fMRI Methods Research Articles

The Last Frontier

Functional magnetic resonance imaging (fMRI) is fast gaining ground as a non-invasive technique for neuroimaging. The method can capture images of the human brain in real time while the subject carries out a cognitive task. This research area is still in its infancy but has immense possibilities to explore the secrets of the human brain, intelligence and thought processes. This article explains the physics behind the fMRI method and describes several studies which use fMRI to explore different facets of the human brain such as learning mathematics, and the deep connections between music and cognitive processes.

Cortical Response to the Natural Speech Envelope Correlates with Neuroimaging Evidence of Cognition in Severe Brain Injury

Recent studies identify severely brain-injured patients with limited or no behavioral responses who successfully perform functional magnetic resonance imaging (fMRI) or electroencephalogram (EEG) mental imagery tasks [1-5]. Such tasks are cognitively demanding [1]; accordingly, recent studies support that fMRI command following in brain-injured patients associates with preserved cerebral metabolism and preserved sleep-wake EEG [5, 6]. We investigated the use of an EEG response that tracks the natural speech envelope (NSE) of spoken language [7-22] in healthy controls and brain-injured patients (vegetative state to emergence from minimally conscious state). As audition is typically preserved after brain injury, auditory paradigms may be preferred in searching for covert cognitive function [23-25]. NSE measures are obtained by cross-correlating EEG with the NSE. We compared NSE latencies and amplitudes with and without consideration of fMRI assessments. NSE latencies showed significant and progressive delay across diagnostic categories. Patients who could carry out fMRI-based mental imagery tasks showed no statistically significant difference in NSE latencies relative to healthy controls; this subgroup included patients without behavioral command following. The NSE may stratify patients with severe brain injuries and identify those patients demonstrating "cognitive motor dissociation" (CMD) [26] who show only covert evidence of command following utilizing neuroimaging or electrophysiological methods that demand high levels of cognitive function. Thus, the NSE is a passive measure that may provide a useful screening tool to improve detection of covert cognition with fMRI or other methods and improve stratification of patients with disorders of consciousness in research studies.

Characterizing contrast origins and noise contribution in spin-echo EPI BOLD at 3 T

In this work, we characterize contrast origins and noise contributions of spin echo (SE) EPI BOLD signal at 3 T. SE BOLD is a fMRI method of choice for imaging brain regions affected by susceptibility artifacts at lower fields, but its sensitivity remains a limiting factor for whole-brain imaging. To resolve this, the signal and noise contributions as well as TE dependence of SE EPI are characterized in this study. By integrating a two-compartment BOLD model with a physiological-thermal noise model, a new SE-BOLD signal model was introduced. The new SE-BOLD model was fit into SE-EPI fMRI data acquired during hypercapnic manipulations at various TEs, using typical fMRI voxel dimensions (3.4 × 3.4 × 5 mm3). Our model predicts intra- and extravascular signal and noise contributions consistent with our understanding of the SE-EPI contrast mechanism. The intravascular BOLD contribution is shown to dominate at TEs lower than tissue T2, but the physiological noise contributions in SE-EPI signal is also shown to be lower than that of gradient-echo (GE). Furthermore, SE-EPI contrast-to-noise ratio (CNR) is not maximized at tissue T2 as is typically assumed. To summarize, a new SE-BOLD model was proposed to characterize SE-BOLD contrast and physiological noise contribution at 3 T. Results suggests that SE-BOLD sensitivity can be improved by using shorter TEs, making it a more attractive choice for fMRI, especially in regions with susceptibility artifacts. Such optimizations could also help extend the application of SE BOLD to WM fMRI studies.

A practical protocol for measurements of spinal cord functional connectivity

Resting state functional magnetic resonance imaging (fMRI) has been used to study human brain function for over two decades, but only recently has this technique been successfully translated to the human spinal cord. The spinal cord is structurally and functionally unique, so resting state fMRI methods developed and optimized for the brain may not be appropriate when applied to the cord. This report therefore investigates the relative impact of different acquisition and processing choices (including run length, echo time, and bandpass filter width) on the detectability of resting state spinal cord networks at 3T. Our results suggest that frequencies beyond 0.08 Hz should be included in resting state analyses, a run length of ~8–12 mins is appropriate for reliable detection of the ventral (motor) network, and longer echo times – yet still shorter than values typically used for fMRI in the brain – may increase the detectability of the dorsal (sensory) network. Further studies are required to more fully understand and interpret the nature of resting state spinal cord networks in health and in disease, and the protocols described in this report are designed to assist such studies.

Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions

Magnetic resonance imaging (MRI) is a highly versatile imaging modality that can be used to measure features of the tumour microenvironment including cell death, proliferation, metabolism, angiogenesis, and hypoxia. Mapping and quantifying these pathophysiological features has the potential to alter the use of adaptive radiotherapy planning. Although these methods are available for use on diagnostic machines, several challenges exist for implementing these functional MRI methods on the MRI-linear accelerators (linacs). This review considers these challenges and potential solutions.

Brain active transmembrane water cycling measured by MR isassociated with neuronal activity.

fMRI is widely used to study brain activity. Unfortunately, conventional fMRI methods assess neuronal activity only indirectly, through hemodynamic coupling. Here, we show that active, steady-state transmembrane water cycling (AWC) could serve as a basis for a potential fMRI mechanism for direct neuronal activity detection. AWC and neuronal actitivity in rat organotypic cortical cultures were simultaneously measured with ahybrid MR-fluorescence system. Perfusion with a paramagnetic MRI contrast agent, Gadoteridol, allows NMR determination of the kinetics of transcytolemmal water exchange. Changes in intracellular calcium concentration, [Cai2+ ] were used as a proxy of neuronal activity and were monitored by fluorescence imaging. When we alter neuronal activity by titrating with extracellular [K+ ] near thenormal value, we see an AWC response resembling Na+ -K+ -ATPase (NKA) Michaelis-Menten behavior. When we treat with thevoltage-gated sodium channel inhibitor, or with an excitatory postsynaptic inhibitor cocktail, we see AWC decrease by up to 71%. AWC was found also to be positively correlated with the basal level of spontaneous activity, which varies in different cultures. These results suggest that AWC is associated with neuronal activity and NKA activity is amajor contributor in coupling AWC to neuronal activity. Although AWC comprises steady-state, homeostatic transmembrane water exchange, our analysis also yields a simultaneous measure of the average cell volume, which reports any slower nettransmembrane water transport.

Renal Functional MRI and Its Application.

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:863-881.

Brain-computer-interface-based intervention re-normalizes brain functional network topology in children with attention deficit/hyperactivity disorder

A brain-computer-interface (BCI)-based attention training game system has shown promise for treating attention deficit/hyperactivity disorder (ADHD) children with inattentive symptoms. However, little is known about brain network organizational changes underlying behavior improvement following BCI-based training. To cover this gap, we aimed to examine the topological alterations of large-scale brain functional networks induced by the 8-week BCI-based attention intervention in ADHD boys using resting-state functional magnetic resonance imaging method. Compared to the non-intervention (ADHD-NI) group, the intervention group (ADHD-I) showed greater reduction of inattention symptoms accompanied with differential brain network reorganizations after training. Specifically, the ADHD-NI group had increased functional connectivity (FC) within the salience/ventral attention network (SVN) and increased FC between task-positive networks (including the SVN, dorsal attention (DAN), somatomotor, and executive control network) and subcortical regions; in contrast ADHD-I group did not have this pattern. In parallel, ADHD-I group had reduced degree centrality and clustering coefficient as well as increased closeness in task-positive and the default mode networks (prefrontal regions) after the training. More importantly, these reduced local functional processing mainly in the SVN were associated with less inattentive/internalizing problems after 8-week BCI-based intervention across ADHD patients. Our findings suggest that the BCI-based attention training facilitates behavioral improvement in ADHD children by reorganizing brain functional network from more regular to more random configurations, particularly renormalizing salience network processing. Future long-term longitudinal neuroimaging studies are needed to develop the BCI-based intervention approach to promote brain maturation in ADHD.

Fractional magnetic resonance imaging based on inhomogeneous main magnetic field

The existing NMR equipment is uneven to face the main magnetic field, mostly adopts the hardware method of magnetic field compensation, such as magnetic field compensation, but it brings bad effects such as image artifact and blurred image. In view of the problem of magnetic field inhomogeneous in magnetic resonance imaging, a fractional domain magnetic resonance imaging (fMRI) method under the main magnetic field inhomogeneous is proposed. First, select a layer of living tissue to be imaged, select several points on the layer and measure the intensity of the magnetic field on the layer. Based on the principle of magnetic resonance imaging, establish the model of the magnetic field intensity distribution in the imaging area, and then establish. The polynomial model of the magnetic field can be divided into the second-order polynomial model and the higher-order polynomial model according to whether there is a significant second-order component in the measured magnetic field. Then, the two models are respectively substituted into the free-induction decay (FID) signals of the magnetic resonance. For the second-order model, the fractional Fourier transform tool can be used to solve the spin density function on one layer of the imaged object. The order model needs to obtain the spin density function at a certain level of the imaging object by solving the algebraic equation, thus establishing the MR signal model with any non-uniform main magnetic field. Experimental results show that this method achieves the same effect as the uniform main magnetic field.

Brain Mechanism for the Feeling of Education Fairness Based on EEG

Fairness plays a very important driving role in the decision-making process of the human society and is also a basic principle for human life. At present, people’s feelings of being treated fairly and the corresponding impacts on their behaviours have received widespread attention from experts and scholars. Considering the individual differences, by using the resting-state fMRI method, this paper investigates the correlation between the internal connections in the brain regions and decision making behaviours related to the feeling of unfairness under the influences of different educational statuses. According to the investigation results, compared with the proponents who receive better education, the subjects were more likely to accept the unfair distribution proposed by those in poorer educational statuses and give better ratings on the unfair distribution. When the proponents in better education status proposed unfair distribution, there were negative correlations between the rejection rate and the functional connectivity of the right mPFC and DLPFC and between the fairness score and the functional connectivity of the left mPFC and the right thalamus. In the resting state, the functional connectivity between the mPFC and the thalamus and DLPFC can help predict individuals’ different decision-making processes on the feeling of unfairness.

Simulations to benchmark time-varying connectivity methods for fMRI.

There is a current interest in quantifying time-varying connectivity (TVC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain’s dynamics. However, given that the ground truth for TVC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exist many TVC methods it is difficult to assess differences in time-varying connectivity between studies. In this paper, we present tvc_benchmarker, which is a Python package containing four simulations to test TVC methods. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating TVC (sliding window, tapered sliding window, multiplication of temporal derivatives, spatial distance and jackknife correlation). These simulations were designed to test each method’s ability to track changes in covariance over time, which is a key property in TVC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future TVC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. Using tvc_benchmarker researchers can easily add, compare and submit their own TVC methods to evaluate its performance.

Functional MRI of brain physiology in aging and neurodegenerative diseases

Brain aging and associated neurodegeneration constitute a major societal challenge as well as one for the neuroimaging community. A full understanding of the physiological mechanisms underlying neurodegeneration still eludes medical researchers, fuelling the development of in vivo neuroimaging markers. Hence it is increasingly recognized that our understanding of neurodegenerative processes likely will depend upon the available information provided by imaging techniques. At the same time, the imaging techniques are often developed in response to the desire to observe certain physiological processes. In this context, functional MRI (fMRI), which has for decades provided information on neuronal activity, has evolved into a large family of techniques well suited for in vivo observations of brain physiology. Given the rapid technical advances in fMRI in recent years, this review aims to summarize the physiological basis of fMRI observations in healthy aging as well as in age-related neurodegeneration. This review focuses on in-vivo human brain imaging studies in this review and on disease features that can be imaged using fMRI methods. In addition to providing detailed literature summaries, this review also discusses future directions in the study of brain physiology using fMRI in the clinical setting.

Neurobiological Correlates in Internet Gaming Disorder: A Systematic Literature Review.

Internet Gaming Disorder (IGD) is a potential mental disorder currently included in the third section of the latest (fifth) edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as a condition that requires additional research to be included in the main manual. Although research efforts in the area have increased, there is a continuing debate about the respective criteria to use as well as the status of the condition as mental health concern. Rather than using diagnostic criteria which are based on subjective symptom experience, the National Institute of Mental Health advocates the use of Research Domain Criteria (RDoC) which may support classifying mental disorders based on dimensions of observable behavior and neurobiological measures because mental disorders are viewed as biological disorders that involve brain circuits that implicate specific domains of cognition, emotion, and behavior. Consequently, IGD should be classified on its underlying neurobiology, as well as its subjective symptom experience. Therefore, the aim of this paper is to review the neurobiological correlates involved in IGD based on the current literature base. Altogether, 853 studies on the neurobiological correlates were identified on ProQuest (in the following scholarly databases: ProQuest Psychology Journals, PsycARTICLES, PsycINFO, Applied Social Sciences Index and s, and ERIC) and on MEDLINE, with the application of the exclusion criteria resulting in reviewing a total of 27 studies, using fMRI, rsfMRI, VBM, PET, and EEG methods. The results indicate there are significant neurobiological differences between healthy controls and individuals with IGD. The included studies suggest that compared to healthy controls, gaming addicts have poorer response-inhibition and emotion regulation, impaired prefrontal cortex (PFC) functioning and cognitive control, poorer working memory and decision-making capabilities, decreased visual and auditory functioning, and a deficiency in their neuronal reward system, similar to those found in individuals with substance-related addictions. This suggests both substance-related addictions and behavioral addictions share common predisposing factors and may be part of an addiction syndrome. Future research should focus on replicating the reported findings in different cultural contexts, in support of a neurobiological basis of classifying IGD and related disorders.

Brain-Computer Interface Systems Based On the Near-Infrared Spectroscopy

The article describes the modern systems of man and computer interaction, or Brain-Computer Interfaces (BCIs), of several types. The interfaces, based on hemodynamic activity measurements of human cerebral cortex by the means of the near-infrared spectroscopy are discussed and also those, which are based on the combinations of the near-infrared spectroscopy, the electroencephalography and the encephalographies of other modalities. The theoretical fundamentals are provided of near-infrared spectroscopy devices functioning and a number of the typical experiments in the field noted. The special focus is on the functional magnetic resonance imaging and other methods of collecting biometrics data in the context of hemodynamic response . Various practical realizations and technical solutions of devices of mentioned type are described. The highlight of possible practical utilizations of human-machine interfaces in the sphere of medicine and/or medical rehabilitation is given. The analysis of practical solutions and applications of BCI systems is performed, which was added by a systematic publications review . Related disciplines have been also considered. Several examples of simultaneous usage of BCIs and robotics systems, which were built for the compensation of human body's lost functions also are provided. Some attention is paid to the Fast Optical Signals and to the researches in the field of optogenetics. The results of the conducted research make us to believe that the peeked field of knowledge is fairly young and at some areas of it the process of pure research and random search of new facts and technical solutions is still ongoing, although with an extremely high intensity. All things considered, the analised research direction should be judged as highly perspective, all the more so as it is joined with wide area of related disciplines with broad spectra of implementations.

Psychopathic traits influence amygdala-anterior cingulate cortex connectivity during facial emotion processing.

There is accumulating evidence that youths with antisocial behavior or psychopathic traits show deficits in facial emotion recognition, but little is known about the neural mechanisms underlying these impairments. A number of neuroimaging studies have investigated brain activity during facial emotion processing in youths with Conduct Disorder (CD) and adults with psychopathy, but few of these studies tested for group differences in effective connectivity—i.e. changes in connectivity during emotion processing. Using functional magnetic resonance imaging and psycho-physiological interaction methods, we investigated the impact of CD and psychopathic traits on amygdala activity and effective connectivity in 46 male youths with CD and 25 typically-developing controls when processing emotional faces. All participants were aged 16–21 years. Relative to controls, youths with CD showed reduced amygdala activity when processing angry or sad faces relative to neutral faces, but the groups did not significantly differ in amygdala-related effective connectivity. In contrast, psychopathic traits were negatively correlated with amygdala–ventral anterior cingulate cortex connectivity for angry vs neutral faces, but were unrelated to amygdala responses to angry or sad faces. These findings suggest that CD and psychopathic traits have differential effects on amygdala activation and functional interactions between limbic regions during facial emotion processing.

Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation.SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample (n = 96) and replication sample (n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes.

Exposure therapy with no exposure

fMRI and patient feedback methods reduce fear responses by changing brain patterns without conscious awareness.

Neuronal Origin of the Temporal Dynamics of Spontaneous BOLD Activity Correlation.

Resting-state functional connectivity (FC) has become a major functional magnetic resonance imaging method to study network organization of human brains. There has been recent interest in the temporal fluctuations of FC calculated using short time windows ("dynamic FC") because this method could provide information inaccessible with conventional "static" FC, which is typically calculated using the entire scan lasting several tens of minutes. Although multiple studies have revealed considerable temporal fluctuations in FC, it is still unclear whether the fluctuations of FC measured in hemodynamics reflect the dynamics of underlying neural activity. We addressed this question using simultaneous imaging of neuronal calcium and hemodynamic signals in mice and found coordinated temporal dynamics of calcium FC and hemodynamic FC measured in the same short time windows. Moreover, we found that variation in transient neuronal coactivation patterns was significantly related to temporal fluctuations of sliding window FC in hemodynamics. Finally, we show that the observed dynamics of FC cannot be fully accounted for by simulated data assuming stationary FC. These results provide evidence for the neuronal origin of dynamic FC and further suggest that information relevant to FC is condensed in temporally sparse events that can be extracted using a small number of time points.

BOLD magnetic resonance imaging in nephrology

Magnetic resonance (MR) imaging, a non-invasive modality that provides anatomic and physiologic information, is increasingly used for diagnosis of pathophysiologic conditions and for understanding renal physiology in humans. Although functional MR imaging methods were pioneered to investigate the brain, they also offer powerful techniques for investigation of other organ systems such as the kidneys. However, imaging the kidneys provides unique challenges due to potential complications from contrast agents. Therefore, development of non-contrast techniques to study kidney anatomy and physiology is important. Blood oxygen level-dependent (BOLD) MR is a non-contrast imaging technique that provides functional information related to renal tissue oxygenation in various pathophysiologic conditions. Here we discuss technical considerations, clinical uses and future directions for use of BOLD MR as well as complementary MR techniques to better understand renal pathophysiology. Our intent is to summarize kidney BOLD MR applications for the clinician rather than focusing on the complex physical challenges that functional MR imaging encompasses; however, we briefly discuss some of those issues.

Mapping the changed hubs and corresponding functional connectivity in idiopathic restless legs syndrome

ObjectiveThe hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, whether the changed pattern in functional network hubs contributes to the onset of leg discomfort symptoms in restless legs syndrome (RLS) patients remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods, we investigated whether alterations of hubs can be detected in RLS. MethodsFirst, we constructed the whole-brain voxelwise functional connectivity and calculated a functional connectivity strength (FCS) map in each of 16 drug-naive idiopathic RLS patients and 26 gender- and age-matched healthy control (HC) subjects. Next, a two-sample t test was applied to compare the FCS maps between HC and RLS patients, and to identify significant changes in FCS in RLS patients. To further elucidate the corresponding changes in the functional connectivity patterns of the aberrant hubs in RLS patients, whole-brain resting-state functional connectivity analyses for the hub areas were performed. ResultsThe hub analysis revealed decreased FCS in the cuneus, fusiform gyrus, paracentral lobe, and precuneus, and increased FCS in the superior frontal gyrus and thalamus in idiopathic drug-naive RLS patients. Subsequent functional connectivity analyses revealed decreased functional connectivity in sensorimotor and visual processing networks and increased functional connectivity in the affective cognitive network and cerebellar–thalamic circuit. Furthermore, the mean FCS value in the superior frontal gyrus was significantly correlated with Hamilton Anxiety Rating Scale scores in RLS patients, and the mean FCS value in the fusiform gyrus was significantly correlated with Hamilton Depression Rating Scale scores. ConclusionsThese findings may provide novel insight into the pathophysiology of RLS.