Between Blood Oxygenation Level Dependent Research Articles

Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population

Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.

Probing the inhibitory motor circuits in adductor laryngeal dystonia during a dystonia-unrelated task

BackgroundThe pathophysiology of adductor laryngeal dystonia (AdLD) remains unknown; however, there is growing evidence that dystonia is associated with disruptions in the inhibitory regulation of sensorimotor cortical areas. Using functional MRI (fMRI) and transcranial magnetic stimulation (TMS) complementarily, we previously demonstrated an overly activated laryngeal motor cortex and revealed correlations between blood-oxygen-level dependent (BOLD) activation and intracortical inhibition in a phonation (dystonia-related) task in adductor laryngeal dystonia (AdLD). ObjectiveHere, we aimed to characterize the brain-based findings in the primary motor cortex (M1) during a dystonia-unrelated (finger tapping) task in AdLD and controls (CTL). MethodsWe examined the between-group differences in task-dependent BOLD activation and intracortical inhibition, measured by the TMS-evoked cortical silent period (cSP), in the M1. The correlations between fMRI and TMS responses were assessed. ResultsThere is more broadly dispersed BOLD activation, not confined to the hand motor cortex, and reduced intracortical inhibition in AdLD compared to CTL. Further, there are more positive correlations between cSP and BOLD activation in a task unrelated to dystonic symptoms in AdLD compared with CTL. This is in contrast to our previous work that demonstrated fewer positive correlations in AdLD during a dystonic phonation task. ConclusionsIn unaffected musculature activation, there is dispersed BOLD activation that is correlated with intracortical inhibition suggesting a possible compensatory strategy in the non-dystonic muscles.

Functional hyperconnectivity is associated with higher levels of sPDGFRβ, a marker of blood brain barrier breakdown, in older adults

AbstractBackgroundApolipoprotein E4 (APOE4) has toxic effects on the cerebrovascular system, neuronal function, and functional connectivity of brain networks (e.g., default‐mode, frontal‐attention, and salience networks). The thalamus, a vital structure that acts as a relay station between many networks, has become of interest in AD research with studies showing atrophy among those with early AD and diminished cortical‐thalamic connections among those with vascular impairment. Additionally, CSF soluble platelet‐derived growth factor receptor‐β (sPDGFRβ), a marker of blood‐brain barrier breakdown, has been associated with AD risk, especially in APOE4 carriers. The present study investigated the relationship between CSF sPDGFRβ and functional connectivity in APOE4 carriers and non‐carriers.MethodOne hundred sixty‐six older adults from the Los Angeles area were selected from a larger cohort (96 females, mean age±SD = 67.4±10.7, 68 APOE4 carriers) based on availability of rsfMRI data. Image data was processed using CONN‐toolbox v19c. Grey matter was parcellated using Harvard/Oxford brain atlas, excluding cerebellum. ROI‐to‐ROI analyses were conducted selecting the thalamus as seed region averaging mean connectivity between the left and right structure of other parcellated brain regions. Linear regression models evaluated significant associations between blood oxygen level‐dependent (BOLD) and network connectivity among seed‐target regions and CSF sPDGFRβvalues, while covarying for age and sex. Comparison of averaged functional connectivity values between seed and all target regions among APOE4 carriers and non‐carriers was also examined.ResultHigher functional connectivity between the thalamus and resting state network hub regions were associated with higher CSF sPDGFRβ (Fig 1A, 1C). Cortical regions within the networks included posterior cingulate gyrus, anterior cingulate gyrus, and frontal eye field. Averaged functional connectivity between these areas was significantly higher in APOE4 carriers compared to non‐carriers (Fig 1B).ConclusionThe thalamus is a key subcortical structure for relaying signals across the brain for memory, attention and sleep/wakefulness cycles, and more recently with relevance to AD. These findings suggest a link between early BBB breakdown and elevated functional connectivity in brain regions known to have high synaptic activity and early pathology among non‐demented older adults at risk for AD.

Functional hyperconnectivity is associated with higher levels of sPDGFRβ, a marker of blood brain barrier breakdown, in older adults

AbstractBackgroundApolipoprotein E4 (APOE4) has toxic effects on the cerebrovascular system, neuronal function, and functional connectivity of brain networks (e.g., default‐mode, frontal‐attention, and salience networks). The thalamus, a vital structure that acts as a relay station between many networks, has become of interest in AD research with studies showing atrophy among those with early AD and diminished cortical‐thalamic connections among those with vascular impairment. Additionally, CSF soluble platelet‐derived growth factor receptor‐β (sPDGFRβ), a marker of blood‐brain barrier breakdown, has been associated with AD risk, especially in APOE4 carriers. The present study investigated the relationship between CSF sPDGFRβ and functional connectivity in APOE4 carriers and non‐carriers.MethodOne hundred sixty‐six older adults from the Los Angeles area were selected from a larger cohort (96 females, mean age+SD=67.4+10.7, 68 APOE4 carriers) based on availability of rsfMRI data. Image data was processed using CONN‐toolbox v19c. Grey matter was parcellated using Harvard/Oxford brain atlas, excluding cerebellum. ROI‐to‐ROI analyses were conducted selecting the thalamus as seed region averaging mean connectivity between the left and right structure of other parcellated brain regions. Linear regression models evaluated significant associations between blood oxygen level‐dependent (BOLD) and network connectivity among seed‐target regions and CSF sPDGFRβvalues, while covarying for age and sex. Comparison of averaged functional connectivity values between seed and all target regions among APOE4 carriers and non‐carriers was also examined.ResultHigher functional connectivity between the thalamus and resting state network hub regions were associated with higher CSF sPDGFRβ (Fig 1A, 1C). Cortical regions within the networks included posterior cingulate gyrus, anterior cingulate gyrus, and frontal eye field. Averaged functional connectivity between these areas was significantly higher in APOE4 carriers compared to non‐carriers (Fig 1B).ConclusionThe thalamus is a key subcortical structure for relaying signals across the brain for memory, attention and sleep/wakefulness cycles, and more recently with relevance to AD. These findings suggest a link between early BBB breakdown and elevated functional connectivity in brain regions known to have high synaptic activity and early pathology among non‐demented older adults at risk for AD.

The Effect of Abnormal Regional Homogeneity and Spontaneous Low-Frequency Brain Activity on Lower Cognitive Ability: A Cross-Sectional Study on Postoperative Children With Tetralogy of Fallot.

Despite intracardiac malformation correction, children with Tetralogy of Fallot (TOF) may still suffer from brain injury. This cross-sectional study was primarily designed to determine the relationship between blood oxygenation level-dependent (BOLD) signal changes after surgery and cognition in school-aged children with TOF. To evaluate the differences between TOF children (n = 9) and healthy children (n = 9), resting-state functional magnetic resonance imaging (rs-fMRI) and the Wechsler Intelligence Scale for Children–Chinese revised edition (WISC-CR) were conducted in this study. The results showed that TOF children had a lower full-scale intelligence quotient (FSIQ, 95.444 ± 5.354, p = 0.022) and verbal intelligence quotient (VIQ, 92.444 ± 4.708, p = 0.003) than healthy children (FSIQ = 118.500 ± 4.330;VIQ = 124.250 ± 4.404), and that significant differences in regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) existed between the two groups. Besides, VIQ had significantly positive correlations with the decreased ALFF value of the middle inferior occipital gyrus (MIOG, beta = 0.908, p = 0.012) after fully adjusting for all covariates. In addition, elevated ReHo values of the left and right precuneus were positively related to ALFF in the MIOG. This study revealed that brain injury substantially influences neural activity and cognition in postoperative TOF children, providing direct evidence of an association between BOLD signal changes and the VIQ and prompting further attention to language development in TOF children.

Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation

Emotion dysregulation is central to the development and maintenance of psychopathology, and is common across many psychiatric disorders. Neurobiological models of emotion dysregulation involve the fronto-limbic brain network, including in particular the amygdala and prefrontal cortex (PFC). Neural variability has recently been suggested as an index of cognitive flexibility. We hypothesized that within-subject neural variability in the fronto-limbic network would be related to inter-individual variation in emotion dysregulation in the context of low affective control. In a multi-site cohort (N = 166, 93 females) of healthy individuals and individuals with emotional dysregulation (attention deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD)), we applied partial least squares (PLS), a multivariate data-driven technique, to derive latent components yielding maximal covariance between blood-oxygen level-dependent (BOLD) signal variability at rest and emotion dysregulation, as expressed by affective lability, depression and mania scores. PLS revealed one significant latent component (r = 0.62, p = 0.044), whereby greater emotion dysregulation was associated with increased neural variability in the amygdala, hippocampus, ventromedial, dorsomedial and dorsolateral PFC, insula and motor cortex, and decreased neural variability in occipital regions. This spatial pattern bears a striking resemblance to the fronto-limbic network, which is thought to subserve emotion regulation, and is impaired in individuals with ADHD, BD, and BPD. Our work supports emotion dysregulation as a transdiagnostic dimension with neurobiological underpinnings that transcend diagnostic boundaries, and adds evidence to neural variability being a relevant proxy of neural efficiency.

The value of a shorter-delay arterial spin labeling protocol for detecting cerebrovascular impairment

The aim of this study was to determine the relationship between blood oxygen level dependent (BOLD) cerebrovascular reactivity (CVR) and cerebral blood flow (CBF) obtained from arterial spin labeling (ASL) using different post labeling delays (PLD). Forty-two patients with steno-occlusive diseases and impaired CVR were divided into two groups, one scanned with a 1.5-second (1.5-s) and the other with a 2.5-second (2.5-s) PLD ASL protocol. For all patients, a region of interest (ROI) was drawn around the CVR impairment. This affected ROI was then left-right flipped across the brain midline to obtain the control ROI. For both groups, the difference in grey matter CVR between affected and control ROI was first tested to confirm significance. The average grey matter CBF of affected and control ROIs were then compared. The same analysis method was used to compare affected and control hemispheres. In both groups of 1.5-s and 2.5-s PLD, CVR values in the affected ROI (-0.049±0.055 and -0.042±0.074%/mmHg, respectively) were significantly lower compared to that in the control ROI (0.152±0.054 and 0.152±0.053%/mmHg, respectively, P<0.0001). In the group with the 1.5-s PLD, CBF in the affected ROI (37.62±11.37 mL/100 g/min) was significantly lower compared to CBF in the control ROI (44.13±11.58 mL/100 g/min, P<0.05). However, in the group with the 2.5-s PLD, no significant differences could be seen between CBF in the affected ROI (40.50±14.82 mL/100 g/min) and CBF in the control ROI (39.68±12.49 mL/100 g/min, P=0.73). In the hemisphere-based analysis, CBF was significantly lower in the affected side than in the control side for the group with the 1.5-s PLD (P<0.05) when CVR was impaired (P<0.0001), but not for the group with the 2.5-s PLD (P=0.49). In conclusion, our study reveals and highlights the value of a shorter-PLD ASL protocol, which is able to reflect CVR impairment. At the same time, we offer a better understanding of the relationship between BOLD CVR and CBF obtained from ASL.

"Switch-Off" of Respiratory Sinus Arrhythmia May Be Associated With the Activation of an Oscillatory Source (Pacemaker) in the Brain Stem.

Recently, we reported on the unusual “switch-off” of respiratory sinus arrhythmia (RSA) by analyzing heart rate (HR) beat-to-beat interval (RRI) signals and respiration in five subjects during a potentially anxiety-provoking first-time functional magnetic resonance imaging (fMRI) scanning with slow spontaneous breathing waves (Rassler et al., 2018). This deviation from a fundamental physiological phenomenon is of interest and merits further research. Therefore, in this study, the interplay between blood-oxygen level-dependent (BOLD) activity in the cerebellum/brain stem, RRI, and respiration was probed. Both the cardiovascular and the respiratory centers are located in the medulla oblongata and pons, indicating that dominant slow rhythmic activity is present in the brain stem. The recording of BOLD signals provides a way to investigate associated neural activity fluctuation in the brain stem. We found slow spontaneous breathing waves associated with two types of slow BOLD oscillations with dominant frequencies at 0.10 and 0.15 Hz in the brain stem. Both BOLD oscillations were recorded simultaneously. One is hypothesized as vessel motion-based phenomenon (BOLDv) associated with the start of expiration; the other one as pattern associated with neural activity (BOLDn) acting as a driving force for spontaneous inspiration and RRI increase (unusual cessation of RSA) about 2–3 s after BOLDv. This time delay of 2–3 s corresponds to the neurovascular coupling time.

Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression

In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.

Spatial Resolution and Imaging Encoding fMRI Settings for Optimal Cortical and Subcortical Motor Somatotopy in the Human Brain.

There is much controversy about the optimal trade-off between blood-oxygen-level-dependent (BOLD) sensitivity and spatial precision in experiments on brain’s topology properties using functional magnetic resonance imaging (fMRI). The sparse empirical evidence and regional specificity of these interactions pose a practical burden for the choice of imaging protocol parameters. Here, we test in a motor somatotopy experiment the impact of fMRI spatial resolution on differentiation between body part representations in cortex and subcortical structures. Motor somatotopy patterns were obtained in a block-design paradigm and visually cued movements of face, upper and lower limbs at 1.5, 2, and 3 mm spatial resolution. The degree of segregation of the body parts’ spatial representations was estimated using a pattern component model. In cortical areas, we observed the same level of segregation between somatotopy maps across all three resolutions. In subcortical areas the degree of effective similarity between spatial representations was significantly impacted by the image resolution. The 1.5 mm 3D EPI and 3 mm 2D EPI protocols led to higher segregation between motor representations compared to the 2 mm 3D EPI protocol. This finding could not be attributed to differential BOLD sensitivity or delineation of functional areas alone and suggests a crucial role of the image encoding scheme – i.e., 2D vs. 3D EPI. Our study contributes to the field by providing empirical evidence about the impact of acquisition protocols for the delineation of somatotopic areas in cortical and sub-cortical brain regions.

MRI-related anxiety in healthy individuals, intrinsic BOLD oscillations at 0.1 Hz in precentral gyrus and insula, and heart rate variability in low frequency bands.

Participation in magnetic resonance imaging (MRI) scanning is associated with increased anxiety, thus possibly impacting baseline recording for functional MRI studies. The goal of the paper is to elucidate the significant hemispheric asymmetry between blood-oxygenation-level-dependent (BOLD) signals from precentral gyrus (PCG) and insula in 23 healthy individuals without any former MRI experience recently published in a PLOSONE paper. In addition to BOLD signals state anxiety and heart rate variability (HRV) were analyzed in two resting state sessions (R1, R2). Phase-locking and time delays from BOLD signals were computed in the frequency band 0.07–0.13 Hz. Positive (pTD) and negative time delays (nTD) were found. The pTD characterize descending neural BOLD oscillations spreading from PCG to insula and nTD characterize ascending vascular BOLD oscillations related to blood flow in the middle cerebral artery. HRV power in two low frequency bands 0.06–0.1 Hz and 0.1–0.14 Hz was computed. Based on the anxiety change from R1 to R2, two groups were separated: one with a strong anxiety decline (large change group) and one with a moderate decline or even anxiety increase (small change group). A significant correlation was found only between the left-hemispheric time delay (pTD, nTD) and anxiety change, with a dominance of nTD in the large change group. The analysis of within-scanner HRV revealed a pronounced increase of low frequency power between both resting states, dominant in the band 0.06–0.1 Hz in the large change group and in the band 0.1–0.14 Hz in the small change group. These results suggest different mechanisms related to anxiety processing in healthy individuals. One mechanism (large anxiety change) could embrace an increase of blood circulation in the territory of the left middle cerebral artery (vascular BOLD) and another (small anxiety change) translates to rhythmic central commands (neural BOLD) in the frequency band 0.1–0.14 Hz.

Yeast biomass formation from carbon dioxide by rational metabolic engineering of Pichia pastoris

A key question in vision research concerns how the brain compensates for self-induced eye and head movements to form the world-centered, spatiotopic representations we perceive. Although human V3A and V6 integrate eye movements with vision, it is unclear which areas integrate head motion signals with visual retinotopic representations, as fMRI typically prevents head movement executions. Here we examined whether human early visual cortex V3A and V6 integrate these signals. A previously introduced paradigm allowed participant head movement during trials, but stabilized the head during data acquisition utilizing the delay between blood-oxygen-level-dependent (BOLD) and neural signals. Visual stimuli simulated either a stable environment or one with arbitrary head-coupled visual motion. Importantly, both conditions were matched in retinal and head motion. Contrasts revealed differential responses in human V6. Given the lack of vestibular responses in primate V6, these results suggest multi-modal integration of visual with neck efference copy signals or proprioception in V6.

Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI

In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.

Human V6 Integrates Visual and Extra-Retinal Cues during Head-Induced Gaze Shifts

SummaryA key question in vision research concerns how the brain compensates for self-induced eye and head movements to form the world-centered, spatiotopic representations we perceive. Although human V3A and V6 integrate eye movements with vision, it is unclear which areas integrate head motion signals with visual retinotopic representations, as fMRI typically prevents head movement executions. Here we examined whether human early visual cortex V3A and V6 integrate these signals. A previously introduced paradigm allowed participant head movement during trials, but stabilized the head during data acquisition utilizing the delay between blood-oxygen-level-dependent (BOLD) and neural signals. Visual stimuli simulated either a stable environment or one with arbitrary head-coupled visual motion. Importantly, both conditions were matched in retinal and head motion. Contrasts revealed differential responses in human V6. Given the lack of vestibular responses in primate V6, these results suggest multi-modal integration of visual with neck efference copy signals or proprioception in V6.

Rigidity in Motor Behavior and Brain Functioning in Patients With Schizophrenia and High Levels of Apathy.

The aim of this study was to investigate whether apathy in schizophrenia is associated with rigidity in behavior and brain functioning. To this end, we studied associations between variability in dynamic functional connectivity (DFC) in relevant functional brain networks, apathy, and variability in physical activity in schizophrenia. Thirty-one patients with schizophrenia, scoring high on apathy, were included and wore an actigraph. Activity variability was calculated on the activity counts using the root of the Mean Squared Successive Difference (MSSD). Furthermore, we calculated DFC on resting-state data as phase interactions between blood oxygen-level dependent (BOLD) signals of 270 brain regions per volume. Variability (MSSD) in DFC was calculated for 3 networks, including the default-mode network (DMN), frontoparietal network, and salience-reward network (SRN). Finally, we calculated correlations between these DFC estimates and apathy and activity variability. First, lower activity variability was associated with higher levels of apathy. Second, higher levels of apathy were associated with lower variability in DFC in the DMN and SRN. Third, higher activity variability was associated with higher variability in DFC in the SRN. In conclusion, patients with schizophrenia and more severe levels of apathy showed less variability in their physical activity and more rigid functional brain network behavior in the DMN and SRN. These networks have been shown relevant for self-reflection, mental simulation, and reward processing, processes that are pivotal for self-initiated goal-directed behavior. Functional rigidity of these networks may therefore contribute to reduced goal-directed behavior, which is characteristic for these patients.

Brain response to food cues varying in portion size is associated with individual differences in the portion size effect in children

Large portions promote intake of energy dense foods (i.e., the portion size effect--PSE), but the neurobiological drivers of this effect are not known. We tested the association between blood oxygen level dependent (BOLD) brain response to food images varied by portion size (PS) and energy density (ED) and children's intake at test-meals of high- and low-ED foods served at varying portions. Children (N = 47; age 7–10 years) participated in a within-subjects, crossover study consisting of 4 meals of increasing PS of high- and low-ED foods and 1 fMRI to evaluate food images at 2 levels of PS (Large, Small) and 2 levels of ED (High, Low). Contrast values between PS conditions (e.g., Large PS - Small PS) were calculated from BOLD signal in brain regions implicated in cognitive control and reward and input as covariates in mixed models to determine if they moderated the PSE curve. Results showed a significant effect of PS on intake. Responses to Large relative to Small PS in brain regions implicated in salience (e.g., ventromedial prefrontal cortex and orbitofrontal cortex) were positively associated with the linear slope (i.e., increase in intake from baseline) of the PSE curve, but negatively associated with the quadratic coefficient for the total meal. Responses to Large PS High ED relative to Small PS High ED cues in regions associated with cognitive control (e.g., dorsolateral prefrontal cortex) were negatively associated with the linear slope of the PSE curve for high-ED foods. Brain responses to PS cues were associated with individual differences in children's susceptibility to overeating from large portions. Responses in food salience regions positively associated with PSE susceptibility while activation in control regions negatively associated with PSE susceptibility.

Multi layer spectral decomposition technique for ERD estimation in EEG μ rhythms: An EEG–fMRI study

Abstract In this paper, power suppression detection in mu band known as event-related desynchronization during a simultaneous EEG–fMRI recording is addressed. Simultaneous EEG-fMRI experiments provide great opportunities to recognize relation between the active areas in the brain, discovered by fMRI, and EEG events. However, decoding EEG is challenging as a result of artifacts which MRI scanner applies on EEG. Ballistocardiogram (BCG) is one of the main destructive artifacts which deteriorates EEG rhythms specially in mu band. The proposed method, is a supervised time-frequency decomposition algorithm which estimates underlying rhythms of EEG signals recorded in MRI scanner. In the proposed method, a multi layer decomposition approach using three criteria is developed to extract brain oscillations. This goal is achieved by eliminating the artifact related components at each decomposition layer as a result of removing useless extracted components in each layer. Performance of the proposed method is evaluated using synthetic and real EEG. The achieved results from synthetic data confirm the ability of the proposed method to extract mu rhythms when different levels of noise exist. In addition, proposed algorithm is applied on a set of real EEG acquired in a simultaneous EEG–fMRI recording. Estimated ERD confirms the superiority of the developed algorithm in terms of estimating changes of mu rhythms. Moreover, an EEG–fMRI integration is performed to explore the correlation between blood oxygenation level dependent (BOLD) in fMRI and ERD of mu rhythm in EEG.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Frequency Clustering Analysis for Resting State Functional Magnetic Resonance Imaging Based on Hilbert-Huang Transform.

Objective: Exploring resting-state functional networks using functional magnetic resonance imaging (fMRI) is a hot topic in the field of brain functions. Previous studies suggested that the frequency dependence between blood oxygen level dependent (BOLD) signals may convey meaningful information regarding interactions between brain regions.Methods: In this article, we introduced a novel frequency clustering analysis method based on Hilbert-Huang Transform (HHT) and a label-replacement procedure. First, the time series from multiple predefined regions of interest (ROIs) were extracted. Second, each time series was decomposed into several intrinsic mode functions (IMFs) by using HHT. Third, the improved k-means clustering method using a label-replacement method was applied to the data of each subject to classify the ROIs into different classes.Results: Two independent resting-state fMRI dataset of healthy subjects were analyzed to test the efficacy of method. The results show almost identical clusters when applied to different runs of a dataset or to different datasets, indicating a stable performance of our framework.Conclusions and Significance: Our framework provided a novel measure for functional segregation of the brain according to time-frequency characteristics of resting state BOLD activities.

Global signal regression acts as a temporal downweighting process in resting-state fMRI

In resting-state functional MRI (rsfMRI), the correlation between blood oxygenation level dependent (BOLD) signals across different brain regions is used to estimate the functional connectivity of the brain. This approach has led to the identification of a number of resting-state networks, including the default mode network (DMN) and the task positive network (TPN). Global signal regression (GSR) is a widely used pre-processing step in rsfMRI that has been shown to improve the spatial specificity of the estimated resting-state networks. In GSR, a whole brain average time series, known as the global signal (GS), is regressed out of each voxel time series prior to the computation of the correlations. However, the use of GSR is controversial because it can introduce artifactual negative correlations. For example, it has been argued that anticorrelations observed between the DMN and TPN are primarily an artifact of GSR. Despite the concerns about GSR, there is currently no consensus regarding its use. In this paper, we introduce a new framework for understanding the effects of GSR. In particular, we show that the main effects of GSR can be well approximated as a temporal downweighting process in which the data from time points with relatively large GS magnitudes are greatly attenuated while data from time points with relatively small GS magnitudes are largely unaffected. Furthermore, we show that a limiting case of this downweighting process in which data from time points with large GS magnitudes are censored can also approximate the effects of GSR. In other words, the correlation maps obtained after GSR show a high degree of spatial similarity (including the presence of anticorrelations between the DMN and TPN) with maps obtained using only the uncensored (i.e. retained) time points. Since the data from these retained time points are unaffected by the censoring process, this finding suggests that the observed anticorrelations inherently exist in the data from time points with small GS magnitudes and are not simply an artifact of GSR.