人脑视觉皮质功能磁共振成像研究

目的研究人脑视觉皮质血氧水平依赖（BOLD）的功能磁共振成像。方法18名正常健康志愿者，在光刺激和非刺激的两种对比条件下，采用EP1技术，采集视觉皮质血氧水平依赖（BOLD）图像。t检验分析得出光刺激状态和非刺激状态信号对比的脑功能图像。结果fMRI图像显示光刺激下脑功能活动激活区主要位于双侧视觉皮质区。结论fMRI可用于在活体人脑上研究各功能区活动，光刺激下的fMRI可对人脑视觉皮质进行定位。     

Does Hypercapnia-Induced Cerebral Vasodilation Modulate the Hemodynamic Response to Neural Activation?

Increases in cerebral blood flow produced by vasoactive agents will increase blood oxygen level-dependent (BOLD) MRI signal intensity. The effects of such vasodilation on activation-related signal changes are incompletely characterized. The two signal changes may be simply additive or there may be more a complex interaction. To investigate this, BOLD MRI was performed in four normal male subjects using T2*-weighted echo planar imaging; brain volumes were acquired every 6.2 s, using a Siemens VISION scanner operating at 2 Tesla; each volume consisted of 64 sequential transverse slices (64 × 64 pixels per slice, 3 × 3 × 3 mm). Sixteen periods of visual stimulation were produced using a flickering checkerboard (8 Hz, 31 s On/31 s Off); this was coupled with five periods of hypercapnia (4% inspired CO₂, 62 s On/124 s Off). Data were analyzed using SPM96. Mean signal intensity, calculated globally for the whole brain, closely mirrored changes in the partial pressure of end-tidal CO₂ (PCO₂), and hypercapnia was associated with widespread significant signal increases (P < 0.001), predominantly within grey matter. As expected, the visual stimulation produced significant signal changes within the occipital cortex (P < 0.001). Within the occipital cortex, no significant interactions (P > 0.001) between the effects of the visual stimulation and PCO₂ were present. The increases in PCO₂ imposed dynamically in the present study would increase cerebral blood flow by between 25 and 40%, an increase within the physiological range and comparable to that induced by neural activation. With this flow change the effects of vasodilation, on an activation-related signal change, are simply additive.     

BOLD and blood volume-weighted fMRI of rat lumbar spinal cord during non-noxious and noxious electrical hindpaw stimulation

Spinal cord fMRI is a useful tool for studying spinal mechanisms of pain, hence for analgesic drug development. Its technical feasibility in both humans and rats has been demonstrated. This study investigates the reproducibility, robustness, and spatial accuracy of fMRI of lumbar spinal cord activation due to transcutaneous noxious and non-noxious electrical stimulation of the hindpaw in alpha-chloralose-anesthetized rats. Blood oxygenation level-dependent (BOLD) and blood volume-weighted fMRI data were acquired without and with intravenous injection of ultra small superparamagnetic iron oxide particles (USPIO), respectively, using a gradient echo (GE) echo planar imaging (EPI) technique at 4.7 T. Neuronal activation in the spinal cord induced by noxious stimulation to the hindpaw (2 ms wide, 5 mA amplitude, known to activate C-fibers) can be robustly detected by both fMRI techniques with excellent reproducibility and peaked at the stimulus frequency of 40 Hz. However, both fMRI techniques were not sensitive to neuronal activation in spinal cord induced by non-noxious stimulation (0.3 ms, 1.5 mA, known only to activate A-fibers). Spatially, the fMRI signal extended approximately 5 mm in the longitudinal direction, covering L(3)-L(5) segments. In the cross-sectional direction, the highest signal change of blood volume-weighted fMRI was in the middle of the ipsilateral dorsal horn, which roughly corresponds to laminae V and VI, while the highest signal change of BOLD fMRI was in the ipsilateral dorsal surface. This study demonstrates that spinal cord fMRI can be performed in anesthetized rats reliably and reproducibly offering it as a potential tool for analgesic drug discovery.     

Regional variability of cerebral blood oxygenation response to hypercapnia

In functional magnetic resonance imaging studies changes in blood oxygenation level-dependent (BOLD) signal intensities during task activation are related to multiple physiological parameters such as cerebral blood flow, volume, and oxidative metabolism, as well as to the regional microvascular anatomy. Consequently, the magnitude of activation-induced BOLD signal changes may vary regionally and between subjects. The aim of this study was to use a uniform global stimulus such as hypercapnia to quantitatively investigate the regional BOLD response in the human brain. In 10 healthy volunteers, T2*-weighted gradient echo images were acquired for a total dynamic scanning time of 9 min during alternating periods of breath holding for 30 s after expiration and self-paced normal breathing for 60 s. Hypercapnia-induced BOLD signal changes in the sensorimotor cortex, frontal cortex, basal ganglia, visual cortex, and cerebellum were significantly different (P < 0.001) and varied from 1.8 to 5.1%. The highest BOLD signal changes were found in the cerebellum and visual cortex, whereas the lowest BOLD signal increase was observed in the frontal cortex. These results demonstrate a regional dependence of the BOLD signal changes during breath hold-induced hypercapnia, indirectly supporting the notion of regional different sensitivities of BOLD responses to task activation.     

Multi-echo fMRI of the cortical laminae in humans at 7 T

Recent developments in ultra high field MRI and receiver coil technology have opened up the possibility of laminar fMRI in humans. This could offer greater insight into human brain function by elucidating both the interaction between brain regions on the basis of laminar activation patterns associated with input and output, and the interactions between laminae in a specific region. We used very high isotropic spatial resolution (0.75 mm voxel size), multi-echo acquisition (gradient-echo) in a 7 T fMRI study of human primary visual cortex (V1) and novel data analysis techniques to quantitatively investigate the echo time dependence of laminar profiles, laminar activation, and physiological noise distributions over an extended region of cortex. We found T(2)* profiles to be explicable in terms of variations in myelin content. Laminar activation profiles vary with echo time (TE): at short TE the highest signal changes are measured at the pial surface; this maximum shifts into grey matter at longer TEs. The top layers peak latest as these have the longest transverse relaxation time. Theoretical simulations and experiment suggest that the intravascular contribution to functional signal changes is significant even at long TE. Based on a temporal noise analysis we argue that the (physiological) noise contributions will ameliorate differences in sensitivity between the layers in a statistical analysis, and correlates with laminar blood volume distribution. We also show that even at this high spatial resolution the physiological noise limit to sensitivity is reached within V1, implying that cortical sub-regions can be examined with this technique.     

Functional connectivity in the rat at 11.7T: Impact of physiological noise in resting state fMRI

Resting state functional MRI (rs-fMRI) of the brain has the potential to elicit networks of functional connectivity and to reveal changes thereof in animal models of neurological disorders. In the present study, we investigate the contribution of physiological noise and its impact on assessment of functional connectivity in rs-fMRI of medetomidine sedated, spontaneously breathing rats at ultrahigh field of 11.7 Tesla. We employed gradient echo planar imaging (EPI) with repetition times of 3s and used simultaneous recordings of physiological parameters. A model of linear regression was applied to quantify the amount of BOLD fMRI signal fluctuations attributable to physiological sources. Our results indicate that physiological noise - mainly originating from the respiratory cycle -dominates the rs-fMRI time course in the form of spatially complex correlation patterns. As a consequence, these physiological fluctuations introduce severe artifacts into seed-based correlation maps and lead to misinterpretation of corresponding connectivity measures. We demonstrate that a scheme of motion correction and linear regression can significantly reduce physiological noise in the rs-fMRI time course, remove artifacts, and hence improve the reproducibility of functional connectivity assessment. In conclusion, physiological noise can severely compromise functional connectivity MRI (fcMRI) of the rodent at high fields and must be carefully considered in design and interpretation of future studies. Motion correction should be considered the primary strategy for reduction of apparent motion related to respiratory fluctuations. Combined with subsequent regression of physiological confounders, this strategy has proven successful in reducing physiological noise and related artifacts affecting functional connectivity analysis. The proposed new and rigorous protocol now opens the potential of fcMRI to elicit the role of brain connectivity in pathological processes without concerns of confounding contributions from physiological noise.     

Dynamic physiological modeling for functional diffuse optical tomography

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n=10, P<0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.     

An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data

The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reducing these signal fluctuations as long as they are repetitive and their timing is known. The filter, applied in image domain, does not require temporal oversampling of the artifact-related fluctuations. Performance is demonstrated for suppression of cardiac and respiratory artifacts in 10-minute brain scans on 6 normal volunteers. Experimental parameters resemble a typical fMRI experiment (17 slices; 1700 ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100 ms TR), allowing identification of artifacts specific to the cardiac and respiratory cycles, aiding assessment of filtering performance. Results show significant reduction in temporal standard deviation (SD(t)) in all subjects. For all 6 datasets with 1700 ms TR combined, the filtering method resulted in an average reduction in SD(t) of 9.2% in 2046 voxels substantially affected by respiratory artifacts, and 12.5% for the 864 voxels containing substantial cardiac artifacts. The maximal SD(t) reduction achieved was 52.7% for respiratory and 55.3% for cardiac filtering. Performance was found to be at least equivalent to the previously published RETROICOR method. Furthermore, the interaction between the filter and fMRI activity detection was investigated using Monte Carlo simulations, demonstrating that filtering algorithms introduce a systematic error in the detected BOLD-related signal change if applied sequentially. It is demonstrated that this can be overcome by combining physiological artifact filtering and detection of BOLD-related signal changes simultaneously. Visual fMRI data from 6 volunteers were analyzed with and without the filter proposed here. Inclusion of the cardio-respiratory regressors in the design matrix yielded a 4.6% t-score increase and 4.0% increase in the number of significantly activated voxels.     

Single shot partial dual echo (SPADE) EPI—an efficient acquisition scheme for reducing susceptibility artefacts in fMRI

SPADE is a new acquisition scheme for fMRI based on dual echo EPI. As in previous work, additional spin echo EPI images are used to recover signal in regions that are affected by susceptibility related sensitivity loss in gradient echo EPI. However, with SPADE the additional spin echo images are only acquired for the affected slices, which reduces the acquisition time and enhances the time normalised signal-to-noise ratio. We demonstrate the feasibility of this approach and discuss potential applications of the SPADE technique in fMRI. We conclude that SPADE provides an efficient acquisition scheme for fMRI applications where whole brain coverage and sensitivity is required.     

Caffeine-induced uncoupling of cerebral blood flow and oxygen metabolism: a calibrated BOLD fMRI study

Although functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) signal changes is a sensitive tool for mapping brain activation, quantitative studies of the physiological effects of pharmacological agents using fMRI alone are difficult to interpret due to the complexities inherent in the BOLD response. Hypercapnia-calibrated BOLD methodology is potentially a more powerful physiological probe of brain function, providing measures of the changes in cerebral blood flow (CBF) and the cerebral metabolic rate of oxygen (CMRO(2)). In this study, we implemented a quantitative R(2)* approach for assessing the BOLD response to improve the stability of repeated measurements, in combination with the calibrated BOLD method, to examine the CBF and CMRO(2) responses to caffeine ingestion. Ten regular caffeine consumers were imaged before and after a 200-mg caffeine dose. A dual-echo arterial spin labeling technique was used to measure CBF and BOLD responses to visual stimulation, caffeine consumption and mild hypercapnia. For a region of interest defined by CBF activation to the visual stimulus, the results were: hypercapnia increased CBF (+46.6%, +/-11.3, mean and standard error), visual stimulation increased both CBF (+47.9%, +/-2.9) and CMRO(2) (+20.7%, +/-1.4), and caffeine decreased CBF (-34.5%, +/-2.6) with a non-significant change in CMRO(2) (+5.2%, +/-6.4). The coupling between CBF and CMRO(2) was significantly different in response to visual stimulation compared to caffeine consumption. A calibrated BOLD methodology using R(2) * is a promising approach for evaluating CBF and CMRO(2) changes in response to pharmacological interventions.     

fMRI resting state networks define distinct modes of long-distance interactions in the human brain

Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect "default" interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.     

Baseline BOLD correlation predicts individuals' stimulus-evoked BOLD responses

To investigate whether individuals' ongoing neuronal activity at resting state can affect their response to brain stimulation, fMRI BOLD signals were imaged from the human visual cortex of fifteen healthy subjects in the absence and presence of visual stimulation. It was found that the temporal correlation strength but not amplitude of baseline BOLD signal fluctuations acquired under the eyes-fixed condition is positively correlated with the amplitude of stimulus-evoked BOLD responses across subjects. Moreover, the spatiotemporal correlations of baseline BOLD signals imply a coherent network covering the visual system, which is topographically indistinguishable from the "resting-state visual network" observed under the eyes-closed condition. The overall findings suggest that the synchronization of ongoing brain activity plays an important role in determining stimulus-evoked brain activity even at an early stage of the sensory system. The tight relationship between baseline BOLD correlation and stimulus-evoked BOLD amplitude provides an essential basis for understanding and interpreting the large inter-subject BOLD variability commonly observed in numerous fMRI studies and potentially for improving group fMRI analysis. This study highlights the importance to integrate the information from both resting-state coherent networks and task-evoked neural responses for a better understanding of how the brain functions.     

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. In a recent study it has been proposed that relevant information in resting-state fMRI can be obtained by inspecting the discrete events resulting in relatively large amplitude BOLD signal peaks. Following this idea, we consider resting fMRI as ‘spontaneous event-related’, we individuate point processes corresponding to signal fluctuations with a given signature, extract a region-specific HRF and use it in deconvolution, after following an alignment procedure. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions.     

Widespread functional connectivity and fMRI fluctuations in human visual cortex in the absence of visual stimulation

To what extent does the visual system's activity fluctuate when no sensory stimulation is present? Here, we studied this issue by examining spontaneous fluctuations in BOLD signal in the human visual system, while subjects were placed in complete darkness. Our results reveal widespread slow fluctuations during such rest periods. In contrast to stimulus-driven activity, during darkness, functionally distinct object areas were fluctuating in unison. These fMRI fluctuations became rapidly spatially de-correlated (39% drop in correlation level, P < 0.008) during visual stimulation. Functional connectivity analysis revealed that the slow spontaneous fluctuations during rest had consistent and specific neuro-anatomical distribution which argued against purely hemodynamic noise sources. Control experiments ruled out eye closure, low luminance and mental imagery as the underlying sources of the spontaneous fluctuations. These results demonstrate that, when no stimulus is present, sensory systems manifest a robust level of slow organized fluctuation patterns.     

Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging

In this study, a new approach to high-speed fMRI using multi-slab echo-volumar imaging (EVI) is developed that minimizes geometrical image distortion and spatial blurring, and enables nonaliased sampling of physiological signal fluctuation to increase BOLD sensitivity compared to conventional echo-planar imaging (EPI). Real-time fMRI using whole brain 4-slab EVI with 286 ms temporal resolution (4mm isotropic voxel size) and partial brain 2-slab EVI with 136 ms temporal resolution (4×4×6 mm(3) voxel size) was performed on a clinical 3 Tesla MRI scanner equipped with 12-channel head coil. Four-slab EVI of visual and motor tasks significantly increased mean (visual: 96%, motor: 66%) and maximum t-score (visual: 263%, motor: 124%) and mean (visual: 59%, motor: 131%) and maximum (visual: 29%, motor: 67%) BOLD signal amplitude compared with EPI. Time domain moving average filtering (2s width) to suppress physiological noise from cardiac and respiratory fluctuations further improved mean (visual: 196%, motor: 140%) and maximum (visual: 384%, motor: 200%) t-scores and increased extents of activation (visual: 73%, motor: 70%) compared to EPI. Similar sensitivity enhancement, which is attributed to high sampling rate at only moderately reduced temporal signal-to-noise ratio (mean: -52%) and longer sampling of the BOLD effect in the echo-time domain compared to EPI, was measured in auditory cortex. Two-slab EVI further improved temporal resolution for measuring task-related activation and enabled mapping of five major resting state networks (RSNs) in individual subjects in 5 min scans. The bilateral sensorimotor, the default mode and the occipital RSNs were detectable in time frames as short as 75 s. In conclusion, the high sampling rate of real-time multi-slab EVI significantly improves sensitivity for studying the temporal dynamics of hemodynamic responses and for characterizing functional networks at high field strength in short measurement times.     

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

Heart rate fluctuations occur in the low-frequency range (<0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms and may be related to low-frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and resting-state fMRI signal timecourses were assessed at 3 T. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44, 162-167) and respiration volume per unit time (Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006b. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31, 1536-1548). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time shifts of 6-12 s and positive correlations at time shifts of 30-42 s (TR=6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 s explained an additional 1% of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.     

The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.     

人脑对事件相关食物视觉刺激反应的功能磁共振成像的初步研究

目的通过功能磁共振成像(fMRI)初步研究人脑对事件相关食物视觉刺激反应.方法本研究利用fMRI技术具有时间分辨率高、对人体无创并且可以对单例数据处理分析的优势,采用平面回波成像(EPI)序列,检测在饥饿状态下,人脑对事件相关食物视觉刺激反应.实验中2名健康志愿受试者,选择事件相关任务模式禁食12 h后扫描.扫描的同时接受随机播放的食物图片、非食物图片和空白模糊图片的视觉刺激.用SPM 2软件处理功能成像数据,对由三类图片所引发的感兴趣区(ROI)做相关t检验统计.结果与食物渴求相关的脑区分布在杏仁核、眶额皮质以及枕叶皮质,与既往的正电子放射扫描成像(PET)实验结果相符.结论确定与食物渴求相关的奖赏通路,对于了解人类在饥饿状态下对食物渴求的复杂认知规律,为进一步寻找有效调控人类摄食行为的手段有重要意义.     

电刺激健康成人小腿前外侧皮肤腰髓功能磁共振成像特征研究

目的通过基于快速自旋回波的功能磁共振序列,检测电刺激健康成人小腿前外侧皮肤时引起腰髓内神经元活动的激活特征;同时,对比横轴位与矢状位的激活信号特征,验证功能磁共振成像技术在腰髓方面研究的可行性及重复性。 方法使用GE 1.5T Signa MR扫描仪及八通道标准脊髓(CTL)线圈用于发射和接受射频(RF)脉冲。12名健康志愿者,男性、女性各6名,年龄23～27岁,平均(25.00±1.13)岁。使用电针刺激仪,以断续脉冲(20Hz),刺激右侧小腿前外侧区皮肤,采用组块设计方法,即R₁-S₁-R₂-S₂-R₃-S₃-R₄。利用单次激发快速自旋回波序列分别采集横轴位与矢状位的功能磁共振图像。使用SPM8软件获得静息态和刺激态图像差异间的t检验图,阈值P＝0.01,设置激活聚类数为0。对矢状位不同脊椎节段内激活像素数及信号强度变化百分比改变使用非参数K相关样本分析法进行统计分析,用非参数Wilcoxon配对检验对矢状位-轴位上分别获取的数据进行统计学分析。 结果除2名志愿者因运动幅度过大被排除外,余下10名志愿者相应脊髓节段内均检测到激活信号。矢状位上,激活信号主要位于T₁₂(10/10)椎体水平,且信号强度变化百分比主要集中于0.0%～2.0%,T₁₁(2/10)、L₁(3/10)椎体水平亦检测到少许激活信号。横轴位上,激活信号主要位于刺激同侧脊髓灰质背侧区(7/10),对侧脊髓灰质背侧区(5/10)及双侧脊髓灰质腹侧区(3/10)也可观察到少许激活。T₁₂椎体水平,横轴位与矢状位的平均激活像素数间比较(Z＝－1.825,P>0.05)及横轴位与矢状位的平均信号强度变化百分比间比较(Z＝－1.376,P>0.05)均差异无统计学意义。 结论利用1.5T超导MR研究腰髓功能磁共振成像是可行的,且激活信号存在一定的特征,主要位于T₁₂椎体水平,并具有一定的重复性;不同方位腰髓功能磁共振成像结果具有一致性。     

Functional MRI response and correlated electrophysiological changes during posterior hypothalamic nucleus deep brain stimulation

Identification of active networks involved in behavior is central to understanding brain function as an emergent property. Functional magnetic resonance imaging (fMRI) allows the identification of areas with increased or decreased activity, but the cellular correlates to changes in fMRI response is still controversial. Deep brain stimulation of the posterior hypothalamic nucleus (PH) is known to facilitate locomotor behaviors and rescue locomotion in rodent models of parkinsonian akinesia by an unknown mechanism. Here, we performed 9.4 T fMRI during deep brain stimulation of PH in the anesthetized rat as a model system to explore the network substrates for its behavioral consequences. In addition, multi-unit and field potential recordings were made to examine the physiological correlates to changes in fMRI response. The most robust and reliable MR signal increases were observed in the somatosensory and motor cortices, with minor limbic and sparse thalamic activation. Electrophysiological experiments demonstrated that increased fMRI response in the neocortex corresponds to general increases in spiking activity, decreased slow oscillations and increased delta band activity. Forelimb movements evoked by intracortical microstimulation had reduced thresholds and larger representational (motor map) areas during and following PH stimulation. These findings identify the sensorimotor cortices as major contributors for behavioral effects of PH stimulation, and that coincident increase in spiking, synaptic activity and MR signal reflect functional facilitation of neocortical output.