Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis

A network approach to the investigation of childhood irritability: probing frustration using social stimuli

BACKGROUND

Self-regulation in early childhood develops within a social context. Variations in such development can be attributed to inter-individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early-life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli.

METHODS

Seventy-three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives.

RESULTS

ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low-frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p < .05, FDR-corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low- and high-frequency neural signal power in the right caudate (p < .05, FDR-corrected).

CONCLUSIONS

Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network-based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability.

Longitudinal manganese-enhanced magnetic resonance imaging of neural projections and activity

Manganese-enhanced magnetic resonance imaging (MEMRI) holds exceptional promise for preclinical studies of brain-wide physiology in awake-behaving animals. The objectives of this review are to update the current information regarding MEMRI and to inform new investigators as to its potential. Mn(II) is a powerful contrast agent for two main reasons: (1) high signal intensity at low doses; and (2) biological interactions, such as projection tracing and neural activity mapping via entry into electrically active neurons in the living brain. High-spin Mn(II) reduces the relaxation time of water protons: at Mn(II) concentrations typically encountered in MEMRI, robust hyperintensity is obtained without adverse effects. By selectively entering neurons through voltage-gated calcium channels, Mn(II) highlights active neurons. Safe doses may be repeated over weeks to allow for longitudinal imaging of brain-wide dynamics in the same individual across time. When delivered by stereotactic intracerebral injection, Mn(II) enters active neurons at the injection site and then travels inside axons for long distances, tracing neuronal projection anatomy. Rates of axonal transport within the brain were measured for the first time in "time-lapse" MEMRI. When delivered systemically, Mn(II) enters active neurons throughout the brain via voltage-sensitive calcium channels and clears slowly. Thus behavior can be monitored during Mn(II) uptake and hyperintense signals due to Mn(II) uptake captured retrospectively, allowing pairing of behavior with neural activity maps for the first time. Here we review critical information gained from MEMRI projection mapping about human neuropsychological disorders. We then discuss results from neural activity mapping from systemic Mn(II) imaged longitudinally that have illuminated development of the tonotopic map in the inferior colliculus as well as brain-wide responses to acute threat and how it evolves over time. MEMRI posed specific challenges for image data analysis that have recently been transcended. We predict a bright future for longitudinal MEMRI in pursuit of solutions to the brain-behavior mystery.

The effects of acute dopamine depletion on resting-state functional connectivity in healthy humans

Alpha-methyl-para-tyrosine (AMPT), a competitive inhibitor of tyrosine hydroxylase, can be used to deplete endogenous dopamine in humans. We examined how AMPT-induced dopamine depletion alters resting-state functional connectivity of the basal ganglia, and canonical resting-state networks, in healthy humans. Fourteen healthy participants (8 females; age [mean ± SD] = 27.93 ± 9.86) completed the study. Following dopamine depletion, the caudate showed reduced connectivity with the medial prefrontal cortex (mPFC) (Cohen's d = 1.89, p<.0001). Moreover, the caudate, putamen, globus pallidus, and midbrain all showed reduced connectivity with the occipital cortex (Cohen's d = 1.48-1.90; p<.0001-0.001). Notably, the dorsal caudate showed increased connectivity with the sensorimotor network (Cohen's d = 2.03, p=.002). AMPT significantly decreased self-reported motivation (t(13)=4.19, p=.001) and increased fatigue (t(13)=4.79, p=.0004). A greater increase in fatigue was associated with a greater reduction in connectivity between the substantia nigra and the mPFC (Cohen's d = 3.02, p<.00001), while decreased motivation was correlated with decreased connectivity between the VTA and left sensorimotor cortex (Cohen's d = 2.03, p=.00004). These findings help us to better understand the role of dopamine in basal ganglia function and may help us better understand neuropsychiatric diseases where abnormal dopamine levels are observed.

Cerebellum Dysfunction in Patients With PRRT2-Related Paroxysmal Dyskinesia

Background and Objectives:

The main culprit gene for paroxysmal kinesigenic dyskinesia, characterized by brief and recurrent attacks of involuntary movements, is PRRT2. The location of the primary dysfunction associated with paroxysmal dyskinesia remains a matter of debate and may vary depending on the etiology. While striatal dysfunction has often been implicated in these patients, evidence from preclinical models indicate that the cerebellum could also play a role. We aimed to investigate the role of the cerebellum in the pathogenesis of PRRT2-related dyskinesia in humans.

Methods:

We enrolled 22 consecutive right-handed patients with paroxysmal kinesigenic dyskinesia with a pathogenic variant of PRRT2, and their matched controls. Participants underwent a multi-modal neuroimaging protocol. We recorded anatomic and diffusion-weighted MRI, as well as resting-state functional MRI during which we tested the after-effects of sham and repetitive transcranial magnetic stimulation applied to the cerebellum on endogenous brain activity. We quantified: (i) the structural integrity of gray matter using voxel-based morphometry; (ii) the structural integrity of white matter using fixel-based analysis; (iii) the strength and direction of functional cerebellar connections using spectral dynamic causal modeling.

Results:

PRRT2 patients had: (i) decreased gray matter volume in the cerebellar lobule VI and in the medial prefrontal cortex; (ii) microstructural alterations of white matter in the cerebellum and along the tracts connecting the cerebellum to the striatum and the cortical motor areas; (iii) dysfunction of cerebellar motor pathways to the striatum and the cortical motor areas, as well as abnormal communication between the associative cerebellum (Crus I) and the medial prefrontal cortex. Cerebellar stimulation modulated communication within the motor and associative cerebellar networks, and tended to restore this communication to the level observed in healthy controls.

Discussion:

Patients with PRRT2-related dyskinesia have converging structural alterations of the motor cerebellum and related pathways with a dysfunction of cerebellar output towards the cerebello-thalamo-striato-cortical network. We hypothesize that abnormal cerebellar output is the primary dysfunction in patients with a PRRT2 pathogenic variant, resulting in striatal dysregulation and paroxysmal dyskinesia. More broadly, striatal dysfunction in paroxysmal dyskinesia might be secondary to aberrant cerebellar output transmitted by thalamic relays in certain disorders.

Clinical trial number:

NCT03481491 (https://ichgcp.net/clinical-trials-registry/NCT03481491)

Enhanced design matrix for task-related fMRI data analysis

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.

Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.

Enhanced Information Flow From Cerebellum to Secondary Visual Cortices Leads to Better Surgery Outcome in Degenerative Cervical Myelopathy Patients: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging

Degenerative cervical myelopathy (DCM) damages the spinal cord, resulting in long-term neurological impairment including motor and visual deficits. Given that visual feedback is crucial in guiding movements, the visual disorder may be a cause of motor deficits in patients with DCM. It has been shown that increased functional connectivity between secondary visual cortices and cerebellum, which are functionally related to the visually guided movements, was correlated with motor function in patients with DCM. One possible explanation is that the information integration between these regions was increased to compensate for impaired visual acuity in patients with DCM and resulted in better visual feedback during motor function. However, direct evidence supporting this hypothesis is lacking. To test this hypothesis and explore in more detail the information flow within the "visual-cerebellum" system, we measured the effective connectivity (EC) among the "visual-cerebellum" system via dynamic causal modeling and then tested the relationship between the EC and visual ability in patients with DCM. Furthermore, the multivariate pattern analysis was performed to detect the relationship between the pattern of EC and motor function in patients with DCM. We found (1) significant increases of the bidirectional connections between bilateral secondary visual cortices and cerebellum were observed in patients with DCM; (2) the increased self-connection of the cerebellum was positively correlated with the impaired visual acuity in patients; (3) the amplitude of effectivity from the cerebellum to secondary visual cortices was positively correlated with better visual recovery following spinal cord decompression surgery; and (4) the pattern of EC among the visual-cerebellum system could be used to predict the pre-operative motor function. In conclusion, this study provided direct evidence that the increased information integration within the "visual-cerebellum" system compensated for visual impairments, which might have importance for sustaining better motor function in patients with DCM.

Adaptive constrained independent vector analysis: An effective solution for analysis of large-scale medical imaging data

There is a growing need for flexible methods for the analysis of large-scale functional magnetic resonance imaging (fMRI) data for the estimation of global signatures that summarize the population while preserving individual-specific traits. Independent vector analysis (IVA) is a data-driven method that jointly estimates global spatio-temporal patterns from multi-subject fMRI data, and effectively preserves subject variability. However, as we show, IVA performance is negatively affected when the number of datasets and components increases especially when there is low component correlation across the datasets. We study the problem and its relationship with respect to correlation across the datasets, and propose an effective method for addressing the issue by incorporating reference information of the estimation patterns into the formulation, as a guidance in high dimensional scenarios. Constrained IVA (cIVA) provides an efficient framework for incorporating references, however its performance depends on a user-defined constraint parameter, which enforces the association between the reference signals and estimation patterns to a fixed level. We propose adaptive cIVA (acIVA) that tunes the constraint parameter to allow flexible associations between the references and estimation patterns, and enables incorporating multiple reference signals, without enforcing inaccurate conditions. Our results indicate that acIVA can reliably estimate high-dimensional multivariate sources from large-scale simulated datasets, when compared with standard IVA. It also successfully extracts meaningful functional networks from a large-scale fMRI dataset for which standard IVA did not converge. The method also efficiently captures subject-specific information, which is demonstrated through observed gender differences in spectral power, higher spectral power in males at low frequencies and in females at high frequencies, within the motor, attention, visual and default mode networks.

Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps

Functional connectivity (FC) maps from brain fMRI data can be derived with dual regression, a proposed alternative to traditional seed-based FC (SFC) methods that detect temporal correlation between a predefined region (seed) and other regions in the brain. As with SFC, incorporating nuisance regressors (NR) into the dual regression must be done carefully, to prevent potential bias and insensitivity of FC estimates. Here, we explore the potentially untoward effects on dual regression that may occur when NR correlate highly with the signal of interest, using both synthetic and real fMRI data to elucidate mechanisms responsible for loss of accuracy in FC maps. Our tests suggest significantly improved accuracy in FC maps derived with dual regression when highly correlated temporal NR were omitted. Single-map dual regression, a simplified form of dual regression that uses neither spatial nor temporal NR, offers a viable alternative whose FC maps may be more easily interpreted, and in some cases be more accurate than those derived with standard dual regression.

A semi-blind online dictionary learning approach for fMRI data

BACKGROUND

Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in recent year. Moreover, the supervised dictionary learning (SDL) that fixed the task stimulus curves as predefined atoms was proposed to improve ODL for functional networks separation. However, SDL cannot estimate the real time courses underlying the brain networks and cannot be applied to the inter-network connectivity analysis. This study aimed at investigating how to add the temporal prior information to ODL to extract the accurate task-related brain networks and the corresponding time courses.

NEW METHOD

To improve the performance of ODL, we propose a semi-blind ODL (semi-ODL) method that incorporates temporal prior information of the task paradigm into the dictionary updating process and optimizes the direction of one or more specific atoms "close" to the task time courses.

RESULTS

Results of the simulated and real fMRI experiment revealed that semi-ODL extracted more accurate task-related component and time courses than ODL and SDL. For one-task fMRI data, semi-ODL and Infomax-ICA showed similar detection power in most cases.

COMPARISON WITH EXISTING METHODS

The semi-ODL outperformed ODL, SDL in robustness to noise, spatial detection power and time course estimation. Moreover, semi-ODL showed comparable performance to Infomax-ICA for one-task fMRI data and outperformed Infomax-ICA in extracting the components related to each task from multi-task fMRI data.

CONCLUSIONS

The semi-ODL method is potentially useful to reveal brain networks underlying various cognitive tasks and the interactions between task-related brain networks.

Functional connectivity density mapping: comparing multiband and conventional EPI protocols

Functional connectivity density mapping (FCDM) is a newly developed data-driven technique that quantifies the number of local and global functional connections for each voxel in the brain. In this study, we evaluated reproducibility, sensitivity, and specificity of both local functional connectivity density (lFCD) and global functional connectivity density (gFCD). We compared these metrics using the human connectome project (HCP) compatible high-resolution (2 mm isotropic, TR = 0.8 s) multiband (MB), and more typical, lower resolution (3.5 mm isotropic, TR = 2.0 s) single-band (SB) resting state functional MRI (rs-fMRI) acquisitions. Furthermore, in order to be more clinically feasible, only rs-fMRI scans that lasted seven minutes were tested. Subjects were scanned twice within a two-week span. We found sensitivity and specificity increased and reproducibility either increased or did not change for the MB compared to the SB acquisitions. The MB scans also showed improved gray matter/white matter contrast compared to the SB scans. The lFCD and gFCD patterns were similar across MB and SB scans and confined predominantly to gray matter. We also observed a strong spatial correlation of FCD between MB and SB scans indicating the two acquisitions provide similar information. These findings indicate high-resolution MB acquisitions improve the quality of FCD data, and seven minute rs-fMRI scan can provide robust FCD measurements.

Multimodal neuroimaging analysis reveals age-associated common and discrete cognitive control constructs

The aims of this study were to determine which cognitive control functions are most sensitive to cross-sectional age differences and to identify neural features in different neuroimaging modalities that associated cognitive control function across the adult lifespan. We employed a joint independent component analysis (jICA) approach to obtain common networks among three different brain-imaging modalities (i.e., structural MRI, resting-state functional MRI, and diffusion tensor imaging) in relation to the cognitive control function. We differentiated three distinct cognitive constructs: one common (across inhibition, shifting, and updating) and two specific (shifting, updating) factors. These common/specific constructs were transformed from three original performance indexes: (a) stop-signal reaction time, (b) switch-cost, and (c) performance sensitivity collected from 156 individuals aged 20 to 78 years old. The current results show that the cross-sectional age difference is associated with a wide spread of brain degeneration that is not limited to the frontal region. Crucially, these findings suggest there are some common and distinct joined multimodal components that correlate with the psychological constructs of common and discrete cognitive control functions, respectively. To support current findings, other fusion ICA models were also analyzed including, parallel ICA (para-ICA) and multiset canonical correlation analysis with jICA (mCCA + jICA). Dynamic interactions among these brain features across different brain modalities could serve as possible developmental mechanisms associated with these age effects.

Temporally constrained ICA with threshold and its application to fMRI data

Background

Although independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal spatially independent brain networks, the order indetermination of ICA leads to the problem of target component selection. The temporally constrained independent component analysis (TCICA) is capable of automatically extracting the desired spatially independent components by adding the temporal prior information of the task to the mixing matrix for fMRI data analysis. However, the TCICA method can only extract a single component that tends to be a mix of multiple task-related components when there exist several independent components related to one task.

Methods

In this study, we proposed a TCICA with threshold (TCICA-Thres) method that performed TCICA outside the threshold and performed FastICA inside the threshold to automatically extract all the target components related to one task. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects. Additionally, the performance of TCICA-Thres was compared with that of FastICA and TCICA.

Results

The results from the simulation and the fMRI data demonstrated that TCICA-Thres better extracted the task-related components than TCICA. Moreover, TCICA-Thres outperformed FastICA in robustness to noise, spatial detection power and computational time.

Conclusions

The proposed TCICA-Thres solves the limitations of TCICA and extends the application of TCICA in fMRI data analysis.

Electronic supplementary material

The online version of this article (10.1186/s12880-018-0300-6) contains supplementary material, which is available to authorized users.

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.

Data-driven approaches for identifying links between brain structure and function in health and disease

Brain imaging technology provides a powerful tool to visualize the living human brain, provide insights into disease mechanisms, and potentially provide a tool to assist clinical decision-making. The brain has a very specific structural substrate providing a foundation for functional information; however, most studies ignore the very interesting and complex relationships between brain structure and brain function. While a variety of approaches have been used to study how brain structure informs function, the study of such relationships in living humans in most cases is limited to noninvasive approaches at the macroscopic scale. The use of data-driven approaches to link structure and function provides a tool which is especially important at the macroscopic scale at which we can study the human brain. This paper reviews data-driven approaches, with a focus on independent component analysis approaches, which leverage higher order statistics to link together macroscopic structural and functional MRI data. Such approaches provide the benefit of allowing us to identify links which do not necessarily correspond spatially (eg, structural changes in one region related to functional changes in other regions). They also provide a "network level" perspective on the data, by enabling us to identify sets of brain regions that covary together. This also opens up the ability to evaluate both within and between network relationships. A variety of examples are presented, including several showing the potential of such approaches to inform us about mental illness, particularly about schizophrenia.

SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis

Independent component analysis (ICA) has become a widely used method for functional magnetic resonance imaging (fMRI) data analysis. However, spatial ICA usually performs better than temporal ICA with regard to the stability and accuracy of functional connectivity detection, and temporal ICA is often not feasible when it is applied to the analysis of real fMRI data of the whole brain because of the excessive spatial dimensions. In this paper, to overcome these problems, we propose a sub-packet constrained temporal ICA (SCTICA) method to take advantage of the a priori information using a multi-objective optimization framework with the Newton iterative algorithm. Moreover, a splitting strategy is presented to improve the feasibility of the temporal ICA for whole brain fMRI data analysis. The experimental results of real data show that the splitting strategy improved the ability of the temporal ICA to analyze whole brain fMRI data. Furthermore, the experimental results also demonstrated that the proposed SCTICA method can not only improve the stability of the temporal ICA, but can also improve the functional connectivity detection ability compared with the classical ICA and ICA with a priori information methods. In brief, the proposed SCTICA method overcomes the problem that prevents temporal ICA from being applied to fMRI data of the whole brain, and the functional connectivity detection performance is greatly improved compared with that of traditional methods.

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features.

Functional Semi-Blind Source Separation Identifies Primary Motor Area Without Active Motor Execution

High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS_M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS_M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS_M1bySS is part of FS_M1, we also collected EEG in a motor condition, i.e. during a voluntary movement task (isometric handgrip) and in a rest condition, i.e. at rest with eyes open and closed. In motor condition, we show that the cortico-muscular coherence (CMC) of FS_M1bySS does not differ from FS_ M1 CMC (0.04 for both sources). Moreover, we show that the FS_M1bySS’s ongoing whole band activity during Motor and both rest conditions displays high mutual information and time correlation with FS_M1 (above 0.900 and 0.800, respectively) whereas much smaller ones with the primary somatosensory cortex [Formula: see text] (about 0.300 and 0.500, [Formula: see text]). FS_M1bySS as a marker of the upper-limb FS_M1 representation obtainable without the execution of an active motor task is a great achievement of the FSS algorithm, relevant in most experimental, neurological and psychiatric protocols.

An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis

Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.

SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis

BACKGROUND AND OBJECTIVE

With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages.

METHODS

In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively.

RESULTS

The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains.

CONCLUSIONS

These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group.

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.

Age-Related Differences in Dynamic Interactions Among Default Mode, Frontoparietal Control, and Dorsal Attention Networks during Resting-State and Interference Resolution

Resting-state fMRI (rs-fMRI) can identify large-scale brain networks, including the default mode (DMN), frontoparietal control (FPN) and dorsal attention (DAN) networks. Interactions among these networks are critical for supporting complex cognitive functions, yet the way in which they are modulated across states is not well understood. Moreover, it remains unclear whether these interactions are similarly affected in aging regardless of cognitive state. In this study, we investigated age-related differences in functional interactions among the DMN, FPN and DAN during rest and the Multi-Source Interference task (MSIT). Networks were identified using independent component analysis (ICA), and functional connectivity was measured during rest and task. We found that the FPN was more coupled with the DMN during rest and with the DAN during the MSIT. The degree of FPN-DMN connectivity was lower in older compared to younger adults, whereas no age-related differences were observed in FPN-DAN connectivity in either state. This suggests that dynamic interactions of the FPN are stable across cognitive states. The DMN and DAN were anti correlated and age-sensitive during the MSIT only, indicating variation in a task-dependent manner. Increased levels of anticorrelation from rest to task also predicted successful interference resolution. Additional analyses revealed that the degree of DMN-DAN anticorrelation during the MSIT was associated to resting cerebral blood flow (CBF) within the DMN. This suggests that reduced DMN neural activity during rest underlies an impaired ability to achieve higher levels of anticorrelation during a task. Taken together, our results suggest that only parts of age-related differences in connectivity are uncovered at rest and thus, should be studied in the functional connectome across multiple states for a more comprehensive picture.

Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data

This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach. A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs. Mean time lag of 0.6s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1-10sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes. The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.

Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

Neuroimaging measures of error-processing: Extracting reliable signals from event-related potentials and functional magnetic resonance imaging

Error-related brain activity has become an increasingly important focus of cognitive neuroscience research utilizing both event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI). Given the significant time and resources required to collect these data, it is important for researchers to plan their experiments such that stable estimates of error-related processes can be achieved efficiently. Reliability of error-related brain measures will vary as a function of the number of error trials and the number of participants included in the averages. Unfortunately, systematic investigations of the number of events and participants required to achieve stability in error-related processing are sparse, and none have addressed variability in sample size. Our goal here is to provide data compiled from a large sample of healthy participants (n=180) performing a Go/NoGo task, resampled iteratively to demonstrate the relative stability of measures of error-related brain activity given a range of sample sizes and event numbers included in the averages. We examine ERP measures of error-related negativity (ERN/Ne) and error positivity (Pe), as well as event-related fMRI measures locked to False Alarms. We find that achieving stable estimates of ERP measures required four to six error trials and approximately 30 participants; fMRI measures required six to eight trials and approximately 40 participants. Fewer trials and participants were required for measures where additional data reduction techniques (i.e., principal component analysis and independent component analysis) were implemented. Ranges of reliability statistics for various sample sizes and numbers of trials are provided. We intend this to be a useful resource for those planning or evaluating ERP or fMRI investigations with tasks designed to measure error-processing.

Association of familial risk for schizophrenia with thalamic and medial prefrontal functional connectivity during attentional control

Anomalies in behavioral correlates of attentional processing and related brain activity are crucial correlates of schizophrenia and associated with familial risk for this brain disorder. However, it is not clear how brain functional connectivity during attentional processes is key for schizophrenia and linked with trait vs. state related variables. To address this issue, we investigated patterns of functional connections during attentional control in healthy siblings of patients with schizophrenia, who share with probands genetic features but not variables related to the state of the disorder. 356 controls, 55 patients with schizophrenia on stable treatment with antipsychotics and 40 healthy siblings of patients with this brain disorder underwent the Variable Attentional Control (VAC) task during fMRI. Independent Component Analysis (ICA) is allowed to identify independent components (IC) of BOLD signal recorded during task performance. Results indicated reduced connectivity strength in patients with schizophrenia as well as in their healthy siblings in left thalamus within an attentional control component and greater connectivity in right medial prefrontal cortex (PFC) within the so-called Default Mode Network (DMN) compared to healthy individuals. These results suggest a relationship between familial risk for schizophrenia and brain functional networks during attentional control, such that this biological phenotype may be considered a useful intermediate phenotype in order to link genes effects to aspects of the pathophysiology of this brain disorder.

A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

Group-independent component analysis (GICA) is a well-established blind source separation technique that has been widely applied to study multi-subject functional magnetic resonance imaging (fMRI) data. The group-independent components (GICs) represent the commonness of all of the subjects in the group. Similar to independent component analysis on the single-subject level, the performance of GICA can be improved for multi-subject fMRI data analysis by incorporating a priori information; however, a priori information is not always considered while looking for GICs in existing GICA methods, especially when no obvious or specific knowledge about an unknown group is available. In this paper, we present a novel method to extract the group intrinsic reference from all of the subjects of the group and then incorporate it into the GICA extraction procedure. Comparison experiments between FastICA and GICA with intrinsic reference (GICA-IR) are implemented on the group level with regard to the simulated, hybrid and real fMRI data. The experimental results show that the GICs computed by GICA-IR have a higher correlation with the corresponding independent component of each subject in the group, and the accuracy of activation regions detected by GICA-IR was also improved. These results have demonstrated the advantages of the GICA-IR method, which can better reflect the commonness of the subjects in the group.

Supervised dictionary learning for inferring concurrent brain networks

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder

Finding neurobiological markers for neurodevelopmental disorders, such as attention deficit and hyperactivity disorder (ADHD), is a major objective of clinicians and neuroscientists. We examined if functional Magnetic Resonance Imaging (fMRI) data from a few distinct visuospatial working memory (VSWM) tasks enables accurately detecting cases with ADHD. We tested 20 boys with ADHD combined type and 20 typically developed (TD) boys in four VSWM tasks that differed in feedback availability (feedback, no-feedback) and reward size (large, small). We used a multimodal analysis based on brain activity in 16 regions of interest, significantly activated or deactivated in the four VSWM tasks (based on the entire participants' sample). Dimensionality of the data was reduced into 10 principal components that were used as the input variables to a logistic regression classifier. fMRI data from the four VSWM tasks enabled a classification accuracy of 92.5%, with high predicted ADHD probability values for most clinical cases, and low predicted ADHD probabilities for most TDs. This accuracy level was higher than those achieved by using the fMRI data of any single task, or the respective behavioral data. This indicates that task-based fMRI data acquired while participants perform a few distinct VSWM tasks enables improved detection of clinical cases.

Independent Component Analysis of EEG-fMRI data for studying epilepsy and epileptic seizures

Here we present a method for classifying fMRI independent components (ICs) by using an optimized algorithm for the individuation of noisy signals from sources of interest. The method was applied to estimate brain activations from combined EEG-fMRI data for the exploration of epilepsy. Spatial ICA was performed using the above-mentioned optimized algorithm and other three popular algorithms. ICs were sorted considering the value: of the coefficients of determination R2, obtained from the multiple regression analysis with morphometric maps of cerebral matter; of the kurtosis, which features the signal energy. The validation of the method was performed comparing the brain activations obtained with those resulted using the General Linear Model (GLM). The ICA-derived activations in different datasets comprised subareas of the GLM-revealed activations, even if the volume and the shape of activated areas do not correspond exactly. The method proposed also detects additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM. Compared with a traditional GLM approach, the ICA one provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain and the temporal constrains.

Feedback associated with expectation for larger-reward improves visuospatial working memory performances in children with ADHD

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Children with ADHD and normal controls were tested in working memory tasks.

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Availability of feedback and expectation for monetary reward were manipulated.

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ADHDs showed improved working memory when feedback was associated with larger-reward.

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Performance improvement in ADHD was associated with brain activity normalization.

We tested the interactive effect of feedback and reward on visuospatial working memory in children with ADHD. Seventeen boys with ADHD and 17 Normal Control (NC) boys underwent functional magnetic resonance imaging (fMRI) while performing four visuospatial 2-back tasks that required monitoring the spatial location of letters presented on a display. Tasks varied in reward size (large; small) and feedback availability (no-feedback; feedback). While the performance of NC boys was high in all conditions, boys with ADHD exhibited higher performance (similar to those of NC boys) only when they received feedback associated with large-reward. Performance pattern in both groups was mirrored by neural activity in an executive function neural network comprised of few distinct frontal brain regions. Specifically, neural activity in the left and right middle frontal gyri of boys with ADHD became normal-like only when feedback was available, mainly when feedback was associated with large-reward. When feedback was associated with small-reward, or when large-reward was expected but feedback was not available, boys with ADHD exhibited altered neural activity in the medial orbitofrontal cortex and anterior insula. This suggests that contextual support normalizes activity in executive brain regions in children with ADHD, which results in improved working memory.

Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p < 0.01) features were identified by general linear modeling and used to generate a classification model for the framework. This feature-optimized classification of ICs with SVM (FOCIS) framework was used to classify both group and single subject ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC < 33, whereas only a few limited ICs are affected by direct splitting when NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

Partial recovery of abnormal insula and dorsolateral prefrontal connectivity to cognitive networks in chronic low back pain after treatment

We previously reported that effective treatment of chronic low back pain (CLBP) reversed abnormal brain structure and functional MRI (fMRI) activity during cognitive task performance, particularly in the left dorsolateral prefrontal cortex (DLPFC). Here, we used resting-state fMRI to examine how chronic pain affects connectivity of brain networks supporting cognitive functioning and the effect of treatment in 14 CLBP patients and 16 healthy, pain-free controls (scans were acquired at baseline for all subjects and at 6-months post-treatment for patients and a matched time-point for 10 controls). The main networks activated during cognitive task performance, task-positive network (TPN) and task-negative network (TNN) (aka default mode) network, were identified in subjects' task fMRI data and used to define matching networks in resting-state data. The connectivity of these cognitive resting-state networks was compared between groups, and before and after treatment. Our findings converged on the bilateral insula (INS) as the region of aberrant cognitive resting-state connectivity in patients pretreatment versus controls. These findings were complemented by an independent, data-driven approach showing altered global connectivity of the INS. Detailed investigation of the INS confirmed reduced connectivity to widespread TPN and TNN areas, which was partially restored post-treatment. Furthermore, analysis of diffusion-tensor imaging (DTI) data revealed structural changes in white matter supporting these findings. The left DLPFC also showed aberrant connectivity that was restored post-treatment. Altogether, our findings implicate the bilateral INS and left DLPFC as key nodes of disrupted cognition-related intrinsic connectivity in CLBP, and the resulting imbalance between TPN and TNN function is partially restored with treatment.

General nonunitary constrained ICA and its application to complex-valued fMRI data

Constrained independent component analysis (C-ICA) algorithms provide an effective way to introduce prior information into the complex- and real-valued ICA framework. The work in this area has focus on adding constraints to the objective function of algorithms that assume a unitary demixing matrix. The unitary condition is required in order to decouple-isolate-the constraints applied for each individual source. This assumption limits the optimization space and, therefore, the separation performance of C-ICA algorithms. We generalize the existing C-ICA framework by using a novel decoupling method that preserves the larger optimization space for the demixing matrix. This framework allows for the constraining of either the sources or the mixing coefficients. A constrained version of the nonunitary entropy bound minimization algorithm is introduced and applied to actual complex-valued fMRI data. We show that constraining the mixing parameters using a temporal constraint improves the estimation of the spatial map and timecourses of task-related components.

The absence of task-related increases in BOLD signal does not equate to absence of task-related brain activation

Most fMRI studies employ general-linear-model-based analyses (GLM-BA) of BOLD signal changes to identify regions that are active (or not) during specific cognitive processes. However, alternate analytic approaches (like independent component analysis) may identify more complex patterns of activation, including in regions not implicated in GLM-BA of the same data. In our opinion, fMRI findings revealed by a GLM-BA cannot exclude any brain regions from contributing to specific cognitive processes.

Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain network changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlated to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea-lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI

OBJECTIVE

The aim was to develop a method for the purpose of localizing epilepsy related hemodynamic foci for patients suffering intractable focal epilepsy using task-free fMRI alone.

METHODS

We studied three groups of subjects: patients with intractable focal epilepsy, healthy volunteers performing motor tasks, and healthy volunteers in resting state. We performed spatial independent component analysis (ICA) on the fMRI alone data and developed a set of IC selection criteria to identify epilepsy related ICs. The method was then tested in the two healthy groups.

RESULTS

In seven out of the nine surgery patients, identified ICs were concordant with surgical resection. Our results were also consistent with presurgical evaluation of the remaining one patient without surgery and may explain why she was not suitable for resection treatment. In the motor task study of ten healthy subjects, our method revealed components with concordant spatial and temporal features as expected from the unilateral motor tasks. In the resting state study of seven healthy subjects, the method successfully rejected all components in four out of seven subjects as non-epilepsy related components.

CONCLUSION

These results suggest the lateralization and localization value of fMRI alone in presurgical evaluation for patients with intractable unilateral focal epilepsy.

SIGNIFICANCE

The proposed method is noninvasive in nature and easy to implement. It has the potential to be incorporated in current presurgical workup for treating intractable focal epilepsy patients.

Temporally and spatially constrained ICA of fMRI data analysis

Constrained independent component analysis (CICA) is capable of eliminating the order ambiguity that is found in the standard ICA and extracting the desired independent components by incorporating prior information into the ICA contrast function. However, the current CICA method produces constraints that are based on only one type of prior information (temporal/spatial), which may increase the dependency of CICA on the accuracy of the prior information. To improve the robustness of CICA and to reduce the impact of the accuracy of prior information on CICA, we proposed a temporally and spatially constrained ICA (TSCICA) method that incorporated two types of prior information, both temporal and spatial, as constraints in the ICA. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects who performed a movement task. Additionally, the performance of TSCICA was compared with the ICA method, the temporally CICA (TCICA) method and the spatially CICA (SCICA) method. The results from the simulation and from the real fMRI data demonstrated that TSCICA outperformed TCICA, SCICA and ICA in terms of robustness to noise. Moreover, the TSCICA method displayed better robustness to prior temporal/spatial information than the TCICA/SCICA method.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

Large-scale brain networks in the awake, truly resting marmoset monkey

Resting-state functional MRI is a powerful tool that is increasingly used as a noninvasive method for investigating whole-brain circuitry and holds great potential as a possible diagnostic for disease. Despite this potential, few resting-state studies have used animal models (of which nonhuman primates represent our best opportunity of understanding complex human neuropsychiatric disease), and no work has characterized networks in awake, truly resting animals. Here we present results from a small New World monkey that allows for the characterization of resting-state networks in the awake state. Six adult common marmosets (Callithrix jacchus) were acclimated to light, comfortable restraint using individualized helmets. Following behavioral training, resting BOLD data were acquired during eight consecutive 10 min scans for each conscious subject. Group independent component analysis revealed 12 brain networks that overlap substantially with known anatomically constrained circuits seen in the awake human. Specifically, we found eight sensory and "lower-order" networks (four visual, two somatomotor, one cerebellar, and one caudate-putamen network), and four "higher-order" association networks (one default mode-like network, one orbitofrontal, one frontopolar, and one network resembling the human salience network). In addition to their functional relevance, these network patterns bear great correspondence to those previously described in awake humans. This first-of-its-kind report in an awake New World nonhuman primate provides a platform for mechanistic neurobiological examination for existing disease models established in the marmoset.

Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations

INTRODUCTION

In healthy conditions, group-level fMRI resting state analyses identify ten resting state networks (RSNs) of cognitive relevance. Here, we aim to assess the ten-network model in severely brain-injured patients suffering from disorders of consciousness and to identify those networks which will be most relevant to discriminate between patients and healthy subjects.

METHODS

300 fMRI volumes were obtained in 27 healthy controls and 53 patients in minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS) and coma. Independent component analysis (ICA) reduced data dimensionality. The ten networks were identified by means of a multiple template-matching procedure and were tested on neuronality properties (neuronal vs non-neuronal) in a data-driven way. Univariate analyses detected between-group differences in networks' neuronal properties and estimated voxel-wise functional connectivity in the networks, which were significantly less identifiable in patients. A nearest-neighbor "clinical" classifier was used to determine the networks with high between-group discriminative accuracy.

RESULTS

Healthy controls were characterized by more neuronal components compared to patients in VS/UWS and in coma. Compared to healthy controls, fewer patients in MCS and VS/UWS showed components of neuronal origin for the left executive control network, default mode network (DMN), auditory, and right executive control network. The "clinical" classifier indicated the DMN and auditory network with the highest accuracy (85.3%) in discriminating patients from healthy subjects.

CONCLUSIONS

FMRI multiple-network resting state connectivity is disrupted in severely brain-injured patients suffering from disorders of consciousness. When performing ICA, multiple-network testing and control for neuronal properties of the identified RSNs can advance fMRI system-level characterization. Automatic data-driven patient classification is the first step towards future single-subject objective diagnostics based on fMRI resting state acquisitions.

On the definition of signal-to-noise ratio and contrast-to-noise ratio for FMRI data

Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.

Mapping a lateralization gradient within the ventral stream for auditory speech perception

Recent models on speech perception propose a dual-stream processing network, with a dorsal stream, extending from the posterior temporal lobe of the left hemisphere through inferior parietal areas into the left inferior frontal gyrus, and a ventral stream that is assumed to originate in the primary auditory cortex in the upper posterior part of the temporal lobe and to extend toward the anterior part of the temporal lobe, where it may connect to the ventral part of the inferior frontal gyrus. This article describes and reviews the results from a series of complementary functional magnetic resonance imaging studies that aimed to trace the hierarchical processing network for speech comprehension within the left and right hemisphere with a particular focus on the temporal lobe and the ventral stream. As hypothesized, the results demonstrate a bilateral involvement of the temporal lobes in the processing of speech signals. However, an increasing leftward asymmetry was detected from auditory-phonetic to lexico-semantic processing and along the posterior-anterior axis, thus forming a "lateralization" gradient. This increasing leftward lateralization was particularly evident for the left superior temporal sulcus and more anterior parts of the temporal lobe.