The effect of sensorimotor activation on functional connectivity mapping with MRI

The correlations in the fluctuations in the blood oxygenation level-dependent (BOLD) MRI signal between anatomically distinct regions of the cortex that are known components of functional systems have been previously studied as possible indicators of functional connectivity. The objective of this study was to examine the effect of sensorimotor brain activity, as assessed by task-based functional magnetic resonance imaging (fMRI), on functional connectivity indices in the same region. Regions of activation for sequential finger motion were determined using a task-based, block-design fMRI study. Functional connectivity measurements based on interregional correlations were acquired at rest and during continuous, sequential finger motion. Connectivity indices were determined using normalized mean correlations within and between three regions of interest activated for the finger motion task. Connectivity indices were also determined for a control region that was not activated for the task. Continuous motor tasks performed during BOLD measurements did not significantly affect the functional connectivity as compared to the connectivity at rest within or between regions known to be activated by the task. However, there appeared to be a trend suggesting a slight reduction in connectivity indices during the motor task. The connectivity within and between those areas not activated for the task remained unchanged between conditions. These results suggest that in the motor system investigated, the recruitment of neurons to perform a specific task may moderately reduce the degree of hemodynamic coupling within and between regions.     

Biophysical and neural basis of resting state functional connectivity: Evidence from non-human primates

Functional MRI (fMRI) has evolved from simple observations of regional changes in MRI signals caused by cortical activity induced by a task or stimulus, to task-free acquisitions of images in a resting state. Such resting state signals contain low frequency fluctuations which may be correlated between voxels, and strongly correlated regions are deemed to reflect functional connectivity within synchronized circuits. Resting state functional connectivity (rsFC) measures have been widely adopted by the neuroscience community, and are being used and interpreted as indicators of intrinsic neural circuits and their functional states in a broad range of applications, both basic and clinical. However, there has been relatively little work reported that validates whether inter-regional correlations in resting state fluctuations of fMRI (rsfMRI) signals actually measure functional connectivity between brain regions, or to establish how MRI data correlate with other metrics of functional connectivity. In this mini-review, we summarize recent studies of rsFC within mesoscopic scale cortical networks (100 μm–10 mm) within a well defined functional region of primary somatosensory cortex (S1), as well as spinal cord and brain white matter in non-human primates, in which we have measured spatial patterns of resting state correlations and validated their interpretation with electrophysiological signals and anatomic connections. Moreover, we emphasize that low frequency correlations are a general feature of neural systems, as evidenced by their presence in the spinal cord as well as white matter. These studies demonstrate the valuable role of high field MRI and invasive measurements in an animal model to inform the interpretation of human imaging studies.     

Hierarchical clustering to measure connectivity in fMRI resting-state data

Low frequency oscillations, which are temporally correlated in functionally related brain regions, characterize the mammalian brain, even when no explicit cognitive tasks are performed. Functional connectivity MR imaging is used to map regions of the resting brain showing synchronous, regional and slow fluctuations in cerebral blood flow and oxygenation. In this study, we use a hierarchical clustering method to detect similarities of low-frequency fluctuations. We describe one measure of correlations in the low frequency range for classification of resting-state fMRI data. Furthermore, we investigate the contribution of motion and hardware instabilities to resting-state correlations and provide a method to reduce artifacts. For all cortical regions studied and clusters obtained, we quantify the degree of contamination of functional connectivity maps by the respiratory and cardiac cycle. Results indicate that patterns of functional connectivity can be obtained with hierarchical clustering that resemble known neuronal connections. The corresponding voxel time series do not show significant correlations in the respiratory or cardiac frequency band.     

Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets

A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions.     

Ictal imaging using functional magnetic resonance

Magnetic resonance imaging (MRI) can now provide maps of human brain function with high spatial and temporal resolution. This noninvasive technique can also map the coritical activation that occurs during focal seizures, as demonstrated here by the results obtained using a conventional 1.5 T clinical MRI system for the investigation of a 4-year-old boy suffering from frequent partial motor seizures of his right side. FLASH images (TE = 60 ms) were acquired every 10 s over a period of 25 min, and activation images derived by subtracting baseline images from images obtained during clinical seizures. Functional MRI revealed sequential activation associated with specific gyri within the left hemisphere with each of five consecutive clinical seizures, and also during a period that was not associated with a detectable clinical seizure. The activated regions included gyri that were structurally abnormal. These results demonstrate (a) that functional MRI can potentially provide new insights into the dynamic events that occur in the epileptic brain and their relationship to brain structure; and (b) that there is the possibility of obtaining similar information in the absence of clinical seizures, suggesting the potential for studies in patients with interictal electrical disturbances.     

Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI

Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique used to investigate the spontaneous correlations of blood-oxygen-level-dependent signals across different regions of the brain. Using functional connectivity tools, it is possible to investigate a specific RS-fMRI network, referred to as "default-mode" (DM) network, that involves cortical regions deactivated in fMRI experiments with cognitive tasks. Previous works have reported a significant effect of aging on DM regions activity. Independent component analysis (ICA) is often used for generating spatially distributed DM functional connectivity patterns from RS-fMRI data without the need for a reference region. This aspect and the relatively easy setup of an RS-fMRI experiment even in clinical trials have boosted the combined use of RS-fMRI and ICA-based DM analysis for noninvasive research of brain disorders. In this work, we considered different strategies for combining ICA results from individual-level and population-level analyses and used them to evaluate and predict the effect of aging on the DM component. Using RS-fMRI data from 20 normal subjects and a previously developed group-level ICA methodology, we generated group DM maps and showed that the overall ICA-DM connectivity is negatively correlated with age. A negative correlation of the ICA voxel weights with age existed in all DM regions at a variable degree. As an alternative approach, we generated a distributed DM spatial template and evaluated the correlation of each individual DM component fit to this template with age. Using a "leave-one-out" procedure, we discuss the importance of removing the bias from the DM template-generation process.     

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation.     

Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas

Functional neuroimaging first allowed researchers to describe the functional segregation of regionally activated areas during a variety of experimental tasks. More recently, functional integration studies have described how these functionally specialized areas, interact within a highly distributed neural network. When applied to the field of neurosciences, structural equation modeling (SEM) uses theoretical and/or empirical hypotheses to estimate the effects of an experimental task within a putative network. SEM represents a linear technique for multivariate analysis of neuroimaging data and has been developed to simultaneously examine ratios of multiple causality in an experimental design; the method attempts to explain a covariance structure within an anatomical constrained model. This method, when combined with the concept of effective connectivity, can provide information on the strength and direction of the functional interactions that take place between identified brain regions of a putative network.     

Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis

In combination with cognitive tasks entailing sequences of sensory and cognitive processes, event-related acquisition schemes allow using functional MRI to examine not only the topography but also the temporal sequence of cortical activation across brain regions (time-resolved fMRI). In this study, we compared two data-driven methods--fuzzy clustering method (FCM) and independent component analysis (ICA)--in the context of time-resolved fMRI data collected during the performance of a newly devised visual imagery task. We analyzed a multisubject fMRI data set using both methods and compared their results in terms of within- and between-subject consistency and spatial and temporal correspondence of obtained maps and time courses. Both FCM and spatial ICA allowed discriminating the contribution of distinct networks of brain regions to the main cognitive stages of the task (auditory perception, mental imagery and behavioural response), with good agreement across methods. Whereas ICA worked optimally on the original time series, averaging with respect to the task onset (and thus introducing some a priori information on the stimulation protocol) was found to be indispensable in the case of FCM. On averaged time series, FCM led to a richer decomposition of the spatio-temporal patterns of activation and allowed a finer separation of the neurocognitive processes subserving the mental imagery task. This study confirms the efficacy of the two examined methods in the data-driven estimation of hemodynamic responses in time-resolved fMRI studies and provides empirical guidelines to their use.     

An algorithm to estimate anatomical connectivity between brain regions using diffusion MRI

The study of anatomical connectivity is essential for interpreting functional MRI data and for establishing how brain areas are linked together into networks to support higher-order functions. Diffusion-weighted MR images (DWI) and tractography provide a unique noninvasive tool to explore the connectional architecture of the brain. The identification of anatomical circuits associated with a specific function can be better accomplished by the joint application of diffusion and functional MRI. In this article, we propose a simple algorithm to identify the set of pathways between two regions of interest. The method is based upon running deterministic tractography from all possible starting positions in the brain and selecting trajectories that intersect both regions. We compare results from single-fiber tractography using diffusion tensor imaging and from multi-fiber tractography using reduced-encoding persistent angular structure (PAS) MRI on standard DWI datasets from healthy human volunteers. Our results show that, in comparison with single-fiber tractography, the multi-fiber technique reveals additional putative routes of connection. We demonstrate highly consistent results of the proposed technique over a cohort of 16 healthy subjects.     

Functional Connectivity Methods and Their Applications in fMRI Data

The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organization of functional communication in the brain network. Based on the principle that distinct brain regions are functionally connected and continuously share information with each other, various approaches to finding these functional networks have been proposed in the literature. In this paper, we present an overview of the most common methods to estimate and characterize functional connectivity in fMRI data. We illustrate these methodologies with resting-state functional MRI data from the Human Connectome Project, providing details of their implementation and insights on the interpretations of the results. We aim to guide researchers that are new to the field of neuroimaging by providing the necessary tools to estimate and characterize brain circuitry.     

FMRI connectivity analysis of acupuncture effects on the whole brain network in mild cognitive impairment patients

The increased risk for the elderly with mild cognitive impairment (MCI) to progress to Alzheimer's disease makes it an appropriate condition for investigation. While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. We sought to investigate the acupuncture effects on the functional connectivity throughout the entire brain in MCI patients compared to healthy controls (HC). The functional magnetic resonance imaging experiment was performed with two different paradigms, namely, deep acupuncture (DA) and superficial acupuncture (SA), at acupoint KI3. We first identified regions showing abnormal functional connectivity in the MCI group compared to HC during the resting state and subsequently tested whether these regions could be modulated by acupuncture. Then, we made the comparison of MCI vs. HC to test whether there were any specific modulatory patterns in the poststimulus resting brain between the two groups. Finally, we made the comparisons of DA vs. SA in each group to test the effect of acupuncture with different needling depths. We found the temporal regions (hippocampus, thalamus, fusiform gyrus) showing abnormal functional connectivity during the resting state. These regions are implicated in memory encoding and retrieving. Furthermore, we found significant changes in functional connectivity related with the abnormal regions in MCI patients following acupuncture. Compared to HC, the correlations related with the temporal regions were enhanced in the poststimulus resting brain in MCI patients. Compared to SA, significantly increased correlations related with the temporal regions were found for the DA condition. The enhanced correlations in the memory-related brain regions following acupuncture may be related to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. The heterogeneous modulatory patterns between DA and SA may suggest that deep muscle insertion of acupuncture is necessary to achieve the appreciable clinical effect.     

fMRI assessment of thalamocortical connectivity during attentional performance

Functional magnetic resonance imaging (fMRI) studies have shown dysfunction in key areas associated with the thalamocortical circuit in patients with schizophrenia. This study examined the functional connectivity involving the frontal-thalamic circuitry during a spatial focusing-of-attention task in 18 unmedicated patients with schizophrenia and 38 healthy controls. Functional connectivity was analyzed by assigning seed regions (in the thalamic nuclei (mediodorsal nucleus (MDN), pulvinar, anterior nucleus (AN)), the dorsolateral prefrontal cortex (Brodmann areas 9 and 46), and the caudate), and correlating their respective activity with that in the non-seed regions voxel-wise. Functional connectivity analysis demonstrated that functional connectivity was significantly impaired in patients, e.g., between the right pulvinar and regions such as the prefrontal and temporal cortices and the cerebellum. On the other hand, enhanced functional connectivity was found in patients e.g., between the AN and regions such as the prefrontal and temporal cortices. In addition, the patients had significantly lower task performance and less (but non-significant) brain activation than those of controls. These results revealed disturbed functional integration in schizophrenia, and suggested that the functional connectivity abnormalities in the thalamocortical circuitry, especially the frontal-thalamic circuitry, may underlie the attention deficits in schizophrenia patients. Further, this study suggested that functional connectivity analysis might be more sensitive than brain activation analysis in detecting the functional abnormalities in schizophrenia.     

Hierarchical features of large-scale cortical connectivity

The analysis of complex networks has revealed patterns of organization in a variety of natural and artificial systems, including neuronal networks of the brain at multiple scales. In this paper, we describe a novel analysis of the large-scale connectivity between regions of the mammalian cerebral cortex, utilizing a set of hierarchical measurements proposed recently. We examine previously identified functional clusters of brain regions in macaque visual cortex and cat cortex and find significant differences between such clusters in terms of several hierarchical measures, revealing differences in how these clusters are embedded in the overall cortical architecture. For example, the ventral cluster of visual cortex maintains structurally more segregated, less divergent connections than the dorsal cluster, which may point to functionally different roles of their constituent brain regions.     

Functional Magnetic Resonance Imaging of the Basal Ganglia and Cerebellum Using a Simple Motor Paradigm

Activation of cortical and subcortical motor areas of the brain, including primary motor cortex, supplementary motor area, basal ganglia and cerebellum, were successfully investigated in seven right-handed, normal volunteers during a simple, rapid, thumb flexion-extension task using functional magnetic resonance imaging. A multi-slice echo-planar imaging sequence was used to cover the entire brain. A signal increase varying from 2% to 6% was observed for the different regions involved in the motor task. Moving the non-dominant thumb was associated with a more bilateral activation pattern in both putamen and cerebellar regions. This study demonstrates the capability of functional magnetic resonance imaging to delineate simultaneously many activated brain areas that are commonly thought to be involved in the performance of motor tasks.     

Quantifying and Comparing Region-of-Interest Activation Patterns in Functional Brain MR Imaging: Methodology Considerations

The general aims of functional brain magnetic resonance imaging (fMRI) studies are to ascertain which areas of the brain are activated during a specific task, the extent of this activation, whether different groups of subjects demonstrate different patterns of activation, and how these groups behave in different tasks. Many steps are involved in answering such questions and if each step is not carefully controlled the results may be influenced. This work has three objectives. Firstly, to present a technique for quantitatively evaluating methods used in functional imaging data analysis. While receiver-operator-characteristic (ROC) analysis has been used effectively to evaluate the ability of post-processing algorithms to detect true activations while rejecting false activations, it is difficult to adapt such a technique for comparisons of methods for quantitating activations. We present a technique based on the ANOVA, between two or more regions of interest (ROIs), subject groups, or activation tasks, over a range of statistical thresholds, which reveals the sensitivity of different activation quantification metrics to noise and other variables. Secondly, we use this technique to compare two methods of quantifying localized brain activation. There are numerous ways of quantifying the amount of activation present in a specific region of the brain in an individual subject. We compare the pixel count approach, which simply counts the number of pixels above an arbitrary statistical threshold, with an approach based on the sum of t-values above the same arbitrary t-value threshold. Finally, we examine the sensitivity of the results from an analysis of variance, to user defined parameters such as threshold and region of interest size. Both simulated and real functional magnetic resonance data are used to demonstrate these techniques.     

The neural correlates of calculation ability in children: an fMRI study

Most studies investigating mental numerical processing involve adult participants and little is known about the functioning of these systems in children. The current study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of numeracy and the influence of age on these correlates with a group of adults and a group of third graders who had average to above average mathematical ability. Participants performed simple and complex versions of exact and approximate calculation tasks while in the magnet. Like adults, children activated a network of brain regions in the frontal and parietal lobes during the calculation tasks, and they recruited additional brain regions for the more complex versions of the tasks. However, direct comparisons between adults and children revealed significant differences in level of activation across all tasks. In particular, patterns of activation in the parietal lobe were significantly different as a function of age. Findings support previous claims that the parietal lobe becomes more specialized for arithmetic tasks with age.     

Flat map areal topography in Macaca mulatta based on combined MRI and histology

Flattened representations are a useful approach to represent the convoluted complex surface of the neocortex of primates and other large-brained mammals. In this study, we compared the flattened representation of neocortical areas obtained from the recently published MRI and histology atlas of the rhesus monkey brain (Saleem KS, Logothetis NK. A combined MRI and histology atlas of the rhesus monkey brain in stereotaxic coordinates. London: Academic; 2007) with other previously published maps. Our results confirm that flat map representations are advantageous due to their ease of use and that current flat maps are well comparable to each other. Some differences arise due to different distinguishing criteria and here too flat maps can help to reveal them.     

Investigation of the large-scale functional brain networks modulated by acupuncture

Previous neuroimaging studies have primarily focused on the neural activities involving the acute effects of acupuncture. Considering that acupuncture can induce long-lasting effects, several researchers have begun to pay attention to the sustained effects of acupuncture on the resting brain. Most of these researchers adopted functional connectivity analysis based on one or a few preselected brain regions and demonstrated various function-guided brain networks underlying the specific effect of acupuncture. Few have investigated how these brain networks interacted at the whole-brain level. In this study, we sought to investigate the functional correlations throughout the entire brain following acupuncture at acupoint ST36 (ACUP) in comparison with acupuncture at nearby nonacupoint (SHAM). We divided the whole brain into 90 regions and constructed functional brain network for each condition. Then we examined the network hubs and identified statistically significant differences in functional correlations between the two conditions. Following ACUP, but not SHAM, the limbic/paralimbic regions such as the amygdala, hippocampus and anterior cingulate gyrus emerged as network hubs. For direct comparisons, increased correlations for ACUP compared to SHAM were primarily related with the limbic/paralimbic and subcortical regions such as the insula, amygdala, anterior cingulate gyrus, and thalamus, whereas decreased correlations were mainly related with the sensory and frontal cortex. The heterogeneous modulation patterns between the two conditions may relate to the functional specific modulatory effects of acupuncture. The preliminary findings may help us to better understand the long-lasting effects of acupuncture on the entire resting brain, as well as the neurophysiological mechanisms underlying acupuncture.     

Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG

Different brain imaging devices are presently available to provide images of the human functional cortical activity, based on hemodynamic, metabolic or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. While the functional connectivity is defined as the temporal coherence among the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally among cortical sites. The structural equation modeling (SEM) is the most used method to estimate effective connectivity in neuroscience, and its typical application is on data related to brain hemodynamic behavior tested by functional magnetic resonance imaging (fMRI), whereas the directed transfer function (DTF) method is a frequency-domain approach based on both a multivariate autoregressive (MVAR) modeling of time series and on the concept of Granger causality.

This study presents advanced methods for the estimation of cortical connectivity by applying SEM and DTF on the cortical signals estimated from high-resolution electroencephalography (EEG) recordings, since these signals exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. To estimate correctly the cortical signals, we used a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual MRI, a distributed source model and a regularized linear inverse source estimates of cortical current density. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high-resolution EEG data, we performed a simulation study, in which different main factors (signal-to-noise ratio, SNR, and simulated cortical activity duration, LENGTH) were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity patterns under reasonable operative conditions, that is, when data exhibit an SNR of at least 3 and a LENGTH of at least 75 s of nonconsecutive EEG recordings at 64 Hz of sampling rate.

Hence, effective and functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high-resolution EEG techniques and linear inverse estimation with SEM or DTF methods. We conclude that the estimation of cortical connectivity can be performed not only with hemodynamic measurements, but also with EEG signals treated with advanced computational techniques.