Real-time animal functional magnetic resonance imaging and its application to neuropharmacological studies

In pharmacological magnetic resonance imaging (phMRI) with anesthetized animals, there is usually only a single time window to observe the dynamic signal change to an acute drug administration since subsequent drug injections are likely to result in altered response properties (e.g., tolerance). Unlike the block-design experiments in which fMRI signal can be elicited with multiple repetitions of a task, these single-event experiments require stable baseline in order to reliably identify drug-induced signal changes. Such factors as subject motion, scanner instability and/or alterations in physiological conditions of the anesthetized animal could confound the baseline signal. The unique feature of such functional MRI (fMRI) studies necessitates a technique that is able to monitor MRI signal in a real-time fashion and to interactively control certain experimental procedures. In the present study, an approach for real-time MRI on a Bruker scanner is presented. The custom software runs on the console computer in parallel with the scanner imaging software, and no additional hardware is required. The utility of this technique is demonstrated in manganese-enhanced MRI (MEMRI) with acute cocaine challenge, in which temporary disruption of the blood-brain barrier (BBB) is a critical step for MEMRI experiments. With the aid of real-time MRI, we were able to assess the outcome of BBB disruption following bolus injection of hyperosmolar mannitol in a near real-time fashion prior to drug administration, improving experimental success rate. It is also shown that this technique can be applied to monitor baseline physiological conditions in conventional fMRI experiments using blood oxygenation level-dependent (BOLD) contrast, further demonstrating the versatility of this technique.     

Nonlinear blood oxygen level-dependent responses for transient activations and deactivations in V1 - insights into the hemodynamic response function with the balloon model

We report studies of the nonlinear nature of blood oxygen level-dependent (BOLD) responses to short transient deactivations in human visual cortex. Both functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) have been used to compare and contrast the hemodynamic response functions (HRFs) associated with transient activation and deactivation in primary visual cortex. We show that signal decreases for short duration deactivations are smaller than corresponding signal increases in activation studies. Moreover, the standard balloon model of BOLD effects may be modified to account for the observed nonlinear nature of deactivations by appropriate changes to simple hemodynamic parameters without recourse to new assumptions about the nature of the coupling between activity and oxygen use.     

BOLD functional MRI in disease and pharmacological studies: room for improvement?

In the past decade the use of blood oxygen level-dependent (BOLD) fMRI to investigate the effect of diseases and pharmacological agents on brain activity has increased greatly. BOLD fMRI does not measure neural activity directly, but relies on a cascade of physiological events linking neural activity to the generation of MRI signal. However, most of the disease and pharmacological studies performed so far have interpreted changes in BOLD fMRI as "brain activation," ignoring the potential confounds that can arise through drug- or disease-induced modulation of events downstream of the neural activity. This issue is especially serious in diseases (like multiple sclerosis, brain tumours and stroke) and drugs (like anaesthetics or those with a vascular action) that are known to influence these physiological events. Here we provide evidence that, to extract meaningful information on brain activity in patient and pharmacological BOLD fMRI studies, it is important to identify, characterise and possibly correct these influences that potentially confound the results. We suggest a series of experimental measures to improve the interpretability of BOLD fMRI studies. We have ranked these according to their potential information and current practical feasibility. First-line, necessary improvements consist of (1) the inclusion of one or more control tasks, and (2) the recording of physiological parameters during scanning and subsequent correction of possible between-group differences. Second-line, highly recommended important aim to make the results of a patient or drug BOLD study more interpretable and include the assessment of (1) baseline brain perfusion, (2) vascular reactivity, (3) the inclusion of stimulus-related perfusion fMRI and (4) the recording of electrophysiological responses to the stimulus of interest. Finally, third-line, desirable improvements consist of the inclusion of (1) simultaneous EEG-fMRI, (2) cerebral blood volume and (3) rate of metabolic oxygen consumption measurements and, when relevant, (4) animal studies investigating signalling between neural cells and blood vessels.     

基于B样条的拉曼光谱基线校正方法

基线漂移是光谱检测仪器普遍存在的现象,会对光谱信号的特征提取带来十分不利的影响,而基线校正是解决该问题的重要手段,也是拉曼光谱信号预处理的重要组成部分。基线校正的原理一般是通过拟合基线的方法来去除光谱信号中的基线漂移。传统的基线校正方法是利用多项式拟合的方法对拉曼光谱信号的基线进行拟合的,但是该方法容易出现过拟合和欠拟合现象,且拟合阶数难以确定。针对传统方法的缺点进行了改进,利用B样条方法对拉曼光谱信号的基线进行拟合,发挥B样条低阶光滑的优点,能够有效地克服多项式方法的缺陷。在实验中,利用该方法对孔雀石绿、罗丹明B的拉曼光谱信号进行了基线校正,观察并比较该方法和传统方法的校正结果。实验结果表明,该方法能够有效地消除拉曼光谱信号的基线漂移,在基线漂移较小和较大的位置,可以采用相同的拟合阶数,不会出现欠拟合和过拟合的现象,获得了良好的基线校正效果,为进一步分析光谱数据提供准确可靠的信息。     

Evaluation of lung tumor perfusion by dynamic contrast-enhanced MRI

The characterization of solid pulmonary lesions with imaging methods remains a diagnostic challenge. The aim of this study was to correlate kinetic parameters of dynamic perfusion magnetic resonance imaging (MRI) with histological tumor classification. Dynamic contrast-enhanced MRI of 31 patients with pulmonary masses (five benign lesions, 26 malignant tumors) was acquired in the tumor areas every 20 s for a mean duration of 124 s. Contrast uptake (CU) was measured by signal analysis in regions of interest (ROIs). The beginning and duration of CU, maximum CU (MCU, % of baseline), maximum contrast upslope (%/s) and the delay to the maximum contrast upslope (s) were calculated. All lesions were classified histologically. The beginning of CU correlated significantly with the MCU delay in all lesions (P=.033). The frequency of a plateau phase was higher in malignant tumors compared to benign lesions (P=.031). Masses with a high MCU showed more frequently a washout of contrast medium after a plateau phase (P=.006) and a higher maximum contrast upslope (P<.001). The MCU delay time was shorter in adenocarcinoma than in squamous cell carcinoma (P=.004). These results indicate that dynamic contrast enhanced MRI might become instrumental in differentiating benign from malignant intrapulmonary tumors and distinguishing adenocarcinoma from squamous cell carcinoma.     

Quantitative NumART2* mapping in functional MRI studies at 1.5 T

Quantitative mapping of the effective transverse relaxation time, T₂* and proton density was performed in a motor activation functional MRI (fMRI) study using multi-echo, echo planar imaging (EPI) and NumART₂* (Numerical Algorithm for Real time T₂*). Comparisons between NumART₂* and conventional single echo EPI with an echo time of 64 ms were performed for five healthy participants examined twice. Simulations were also performed to address specific issues associated with the two techniques, such as echo time-dependent signal variation. While the single echo contrast varied with the baseline T₂* value, relative changes in T₂* remained unaffected. Statistical analysis of the T₂* maps yielded fMRI activation patterns with an improved statistical detection relative to conventional EPI but with less activated voxels, suggesting that NumART₂* has superior spatial specificity. Two effects, inflow and dephasing, that may explain this finding were investigated. Particularly, a statistically significant increase in proton density was found in a brain area that was detected as activated by conventional EPI but not by NumART₂* while no such changes were observed in brain areas that showed stimulus correlated signal changes on T₂* maps.     

Direct measurement of oxygen extraction with fMRI using 6% CO2 inhalation

The blood-oxygenation-level-dependent (BOLD) signal is an indirect hemodynamic signal that is sensitive to cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen. Therefore, the BOLD signal amplitude and dynamics cannot be interpreted unambiguously without additional physiological measurements, and thus, there remains a need for a functional magnetic resonance imaging (fMRI) signal, which is more closely related to the underlying neuronal activity. In this study, we measured CBF with continuous arterial spin labeling, CBV with an exogenous contrast agent and BOLD combined with intracortical electrophysiological recording in the primary visual cortex of the anesthetized monkey. During inhalation of 6% CO2, it was observed that CBF and CBV are not further increased by a visual stimulus, although baseline CBF for 6% CO2 is below the maximal value of CBF. In contrast, the electrophysiological response to the stimulation was found to be preserved during hypercapnia. As a consequence, the simultaneously measured BOLD signal responds negatively to a visual stimulation for 6% CO2 inhalation in the same voxels responding positively during normocapnia. These observations suggest that the fMRI response to a sensory stimulus for 6% CO2 inhalation occurs in the absence of a hemodynamic response, and it therefore directly reflects oxygen extraction into the tissue.     

Statistical methods for detecting activated regions in functional MRI of the brain

Two statistical tests for detecting activated pixels in functional MRI (fMRI) data are presented. The first test (t-test) is the optimal solution to the problem of detecting a known activation signal in Gaussian white noise. The results of this test are shown to be equivalent to the cross-correlation method that is widely used for activation detection in fMRI. The second test (F test) is the optimal solution when the measured data are modeled to consist of an unknown activation signal that lies in a known lower dimensional subspace of the measurement space with added Gaussian white noise. A model for the signal subspace based on a truncated trigonometric Fourier series is proposed for periodic activation–baseline imaging paradigms. The advantage of the second method is that it does not assume any information about the shape or delay of the activation signal, except that it is periodic with the same period as the activation–baseline pattern. The two models are applied to experimental echo-planar fMRI data sets and the results are compared.     

Quantification of venous blood signal contribution to BOLD functional activation in the auditory cortex at 3 T

Most modern techniques for functional magnetic resonance imaging (fMRI) rely on blood-oxygen-level-dependent (BOLD) contrast as the basic principle for detecting neuronal activation. However, the measured BOLD effect depends on a transfer function related to neurophysiological changes accompanying electrical neural activation. The spatial accuracy and extension of the region of interest are determined by vascular effect, which introduces incertitude on real neuronal activation maps. Our efforts have been directed towards the development of a new methodology that is capable of combining morphological, vascular and functional information; obtaining new insight regarding foci of activation; and distinguishing the nature of activation on a pixel-by-pixel basis. Six healthy volunteers were studied in a parametric auditory functional experiment at 3 T; activation maps were overlaid on a high-resolution brain venography obtained through a novel technique. The BOLD signal intensities of vascular and nonvascular activated voxels were analyzed and compared: it was shown that nonvascular active voxels have lower values for signal peak (P<10(-7)) and area (P<10(-8)) with respect to vascular voxels. The analysis showed how venous blood influenced the measured BOLD signals, supplying a technique to filter possible venous artifacts that potentially can lead to misinterpretation of fMRI results. This methodology, although validated in the auditory cortex activation, maintains a general applicability to any cortical fMRI study, as the basic concepts on which it relies on are not limited to this cortical region. The results obtained in this study can represent the basis for new methodologies and tools that are capable of adding further characterization to the BOLD signal properties.     

BOLD signal and vessel dynamics: a hierarchical cluster analysis

The aim of the present study was to analyze blood oxygenation level-dependent (BOLD) signal variation during an apnea-based task in order to assess the capability of a functional magnetic resonance imaging (fMRI) procedure to estimate cerebral vascular dynamic effects. We measured BOLD contrast by hierarchical cluster analysis in healthy subjects undergoing an fMRI experiment, in which the task paradigm was one phase of inspirational apnea (IA). By processing the time courses of the fMRI experiment, analysis was performed only on a subclass of all the possible signal patterns; basically, root mean square and absolute variation differences have been calculated. Considering the baseline value obtained by computing the mean value of the initial rest period as reference, particular voxels showed relative important variations during the IA task and during the recovery phase following the IA. We focused our interest on the signal response of voxels that would correspond mainly to white and gray matter regions and that also may be affected by the proximity of large venous vessels. The results are presented as maps of space-temporal distribution of time series variations with two levels of hierarchical clustering among voxels with low to high initial response. Furthermore, we have presented a clustering of the signal response delay, conducting to a partition and identification of specified brain sites.     

中红外谐波检测背景信号漂移的修正方法

可调谐半导体激光吸收光谱（TDLAS）由于具有高灵敏度、高分辨率、非侵入及实时检测等特点,被广泛应用于燃烧诊断、痕量气体监测、工业过程控制等领域中。波长调制光谱（WMS）的二次谐波（2f）检测是最常用的TDLAS气体传感方法之一。激光器作为TDLAS-WMS在线检测系统中最核心的部件之一,在长期运行过程中会由于其工作温度等因素变化,引起输出激光波长漂移和2f背景信号基线变化,从而导致气体浓度反演的精确度和TDLAS-WMS在线检测系统的稳定性降低。针对上述问题,根据NO气体分子在中红外波段5.176~5.189 μm的基频吸收特性,选择峰值发射波长位于5.184 μm的分布反馈式连续波量子级联激光器（DFB-CW QCL）,分析了输出激光中心波长对应的峰值采样点位置随采样时间变化的漂移规律和2f吸收及其背景信号的漂移特性。基于上述分析,提出了以2f信号平均峰峰值替代2f信号峰值建立气体浓度反演模型以修正2f背景信号基线漂移,并结合以信噪比最优为2f背景信号波长漂移修正原则的2f背景信号漂移综合修正方法,以消除TDLAS-WMS在线检测系统长期连续检测过程中2f背景信号漂移对气体浓度反演结果的不利影响。研究结果表明,2f信号平均峰峰值随配置的NO样气浓度的增加而增大,这两者呈现较好的线性关系,其拟合曲线的线性拟合度R2达到了0.999 9。在使用体积浓度为20×10-6 NO气体样品开展的连续60 min监测实验中,波长漂移修正后,反演浓度的标准偏差由波长漂移修正前的0.19×10-6下降到了0.07×10-6,反演浓度的最大相对误差由波长漂移修正前的6.30%下降到了3.85%,相对误差均方值由波长漂移修正前的24.39%下降到了9.99%。结果显示,该2f背景信号漂移综合修正方法可以有效地抑制2f背景信号漂移对气体浓度反演结果的影响,显著提高了TDLAS-WMS在线检测系统连续监测的灵敏度、精确度和稳定性。     

Effects of mild hypoxic hypoxia on poststimulus undershoot of blood-oxygenation-level-dependent fMRI signal in the human visual cortex

Characteristics of the blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal poststimulus undershoot in the visual cortex were studied at varying levels of arterial blood oxygen saturation (Ysat). Undershoot with an amplitude of -0.6+/-0.2% appeared after positive BOLD response (+1.7+/-0.5%) under control conditions. Cerebral blood volume (CBV), as determined with vascular-space-occupancy-dependent fMRI, increased by 26-43% during the positive BOLD peak, but the CBV proceeded at baseline level during the BOLD poststimulus undershoot. Mild hypoxic hypoxia (Ysat ranging from 0.82 to 0.89) had no effect on the amplitude or duration of poststimulus undershoot in activated BOLD pixels. Hypoxia did not influence CBV during the BOLD poststimulus undershoot. In contrast, the positive BOLD signal at the level of all activated pixels was smaller in hypoxia than in normoxia. The present results show that the BOLD poststimulus undershoot is not influenced by curtailed oxygen availability and that, during the undershoot, CBV is not different from resting state.     

Comparison between end-tidal CO₂ and respiration volume per time for detecting BOLD signal fluctuations during paced hyperventilation

Respiratory motion and capnometry monitoring were performed during blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) of the brain while a series of paced hyperventilation tasks were performed that caused significant hypocapnia. Respiration volume per time (RVT) and end-tidal carbon dioxide (ETCO(2)) were determined and compared for their ability to explain BOLD contrast changes in the data. A 35% decrease in ETCO(2) was observed along with corresponding changes in RVT. A best-fit ETCO(2) response function, with an average initial peak delay time of 12 s, was empirically determined. ETCO(2) data convolved with this response function was more strongly and prevalently correlated to BOLD signal changes than RVT data convolved with the corresponding respiration response function. The results suggest that ETCO(2) better models BOLD signal fluctuations in fMRI experiments with significant transient hypocapnia. This is due to hysteresis in the ETCO(2) response when moving from hypocapnia to normocapnia, compared to moving from normocapnia to hypocapnia.     

A two-compartment gel phantom for optimization and quality assurance in clinical BOLD fMRI

Clinical applications of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) depend heavily on robust paradigms, imaging methods and analysis procedures. In this work, as a means to optimize and perform quality assurance of the entire imaging and analysis chain, a phantom that provides a well known and reproducible signal change similar to a block type fMRI experiment is presented. It consists of two gel compartments with slightly different T2 that dynamically enter and leave the imaged volume. The homogeneous gel in combination with a cylindrical geometry results in a well-defined T*2 difference causing a signal difference between the two compartments in T*2-weighted MR images. From time series data obtained with the phantom, maps of percent signal change (PSC) and t-values are calculated. As an example of image parameter optimisation, the phantom is demonstrated to be useful for accurate determination of the influence of echo time (TE) on BOLD fMRI results, taking the t-value as a measure of sensitivity. In addition, the phantom is proposed as a tool for quality assurance (QA) since reproducible time series and t-maps are obtained in a series of independent repeat experiments. The phantom is relatively simple to build and can therefore be used by any clinical fMRI center.     

A novel local PCA-based method for detecting activation signals in fMRI.

A novel local principal component analysis (LPCA) technique is presented for activation signal detection in functional magnetic resonance imaging (fMRI) without explicit knowledge about the shape of the model activation signal. Unlike the traditional PCA methods, our LPCA algorithm is based on a measure of separation between two clusters formed by the signal segments in active periods and inactive periods, which is computed in an eigen-subspace. In addition, we only applied PCA to the temporal sequence of each individual voxel instead of applying PCA to the fMRI data set. In our algorithm, we first applied a linear regression procedure to alleviate the baseline drift artifact. Then, the baseline-corrected temporal signals were partitioned into active and inactive segments according to the paradigm used for the fMRI data acquisition. Principal components were computed from all these segments for each voxel by PCA. By projecting the segments of each voxel onto a linear subspace formed by the corresponding most dominant principal components, two separate clusters were formed from active and inactive segments. An activation measure was defined based on the degree of separation between these two clusters in the projection space. We show experimental results on the activation signal detection from various sets of fMRI data with different types of stimulation by using the proposed LPCA algorithm and the standard t-test method for comparison. Our experiments indicate that the LPCA algorithm in general provides substantial signal-to-noise ratio improvement over the t-test method.     

EEG quality during simultaneous functional MRI of interictal epileptiform discharges

This article concerns the evaluation of the quality of interictal epileptiform EEG discharges recorded throughout simultaneous echo planar imaging (EPI). BOLD (blood oxygen level dependent) functional MRI (fMRI) images were acquired continuously on a patient with intractable epilepsy. EEG was sampled simultaneously, during and after imaging, with removal of pulse and imaging artifacts by subtraction of channel-specific running averages. Contiguous EEG epochs recorded with and without fMRI (fMRI+ve vs. fMRI−ve) were next randomized and presented to two blinded observers. Epileptiform discharges were identified retrospectively, and comparison was made in terms of the number of identified events, their amplitude, and spatiotemporal distribution. A spectral analysis was also performed on the EEG. In the randomized comparison of EEG segments, 80 (fMRI+ve) vs. 69 (fMRI−ve) discharges were noted with good interobserver agreement (69%). There were no significant differences in amplitude or spatio-temporal distribution. Comparison of the events detected and measured by two expert observers demonstrated that the Interictal Epileptiform Discharge (IED) characteristics were indistinguishable with and without scanning. We review briefly the existing literature on EEG recording quality for combined EEG/fMRI.     

Functional connectivity in the rat brain: a complex network approach

Functional connectivity analyses of fMRI data can provide a wealth of information on the brain functional organization and have been widely applied to the study of the human brain. More recently, these methods have been extended to preclinical species, thus providing a powerful translational tool. Here, we review methods and findings of functional connectivity studies in the rat. More specifically, we focus on correlation analysis of pharmacological MRI (phMRI) responses, an approach that has enabled mapping the patterns of connectivity underlying major neurotransmitter systems in vivo. We also review the use of novel statistical approaches based on a network representation of the functional connectivity and their application to the study of the rat brain functional architecture.     

A data post-processing protocol for dynamic MRI data to discriminate brain activity from global physiological effects

All fMRI techniques measure stimulus induced focal metabolic and physiological changes in activated brain areas. During the entire fMRI experiment it is necessary to maintain the general physiological condition of the subject as stable as possible. This is not always an easy task. The typical block design in standard fMRI experiments minimizes most of the problems related with these general physiological changes. However in some fMRI experiments, like pharmacological MRI, the experimental setup makes the use of a blocked design impossible. Therefore signal correction algorithms have been developed to correct for these physiological signal instabilities. These algorithms often require elaborate calculation efforts and the data interpretation is often very difficult if no prior knowledge on the nature of the changes exists.In this work we present an algorithm, which has the advantage of being low in calculation effort and the resulting data after correction are easy to interpret. It makes use of a datafit between the general physiological and focal activation related signal changes to eliminate the generalized effects. This algorithm has been tested on simulated and experimentally obtained signal traces suffering both from substantial general signal changes overwhelming the smaller focal activation induced signal changes.     

Model-based PRFS thermometry using fat as the internal reference and the extended Prony algorithm for model fitting

A model-based proton resonance frequency shift (PRFS) thermometry method was developed to significantly reduce the temperature quantification errors encountered in the conventional phase mapping method and the spatiotemporal limitations of the spectroscopic thermometry method. Spectral data acquired using multi-echo gradient echo (GRE) is fit into a two-component signal model containing temperature information and fat is used as the internal reference. The noniterative extended Prony algorithm is used for the signal fitting and frequency estimate. Monte Carlo simulations demonstrate the advantages of the method for optimal water-fat separation and temperature estimation accuracy. Phantom experiments demonstrate that the model-based method effectively reduces the interscan motion effects and frequency disturbances due to the main field drift. The thermometry result of ex vivo goose liver experiment with high intensity focused ultrasound (HIFU) heating was also presented in the paper to indicate the feasibility of the model-based method in real tissue.     

Real-time MR artifacts filtering during continuous EEG/fMRI acquisition

The purpose of this study was the development of a real-time filtering procedure of MRI artifacts in order to monitor the EEG activity during continuous EEG/fMRI acquisition. The development of a combined EEG and fMRI technique has increased in the past few years. Preliminary “spike-triggered” applications have been possible because in this method, EEG knowledge was only necessary to identify a trigger signal to start a delayed fMRI acquisition. In this way, the two methods were used together but in an interleaved manner. In real simultaneous applications, like event-related fMRI study, artifacts induced by MRI events on EEG traces represent a substantial obstacle for a right analysis. Up until now, the methods proposed to solve this problem are mainly based on procedures to remove post-processing artifacts without the possibility to control electrophysiological behavior of the patient during fMRI scan. Moreover, these methods are not characterized by a strong “prior knowledge” of the artifact, which is an imperative condition to avoid any loss of information on the physiological signals recovered after filtering. In this work, we present a new method to perform simultaneous EEG/fMRI study with real-time artifacts filtering characterized by a procedure based on a preliminary analytical study of EPI sequence parameters-related EEG-artifact shapes. Standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes included: high-performance acquisition device; electrodes/cap/wires/cables materials and geometric design; shielding box for EEG signal receiver; optical fiber link; and software. The effects of the RF pulse and time-varying magnetic fields were minimized by using a correct head cap wires-locked environment montage and then removed during EEG/fMRI acquisition with a subtraction algorithm that takes in account the most significant EPI sequence parameters. The on-line method also allows a further post-processing utilization.