Carlos Faraco

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.

Gyral folding pattern analysis via surface profiling.

Folding is an essential shape characteristic of the human cerebral cortex. Descriptors of cortical folding patterns have been studied for decades. However, many previous studies are either based on local shape descriptors such as curvature, or based on global descriptors such as gyrification index or spherical wavelets. This paper proposes a gyrus-scale folding pattern analysis technique via cortical surface profiling. Firstly, we sample the cortical surface into 2D profiles and model them using a power function. This step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. Secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical surface, and apply the affinity propagation clustering algorithm to parcellate the cortex into cortical regions with strict hierarchy and smooth transitions among them. Finally, a second-round surface profiling is performed on the parcellated cortical surface, and the number of hinges is detected to describe the gyral folding pattern. We have applied the surface profiling method to two normal brain datasets and a schizophrenia patient dataset. The experimental results demonstrate that the proposed method can accurately classify human gyri into 2-hinge, 3-hinge and 4-hinge patterns. The distribution of these folding patterns on brain lobes and the relationship between fiber density and gyral folding patterns are further investigated. Results from the schizophrenia dataset are consistent with commonly found abnormality in former studies by others, which demonstrates the potential clinical applications of the proposed technique.

A computational model of cerebral cortex folding.

The geometric complexity and variability of the human cerebral cortex have long intrigued the scientific community. As a result, quantitative description of cortical folding patterns and the understanding of underlying folding mechanisms have emerged as important research goals. This paper presents a computational 3D geometric model of cerebral cortex folding initialized by MRI data of a human fetal brain and deformed under the governance of a partial differential equation modeling cortical growth. By applying different simulation parameters, our model is able to generate folding convolutions and shape dynamics of the cerebral cortex. The simulations of this 3D geometric model provide computational experimental support to the following hypotheses: (1) Mechanical constraints of the skull regulate the cortical folding process. (2) The cortical folding pattern is dependent on the global cell growth rate of the whole cortex. (3) The cortical folding pattern is dependent on relative rates of cell growth in different cortical areas. (4) The cortical folding pattern is dependent on the initial geometry of the cortex.

Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data

Resting state fMRI (rsfMRI) has been demonstrated to be an effective modality by which to explore the functional networks of the human brain, as the low-frequency oscillations in rsfMRI time courses between spatially distant brain regions show the evidence of correlated activity patterns in the brain. This paper proposes a novel surface-based data-driven framework to explore these networks through the use of high resolution rsfMRI data. Guided by DTI defined fiber pathways and constrained by the gray matter, we map the rsfMRI BOLD signals onto the cortical surface generated by DTI-based tissue segmentation. We then use a data-driven affinity propagation clustering algorithm to identify these functional networks. Our experimental results demonstrate that the framework has high reproducibility and that several networks are detected reliably among individual subjects. Furthermore, our results exhibit that functional networks are highly correlated with structural connections. Finally, our framework is able to reveal visual sub-networks, indicating its potential role in sub-network exploration.

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.

Lack of long-term fMRI differences after multiple sports-related concussions

Introduction: Mild traumatic brain injury (mTBI) or concussion has been acutely associated with several cognitive symptoms, including deficits in response inhibition, working memory and motor performance. The pervasiveness of these cognitive symptoms has been more controversial. The effects of multiple concussions on neuropsychological functioning and brain activation following at least 6-months post-mTBI were examined. Methods: Twenty right-handed male athletes with a history of at least two concussions and 20 age/pre-morbid IQ/athletic-experience matched controls underwent neuropsychological assessment and fMRI scanning where they performed versions of a colour-word Stroop interference task, an operation-span working memory task and a finger-tapping task. Results: The Attention index score on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was lower for the concussion group, but only at liberal statistical threshold. Total RBANS score approached statistical significance. Reaction time during neurobehavioural tasks was similar across groups, but accuracy was reduced in the concussed group on the working memory task. Despite expected activation patterns within each group, there were no group differences in neural activation on any functional tasks using either whole-brain or ROI-specific analyses at liberal statistical thresholds. Conclusion: There were minimal differences between the two closely matched groups. Results point to the relative plasticity of younger adults’ cognitive abilities following concussion.

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.