Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers

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.

ICA extracts epileptic sources from fMRI in EEG-negative patients: a retrospective validation study

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.

Alterations of the salience network in obesity: a resting-state fMRI study

Obesity is a major health problem in modern societies. It has been related to abnormal functional organization of brain networks believed to process homeostatic (internal) and/or salience (external) information. This study used resting-state functional magnetic resonance imaging analysis to delineate possible functional changes in brain networks related to obesity. A group of 18 healthy adult participants with obesity were compared with a group of 16 lean participants while performing a resting-state task, with the data being evaluated by independent component analysis. Participants also completed a neuropsychological assessment. Results showed that the functional connectivity strength of the putamen nucleus in the salience network was increased in the obese group. We speculate that this abnormal activation may contribute to overeating through an imbalance between autonomic processing and reward processing of food stimuli. A correlation was also observed in obesity between activation of the putamen nucleus in the salience network and mental slowness, which is consistent with the notion that basal ganglia circuits modulate rapid processing of information.

Thalamus, brainstem and salience network connectivity changes during propofol-induced sedation and unconsciousness

In this functional magnetic resonance imaging study, we examined the effect of mild propofol sedation and propofol-induced unconsciousness on resting state brain connectivity, using graph analysis based on independent component analysis and a classical seed-based analysis. Contrary to previous propofol research, which mainly emphasized the importance of connectivity in the default mode network (DMN) and external control network (ECN), we focused on the salience network, thalamus, and brainstem. The importance of these brain regions in brain arousal and organization merits a more detailed examination of their connectivity response to propofol. We found that the salience network disintegrated during propofol-induced unconsciousness. The thalamus decreased connectivity with the DMN, ECN, and salience network, while increasing connectivity with sensorimotor and auditory/insular cortices. Brainstem regions disconnected from the DMN with unconsciousness, while the pontine tegmental area increased connectivity with the insulae during mild sedation. These findings illustrate that loss of consciousness is associated with a wide variety of decreases and increases of both cortical and subcortical connectivity. It furthermore stresses the necessity of also examining resting state connectivity in networks representing arousal, not only those associated with awareness.

Shifted coupling of EEG driving frequencies and fMRI resting state networks in schizophrenia spectrum disorders

Introduction

The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN).

Methods

Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps.

Results

The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls.

Conclusions

By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling.

Significance

The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

Impact of functional MRI data preprocessing pipeline on default-mode network detectability in patients with disorders of consciousness

An emerging application of resting-state functional MRI (rs-fMRI) is the study of patients with disorders of consciousness (DoC), where integrity of default-mode network (DMN) activity is associated to the clinical level of preservation of consciousness. Due to the inherent inability to follow verbal instructions, arousal induced by scanning noise and postural pain, these patients tend to exhibit substantial levels of movement. This results in spurious, non-neural fluctuations of the rs-fMRI signal, which impair the evaluation of residual functional connectivity. Here, the effect of data preprocessing choices on the detectability of the DMN was systematically evaluated in a representative cohort of 30 clinically and etiologically heterogeneous DoC patients and 33 healthy controls. Starting from a standard preprocessing pipeline, additional steps were gradually inserted, namely band-pass filtering (BPF), removal of co-variance with the movement vectors, removal of co-variance with the global brain parenchyma signal, rejection of realignment outlier volumes and ventricle masking. Both independent-component analysis (ICA) and seed-based analysis (SBA) were performed, and DMN detectability was assessed quantitatively as well as visually. The results of the present study strongly show that the detection of DMN activity in the sub-optimal fMRI series acquired on DoC patients is contingent on the use of adequate filtering steps. ICA and SBA are differently affected but give convergent findings for high-grade preprocessing. We propose that future studies in this area should adopt the described preprocessing procedures as a minimum standard to reduce the probability of wrongly inferring that DMN activity is absent.

Characterization of resting state activity in MCI individuals

Objectives. Aging is the major risk factor for Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). The aim of this study was to identify novel modifications of brain functional connectivity in MCI patients. MCI individuals were compared to healthy elderly subjects.

Methods. We enrolled 37 subjects (age range 60–80 y.o.). Of these, 13 subjects were affected by MCI and 24 were age-matched healthy elderly control (HC). Subjects were evaluated with Mini Mental State Examination (MMSE), Frontal Assessment Battery (FAB), and prose memory (Babcock story) tests. In addition, with functional Magnetic Resonance Imaging (fMRI), we investigated resting state network (RSN) activities. Resting state (Rs) fMRI data were analyzed by means of Independent Component Analysis (ICA). Subjects were followed-up with neuropsychological evaluations for three years.

Results. Rs-fMRI of MCI subjects showed increased intrinsic connectivity in the Default Mode Network (DMN) and in the Somatomotor Network (SMN). Analysis of the DMN showed statistically significant increased activation in the posterior cingulate cortex (PCC) and left inferior parietal lobule (lIPL). During the three years follow-up, 4 MCI subjects converted to AD. The subset of MCI AD-converted patients showed increased connectivity in the right Inferior Parietal Lobule (rIPL). As for SMN activity, MCI and MCI-AD converted groups showed increased level of connectivity in correspondence of the right Supramarginal Gyrus (rSG).

Conclusions. Our findings indicate alterations of DMN and SMN activity in MCI subjects, thereby providing potential imaging-based markers that can be helpful for the early diagnosis and monitoring of these patients.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities.

An automated method for identifying artifact in independent component analysis of resting-state FMRI

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

Functional activity maps based on significance measures and Independent Component Analysis

The use of functional imaging has been proven very helpful for the process of diagnosis of neurodegenerative diseases, such as Alzheimer's Disease (AD). In many cases, the analysis of these images is performed by manual reorientation and visual interpretation. Therefore, new statistical techniques to perform a more quantitative analysis are needed. In this work, a new statistical approximation to the analysis of functional images, based on significance measures and Independent Component Analysis (ICA) is presented. After the images preprocessing, voxels that allow better separation of the two classes are extracted, using significance measures such as the Mann-Whitney-Wilcoxon U-Test (MWW) and Relative Entropy (RE). After this feature selection step, the voxels vector is modelled by means of ICA, extracting a few independent components which will be used as an input to the classifier. Naive Bayes and Support Vector Machine (SVM) classifiers are used in this work. The proposed system has been applied to two different databases. A 96-subjects Single Photon Emission Computed Tomography (SPECT) database from the "Virgen de las Nieves" Hospital in Granada, Spain, and a 196-subjects Positron Emission Tomography (PET) database from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Values of accuracy up to 96.9% and 91.3% for SPECT and PET databases are achieved by the proposed system, which has yielded many benefits over methods proposed on recent works.

Neural underpinnings of behavioural strategies that prioritize either cognitive task performance or pain

We previously discovered that when faced with a challenging cognitive task in the context of pain, some people prioritize task performance, while in others, pain results in poorer performance. These behaviours, designated respectively as A- and P-types (for attention dominates vs pain dominates), may reflect pain coping strategies, resilience or vulnerabilities to develop chronic pain, or predict the efficacy of treatments such as cognitive behavioural therapy. Here, we used a cognitive interference task and pain stimulation in 80 subjects to interrogate psychophysical, psychological, brain structure and function that distinguish these behavioural strategies. During concurrent pain, the A group exhibited faster task reaction times (RTs) compared to nonpain trials, whereas the P group had slower RTs during pain compared to nonpain trials, with the A group being 143 ms faster than the P group. Brain imaging revealed structural and functional brain features that characterized these behavioural strategies. Compared to the performance-oriented A group, the P group had (1) more gray matter in regions implicated in pain and salience (anterior insula, anterior midcingulate cortex, supplementary motor area, orbitofrontal cortex, thalamus, caudate), (2) greater functional connectivity in sensorimotor and salience resting-state networks, (3) less white matter integrity in the internal and external capsule, anterior thalamic radiation and corticospinal tract, but (4) were indistinguishable based on sex, pain sensitivity, neuroticism, and pain catastrophizing. These data may represent neural underpinnings of how task performance vs pain is prioritized and provide a framework for developing personalized pain therapy approaches that are based on behaviour-structure-function organization.

Spatiotemporal Segregation of Neural Response to Auditory Stimulation: An fMRI Study Using Independent Component Analysis and Frequency-Domain Analysis

Although auditory processing has been widely studied with conventional parametric methods, there have been a limited number of independent component analysis (ICA) applications in this area. The purpose of this study was to examine spatiotemporal behavior of brain networks in response to passive auditory stimulation using ICA. Continuous broadband noise was presented binaurally to 19 subjects with normal hearing. ICA was performed to segregate spatial networks, which were subsequently classified according to their temporal relation to the stimulus using power spectrum analysis. Classification of separated networks resulted in 3 stimulus-activated, 9 stimulus-deactivated, 2 stimulus-neutral (stimulus-dependent but not correlated with the stimulation timing), and 2 stimulus-unrelated (fluctuations that did not follow the stimulus cycles) components. As a result of such classification, spatiotemporal subdivisions were observed in a number of cortical structures, namely auditory, cingulate, and sensorimotor cortices, where parts of the same cortical network responded to the stimulus with different temporal patterns. The majority of the classified networks seemed to comprise subparts of the known resting-state networks (RSNs); however, they displayed different temporal behavior in response to the auditory stimulus, indicating stimulus-dependent temporal segregation of RSNs. Only one of nine deactivated networks coincided with the “classic” default-mode network, suggesting the existence of a stimulus-dependent default-mode network, different from that commonly accepted.

Reliability and spatial specificity of rat brain sensorimotor functional connectivity networks are superior under sedation compared with general anesthesia

Functional connectivity networks derived from resting-state functional MRI (rsfMRI) have received increasing interest to further our understanding of brain function. The anesthesia in rodent models may influence the interpretation and comparison of results from functional connectivity MRI (fcMRI). More research is required on this aspect. In this study, we investigated rat brain connectivity networks under 1.5% isoflurane anesthesia in comparison with medetomidine sedation. rsfMRI data were acquired under both anesthesia conditions within one imaging session. Male Wistar rats (n = 17) were scanned at 11.7 T with focus on the sensorimotor system. The data underwent a per-subject independent component analysis (ICA), after which individual components were grouped using hierarchical clustering. Consistent and reliable networks were identified under medetomidine in sensorimotor cortex (three networks) and striatum (two networks). The incidence of these networks was drastically reduced under isoflurane. Seed correlation analysis confirmed these results and revealed globally elevated correlations with low topical specificity under isoflurane, stemming from low-frequency global signal fluctuations. Global signal removal thus enhanced slightly regional specificity under isoflurane and showed anti-correlations of cortico-striatal connections in both anesthesia regimes. Functional connectivity networks are thus reliably detected in medetomidine-sedated animals on an individual basis using ICA. Their occurrence, however, is heavily compromised under isoflurane as a result of global signal fluctuations potentially stemming from burst-suppression-like neural activity. Anesthesia and pharmacologically induced modulations may provide insight into network mechanisms in the future. As an agent for fcMRI in brain disease studies, light sedation using medetomidine preserves connectivity networks in a greater level of detail, and may therefore be considered superior to standard isoflurane anesthesia.

PHYCAA+: an optimized, adaptive procedure for measuring and controlling physiological noise in BOLD fMRI

The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response). To address these issues, we have developed PHYCAA+, a significantly improved version of the PHYCAA algorithm (Churchill et al., 2011) that (1) down-weights the variance of voxels in probable non-neuronal tissue, and (2) identifies the multivariate physiological noise subspace in gray matter that is linked to non-neuronal tissue. This model estimates physiological noise directly from EPI data, without requiring external measures of heartbeat and respiration, or manual selection of physiological components. The PHYCAA+ model significantly improves the prediction accuracy and reproducibility of single-subject analyses, compared to PHYCAA and a number of commonly-used physiological correction algorithms. Individual subject denoising with PHYCAA+ is independently validated by showing that it consistently increased between-subject activation overlap, and minimized false-positive signal in non gray-matter loci. The results are demonstrated for both block and fast single-event task designs, applied to standard univariate and adaptive multivariate analysis models.

Time course based artifact identification for independent components of resting-state FMRI

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

Resting-state fMRI in the Human Connectome Project

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.

Automatic selection of resting-state networks with functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥92%) and the occipital network, which includes the medial visual cortical areas (≥94%), and consistent for the attention network (≥80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.

Effect of scanner acoustic background noise on strict resting-state fMRI

Functional MRI (fMRI) resting-state experiments are aimed at identifying brain networks that support basal brain function. Although most investigators consider a 'resting-state' fMRI experiment with no specific external stimulation, subjects are unavoidably under heavy acoustic noise produced by the equipment. In the present study, we evaluated the influence of auditory input on the resting-state networks (RSNs). Twenty-two healthy subjects were scanned using two similar echo-planar imaging sequences in the same 3T MRI scanner: a default pulse sequence and a reduced "silent" pulse sequence. Experimental sessions consisted of two consecutive 7-min runs with noise conditions (default or silent) counterbalanced across subjects. A self-organizing group independent component analysis was applied to fMRI data in order to recognize the RSNs. The insula, left middle frontal gyrus and right precentral and left inferior parietal lobules showed significant differences in the voxel-wise comparison between RSNs depending on noise condition. In the presence of low-level noise, these areas Granger-cause oscillations in RSNs with cognitive implications (dorsal attention and entorhinal), while during high noise acquisition, these connectivities are reduced or inverted. Applying low noise MR acquisitions in research may allow the detection of subtle differences of the RSNs, with implications in experimental planning for resting-state studies, data analysis, and ergonomic factors.

Default mode network functional connectivity altered in failed back surgery syndrome

The purpose of this study was to identify alterations in the default mode network of failed back surgery syndrome patients as compared to healthy subjects. Resting state functional magnetic resonance imaging was conducted at 3 Tesla and data were analyzed with an independent component analysis. Results indicate an overall reduced functional connectivity of the default mode network and recruitment of additional pain modulation brain regions, including dorsolateral prefrontal cortex, insula, and additional sensory motor integration brain regions, including precentral and postcentral gyri, for failed back surgery syndrome patients.

PERSPECTIVE

This article presents alterations in the default mode network of chronic low back pain patients with failed back surgery syndrome as compared to healthy participants.

The Use of a priori Information in ICA-Based Techniques for Real-Time fMRI: An Evaluation of Static/Dynamic and Spatial/Temporal Characteristics

Real-time brain functional MRI (rt-fMRI) allows in vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitor a target independent component (IC). We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off-line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time, and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability.

ICA analysis of fMRI with real-time constraints: an evaluation of fast detection performance as function of algorithms, parameters and a priori conditions

Independent component analysis (ICA) techniques offer a data-driven possibility to analyze brain functional MRI data in real-time. Typical ICA methods used in functional magnetic resonance imaging (fMRI), however, have been until now mostly developed and optimized for the off-line case in which all data is available. Real-time experiments are ill-posed for ICA in that several constraints are added: limited data, limited analysis time and dynamic changes in the data and computational speed. Previous studies have shown that particular choices of ICA parameters can be used to monitor real-time fMRI (rt-fMRI) brain activation, but it is unknown how other choices would perform. In this rt-fMRI simulation study we investigate and compare the performance of 14 different publicly available ICA algorithms systematically sampling different growing window lengths (WLs), model order (MO) as well as a priori conditions (none, spatial or temporal). Performance is evaluated by computing the spatial and temporal correlation to a target component as well as computation time. Four algorithms are identified as best performing (constrained ICA, fastICA, amuse, and evd), with their corresponding parameter choices. Both spatial and temporal priors are found to provide equal or improved performances in similarity to the target compared with their off-line counterpart, with greatly reduced computation costs. This study suggests parameter choices that can be further investigated in a sliding-window approach for a rt-fMRI experiment.

Semantic network disconnection in formal thought disorder

BACKGROUND

Structural and functional findings in schizophrenic patients with formal thought disorder (FTD) show abnormalities within left-side semantic areas. The present study investigate the network function of the involved brain regions as a function of FTD severity.

METHODS

We examined a group of 16 schizophrenia patients differing in FTD, but not in overall symptom severity, and 18 matched healthy controls. A passive word reading paradigm was applied during functional MRI (fMRI). A concatenated independent component analysis approach separated the fMRI signal into independent components, and spatial similarity was used to estimate the individual differences in spatial configuration of networks.

RESULTS

The semantic network was identified for both groups encompassing structures of the left inferior frontal gyrus, the left angular gyrus and the left middle temporal gyrus. The differences between the semantic networks of patients and controls increased with increasing severity of FTD. This difference was due to a decreasing contribution of the left inferior frontal gyrus (Brodmann area 45 and 47).

CONCLUSION

Severity of FTD was correlated with a disruption of the left semantic network in schizophrenic patients. We suggest that FTD is a consequence of a frontal-parietal/temporal disconnection due to a complex interaction between structural and functional abnormalities within the left semantic network.

Group information guided ICA for fMRI data analysis

Group independent component analysis (ICA) has been widely applied to studies of multi-subject fMRI data for computing subject specific independent components with correspondence across subjects. However, the independence of subject specific independent components (ICs) derived from group ICA has not been explicitly optimized in existing group ICA methods. In order to preserve independence of ICs at the subject level and simultaneously establish correspondence of ICs across subjects, we present a new framework for obtaining subject specific ICs, which we coined group-information guided ICA (GIG-ICA). In this framework, group information captured by standard ICA on the group level is exploited as guidance to compute individual subject specific ICs using a multi-objective optimization strategy. Specifically, we propose a framework with two stages: at first, group ICs (GICs) are obtained using standard group ICA tools, and then the GICs are used as references in a new one-unit ICA with spatial reference (ICA-R) using a multi-objective optimization solver. Comparison experiments with back-reconstruction (GICA1 and GICA3) and dual regression on simulated and real fMRI data have demonstrated that GIG-ICA is able to obtain subject specific ICs with stronger independence and better spatial correspondence across different subjects in addition to higher spatial and temporal accuracy.

Default network is not hypoactive in dementia with fluctuating cognition: an Alzheimer disease/dementia with Lewy bodies comparison

Default mode network resting state activity in posterior cingulate cortex is abnormally reduced in Alzheimer disease (AD) patients. Fluctuating cognition and electroencephalogram abnormalities are established core and supportive elements respectively for the diagnosis of dementia with Lewy bodies (DLB). Our aim was to assess whether patients with DLB with both of these features have different default mode network patterns during resting state functional magnetic resonance imaging compared with AD. Eighteen patients with DLB, 18 AD patients without fluctuating cognition, and 15 control subjects were selected after appropriate matching and followed for 2-5 years to confirm diagnosis. Independent component analysis with functional connectivity (FC) and Granger causality approaches were applied to isolate and characterize resting state networks. FC was reduced in AD and DLB patients compared with control subjects. Posterior cingulate cortex activity was lower in AD than in control subjects and DLB patients (p < 0.05). Right hemisphere FC was reduced in DLB patients in comparison with control subjects but not in patients with AD, and was correlated with severity of fluctuations (ρ = -0.69; p < 0.01). Causal flow analysis showed differences between patients with DLB and AD and control subjects.

Resting state networks and consciousness: alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness States

In order to better understand the functional contribution of resting state activity to conscious cognition, we aimed to review increases and decreases in functional magnetic resonance imaging (fMRI) functional connectivity under physiological (sleep), pharmacological (anesthesia), and pathological altered states of consciousness, such as brain death, coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. The reviewed resting state networks were the DMN, left and right executive control, salience, sensorimotor, auditory, and visual networks. We highlight some methodological issues concerning resting state analyses in severely injured brains mainly in terms of hypothesis-driven seed-based correlation analysis and data-driven independent components analysis approaches. Finally, we attempt to contextualize our discussion within theoretical frameworks of conscious processes. We think that this “lesion” approach allows us to better determine the necessary conditions under which normal conscious cognition takes place. At the clinical level, we acknowledge the technical merits of the resting state paradigm. Indeed, fast and easy acquisitions are preferable to activation paradigms in clinical populations. Finally, we emphasize the need to validate the diagnostic and prognostic value of fMRI resting state measurements in non-communicating brain damaged patients.

Modifications of default-mode network connectivity in patients with cerebral glioma

Purpose

The aim of the study was to evaluate connectivity modifications in the Default Mode Network (DMN) in patients with cerebral glioma, and to correlate these modifications to tumor characteristics.

Methods

Twenty-four patients with a left-hemisphere cerebral tumor (14 grade II and 10 grade IV gliomas) and 14 healthy age-matched right-hand volunteers were enrolled in the study. Subjects underwent fMRI while performing language tasks for presurgical mapping. Data was analyzed with independent component analysis in order to identify the DMN. DMN group maps were produced by random-effect analysis (p<0.001, FDR-corrected). An analysis of variance across the three groups (p<0.05) and post-hoc t-test contrasts between pairs of groups were calculated (p<0.05, FDR-corrected).

Results

All three groups showed typical DMN areas. However, reduced DMN connectivity was detected in tumor patients with respect to controls. A significantly increased and reduced integration of DMN areas was observed in the hippocampal and prefrontal regions, respectively. Modifications were closely related to tumor grading. Moreover, the DMN lateralized to the hemisphere contralateral to tumor in the low-grade, but not in the high-grade tumor patients.

Conclusion

Modifications of DMN connectivity were induced by gliomas and differed for high and low grade tumors.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering.

Auditory resting-state network connectivity in tinnitus: a functional MRI study

The underlying functional neuroanatomy of tinnitus remains poorly understood. Few studies have focused on functional cerebral connectivity changes in tinnitus patients. The aim of this study was to test if functional MRI “resting-state” connectivity patterns in auditory network differ between tinnitus patients and normal controls. Thirteen chronic tinnitus subjects and fifteen age-matched healthy controls were studied on a 3 tesla MRI. Connectivity was investigated using independent component analysis and an automated component selection approach taking into account the spatial and temporal properties of each component. Connectivity in extra-auditory regions such as brainstem, basal ganglia/NAc, cerebellum, parahippocampal, right prefrontal, parietal, and sensorimotor areas was found to be increased in tinnitus subjects. The right primary auditory cortex, left prefrontal, left fusiform gyrus, and bilateral occipital regions showed a decreased connectivity in tinnitus. These results show that there is a modification of cortical and subcortical functional connectivity in tinnitus encompassing attentional, mnemonic, and emotional networks. Our data corroborate the hypothesized implication of non-auditory regions in tinnitus physiopathology and suggest that various regions of the brain seem involved in the persistent awareness of the phenomenon as well as in the development of the associated distress leading to disabling chronic tinnitus.

The neurodynamic organization of modality-dependent hallucinations

The pathophysiology of hallucinations remains mysterious. This research aims to specifically explore the interaction between hallucinations and spontaneous resting-state activity. We used multimodal magnetic resonance imaging during hallucinations occurrence in 20 drug-free adolescents with a "brief psychotic disorder." They were furthermore compared with 20 matched controls at rest or during exteroceptive stimuli. Anatomical and functional symptom-mapping demonstrated reduced cortical thickness and increased blood oxygen level-dependent signal in modality-dependent association sensory cortices during auditory, visual, and multisensory hallucinations. On the contrary, primary-sensory-cortex recruitment was not systematic and was shown to be associated with increased vividness of the hallucinatory experiences. Spatiotemporal activity patterns in the default-mode network (DMN) during hallucinations and symptom-free periods in patients were compared with patterns measured in healthy individuals. A disengagement of the DMN was concomitant to hallucinations, as for exogenous stimulations in healthy participants. Specifically, spatial and temporal instabilities of the DMN correlated with the severity of hallucinations but persisted during symptom-free periods. These results suggest that hallucinatory experiences emerge from a spontaneous DMN withdrawal, providing a convincing model for hallucinations beyond the auditory modality.

Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection

Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge.

Optimizing preprocessing and analysis pipelines for single-subject fMRI: 2. Interactions with ICA, PCA, task contrast and inter-subject heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or "pipeline") may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets.

Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI

A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R(2)* and initial signal intensity (S(0)) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R(2)* and S(0) change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like "functional network" components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.

Prefrontal transcranial direct current stimulation changes connectivity of resting-state networks during fMRI

Transcranial direct current stimulation (tDCS) has been proposed for experimental and therapeutic modulation of regional brain function. Specifically, anodal tDCS of the dorsolateral prefrontal cortex (DLPFC) together with cathodal tDCS of the supraorbital region have been associated with improvement of cognition and mood, and have been suggested for the treatment of several neurological and psychiatric disorders. Although modeled mathematically, the distribution, direction, and extent of tDCS-mediated effects on brain physiology are not well understood. The current study investigates whether tDCS of the human prefrontal cortex modulates resting-state network (RSN) connectivity measured by functional magnetic resonance imaging (fMRI). Thirteen healthy subjects underwent real and sham tDCS in random order on separate days. tDCS was applied for 20 min at 2 mA with the anode positioned over the left DLPFC and the cathode over the right supraorbital region. Patterns of resting-state brain connectivity were assessed before and after tDCS with 3 T fMRI, and changes were analyzed for relevant networks related to the stimulation-electrode localizations. At baseline, four RSNs were detected, corresponding to the default mode network (DMN), the left and right frontal-parietal networks (FPNs) and the self-referential network. After real tDCS and compared with sham tDCS, significant changes of regional brain connectivity were found for the DMN and the FPNs both close to the primary stimulation site and in connected brain regions. These findings show that prefrontal tDCS modulates resting-state functional connectivity in distinct functional networks of the human brain.

Mapping hemodynamic correlates of seizures using fMRI: A review

Functional magnetic resonance imaging (fMRI) is able to detect changes in blood oxygenation level associated with neuronal activity throughout the brain. For more than a decade, fMRI alone or in combination with simultaneous EEG recording (EEG-fMRI) has been used to investigate the hemodynamic changes associated with interictal and ictal epileptic discharges. This is the first literature review to focus on the various fMRI acquisition and data analysis methods applied to map epileptic seizure-related hemodynamic changes from the first report of an fMRI scan of a seizure to the present day. Two types of data analysis approaches, based on temporal correlation and data driven, are explained and contrasted. The spatial and temporal relationship between the observed hemodynamic changes using fMRI and other non-invasive and invasive electrophysiological and imaging data is considered. We then describe the role of fMRI in localizing and exploring the networks involved in spontaneous and triggered seizure onset and propagation. We also discuss that fMRI alone and combined with EEG hold great promise in the investigation of seizure-related hemodynamic changes non-invasively in humans. We think that this will lead to significant improvements in our understanding of seizures with important consequences for the treatment of epilepsy.

Extracting functional networks with spatial independent component analysis: the role of dimensionality, reliability and aggregation scheme

PURPOSE OF REVIEW

Clinical studies have differentiated functional brain networks in neurological patient and control populations using independent component analysis (ICA) applied to functional MRI (fMRI). Principal component analysis (PCA) is used to reduce the data dimensionality to make this feasible. The role of this choice is reviewed in connection with the accuracy and the reliability of the ICA results and the schemes of data aggregation in population studies.

RECENT FINDINGS

It has been pointed out recently that it is important to critically explore the ICA model orders without relying on strictly predetermined PCA cutoffs for the number of components. We further illustrate this aspect empirically by showing that a large enough range of dimensions may exist where ICA components remain accurate but also that the minimum PCA dimension required to reliably extract the best ICA maps may vary substantially across subjects. Moreover, with the aid of a simple simulation, we show that reliable independent components can still be recovered beyond a theoretical PCA cutoff.

SUMMARY

The role of the PCA cutoff and its impact on the accuracy and reliability of the ICA results should be carefully considered in future clinical fMRI studies.

Automatic identification of functional clusters in FMRI data using spatial dependence

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.

Can resting-state functional MRI serve as a complement to task-based mapping of sensorimotor function? A test-retest reliability study in healthy volunteers

PURPOSE

To investigate if resting-state functional MRI (fMRI) reliably can serve as a complement to task-based fMRI for presurgical mapping of the sensorimotor cortex.

MATERIALS AND METHODS

Functional data were obtained in 10 healthy volunteers using a 3 Tesla MRI system. Each subject performed five bilateral finger tapping experiments interleaved with five resting-state experiments. Following preprocessing, data from eight volunteers were further analyzed with the general linear model (finger tapping data) and independent component analysis (rest data). Test-retest reliability estimates (hit rate and false alarm rate) for resting-state fMRI activation of the sensorimotor network were compared with the reliability estimates for task-evoked activation of the sensorimotor cortex. The reliability estimates constituted a receiver operating characteristics curve from which the area under the curve (AUC) was calculated. Statistical testing was performed to compare the two groups with respect to reliability.

RESULTS

The AUC was generally higher for the task experiments, although median AUC was not significantly different on a group level. Also, the two groups showed comparable levels of within-group variance.

CONCLUSION

Test-retest reliability was comparable between resting-state measurements and task-based fMRI, suggesting that presurgical mapping of functional networks can be a supplement to task-based fMRI in cases where patient status excludes task-based fMRI.

A feature-selective independent component analysis method for functional MRI

In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features in the sample space of the independent components during ICA estimation. The feature-selective scheme is achieved through a filtering operation in the source sample space followed by a projection onto the demixing vector space by a least squares projection in an iterative ICA process. We perform ICA estimation of artificial activations superimposed into a resting state fMRI dataset to show that the feature-selective scheme improves the detection of injected activation from the independent component estimated by ICA. We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.

EEGIFT: group independent component analysis for event-related EEG data

Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.

Behavioral interpretations of intrinsic connectivity networks

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD

This paper presents a paradigm for generating a quantifiable marker of pathology that supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such as autism spectrum disorder (ASD). This is achieved by creating high-dimensional nonlinear pattern classifiers using support vector machines (SVM), that learn the underlying pattern of pathology using numerous atlas-based regional features extracted from diffusion tensor imaging (DTI) data. These classifiers, in addition to providing insight into the group separation between patients and controls, are applicable on a single subject basis and have the potential to aid in diagnosis by assigning a probabilistic abnormality score to each subject that quantifies the degree of pathology and can be used in combination with other clinical scores to aid in diagnostic decision. They also produce a ranking of regions that contribute most to the group classification and separation, thereby providing a neurobiological insight into the pathology. As an illustrative application of the general framework for creating diffusion based abnormality classifiers we create classifiers for a dataset consisting of 45 children with ASD (mean age 10.5 ± 2.5 yr) as compared to 30 typically developing (TD) controls (mean age 10.3 ± 2.5 yr). Based on the abnormality scores, a distinction between the ASD population and TD controls was achieved with 80% leave one out (LOO) cross-validation accuracy with high significance of p<0.001, ~84% specificity and ~74% sensitivity. Regions that contributed to this abnormality score involved fractional anisotropy (FA) differences mainly in right occipital regions as well as in left superior longitudinal fasciculus, external and internal capsule while mean diffusivity (MD) discriminates were observed primarily in right occipital gyrus and right temporal white matter.

Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness

OBJECTIVES

Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma.

EXPERIMENTAL DESIGN

A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)].

PRINCIPAL OBSERVATIONS

All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin.

CONCLUSIONS

When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.

On the relationship between seed-based and ICA-based measures of functional connectivity

Brain functional connectivity (FC) refers to inter-regional synchrony of low frequency fluctuations in blood oxygenation level dependent functional magnetic resonance imaging. FC has been evaluated both during task performance and in the "resting" state, yielding reports of FC differences correlated with behavior and diagnosis. Two methodologies are widely used for evaluating FC from blood oxygenation level dependent functional magnetic resonance imaging data: Temporal correlation with a specified seed voxel or small region of interest; and spatial independent component analysis. While results from seed-based and independent component analysis methodologies are generally similar, they are conceptually different. This study is intended to elucidate and illustrate, qualitatively and quantitatively, the relationship between seed and independent component analysis derived measures of FC. Seed-based FC measures are shown to be the sum of independent component analysis-derived within network connectivities and between network connectivities. We present a simple simulation and an experiment on visuomotor activity that highlight this relationship between the two methods.

Comparison of hypothesis- and a novel hybrid data/hypothesis-driven method of functional MR imaging analysis in patients with brain gliomas

BACKGROUND AND PURPOSE

An alternative technique, which is less influenced by tumor- and patient-related factors, is required to overcome the limits of GLM analysis of fMRI data in patients. The aim of this study was to statistically assess differences in the identification of language regions and hemispheric lateralization of language function between controls and patients as estimated by both the GLM and a novel combined ICA-GLM procedure.

MATERIALS AND METHODS

We retrospectively evaluated 42 patients with pathologically confirmed brain gliomas of the left frontal and/or temporoparietal lobes and a control group of 14 age-matched healthy volunteers who underwent BOLD fMRI to lateralize language functions in the cerebral hemispheres. Data were processed by using a classic GLM and ICA-GLM.

RESULTS

ICA-GLM demonstrated a higher sensitivity in detecting language activation, specifically in the left TPJ of patients. There were no significant differences between the GLM and ICA-GLM in controls; however, statistically significant differences were observed by using ICA-GLM for the LI in patients. For the computation of the LI, ICA-GLM was less influenced by the chosen statistical threshold compared with the GLM.

CONCLUSIONS

We suggest the use of the ICA-GLM as a valid alternative to the classic GLM method for presurgical mapping in patients with brain tumors and to replicate the present results in a broader sample of patients.