Improvement of spectral density-based activation detection of event-related fMRI data

For event-related data obtained from an experimental paradigm with a periodic design, spectral density at the fundamental frequency of the paradigm has been used as a template-free activation detection measure. In this article, we build and expand upon this detection measure to create an improved, integrated measure. Such an integrated measure linearly combines information contained in the spectral densities at the fundamental frequency as well as the harmonics of the paradigm and in a spatial correlation function characterizing the degree of co-activation among neighboring voxels. Several figures of merit are described and used to find appropriate values for the coefficients in the linear combination. Using receiver-operating characteristic analysis on simulated functional magnetic resonance imaging (fMRI) data sets, we quantify and validate the improved performance of the integrated measure over the spectral density measure based on the fundamental frequency as well as over some other popular template-free data analysis methods. We then demonstrate the application of the new method on an experimental fMRI data set. Finally, several extensions to this work are suggested.     

Activation detection in event-related fMRI data based on spatio-temporal properties.

Template-based activation detection methods, such as cross-correlation, could be difficult to apply in event-related functional MRI data because accurate a priori knowledge about the activation signal patterns is often not available. As a result, several categories of template-free data analysis techniques have been introduced in the fMRI literature. One previously described template-free activation detection technique is based on the feature that activated voxels yield reproducible time course patterns as the subject undergoes the same simulation in repeated epochs. In this paper, spatial information is incorporated as a second feature and a combined univariate measure is formed. The resulting method is shown to offer measurable improvement in detecting activation regions in simulated data in a highly computationally efficient manner. Its practical utility is demonstrated with an experimental data set obtained with a visually guided motor paradigm.     

Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations

The task induced blood oxygenation level dependent signal changes observed using functional magnetic resonance imaging (fMRI) are critically dependent on the relationship between neuronal activity and hemodynamic response. Therefore, understanding the nature of neurovascular coupling is important when interpreting fMRI signal changes evoked via task. In this study, we used regional homogeneity (ReHo), a measure of local synchronization of the BOLD time series, to investigate whether the similarities of one voxel with the surrounding voxels are a property of neurovascular coupling. FMRI scans were obtained from fourteen subjects during bilateral finger tapping (FTAP), digit–symbol substitution (DSST) and periodic breath holding (BH) paradigm. A resting-state scan was also obtained for each of the subjects for 4 min using identical imaging parameters. Inter-voxel correlation analyses were conducted between the resting-state ReHo, resting-state amplitude of low frequency fluctuations (ALFF), BH responses and task activations within the masks related to task activations. There was a reliable mean voxel-wise spatial correlation between ReHo and other neurovascular variables (BH responses and ALFF). We observed a moderate correlation between ReHo and task activations (FTAP: r = 0.32; DSST: r = 0.22) within the task positive network and a small yet reliable correlation within the default mode network (DSST: r = − 0.08). Subsequently, a linear regression was used to estimate the contribution of ReHo, ALFF and BH responses to the task activated voxels. The unique contribution of ReHo was minimal. The results suggest that regional synchrony of the BOLD activity is a property that can explain the variance of neurovascular coupling and task activations; but its contribution to task activations can be accounted for by other neurovascular factors such as the ALFF.     

Measurement and characterization of the human spinal cord SEEP response using event-related spinal fMRI

Although event-related fMRI is able to reliably detect brief changes in brain activity and is now widely used throughout systems and cognitive neuroscience, there have been no previous reports of event-related spinal cord fMRI. This is likely attributable to the various technical challenges associated with spinal fMRI (e.g., imaging a suitable length of the cord, reducing image artifacts from the vertebrae and intervertebral discs, and dealing with physiological noise from spinal cord motion). However, with many of these issues now resolved, the largest remaining impediment for event-related spinal fMRI is a deprived understanding of the spinal cord fMRI signal time course. Therefore, in this study, we used a proton density-weighted HASTE sequence, with functional contrast based on signal enhancement by extravascular water protons (SEEP), and a motion-compensating GLM analysis to (i) characterize the SEEP response function in the human cervical spinal cord and (ii) demonstrate the feasibility of event-related spinal fMRI. This was achieved by applying very brief (1 s) epochs of 22°C thermal stimulation to the palm of the hand and measuring the impulse response function. Our results suggest that the spinal cord SEEP response (time to peak ≈8 s; FWHM ≈4 s; and probably lacking pre- and poststimulus undershoots) is slower than previous estimates of SEEP or BOLD responses in the brain, but faster than previously reported spinal cord BOLD responses. Finally, by detecting and mapping consistent signal-intensity changes within and across subjects, and validating these regions with a block-designed experiment, this study represents the first successful demonstration of event-related spinal fMRI.     

Across-trial averaging of event-related EEG responses and beyond

Internally and externally triggered sensory, motor and cognitive events elicit a number of transient changes in the ongoing electroencephalogram (EEG): event-related brain potentials (ERPs), event-related synchronization and desynchronization (ERS/ERD), and event-related phase resetting (ERPR). To increase the signal-to-noise ratio of event-related brain responses, most studies rely on across-trial averaging in the time domain, a procedure that is, however, blind to a significant fraction of the elicited cortical activity. Here, we outline the key concepts underlying the limitations of time-domain averaging and consider three alternative methodological approaches that have received increasing interest: time-frequency decomposition of the EEG (using the continuous wavelet transform), blind source separation of the EEG (using Independent Component Analysis) and the analysis of event-related brain responses at the level of single trials. In addition, we provide practical guidelines on the implementation of these methods and on the interpretation of the results they produce.     

Elevated responses to constant facial emotions in different faces in the human amygdala: an fMRI study of facial identity and expression

Background  

Human faces provide important signals in social interactions by inferring two main types of information, individual identity and emotional expression. The ability to readily assess both, the variability and consistency among emotional expressions in different individuals, is central to one's own interpretation of the imminent environment. A factorial design was used to systematically test the interaction of either constant or variable emotional expressions with constant or variable facial identities in areas involved in face processing using functional magnetic resonance imaging.     

Rate dependence of human visual cortical response due to brief stimulation: an event-related fMRI study

The human brain response to a wide range of visual stimulus rates presented over a prolonged time period has been investigated by various neuroimaging techniques. However, to date, no imaging study has been performed to study the dynamic human brain response to various stimulus rates when presented in a short time. This report describes activation in the human brain due to brief visual stimulus presentation (1 s) for stimulus rates varying from 1 to 20 Hz using event-related functional MRI (fMRI). Our results show that the amplitude of the fMRI response increases with the stimulus frequency and plateaus at 6 Hz. This finding differs slightly from the results of previous blocked task paradigm experiments (with a longer time of stimulus presentation), in which the response peaks at approximately 8 Hz and then decreases. Our results are in close agreement with previously published psychophysical studies, suggesting that the fMRI signal in this experiment is indicative of cortical activity related to visual processing.     

The representation of the verb's argument structure as disclosed by fMRI

Background  

In the composition of an event the verb's argument structure defines the number of participants and their relationships. Previous studies indicated distinct brain responses depending on how many obligatory arguments a verb takes. The present functional magnetic resonance imaging (fMRI) study served to verify the neural structures involved in the processing of German verbs with one (e.g. "snore") or three (e.g. "gives") argument structure. Within a silent reading design, verbs were presented either in isolation or with a minimal syntactic context ("snore" vs. "Peter snores").     

Improved detection of time windows of brain responses in fMRI using modified temporal clustering analysis

Temporal clustering analysis (TCA) has been proposed recently as a method to detect time windows of brain responses in functional MRI (fMRI) studies when the timing and location of the activation are completely unknown. Modifications to the TCA technique are introduced in this report to further improve the sensitivity in detecting brain activation. The modified TCA is based on the integrated signal intensity of a temporal cluster at each time point, while the original TCA is based only on the size of a temporal cluster at each time point. A temporal cluster at each time point is defined, in both TCA methods, as a group of pixels reaching their maximum (or minimum) values at the same time. Both computer simulation and in vivo fMRI experiments have been performed. Compared with the original TCA, the modified TCA shows a significant improvement in the sensitivity to detect activation peaks for determining time windows of brain responses.     

Complementary aspects of diffusion imaging and fMRI; I: structure and function

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.     

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.     

White versus gray matter: fMRI hemodynamic responses show similar characteristics, but differ in peak amplitude

ABSTRACT: BACKGROUND: There is growing evidence for the idea of fMRI activation in white matter. In the current study, we compared hemodynamic response functions (HRF) in white matter and gray matter using 4 T fMRI. White matter fMRI activation was elicited in the isthmus of the corpus callosum at both the group and individual levels (using an established interhemispheric transfer task). Callosal HRFs were compared to HRFs from cingulate and parietal activation. RESULTS: Examination of the raw HRF revealed similar overall response characteristics. Finite impulse response modeling confirmed that the WM HRF characteristics were comparable to those of the GM HRF, but had significantly decreased peak response amplitudes. CONCLUSIONS: Overall, the results matched a priori expectations of smaller HRF responses in white matter due to the relative drop in cerebral blood flow (CBF) and cerebral blood volume (CBV). Importantly, the findings demonstrate that despite lower CBF and CBV, white matter fMRI activation remained within detectable ranges at 4 T.     

White versus gray matter: fMRI hemodynamic responses show similar characteristics, but differ in peak amplitude

Background

The first mammalian protein histidine phosphatase (PHP) was discovered in the late 90s of the last century. One of the known substrates of PHP is ATP-citrate lyase (ACL), which is responsible - amongst other functions - for providing acetyl-CoA for acetylcholine synthesis in neuronal tissues. It has been shown in previous studies that PHP downregulates the activity of ACL by dephosphorylation. According to this our present work focused on the influence of PHP activity on the acetylcholine level in cholinergic neurons.

Results

The amount of PHP in SN56 cholinergic neuroblastoma cells was increased after overexpression of PHP by using pIRES2-AcGFP1-PHP as a vector. We demonstrated that PHP overexpression reduced the acetylcholine level and induced cell death. The acetylcholine content of SN56 cells was measured by fast liquid chromatography-tandem mass spectrometry method. Overexpression of the inactive H53A-PHP mutant also induced cell damage, but in a significantly reduced manner. However, this overexpression of the inactive PHP mutant did not change the acetylcholine content of SN56 cells significantly. In contrast, PHP downregulation, performed by RNAi-technique, did not induce cell death, but significantly increased the acetylcholine content in SN56 cells.

Conclusions

We could show for the first time that PHP downregulation increased the acetylcholine level in SN56 cells. This might be a potential therapeutic strategy for diseases involving cholinergic deficits like Alzheimer's disease.     

Nonlinear responses of the periodic structure composed of single negative materials

We investigate the nonlinear responses of the one-dimensional periodic structure containing alternate single negative materials. The transmission property of this nonlinear periodic structure is studied and the bistable behavior is found. Similar as in the linear periodic structure, the transmission property of this nonlinear structure in the zero phase (zero-?) gap region is found to be relatively insensitive to the incident angle from 0° to 30°, in comparison with that in the Bragg gap. Especially, the transmissions associated with the zero-? gap reach the peaks at almost the same input intensity for different incident angles. Another merit of the transmission property in the zero-? gap is that a lower threshold is needed to achieve the bistability than in the Bragg gap. The electric field distribution in the system is further studied. We observe the zero-? gap soliton and compare it with the usual Bragg gap soliton. The differences between these two kinds of solitons are analyzed, and the zero-? gap soliton is shown to be insensitive to the incident angle. In the end, we find that the transmission and the soliton in the zero-? gap are also weakly dependent on the loss and the scaling in contrast to that in the Bragg gap.     

Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling

We investigated the use and implementation of a nonlinear methodology for establishing which changes in neurophysiological signals cause changes in the blood oxygenation level-dependent (BOLD) contrast measured in functional magnetic resonance imaging. Unlike previous analytical approaches, which used linear correlation to establish covariations between neural activity and BOLD, we propose a directed information-theoretic measure, the transfer entropy, which can elucidate even highly nonlinear causal relationships between neural activity and BOLD signal. In this study we investigated the practicality of such an analysis given the limited data samples that can be collected experimentally due to the low temporal resolution of BOLD signals. We implemented several algorithms for the estimation of transfer entropy and we tested their effectiveness using simulated local field potentials (LFPs) and BOLD data constructed to match the main statistical properties of real LFP and BOLD signals measured simultaneously in monkey primary visual cortex. We found that using the advanced methods of entropy estimation implemented and described here, a transfer entropy analysis of neurovascular coupling based on experimentally attainable data sets is feasible.     

Coherent NMR responses in spin systems with complex spectral structure

We show that for nonresonant excitation of a quantum system, with an NMR spectrum consisting of two inhomogeneously broadened lines, by a combination of an extended and a short delta-shaped pulse, in the free-precession and spin-echo signals after the second pulse we observe the effect of doubling of the additional emission signals. We establish that the nature of this effect is due to zero beats arising when the variable oscillation frequency of the magnetic moments of the material matches the detuning of the pulse carrier frequency from the resonant frequencies of the spectral lines. Within the theory obtained, we propose a formula which we can use, if we know the times at which the additional emission signals arise, to very accurately determine the frequency shift of the lines in the complex spectrum of the material. The theoretical results agree well with experimental data on generation of multiple NMR signals in toluene, the spectrum of which consists of two non-overlapping lines. __________ Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 73, No. 3, pp. 304–308, May–June, 2006.     

Usefulness of event-related potentials in the assessment of mild cognitive impairment

Background  

The aim of this study was to determine if changes in latencies and amplitudes of the major waves of Auditory Event-Related Potentials (AERP), correlate with memory status of patients with mild cognitive impairment (MCI) and conversion to Alzheimer's disease (AD).