A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion

Simultaneously capturing excessive variations and smooth dynamics of the underlying neural activity using spatiotemporal basis expansion and multisubject fMRI data

In the last decade, dictionary learning (DL) has gained popularity over independent component analysis (ICA) within the blind source separation (BSS) framework for functional magnetic resonance imaging (fMRI) signals. Despite its rising popularity, a primary challenge in DL remains model fitting. It is susceptible to overfitting because the conventional loss function strives to correspond too closely to the training data. However, in the case of multi-subject (MS) analysis, it becomes imperative to overfit in order to acquire the source diversities across different brains. In this paper, an attempt has been made to resolve this predicament by concurrently preserving and mitigating the effect of high variance. A novel algorithm named joint analysis and synthesis DL (JASDL) has been proposed that simultaneously learns the overfitted trends to retain the data-centric cross-subject diversities and wellfitted trends by adequately regularizing the model complexity. This fusion was achieved by benefiting from modeling each subject's data in terms of both spatiotemporal (ST) prior information (PI) and MS-ST components. The PI consisted of biological priors derived from neuroscience knowledge, such as brain network templates, and mathematical priors derived from basis functions, such as three-dimensional (3D) cubic basis splines (B-splines). In contrast, MS-ST components were estimated using the computationally most parsimonious sparse ST blind source separation (ssBSS) method. Using the proposed analysis/synthesis cost function that exploits tri and quad-factorization for matrix approximation, the JASDL algorithm can model temporal smoothness and spatial reduction of false positives while retaining MS variations. Its efficacy was evaluated by comparing it with existing DL techniques using both experimental and synthetic fMRI datasets. Overall, the mean of correlation and F-score was found to be [Formula: see text] higher for the JASDL synthesis dictionary than the state-of-the-art subject-wise sequential DL (swsDL).

Deep linear matrix approximate reconstruction with integrated BOLD signal denoising reveals reproducible hierarchical brain connectivity networks from multiband multi-echo fMRI

The hierarchical modular functional structure in the human brain has not been adequately depicted by conventional functional magnetic resonance imaging (fMRI) acquisition techniques and traditional functional connectivity reconstruction methods. Fortunately, rapid advancements in fMRI scanning techniques and deep learning methods open a novel frontier to map the spatial hierarchy within Brain Connectivity Networks (BCNs). The novel multiband multi-echo (MBME) fMRI technique has increased spatiotemporal resolution and peak functional sensitivity, while the advanced deep linear model (multilayer-stacked) named DEep Linear Matrix Approximate Reconstruction (DELMAR) enables the identification of hierarchical features without extensive hyperparameter tuning. We incorporate a multi-echo blood oxygenation level-dependent (BOLD) signal and DELMAR for denoising in its first layer, thereby eliminating the need for a separate multi-echo independent component analysis (ME-ICA) denoising step. Our results demonstrate that the DELMAR/Denoising/Mapping strategy produces more accurate and reproducible hierarchical BCNs than traditional ME-ICA denoising followed by DELMAR. Additionally, we showcase that MBME fMRI outperforms multiband (MB) fMRI in terms of hierarchical BCN mapping accuracy and precision. These reproducible spatial hierarchies in BCNs have significant potential for developing improved fMRI diagnostic and prognostic biomarkers of functional connectivity across a wide range of neurological and psychiatric disorders.

Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks

Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject's data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be 26 % higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.

A survey of brain functional network extraction methods using fMRI data

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

FMRI Data Analysis Preserving Map Variability Via Unsupervised Object-Centric Learning

A novel data-driven functional magnetic resonance imaging (fMRI) data analysis method is proposed using a deep object-centric learning paradigm. The method can faithfully estimate the variabilities in the spatial neural activation maps, which capture functional interconnections in the brain, over fMRI volumes. The key idea is to treat the component maps composing individual fMRI volumes as "objects," whose latent representations are separately learned by a set of autoencoders. Numerical tests using synthetic and real data sets verify the advantages of the proposed method compared to existing matrix factorization-based approaches.

Causality methods to study the functional connectivity in brain networks: the basal ganglia - thalamus causal interactions

This study uses methods recently developed to study the complex evolution of atmospheric phenomena which have some similarities with the dynamics of the human brain. In both cases, it is possible to record the activity of particular centers (geographic regions or brain nuclei) but not to make an experimental modification of their state. The study of "causality", which is necessary to understand the dynamics of these complex systems and to develop robust models that can predict their evolution, is hampered by the experimental restrictions imposed by the nature of both systems. The study was performed with data obtained in the thalamus and basal ganglia of awake humans executing different tasks. This work studies the linear, non-linear and more complex relationships of these thalamic centers with the cortex and main BG nuclei, using three complementary techniques: the partial correlation regression method, the Gaussian process regression/distance correlation and a model-free method based on nearest-neighbor that computes the conditional mutual information. These causality methods indicated that the basal ganglia present a different functional relationship with the anterior-ventral (motor), intralaminar and medio-dorsal thalamic centers, and that more than 60% of these thalamus-basal ganglia relationships present a non-linear dynamic (35 of the 57 relationships found). These functional interactions were observed for basal ganglia nuclei with direct structural connections with the thalamus (primary somatosensory and motor cortex, striatum, internal globus pallidum and substantia nigra pars reticulata), but also for basal ganglia without structural connections with the thalamus (external globus pallidum and subthalamic nucleus). The motor tasks induced rapid modifications of the thalamus-basal ganglia interactions. These findings provide new perspectives of the thalamus - BG interactions, many of which may be supported by indirect functional relationships and not by direct excitatory/inhibitory interactions.

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

The conventional dictionary learning (DL) algorithms aim to adapt the dictionary/sparse code to individual functional magnetic resonance imaging (fMRI) data. Thus, lacking the capability to consolidate the spatiotemporal diversities offered by other subjects. Considering that subject-wise (sw) data matrix can be decomposed into the sparse linear combination of multi-subject (MS) time courses and MS spatial maps, two new algorithms, sw sequential DL (swsDL) and sw block DL (swbDL), have been proposed. They are based on the novel framework, defined by the mixing model, where base matrices prepared by operating a computationally fast sparse spatiotemporal blind source separation method over multiple subjects are employed to adapt the mixing matrices to sw training data. They solve the optimization models formulated using [Formula: see text]/[Formula: see text]-norm penalization/constraints through dictionary/sparse code pair update and alternating minimization approach. They are unique because no existing sparse DL method can incorporate MS spatiotemporal components while updating sw atoms/sparse codes, which can eventually be assembled using neuroscience knowledge to extract group-level dynamics. Various fMRI datasets are used to evaluate and compare the performance of the proposed algorithms with existing state-of-the-art algorithms. Specifically, overall, a [Formula: see text] increase in the mean correlation value and [Formula: see text] reduction in the mean computation time exhibited by swsDL and swbDL, respectively, over the adaptive consistent sequential dictionary algorithm.

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

Morphological Component Analysis of functional MRI Brain Networks

OBJECTIVE

Sparse representations have been utilized to identify functional connectivity (FC) of networks, while ICA employs the assumption of independence among the network sources to demonstrate FC. Here, we investigate a sparse decomposition method based on Morphological Component Analysis and K-SVD dictionary learning-MCA-KSVD-and contrast the effect of the sparsity constraint vs. the independency constraint on FC and denoising.

METHODS

Using a K-SVD algorithm, fMRI signals are decomposed into morphological components which have sparse spatial overlap. We present simulations when the independency assumption of ICA fails and MCA-KSVD recovers more accurate spatial-temporal structures. Denoising performance of both methods is investigated at various noise levels. A comprehensive experimental study was conducted on resting-state and task fMRI.

RESULTS

Validations show that ICA is advantageous when network components are well-separated and sparse. In such cases, the MCA-KSVD method has modest value over ICA in terms of network delineation but is significantly more effective in reducing spatial and temporal noise. Results demonstrate that the sparsity constraint yields sparser networks with higher spatial resolution while suppressing weak signals. Temporally, this localization effect yields higher contrast-to-noise ratios (CNRs) of time series.

CONCLUSION

While marginally improving the spatial decomposition, MCA-KSVD denoises fMRI data much more effectively than ICA, preserving network structures and improving CNR, especially for weak networks.

SIGNIFICANCE

A sparsity-based decomposition approach may be useful for investigating functional connectivity in noisy cases. It may serve as an efficient decomposition method for reduced acquisition time and may prove useful for detecting weak network activations.

Adaptive Brain Activity Detection in Structured Interference and Partially Homogeneous Locally Correlated Disturbance

OBJECTIVE

In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations.

METHODS

In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of fac-torizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covari-ance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection.

RESULTS

Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain.

CONCLUSION

The proposed method can offer better activation maps in terms of accuracy and spatial smoothness.

SIGNIFICANCE

The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data.

Discriminating mild traumatic brain injury using sparse dictionary learning of functional network dynamics

Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders. However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject-specific time-varying evolution of a set of overlapping group-level sparse connectivity components (SCCs) based on the resting-state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (<3 days postinjury) and 31 healthy controls (HCs). The identified SCCs were consistently distributed in the cohort of subjects without significant inter-group differences in connectivity patterns. Nevertheless, subject-specific temporal expression of these SCCs could be used to discriminate patients with mTBI from HCs with a classification accuracy of 74.2% (specificity 64.5% and sensitivity 83.9%) using leave-one-out cross-validation. Taken together, our findings indicate neuroimaging biomarkers for mTBI individual diagnosis based on the temporal expression of SCCs underlying time-resolved functional connectivity.

The causal interaction in human basal ganglia

The experimental study of the human brain has important restrictions, particularly in the case of basal ganglia, subcortical centers whose activity can be recorded with fMRI methods but cannot be directly modified. Similar restrictions occur in other complex systems such as those studied by Earth system science. The present work studied the cause/effect relationships between human basal ganglia with recently introduced methods to study climate dynamics. Data showed an exhaustive (identifying basal ganglia interactions regardless of their linear, non-linear or complex nature) and selective (avoiding spurious relationships) view of basal ganglia activity, showing a fast functional reconfiguration of their main centers during the execution of voluntary motor tasks. The methodology used here offers a novel view of the human basal ganglia which expands the perspective provided by the classical basal ganglia model and may help to understand BG activity under normal and pathological conditions.

Robust Subspace Detectors Based on α-Divergence With Application to Detection in Imaging

Robust variants of Wald, Rao and likelihood ratio (LR) tests for the detection of a signal subspace in a signal interference subspace corrupted by contaminated Gaussian noise are proposed in this paper. They are derived using the α- divergence, and the trade-off between the robustness and the power (the probability of detection) of the tests is adjustable using a single hyperparameter α . It is shown that when α→ 1 , these tests are equivalent to their well known classical counterparts. For example the robust LR test coincides with the LR test or the matched subspace detector (MSD). Asymptotic results are provided to support the proposed tests and robustness to outliers is obtained using values of . Numerical experiments illustrating the performance of these tests on simulated, real functional magnetic resonance imaging (fMRI), hyperspectral and synthetic aperture radar (SAR) data are also presented.

The effects of lutein and zeaxanthin on resting state functional connectivity in older Caucasian adults: a randomized controlled trial

The carotenoids lutein (L) and zeaxanthin (Z) accumulate in retinal regions of the eye and have long been shown to benefit visual health. A growing literature suggests cognitive benefits as well, particularly in older adults. The present randomized controlled trial sought to investigate the effects of L and Z on brain function using resting state functional magnetic resonance imaging (fMRI). It was hypothesized that L and Z supplementation would (1) improve intra-network integrity of default mode network (DMN) and (2) reduce inter-network connectivity between DMN and other resting state networks. 48 community-dwelling older adults (mean age = 72 years) were randomly assigned to receive a daily L (10 mg) and Z (2 mg) supplement or a placebo for 1 year. Resting state fMRI data were acquired at baseline and post-intervention. A dictionary learning and sparse coding computational framework, based on machine learning principles, was used to investigate intervention-related changes in functional connectivity. DMN integrity was evaluated by calculating spatial overlap rate with a well-established DMN template provided in the neuroscience literature. Inter-network connectivity was evaluated via time series correlations between DMN and nine other resting state networks. Contrary to expectation, results indicated that L and Z significantly increased rather than decreased inter-network connectivity (Cohen's d = 0.89). A significant intra-network effect on DMN integrity was not observed. Rather than restoring what has been described in the available literature as a "youth-like" pattern of intrinsic brain activity, L and Z may facilitate the aging brain's capacity for compensation by enhancing integration between networks that tend to be functionally segregated earlier in the lifespan.

Deep Generative Analysis for Task-Based Functional MRI Experiments

While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces-time series of brain volumes-continue to pose daunting analysis challenges. The current standard ("mass univariate") approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel ("voxel"), computing test statistics for each model, and correcting for false positives post hoc using bootstrap or other resampling methods. Despite its simplicity, this approach has enjoyed great success over the last two decades due to: 1) its ability to produce effect maps highlighting brain regions whose activity significantly correlates with a given variable of interest; and 2) its modeling of experimental effects as separable and thus easily interpretable. However, this approach suffers from several well-known drawbacks, namely: inaccurate assumptions of linearity and noise Gaussianity; a limited ability to capture individual effects and variability; and difficulties in performing proper statistical testing secondary to independently fitting voxels. In this work, we adopt a different approach, modeling entire volumes directly in a manner that increases model flexibility while preserving interpretability. Specifically, we use a generalized additive model (GAM) in which the effects of each regressor remain separable, the product of a spatial map produced by a variational autoencoder and a (potentially nonlinear) gain modeled by a covariate-specific Gaussian Process. The result is a model that yields group-level effect maps comparable or superior to the ones obtained with standard fMRI analysis software while also producing single-subject effect maps capturing individual differences. This suggests that generative models with a decomposable structure might offer a more flexible alternative for the analysis of task-based fMRI data.

Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts

BACKGROUND

Spatial sparsity has been found to be in line with the intrinsic characteristic of brain activation. However, identifying a sparse representation of complex-valued fMRI data is challenging due to high noise within the phase data.

NEW METHODS

We propose to reduce the noise by combining real and imaginary parts of complex-valued fMRI data along spatial and temporal dimensions to form a real-valued spatiotemporal concatenation model. This model not only enables flexible usage of existing real-valued sparse representation algorithms but also allows for the reconstruction of complex-valued spatial and temporal components from their real and imaginary estimates. We propose to select components from both real and imaginary estimates to reconstruct the complex-valued component, using phase denoising to recover weak brain activity from high-amplitude noise.

RESULTS

The K-SVD algorithm was used to obtain a sparse representation within the spatiotemporal concatenation model. The results from simulated and experimental complex-valued fMRI datasets validated the efficacy of our method.

COMPARISON WITH EXISTING METHODS

Compared to a magnitude-only approach, the proposed method detected additional voxels manifest within several specific regions expected to be involved but likely missing from the magnitude-only data, e.g., in the anterior cingulate cortex region. Simulation results showed that the additional voxels were accurate and unique information from the phase data. Compared to a complex-valued dictionary learning algorithm, our method exhibited lower noise for both magnitude and phase maps.

CONCLUSIONS

The proposed method is robust to noise and effective for identifying a sparse representation of the natively complex-valued fMRI data.

Sparse representation and dictionary learning model incorporating group sparsity and incoherence to extract abnormal brain regions associated with schizophrenia

Schizophrenia is a complex mental illness, the mechanism of which is currently unclear. Using sparse representation and dictionary learning (SDL) model to analyze functional magnetic resonance imaging (fMRI) dataset of schizophrenia is currently a popular method for exploring the mechanism of the disease. The SDL method decomposed the fMRI data into a sparse coding matrix X and a dictionary matrix D. However, these traditional methods overlooked group structure information in X and the coherence between the atoms in D. To address this problem, we propose a new SDL model incorporating group sparsity and incoherence, namely GS2ISDL to detect abnormal brain regions. Specifically, GS2ISDL uses the group structure information that defined by AAL anatomical template from fMRI dataset as priori to achieve inter-group sparsity in X. At the same time, L ₁ - norm is enforced on X to achieve intra-group sparsity. In addition, our algorithm also imposes incoherent constraint on the dictionary matrix D to reduce the coherence between the atoms in D, which can ensure the uniqueness of X and the discriminability of the atoms. To validate our proposed model GS2ISDL, we compared it with both IK-SVD and SDL algorithm for analyzing fMRI dataset collected by Mind Clinical Imaging Consortium (MCIC). The results show that the accuracy, sensitivity, recall and MCC values of GS2ISDL are 93.75%, 95.23%, 80.50% and 88.19%, respectively, which outperforms both IK-SVD and SDL. The ROIs extracted by GS2ISDL model (such as Precentral gyrus, Hippocampus and Caudate nucleus, etc.) are further verified by the literature review on schizophrenia studies, which have significant biological significance.

Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning

Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.

Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study

In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.

The dynamic of basal ganglia activity with a multiple covariance method: influences of Parkinson's disease

The closed-loop cortico-subcortical pathways of basal ganglia have been extensively used to describe the physiology of these centres and to justify the functional disorders of basal ganglia diseases. This approach justifies some experimental and clinical data but not others, and furthermore, it does not include a number of subcortical circuits that may produce a more complex basal ganglia dynamic than that expected for closed-loop linear networks. This work studied the functional connectivity of the main regions of the basal ganglia motor circuit with magnetic resonance imaging and a new method (functional profile method), which can analyse the multiple covariant activity of human basal ganglia. The functional profile method identified the most frequent covariant functional status (profiles) of the basal ganglia motor circuit, ordering them according to their relative frequency and identifying the most frequent successions between profiles (profile transitions). The functional profile method classified profiles as input profiles that accept the information coming from other networks, output profiles involved in the output of processed information to other networks and highly interconnected internal profiles that accept transitions from input profiles and send transitions to output profiles. Profile transitions showed a previously unobserved functional dynamic of human basal ganglia, suggesting that the basal ganglia motor circuit may work as a dynamic multiple covariance network. The number of internal profiles and internal transitions showed a striking decrease in patients with Parkinson’s disease, a fact not observed for input and output profiles. This suggests that basal ganglia of patients with Parkinson’s disease respond to requirements coming from other neuronal networks, but because the internal processing of information is drastically weakened, its response will be insufficient and perhaps also self-defeating. These marked effects were found in patients with few motor disorders, suggesting that the functional profile method may be an early procedure to detect the first stages of the Parkinson’s disease when the motor disorders are not very evident. The multiple covariance activity found presents a complementary point of view to the cortico-subcortical closed-loop model of basal ganglia. The functional profile method may be easily applied to other brain networks, and it may provide additional explanations for the clinical manifestations of other basal ganglia disorders.

Joint representation of connectome-scale structural and functional profiles for identification of consistent cortical landmarks in macaque brain

Discovery and representation of common structural and functional cortical architectures has been a significant yet challenging problem for years. Due to the remarkable variability of structural and functional cortical architectures in human brain, it is challenging to jointly represent a common cortical architecture which can comprehensively encode both structure and function characteristics. In order to better understand this challenge and considering that macaque monkey brain has much less variability in structure and function compared with human brain, in this paper, we propose a novel computational framework to apply our DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) and HAFNI (Holistic Atlases of Functional Networks and Interactions) frameworks on macaque brains, in order to jointly represent structural and functional connectome-scale profiles for identification of a set of consistent and common cortical landmarks across different macaque brains based on multimodal DTI and resting state fMRI (rsfMRI) data. Experimental results demonstrate that 100 consistent and common cortical landmarks are successfully identified via the proposed framework, each of which has reasonably accurate anatomical, structural fiber connection pattern, and functional correspondences across different macaque brains. This set of 100 landmarks offer novel insights into the structural and functional cortical architectures in macaque brains.

Analysis of brain functional connectivity network in MS patients constructed by modular structure of sparse weights from cognitive task-related fMRI

Cognitive dysfunction in multiple sclerosis (MS) seems to be the result of neural disconnections, leading to a wide range of brain functional network alterations. It is assumed that the analysis of the topological structure of brain connectivity network can be used to assess cognitive impairments in MS disease. We aimed to identify these brain connectivity pattern alterations and detect the significant features for the distinction of MS patients from healthy controls (HC). In this regard, the importance of functional brain networks construction for better exhibition of changes, inducing the improved reflection of functional organization structure should be precisely considered. In this paper, we strove to introduce a framework for modeling the functional connectivity network by considering the two most important intrinsic sparse and modular structures of brain. For the proposed approach, we first derived group-wise sparse representation via learning a common over-complete dictionary matrix from the aggregated cognitive task-based functional magnetic resonance imaging (fMRI) data of all subjects of the two groups to be able to investigate between-group differences. We then applied the modularity concept on achieved sparse coefficients to compute the connectivity strength between the two brain regions. We examined the changes in network topological properties between relapsing-remitting MS (RRMS) and matched HC groups by considering the pairwise connections of regions of the resulted weighted networks and extracting graph-based measures. We found that the informative brain regions were related to their important connectivity weights, which could distinguish MS patients from the healthy controls. The experimental findings also proved the discrimination ability of the modularity measure among all the global features. In addition, we identified such local feature subsets as eigenvector centrality, eccentricity, node strength, and within-module degree, which significantly differed between the two groups. Moreover, these nodal graph measures have been served as the detectors of brain regions, affected by different cognitive deficits. In general, our findings illustrated that integration of sparse representation, modular structure, and pairwise connectivity strength in combination with the graph properties could help us with the early diagnosis of cognitive alterations in the case of MS.

Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

OBJECTIVE

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain's spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks.

METHODS

The influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety).

RESULTS

We found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%.

CONCLUSION

The amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer's disease, minimal cognitive impairment).

Discovering and characterizing dynamic functional brain networks in task FMRI

Many existing studies for the mapping of function brain networks impose an implicit assumption that the networks' spatial distributions are constant over time. However, the latest research reports reveal that functional brain networks are dynamical and have time-varying spatial patterns. Furthermore, how these functional networks evolve over time has not been elaborated and explained in sufficient details yet. In this paper, we aim to discover and characterize the dynamics of functional brain networks via a windowed group-wise dictionary learning and sparse coding approach. First, we aggregated the sampled subjects' fMRI signals into one big data matrix, and learned a common dictionary for all individuals via a group-wise dictionary learning step. Second, we obtained the dynamic time-varying functional networks by using the windowed time-varying sparse coding approach. Experimental results demonstrated that our windowed group-wise dictionary learning and sparse coding method can effectively detect the task-evoked networks and also characterize how these networks evolve over time. This work sheds novel insights on the dynamics mechanism of functional brain networks.

The organization of the basal ganglia functional connectivity network is non-linear in Parkinson's disease

The motor symptoms in Parkinson's disease (PD) have been linked to changes in the excitatory/inhibitory interactions of centers involved in the cortical-subcortical closed-loop circuits which connect basal ganglia (BG) and the brain cortex. This approach may explain some motor symptoms of PD but not others, which has driven the study of BG from new perspectives. Besides their cortical-subcortical linear circuits, BG have a number of subcortical circuits which directly or indirectly connect each BG with all the others. This suggests that BG may work as a complex network whose output is the result of massive functional interactions between all of their nuclei (decentralized network; DCN), more than the result of the linear excitatory/inhibitory interactions of the cortical-subcortical closed-loops. The aim of this work was to study BG as a DCN, and to test whether the DCN behavior of BG changes in PD. BG activity was recorded with MRI methods and their complex interactions were studied with a procedure based on multiple correspondence analysis, a data-driven multifactorial method which can work with non-linear multiple interactions. The functional connectivity of twenty parkinsonian patients and eighteen age-matched controls were studied during resting and when they were performing sequential hand movements. Seven functional configurations were identified in the control subjects during resting, and some of these interactions changed with motor activity. Five of the seven interactions found in control subjects changed in Parkinson's disease. The BG response to the motor task was also different in PD patients and controls. These data show the basal ganglia as a decentralized network where each region can perform multiple functions and each function is performed by multiple regions. This framework of BG interactions may provide new explanations concerning motor symptoms of PD which are not explained by current BG models.

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The classical basal ganglia model is based on linear excitatory/inhibitory interactions.

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The classical model only explains part of the motor disorders of Parkinson's disease.

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fcMRI images were studied with Multiple Correspondence Analysis (MCA).

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MCA showed multiple non-linear interactions between basal ganglia.

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Parkinson's disease induced marked changes of non-linear basal ganglia interactions.

Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis

Resting state functional magnetic resonance imaging is used to study how brain regions are functionally connected by measuring temporal correlation of the fMRI signals, when a subject is at rest. Sparse dictionary learning is used to estimate a dictionary of resting state networks by decomposing the whole brain signals into several temporal features (atoms), each being shared by a set of voxels associated to a network. Recently, we proposed and validated a new method entitled Sparsity-based Analysis of Reliable K-hubness (SPARK), suggesting that connector hubs of brain networks participating in inter-network communication can be identified by counting the number of atoms involved in each voxel (sparse number k). However, such hub analysis can be corrupted by the presence of noise-related atoms, where physiological fluctuations in cardiorespiratory processes may remain even after band-pass filtering and regression of confound signals from the white matter and cerebrospinal fluid. Handling this issue might require manual classification of noisy atoms, which is a time-consuming and subjective task. Motivated by the fact that the physiological fluctuations are often localized in tissues close to large vasculatures, i.e. sagittal sinus, we propose an automatic classification of physiological noise-related atoms for SPARK using spatial priors and a stepwise regression procedure. We measured the degree to which the noise-characteristic time-courses within the mask are explained by each atom, and classified noise-related atoms using a subject-specific threshold estimated using a bootstrap resampling based strategy. Using real data from healthy subjects (N = 25), manual classification of the atoms by two independent reviewers showed the presence of sagittal sinus related noise in 65% of the runs. Applying the same manual classification after the proposed automatic removal method reduced this rate to 19%. A 10-fold cross-validation on real data showed good specificity and accuracy of the proposed automated method in classifying the target noise (area under the ROC curve= 0.89), when compared to the manual classification considered as the reference. We demonstrated decrease in k-hubness values in the voxels involved in the sagittal sinus at both individual and group levels, suggesting a significant improvement of SPARK, which is particularly important when considering clinical applications.

A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA

BACKGROUND

The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise.

NEW METHOD

We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually.

COMPARISON WITH EXISTING METHODS

We apply this method to compare the performances of two different factorization-based methods, ICA and its multiset extension independent vector analysis (IVA), for the analysis of fMRI data from 109 patients with schizophrenia and 138 healthy controls during the performance of three tasks.

RESULTS

Through this application of GDMs, we find that IVA can determine regions that are more discriminatory between patients and controls than ICA, though IVA is less effective at emphasizing regions found in only a subset of the tasks.

CONCLUSIONS

These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses.

Blind fMRI source unmixing via higher-order tensor decompositions

BACKGROUND

The growing interest in neuroimaging technologies generates a massive amount of biomedical data of high dimensionality. Tensor-based analysis of brain imaging data has been recognized as an effective analysis that exploits its inherent multi-way nature. In particular, the advantages of tensorial over matrix-based methods have previously been demonstrated in the context of functional magnetic resonance imaging (fMRI) source localization. However, such methods can also become ineffective in realistic challenging scenarios, involving, e.g., strong noise and/or significant overlap among the activated regions. Moreover, they commonly rely on the assumption of an underlying multilinear model generating the data.

NEW METHOD

This paper aims at investigating the possible gains from exploiting the 4-dimensional nature of the brain images, through a higher-order tensorization of the fMRI signal, and the use of less restrictive generative models. In this context, the higher-order block term decomposition (BTD) and the PARAFAC2 tensor models are considered for the first time in fMRI blind source separation. A novel PARAFAC2-like extension of BTD (BTD2) is also proposed, aiming at combining the effectiveness of BTD in handling strong instances of noise and the potential of PARAFAC2 to cope with datasets that do not follow the strict multilinear assumption.

COMPARISON WITH EXISTING METHODS

The methods were tested using both synthetic and real data and compared with state of the art methods.

CONCLUSIONS

The simulation results demonstrate the effectiveness of BTD and BTD2 for challenging scenarios (presence of noise, spatial overlap among activation regions and inter-subject variability in the haemodynamic response function (HRF)).

Improved Application of Sparse Representation Classifier in fMRI-based Brain State Decoding

Multivariate pattern analysis techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Among various multivariate pattern analysis methods, sparse representation classifier (SRC) exhibit state-of-the-art classification performance for image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to investigate the feasibility of SRC in fMRI-based decoding and how to improve the performance of SRC. In this study, two SRC variants were proposed to improve SRC. We performed experimental tests on real fMRI data to compare the performance of SRC, the non-negative SRC (NSRC), two SRC variants, and the support vector machine (SVM). The results of the real fMRI experiments showed that the two SRC variants and NSRC exhibited much better classification performance than the SRC. Moreover, the performance of the second SRC variant is the best among the five classifiers.

The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics

Data-driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data-driven methods that are based on two different forms of diversity-statistical properties of the data-statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.

Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy

Hubs of brain networks are brain regions exhibiting denser connections than others, promoting long-range communication. Studies suggested the reorganization of hubs in epilepsy. The patterns of connector hub abnormalities specific to mesial temporal lobe epilepsy (mTLE) are unclear. We wish to quantify connector hub abnormalities in mTLE and identify epilepsy-related resting state networks involving abnormal connector hubs. A recently developed sparsity-based analysis of reliable k-hubness (SPARK) allowed us to address this question by using resting state functional MRI in 20 mTLE patients and 17 healthy controls. Handling the multicollinearity of functional networks, SPARK measures a new metric of hubness by counting the number (k) of networks involved in each voxel, and identifies which networks are actually associated to each connector hub. This measure provides new information about the network architecture involving connector hubs and a realistic range of k-hubness. We quantified the disruption and emergence of connector hubs in individual epileptic subjects and assessed the lateralization of networks involving connector hubs. In mTLE, we found pathological disruptions of normal connector hubs in the mTL and within the default mode network. Right mTLE had remarkably higher emergence of new connector hubs in the mTL than left mTLE. Different patterns of lateralization of the salience network involving the abnormal hippocampus were found in right versus left mTLE. The temporal, cerebellar, default mode, subcortical and motor networks also contributed to the lateralization of hippocampal networks. We finally observed an asymmetrical connector hub reorganization and overall regularization of epilepsy-related resting state networks in mTLE, characterized by the disruption of distant connections and the emergence of local connections.

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Individually reproducible brain network hubs in mesial Temporal Lobe Epilepsy (mTLE).

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We observed asymmetrical connector hub reorganization and network regularization in mTLE.

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We found connector hub disruptions within the mTL and default mode network.

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Emergence of new connector hubs in the mTL was prominent in right but not in left mTLE.

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Lateralization of hippocampal connectivity was associated with the salience network.

Latent source mining in FMRI via restricted Boltzmann machine

Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set. In this article, we propose to apply RBM to fMRI time courses instead of volumes for BSS. The proposed method not only interprets fMRI time courses explicitly to take advantages of deep learning models in latent feature learning but also substantially reduces model complexity and increases the scale of training set to improve training efficiency. Our experimental results based on Human Connectome Project (HCP) datasets demonstrated the superiority of the proposed method over ICA and the one that applied RBM to fMRI volumes in identifying task-related components, resulted in more accurate and specific representations of task-related activations. Moreover, our method separated out components representing intermixed effects between task events, which could reflect inherent interactions among functionally connected brain regions. Our study demonstrates the value of RBM in mining complex structures embedded in large-scale fMRI data and its potential as a building block for deeper models in fMRI data analysis.

Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

Sparse coding reveals greater functional connectivity in female brains during naturalistic emotional experience

Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging) studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA), sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.

Signal sampling for efficient sparse representation of resting state FMRI data

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain's signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain's signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority.

Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation

Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage. Our analysis based on group-level sparse representation revealed that multiple functional networks involved in memory, emotion, attention, and vigilance processing were impaired by SD. Of particular interest, the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited. These results not only further elucidate the impact of SD on brain function but also demonstrate the ability of the proposed approach to provide new insights into the functional organization of the resting-state brain by permitting spatial overlap between networks and facilitating the description of the varied relationships of the overlapping regions with other regions of the brain in the context of different functional systems. Hum Brain Mapp 38:4671-4689, 2017. © 2017 Wiley Periodicals, Inc.

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

Dual Temporal and Spatial Sparse Representation for Inferring Group-Wise Brain Networks From Resting-State fMRI Dataset

Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. First, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject. Next, based on the current brain science knowledge, a simple mathematical model is proposed to describe the complex nonlinear dynamic coupling mechanism of the brain networks, with which the group-wise intrinsic connectivity networks (ICNs) can be inferred by sparse representation for these brain functional networks (spatial domain) of all subjects. Experiments on Leiden_2180 dataset show that most group-wise ICNs obtained by the proposed DTSSR are interpretable by current brain science knowledge and are consistent with previous literature reports. The robustness of DTSSR and the reproducibility of the results are demonstrated by experiments on three different datasets (Leiden_2180, Leiden_2200, and our own dataset). The present work also shed new light on exploring the coupling mechanism of brain networks from perspective of information science.

Elucidating functional differences between cortical gyri and sulci via sparse representation HCP grayordinate fMRI data

The highly convoluted cerebral cortex is characterized by two different topographic structures: convex gyri and concave sulci. Increasing studies have demonstrated that cortical gyri and sulci exhibit different structural connectivity patterns. Inspired by the intrinsic structural differences between gyri and sulci, in this paper, we present a data-driven framework based on sparse representation of fMRI data for functional network inferences, then examine the interactions within and across gyral and sulcal functional networks and finally elucidate possible functional differences using graph theory based properties. We apply the proposed framework to the high-resolution Human Connectome Project (HCP) grayordinate fMRI data. Extensive experimental results on both resting state fMRI data and task-based fMRI data consistently suggested that gyri are more functionally integrated, while sulci are more functionally segregated in the organizational architecture of cerebral cortex, offering novel understanding of the byzantine cerebral cortex.