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BRAINET
BRAINet: Structural and Functional Brain Network Analytics
The idea that the brain is composed of functional and structural networks and that defects of these networks are either the cause or the result of the majority of neurological/psychiatric diseases, is widely accepted and is supported by studies on different patient groups. The use of fMRI (functional MRI) for studying the functional networks (fNETs), and dMRI (diffusion MRI) for studying the structural networks (sNETs) have increased within the last decade. Despite the advantages of these modalities, there are multiple open problems in brain network modeling. Some of the major challenges are:
  • Functionally homogeneous, spatially consistent across a population brain parcellation
  • Directed, weighted and dynamic structural and functional connectivity definitions
  • Network thresholding and multiple comparison problem
  • Reference network construction
  • Structure-Function relation
  • Clinical translations for early diagnosis, disease monitoring, pharmacological studies
BRAINet is a series of projects endeavoring to tackle with these problems within a unified functional and structural network modeling setup. We have chosen the Alzheimer’s Disease as our primary application area, though the methods are not disease specific.
The project involves researchers from different institutions and fields, including engineering (Electrical-Electronics Eng., Computer Science), basic sciences (Physics, Mathematics) and medicine (Neurology). The team uses I.U. Hulusi Behcet Life Sciences Center MRI facilities for data collection.
Fibers Color-coded FA Parcellation Segmentation
Sample screens from the project's BRAINet MITK plug-in

Dissemination Activities:

Selected Literature:
  1. Sporns O, Structure and function of complex brain networks, Dialogues Clin. Neuroscience (2013 Sep 1)
  2. Bullmore E, Sporns O, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat Rev Neurosci (2009 Mar 1) 10: 186-98
  3. Fornito A, et al, Graph analysis of the human connectome: promise, progress, and pitfalls, Neuroimage (2013 Oct 15) 80: 426-44
  4. Kaiser Marcus, A tutorial in connectome analysis: Topological and spatial features of brain networks. NeuroImage (2011 Jan 1) 57: 892-907
  5. Telesford QK, et al, The brain as a complex system: using network science as a tool for understanding the brain. Brain Connect (2011 Jan 1) 1: 295-308
  6. Hagmann P, et al, Mapping the Structural Core of Human Cerebral Cortex. PLoS Biology (2008 Jan 1) 6: e159
  7. Passingham RE, What we can and cannot tell about the wiring of the human brain. Neuroimage (2013 Oct 15) 80: 14-7
  8. Horn A, Ostwald D, Reisert M, Blankenburg F. The structural-functional connectome and the default mode network of the human brain. Neuroimage (2013 Oct 4)
  9. Bastiani M, et al, Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm. Neuroimage (2012 Jun 12) 62: 1732-1749
  10. Smith SM, et al, Network modelling methods for FMRI. Neuroimage (2011 Jan 15) 54: 875-91
  11. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. NeuroImage (2010) 53:1197-207
  12. Meskaldji DE, et al. Adaptive strategy for statistical analysis of connectomes. PLoS One 6, (2011) e230009
  13. Chen G. et al. Classification of Alzheimer disease, mild cognitive impairment and normal cognitive status with large-scale network analysis based on resting state functional MRI imaging. Radiology (2011) 259, 213-221
  14. Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. Neuroimage (2011) 56, 2068-2079
  15. Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science (2002) 296, 910-913
  16. Hirschberger M, et al. Randomly generating portfolio-selection covariance matrices with specified distributional characteristics. Eur. Open J Res. (2004) 177, 1610-1625
  17. Cabeen RP, Bastin ME, Laidlaw DH. A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging. Neuroimage (2016 Nov 12) 146: 100-112
  18. Horn A, Blankenburg F. Toward a standardized structural-functional group connectome in MNI space. Neuroimage (2016 Jan 1) 124 (Pt A): 310-322.
  19. Zhu Dajiang, et al, Fusing DTI and FMRI Data: A Survey of Methods and Applications. NeuroImage (2013 Jan 1)
  20. Bowman FD, Zhang L, Derado G, Chen S, Determining functional connectivity using fMRI data with diffusion-based anatomical weighting. Neuroimage (2012 Sep 1) 62: 1769-79
  21. Messe A, Benali H, Marrelec G, Relating Structural and Functional Connectivity in MRI: A Simple Model for a Complex Brain. IEEE Trans Med Imaging (2015 Jan 1) 34: 27-37
  22. Abdelnour Farras, Voss Henning U., Raj Ashish. Network diffusion accurately models the relationship between structural and functional brain connectivity networks. NeuroImage (2014 Apr 15) 90: 335-47
  23. Ghanbari Y, Smith AR, Schultz RT, Verma R. Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding. Med Image Anal (2014 Dec 1) 18: 1337-48
  24. Karahan Esin, Rojas-Lopez Pedro A., Bringas-Vega Maria L., Valdes-Hernandez Pedro A., Valdes-Sosa Pedro A. Tensor Analysis and Fusion of Multimodal Brain Images. Proceedings of the IEEE (2015 Jan 1) 103: 1531-1559
  25. Cichocki A.Tensor Decompositions: A New Concept in Brain Network Analysis? (2013) http://arxiv.org/abs/1305.0395
Resources:
  1. Repository for Machine Learning on Connectome Data
  2. Brain Connectome Toolbox (Matlab)
  3. Neuroimage Special Issue on Shared Data Sources


Team Members and Collaborators:
Burak Acar, PhD, Alkan Kabakcioglu, PhD, E. Sule Yazici, PhD, Hakan Gurvit, MD, Basar Bilgic, MD, Asli Demirtas-Tatlidede, MD, Tamer Demiralp, MD, A. Taylan Cemgil, PhD, Ender Konukoglu, PhD
Students:
Demet Yuksel, Gokhan Gumus, Cigdem Ulasoglu, Elif Yavas, Elif Kurt, Ezgi Soncu, Abdullah Karaslanli, Goktekin Durusoy, Abdulkadir Yazici (Software support)
Past Members, Students and Collaborators:
Basak Kilic, MS, Erhan Ozacar, MS, Kaan Ege Ozgun, BS, Caspar J. Goch, PhD, Evren Ozarslan, PhD
Resources / Projects:
Collaborators:
Partners
Funding:
TUBITAK ARDEB 1003 (114E053) 2014-2016
Bogazici University BAP (10520) 2015-2018
 



BRAINet Platform (An MITK Plug-in)

GUI
The BRAINet platform is a custom plug-in developed under MITK (www.mitk.org). The platform has been designed to
  • load and display fMRI, DWI, T1, T2, Parcellation, Segmentation and  precomputed ICA-maps,
  • perform DTI reconstruction,
  • run deterministic and probabilistic tractography on DTI
  • network node definition & refinement
  • Multi-connectivity metric sNET and fNET generation
  • Ability to define cNET connectivity
  • Visualization and export functions
Assessment of Functional Connectivity Methods in Dementia
fMRI maps
Using resting-state fMRI to investigate functional connectivity measures and detect abnormality within and between resting-state networks have yielded promising results that disclose information about the nature of neurodegenerative diseases. The main motivation behind this work was to understand the changes of functional connectivity measures within the components of Default Mode Network (DMN) for people suffering from dementia. The analyses were conducted on three subject groups: subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and Alzheimer’s disease (AD).  By using varying resting-state fMRI methods, such as seed-based, independent component and cluster analyses, it was possible to differentiate between SCI, MCI and AD patients by investigating the dementia related changes within the DMN. Independent of the method of choice, the obtained results indicated a similar pattern of change in connecitivity measures that showed significant differences between each group.