Group independent component analysis of language fMRI from word generation tasks

Independent component analysis: a reliable alternative to general linear model for task-based fMRI

Background

Functional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM).

Methods

Sixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the performance of each technique.

Results

In the control group, ICA and GLM maps were similar without any superiority of either technique. In Group 1 and Group 2, ICA performed statistically better than GLM, with a p-value of < 0.01801 and < 0.0237, respectively. This indicated that ICA performs as well as GLM when the subjects are able to cooperate well (less movement, good task performance), but ICA could outperform GLM in the patient groups. When both techniques were combined, 240 out of 259 scans produced reliable results, showing that the sensitivity of task-based fMRI can be increased when both techniques are integrated with the clinical setup.

Conclusion

ICA may be slightly more advantageous, compared to GLM, in patients with brain lesions, across the range of pathologies included in our population and independent of symptoms chronicity. Our findings suggest that GLM analysis may be more susceptible to brain activity perturbations induced by a variety of lesions or scanner-induced artifacts due to motion or other factors. In our research, we demonstrated that ICA is able to provide fMRI results that can be used in surgery, taking into account patient and task-wise aspects that differ from those when fMRI is used in research.

Both activation and deactivation of functional networks support increased sentence processing costs

The research on the neural correlates underlying the language system has gradually moved away from the traditional Broca-Wernicke framework to a network perspective in the past 15 years. Language processing is found to be supported by the co-activation of both core and peripheral brain regions. However, the dynamic co-activation patterns of these brain regions serving different language functions remain to be fully revealed. The present functional magnetic resonance imaging (fMRI) study focused on sentence processing at different syntactic complexity levels to examine how the co-activation of different brain networks will be modulated by increased processing costs. Chinese relative clauses were used to probe the two dimensions of syntactic complexity: embeddedness (left-branching vs. center-embedded) and gap-filler dependency (subject-gap vs. object-gap) using the general linear model (GLM) approach, independent component analysis (ICA) and graph theoretical analysis. In contrast to localized activation revealed by the GLM approach, ICA identified more extensive networks both positively and negatively correlated with the task. We found that the posterior default mode network was anti-correlated to the gap-filler integration costs with increased deactivation for the left-branching object relative clauses compared to subject relative clauses, suggesting the involvement of this network in leveraging the cognitive resources based on the complexity level of the language task. Concurrent activation and deactivation of networks were found to be associated with the higher costs induced by center-embedding and its interaction with gap-filler integration. The graph theoretical analysis further unveiled that center-embeddedness imposed more attentional demand on the subject relative clause, as characterized by its higher degree and strength in the ventral attention network, and higher processing costs of syntactic reanalysis on the object relative clause, as characterized by increased intermodular connections of the language network with other networks. The results suggest that network activation and deactivation profiles are modulated by different dimensions of syntactic complexity to serve the higher demand of creating a coherent semantic representation.

Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging

Neurosurgery has been at the forefront of a paradigm shift from a localizationist perspective to a network-based approach to brain mapping. Over the last 2 decades, we have seen dramatic improvements in the way we can image the human brain and noninvasively estimate the location of critical functional networks. In certain patients with brain tumors and epilepsy, intraoperative electrical stimulation has revealed direct links between these networks and their function. The focus of these techniques has rightfully been identification and preservation of so-called "eloquent" brain functions (i.e., motor and language), but there is building momentum for more extensive mapping of cognitive and emotional networks. In addition, there is growing interest in mapping these functions in patients with a broad range of neurosurgical diseases. Resting-state functional MRI (rs-fMRI) is a noninvasive imaging modality that is able to measure spontaneous low-frequency blood oxygen level-dependent signal fluctuations at rest to infer neuronal activity. Rs-fMRI may be able to map cognitive and emotional networks for individual patients. In this review, the authors give an overview of the rs-fMRI technique and associated cognitive and emotional resting-state networks, discuss the potential applications of rs-fMRI, and propose future directions for the mapping of cognition and emotion in neurosurgical patients.

Functional Connectivity in Adult Brain Tumor Patients: A Systematic Review

Brain tumor (BT) patients often experience reduced cognitive abilities and disrupted adaptive functioning before and after treatment. An innovative approach to understanding the underlying brain networks associated with these outcomes has been to study the brain's functional connectivity (FC), the spatially distributed and temporally correlated activity throughout the brain, and how it can be affected by a tumor. The present review synthesized the extant BT FC literature that utilizes functional magnetic resonance imaging to study FC strength of commonly observed networks during rest and task. A systematic review of English articles using PubMed was conducted. Search terms included brain tumor OR glioma AND functional connectivity, independent component analysis, ICA, psychophysiological interaction, OR PPI. Studies in which participants were diagnosed with BTs as adults that evaluated specific networks of interest using independent component analysis or seed-based component analysis were included. Twenty-five studies met inclusion criteria. BT patients often presented with decreases in FC strength within well-established networks and increases in atypical FC patterns. Network differences were tumor adjacent and distal, and left hemisphere tumors generally had a greater impact on FC. FC alterations often correlated with behavioral or cognitive outcomes when assessed. Overall, BTs appear to lead to various alterations in FC across different functional networks, and the most common change is a decrease in expected FC strength. More longitudinal studies are needed to determine the time course of network alterations across treatment and recovery, the role of medical treatments in BT survivors' FC, and the potential of FC patterns as biomarkers of cognitive outcomes.

Functional MRI Task Comparison for Language Mapping in Neurosurgical Patients

BACKGROUND AND PURPOSE

Language task-based functional MRI (fMRI) is increasingly used for presurgical planning in patients with brain lesions. Different paradigms elicit activations of different components of the language network. The aim of this study is to optimize fMRI clinical usage by comparing the effectiveness of three language tasks for language lateralization and localization in a large group of patients with brain lesions.

METHODS

We analyzed fMRI data from a sequential retrospective cohort of 51 patients with brain lesions who underwent presurgical fMRI language mapping. We compared the effectiveness of three language tasks (Antonym Generation, Sentence Completion (SC), and Auditory Naming) for lateralizing language function and for activating cortex within patient-specific regions-of-interest representing eloquent language areas, and assessed the degree of spatial overlap of the areas of activation elicited by each task.

RESULTS

The tasks were similarly effective for lateralizing language within the anterior language areas. The SC task produced higher laterality indices within the posterior language areas and had a significantly higher agreement with the clinical report. Dice coefficients between the task pairs were in the range of .351-.458, confirming substantial variation in the components of the language network activated by each task.

CONCLUSIONS

SC task consistently produced large activations within the dominant hemisphere and was more effective for lateralizing language within the posterior language areas. The low degree of spatial overlap among the tasks strongly supports the practice of using a battery of tasks to help the surgeon to avoid eloquent language areas.

State-Dependent Effects of Ventromedial Prefrontal Cortex Continuous Thetaburst Stimulation on Cocaine Cue Reactivity in Chronic Cocaine Users

Cue-induced craving is a significant barrier to obtaining abstinence from cocaine. Neuroimaging research has shown that cocaine cue exposure evokes elevated activity in a network of frontal-striatal brain regions involved in drug craving and drug seeking. Prior research from our laboratory has demonstrated that when targeted at the medial prefrontal cortex (mPFC), continuous theta burst stimulation (cTBS), an inhibitory form of non-invasive brain stimulation, can decrease drug cue-related activity in the striatum in cocaine users and alcohol users. However, it is known that there are individual differences in response to repetitive transcranial magnetic stimulation (rTMS), with some individuals being responders and others non-responders. There is some evidence that state-dependent effects influence response to rTMS, with baseline neural state predicting rTMS treatment outcomes. In this single-blind, active sham-controlled crossover study, we assess the striatum as a biomarker of treatment response by determining if baseline drug cue reactivity in the striatum influences striatal response to mPFC cTBS. The brain response to cocaine cues was measured in 19 cocaine-dependent individuals immediately before and after real and sham cTBS (110% resting motor threshold, 3600 total pulses). Group independent component analysis (ICA) revealed a prominent striatum network comprised of bilateral caudate, putamen, and nucleus accumbens, which was modulated by the cocaine cue reactivity task. Baseline drug cue reactivity in this striatal network was inversely related to change in striatum reactivity after real (vs. sham) cTBS treatment (ρ = -.79; p < .001; R ² _Adj = .58). Specifically, individuals with a high striatal response to cocaine cues at baseline had significantly attenuated striatal activity after real but not sham cTBS (t ₉ = -3.76; p ≤ .005). These data demonstrate that the effects of mPFC cTBS on the neural circuitry of craving are not uniform and may depend on an individual's baseline frontal-striatal reactivity to cues. This underscores the importance of assessing individual variability as we develop brain stimulation treatments for addiction.

Intrinsic network connectivity and own body perception in gender dysphoria

Gender dysphoria (GD) is characterized by incongruence between one's identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception -default mode network (DMN) and salience network - and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher "self" ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.

Not single brain areas but a network is involved in language: Applications in presurgical planning

OBJECTIVES

Language is an important human function, and is a determinant of the quality of life. In conditions such as brain lesions, disruption of the language function may occur, and lesion resection is a solution for that. Presurgical planning to determine the language-related brain areas would enhance the chances of language preservation after the operation; however, availability of a normative language template is essential.

PATIENTS AND METHODS

In this study, using data from 60 young individuals who were meticulously checked for mental and physical health, and using fMRI and robust imaging and data analysis methods, functional brain maps for the language production, perception and semantic were produced.

RESULTS

The obtained templates showed that the language function should be considered as the product of the collaboration of a network of brain regions, instead of considering only few brain areas to be involved in that.

CONCLUSION

This study has important clinical applications, and extends our knowledge on the neuroanatomy of the language function.

Challenges and techniques for presurgical brain mapping with functional MRI

Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.

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fMRI is increasingly used for presurgical brain mapping for surgical planning.

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Understanding of the limitations of fMRI is critical for its clinical use.

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Clinical fMRI's challenges and potential solutions are discussed.

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Intraoperative use and validation of fMRI are discussed.

Lexical-Semantic Search Under Different Covert Verbal Fluency Tasks: An fMRI Study

Background: Verbal fluency is a measure of cognitive flexibility and word search strategies that is widely used to characterize impaired cognitive function. Despite the wealth of research on identifying and characterizing distinct aspects of verbal fluency, the anatomic and functional substrates of retrieval-related search and post-retrieval control processes still have not been fully elucidated. Methods: Twenty-one native English-speaking, healthy, right-handed, adult volunteers (mean age = 31 years; range = 21-45 years; 9 F) took part in a block-design functional Magnetic Resonance Imaging (fMRI) study of free recall, covert word generation tasks when guided by phonemic (P), semantic-category (C), and context-based fill-in-the-blank sentence completion (S) cues. General linear model (GLM), Independent Component Analysis (ICA), and psychophysiological interaction (PPI) were used to further characterize the neural substrate of verbal fluency as a function of retrieval cue type. Results: Common localized activations across P, C, and S tasks occurred in the bilateral superior and left inferior frontal gyrus, left anterior cingulate cortex, bilateral supplementary motor area (SMA), and left insula. Differential task activations were centered in the occipital, temporal and parietal regions as well as the thalamus and cerebellum. The context-based fluency task, i.e., the S task, elicited higher differential brain activity in a lateralized frontal-temporal network typically engaged in complex language processing. P and C tasks elicited activation in limited pathways mainly within the left frontal regions. ICA and PPI results of the S task suggested that brain regions distributed across both hemispheres, extending beyond classical language areas, are recruited for lexical-semantic access and retrieval during sentence completion. Conclusion: Study results support the hypothesis of overlapping, as well as distinct, neural networks for covert word generation when guided by different linguistic cues. The increased demand on word retrieval is met by the concurrent recruitment of classical as well as non-classical language-related brain regions forming a large cognitive neural network. The retrieval-related search and post-retrieval control processes that subserve verbal fluency, therefore, reverberates across distinct functional networks as determined by respective task demands.

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.

Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way.

Neural Processes in the Human Temporoparietal Cortex Separated by Localized Independent Component Analysis

The human temporoparietal junction (TPJ) is a topic of intense research. Imaging studies have identified TPJ activation in association with many higher-order functions such as theory-of-mind, episodic memory, and attention, causing debate about the distribution of different processes. One major challenge is the lack of consensus about the anatomical location and extent of the TPJ. Here, we address this problem using data-driven analysis to test the hypothesis that the bilateral TPJ can be parcellated into subregions. We applied independent component analysis (ICA) to task-free fMRI data within a local region around the bilateral TPJ, iterating the ICA at multiple model orders and in several datasets. The localized analysis allowed finer separation of processes and the use of multiple dimensionalities provided qualitative information about lateralization. We identified four subdivisions that were bilaterally symmetrical and one that was right biased. To test whether the independent components (ICs) reflected true subdivisions, we performed functional connectivity analysis using the IC coordinates as seeds. This confirmed that the subdivisions belonged to distinct networks. The right-biased IC was connected with a network often associated with attentional processing. One bilateral subdivision was connected to sensorimotor regions and another was connected to auditory regions. One subdivision that presented as distinct left- and right-biased ICs was connected to frontoparietal regions. Another subdivision that also had left- and right-biased ICs was connected to social or default mode networks. Our results show that the TPJ in both hemispheres hosts multiple neural processes with connectivity patterns consistent with well developed specialization and lateralization.

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.

Independent component analysis of functional networks for response inhibition: Inter-subject variation in stop signal reaction time

Cognitive control is a critical executive function. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined regional responses to stop signal inhibition. In contrast to this parameterized approach, independent component analysis (ICA) elucidates brain networks subserving cognitive control. In our earlier work of 59 adults performing the SST during fMRI, we characterized six independent components (ICs). However, none of these ICs correlated with stop signal performance, raising questions about their behavioral validity. Here, in a larger sample (n = 100), we identified and explored 23 ICs for correlation with the stop signal reaction time (SSRT), a measure of the efficiency of response inhibition. At a corrected threshold (P < 0.0005), a paracentral lobule-midcingulate network and a left inferior parietal-supplementary motor-somatomotor network showed a positive correlation between SE beta weight and SSRT. In contrast, a midline cerebellum-thalamus-pallidum network showed a negative correlation between SE beta weight and SSRT. These findings suggest that motor preparation and execution prolongs the SSRT, likely via an interaction between the go and stop processes as suggested by the race model. Behaviorally, consistent with this hypothesis, the difference in G and SE reaction times is positively correlated with SSRT across subjects. These new results highlight the importance of cognitive motor regions in response inhibition and support the utility of ICA in uncovering functional networks for cognitive control in the SST.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance.

Attention shifts the language network reflecting paradigm presentation

OBJECTIVES

Functional magnetic resonance imaging (fMRI) is a reliable and non-invasive method with which to localize language function in pre-surgical planning. In clinical practice, visual stimulus presentation is often difficult or impossible, due to the patient's restricted language or attention abilities. Therefore, our aim was to investigate modality-specific differences in visual and auditory stimulus presentation.

METHODS

Ten healthy subjects participated in an fMRI study comprising two experiments with visual and auditory stimulus presentation. In both experiments, two language paradigms (one for language comprehension and one for language production) used in clinical practice were investigated. In addition to standard data analysis by the means of the general linear model (GLM), independent component analysis (ICA) was performed to achieve more detailed information on language processing networks.

RESULTS

GLM analysis revealed modality-specific brain activation for both language paradigms for the contrast visual > auditory in the area of the intraparietal sulcus and the hippocampus, two areas related to attention and working memory. Using group ICA, a language network was detected for both paradigms independent of stimulus presentation modality. The investigation of language lateralization revealed no significant variations. Visually presented stimuli further activated an attention-shift network, which could not be identified for the auditory presented language.

CONCLUSION

The results of this study indicate that the visually presented language stimuli additionally activate an attention-shift network. These findings will provide important information for pre-surgical planning in order to preserve reading abilities after brain surgery, significantly improving surgical outcomes. Our findings suggest that the presentation modality for language paradigms should be adapted on behalf of individual indication.

A comprehensive neuropsychological mapping battery for functional magnetic resonance imaging

Existing batteries for FMRI do not precisely meet the criteria for comprehensive mapping of cognitive functions within minimum data acquisition times using standard scanners and head coils. The goal was to develop a battery of neuropsychological paradigms for FMRI that can also be used in other brain imaging techniques and behavioural research. Participants were 61 healthy, young adult volunteers (48 females and 13 males, mean age: 22.25 ± 3.39 years) from the university community. The battery included 8 paradigms for basic (visual, auditory, sensory-motor, emotional arousal) and complex (language, working memory, inhibition/interference control, learning) cognitive functions. Imaging was performed using standard functional imaging capabilities (1.5-T MR scanner, standard head coil). Structural and functional data series were analysed using Brain Voyager QX2.9 and Statistical Parametric Mapping-8. For basic processes, activation centres for individuals were within a distance of 3-11 mm of the group centres of the target regions and for complex cognitive processes, between 7 mm and 15 mm. Based on fixed-effect and random-effects analyses, the distance between the activation centres was 0-4 mm. There was spatial variability between individual cases; however, as shown by the distances between the centres found with fixed-effect and random-effects analyses, the coordinates for individual cases can be used to represent those of the group. The findings show that the neuropsychological brain mapping battery described here can be used in basic science studies that investigate the relationship of the brain to the mind and also as functional localiser in clinical studies for diagnosis, follow-up and pre-surgical mapping.

Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling

The study of human olfaction is complicated by the myriad of processing demands in conscious perceptual and emotional experiences of odors. Combining functional magnetic resonance imaging with convergent multivariate network analyses, we examined the spatiotemporal behavior of olfactory-generated blood-oxygenated-level-dependent signal in healthy adults. The experimental functional magnetic resonance imaging (fMRI) paradigm was found to offset the limitations of olfactory habituation effects and permitted the identification of five functional networks. Analysis delineated separable neuronal circuits that were spatially centered in the primary olfactory cortex, striatum, dorsolateral prefrontal cortex, rostral prefrontal cortex/anterior cingulate, and parietal-occipital junction. We hypothesize that these functional networks subserve primary perceptual, affective/motivational, and higher order olfactory-related cognitive processes. Results provided direct evidence for the existence of parallel networks with top-down modulation for olfactory processing and clearly distinguished brain activations that were sniffing-related versus odor-related. A comprehensive neurocognitive model for olfaction is presented that may be applied to broader translational studies of olfactory function, aging, and neurological disease.

Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA

Animal studies indicate that different functional networks (FNs), each with a unique timecourse, may overlap at common brain regions. For understanding how different FNs overlap in the human brain and how the timecourses of overlapping FNs are modulated by cognitive tasks, we applied spatial independent component analysis (sICA) to functional magnetic resonance imaging (fMRI) data. These data were acquired from healthy participants while they performed a visual task with parametric loads of attention and working memory. sICA identified a total of 14 FNs, and they showed different extents of overlap at a majority of brain regions exhibiting any functional activity. More FNs overlapped at the higher-order association cortex including the anterior and posterior cingulate, precuneus, insula, and lateral and medial frontoparietal cortices (FPCs) than at the primary sensorimotor cortex. Furthermore, overlapping FNs exhibited concurrent but different task-related modulations of timecourses. FNs showing task-related up- vs. down-modulation of timecourses overlapped at both the lateral and medial FPCs and subcortical structures including the thalamus, striatum, and midbrain ventral tegmental area (VTA). Such task-related, concurrent, but opposite changes in timecourses in the same brain regions may not be detected by current analyses based on General-Linear-Model (GLM). The present findings indicate that multiple cognitive processes may associate with common brain regions and exhibit simultaneous but different modulations in timecourses during cognitive tasks.

The influence of stimulus detection on activation patterns during auditory hallucinations

INTRODUCTION

Neuroimaging studies investigating auditory verbal hallucinations (AVH) have revealed involvement of several cortical structures. These findings may however be biased by brain activity related to stimulus detection and motor processes associated with the task to indicate the presence of AVH. Disentangling brain activation specifically related to AVH and to additional cognitive processes may help focus on the true neuronal substrates of AVH and strengthen the development of new focal treatment strategies.

METHODS

Brain activation during AVH as indicated by button press was compared to brain activation during auditory stimulus detection indicated by button press. We performed two neuroimaging meta-analyses, assessing 10 AVH and 11 auditory stimulus detection studies. A random-effects activation likelihood estimation was performed using GingerALE to assess commonalities and differences across AVH and stimulus detection studies.

RESULTS

Activity in the claustrum, pulvinar area, medial geniculum body, pyramis, culmen, putamen, insula, and parahippocampal, medial frontal, precentral, postcentral, superior temporal and right inferior frontal gyri was found to be specifically related to AVH. The pars opercularis of the left inferior frontal gyrus and the left transverse temporal gyrus were activated to a similar extent during AVH and auditory stimulus detection.

DISCUSSION

Development of new focal treatment strategies for AVH may focus on the areas uniquely activated in the AVH analysis. The pars opercularis and the transverse temporal gyrus may not be directly involved in the experience of AVH itself, but rather in auditory stimulus detection.

Group independent component analysis and functional MRI examination of changes in language areas associated with brain tumors at different locations

Object

This study investigates the effect of tumor location on alterations of language network by brain tumors at different locations using blood oxygenation level dependent (BOLD) fMRI and group independent component analysis (ICA).

Subjects and Methods

BOLD fMRI data were obtained from 43 right handed brain tumor patients. Presurgical mapping of language areas was performed on all 43 patients with a picture naming task. All data were retrospectively analyzed using group ICA. Patents were divided into three groups based on tumor locations, i.e., left frontal region, left temporal region or right hemisphere. Laterality index (LI) was used to assess language lateralization in each group.

Results

The results from BOLD fMRI and ICA revealed the different language activation patterns in patients with brain tumors located in different brain regions. Language areas, such as Broca’s and Wernicke’s areas, were intact in patients with tumors in the right hemisphere. Significant functional changes were observed in patients with tumor in the left frontal and temporal areas. More specifically, the tumors in the left frontal region affect both Broca’s and Wernicke’s areas, while tumors in the left temporal lobe affect mainly Wernicke’s area. The compensated activation increase was observed in the right frontal areas in patients with left hemisphere tumors.

Conclusion

Group ICA provides a model free alternative approach for mapping functional networks in brain tumor patients. Altered language activation by different tumor locations suggested reorganization of language functions in brain tumor patients and may help better understanding of the language plasticity.

Defining language networks from resting-state fMRI for surgical planning--a feasibility study

Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision-making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood-oxygen-level dependent (BOLD) signal change while patients perform carefully timed language vs. control tasks. This task-based fMRI critically depends on task performance, excluding many patients who have difficulty performing language tasks due to neurologic deficits. On the basis of recent discovery of resting-state fMRI (rs-fMRI), we propose a "task-free" paradigm acquiring fMRI data when patients simply are at rest. This paradigm is less demanding for patients to perform and easier for technologists to administer. We investigated the feasibility of this approach in right-handed healthy control subjects. First, group independent component analysis (ICA) was applied on the training group (14 subjects) to identify group level language components based on expert rating results. Then, four empirically and structurally defined language network templates were assessed for their ability to identify language components from individuals' ICA output of the testing group (18 subjects) based on spatial similarity analysis. Results suggest that it is feasible to extract language activations from rs-fMRI at the individual subject level, and two empirically defined templates (that focuses on frontal language areas and that incorporates both frontal and temporal language areas) demonstrated the best performance. We propose a semi-automated language component identification procedure and discuss the practical concerns and suggestions for this approach to be used in clinical fMRI language mapping.

Reduced connectivity in the self-processing network of schizophrenia patients with poor insight

Lack of insight (unawareness of illness) is a common and clinically relevant feature of schizophrenia. Reduced levels of self-referential processing have been proposed as a mechanism underlying poor insight. The default mode network (DMN) has been implicated as a key node in the circuit for self-referential processing. We hypothesized that during resting state the DMN network would show decreased connectivity in schizophrenia patients with poor insight compared to patients with good insight. Patients with schizophrenia were recruited from mental health care centers in the north of the Netherlands and categorized in groups having good insight (n= 25) or poor insight (n = 19). All subjects underwent a resting state fMRI scan. A healthy control group (n = 30) was used as a reference. Functional connectivity of the anterior and posterior part of the DMN, identified using Independent Component Analysis, was compared between groups. Patients with poor insight showed lower connectivity of the ACC within the anterior DMN component and precuneus within the posterior DMN component compared to patients with good insight. Connectivity between the anterior and posterior part of the DMN was lower in patients than controls, and qualitatively different between the good and poor insight patient groups. As predicted, subjects with poor insight in psychosis showed decreased connectivity in DMN regions implicated in self-referential processing, although this concerned only part of the network. This finding is compatible with theories implying a role of reduced self-referential processing as a mechanism contributing to poor insight.

SINGLE-TRIAL BASED INDEPENDENT COMPONENT ANALYSIS ON MISMATCH NEGATIVITY IN CHILDREN

Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential — mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.

Special surgical considerations for functional brain mapping

The development of functional mapping techniques gives neurosurgeons many options for preoperative planning. Integrating functional and anatomic data can inform patient selection and surgical planning and makes functional mapping more accessible than when only invasive studies were available. However, the applications of functional mapping to neurosurgical patients are still evolving. Functional imaging remains complex and requires an understanding of the underlying physiologic and imaging characteristics. Neurosurgeons must be accustomed to interpreting highly processed data. Successful implementation of functional image-guided procedures requires efficient interactions between neurosurgeon, neurologist, radiologist, neuropsychologist, and others, but promises to enhance the care of patients.

Abnormal functional lateralization and activity of language brain areas in typical specific language impairment (developmental dysphasia)

Atypical functional lateralization and specialization for language have been proposed to account for developmental language disorders, yet results from functional neuroimaging studies are sparse and inconsistent. This functional magnetic resonance imaging study compared children with a specific subtype of specific language impairment affecting structural language (n = 21), to a matched group of typically developing children using a panel of four language tasks neither requiring reading nor metalinguistic skills, including two auditory lexico-semantic tasks (category fluency and responsive naming) and two visual phonological tasks based on picture naming. Data processing involved normalizing the data with respect to a matched pairs paediatric template, groups and between-groups analysis, and laterality indices assessment within regions of interest using single and combined task analysis. Children with specific language impairment exhibited a significant lack of left lateralization in all core language regions (inferior frontal gyrus-opercularis, inferior frontal gyrus-triangularis, supramarginal gyrus and superior temporal gyrus), across single or combined task analysis, but no difference of lateralization for the rest of the brain. Between-group comparisons revealed a left hypoactivation of Wernicke's area at the posterior superior temporal/supramarginal junction during the responsive naming task, and a right hyperactivation encompassing the anterior insula with adjacent inferior frontal gyrus and the head of the caudate nucleus during the first phonological task. This study thus provides evidence that this subtype of specific language impairment is associated with atypical lateralization and functioning of core language areas.

Answering six questions in extracting children's mismatch negativity through combining wavelet decomposition and independent component analysis

This study combines wavelet decomposition and independent component analysis (ICA) to extract mismatch negativity (MMN) from electroencephalography (EEG) recordings. As MMN is a small event-related potential (ERP), a systematic ICA based approach is designed, exploiting MMN's temporal, frequency and spatial information. Moreover, this study answers which type of EEG recordings is more appropriate for ICA to extract MMN, what kind of the preprocessing is beneficial for ICA decomposition, which algorithm of ICA can be chosen to decompose EEG recordings under the selected type, how to determine the desired independent component extracted by ICA, how to improve the accuracy of the back projection of the selected independent component in the electrode field, and what can be finally obtained with the application of ICA. Results showed that the proposed method extracted MMN with better properties than those estimated by difference wave only using temporal information or ICA only using spatial information. The better properties mean that the deviant with larger magnitude of deviance to repeated stimuli in the oddball paradigm can elicit MMN with larger peak amplitude and shorter latency. As other ERPs also have the similar information exploited here, the proposed method can be used to study other ERPs.

Semantic association investigated with functional MRI and independent component analysis

Semantic association, an essential element of human language, enables discourse and inference. Neuroimaging studies have revealed localization and lateralization of semantic circuitry, making substantial contributions to cognitive neuroscience. However, because of methodological limitations, these investigations have only identified individual functional components rather than capturing the behavior of the entire network. To overcome these limitations, we have implemented group independent component analysis (ICA) to investigate the cognitive modules used by healthy adults performing the fMRI semantic decision task. When compared with the results of a standard general linear modeling (GLM) analysis, ICA detected several additional brain regions subserving semantic decision. Eight task-related group ICA maps were identified, including left inferior frontal gyrus (BA44/45), middle posterior temporal gyrus (BA39/22), angular gyrus/inferior parietal lobule (BA39/40), posterior cingulate (BA30), bilateral lingual gyrus (BA18/23), inferior frontal gyrus (L>R, BA47), hippocampus with parahippocampal gyrus (L>R, BA35/36), and anterior cingulate (BA32/24). Although most of the components were represented bilaterally, we found a single, highly left-lateralized component that included the inferior frontal gyrus and the medial and superior temporal gyri, the angular and supramarginal gyri, and the inferior parietal cortex. The presence of these spatially independent ICA components implies functional connectivity and can be equated with their modularity. These results are analyzed and presented in the framework of a biologically plausible theoretical model in preparation for similar analyses in patients with right- or left-hemispheric epilepsies.

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.

FMRI language mapping in children: a panel of language tasks using visual and auditory stimulation without reading or metalinguistic requirements

In the context of presurgical mapping or investigation of neurological and developmental disorders in children, language fMRI raises the issue of the design of a tasks panel achievable by young disordered children. Most language tasks shown to be efficient with healthy children require metalinguistic or reading abilities, therefore adding attentional, cognitive and academic constraints that may be problematic in this context. This study experimented a panel of four language tasks that did not require high attentional skills, reading, or metalinguistic abilities. Two reference tasks involving auditory stimulation (words generation from category, "category"; auditory responsive naming, "definition") were compared with two new tasks involving visual stimulation. These later were designed to tap spontaneous phonological production, in which the names of pictures to be named involve a phonological difference (e.g. in French poule/boule/moule; "phon-diff") or change of segmentation (e.g. in French car/car-te/car-t-on; "phon-seg"). Eighteen healthy children participated (mean age: 12.7+/-3 years). Data processing involved normalizing the data via a matched pairs pediatric template, and inter-task and region of interest analyses with laterality assessment. The reference tasks predominantly activated the left frontal and temporal core language regions, respectively. The new tasks activated these two regions simultaneously, more strongly for the phon-seg task. The union and intersection of all tasks provided more sensitive or specific maps. The study demonstrates that both reference and new tasks highlight core language regions in children, and that the latter are useful for the mapping of spontaneous phonological processing. The use of several different tasks may improve the sensitivity and specificity of fMRI.