Spontaneous Blood Oxygenation Level-dependent Signal Research Articles

Resting‐state functional magnetic resonance imaging versus task‐based activity for language mapping and correlation with perioperative cortical mapping

IntroductionPreoperative language mapping using functional magnetic resonance imaging (fMRI) aims to identify eloquent areas in the vicinity of surgically resectable brain lesions. fMRI methodology relies on the blood‐oxygen‐level‐dependent (BOLD) analysis to identify brain language areas. Task‐based fMRI studies the BOLD signal increase in brain areas during a language task to identify brain language areas, which requires patients' cooperation, whereas resting‐state fMRI (rsfMRI) allows identification of functional networks without performing any explicit task through the analysis of the synchronicity of spontaneous BOLD signal oscillation between brain areas. The aim of this study was to compare preoperative language mapping using rsfMRI and task fMRI to cortical mapping (CM) during awake craniotomies.MethodsFifty adult patients surgically treated for a brain lesion were enrolled. All patients had a presurgical language mapping with both task fMRI and rsfMRI. Identified language networks were compared to perioperative language mapping using electric cortical stimulation.ResultsResting‐state fMRI was able to detect brain language areas during CM with a sensitivity of 100% compared to 65.6% with task fMRI. However, we were not able to perform a specificity analysis and compare task‐based and rest fMRI with our perioperative setting in the current study. In second‐order analysis, task fMRI imaging included main nodes of the SN and main areas involved in semantics were identified in rsfMRI.ConclusionResting‐state fMRI for presurgical language mapping is easy to implement, allowing the identification of functional brain language network with a greater sensitivity than task‐based fMRI, at the cost of some precautions and a lower specificity. Further study is required to compare both the sensitivity and the specificity of the two methods and to evaluate the clinical value of rsfMRI as an alternative tool for the presurgical identification of brain language areas.

Erroneous Resting-State fMRI Connectivity Maps Due to Prolonged Arterial Arrival Time and How to Fix Them.

In resting-state functional MRI (rs-fMRI), functional networks are assessed utilizing the temporal correlation between spontaneous blood oxygen level-dependent signal fluctuations of spatially remote brain regions. Recently, several groups have shown that temporal shifts are present in rs-fMRI maps in patients with cerebrovascular disease due to spatial differences in arterial arrival times, and that this can be exploited to map arrival times in the brain. This suggests that rs-fMRI connectivity mapping may be similarly sensitive to such temporal shifts, and that standard rs-fMRI analysis methods may fail to identify functional connectivity networks. To investigate this, we studied the default mode network (DMN) in Moyamoya disease patients and compared it with normal healthy volunteers. Our results show that using standard independent component analysis (ICA) and seed-based approaches, arterial arrival delays lead to inaccurate incomplete characterization of functional connectivity within the DMN in Moyamoya disease patients. Furthermore, we propose two techniques to correct these errors, for seed-based and ICA methods, respectively. Using these methods, we demonstrate that it is possible to mitigate the deleterious effects of arterial arrival time on the assessment of functional connectivity of the DMN. As these corrections have not been applied to the vast majority of >200 prior rs-fMRI studies in patients with cerebrovascular disease, we suggest that they be interpreted with great caution. Correction methods should be applied in any rs-fMRI connectivity study of subjects expected to have abnormally delayed arterial arrival times.

Altered intrinsic activity and functional connectivity in two animal models with opposing fear memories.

Post-traumatic stress disorder and sepsis-associated encephalopathy are two common neuropsychiatric disorders, but to some extent with opposing cognitive performance. However, the neural mechanisms underlying these two different phenomena remain poorly understood. In our study, the amplitude of low-frequency fluctuations (ALFF) of spontaneous blood oxygen level-dependent signals was used to assess regional disturbances in two animal models with opposing cognitive performance, namely post-traumatic stress disorder and sepsis-associated encephalopathy. Then, we examined whether the memory-relevant brain regions with impaired ALFF altered functional connectivity within the whole brain. Here, we showed that lipopolysaccharide challenge and footshock induced significantly different cognitive performance. We found significant group differences in ALFF in the bilateral dentate gyrus. In addition, our study showed that footshocks induced a significant decrease in functional connectivity between the left dentate gyrus and the visual cortex compared with the lipopolysaccharide group. Notably, cognitive performance was associated with ALFF values in the right dentate gyrus. In conclusion, our data suggested that damaged regional spontaneous activity and abnormal functional connectivity might be differently involved in two opposing fear memories.

Frequency of Spontaneous BOLD Signal Differences between Moderate and Late Preterm Newborns and Term Newborns.

Little is known about the frequency features of spontaneous neural activity in the brains of moderate and late preterm (MLPT) newborns. We used resting-state functional magnetic resonance imaging (rs-fMRI) and the amplitude of low-frequency fluctuation (ALFF) method to investigate the frequency properties of spontaneous blood oxygen level-dependent (BOLD) signals in 26 MLPT and 35 term newborns. Two frequency bands, slow-4 (0.027-0.073Hz) and slow-5 (0.01-0.027Hz), were analyzed. Our results showed widespread differences in ALFF between the two bands; differences occurred mainly in the primary sensory and motor cortices and to a lesser extent in association cortices and subcortical areas. Compared with term newborns, MLPT newborns showed significantly altered neural activity predominantly in the primary sensory and motor cortices and in the posterior cingulate gyrus/precuneus. In addition, a significant interaction between frequency bands and groups was observed in the primary somatosensory cortex. Intriguingly, these primary sensory and motor regions have been proven to be the major cortical hubs during the neonatal period. Our results revealed the frequency of spontaneous BOLD signal differences between MLPT and term newborns, which contribute to the understanding of regional development of spontaneous brain rhythms of MLPT newborns.

Evaluating rehabilitation interventions in Parkinson's disease with functional MRI: a promising neuroprotective strategy.

Evaluating rehabilitation interventions in Parkinson's disease with functional MRI: a promising neuroprotective strategy.

Frequency of spontaneous BOLD signal shifts during infancy and correlates with cognitive performance

Numerous studies have been conducted to delineate the early development of different functional networks, based on measuring the temporal synchronization of spontaneous blood oxygenation level-dependent (BOLD) signals acquired using resting state functional MRI (rsfMRI). However, little attention has been paid to the change of the frequency properties of these signals during early brain development. Such frequency properties may reflect important physiological changes and potentially have significant cognitive consequences. In this study, leveraging a large (N=86 subjects), longitudinal sample of human infants scanned during the first two years of life, we aimed to specifically delineate the developmental changes of the frequency characteristics of spontaneous BOLD signals. Both whole-brain and network-level examinations were carried out and the frequency-behavior relationship was explored. Our results revealed a clear right-ward shift of BOLD signal frequency during the first year of life. Moreover, the power at the peak-frequency for sensorimotor and lateral visual networks correlates with domain-specific Mullen Scales in 1-year-olds, suggesting the behavioral significance of the BOLD signal frequency during infancy. Findings from this study shed light into early functional brain development and provide a new perspective for future searches for functional developmental abnormalities.

Synchronous Multiscale Neuroimaging Environment for Critically Sampled Physiological Analysis of Brain Function: Hepta-Scan Concept

Functional connectivity of the resting-state networks of the brain is thought to be mediated by very-low-frequency fluctuations (VLFFs <0.1 Hz) in neuronal activity. However, vasomotor waves and cardiorespiratory pulsations influence indirect measures of brain function, such as the functional magnetic resonance imaging blood-oxygen-level-dependent (BOLD) signal. How strongly physiological oscillations correlate with spontaneous BOLD signals is not known, partially due to differences in the data-sampling rates of different methods. Recent ultrafast inverse imaging sequences, including magnetic resonance encephalography (MREG), enable critical sampling of these signals. In this study, we describe a multimodal concept, referred to as Hepta-scan, which incorporates synchronous MREG with scalp electroencephalography, near-infrared spectroscopy, noninvasive blood pressure, and anesthesia monitoring. Our preliminary results support the idea that, in the absence of aliased cardiorespiratory signals, VLFFs in the BOLD signal are affected by vasomotor and electrophysiological sources. Further, MREG signals showed a high correlation coefficient between the ventromedial default mode network (DMNvmpf) and electrophysiological signals, especially in the VLF range. Also, oxy- and deoxyhemoglobin and vasomotor waves were found to correlate with DMNvmpf. Intriguingly, usage of shorter time windows in these correlation measurements produced significantly (p<0.05) higher positive and negative correlation coefficients, suggesting temporal nonstationary behavior between the measurements. Focus on the VLF range strongly increased correlation strength.

The effect of echo time and post-processing procedure on blood oxygenation level-dependent (BOLD) functional connectivity analysis

While spontaneous BOLD fMRI signal is a common tool to map functional connectivity, unexplained inter- and intra-subject variability frequently complicates interpretation. Similar to evoked BOLD fMRI responses, spontaneous BOLD signal is expected to vary with echo time (TE) and corresponding intra/extravascular sensitivity. This may contribute to discrepant conclusions even following identical post-processing pipelines. Here we applied commonly-utilized independent component analysis (ICA) as well as seed-based correlation analysis and investigated default mode network (DMN) and visual network (VN) detection from BOLD data acquired at three TEs (3T; TR=2500ms; TE=15ms, 35ms, and 55ms) and from quantitative R2* maps. Explained variance in ICA analysis was significantly higher (P<0.05) when R2*-derived maps were considered relative to single-TE data with no post-processing. While explained variance in the BOLD data increased with motion correction, R2* derived DMN and VN were minimally affected by motion correction. Explained variance increased in all data when physiological noise confounds were removed using CompCor. Notably, the R2*-derived connectivity patterns were least affected by motion and physiological noise confounds in a seed-based correlation analysis. Intermediate (35ms) and long (55ms) TE data provided similar spatial and temporal characteristics only after reducing motion and physiological noise contamination. Results provide an exemplar for how 3T spontaneous BOLD network detection varies with TE and post-processing procedure over the range of commonly acquired TE values.

Spontaneous BOLD Signal Fluctuations in Young Healthy Subjects and Elderly Patients with Chronic Kidney Disease

Spontaneous fluctuations in blood oxygenation level-dependent (BOLD) images are the basis of resting-state fMRI and frequently used for functional connectivity studies. However, there may be intrinsic information in the amplitudes of these fluctuations. We investigated the possibility of using the amplitude of spontaneous BOLD signal fluctuations as a biomarker for cerebral vasomotor reactivity.We compared the coefficient of variation (CV) of the time series (defined as the temporal standard deviation of the time series divided by the mean signal intensity) in two populations: 1) Ten young healthy adults and 2) Ten hypertensive elderly subjects with chronic kidney disease (CKD).We found a statistically significant increase (P<0.01) in the CV values for the CKD patients compared with the young healthy adults in both gray matter (GM) and white matter (WM). The difference was independent of the exact segmentation method, became more significant after correcting for physiological signals using RETROICOR, and mainly arose from very low frequency components of the BOLD signal fluctuation (f<0.025 Hz). Furthermore, there was a strong relationship between WM and GM signal fluctuation CV's (R2 = 0.87) in individuals, with a ratio of about 1∶3.These results suggest that amplitude of the spontaneous BOLD signal fluctuations may be used to assess the cerebrovascular reactivity mechanisms and provide valuable information about variations with age and different disease states.

Functional connectivity patterns of medial and lateral macaque frontal eye fields reveal distinct visuomotor networks

It has been previously shown that small- and large-amplitude saccades have different functions during vision in natural environments. Large saccades are associated with reaching movements toward objects, whereas small saccades facilitate the identification of more detailed object features necessary for successful grasping and manual manipulation. To determine whether these represent dichotomous processing streams, we used resting-state functional MRI to examine the functional connectivity patterns of the medial and lateral frontal eye field (FEF) regions that encode large- and small-amplitude saccades, respectively. We found that the spontaneous blood oxygen level-dependent signals of the medial FEF were functionally correlated with areas known to be involved in reaching movements and executive control processes, whereas lateral FEF was functionally correlated with cortical areas involved in object processing and in grasping, fixation, and manipulation of objects. The results provide strong evidence for two distinct visuomotor network systems in the primate brain that likely reflect the alternating phases of vision for action in natural environments.

Infraslow LFP correlates to resting-state fMRI BOLD signals

The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (<0.5 Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1-100 Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.

The change of functional connectivity specificity in rats under various anesthesia levels and its neural origin.

Spatiotemporal correlations of spontaneous blood oxygenation level dependent (BOLD) signals measured in the resting brain have been found to imply many resting-state coherent networks under both awake/conscious and anesthetized/unconscious conditions. To understand the resting-state brain networks in the unconscious state, spontaneous BOLD signals from the rat sensorimotor cortex were studied across a wide range of anesthesia levels induced by isoflurane. Distinct resting-state networks covering functionally specific sub-regions of the sensorimotor system were observed under light anesthesia with 1.0% isoflurane; however, they gradually merged into a highly synchronized and spatially less-specific network under deep anesthesia with 1.8% isoflurane. The EEG power correlations recorded using three electrodes from a separate group of rats showed similar dependency on anesthesia depth, suggesting the neural origin of the change in functional connectivity specificity. The specific-to-less-specific transition of resting-state networks may reflect a functional reorganization of the brain at different anesthesia levels or brain states.

Role of Ongoing, Intrinsic Activity of Neuronal Populations for Quantitative Neuroimaging of Functional Magnetic Resonance Imaging–Based Networks

A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI-conducted with or without tasks-is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. In task fMRI experiments, the baseline is differenced away to disclose areas of small evoked changes in the blood oxygenation level-dependent (BOLD) signal. In resting-state fMRI experiments, the spotlight is on regions revealed by correlations of tiny fluctuations in the baseline (or spontaneous) BOLD signal. Interpretation of fMRI-based networks is obscured further, because the BOLD signal indirectly reflects neuronal activity, and difference/correlation maps are thresholded. Since the small changes of BOLD signal typically observed in cognitive fMRI experiments represent a minimal fraction of the total energy/activity in a given area, the relevance of fMRI-based networks is uncertain, because the majority of neuronal energy/activity is ignored. Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMR(O2)). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of ΔCMR(O2) and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMR(O2).

Abnormal small-world architecture of top–down control networks in obsessive–compulsive disorder

Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder that is characterized by recurrent intrusive thoughts, ideas or images and repetitive ritualistic behaviours. Although focal structural and functional abnormalities in specific brain regions have been widely studied in populations with OCD, changes in the functional relations among them remain poorly understood. This study examined OCD-related alterations in functional connectivity patterns in the brain's top-down control network. We applied resting-state functional magnetic resonance imaging to investigate the correlation patterns of intrinsic or spontaneous blood oxygen level-dependent signal fluctuations in 18 patients with OCD and 16 healthy controls. The brain control networks were first constructed by thresholding temporal correlation matrices of 39 brain regions associated with top-down control and then analyzed using graph theory-based approaches. Compared with healthy controls, the patients with OCD showed decreased functional connectivity in the posterior temporal regions and increased connectivity in various control regions such as the cingulate, precuneus, thalamus and cerebellum. Furthermore, the brain's control networks in the healthy controls showed small-world architecture (high clustering coefficients and short path lengths), suggesting an optimal balance between modularized and distributed information processing. In contrast, the patients with OCD showed significantly higher local clustering, implying abnormal functional organization in the control network. Further analysis revealed that the changes in network properties occurred in regions of increased functional connectivity strength in patients with OCD. The patient group in the present study was heterogeneous in terms of symptom clusters, and most of the patients with OCD were medicated. Our preliminary results suggest that the organizational patterns of intrinsic brain activity in the control networks are altered in patients with OCD and thus provide empirical evidence for aberrant functional connectivity in the large-scale brain systems in people with this disorder.

Simultaneous fMRI and Electrophysiology in the Rodent Brain

To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in which functional MRI data and in vivo intracortical recording can be performed simultaneously. The combination of MRI and electrical recording is technically challenging because the electrodes used for recording distort the MRI images and the MRI acquisition induces noise in the electrical recording. To minimize the mutual interference of the two modalities, glass microelectrodes were used rather than metal and a noise removal algorithm was implemented for the electrophysiology data. In our studies, two microelectrodes were separately implanted in bilateral primary somatosensory cortices (SI) of the rat and fixed in place. One coronal slice covering the electrode tips was selected for functional MRI. Electrode shafts and fixation positions were not included in the image slice to avoid imaging artifacts. The removed scalp was replaced with toothpaste to reduce susceptibility mismatch and prevent Gibbs ringing artifacts in the images. The artifact structure induced in the electrical recordings by the rapidly-switching magnetic fields during image acquisition was characterized by averaging all cycles of scans for each run. The noise structure during imaging was then subtracted from original recordings. The denoised time courses were then used for further analysis in combination with the fMRI data. As an example, the simultaneous acquisition was used to determine the relationship between spontaneous fMRI BOLD signals and band-limited intracortical electrical activity. Simultaneous fMRI and electrophysiological recording in the rodent will provide a platform for many exciting applications in neuroscience in addition to elucidating the relationship between the fMRI BOLD signal and neuronal activity.

Alcohol increases spontaneous BOLD signal fluctuations in the visual network

Brain activity during resting wakefulness is characterized by slow (< 0.1 Hz) fluctuations of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals that are topographically organized in discrete functional connectivity networks (resting-state networks, RSNs). The present study aimed at revealing possible network-specific alcohol-induced changes in resting-state fMRI (RS-fMRI) signals. RS-fMRI was carried out on eight healthy subjects in four consecutive 6-min sessions, one before and three after a 0.7 g/kg dose of ethyl alcohol. Control experiments were carried out in different days without alcohol administration. Independent component analysis (ICA) was performed on all experimental and control scans to extract individual and group-level RSN maps in a dynamic network analysis. Alcohol administration significantly increased the overall strength of the visual network ICA component, reaching the peak at 90 min. Within the visual network, the alcohol-induced increase was more pronounced in the primary regions of the occipital cortex and less pronounced in the secondary regions of the occipito-temporal cortex. Other major RSN components, such as the default-mode, the fronto-parietal, the sensori-motor, the self-referential and the auditory components, did not exhibit alcohol-induced changes during the same time window. Alcohol-induced effects on the resting-state functional connectivity of the visual network observed in the present study demonstrate that the visual system is a selective and primary target of acute alcohol administration. The strong enhancement of spontaneous BOLD fluctuations in the primary visual cortex in an acute alcoholic state may impair the normal activation response to visual stimuli and affect visual perception.

Low Frequency Fluctuation Component Analysis in Active Stimulation fMRI Paradigm

Purpose : To separate and evaluate the low frequency spontaneous fluctuation BOLD signals from the functional magnetic resonance imaging data using sensorimotor active task. Materials and Methods : Twenty female archery players and twenty three control subjects were included in this study. Finger-tapping task consisted of three cycles of right finger tapping, with a subsequent 30 second rest. Blood oxygenation level-dependent (BOLD) data were collected using -weighted echo planar imaging at a 3.0 T scanner. A 3-D FSPGR T1-weighted images were used for structural reference. Image processing and statistical analyses were performed using SPM5 for active finger-tapping task and GIFT program was used for statistical analyses of low frequency spontaneous fluctuation BOLD signal. Results : Both groups showed the activation in the left primary motor cortex and supplemental motor area and in the right cerebellum for right finger-tapping task. ICA analysis using GIFT revealed independent components corresponding to contralateral and ipsilateral sensorimotor network and cognitive-related neural network. Conclusion : The current study demonstrated that the low frequency spontaneous fluctuation BOLD signals can be separated from the fMRI data using finger tapping paradigm. Also, it was found that these independent components correspond to spontaneous and coherent neural activity in the primary sensorimotor network and in the motor-cognitive network.

Resting state networks in human cervical spinal cord observed with fMRI

It has been reported that spontaneous fluctuations of blood oxygen level dependent (BOLD) signals can be detected in human brain and constitute resting state networks. It has not been reported whether resting state networks also exist in human spinal cord. In the present study, we investigate spontaneous BOLD signal changes in human cervical spinal cord during resting state. fMRI data were analyzed with independent component analysis and SPM software package. Acceptable reproducibility of spatial maps of BOLD signal changes was found across sessions, with the highest correlation values ranging from 0.18 to 0.44. The dominant frequency of signal changes from independent components with the highest correlation values was approximately the frequency range of the respiratory circle. Activities of spinal motor neurons innervating the scalenes were considered as a major factor in the production of BOLD signal fluctuations were observed in this study. Our findings suggest that BOLD fMRI can be applied to study the features of low-frequency rhythmic activities and corresponding mechanisms in the spinal cord during resting state.

Electrophysiological correlates of the brain's intrinsic large-scale functional architecture

Spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signals demonstrate consistent temporal correlations within large-scale brain networks associated with different functions. The neurophysiological correlates of this phenomenon remain elusive. Here, we show in humans that the slow cortical potentials recorded by electrocorticography demonstrate a correlation structure similar to that of spontaneous BOLD fluctuations across wakefulness, slow-wave sleep, and rapid-eye-movement sleep. Gamma frequency power also showed a similar correlation structure but only during wakefulness and rapid-eye-movement sleep. Our results provide an important bridge between the large-scale brain networks readily revealed by spontaneous BOLD signals and their underlying neurophysiology.