Degang Zhang

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work.

Complex span tasks and hippocampal recruitment during working memory

The working memory (WM) system is vital to performing everyday functions that require attentive, non-automatic processing of information. However, its interaction with long term memory (LTM) is highly debated. Here, we used fMRI to examine whether a popular complex WM span task, thought to force the displacement of to-be-remembered items in the focus of attention to LTM, recruited medial temporal regions typically associated with LTM functioning to a greater extent and in a different manner than traditional neuroimaging WM tasks during WM encoding and maintenance. fMRI scans were acquired while participants performed the operation span (OSPAN) task and an arithmetic task. Results indicated that performance of both tasks resulted in significant activation in regions typically associated with WM function. More importantly, significant bilateral activation was observed in the hippocampus, suggesting it is recruited during WM encoding and maintenance. Right posterior hippocampus activation was greater during OSPAN than arithmetic. Persitimulus graphs indicate a possible specialization of function for bilateral posterior hippocampus and greater involvement of the left for WM performance. Recall time-course activity within this region hints at LTM involvement during complex span.

Axonal Fiber Terminations Concentrate on Gyri

Convoluted cortical folding and neuronal wiring are 2 prominent attributes of the mammalian brain. However, the macroscale intrinsic relationship between these 2 general cross-species attributes, as well as the underlying principles that sculpt the architecture of the cerebral cortex, remains unclear. Here, we show that the axonal fibers connected to gyri are significantly denser than those connected to sulci. In human, chimpanzee, and macaque brains, a dominant fraction of axonal fibers were found to be connected to the gyri. This finding has been replicated in a range of mammalian brains via diffusion tensor imaging and high-angular resolution diffusion imaging. These results may have shed some lights on fundamental mechanisms for development and organization of the cerebral cortex, suggesting that axonal pushing is a mechanism of cortical folding.

Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain.

Visual analytics of brain networks.

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging.

Increased cortico-subcortical functional connectivity in schizophrenia

It has been widely reported that structural and functional connectivities are disturbed in cortical networks in schizophrenia (SZ). However, much less is known about the structural and functional connectivities between cortical and subcortical regions in SZ. Here, diffusion tensor imaging (DTI) data was used to identify consistent cortico-subcortical structural connection patterns across SZ patients and controls, and thus 13 common cortical Regions of Interest (ROIs) were determined. DTI and resting state fMRI (R-fMRI) datasets were used to assess the structural and functional connectivities between the 13 cortical ROIs and 12 subcortical regions in 8 SZ patients and 10 normal controls. It was found that there are significantly increased functional connectivities for 7 cortico-subcortical connections between the 13 cortical ROIs and 12 subcortical regions. Among most of these connections, the functional connectivity strength was doubled in SZ in comparison to controls. The cortical ROIs with functional hyper-connectivities to subcortical regions are localized in frontal and parietal lobes. However, no significant difference in the structural connectivity between these cortical and subcortical regions was found between SZ and controls. Additional analysis results showed 4 significantly increased and 2 significantly decreased cortico-cortical connections. Our study results suggest the functional hyper-connectivity between cortical and subcortical regions, adding further evidence to literature findings that SZ is a disorder of connectivity between components of large-scale brain networks. The result of either increased or decreased functional connectivities among cortical ROIs exhibits the complex pattern of disturbance of brain networks in SZ.

Discovering dense and consistent landmarks in the brain

The lack of consistent and reliable functionally meaningful landmarks in the brain has significantly hampered the advancement of brain imaging studies. In this paper, we use white matter fiber connectivity patterns, obtained from diffusion tensor imaging (DTI) data, as predictors of brain function, and to discover a dense, reliable and consistent map of brain landmarks within and across individuals. The general principles and our strategies are as follows. 1) Each brain landmark should have consistent structural fiber connectivity pattern across a group of subjects. We will quantitatively measure the similarity of the fiber bundles emanating from the corresponding landmarks via a novel trace-map approach, and then optimize the locations of these landmarks by maximizing the group-wise consistency of the shape patterns of emanating fiber bundles. 2) The landmark map should be dense and distributed all over major functional brain regions. We will initialize a dense and regular grid map of approximately 2000 landmarks that cover the whole brains in different subjects via linear brain image registration. 3) The dense map of brain landmarks should be reproducible and predictable in different datasets of various subject populations. The approaches and results in the above two steps are evaluated and validated via reproducibility studies. The dense map of brain landmarks can be reliably and accurately replicated in a new DTI dataset such that the landmark map can be used as a predictive model. Our experiments show promising results, and a subset of the discovered landmarks are validated via task-based fMRI.

Bridging low-level features and high-level semantics via fMRI brain imaging for video classification

The multimedia content analysis community has made significant effort to bridge the gap between low-level features and high-level semantics perceived by human cognitive systems such as real-world objects and concepts. In the two fields of multimedia analysis and brain imaging, both topics of low-level features and high level semantics are extensively studied. For instance, in the multimedia analysis field, many algorithms are available for multimedia feature extraction, and benchmark datasets are available such as the TRECVID. In the brain imaging field, brain regions that are responsible for vision, auditory perception, language, and working memory are well studied via functional magnetic resonance imaging (fMRI). This paper presents our initial effort in marrying these two fields in order to bridge the gaps between low-level features and high-level semantics via fMRI brain imaging. Our experimental paradigm is that we performed fMRI brain imaging when university student subjects watched the video clips selected from the TRECVID datasets. At current stage, we focus on the three concepts of sports, weather, and commercial-/advertisement specified in the TRECVID 2005. Meanwhile, the brain regions in vision, auditory, language, and working memory networks are quantitatively localized and mapped via task-based paradigm fMRI, and the fMRI responses in these regions are used to extract features as the representation of the brains comprehension of semantics. Our computational framework aims to learn the most relevant low-level feature sets that best correlate the fMRI-derived semantics based on the training videos with fMRI scans, and then the learned models are applied to larger scale test datasets without fMRI scans for category classifications. Our result shows that: 1) there are meaningful couplings between brains fMRI responses and video stimuli, suggesting the validity of linking semantics and low-level features via fMRI; 2) The computationally learned low-level feature sets from fMRI-derived semantic features can significantly improve the classification of video categories in comparison with that based on original low-level features.

Automatic cortical surface parcellation based on fiber density information

It is widely believed that the structural connectivity of a brain region largely determines its function. High resolution Diffusion Tensor Imaging (DTI) is now able to image the axonal fibers in vivo and the DTI tractography result provides rich connectivity information. In this paper, a novel method is proposed to employ fiber density information for automatic cortical parcellation based on the premise that fibers connecting to the same cortical region should be within the same functional brain network and their aggregation on the cortex can define a functionally coherent region. This method consists of three steps. Firstly, the fiber density is calculated on the cortical surface. Secondly, a flow field is obtained by calculating the fiber density gradient and a flow field tracking method is utilized for cortical parcellation. Finally, an atlas-based warping method is used to label the parcellated regions. Our method was applied to parcellate and label the cortical surfaces of eight healthy brain DTI images, and interesting results are obtained. In addition, the labeled regions are used as ROIs to construct structural networks for different brains, and the graph properties of these networks are measured.

Diffusion tensor imaging reveals evolution of primate brain architectures

Evolution of the brain has been an inherently interesting problem for centuries. Recent studies have indicated that neuroimaging is a powerful technique for studying brain evolution. In particular, a variety of reports have demonstrated that consistent white matter fiber connection patterns derived from diffusion tensor imaging (DTI) tractography reveal common brain architecture and are predictive of brain functions. In this paper, based on our recently discovered 358 dense individualized and common connectivity-based cortical landmarks (DICCCOL) defined by consistent fiber connection patterns in DTI datasets of human brains, we derived 65 DICCCOLs that are common in macaque monkey, chimpanzee and human brains and 175 DICCCOLs that exhibit significant discrepancies amongst these three primate species. Qualitative and quantitative evaluations not only demonstrated the consistencies of anatomical locations and structural fiber connection patterns of these 65 common DICCCOLs across three primates, suggesting an evolutionarily preserved common brain architecture but also revealed regional patterns of evolutionarily induced complexity and variability of those 175 discrepant DICCCOLs across the three species.