Functional MRI today

Functional MRI of murine olfactory bulbs at 15.2T reveals characteristic activation patters when stimulated by different odors

Thanks to its increased sensitivity, single-shot ultrahigh field functional MRI (UHF fMRI) could lead to valuable insight about subtle brain functions such as olfaction. However, UHF fMRI experiments targeting small organs next to air voids, such as the olfactory bulb, are severely affected by field inhomogeneity problems. Spatiotemporal Encoding (SPEN) is an emerging single-shot MRI technique that could provide a route for bypassing these complications. This is here explored with single-shot fMRI studies on the olfactory bulbs of male and female mice performed at 15.2T. SPEN images collected on these organs at a 108 µm in-plane resolution yielded remarkably large and well-defined responses to olfactory cues. Under suitable T2* weightings these activation-driven changes exceeded 5% of the overall signal intensity, becoming clearly visible in the images without statistical treatment. The nature of the SPEN signal intensity changes in such experiments was unambiguously linked to olfaction, via single-nostril experiments. These experiments highlighted specific activation regions in the external plexiform region and in glomeruli in the lateral part of the bulb, when stimulated by aversive or appetitive odors, respectively. These strong signal activations were non-linear with concentration, and shed light on how chemosensory signals reaching the olfactory epithelium react in response to different cues. Second-level analyses highlighted clear differences among the appetitive, aversive and neutral odor maps; no such differences were evident upon comparing male against female olfactory activation regions.

Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan

Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI) signal in the human brain across the adult lifespan using Hurst exponent (HE). We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females) from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected). However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected). Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process.

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7" iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study

OBJECTIVE

In the last few years, the interest in applying virtual reality systems for neurorehabilitation is increasing. Their compatibility with neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), allows for the investigation of brain reorganization with multimodal stimulation and real-time control of the changes occurring in brain activity. The present study was aimed at testing a novel semi-immersive visuo-motor task (VMT), which has the features of being adopted in the field of neurorehabilitation of the upper limb motor function.

APPROACH

A virtual environment was simulated through a three-dimensional hand-sensing device (the LEAP Motion Controller), and the concomitant VMT-related prefrontal cortex (PFC) response was monitored non-invasively by fNIRS. Upon the VMT, performed at three different levels of difficulty, it was hypothesized that the PFC would be activated with an expected greater level of activation in the ventrolateral PFC (VLPFC), given its involvement in the motor action planning and in the allocation of the attentional resources to generate goals from current contexts. Twenty-one subjects were asked to move their right hand/forearm with the purpose of guiding a virtual sphere over a virtual path. A twenty-channel fNIRS system was employed for measuring changes in PFC oxygenated-deoxygenated hemoglobin (O2Hb/HHb, respectively).

MAIN RESULTS

A VLPFC O2Hb increase and a concomitant HHb decrease were observed during the VMT performance, without any difference in relation to the task difficulty.

SIGNIFICANCE

The present study has revealed a particular involvement of the VLPFC in the execution of the novel proposed semi-immersive VMT adoptable in the neurorehabilitation field.

Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

BACKGROUND

Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience.

NEW METHOD

To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise.

RESULTS

We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD.

COMPARISON WITH EXISTING METHOD(S)

Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution.

CONCLUSIONS

We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Coding of level of ambiguity within neural systems mediating choice

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study

Previous functional near-infrared spectroscopy (fNIRS) studies indicated that the prefrontal cortex (PFC) is involved in the maintenance of the postural balance after external perturbations. So far, no studies have been conducted to investigate the PFC hemodynamic response to virtual reality (VR) tasks that could be adopted in the field of functional neurorehabilitation. The aim of this fNIRS study was to assess PFC oxygenation response during an incremental and a control swing balance task (ISBT and CSBT, respectively) in a semi-immersive VR environment driven by a depth-sensing camera. It was hypothesized that: i) the PFC would be bilaterally activated in response to the increase of the ISBT difficulty, as this cortical region is involved in the allocation of attentional resources to maintain postural control; and ii) the PFC activation would be greater in the right than in the left hemisphere considering its dominance for visual control of body balance. To verify these hypotheses, 16 healthy male subjects were requested to stand barefoot while watching a 3 dimensional virtual representation of themselves projected onto a screen. They were asked to maintain their equilibrium on a virtual blue swing board susceptible to external destabilizing perturbations (i.e., randomizing the forward-backward direction of the impressed pulse force) during a 3-min ISBT (performed at four levels of difficulty) or during a 3-min CSBT (performed constantly at the lowest level of difficulty of the ISBT). The center of mass (COM), at each frame, was calculated and projected on the floor. When the subjects were unable to maintain the COM over the board, this became red (error). After each error, the time required to bring back the COM on the board was calculated (returning time). An eight-channel continuous wave fNIRS system was employed for measuring oxygenation changes (oxygenated-hemoglobin, O2Hb; deoxygenated-hemoglobin, HHb) related to the PFC activation (Brodmann Areas 10, 11 and 46). The results have indicated that the errors increased between the first and the second level of difficulty of the ISBT, then decreased and remained constant; the returning time progressively increased during the first three levels of difficulty and then remained constant. During the CSBT, the errors and the returning time did not change. In the ISBT, the increase of the first three levels of difficulty was accompanied by a progressive increase in PFC O2Hb and a less consistent decrease in HHb. A tendency to plateau was observable for PFC O2Hb and HHb changes in the fourth level of difficulty of the ISBT, which could be partly explained by a learning effect. A right hemispheric lateralization was not found. A lower amplitude of increase in O2Hb and decrease in HHb was found in the PFC in response to the CSBT with respect to the ISBT. This study has demonstrated that the oxygenation increased over the PFC while performing an ISBT in a semi-immersive VR environment. These data reinforce the involvement of the PFC in attention-demanding balance tasks. Considering the adaptability of this virtual balance task to specific neurological disorders, the absence of motion sensing devices, and the motivating/safe semi-immersive VR environment, the ISBT adopted in this study could be considered valuable for diagnostic testing and for assessing the effectiveness of functional neurorehabilitation.

Current status and future perspectives of magnetic resonance high-field imaging: a summary

There are several magnetic resonance (MR) imaging techniques that benefit from high-field MR imaging. This article describes a range of novel techniques that are currently being used clinically or will be used in the future for clinical purposes as they gain popularity. These techniques include functional MR imaging, diffusion tensor imaging, cortical thickness assessment, arterial spin labeling perfusion, white matter hyperintensity lesion assessment, and advanced MR angiography.

Functional MRI: A confluence of fortunate circumstances

Functional MRI has existed for about twenty years and by almost all measures has been incredibly successful. What are the reasons behind this success? In this review, eight extremely fortunate circumstances came together to produce BOLD based fMRI as we know it today. They are as follows: 1. The MRI signal, 2. The MRI relaxation rates, 3. The oxygen-dependent magnetic susceptibility of blood, 4. Neuronal-hemodynamic coupling, 5. The spatial scale of brain activation, 6. The prevalence of scanners able to perform echo planar imaging (EPI), 7. The parallel development of computing power, and 8. The very large group of neuroscientists who, pre-1991, were perfectly poised, willing, and able to exploit the capability of fMRI. These circumstances are discussed in detail. The desired goal of this review is primarily to convey the field of fMRI from the perspective of what was critically important before, during and after its inception and how things might have been if these circumstances would have been different. While there are many instances where circumstances could have been better, it is clear that they worked out extremely well, as the field of fMRI, a major aspect of functional neuroimaging today, is thriving.

A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults

Upon stimulation, real time maps of cortical hemodynamic responses can be obtained by non-invasive functional near-infrared spectroscopy (fNIRS) which measures changes in oxygenated and deoxygenated hemoglobin after positioning multiple sources and detectors over the human scalp. The current commercially available transportable fNIRS systems have a time resolution of 1-10 Hz, a depth sensitivity of about 1.5 cm, and a spatial resolution of about 1cm. The goal of this brief review is to report infants, children and adults fNIRS language studies. Since 1998, 60 studies have been published on cortical activation in the brain's classic language areas in children/adults as well as newborns using fNIRS instrumentations of different complexity. In addition, the basic principles of fNIRS including features, strengths, advantages, and limitations are summarized in terms that can be understood even by non specialists. Future prospects of fNIRS in the field of language processing imaging are highlighted.

Analysis of fMRI time series with mutual information

Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.

Interactions within and between corticostriatal loops during component processes of category learning

We examined dynamic interactions between cortex and BG during stimulus-response and feedback processing phases of categorization. First, we dissociated stimulus-response processing from feedback processing using "jittered" intervals of time between response and feedback to examine how each recruits the four primary corticostriatal loops (motor, executive, visual, and motivational). Second, we examined dynamic interactions within and between corticostriatal loops using Granger causality mapping. On each trial, subjects viewed one of six abstract visual stimuli, pressed a button indicating category membership, and then received feedback as to whether the decision was right or wrong. Stimulus-response processing was associated with greater activity in the visual loop, whereas feedback processing resulted in activity in the executive loop that was sensitive to feedback valence. Granger causality mapping showed patterns of directed influence within corticostriatal loops and between loops from the motor to the executive, to the visual, and finally to the motivational loop. These patterns of interaction are consistent with functional integration of motor processing in the motor loop with feedback processing in the executive loop and maintenance of stimulus-response history for future responses in the motivational loop.

Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network. Part II: flow variations induced by global or localized modifications of arteriolar diameters

In a companion paper (Lorthois et al., Neuroimage, in press), we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking into account the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, volumes of functional vascular territories, regional flow at voxel and network scales, etc. Using the same approach, we study flow reorganizations induced by global arteriolar vasodilations (an isometabolic global increase in cerebral blood flow). For small to moderate global vasodilations, the relationship between changes in volume and changes in flow is in close agreement with Grubb's law, providing a quantitative tool for studying the variations of its exponent with underlying vascular architecture. A significant correlation between blood flow and vascular structure at the voxel scale, practically unchanged with respect to baseline, is demonstrated. Furthermore, the effects of localized arteriolar vasodilations, representative of a local increase in metabolic demand, are analyzed. In particular, localized vasodilations induce flow changes, including vascular steal, in the neighboring arteriolar trunks at small distances (<300 μm), while their influence in the neighboring veins is much larger (about 1 mm), which provides an estimate of the vascular point spread function. More generally, for the first time, the hemodynamic component of various functional neuroimaging techniques has been isolated from metabolic and neuronal components, and a direct relationship with several known characteristics of the BOLD signal has been demonstrated.

Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single "representative" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI.

Functional MRI and multivariate autoregressive models

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.

The effect of different anesthetics on neurovascular coupling

To date, the majority of neurovascular coupling studies focused on the thalamic afferents' activity in layer IV and the corresponding large spiking activity as responsible for functional hyperemia. This paper highlights the role of the secondary and late cortico-cortical transmission in neurovascular coupling. Simultaneous scalp electroencephalography (EEG) and diffuse optical imaging (DOI) measurements were obtained during multiple conditions of event-related electrical forepaw stimulation in 33 male Sprague-Dawley rats divided into 6 groups depending on the maintaining anesthetic - alpha-chloralose, pentobarbital, ketamine-xylazine, fentanyl-droperidol, isoflurane, or propofol. The somatosensory evoked potentials (SEP) were decomposed into four components and the question of which best predicts the hemodynamic responses was investigated. Results of the linear regression analysis show that the hemodynamic response is best correlated with the secondary and late cortico-cortical transmissions and not with the initial thalamic input activity in layer IV. Baseline cerebral blood flow (CBF) interacts with neural activity and influences the evoked hemodynamic responses. Finally, neurovascular coupling appears to be the same across all anesthetics used.

Is it time to re-prioritize neuroimaging databases and digital repositories?

The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

An fMRI normative database for connectivity networks using one-class support vector machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal patterns and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored.

Multimodal and Multi-tissue Measures of Connectivity Revealed by Joint Independent Component Analysis

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Surface-based functional magnetic resonance imaging analysis of partial brain echo planar imaging data at 1.5 T

Surface-based functional magnetic resonance imaging (fMRI) analysis is more sensitive and accurate than volume-based analysis for detecting neural activation. However, these advantages are less important in practical fMRI experiments with commonly used 1.5-T magnetic resonance devices because of the resolution gap between the echo planar imaging data and the cortical surface models. We expected high-resolution segmented partial brain echo planar imaging (EPI) data to overcome this problem, and the activation patterns of the high-resolution data could be different from the low-resolution data. For the practical applications of surface-based fMRI analysis using segmented EPI techniques, the effects of some important factors (e.g., activation patterns, registration and local distortions) should be intensively evaluated because the results of surface-based fMRI analyses could be influenced by them. In this study, we demonstrated the difference between activations detected from low-resolution EPI data, which were covering whole brain, and high-resolution segmented EPI data covering partial brain by volume- and surface-based analysis methods. First, we compared the activation maps of low- and high-resolution EPI datasets detected by volume- and surface-based analyses, with the spatial patterns of activation clusters, and analyzed the distributions of activations in occipital lobes. We also analyzed the high-resolution EPI data covering motor areas and fusiform gyri of human brain, and presented the differences of activations detected by volume- and surface-based methods.

Ethical Issues in Neuroimaging Health Research

Neuroimaging is increasingly used to understand conditions like stroke and epilepsy. However, there is growing recognition that neuroimaging can raise ethical issues. We used interpretative phenomenological analysis to analyse interview data pre-and post-scan to explore these ethical issues. Findings show participants can become anxious prior to scanning and the protocol for managing incidental findings is unclear. Participants lacked a frame of reference to contextualize their expectations and often drew on medical narratives. Recommendations to reduce anxiety include dialogue between researcher and participant to clarify understanding during consent and the use of a `virtual tour' of the neuroimaging experience.

New Horizons for the Next Era of Human Brain Imaging, Cognitive, and Behavioral Research: Pacific Rim Interactivity

Beginning in the 1990's, substantial advances have been made in the ability to image the living human brain. Functional MRI, PET, and other modalities have been developed to provide a rich means for assessing brain function and structure across spatial and temporal dimensions. Such methods are now the preferred means to examine the brain in vivo, with several thousand articles now appearing in the literature each year. The next era of human brain imaging is upon us now as technological developments reach a level where data can be processed quickly and combined with other biological information to provide fundamentally new applications and insights. This new era will involve and require the collaborative participation of leading research groups from around the world to share information and expertise for understanding observed effects and synthesizing these into new knowledge. One particular community that is gaining in its prominence in the field is that of the Pacific Rim, whose collective research efforts present an important corpus of research effort into brain structure and function. The Pacific Rim represents an important collection of researchers interested in the greater sharing of ideas. In this special issue of Brain Imaging and Behavior, we focus on emerging areas of research that utilize brain imaging methodology, and discuss how current developments are driving the expansion of functional imaging research. Moreover, we focus on the robust interaction of researchers from around the Pacific Rim whose collaborations are significantly shaping the future of brain imaging.

Detection of time-varying signals in event-related fMRI designs

In neuroimaging research on attention, cognitive control, decision-making, and other areas where response time (RT) is a critical variable, the temporal variability associated with the decision is often assumed to be inconsequential to the hemodynamic response (HDR) in rapid event-related designs. On this basis, the majority of published studies model brain activity lasting less than 4 s with brief impulses representing the onset of neural or cognitive events, which are then convolved with the hemodynamic impulse response function (HRF). However, electrophysiological studies have shown that decision-related neuronal activity is not instantaneous, but in fact, often lasts until the motor response. It is therefore possible that small differences in neural processing durations, similar to human RTs, will produce noticeable changes in the HDR, and therefore in the results of regression analyses. In this study we compare the effectiveness of traditional models that assume no temporal variance with a model that explicitly accounts for the duration of very brief epochs of neural activity. Using both simulations and fMRI data, we show that brief differences in duration are detectable, making it possible to dissociate the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.

Cognitive neuroscience of aging

The number of reports on the cognitive neuroscience of aging has increased in recent years, and most of these studies have found many similarities in the patterns of activity in young and old adults, indicating that basic neural mechanisms are maintained into older age. Despite these overall similarities, older adults often have less activity in some regions, such as medial temporal areas during memory processing and visual regions across a variety of cognitive domains. It seems clear that age reductions in cognitive function can be tied, at least in part, to these reductions in brain activity. On the other hand, older adults typically also overrecruit some brain areas, mainly the ventral or dorsal prefrontal cortex during memory tasks, as well as both the frontal and parietal regions during tasks engaging cognitive control processes, such as attention. Sometimes this overrecruitment appears to be in response to altered function in other brain regions and is often seen in those older adults who perform better on the task at hand. These findings have provided rather convincing support for the idea that overrecruitment can be compensatory in the elderly. Nevertheless, not all age increases can be interpreted as compensatory, and some are more indicative of neural inefficiency. The challenge facing future research will be to understand the task conditions that promote compensation in older adults, the role of the various brain areas in aiding cognitive function, and how these compensatory mechanisms can be elicited to enhance quality of life in the elderly.

Artificial shifting of fMRI activation localized by volume- and surface-based analyses

Spatial smoothing is an important post-processing procedure that is used to increase the signal-to-noise ratio (SNR) of blood oxygenation level-dependent signals (BOLD) in common functional magnetic resonance imaging (fMRI) applications. However, recent studies have shown that smoothing artificially shifts probabilistic local maxima of fMRI activations. In this study, we show shifting of the localization of functional centers in hand motor areas of the cerebral cortex by three-dimensional isotropic Gaussian kernel smoothing or two-dimensional heat kernel smoothing in volume- and surface-based fMRI analyses. Activation maps derived from smoothed echo planar imaging (EPI) data by volume- and surface-based analyses were assigned to the nodes of individual cortical surface models, and local maxima in the primary motor area (M1) and the primary somatosensory cortex (S1) were compared with those derived from non-smoothed risk map analysis, which is commonly used in presurgical applications. For each analysis, the Euclidean and geodesic distances between the correlation coefficients of local maxima derived from smoothed and non-smoothed EPI data were measured. The results show that the correlation coefficients derived from the volume- and surface-based analyses were about 29.4% and 42.9% higher for smoothed than for non-smoothed risk map analyses, and show minimum shifting of localizations by 12.1 mm and 6.9 mm on average in Euclidean distance, respectively, and about 9.5 mm and 5.7 mm on average in geodesic distance, respectively.

Regional differences in the coupling of cerebral blood flow and oxygen metabolism changes in response to activation: implications for BOLD-fMRI

Functional magnetic resonance imaging (fMRI) based on blood oxygenation level dependent (BOLD) signal changes is a sensitive tool for mapping brain activation, but quantitative interpretation of the BOLD response is problematic. The BOLD response is primarily driven by cerebral blood flow (CBF) changes, but is moderated by M, a scaling parameter reflecting baseline deoxyhemoglobin, and n, the ratio of fractional changes in CBF to cerebral metabolic rate of oxygen consumption (CMRO(2)). We compared M and n between cortical (visual cortex, VC) and subcortical (lentiform nuclei, LN) regions using a quantitative approach based on calibrating the BOLD response with a hypercapnia experiment. Although M was similar in both regions (~5.8%), differences in n (2.21+/-0.03 in VC and 1.58+/-0.03 in LN; Cohen d=1.71) produced substantially weaker (~3.7x) subcortical than cortical BOLD responses relative to CMRO(2) changes. Because of this strong sensitivity to n, BOLD response amplitudes cannot be interpreted as a quantitative reflection of underlying metabolic changes, particularly when comparing cortical and subcortical regions.

The neuroimaging research process from the participants' perspective

The aim of this study was to investigate participants' experiences of taking part in research conducted using fMRI or MEG procedures. Forty-four participants completed a questionnaire after taking part in either fMRI or MEG experiments; the questionnaire asked about experiences of and attitudes toward fMRI/MEG. Ten follow-up interviews were conducted to enable an in-depth analysis of these attitudes and experiences. The findings were generally positive: all participants thought fMRI and MEG were safe procedures, 93% would recommend participating in neuroimaging research to their friends and family, and participants were positive about participating in future neuroimaging research. However, some negative issues were identified. Some participants reported feeling nervous prior to scanning procedures, several participants reported side-effects after taking part, a number of participants were upset at being in a confined space and some participants did not feel confident about exiting the scanner in an emergency. Several recommendations for researchers are made, including a virtual tour of the scanning equipment during the consenting process in order to better prepare potential participants for the scanning experience and to minimize the potential psychological discomfort sometimes experienced in neuroimaging research.