Fondazione Macchi, O.U. Neuroradiology, Italy
  • 3 Ospedale di Circolo e Fondazione Macchi, O.U. Health Physics, Italy

Introduction and Motivations. Functional magnetic resonance methods detect in vivo brain hemodynamic responses related to specific task. After defined statistical processes pipeline, relevant information contained in the fMRI Blood-Oxygen-Level-Dependent signals are transformed in a 3D Statistical Parametric Maps. In our previous works, we investigated how to conveniently summarize these original 3D distributions by a more compact description composed of a set of indexes calculated for each functional area. These values, properly named Activation Weighted Indexes (AWIs), vary between 0 and 1 and regard all brain regions contained into specific template used for co-registration (e.g. using Juelich standard there will be 121 indexes). The proposed AWI based feature extraction procedure reduces the three-dimensions SPM distribution to the one-dimension AWI distribution The resulting quantized data structure of task-related brain activations, that we named Activation Weighted Vector (AWV), is a histogram, where each bin represents the level of the functionality of each brain structure. The expressiveness of AWV depends by the normalization procedure for fMRI scans: the more regions standard atlas has, the more AWIs are computable. Proceeding from these considerations, we addressed the problem of generating an inclusive template for AWI analysis satisfying the right trade-off between exhaustion power and visual usability. Description of CRAIIM Hybrid Atlas. The hybrid atlas is the outcome of the joint operation between different brain templates found into the FMRIB Software Library. They are Juelich histological atlas and Harvard-Oxford cortical and subcortical structural atlases. Both are probabilistic labelled and registered in MNI152 space. Juelich model was created by averaging multi-subject post-mortem cyto and myelo-architectonic segmentations, which has detected 52 grey matter structures and 10 white matters ones. Harvard-Oxford models cover 48 cortical and 21 subcortical structural regions, computed by segmentation of T1-weighted images of healthy male and female. The joint process was possible choosing a reference atlas and then adding lacking anatomical structures. In our case, Juelich atlas was the template from which regions dearth were easily included choosing them from Harvard-Oxford atlases. This procedure, shaped with complete or partial union operations, gives rise to an hybrid atlas that covers 161 regions, in which 121 are the 100% of Juelich, and other 40 are a variable percentage of Harvard-Oxford original brain volumes. The benefits of this hybrid atlas are the integration of fundamental neuroanatomy models useful for co-registration that in the standard template were absent, e.g. many frontal and temporal cortexes, subcallosal portions, cingulate gyrus and thalamus halves. The limitation is that these last regions are in some cases a minor proportion of the Harvard-Oxford template. The hybridized atlas is in NIFTI format and its FSL-like name is CRAIIM-thr0-1mm.nii.gz (where thr0 is a voxel probability threshold, i.e. Prob. ≥ 0 to belong a certain anatomical label; and 1mm means the voxels resolution). Clinical Applications. As above described, CRAIIM hybrid atlas has 161 regions. With this registration template, the AWV procedure generates vectors of 161 indexes from each SPMs yielding important advantages both in the analysis of collection of data and in the individual data interpretation. The data represented in a “well dimensioned” vector space are used to perform efficiently quantitative measures of dissimilarity among brains functional activations basing on a given selected metrics. Unsupervised learning methods are naturally applied to learn the structure of the data and then to discover and recognize salient neurological patterns that unearth hidden features, also permitting their interpretation like individual brain signatures. With AWV analysis, the Radiologists investigate activation about macro categories alike white/grey matters contribution or left/right hemispheres functional balancement. In addition, AWVs allow physician to examine geographically defined zones such as frontal or temporal cortexes, motor or visual systems, limbic core, callosum commissure, or more detailed contribution akin Broca’s areas, Wernicke’s areas, &c. AWVs also facilitate severe assessment modalities for patients monitoring. For example, within homogenous group, clinicians could discover interesting level differences; or between heterogeneous ones, they could observe interesting likeness. In addition, including time factor for each subject, longitudinal studies based on AWV could highlight brain plasticity processes more suitable than classical SPM qualitative visual inspection. These kind of clinical evaluations have claim among pre/post neurosurgical operation controls or during long-term ordinary patients’ checks. Obviously, AWV analysis is a math tool well applicable with active paradigm as well as passive ones, e.g. for resting state fMRI acquisition, keeping in mind the greater role that could have distributed functional connectivity instead locally defined expected activation. Present and Future Works. Hybrid atlas has enhanced activated weighted vector analysis thanks to its peculiarities. It has clinical utility for AWV methodology, but some kind of incompleteness for all brain region representation: it is missing of special structure like cerebellum and, in general, has some portion that is a percentage of its original atlases. Other own quality regards the resolution about only 1mm. Future work is planned to generalize the atlas building procedure with which to easily generate solutions for diversified functional neuroradiological applications honouring the salience of existing specialized template.

Acknowledgements

The authors would like to thank all staff of CRAIIM (Research Centre in Image Analysis and Medical Informatics). In particular, a special gratitude for Dr. Valentina Pedoia for her technical support and Dr. Sergio Balbi for his clinical suggestions. This work will be presented at the INCF Neuroinformatics Conference 2016 in Reading, UK (September 3rd and 4th).

References

(1) Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences. 1990 Dec 1;87(24):9868-72.
(2) Lindquist MA. The statistical analysis of fMRI data. Statistical Science. 2008;23 (4):439-64. doi:10.1214/09-STS282
(3) Pedoia, V., Colli, V., Strocchi, S., Vite, C., Binaghi, E. and Conte, L., 2011, March. fMRI analysis software tools: an evaluation framework. In SPIE Medical Imaging (pp. 796528-796528). International Society for Optics and Photonics. doi: 10.1117/12.877067.
(4) Pedoia V, Strocchi S, Minotto R, Binaghi E. Hemispheric dominance evaluation by using fMRI activation weighted vector. Computational Modelling of Objects Represented in Images III: Fundamentals, Methods and Applications. 2012 Aug 24:303.
(5) Pedoia V, Strocchi S, Colli V, Binaghi E, Conte L. Functional magnetic resonance imaging: Comparison between activation maps and computation pipelines in a clinical context. Magnetic resonance imaging. 2013 May 31;31(4):555-66. doi: 10.1016/j.mri.2012.10.013
(6) Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012 Aug 15;62(2):782-90. doi: 10.1016/j.neuroimage.2011.09.015
(7) Duda RO, Hart PE, Stork DG. Pattern classification. John Wiley & Sons; 2012 Nov 9.
(8) Mitchell TM. Machine learning. 1997. Burr Ridge, IL: McGraw Hill. 1997;45:995.
(9)Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature neuroscience. 2015 Oct 12. doi:10.1038/nn.4135
(10) Van Den Heuvel MP, Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. European Neuropsychopharmacology. 2010 Aug 31;20(8):519-34. 10.1016/j.euroneuro.2010.03.008

Keywords: Brain Atlas, Functional Neuroimaging, fMRI data analysis, unsupervised learning, neuroradiology, fsl

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Neuroimaging

Citation: Vergani AA, Minotto R, Strocchi S and Binaghi E (2016). FSL-BASED HYBRID ATLAS PROMOTES ACTIVATION WEIGHTED VECTOR ANALYSIS IN FUNCTIONAL NEURORADIOLOGY. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00077

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Received: 29 May 2016; Published Online: 18 Jul 2016.

* Correspondence: Dr. Alberto A Vergani, University of Insubria, Department of Theoretical and Applied Science, Varese, Italy, aavergani@uninsubria.it