Model-free fMRI group analysis using FENICA

Separating vascular and neuronal effects of age on fMRI BOLD signals

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

A Meta-analysis on the neural basis of planning: Activation likelihood estimation of functional brain imaging results in the Tower of London task

The ability to mentally design and evaluate series of future actions has often been studied in terms of planning abilities, commonly using well-structured laboratory tasks like the Tower of London (ToL). Despite a wealth of studies, findings on the specific localization of planning processes within prefrontal cortex (PFC) and on the hemispheric lateralization are equivocal. Here, we address this issue by integrating evidence from two different sources of data: First, we provide a systematic overview of the existing lesion data on planning in the ToL (10 studies, 211 patients) which does not indicate any evidence for a general lateralization of planning processes in (pre)frontal cortex. Second, we report a quantitative meta-analysis with activation likelihood estimation based on 31 functional neuroimaging datasets on the ToL. Separate meta-analyses of the activation patterns reported for Overall Planning (537 participants) and for Planning Complexity (182 participants) congruently show bilateral contributions of mid-dorsolateral PFC, frontal eye fields, supplementary motor area, precuneus, caudate, anterior insula, and inferior parietal cortex in addition to a left-lateralized involvement of rostrolateral PFC. In contrast to previous attributions of planning-related brain activity to the entire dorsolateral prefrontal cortex (dlPFC) and either its left or right homolog derived from single studies on the ToL, the present meta-analyses stress the pivotal role specifically of the mid-dorsolateral part of PFC (mid-dlPFC), presumably corresponding to Brodmann Areas 46 and 9/46, and strongly argue for a bilateral rather than lateralized involvement of the dlPFC in planning in the ToL. Hum Brain Mapp 38:396-413, 2017. © 2016 Wiley Periodicals, Inc.

On characterizing population commonalities and subject variations in brain networks

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs.

Olfactory training induces changes in regional functional connectivity in patients with long-term smell loss

Recently, olfactory training has been introduced as a promising treatment for patients with olfactory dysfunction. However, less is known about the neuronal basis and the influence on functional networks of this training. Thus, we aimed to investigate the neuroplasticity of chemosensory perception through an olfactory training program in patients with smell loss. The experimental setup included functional MRI (fMRI) experiments with three different types of chemosensory stimuli. Ten anosmic patients (7f, 3m) and 14 healthy controls (7f, 7m) underwent the same testing sessions. After a 12-week olfactory training period, seven patients (4f, 3m) were invited for follow-up testing using the same fMRI protocol. Functional networks were identified using independent component analysis and were further examined in detail using functional connectivity analysis. We found that anosmic patients and healthy controls initially use the same three networks to process chemosensory input: the olfactory; the somatosensory; and the integrative network. Those networks did not differ between the two groups in their spatial extent, but in their functional connectivity. After the olfactory training, the sensitivity to detect odors significantly increased in the anosmic group, which was also manifested in modifications of functional connections in all three investigated networks. The results of this study indicate that an olfactory training program can reorganize functional networks, although, initially, no differences in the spatial distribution of neural activation were observed.

Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data

Task-based fMRI activation mapping has been widely used in clinical neuroscience in order to assess different functional activity patterns in conditions such as prenatal alcohol exposure (PAE) affected brains and healthy controls. In this paper, we propose a novel, alternative approach of group-wise sparse representation of the fMRI data of multiple groups of subjects (healthy control, exposed non-dysmorphic PAE and exposed dysmorphic PAE) and assess the systematic functional activity differences among these three populations. Specifically, a common time series signal dictionary is learned from the aggregated fMRI signals of all three groups of subjects, and then the weight coefficient matrices (named statistical coefficient map (SCM)) associated with each common dictionary were statistically assessed for each group separately. Through inter-group comparisons based on the correspondence established by the common dictionary, our experimental results have demonstrated that the group-wise sparse coding strategy and the SCM can effectively reveal a collection of brain networks/regions that were affected by different levels of severity of PAE.

A Window into the Brain: Advances in Psychiatric fMRI

Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research. It provides a means to assay differences in brain systems that underlie psychiatric illness, treatment response, and properties of brain structure and function that convey risk factor for mental diseases. Here we review recent advances in fMRI methods in general use and progress made in understanding the neural basis of mental illness. Drawing on concepts and findings from psychiatric fMRI, we propose that mental illness may not be associated with abnormalities in specific local regions but rather corresponds to variation in the overall organization of functional communication throughout the brain network. Future research may need to integrate neuroimaging information drawn from different analysis methods and delineate spatial and temporal patterns of brain responses that are specific to certain types of psychiatric disorders.

Same same but different. Different trigeminal chemoreceptors share the same central pathway

Intranasal trigeminal sensations are important in everyday life of human beings, as they play a governing role in protecting the airways from harm. Trigeminal sensations arise from the binding of a ligand to various sub-types of transient receptor potential (TRP) channels located on mucosal branches of the trigeminal nerve. Which underlying neural networks are involved in the processing of various trigeminal inputs is still unknown. To target this unresolved question fourteen healthy human subjects were investigated by completing three functional magnetic resonance imaging (fMRI) scanning sessions during which three trigeminal substances, activating varying sub-types of chemoreceptors and evoking different sensations in the nose were presented: CO2, menthol and cinnamaldehyde. We identified similar functional networks responding to all stimuli: an olfactory network, a somatosensory network and an integrative network. The processing pathway of all three stimulants was represented by the same functional networks, although CO2 evokes painful but virtually odorless sensations, and the two other stimulants, menthol and cinnamaldehyde are perceived as mostly non painful with a clear olfactory percept. Therefore, our results suggest a common central processing pathway for trigeminal information regardless of the trigeminal chemoreceptor and sensation type.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain's traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations.

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.

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.

Multivoxel pattern analysis for FMRI data: a review

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.

Dual processing streams in chemosensory perception

Higher order sensory processing follows a general subdivision into a ventral and a dorsal stream for visual, auditory, and tactile information. Object identification is processed in temporal structures (ventral stream), whereas object localization leads to activation of parietal structures (dorsal stream). To examine whether the chemical senses demonstrate a similar dissociation, we investigated odor identification and odor localization in 16 healthy young subjects using functional MRI. We used two odors—(1) eucalyptol; (2) a mixture of phenylethanol and carbon dioxide)—which were delivered to only one nostril. During odor identification subjects had to recognize the odor; during odor localization they had to detect the stimulated nostril. We used general linear model (GLM) as a classical method as well as independent component analysis (ICA) in order to investigate a possible neuroanatomical dissociation between both tasks. Both methods showed differences between tasks—confirming a dual processing stream in the chemical senses—but revealed complementary results. Specifically, GLM identified the left intraparietal sulcus and the right superior frontal sulcus to be more activated when subjects were localizing the odorants. For the same task, ICA identified a significant cluster in the left parietal lobe (paracentral lobule) but also in the right hippocampus. While GLM did not find significant activations for odor identification, ICA revealed two clusters (in the left central fissure and the left superior frontal gyrus) for this task. These data demonstrate that higher order chemosensory processing shares the general subdivision into a ventral and a dorsal processing stream with other sensory systems and suggest that this is a global principle, independent of sensory channels.

An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.

A fast-FENICA method on resting state fMRI data

For resting-state fMRI data, independent component analysis (ICA) is an excellent method which enables the decomposition of high-dimensional data into discrete spatial and temporal components. Fully exploratory network ICA (FENICA), a fully automated and purely data-driven ICA-based analysis for group assessment of resting-state networks, was proposed by Schöpf et al. (2010). FENICA is a novel and effective group assessment method, but it is not without limitations, such as those related to memory and time costs in running. Here we present Fast-FENICA, which is based on an energy sifting algorithm for interested networks, a linear candidate networks formation strategy and a correlation coefficients ranking algorithm of network matrix. It is demonstrated that the energy sifting algorithm for interested networks and linear candidate networks formation strategy can transform the stubborn computing time and memory cost limitations of FENICA from a quadratic level to a linear level and thus speed up the group evaluation. Furthermore, the correlation coefficients ranking algorithm can further increase the calculation speed and float up the consistent networks effectively. In comparison to FENICA, the hybrid data and true data experimental results demonstrate that Fast-FENICA not only contributes to the practicability and efficiency without decreasing the detecting ability of functional networks, but also ranks the common functional networks based on the whole spatial consistency at a group level. This proposed effective group analysis method is expected to have wide applicability.

Differential modulation of the default mode network via serotonin-1A receptors

Reflecting one's mental self is a fundamental process for evaluating the personal relevance of life events and for moral decision making and future envisioning. Although the corresponding network has been receiving growing attention, the driving neurochemical mechanisms of the default mode network (DMN) remain unknown. Here we combined positron emission tomography and functional magnetic resonance imaging to investigate modulations of the DMN via serotonin-1A receptors (5-HT(1A)), separated for 5-HT autoinhibition (dorsal raphe nucleus) and local inhibition (heteroreceptors in projection areas). Using two independent approaches, regional 5-HT(1A) binding consistently predicted DMN activity in the retrosplenial cortex for resting-state functional magnetic resonance imaging and the Tower of London task. On the other hand, both local and autoinhibitory 5-HT(1A) binding inversely modulated the posterior cingulate cortex, the strongest hub in the resting human brain. In the frontal part of the DMN, a negative association was found between the dorsal medial prefrontal cortex and local 5-HT(1A) inhibition. Our results indicate a modulation of key areas involved in self-referential processing by serotonergic neurotransmission, whereas variations in 5-HT(1A) binding explained a considerable amount of the individual variability in the DMN. Moreover, the brain regions associated with distinct introspective functions seem to be specifically regulated by the different 5-HT(1A) binding sites. Together with previously reported modulations of dopamine and GABA, this regional specialization suggests complex interactions of several neurotransmitters driving the default mode network.

Studying the freely-behaving brain with fMRI

Given that the brain evolved to function in the real world then it seems reasonable to want to examine how it operates in that context. But of course the world is complex, as are the brain's responses to it, and MRI scanners are inherently restrictive environments. This combination of challenges makes the prospect of studying the freely-behaving brain with fMRI disconcerting to anyone sensible. When designing naturalistic fMRI experiments it is necessary to ensure that the thoughts or behaviours under scrutiny are not unduly perturbed or constrained by the imaging process, while still being amenable to experimental manipulation and control, and result in meaningful and interpretable data. This is difficult to achieve. Here, briefly, and in a highly subjective and selective manner, I consider: why we might want to deploy free-behaviour designs in an fMRI context, how to go about it, review some examples of it in action, and decide finally whether it is worth it (it is).

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.