Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA

Functional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the large scale. Most studies assume a simple relationship between neural and BOLD activity, in spite of the fact that it is important to elucidate how the “when” and “what” components of neural activity are correlated to the “where” of fMRI data. Here we conducted simultaneous recordings of neural and BOLD signal fluctuations in primary visual (V1) cortex of anesthetized monkeys. We explored the neurovascular relationship during periods of spontaneous activity by using temporal kernel canonical correlation analysis (tkCCA). tkCCA is a multivariate method that can take into account any features in the signals that univariate analysis cannot. The method detects filters in voxel space (for fMRI data) and in frequency–time space (for neural data) that maximize the neurovascular correlation without any assumption of a hemodynamic response function (HRF). Our results showed a positive neurovascular coupling with a lag of 4–5 s and a larger contribution from local field potentials (LFPs) in the γ range than from low-frequency LFPs or spiking activity. The method also detected a higher correlation around the recording site in the concurrent spatial map, even though the pattern covered most of the occipital part of V1. These results are consistent with those of previous studies and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI.     

Component structure of event-related fMRI responses in the different neurovascular compartments

In most functional magnetic resonance imaging (fMRI) studies, brain activity is localized by observing changes in the blood oxygenation level-dependent (BOLD) signal that are believed to arise from capillaries, venules and veins in and around the active neuronal population. However, the contribution from veins can be relatively far downstream from active neurons, thereby limiting the ability of BOLD imaging methods to precisely pinpoint neural generators. Hemodynamic measures based on apparent diffusion coefficients (ADCs) have recently been used to identify more upstream functional blood flow changes in the capillaries, arterioles and arteries. In particular, we recently showed that, due to the complementary vascular sensitivities of ADC and BOLD signals, the voxels conjointly activated by both measures may identify the capillary networks of the active neuronal areas. In this study, we first used simultaneously acquired ADC and BOLD functional imaging signals to identify brain voxels activated by ADC only, by both ADC and BOLD and by BOLD only, thereby delineating voxels relatively dominated by the arterial, capillary, and draining venous neurovascular compartments, respectively. We then examined the event-related fMRI BOLD responses in each of these delineated neurovascular compartments, hypothesizing that their event-related responses would show different temporal componentries. In the regions activated by both the BOLD and ADC contrasts, but not in the BOLD-only areas, we observed an initial transient signal reduction (an initial dip), consistent with the local production of deoxyhemoglobin by the active neuronal population. In addition, the BOLD-ADC overlap areas and the BOLD-only areas showed a clear poststimulus undershoot, whereas the compartment activated by only ADC did not show this component. These results indicate that using ADC contrast in conjunction with BOLD imaging can help delineate the various neurovascular compartments, improve the localization of active neural populations, and provide insight into the physiological mechanisms underlying the hemodynamic signals.     

Non-neural BOLD variability in block and event-related paradigms

Block and event-related stimulus designs are typically used in fMRI studies depending on the importance of detection power or estimation efficiency. The extent of vascular contribution to variability in block and event-related fMRI-BOLD response is not known. With scaling, the extent of vascular variability in the fMRI-BOLD response during block and event-related design tasks was investigated. Blood oxygen level-dependent (BOLD) contrast data from healthy volunteers performing a block design motor task and an event-related memory task requiring performance of a motor response were analyzed from the regions of interest (ROIs) surrounding the primary and supplementary motor cortices. Average BOLD signal change was significantly larger during the block design compared to the event-related design. In each subject, BOLD signal change across voxels in the ROIs had higher variation during the block design task compared to the event-related design task. Scaling using the resting state fluctuation of amplitude (RSFA) and breath-hold (BH), which minimizes BOLD variation due to vascular origins, reduced the within-subject BOLD variability in every subject during both tasks but significantly reduced BOLD variability across subjects only during the block design task. The strong non-neural source of intra- and intersubject variability of BOLD response during the block design compared to event-related task indicates that study designs optimizing for statistical power through enhancement of the BOLD contrast (for, e.g., block design) can be affected by enhancement of non-neural sources of BOLD variability.     

结合局部一致性和低频振幅探究躯体症状障碍患者大脑自发性活动的改变

躯体症状障碍（somatic symptom disorder,SSD）是一种常见的医学疾病,致病原因涉及生物学、心理学及社会因素.目前关于SSD的神经机制知之甚少.本研究通过静息态功能磁共振成像（functional magnetic resonance imaging,fMRI）,结合低频振幅（amplitudes of low-frequency fluctuation,ALFF）和局部一致性（regional homogeneity,ReHo）分析探究45位SSD患者和43位健康对照自发性脑活动特征的区别.结果发现：与对照组相比,SSD患者右侧扣带回中部的ReHo值显著升高,而右侧楔前叶、左侧颞下回延伸到左侧颞中回和左侧海马旁回、右侧脑桥的ReHo值显著降低.同时,SSD患者扣带回中部延伸至左侧额中回、右侧脑岛延伸至右侧额下回、左侧额中回延伸至左侧前扣带回的ALFF值均显著升高.这些脑区的脑功能与自我加工、情绪处理、身体知觉等有关,与SSD发病机制有重要联系.     

Physiological characterization of a robust survival rodent fMRI method

Anesthetics are commonly used in preclinical functional MRI studies. It is well-appreciated that proper choice of anesthetics is of critical importance for maintaining a physiologically normal range of autonomic functioning. A recent study, using a low dose of dexmedetomidine (active isomer of medetomidine) in combination with a low dose of isoflurane, suggested stable measurements across repeated fMRI experiments in individual animals with each session lasting up to several hours. The rat default mode network has been successfully identified using this preparation, indicating that this protocol minimally disturbs brain network functions. However, medetomidine is known to cause peripheral vasoconstriction, respiratory suppression, and bradycardia, each of which could independently confound the BOLD signal. The goal of this study was to systematically characterize physiological conditions for fMRI experiments under this anesthetic regimen. To this end, we acquired somatosensory stimulation “task-evoked” and resting-state fMRI to evaluate the integrity of neurovascular coupling and brain network function during three time windows (0–30 min, 30–90 min, and 90–150 min) following dexmedetomidine initiation. Results demonstrate that both evoked BOLD response and resting-state fMRI signal remained stable during the 90–150 min time window, while autonomic physiological parameters maintained near-normal conditions during this period. Our data suggest that using a spontaneously-inhaled, low dose of isoflurane in combination with a continuous low dose of dexmedetomidine is a viable option for longitudinal imaging studies in rats.     

Illuminating the BOLD signal: combined fMRI-fNIRS studies

Functional magnetic resonance imaging (fMRI) is currently combined with electrophysiological methods to identify the relationship between neuronal activity and the blood oxygenation level-dependent (BOLD) signal. Several processes like neuronal activity, synaptic activity, vascular dilation, blood volume and oxygenation changes underlie both response modalities, that is, the electrophysiological signal and the vascular response. However, accessing single process relationships is absolutely mandatory when aiming at a deeper understanding of neurovascular coupling and necessitates studies on the individual building blocks of the vascular response. Combined fMRI and functional near-infrared spectroscopy studies have been performed to validate the correlation of the BOLD signal to the hemodynamic changes in the brain. Here we review the current status of the integration of both technologies and judge these studies in the light of recent findings on neurovascular coupling.     

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.     

Fractional amplitude of low-frequency fluctuation changes in monkeys with spinal cord injury: A resting-state fMRI study

Purpose

Although functional magnetic resonance imaging (fMRI) has revealed that spinal cord injury (SCI) causes anomalous changes in task-induced brain activation, its effect during the resting state remains unclear. The aim of this study is to explore the changes of the brain resting-state function in non-human primates with unilateral SCI.

Materials and methods

Eleven adult female rhesus monkeys were subjected to resting-state fMRI: five with unilateral thoracic SCI and six healthy monkeys, to obtain the fractional amplitude of low-frequency fluctuations (fALFF) of the blood oxygenation level-dependent (BOLD) contrast signal to determine the influence of SCI on the cerebral resting-state function.

Results

The SCI-induced fALFF vary significantly in several encephalic regions, including the left cerebellum, the left thalamus, the right lateral geniculate nucleus, the right superior parietal lobule, and the posterior cingulate gyrus.

Conclusion

Analysis of the resting-state fMRI provides evidence of abnormal spontaneous brain activations in primates with SCI, which may help us understand the pathophysiologic mechanisms underlying the changes in neural plasticity in the central nervous system after SCI.     

Altered regional homogeneity of brain spontaneous signals in SIV infected rhesus macaque model

BackgroundRegional homogeneity (ReHo), a measurement from resting-state functional magnetic imaging (rs-fMRI) to reflect local synchronization of brain activities, has been widely explored in previous studies of neurological diseases. SIV infected model for detecting the neurological changes with progression was studied.MethodsIn the study, six rhesus macaques infected by simian immunodeficiency virus (SIV) were scanned by resting-state fMRI at the following time points: before SIV inoculation (baseline), 12 weeks and 24 weeks post inoculation (12 wpi, 24 wpi). Meanwhile, the immunological parameters including serum percentage of CD4 + T cell, CD4/CD8 ratio and absolute CD4 + T cell number were measured and analyzed.ResultsIn comparison of baseline, significant decreased ReHo was found in the left superior frontal gyrus, left superior temporal gyrus, left hippocampus, right precuneus, left angular gyrus, and bilateral occipital gyrus; in contrast increased ReHo in putamen at 12 wpi. Moreover, at the time of 24 wpi, decreased ReHo was observed in the right postcentral gyrus, left precentral gyrus, posterior cingulated gyrus and thalamus, while ReHo was increased in the left putamen, hippocampus, left anterior cingulated cortex and precentral cortex. The correlation analysis revealed that ReHo in the superior frontal gyrus showed negative association with CD4/CD8 ratio and positive with absolute CD4 + T cell number. The correlation analysis showed that percentage of CD4 + was correlated with the ReHo values in right middle frontal gyrus, bilateral thalamus and amygdala positively; negative relationship with left putamen, left superior frontal gyrus, left superior and middle temporal gyrus.ConclusionThe study first indicates that hippocampus, putamen, frontal and occipital lobe were impaired by using rs-fMRI and correlated with immunological parameters. Thus, ReHo value can be utilized as a noninvasive biomarker of spontaneous brain activity changes caused by the progression of neurological impairments.     

Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI

Analysis of resting-state functional magnetic resonance imaging (fMRI) data is based on detecting low-frequency signal fluctuations in functionally connected brain areas. These synchronous fluctuations in resting-state networks have been observed in several studies with healthy subjects. In this study, we explored if independent component analysis (ICA) can be used to localize the sensorimotor area from resting-state fMRI data in patients with brain tumors. Finger-tapping activation task and resting-state blood-oxygenation-level-dependent fMRI data were acquired from 8 patients with brain tumors and 10 healthy volunteers. Sensorimotor task independent components (IC_task) were used to verify resting-state independent components (IC_rest) individually. In addition, sensorimotor IC_rests were compared between the groups and no significant differences were detected in volume, spatial correlation or temporal correlation. These results show that it is possible to localize a sensorimotor area from resting-state data using ICA in patients with brain tumors. This offers a complementary method for assessing the sensorimotor area in subjects with brain tumors who have difficulties in performing motor paradigms.     

Assessment of data acquisition parameters,and analysis techniques for noise reduction in spinal cord fMRI data

Purpose

The purpose of this work is to characterize the noise in spinal cord functional MRI, assess current methods aimed at reducing noise, and optimize imaging parameters.

Methods

Functional MRI data were acquired at multiple echo times and the contrast-to-noise ratio (CNR) was calculated. Independently, the repetition time was systematically varied with and without parallel imaging, to maximize BOLD sensitivity and minimize type I errors. Noise in the images was characterized by examining the frequency spectrum, and investigating whether autocorrelations exist. The efficacy of several physiological noise reduction methods in both null (no stimuli) and task (thermal pain paradigm) data was also assessed. Finally, our previous normalization methods were extended.

Results

The echo time with the highest functional CNR at 3 Tesla is at roughly 75 msec. Parallel imaging reduced the variance and the presence of autocorrelations, however the BOLD response in task data was more robust in data acquired without parallel imaging. Model-free based approaches further increased the detection of active voxels in the task data. Finally, inter-subject registration was improved.

Conclusions

Results from this study provide a rigorous characterization of the properties of the noise and assessment of data acquisition and analysis methods for spinal cord and brainstem fMRI.     

An investigation of the relationship between BOLD and perfusion signal changes during epileptic generalised spike wave activity

In pathological conditions interpretation of functional magnetic resonance imaging (fMRI) results can be difficult. This is due to a reliance on the assumed coupling between neuronal activity and changes in cerebral blood flow (CBF) and oxygenation. We wanted to investigate the coupling between blood oxygen level dependant contrast (BOLD) and CBF time courses in epilepsy patients with generalised spike wave activity (GSW) to better understand the underlying mechanisms behind the EEG-fMRI signal changes observed, especially in regions of negative BOLD response (NBR). Four patients with frequent GSW were scanned with simultaneous electroencephalographic (EEG)-fMRI with BOLD and arterial spin labeling (ASL) sequences. We examined the relationship between simultaneous CBF and BOLD measurements by looking at the correlation of the two signals in terms of percentage signal change on a voxel-by-voxel basis. This method is not reliant on coincident activation. BOLD and CBF were positively correlated in patients with epilepsy during background EEG activity and GSW. The subject average value of the Delta CBF/Delta BOLD slope lay between +19 and +36 and also showed spatial variation which could indicate areas with altered vascular response. There was not a significant difference between Delta CBF/Delta BOLD during GSW, suggesting that neurovascular coupling to BOLD signal is generally maintained between states and, in particular, within areas of NBR.     

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.     

Barrier height imaging of Si(1 1 1)3 × 1-Ag reconstructed surfaces

We investigated the bias voltage polarity dependence of atomically resolved barrier height (BH) images on Si(1 1 1)3 × 1-Ag surfaces. The BH images were very similar to scanning tunneling microscopy (STM) images in both the empty and filled states. This similarity strongly supports the interpretation that the BH image reflects the vertical decay rate of the surface local density of states (LDOS). Differences in contrast and protrusion shapes between BH and STM images were observed. We attributed these differences to the geometric contribution to the STM image and to the improved spatial resolution of the BH image due to the lock-in technique.     

Comparison of α-chloralose,medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat

Functional connectivity measures based upon low-frequency blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) signal fluctuations have become a widely used tool for investigating spontaneous brain activity in humans. Still unknown, however, is the precise relationship between neural activity, the hemodynamic response and fluctuations in the MRI signal. Recent work from several groups had shown that correlated low-frequency fluctuations in the BOLD signal can be detected in the anesthetized rat — a first step toward elucidating this relationship. Building on this preliminary work, through this study, we demonstrate that functional connectivity observed in the rat depends strongly on the type of anesthesia used. Power spectra of spontaneous fluctuations and the cross-correlation-based connectivity maps from rats anesthetized with α-chloralose, medetomidine or isoflurane are presented using a high-temporal-resolution imaging sequence that ensures minimal contamination from physiological noise. The results show less localized correlation in rats anesthetized with isoflurane as compared with rats anesthetized with α-chloralose or medetomidine. These experiments highlight the utility of using different types of anesthesia to explore the fundamental physiological relationships of the BOLD signal and suggest that the mechanisms contributing to functional connectivity involve a complicated relationship between changes in neural activity, neurovascular coupling and vascular reactivity.     

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.     

Neural and vascular variability and the fMRI-BOLD response in normal aging

Neural, vascular and structural variables contributing to the blood oxygen level-dependent (BOLD) signal response variability were investigated in younger and older humans. Twelve younger healthy human subjects (six male and six female; mean age: 24 years; range: 19–27 years) and 12 older healthy subjects (five male and seven female; mean age: 58 years; range: 55–71 years) with no history of head trauma and neurological disease were scanned. Functional magnetic resonance imaging measurements using the BOLD contrast were made when participants performed a motor, cognitive or a breath hold (BH) task. Activation volume and the BOLD response amplitude were estimated for the younger and older at both group and subject levels. Mean activation volume was reduced by 45%, 40% and 38% in the elderly group during the motor, cognitive and BH tasks, respectively, compared to the younger. Reduction in activation volume was substantially higher compared to the reduction in the gray matter volume of 14% in the older compared to the younger. A significantly larger variability in the intersubject BOLD signal change occurred during the motor task, compared to the cognitive task. BH-induced BOLD signal change between subjects was significantly less-variable in the motor task-activated areas in the younger compared to older whereas such a difference between age groups was not observed during the cognitive task. Hemodynamic scaling using the BH signal substantially reduced the BOLD signal variability during the motor task compared to the cognitive task. The results indicate that the origin of the BOLD signal variability between subjects was predominantly vascular during the motor task while being principally a consequence of neural variability during the cognitive task. Thus, in addition to gray matter differences, the type of task performed can have different vascular variability weighting that can influence age-related differences in brain functional response.     

Functional phantom for fMRI: a feasibility study

Functional magnetic resonance imaging (fMRI) reveals changes in blood oxygen level-dependent (BOLD) signal after considerable processing. This paper describes the implementation and testing of an fMRI phantom where electric current applied to a thin wire within a proton-rich medium substituted BOLD distortion of the magnetic field; the scanner detects these two distortions as practically identical signal changes. The magnitude of the change depended on the current strength. The phantom has a number of possible applications. Signal changes across sessions, days, instruments and individuals could be monitored. Placing the phantom close to a subject during an fMRI experiment could allow differentiating sensitivity changes in the scanner due to instrumentation from changes in the subject's state and performance during the experiment. The spatial extent of brain activations and effects of various changes in the chain of image formation could be analyzed using current-induced "activations". Furthermore, the phantom could expedite fMRI sequence development by reducing the need to scan human subjects, who introduce uncertainty to the signal. Thus, this fMRI phantom could be useful for both cognitive fMRI studies and scanner calibration.     

Input permutation method to detect active voxels in fMRI study

Correctly identifying voxels or regions of interest (ROI) that actively respond to a given stimulus is often an important objective/step in many functional magnetic resonance imaging (fMRI) studies. In this article, we study a nonparametric method to detect active voxels, which makes minimal assumption about the distribution of blood oxygen level-dependent (BOLD) signals. Our proposal has several interesting features. It uses time lagged correlation to take into account the delay in response to the stimulus, due to hemodynamic variations. We introduce an input permutation method (IPM), a type of block permutation method, to approximate the null distribution of the test statistic. Also, we propose to pool the permutation-derived statistics of preselected voxels for a better approximation to the null distribution. Finally, we control multiple testing error rate using the local false discovery rate (FDR) by Efron [Correlation and large-scale simultaneous hypothesis testing. J Am Stat Assoc 102 (2007) 93–103] and Park et al. [Estimation of empirical null using a mixture of normals and its use in local false discovery rate. Comput Stat Data Anal 55 (2011) 2421–2432] to select the active voxels.     

Learning-based structurally-guided construction of resting-state functional correlation tensors

Functional magnetic resonance imaging (fMRI) measures changes in blood-oxygenation-level-dependent (BOLD) signals to detect brain activities. It has been recently reported that the spatial correlation patterns of resting-state BOLD signals in the white matter (WM) also give WM information often measured by diffusion tensor imaging (DTI). These correlation patterns can be captured using functional correlation tensor (FCT), which is analogous to the diffusion tensor (DT) obtained from DTI. In this paper, we propose a noise-robust FCT method aiming at further improving its quality, and making it eligible for further neuroscience study. The novel FCT estimation method consists of three major steps: First, we estimate the initial FCT using a patch-based approach for BOLD signal correlation to improve the noise robustness. Second, by utilizing the relationship between functional and diffusion data, we employ a regression forest model to learn the mapping between the initial FCTs and the corresponding DTs using the training data. The learned forest can then be applied to predict the DTI-like tensors given the initial FCTs from the testing fMRI data. Third, we re-estimate the enhanced FCT by utilizing the DTI-like tensors as a feedback guidance to further improve FCT computation. We have demonstrated the utility of our enhanced FCTs in Alzheimer's disease (AD) diagnosis by identifying mild cognitive impairment (MCI) patients from normal subjects.