人脑初级视皮层内事件相关型视觉刺激fMRI BOLD响应特性初步研究

基于脑血氧水平依赖(BOLD)对比的功能磁共振成像（fMRI）方法是研究脑功能活动的一种重要的无损伤探测手段，BOLD的机制及它与脑神经活动关系一直是国际上十分活跃的研究领域，尤其是在实验研究方面. 视觉刺激所引起的脑的初级视皮层(V1) 的BOLD响应的时间特性已有较多的研究，但是在这些研究中，有的结论认为BOLD对视觉刺激的响应是线性的，有的结论却是相反的. 我们采用事件关联型核磁共振功能成像（ER-fMRI）方法，研究不同的短暂视觉刺激持续时间下的BOLD响应，得到了视觉刺激下的脑激活图. 同时找出了视觉刺激时间分别是1、2、3、4、5和6 s的V1区的BOLD响应曲线，并初步显示出BOLD响应与刺激持续时间的线性关系.     

A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response

Blood oxygenation level-dependent (BOLD) contrast-based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is the comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian or Gamma function. The parameters of the two models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better than the Gamma model in fitting the data. The model parameters are different in the left and right occipital regions, which indicate that the dynamic processes seem different in various cerebral functional regions.     

Projection screen or video goggles as stimulus modality in functional magnetic resonance imaging

The purpose of this study was to investigate the reliability of functional magnetic resonance imaging (fMRI) by using either a projection screen or video goggles as stimulus modality. A sequence of visual stimuli were presented to the same subject at different occasions. The sequence was optimized with a genetic algorithm. In five sessions the stimuli were presented using a projection screen viewed through a mirror in the head coil and in five sessions using video goggles. Failure to detect visual activation in the medial left hemisphere was observed in sessions using the projection screen as stimulus modality. Decreased thresholds for P values and cluster size resulted in activation outside the occipital lobe and did not significantly increase activated areas in this region. Results in this study indicate that presentation of fMRI tasks with visual routes is more reliable with direct input through video goggles than with the conventional use of projection screens. Failure to detect crucial visual areas has severe consequences for tumor surgery in the visual cortex. Inferior visual impression might also have negative consequences for cognitive tests with high demand on attention and perception.     

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.     

Nonlinear responses of cerebral blood volume, blood flow and blood oxygenation signals during visual stimulation

Hemodynamic-based functional magnetic resonance imaging (fMRI) techniques provide a great utility for noninvasive functional brain mapping. However, because the hemodynamic signals reflect underlying neural activity indirectly, characterization of these signals following brain activation is essential for experimental design and data interpretation. In this report, the linear (or nonlinear) responses to neuronal activation of three hemodynamic parameters based primarily on changes of cerebral blood volume, blood flow and blood oxygenation were investigated by testing these hemodynamic responses' additivity property. Using a recently developed fMRI technique that acquires vascular space occupancy (VASO), arterial spin labeling (ASL) perfusion and blood oxygenation level-dependent (BOLD) signals simultaneously, the additivity property of the three hemodynamic responses in human visual cortex was assessed using various visual stimulus durations. Experiments on healthy volunteers showed that all three hemodynamic-weighted signals responded nonlinearly to stimulus durations less than 4 s, with the degree of nonlinearity becoming more severe as the stimulus duration decreased. Vascular space occupancy and ASL perfusion signals showed similar nonlinearity properties, whereas the BOLD signal was the most nonlinear. These data suggest that caution should be taken in the interpretation of hemodynamic-based signals in fMRI.     

Hemodynamic responses in human multisensory and auditory association cortex to purely visual stimulation

Background  

Recent findings of a tight coupling between visual and auditory association cortices during multisensory perception in monkeys and humans raise the question whether consistent paired presentation of simple visual and auditory stimuli prompts conditioned responses in unimodal auditory regions or multimodal association cortex once visual stimuli are presented in isolation in a post-conditioning run. To address this issue fifteen healthy participants partook in a "silent" sparse temporal event-related fMRI study. In the first (visual control) habituation phase they were presented with briefly red flashing visual stimuli. In the second (auditory control) habituation phase they heard brief telephone ringing. In the third (conditioning) phase we coincidently presented the visual stimulus (CS) paired with the auditory stimulus (UCS). In the fourth phase participants either viewed flashes paired with the auditory stimulus (maintenance, CS-) or viewed the visual stimulus in isolation (extinction, CS+) according to a 5:10 partial reinforcement schedule. The participants had no other task than attending to the stimuli and indicating the end of each trial by pressing a button.     

Neonatal auditory activation detected by functional magnetic resonance imaging

The objective of this study was to detect auditory cortical activation in non-sedated neonates employing functional magnetic resonance imaging (fMRI). Using echo-planar functional brain imaging, subjects were presented with a frequency-modulated pure tone; the BOLD signal response was mapped in 5 mm-thick slices running parallel to the superior temporal gyrus. Twenty healthy neonates (13 term, 7 preterm) at term and 4 adult control subjects. Blood oxygen level-dependent (BOLD) signal in response to auditory stimulus was detected in all 4 adults and in 14 of the 20 neonates. FMRI studies of adult subjects demonstrated increased signal in the superior temporal regions during auditory stimulation. In contrast, signal decreases were detected during auditory stimulation in 9 of 14 newborns with BOLD response. fMRI can be used to detect brain activation with auditory stimulation in human infants.     

Blood Oxygenation Level Dependent Signal Time Courses During Prolonged Visual Stimulation

Previous functional magnetic resonance imaging (MRI) studies using extended visual stimulation have reported disparate results. Two studies have shown that blood oxygen level dependent (BOLD) contrast decays over time which is cited as evidence of recoupling between oxygen utilisation and cerebral blood flow during stimulus presentation. These findings have serious implications for the design of functional MRI experiments because they raise the possibility that BOLD contrast may not accurately reflect neuronal activity. Another study reported no decay of BOLD contrast. These studies used different visual stimuli and imaging techniques. We have performed a series of experiments, using different MRI techniques (echo-planar imaging and fast low angle shot) and two different visual stimuli to assess which of these factors may explain the previous results. In all of our experiments the signal time course from areas of significant activation remained largely elevated throughout the duration of stimulation and this is not affected by the imaging method used. Our data, in accordance with that of Bandettini et al., suggest that recoupling between blood flow and oxygen extraction is not a general phenomenon in the human brain when visual stimuli are presented for an extended time.     

Integration of EEG source imaging and fMRI during continuous viewing of natural movies

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705–717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner.     

Dynamics and nonlinearities of the BOLD response at very short stimulus durations

In designing a functional imaging experiment or analyzing data, it is typically assumed that task duration and hemodynamic response are linearly related to each other. However, numerous human and animal studies have previously reported a deviation from linearity for short stimulus durations (<4 s). Here, we investigated nonlinearities of blood-oxygenation-level-dependent (BOLD) signals following visual stimulation of 5 to 1000 ms duration at two different luminance levels in human subjects. It was found that (a) a BOLD response to stimulus durations as short as 5 ms can be reliably detected; this stimulus duration is shorter than employed in any previous study investigating BOLD signal time courses; (b) the responses are more nonlinear than in any other previous study: the BOLD response to 1000 ms stimulation is only twice as large as the BOLD response to 5 ms stimulation although 200 times more photons were projected onto the retina; (c) the degree of nonlinearity depends on stimulus intensity; that is, nonlinearities have to be characterized not only by stimulus duration but also by stimulus features like luminance. These findings are especially of most practical importance in rapid event-related functional magnetic resonance imaging (fMRI) experimental designs. In addition, an 'initial dip' response--thought to be generated by a rapid increase in cerebral metabolic rate of oxygen metabolism (CMRO2) relative to cerebral blood flow--was observed and shown to colocalize well with the positive BOLD response. Highly intense stimulation, better tolerated by human subjects for short stimulus durations, causes early CMRO2 increase, and thus, the experimental design utilized in this study is better for detecting the initial dip than standard fMRI designs. These results and those from other groups suggest that short stimulation combined with appropriate experimental designs allows neuronal events and interactions to be examined by BOLD signal analysis, despite its slow evolution.     

Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study

Signal fluctuations in functional magnetic resonance imaging (fMRI) can result from a number of sources that may have a neuronal, physiologic or instrumental origin. To determine the relative contribution of these sources, we recorded physiological (respiration and cardiac) signals simultaneously with fMRI in human volunteers at rest with their eyes closed. State-of-the-art technology was used including high magnetic field (7 T), a multichannel detector array and high-resolution (3 mm³) echo-planar imaging. We investigated the relative contribution of thermal noise and other sources of variance to the observed fMRI signal fluctuations both in the visual cortex and in the whole brain gray matter. The following sources of variance were evaluated separately: low-frequency drifts due to scanner instability, effects correlated with respiratory and cardiac cycles, effects due to variability in the respiratory flow rate and cardiac rate, and other sources, tentatively attributed to spontaneous neuronal activity. We found that low-frequency drifts are the most significant source of fMRI signal fluctuations (3.0% signal change in the visual cortex, TE=32 ms), followed by spontaneous neuronal activity (2.9%), thermal noise (2.1%), effects due to variability in physiological rates (respiration 0.9%, heartbeat 0.9%), and correlated with physiological cycles (0.6%). We suggest the selection and use of four lagged physiological noise regressors as an effective model to explain the variance related to fluctuations in the rates of respiration volume change and cardiac pulsation. Our results also indicate that, compared to the whole brain gray matter, the visual cortex has higher sensitivity to changes in both the rate of respiration and the spontaneous resting-state activity. Under the conditions of this study, spontaneous neuronal activity is one of the major contributors to the measured fMRI signal fluctuations, increasing almost twofold relative to earlier experiments under similar conditions at 3 T.     

Anatomical correlates of the functional organization in the human occipitotemporal cortex

The connectivity between functionally distinct areas in the human brain is unknown because of the limitations posed by current postmortem anatomical labeling techniques. Diffusion tensor imaging (DTI) has previously been used to define large white matter tracts based on well-known anatomical landmarks in the living human brain. In the present study, we used DTI coupled with functional magnetic resonance imaging (fMRI) to assess neuronal connections between human striate and functionally defined extrastriate ventral cortical areas. Functional areas were identified with conventional fMRI mapping procedures and then used as seeding points in a DTI analysis to ascertain connectivity patterns between cortical areas, thus yielding the pattern of connections between human occipitoventral visual areas in vivo.     

Identification of activated regions during a language task

Functional magnetic resonance imaging (fMRI) techniques are based on the assumption that changes in neural activity are accompanied by modulation in the blood-oxygenation-level-dependent (BOLD) signal. In addition to conventional increases in BOLD signals, sustained negative BOLD signal changes are occasionally observed in many fMRI experiments, which show regions of cortex that seem to respond in antiphase with primary stimulus. The existence of this so-called negative BOLD response (NBR) has been observed and investigated in many functional studies. Several theoretical mechanisms have been proposed to account for it, but its origin has never been fully explained. In this study, the variability of fMRI activation, including the sources of the negative BOLD signal, during phonological and semantic language tasks, was investigated in six right-handed healthy subjects. We found significant activations in the brain regions, mainly in the left hemisphere, involved in the language stimuli [prominent in the inferior frontal gyrus, approximately Brodmann Areas (BA)7, BA44, BA45 and BA47, and in the precuneus]. Moreover, we observed activations in motor regions [precentral gyrus and supplementary motor area (SMA)], a result that suggests a specific role of these areas (particularly the SMA) in language processing. Functional analysis have also shown that certain brain regions, including the posterior cingulate cortex and the anterior cingulate cortex, have consistently greater activity during resting states compared to states of performing cognitive tasks. In our study, we observed diffuse NBR at the cortical level and a stronger negative response in correspondence to the main sinuses. These phenomena seem to be unrelated to a specific neural activity, appearing to be expressions of a mechanical variation in hemodynamics. We discussed about the importance of these responses that are anticorrelated with the stimulus. Our data suggest that particular care must be considered in the interpretation of fMRI findings, especially in the case of presurgical studies.     

An improved temporal clustering analysis method applied to whole-brain data in fMRI study

Temporal clustering analysis (TCA) has been proposed as a method to detect the brain responses of an fMRI time series when the time and location of the activation are completely unknown. But TCA is still incompetent in dealing with the time series of the whole brain due to the existence of many inactive pixels. If only active pixels are considered, the sensitivity of TCA will be improved greatly and it could be applied to the whole brain. In this study, some modifications were made to TCA to remove inactive pixels, and the applicability of the modified TCA to the whole brain was validated with a set of visual fMRI data. Based on the time series of the modified TCA, activations of the whole brain corresponding to the visual stimulation were detected. Compared with the previous TCA, the modified TCA method shows a significant improvement in the sensitivity to detect activation peaks of the whole brain.     

Top-down and bottom-up modulation of language related areas – An fMRI Study

Background  

One major problem for cognitive neuroscience is to describe the interaction between stimulus and task driven neural modulation. We used fMRI to investigate this interaction in the human brain. Ten male subjects performed a passive listening and a semantic categorization task in a factorial design. In both tasks, words were presented auditorily at three different rates.     

A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.     

Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys

Pattern recognition methods have shown that functional magnetic resonance imaging (fMRI) data can reveal significant information about brain activity. For example, in the debate of how object categories are represented in the brain, multivariate analysis has been used to provide evidence of a distributed encoding scheme [Science 293:5539 (2001) 2425–2430]. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success [Nature reviews 7:7 (2006) 523–534]. In this study, we compare four popular pattern recognition methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis (LDA) and Gaussian naïve Bayes (GNB), using data collected at high field (7 Tesla) with higher resolution than usual fMRI studies. We investigate prediction performance on single trials and for averages across varying numbers of stimulus presentations. The performance of the various algorithms depends on the nature of the brain activity being categorized: for several tasks, many of the methods work well, whereas for others, no method performs above chance level. An important factor in overall classification performance is careful preprocessing of the data, including dimensionality reduction, voxel selection and outlier elimination.     

Iterative temporal clustering analysis for the detection of multiple response peaks in fMRI

The temporal clustering analysis (TCA) is a novel and effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown. Performing the TCA method once can only detect the largest peak of the activation time windows well, if multiple response peaks at the same location of the brain occur. However, this limitation can be removed by using a TCA method in an iterative way in order for the smaller peaks to be detected. Our in vivo fMRI experiments with event-related visual tasks have demonstrated this ability.     

Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation

We investigate the relationship between the temporal variation in the magnitude of occipital visual evoked potentials (VEPs) and of haemodynamic measures of brain activity obtained using both blood oxygenation level dependent (BOLD) and perfusion sensitive (ASL) functional magnetic resonance imaging (fMRI). Volunteers underwent a continuous BOLD fMRI scan and/or a continuous perfusion-sensitive (gradient and spin echo readout) ASL scan, during which 30 second blocks of contrast reversing visual stimuli (at 4 Hz) were interleaved with 30 second blocks of rest (visual fixation). Electroencephalography (EEG) and fMRI were simultaneously recorded and following EEG artefact cleaning, VEPs were averaged across the whole stimulation block (120 reversals, VEP₁₂₀) and at a finer timescale (15 reversals, VEP₁₅). Both BOLD and ASL time-series were linearly modelled to establish: (1) the mean response to visual stimulation, (2) transient responses at the start and end of each stimulation block, (3) the linear decrease between blocks, (4) the nonlinear between-block variation (covariation with VEP₁₂₀), (5) the linear decrease within block and (6) the nonlinear variation within block (covariation with VEP₁₅).     

Nonlinear statistical properties of fMRI signals in human visual cortex during resting-state

The purpose of the current work is to study nonlinear dynamics in neuronal activity within human brain visual cortex based on blood-oxygen-level dependent (BOLD) contrast imaging. In particular, based on functional magnetic resonance imaging (fMRI) signals, measures of fractality, complexity, and state disorder are estimated from central and peripheral eccentricity bands across three visual areas. Statistical results from analysis of 48750 resting-state fMRI signals show evidence that nonlinear dynamics of neuronal activity in resting-state in central and peripheral eccentricity bands of human visual cortex are persistent. However, they exhibit heterogeneous variability across eccentricity bands and visual areas. Also, information content in first visual area is more ordered than in the second one, whilst information content in the third visual area is the least ordered. These interesting nonlinear statistical properties are a further step toward understanding neuronal activity and nonlinear dynamics in human brain visual cortex.