Analysis Of Brain Imaging Data Research Articles

Effective Alzheimer’s disease detection using enhanced Xception blending with snapshot ensemble

Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models to detect AD from brain MRIs. We trained an enhanced Xception architecture once to produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with a RF meta-learner using a blending algorithm. The efficacy of our ensemble technique is confirmed by the experimental findings, which categorize Alzheimer’s into four groups with 99.14% accuracy. This methodology may help medical practitioners provide patients with Alzheimer’s with individualized care. Subsequent efforts will concentrate on enhancing the model’s efficacy via its generalization to a variety of datasets.

Perceiving humanness across ages: neural correlates and behavioral patterns.

Humanness perception, which attributes fundamental and unique human characteristics to other objects or people, has significant consequences for people's interactions. Notably, the failure to perceive humanness in older adults can lead to prejudice. This study investigates the effect of a target's age on humanness perception in terms of two dimensions: agency (the ability to act and do) and experience (the ability to feel and sense). We also examined brain activity using a magnetic resonance imaging (MRI) scanner in order to understand the underlying neural mechanisms. Healthy university students viewed the facial images of older and younger individuals and judged the humanness of each individual in terms of agency and experience while inside the MRI scanner. The results indicated that older adults were rated higher on experience, and no difference was found in ratings for agency between younger and older face images. Analysis of brain imaging data indicated that positive functional connectivity between the ventral and dorsal regions of the medial prefrontal cortex (mPFC) was greater when judging the humanness of younger faces than older faces. We also found that the negative functional connectivity between the left inferior frontal gyrus and postcentral gyrus was greater when judging the humanness of older faces as compared to that of younger faces. Although the current study did not show distinct brain activities related to humanness perception, it suggests the possibility that different brain connectivities are related to humanness perception regarding targets belonging to different age groups.

Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

Predictive analysis of brain imaging data based on deep learning algorithms

Abstract This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using Kendall’s concordance coefficient (KCC), and to analyze the differences between the datasets using the two-sample t-test. Secondly, a batch normalized convolutional neural network (BN-CNN)-based prediction method for brain imaging data has been developed. This method extracts spatial and temporal features in two convolutional layers, followed by a fully connected layer for classification. Experimental results show that this method is helpful in predicting missing structural data in brain imaging. Secondly, a batch normalized convolutional neural network (BN-CNN) based brain imaging data prediction method is developed, which extracts spatial and temporal features in two convolutional layers. Then it connects to a fully connected layer for classification. The experimental results show that this method’s structural similarity index (SSIM) and feature similarity index (FSIM) in brain imaging data prediction of missing data reaches 0.9446 and 0.9465, respectively, which is significantly better than that of other GAN benchmarks. In applying the method to epilepsy and Parkinson’s cases, this algorithm is used to epilepsy and Parkinson’s cases, and a two-sample t-test analyzes the differences in the data sets. In the application of epilepsy and Parkinson’s cases, the algorithm in this paper has an average prediction accuracy of 93.37%, effectively reducing the rate of incorrect predictions. Deep learning algorithms are highly efficient and accurate in predicting brain imaging data, which is crucial for future clinical diagnosis and treatment.

How the Patient-clinician Relationship Influences Treatment Experiences and Outcomes in Chronic Pain Patients: An fMRI Hyperscan Study

The patient-clinician therapeutic alliance is a powerful modulator shaping patients' treatment experience and clinical outcomes. Before and after a 3-week electro-acupuncture intervention (6 sessions), fMRI hyper-scanning was applied to investigate the influence of either an Augmented (warm/attentive) or a Limited (neutral/business-like) clinician interaction style. Eighteen women with fibromyalgia (mean age=41.41, SD=12.20, each group N=9), randomly assigned to an Augmented or Limited patient-clinician dyadic interaction style (trained acupuncturists), underwent a simultaneously recorded fMRI hyper-scanning procedure with a video connection between two scanners and an evoked cuff pressure pain/treatment paradigm. Questionnaires were used to assess the patients' experience by targeting treatment-related changes such as clinical pain levels, therapeutic alliance, trust, the clinician's warmth and competence, and the probability of continuing the treatment with this clinician after study participation. As rated by patients, pooled therapeutic alliance and clinicians' warmth in the Augmented (mean_alliance=43.21, SD=2.40; mean_warmth=4.00, SD=0.0) were significantly higher than in the Limited group (mean_alliance=22.79, SD=13.15; mean_warmth=2.54, SD=1.10; t_alliance(16)=4.31, p<0.001; t_warmth(16)=3.74; p=0.002). Additionally, over the course of all 6 acupuncture treatment sessions, most patients experienced immediate clinical pain relief after each treatment session (p<0.001), irrespective of augmented or limited group assignment. Pain catastrophizing was significantly reduced after the intervention (t(16)=3.79; p=0.002) in the Augmented (∆-7.88) but not the Limited group (∆1.44). Importantly, only the Augmented group reported a high probability to continue with the treatment after the study as well as a high pain relief expectation for future acupuncture treatments even though both groups experienced similar pain relief treatment outcomes over the course of the study intervention. Brain imaging data analysis focused on social mirroring areas such as the temporo-parietal junction (TPJ). In summary, this study highlights the importance of investigating variable interaction quality during longitudinal clinical therapies and its influence on clinical outcomes, treatment compliance, and brain processing of nociceptive signaling. Grant support from NIH - National Center for Complementary and Integrative Health (NCCIH: R61-AT009306, R33-AT009306), Neuroimaging Pilot Funding Initiative at the Martinos Center for Biomedical imaging (MGH R90DA023427), Korea Institute of Oriental Medicine (KIOM), Foundation for the Science of the Therapeutic Encounter, National Center for Research Resources (P41RR14075; CRC 1 UL1 RR025758), Harvard Clinical and Translational Science Center, Martinos Computing facilities, and National Institutes of Health (Grant Nos. S10RR023401, S10RR019307, S10RR019254, and S10RR023043). The funders had no role in study design, data collection and/or interpretation of the results. Also, all authors declare no existence of competing conflicts of interest.

Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience.

Advancements in population neuroscience are spurred by the availability of large scale, open datasets, such as the Human Connectome Project or recently introduced UK Biobank. With the increasing data availability, analyses of brain imaging data employ more and more sophisticated machine learning algorithms. However, all machine learning algorithms must balance generalization and complexity. As the detail of neuroimaging data leads to high-dimensional data spaces, model complexity and hence the chance of overfitting increases. Different methodological approaches can be applied to alleviate the problems that arise in high-dimensional settings by reducing the original information into meaningful and concise features. One popular approach is dimensionality reduction, which allows to summarize high-dimensional data into low-dimensional representations while retaining relevant trends and patterns. In this paper, principal component analysis (PCA) is discussed as widely used dimensionality reduction method based on current examples of population-based neuroimaging analyses.

Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.

Abstracts from Society for Acupuncture ResearchPandemics, Pain, &amp; Public Health:Roles and Relevance of Traditional East Asian MedicineJune 14–17, 2021

Abstracts from Society for Acupuncture ResearchPandemics, Pain, &amp; Public Health:Roles and Relevance of Traditional East Asian Medicine<i>June 14–17, 2021</i>

NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies.

Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise and extracting neuronal activity-related sources. The application of ICA remains challenging due to its complexity in usage, and an easy-to-use toolbox dedicated to ICA processing is still lacking in the fNIRS community. In this study, we propose NIRS-ICA, an open-source MATLAB toolbox to ease the difficulty of ICA application for fNIRS studies. NIRS-ICA incorporates commonly used ICA algorithms for source separation, user-friendly GUI, and quantitative evaluation metrics assisting source selection, which facilitate both removing noise and extracting neuronal activity-related sources. The options used in the processing can also be reported easily, which promotes using ICA in a more reproducible way. The proposed toolbox is validated and demonstrated based on both simulative and real fNIRS datasets. We expect the release of the toolbox will extent the application for ICA in the fNIRS community.

Subtypes of inhibitory and reward activation associated with substance use variation in adolescence: A latent profile analysis of brain imaging data.

The present study identified subgroups based on inhibitory and reward activation, two key neural functions involved in risk-taking behavior, and then tested the extent to which subgroup differences varied by age, sex, behavioral and familial risk, and substance use. Participants were 145 young adults (18-21 years old; 40.0% female) from the Michigan Longitudinal Study. Latent profile analysis (LPA) was used to establish subgroups using task-based brain activations. Demographic and substance use differences between subgroups were then examined in logistic regression analyses. Whole-brain task activations during a functional magnetic resonance imaging go/no-go task and monetary incentive delay task were used to identify beta weights as input for LPA modeling. A four-class model showed the best fit with the data. Subgroups were categorized as: (1) low inhibitory activation/moderate reward activation (39.7%), (2) moderate inhibitory activation/low reward activation (22.7%), (3) moderate inhibitory activation/high reward activation (25.2%), and (4) high inhibitory activation/high reward activation (12.4%). Compared with the other subgroups, Class 2 was older, less likely to have parental alcohol use disorder, and had less alcohol use. Class 4 was the youngest and had greater marijuana use. Classes 1 and 3 did not differ significantly from the other subgroups. These findings demonstrate that LPA applied to brain activations can be used to identify distinct neural profiles that may explain heterogeneity in substance use outcomes and may inform more targeted substance use prevention and intervention efforts.

Dimensionality Reduction Methods for Brain Imaging Data Analysis

The past century has witnessed the grand success of brain imaging technologies, such as electroencephalography and magnetic resonance imaging, in probing cognitive states and pathological brain dynamics for neuroscience research and neurology practices. Human brain is “the most complex object in the universe,” and brain imaging data ( BID ) are routinely of multiple/many attributes and highly non-stationary. These are determined by the nature of BID as the recordings of the evolving processes of the brain(s) under examination in various views. Driven by the increasingly high demands for precision, efficiency, and reliability in neuro-science and engineering tasks, dimensionality reduction has become a priority issue in BID analysis to handle the notoriously high dimensionality and large scale of big BID sets as well as the enormously complicated interdependencies among data elements. This has become particularly urgent and challenging in this big data era. Dimensionality reduction theories and methods manifest unrivaled potential in revealing key insights to BID via offering the low-dimensional/tiny representations/features, which may preserve critical characterizations of massive neuronal activities and brain functional and/or malfunctional states of interest. This study surveys the most salient work along this direction conforming to a 3-dimensional taxonomy with respect to (1) the scale of BID , of which the design with this consideration is important for the potential applications; (2) the order of BID , in which a higher order denotes more BID attributes manipulatable by the method; and (3) linearity , in which the method’s degree of linearity largely determines the “fidelity” in BID exploration. This study defines criteria for qualitative evaluations of these works in terms of effectiveness, interpretability, efficiency, and scalability. The classifications and evaluations based on the taxonomy provide comprehensive guides to (1) how existing research and development efforts are distributed and (2) their performance, features, and potential in influential applications especially when involving big data. In the end, this study crystallizes the open technical issues and proposes research challenges that must be solved to enable further researches in this area of great potential.

Classifying Cognitive Normal and Early Mild Cognitive Impairment of Alzheimer’s Disease by Applying Restricted Boltzmann Machine to fMRI Data

Background: Neuroimaging is an important tool in early detection of Alzheimer’s disease (AD), which is a serious neurodegenerative brain disease among the elderly subjects. Independent component analysis (ICA) is arguably one of the most widely used algorithm for the analysis of brain imaging data, which can be used to extract intrinsic networks of brain from functional magnetic resonance imaging (fMRI). Method: Witnessed by recent studies, a more flexible model known as restricted Boltzmann machine (RBM) can also be used to extract spatial maps and time courses of intrinsic networks from resting state fMRI, moreover, RBM shows superior temporal features than ICA. Here, we seek to employ RBM to improve the performance of classifying individuals. Experiments are performed on healthy controls and subjects at the early stage of AD, i.e., cognitive normal (CN) and early mild cognitive impairment participants (EMCI), and two types of data, i.e., structural magnetic resonance imaging (sMRI) and fMRI data. Results: (1) By separately employing ICA for sMRI and fMRI, the features extracted from fMRI improve classification accuracy by 7.5% for CN and EMCI; (2) instead of applying ICA to fMRI, using RBM further improves classification accuracy by 7.75% for CN and EMCI; (3) the lesions at the early stage of AD are more likely to occur in the regions around slices 4, 6, 10, 14, 19, 51 and 59 of the whole brain in the longitudinal direction. Conclusion: By using fMRI instead of sMRI and RBM instead of ICA, we can classify CN and EMCI more efficiently.

Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis.

There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a 'default mode network' pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a "Large-N" study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning

Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage — representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain.

Explainable deep learning models for dementia identification via magnetic resonance imaging

AbstractBackgroundToday, to diagnose dementia, clinicians evaluate cognitive tests performed by patients and briefly analyze brain imaging data to look for biomarkers. While valuable information is present in MRI scans, these latter remain challenging to analyze and interpret. Artificial intelligence models have shown promising results to improve the current practice by supporting practitioners in the evaluation of imaging data. Nonetheless, the majority of developed statistical models are more often than not black‐box systems that issue predictions without any clear interpretability, hindering their practical applications.MethodsWe propose an interpretable method based on deep learning that works on minimally preprocessed T1‐weighted 3D scans of the brain. Relying on FullGrad [1], we can dissect the predictions of the model given an input scan. Once the model is trained, it can not only give an automated diagnostic but also generate a heatmap highlighting the regions of the brain that our model points to be responsible for its prediction of dementia. To ensure practicality, we integrate our model in a convenient app that can smoothly be run from a browser, as shown in the attached screenshot.ResultsWe trained and evaluated our model on the OASIS dataset [2]. The specific explanation obtained by our model points at well‐known biomarkers, notably by highlighting the voxels of the hippocampus of patients with dementia. Interestingly, as it can be seen in the second annex, we notice that across individuals, our model focuses more on the voxels located in the right hippocampus.ConclusionsIn this study, we show how machine learning can identify dementia patients using MRI images while ensuring interpretable decisions of the models. Our tools, including the bespoke "explainer" viewer overlaid on each patient's brain, will enable the development of better and more reliable machine‐learning based diagnostics and nurture the trust of practitioners in computer‐aided diagnostics. Furthermore, this will help to discover currently unknown biomarkers and thus lead to a better understanding of the disease.

Nonparametric matrix response regression with application to brain imaging data analysis.

With the rapid growth of neuroimaging technologies, a great effort has been dedicated recently to investigate the dynamic changes in brain activity. Examples include time course calcium imaging and dynamic brain functional connectivity. In this paper, we propose a novel nonparametric matrix response regression model to characterize the nonlinear association between 2D image outcomes and predictors such as time and patient information. Our estimation procedure can be formulated as a nuclear norm regularization problem, which can capture the underlying low-rank structure of the dynamic 2D images. We present a computationally efficient algorithm, derive the asymptotic theory, and show that the method outperforms other existing approaches in simulations. We then apply the proposed method to a calcium imaging study for estimating the change of fluorescent intensities of neurons, and an electroencephalography study for a comparison in the dynamic connectivity covariance matrices between alcoholic and control individuals. For both studies, the method leads to a substantial improvement in predictionerror.

VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data

AbstractWe present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.

MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain.

Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and a variety of versions constructed. These atlases diverge in the criteria based on which they are plotted. The criteria range from cytoarchitectonic features, neurotransmitter receptor distributions, myelination fingerprints, and transcriptomic patterns to structural and functional connectomic profiles. Among them, brainnetome atlas is tightly related to brain connectome information and built by parcellating the brain on the basis of the anatomical connectivity profiles derived from structural neuroimaging data. The pipeline for building the brainnetome atlas has been published as a toolbox named ATPP (A Pipeline for Automatic Tractography-Based Brain Parcellation). In this paper, we present a variation of ATPP, which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species. The new toolbox, MonkeyCBP, has major alterations in three aspects: brain extraction, image registration, and validity indices. By parcellating two different brain regions (posterior cingulate cortex) and (frontal pole) of the rhesus monkey, we demonstrate the efficacy of these alterations. The toolbox has been made public (https://github.com/bheAI/MonkeyCBP_CLI, https://github.com/bheAI/MonkeyCBP_GUI). It is expected that the toolbox can benefit the non-human primate neuroimaging community with high-throughput computation and low labor involvement.

Predictors and associating factors of nasogastric tube removal: Clinical and brain imaging data analysis in post-stroke dysphagia

Post-stroke dysphagia is a frequent complication. Although most patients with dysphagia recover after the acute phase, some patients require long-term enteral feeding, either through a nasogastric (NG) or gastrostomy tube; the effectiveness of using either tube is still under debate. This study elucidated the natural course of NG tube installation and removal and examined the predictors and associating factors based on clinical and brain imaging data. This retrospective cohort study with medical record reviews recruited patients received NG tube installation after their acute stroke events between January 1, 2016, and December 31, 2016. Inclusion criteria were subjects above 20 years of age and with a diagnosis of a newly onset stroke except SAH whose comprehensive clinical and imaging data were available. Survival analysis was performed for the right-censored data because some patients were lost to follow-up after discharge or transferal. In total we recruited 135 patients. Among these patients, the timing of their NG tube removal reached a plateau at 12-16 weeks after stroke. The modified Rankin score on discharge, representing the overall subacute disease status, was the most significant factor. Other clinical variables could be divided into 2 categories: baseline patient characteristics and stroke event severity. Moreover, semi-quantitative brain imaging scores corresponding to the aforementioned 3 categories were correlated significantly. In Taiwan, the NG tube removal rate reached a plateau at around 12-16 weeks after stroke onset. Variables related to long-term NG tube use were divided into baseline characteristics of patient and stroke event severity.

Multimodal Dynamic Brain Connectivity Analysis Based on Graph Signal Processing for Former Athletes With History of Multiple Concussions

The study of structure-function relationships in the brain has been an active area of research in neuroscience. The availability of brain imaging data that captures the structural connectivity and functional co-activation of the brain regions has led to the study of multimodal technical frameworks that can help disentangle the mechanisms linking cognitive abilities and brain structural alterations. This paper analyzes the diffusion and resting state functional magnetic resonance imaging (dMRI and rs-fMRI) data collected from a population consisting of former athletes with a history of multiple concussions and healthy controls with no reported history of concussion. For each subject, the structural connectome is represented by a graph with its nodes associated with cortical brain regions and the adjacency matrix derived from dMRI. Each cortical brain region is associated with a blood oxygen level dependent (BOLD) signal derived from fMRI. This paper uses the tools from graph signal processing (GSP) to select the brain regions of interest (ROIs) that have significant statistical differences in the extracted high and low graph frequency components of the region specific BOLD signal across former athletes and healthy controls, where the graph frequencies represent the extent of spatial variations of the BOLD signal across the brain. The selected ROIs have also been previously identified to be affected in the existing clinical studies on traumatic brain injuries (TBI). Furthermore, the dynamic functional connectivity profiles of the selected ROIs are determined by leveraging the high and low graph frequency components of the BOLD signal and a sliding window based approach. Interestingly, the graph frequency functional connectivity profiles reveal unique characteristics that are not apparent in the unimodal dynamic functional connectivity profiles based on fMRI. Our analysis reveals statistically significant differences in the dwell times in multiple dynamic graph frequency functional connectivity states for the two groups of subjects. Therefore, the results presented in this paper underline the significance of graph signal processing tools for multimodal analysis of brain imaging data and also provide promising direction for applications in clinical research and medical diagnosis.