Armin Iraji

Resting State Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage: Independent Component and Seed-Based Analyses

Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting.

Imaging brain plasticity after trauma

The brain is highly plastic after stroke or epilepsy; however, there is a paucity of brain plasticity investigation after traumatic brain injury (TBI). This mini review summarizes the most recent evidence of brain plasticity in human TBI patients from the perspective of advanced magnetic resonance imaging. Similar to other forms of acquired brain injury, TBI patients also demonstrated both structural reorganization as well as functional compensation by the recruitment of other brain regions. However, the large scale brain network alterations after TBI are still unknown, and the field is still short of proper means on how to guide the choice of TBI rehabilitation or treatment plan to promote brain plasticity. The authors also point out the new direction of brain plasticity investigation.

The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective.

Diffusion kurtosis imaging discriminates patients with white matter lesions from healthy subjects

This work illustrates that DKI reveals white matter lesions and also discriminates healthy subjects from patientswith white matter lesions. To show this capability, we have investigated DKI images of a healthy subject and apatient with white matter lesions. The analysis was performed both between and within subjects. Regions of Interest (ROIs) for lesion and normal white matterin the patient images are selected manually (for within subject study) and also the corresponding ROIs in the healthy subject are defined (for between subject study). The results of comparing the estimated values for apparent diffusion and kurtosis parameters show that both Dapp and Kapp can distinguish normal and abnormal tissues. Kapp (Dapp) of the normal regions is greater (lower) than that ofthe abnormal regions. Another investigation over all voxels in the brain shows an important feature of kurtosis in determining white matter lesions.

Connectome-scale assessment of structural and functional connectivity in mild traumatic brain injury at the acute stage

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits each year in the United States. The large-scale structural and functional network connectivity changes of mTBI are still unknown. This study was designed to determine the connectome-scale brain network connectivity changes in mTBI at both structural and functional levels. 40 mTBI patients at the acute stage and 50 healthy controls were recruited. A novel approach called Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) was applied for connectome-scale analysis of both diffusion tensor imaging and resting state functional MRI data. Among 358 networks identified on DICCCOL analysis, 41 networks were identified as structurally discrepant between patient and control groups. The involved major white matter tracts include the corpus callosum, and superior and inferior longitudinal fasciculi. Functional connectivity analysis identified 60 connectomic signatures that differentiate patients from controls with 93.75% sensitivity and 100% specificity. Analysis of functional domains showed decreased intra-network connectivity within the emotion network and among emotion-cognition interactions, and increased interactions among action-emotion and action-cognition as well as within perception networks. This work suggests that mTBI may result in changes of structural and functional connectivity on a connectome scale at the acute stage.

Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is a major public health concern. Functional MRI has reported alterations in several brain networks following mTBI. However, the connectome-scale brain network changes are still unknown. In this study, sixteen mTBI patients were prospectively recruited from an emergency department and followed up at 4–6 weeks after injury. Twenty-four healthy controls were also scanned twice with the same time interval. Three hundred fifty-eight brain landmarks that preserve structural and functional correspondence of brain networks across individuals were used to investigate longitudinal brain connectivity. Network-based statistic (NBS) analysis did not find significant difference in the group-by-time interaction and time effects. However, 258 functional pairs show group differences in which mTBI patients have higher functional connectivity. Meta-analysis showed that “Action” and “Cognition” are the most affected functional domains. Categorization of connectomic signatures using multiview group-wise cluster analysis identified two patterns of functional hyperconnectivity among mTBI patients: (I) between the posterior cingulate cortex and the association areas of the brain and (II) between the occipital and the frontal lobes of the brain. Our results demonstrate that brain concussion renders connectome-scale brain network connectivity changes, and the brain tends to be hyperactivated to compensate the pathophysiological disturbances.

Longitudinal analysis of brain recovery after mild traumatic brain injury based on groupwise consistent brain network clusters

Traumatic brain injury (TBI) affects over 1.5 million Americans each year, and more than 75% of TBI cases are classified as mild (mTBI). Several functional network alternations have been reported after mTBI; however, the network alterations on a large scale, particularly on connectome scale, are still unknown. To analyze brain network, in a previous work, 358 landmarks named dense individualized common connectivity based cortical landmarks (DICCCOL) were identified on cortical surface. These landmarks preserve structural connection consistency and maintain functional correspondence across subjects. Hence DICCCOLs have been shown powerful in identifying connectivity signatures in affected brains. However, on such fine scales, the longitudinal changes in brain network of mTBI patients were complicated by the noise embedded in the systems as well as the normal variability of individuals at different times. Faced with such problems, we proposed a novel framework to analyze longitudinal changes from the perspective of network clusters. Specifically, multiview spectral clustering algorithm was applied to cluster brain networks based on DICCCOLs. And both structural and functional networks were analyzed. Our results showed that significant longitudinal changes were identified from mTBI patients that can be related to the neurocognitive recovery and the brain’s effort to compensate the effect of injury.

Temporal Concatenated Sparse Coding of Resting State fMRI Data Reveal Network Interaction Changes in mTBI

Resting state fMRI (rsfMRI) has been a useful imaging modality for network level understanding and diagnosis of brain diseases, such as mild traumatic brain injury (mTBI). However, there call for effective methodologies which can detect group-wise and longitudinal changes of network interactions in mTBI. The major challenges are two folds: (1) There lacks an individualized and common network system that can serve as a reference platform for statistical analysis; (2) Networks and their interactions are usually not modeled in the same algorithmic structure, which results in bias and uncertainty. In this paper, we propose a novel temporal concatenated sparse coding (TCSC) method to address these challenges. Based on the sparse graph theory the proposed method can model the commonly shared spatial maps of networks and the local dynamics of the networks in each subject in one algorithmic structure. Obviously, the local dynamics are not comparable across subjects in rsfMRI or across groups; however, based on the correspondence established by the common spatial profiles, the interactions of these networks can be modeled individually and statistically assessed in a group-wise fashion. The proposed method has been applied on an mTBI dataset with acute and sub-acute stages, and experimental results have revealed meaningful network interaction changes in mTBI.

Group-wise sparse representation of brain states reveal network abnormalities in mild traumatic brain injury

Mild traumatic brain injury (mTBI) is a leading public health care burden. Recent research has shown that the functional impairment in mTBI patients could be captured by resting state fMRI (rsfMRI) at network level. Moreover exploring brain response to mTBI over time at large scale network level can help physicians better diagnose brain injury and order appropriate rehabilitation plan. Therefore, there is a need for methodological innovation that could assess brain impairment in rsfMRI data and further define biomarkers for network changes. In this paper, we propose a novel group-wise sparse representation of brain states (GSRBS) approach, based on rsfMRI data, to explore the effect of mTBI on functional networks across different groups and longitudinal stages. Specifically, a dictionary of brain networks is learned from the volumes of rsfMRI data, and at each time point these networks are linearly and sparsely combined to realize a brain state. Our results showed that group-wise statistical difference on the network composition of brain states could be found between healthy controls and mTBI patients at two different temporal stages.