Computational brain connectivity mapping: A core health and scientific challenge

One third of the burden of all the diseases in Europe is due to problems caused by diseases affecting brain. Although exceptional progress have been obtained for exploring the brain during the past decades, it is still terra-incognita and calls for specific efforts in research to better understand its architecture and functioning. To take up this great challenge of modern science and to solve the limited view of the brain provided just by one imaging modality, this article advocates the idea developed in my research group of a global approach involving new generation of models for brain connectivity mapping and strong interactions between structural and functional connectivities. Capitalizing on the strengths of integrated and complementary non invasive imaging modalities such as diffusion Magnetic Resonance Imaging (dMRI) and Electro & Magneto-Encephalography (EEG & MEG) will contribute to achieve new frontiers for identifying and characterizing structural and functional brain connectivities and to provide a detailed mapping of the brain connectivity, both in space and time. Thus leading to an added clinical value for high impact diseases with new perspectives in computational neuro-imaging and cognitive neuroscience.

[1]  Daniel C. Alexander,et al.  MicroTrack: An Algorithm for Concurrent Projectome and Microstructure Estimation , 2010, MICCAI.

[2]  Douglas L. Rosene,et al.  The Geometric Structure of the Brain Fiber Pathways , 2012, Science.

[3]  Carl-Fredrik Westin,et al.  Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data , 2014 .

[4]  Ning Yang,et al.  Fusing DTI and fMRI data: A survey of methods and applications , 2014, NeuroImage.

[5]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[6]  Théodore Papadopoulo,et al.  Propagation of epileptic spikes revealed by diffusion-based constrained MEG source reconstruction , 2013 .

[7]  P. Basser,et al.  Axcaliber: A method for measuring axon diameter distribution from diffusion MRI , 2008, Magnetic resonance in medicine.

[8]  Théodore Papadopoulo,et al.  Using diffusion MRI information in the Maximum Entropy on Mean framework to solve MEG/EEG inverse problem , 2014 .

[9]  Rachid Deriche,et al.  Higher-Order Tensors in Diffusion Imaging , 2014, Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data.

[10]  P. Bhattacharya.  Diffusion MRI: Theory, methods, and applications, Derek K. Jones (Ed.). Oxford University press (2011), $152.77 , 2012 .

[11]  Olivier D. Faugeras,et al.  A common formalism for the Integral formulations of the forward EEG problem , 2005, IEEE Transactions on Medical Imaging.

[12]  Carlo Pierpaoli,et al.  Mean apparent propagator (MAP) MRI: A novel diffusion imaging method for mapping tissue microstructure , 2013, NeuroImage.

[13]  Rachid Deriche,et al.  Constrained diffusion kurtosis imaging using ternary quartics & MLE , 2014, Magnetic resonance in medicine.

[14]  N. Balak,et al.  Costs of disorders of the brain in Europe , 2007, European journal of neurology.

[15]  Jean-Philippe Thiran,et al.  Structural connectomics in brain diseases , 2013, NeuroImage.

[16]  J. Olesen,et al.  Cost of disorders of the brain in Europe , 2005, European journal of neurology.

[17]  Rachid Deriche,et al.  Continuous diffusion signal, EAP and ODF estimation via Compressive Sensing in diffusion MRI , 2013, Medical Image Anal..

[18]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[19]  Rachid Deriche,et al.  A nested cortex parcellation combining analysis of MEG forward problem and diffusion MRI tractography , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[20]  P. Basser.  Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy , 2009 .

[21]  Flavio Dell'Acqua,et al.  Comment on “The Geometric Structure of the Brain Fiber Pathways” , 2012, Science.

[22]  Rachid Deriche,et al.  Multiple q-shell diffusion propagator imaging , 2011, Medical Image Anal..

[23]  R. Cameron Craddock,et al.  Clinical applications of the functional connectome , 2013, NeuroImage.

[24]  I. Wilkinson,et al.  Introduction to functional magnetic resonance imaging , 1999 .

[25]  Rachid Deriche,et al.  AxTract: Microstructure-Driven Tractography Based on the Ensemble Average Propagator , 2015, IPMI.

[26]  Rachid Deriche,et al.  Optimal real-time Q-ball imaging using regularized Kalman filtering with incremental orientation sets , 2009, Medical Image Anal..

[27]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[28]  S. Small,et al.  AN INTRODUCTION TO FUNCTIONAL MAGNETIC RESONANCE IMAGING , 1999 .

[29]  J. Os,et al.  The size and burden of mental disorders and other disorders of the brain in Europe 2010 , 2011, European Neuropsychopharmacology.

[30]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[31]  Rachid Deriche,et al.  From Second to Higher Order Tensors in Diffusion-MRI , 2009, Tensors in Image Processing and Computer Vision.

[32]  Rachid Deriche,et al.  A survey of current trends in diffusion MRI for structural brain connectivity , 2016, Journal of neural engineering.

[33]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[34]  Ragini Verma,et al.  On facilitating the use of HARDI in population studies by creating rotation-invariant markers , 2015, Medical Image Anal..

[35]  R. Deriche,et al.  Design of multishell sampling schemes with uniform coverage in diffusion MRI , 2013, Magnetic resonance in medicine.

[36]  Rachid Deriche,et al.  Complete Set of Invariants of a 4 th Order Tensor: The 12 Tasks of HARDI from Ternary Quartics , 2014, MICCAI.