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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
  • Spatial resolution limits of dMRI that limits the assessment of brain microstructure in-vivo
  • Temporal resolution limits of fMRI that limits the assessment of brain dynamics
  • Challenges in statistical network analysis with multiple comparison problem
  • 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, Physiology). 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

Invited Talks:

Selected Literature:
Diffusion MRI
  1. Liu C, Ízarslan E, Multimodal integration of diffusion MRI for better characterization of tissue biology. NMR Biomed (2018 Jul 16): e3939
Connectome Approach:
  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
Structural Connectome:
  1. Hagmann P, et al, Mapping the Structural Core of Human Cerebral Cortex. PLoS Biology (2008 Jan 1) 6: e159
  2. Bastiani M, et al, Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm. Neuroimage (2012 Jun 12) 62: 1732-1749
  3. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison.
    Panagiotaki E, Schneider T, Siow B, Hall MG, Lythgoe MF, Alexander DC. Neuroimage (2012 Feb 1) 59: 2241-54
  4. Inferring Microstructural Information of White Matter from Diffusion MRI. Assaf Y, Cohen Y. In: Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy
  5. The CONNECT project: Combining macro- and micro-structure.
    Assaf Y, Alexander DC, et al. Neuroimage (2013 Oct 15) 80: 273-82
  6. Passingham RE, What we can and cannot tell about the wiring of the human brain. Neuroimage (2013 Oct 15) 80: 14-7
  7. Fiber clustering versus the parcellation-based connectome. O'Donnell LJ, Golby AJ, Westin CF. Neuroimage (2013 Oct 15) 80: 283-9
  8. Microstructural grey matter parcellation and its relevance for connectome analyses. Caspers S, Eickhoff SB, Zilles K, Amunts K. Neuroimage (2013 Oct 15) 80: 18-26
  9. Structural connectomics in brain diseases. Griffa A, Baumann PS, Thiran JP, Hagmann P. Neuroimage (2013 Oct 15) 80: 515-26
Functional Connectome:
  1. Smith SM, et al, Network modelling methods for FMRI. Neuroimage (2011 Jan 15) 54: 875-91
  2. Horn A, Blankenburg F. Toward a standardized structural-functional group connectome in MNI space. Neuroimage (2016 Jan 1) 124 (Pt A): 310-322.
  3. Zhu Dajiang, et al, Fusing DTI and FMRI Data: A Survey of Methods and Applications. NeuroImage (2013 Jan 1)
  1. 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)
  2. Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease. Raj A, LoCastro E, Kuceyeski A, Tosun D, Relkin N, Weiner M. Cell Rep (2015 Jan 14)
  3. 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
  4. New approaches for exploring anatomical and functional connectivity in the human brain. Ramnani N, Behrens TE, Penny W, Matthews PM. Biol Psychiatry (2004 Nov 1) 56: 613-9
  5. A watershed model of individual differences in fluid intelligence. Kievit RA, Davis SW, Griffiths J, Correia MM, Cam-Can , Henson RN. Neuropsychologia (2016 Oct 1) 91: 186-198
Network Analysis
  1. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. NeuroImage (2010) 53:1197-207
  2. Meskaldji DE, et al. Adaptive strategy for statistical analysis of connectomes. PLoS One 6, (2011) e230009
  3. 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
  4. Cichocki A.Tensor Decompositions: A New Concept in Brain Network Analysis? (2013)
  1. Repository for Machine Learning on Connectome Data
  2. Brain Connectome Toolbox (Matlab)
  3. Neuroimage Special Issue on Shared Data Sources
  4. The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge

Core Team Members:
(PI) Burak Acar, PhD; Alkan Kabakcioglu, PhD; E. Sule Yazici, PhD; Hakan Gurvit, MD; Basar Bilgic, MD; Asli Demirtas-Tatlidede, MD; Tamer Demiralp, MD; Caspar J. Goch, PhDl; Evren Ozarslan, PhD
Demet Yuksel, Goktekin Durusoy, Abdullah Karaslaanli, Zeynep Kahraman, Cigdem Ulasoglu, Elif Yavas, Elif Kurt, Ezgi Soncu, Zerrin Yildirim, Basak Kilic, Erhan Ozacar, Kaan Ege Ozgun, Gokhan Gumus, Umut Kucukarslan, Mehmet S. Onay, Gurur Gamgam, Abdullah Hayran, Fatih Sogukpinar

Resources / Projects:
Core Collaborators:
Core Funding:
TUBITAK ARDEB 1003 (114E053) 2014-2016
Bogazici University BAP (10520) 2015-2018
Istanbul University BAP (53037)

BRAINet Platform (An MITK Plug-in) -
D. Yuksel, G. Durusoy, A. Karaaslanli, Z. Kahraman, K.E. Ozgun, G. Gumus

The BRAINet platform is a custom plug-in developed under MITK ( 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
  • Visualization and export functions
Assessment of Functional Connectivity Methods in Dementia -
 B. Kilic
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.
fMRI Guided Personalization of Cortical Parcellation Maps -
M.S. Onay, U. Kucukarslan
ParcellationChange in intra-parcel BOLD correlations Joint analysis of structural and functional brain connectomes (sNET & fNET) requires networks to be defined over the same network nodes, ie. cortical parcellation regions. Such an analysis assumes that each network node (cortical parcellation) is spatially well-defined to assure inter-subject correspondence (which is provided by using an atlas) and is functionally compact (ie. high intra-parcel functional connectivity). Here, we propose to use a 3D elastic warping strategy (inspired by the Demon's algorithm) to iteratively modify cortical parcellation maps to increase intra-parcel functional connectivity. Results suggest a statistically significant increase in intra-parcel BOLD signal correlations.
BRAINet Platform Integration with High-Res Schaefer Parcellation Atlases
F. Sogukpinar, A. Hayran
Schaefer Parcellation
Cortical parcellation is required to define a common node set for consistent joint analysis of structural and functional networks over a population. The built network models are limited by the resolution of the parcellations used. Too coarse parcellations are poor in localization of functional/structural units of the brain, while too high parcellations (voxel-level analysis at the extreme end) prohibit population studies due to difficulties in across-subject correspondences. The Destrieux atlas (148 parcels) has been commonly used. In this work, the recently developed Schaefer atlas (A. Schaefer et al, Cerebral Cortex, 28, 2018) with multi-resolution parcellations (100-1000) has been integrated with BRAINet. Initial comparisons of global network parameters between 148 and 400 node networks showed high correlation, confirming the usability of the new atlas in BRAINer network analysis.