- Sporns O, Structure and function of complex brain networks, Dialogues Clin. Neuroscience (2013 Sep 1)
- Bullmore E, Sporns O, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat Rev Neurosci (2009 Mar 1) 10: 186-98
- Fornito A, et al, Graph analysis of the human connectome: promise, progress, and pitfalls, Neuroimage (2013 Oct 15) 80: 426-44
- Kaiser Marcus, A tutorial in connectome analysis: Topological and spatial features of brain networks. NeuroImage (2011 Jan 1) 57: 892-907
- 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
- Hagmann P, et al, Mapping the Structural Core of Human Cerebral Cortex. PLoS Biology (2008 Jan 1) 6: e159
- Bastiani M, et al, Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm. Neuroimage (2012 Jun 12) 62: 1732-1749
- Griffa A, Baumann PS, Thiran JP, Hagmann P. Structural connectomics in brain diseases. Neuroimage (2013 Oct 15) 80: 515-26
- Smith SM, et al, Network modelling methods for FMRI. Neuroimage (2011 Jan 15) 54: 875-91
- Horn A, Blankenburg F. Toward a standardized structural-functional group connectome in MNI space. Neuroimage (2016 Jan 1) 124 (Pt A): 310-322.
- Zhu Dajiang, et al, Fusing DTI and FMRI Data: A Survey of Methods and Applications. NeuroImage (2013 Jan 1)
- Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. NeuroImage (2010) 53:1197-207
- Cichocki A.Tensor Decompositions: A New Concept in Brain Network Analysis? (2013) http://arxiv.org/abs/1305.0395
Zhang Z, Allen GI, Zhu H, Dunson D. Tensor network factorizations: Relationships between brain structural connectomes and traits. Neuroimage (2019 Aug 15) 197: 330-343
BRAINet: Structural and Functional Brain Network Analysis
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
- Structure-Function relation
- Machine Learning for graphs
- 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.
Watch the short video published by Bogazici Buzz
- Repository for Machine Learning on Connectome Data
- Brain Connectome Toolbox (Matlab)
- Neuroimage Special Issue on Shared Data Sources
- The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge
- OpenData - Various Graph Datasets
- ADNI: Alzheimer's Disease Neuroimaging Initiative
- BrainWeb : Simulated Brain Database (McGill University)
- Human Connectome Project
- Human Brain Project
BRAINet projects approach to connectome analysis is based on exploiting multi-modal connectome, as such all modalities need to be co-registered before proceeding to connectome construction and analysis. Co-registration of multi-modal data is a challenging task without any general purpose solution. Hence, we pursue a customized pre-processing pipeline that is designed to utilize the best components of well-known software toolboxes. Primarily, we use FSL for fMRI processing and registration of scalar volumes (such as T1), FreeSurfer for parcellation (atlas registration) and Tortoise for DWI processing. All co-registered volumes are loaded to the BRAINet Platform for manual/visual verificiation by expert neurologists as the final quality check. Cases labeled as poorly co-registered by the neurologists are excluded for further processing and are not manually corrected. Hence, all data used in BRAINet has been identically pre-processed without manual intervention.
A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e. sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes to define a low-dimensional space via tensor factorization. The proposed B-tensor encapsulates the whole population either with uni-modal (3D Tensor) or multi-modal (4D Tensor) networks. We show on a cohort of 47 subjects, spanning the spectrum of dementia, that diagnosis with an accuracy of 77% to 100% is achievable in a 5D connectome space using different structural and functional connectome constructions in a uni-modal and multi-modal fashion. We further show that multi-modal tensor factorization improves the results suggesting complementary information in structure and function.
A neurological assessment of the connectivity patterns identified largely agrees with prior knowledge, yet also suggests new associations that may play a role in the disease progress. The precuneus (L/R30), the angular gyrus (L/R25), the supramarginal gyrus (L/R26), the superior parietal lobule (L/R27), the precentral gyrus (L/R29), the postcentral gyrus (L/R28), the dorsal & ventral parts of posterior cingulate cortex (L/R 9-10) are observed to be the most conspicuous regions.