Spatial heterogeneity of the relation between resting‐state connectivity and blood flow: An important consideration for pharmacological studies

Resting state fMRI (RSfMRI) and arterial spin labeling (ASL) provide the field of pharmacological Neuroimaging tool for investigating states of brain activity in terms of functional connectivity or cerebral blood flow (CBF). Functional connectivity reflects the degree of synchrony or correlation of spontaneous fluctuations—mostly in the blood oxygen level dependent (BOLD) signal—across brain networks; but CBF reflects mean delivery of arterial blood to the brain tissue over time. The BOLD and CBF signals are linked to common neurovascular and hemodynamic mechanisms that necessitate increased oxygen transportation to the site of neuronal activation; however, the scale and the sources of variation in static CBF and spatiotemporal BOLD correlations are likely different. We tested this hypothesis by examining the relation between CBF and resting‐state‐network consistency (RSNC)—representing average intranetwork connectivity, determined from dual regression analysis with eight standard networks of interest (NOIs)—in a crossover placebo‐controlled study of morphine and alcohol. Overall, we observed spatially heterogeneous relations between RSNC and CBF, and between the experimental factors (drug‐by‐time, time, drug and physiological rates) and each of these metrics. The drug‐by‐time effects on CBF were significant in all networks, but significant RSNC changes were limited to the sensorimotor, the executive/salience and the working memory networks. The post‐hoc voxel‐wise statistics revealed similar dissociations, perhaps suggesting differential sensitivity of RSNC and CBF to neuronal and vascular endpoints of drug actions. The spatial heterogeneity of RSNC/CBF relations encourages further investigation into the role of neuroreceptor distribution and cerebrovascular anatomy in predicting spontaneous fluctuations under drugs. Hum Brain Mapp 35:929–942, 2014. © 2012 Wiley Periodicals, Inc.     

Regional variations in vascular density correlate with resting‐state and task‐evoked blood oxygen level‐dependent signal amplitude

Functional magnetic resonance imaging (fMRI) has become one of the primary tools used for noninvasively measuring brain activity in humans. For the most part, the blood oxygen level‐dependent (BOLD) contrast is used, which reflects the changes in hemodynamics associated with active brain tissue. The main advantage of the BOLD signal is that it is relatively easy to measure and thus is often used as a proxy for comparing brain function across population groups (i.e., control vs. patient). However, it is particularly weighted toward veins whose structural architecture is known to vary considerably across the brain. This makes it difficult to interpret whether differences in BOLD between cortical areas reflect true differences in neural activity or vascular structure. We therefore investigated how regional variations of vascular density (VAD) relate to the amplitude of resting‐state and task‐evoked BOLD signals. To address this issue, we first developed an automated method for segmenting veins in images acquired with susceptibility‐weighted imaging, allowing us to visualize the venous vascular tree across the brain. In 19 healthy subjects, we then applied voxel‐based morphometry (VBM) to T1‐weighted images and computed regional measures of gray matter density (GMD). We found that, independent of spatial scale, regional variations in resting‐state and task‐evoked fMRI amplitudes were better correlated to VAD compared to GMD. Using a general linear model (GLM), it was observed that the bulk of regional variance in resting‐state activity could be modeled by VAD. Cortical areas whose resting‐state activity was most suppressed by VAD correction included Cuneus, Precuneus, Culmen, and BA 9, 10, and 47. Taken together, our results suggest that resting‐state BOLD signals are significantly related to the underlying structure of the brain vascular system. Calibrating resting BOLD activity by venous structure may result in a more accurate interpretation of differences observed between cortical areas and/or individuals. Hum Brain Mapp 35:1906–1920, 2014. © 2013 Wiley Periodicals, Inc .     

Dual‐echo ASL contributes to decrypting the link between functional connectivity and cerebral blow flow

Arterial spin labeling (ASL) MRI with a dual‐echo readout module (DE‐ASL) enables noninvasive simultaneous acquisition of cerebral blood flow (CBF)‐weighted images and blood oxygenation level dependent (BOLD) contrast. Up to date, resting‐state functional connectivity (FC) studies based on CBF fluctuations have been very limited, while the BOLD is still the method most frequently used. The purposes of this technical report were (i) to assess the potentiality of the DE‐ASL sequence for the quantification of resting‐state FC and brain organization, with respect to the conventional BOLD (cvBOLD) and (ii) to investigate the relationship between a series of complex network measures and the CBF information. Thirteen volunteers were scanned on a 3 T scanner acquiring a pseudocontinuous multislice DE‐ASL sequence, from which the concomitant BOLD (ccBOLD) simultaneously to the ASL can be extracted. In the proposed comparison, the brain FC and graph‐theoretical analysis were used for quantifying the connectivity strength between pairs of regions and for assessing the network model properties in all the sequences. The main finding was that the ccBOLD part of the DE‐ASL sequence provided highly comparable connectivity results compared to cvBOLD. As expected, because of its different nature, ASL sequence showed different patterns of brain connectivity and graph indices compared to BOLD sequences. To conclude, the resting‐state FC can be reliably detected using DE‐ASL, simultaneously to CBF quantifications, whereas a single fMRI experiment precludes the quantitative measurement of BOLD signal changes. Hum Brain Mapp 38:5831–5844, 2017. © 2017 Wiley Periodicals, Inc.     

Fluctuations of the EEG‐fMRI correlation reflect intrinsic strength of functional connectivity in default mode network

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting‐state functional connectivity is temporally variable in human brain. Combined full‐band electroencephalography‐fMRI (fbEEG‐fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood‐oxygen‐level‐dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG‐fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting‐state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short‐time‐window comparisons of infra‐slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG‐BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG‐fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG‐BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on‐average low correlations between infra‐slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.     

Altered amygdalar resting‐state connectivity in depression is explained by both genes and environment

Recent findings indicate that alterations of the amygdalar resting‐state fMRI connectivity play an important role in the etiology of depression. While both depression and resting‐state brain activity are shaped by genes and environment, the relative contribution of genetic and environmental factors mediating the relationship between amygdalar resting‐state connectivity and depression remain largely unexplored. Likewise, novel neuroimaging research indicates that different mathematical representations of resting‐state fMRI activity patterns are able to embed distinct information relevant to brain health and disease. The present study analyzed the influence of genes and environment on amygdalar resting‐state fMRI connectivity, in relation to depression risk. High‐resolution resting‐state fMRI scans were analyzed to estimate functional connectivity patterns in a sample of 48 twins (24 monozygotic pairs) informative for depressive psychopathology (6 concordant, 8 discordant and 10 healthy control pairs). A graph‐theoretical framework was employed to construct brain networks using two methods: (i) the conventional approach of filtered BOLD fMRI time‐series and (ii) analytic components of this fMRI activity. Results using both methods indicate that depression risk is increased by environmental factors altering amygdalar connectivity. When analyzing the analytic components of the BOLD fMRI time‐series, genetic factors altering the amygdala neural activity at rest show an important contribution to depression risk. Overall, these findings show that both genes and environment modify different patterns the amygdala resting‐state connectivity to increase depression risk. The genetic relationship between amygdalar connectivity and depression may be better elicited by examining analytic components of the brain resting‐state BOLD fMRI signals. Hum Brain Mapp 36:3761–3776, 2015. © 2015 Wiley Periodicals, Inc.     

Default‐mode network functional connectivity is closely related to metabolic activity

Over the last decade, the brain's default‐mode network (DMN) and its function has attracted a lot of attention in the field of neuroscience. However, the exact underlying mechanisms of DMN functional connectivity, or more specifically, the blood‐oxygen level‐dependent (BOLD) signal, are still incompletely understood. In the present study, we combined 2‐deoxy‐2‐[¹⁸F]fluoroglucose positron emission tomography (FDG‐PET), proton magnetic resonance spectroscopy (¹H‐MRS), and resting‐state functional magnetic resonance imaging (rs‐fMRI) to investigate more directly the association between local glucose consumption, local glutamatergic neurotransmission and DMN functional connectivity during rest. The results of the correlation analyzes using the dorsal posterior cingulate cortex (dPCC) as seed region showed spatial similarities between fluctuations in FDG‐uptake and fluctuations in BOLD signal. More specifically, in both modalities the same DMN areas in the inferior parietal lobe, angular gyrus, precuneus, middle, and medial frontal gyrus were positively correlated with the dPCC. Furthermore, we could demonstrate that local glucose consumption in the medial frontal gyrus, PCC and left angular gyrus was associated with functional connectivity within the DMN. We did not, however, find a relationship between glutamatergic neurotransmission and functional connectivity. In line with very recent findings, our results lend further support for a close association between local metabolic activity and functional connectivity and provide further insights towards a better understanding of the underlying mechanism of the BOLD signal. Hum Brain Mapp 36:2027–2038, 2015. © 2015 Wiley Periodicals, Inc.     

Enhanced phase regression with savitzky‐golay filtering for high‐resolution BOLD fMRI

Phase regression exploits the temporal evolution of phase in individual voxels to suppress blood oxygenation level dependent (BOLD) signal fluctuations caused by larger vessels and draining veins while preserving signal changes from microvascular effects. However, this process does not perform well when phase time series have low signal‐to‐noise ratios because of high levels of physiological noise. We demonstrate that Savitzky‐Golay filters may be used to recover the underlying change in phase and completely restore the efficacy of phase regression. We do not make a priori assumptions regarding phase evolution and perform a data‐driven exploration of parameter space to select the Savitzky‐Golay filter parameters that minimize temporal variance in each voxel after phase regression. This approach is shown to work well on data acquired with single‐shot and multi‐shot pulse sequences, and should therefore be useful for both human and animal gradient‐echo fMRI at high spatial resolutions at high fields. The ability to improve the spatial specificity of BOLD activation may be especially advantageous for clinical applications of fMRI that rely upon the accuracy of individual subject's activation maps to assist with presurgical planning and clinical decision‐making. Enhanced phase regression with Savitzky‐Golay filtering may also find other uses in analyses of resting state functional connectivity. Hum Brain Mapp 35:3832–3840, 2014. © 2014 Wiley Periodicals, Inc.     

Localized reductions in resting‐state functional connectivity in children with prenatal alcohol exposure

Fetal alcohol spectrum disorders (FASD) are characterized by impairment in cognitive function that may or may not be accompanied by craniofacial anomalies, microcephaly, and/or growth retardation. Resting‐state functional MRI (rs‐fMRI), which examines the low‐frequency component of the blood oxygen level dependent (BOLD) signal in the absence of an explicit task, provides an efficient and powerful mechanism for studying functional brain networks even in low‐functioning and young subjects. Studies using independent component analysis (ICA) have identified a set of resting‐state networks (RSNs) that have been linked to distinct domains of cognitive and perceptual function, which are believed to reflect the intrinsic functional architecture of the brain. This study is the first to examine resting‐state functional connectivity within these RSNs in FASD. Rs‐fMRI scans were performed on 38 children with FASD (19 with either full fetal alcohol syndrome (FAS) or partial FAS (PFAS), 19 nonsyndromal heavily exposed (HE)), and 19 controls, mean age 11.3 ± 0.9 years, from the Cape Town Longitudinal Cohort. Nine resting‐state networks were generated by ICA. Voxelwise group comparison between a combined FAS/PFAS group and controls revealed localized dose‐dependent functional connectivity reductions in five regions in separate networks: anterior default mode, salience, ventral and dorsal attention, and R executive control. The former three also showed lower connectivity in the HE group. Gray matter connectivity deficits in four of the five networks appear to be related to deficits in white matter tracts that provide intra‐RSN connections. Hum Brain Mapp 38:5217–5233, 2017. © 2017 Wiley Periodicals, Inc.     

Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals

After its discovery in 1990, blood oxygenation level-dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) has been widely used to map brain activation in humans and animals. Since fMRI relies on signal changes induced by neural activity, its signal source can be complex and is also dependent on imaging parameters and techniques. In this review, we identify and describe the origins of BOLD fMRI signals, including the topics of (1) effects of spin density, volume fraction, inflow, perfusion, and susceptibility as potential contributors to BOLD fMRI, (2) intravascular and extravascular contributions to conventional gradient-echo and spin-echo BOLD fMRI, (3) spatial specificity of hemodynamic-based fMRI related to vascular architecture and intrinsic hemodynamic responses, (4) BOLD signal contributions from functional changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of O(2) utilization (CMRO(2)), (5) dynamic responses of BOLD, CBF, CMRO(2), and arterial and venous CBV, (6) potential sources of initial BOLD dips, poststimulus BOLD undershoots, and prolonged negative BOLD fMRI signals, (7) dependence of stimulus-evoked BOLD signals on baseline physiology, and (8) basis of resting-state BOLD fluctuations. These discussions are highly relevant to interpreting BOLD fMRI signals as physiological means.     

Evidence that neurovascular coupling underlying the BOLD effect increases with age during childhood

Functional MRI using blood–oxygen‐level‐dependent (BOLD) imaging has provided unprecedented insights into the maturation of the human brain. Task‐based fMRI studies have shown BOLD signal increases with age during development (ages 5–18) for many cognitive domains such as language and executive function, while functional connectivity (resting‐state) fMRI studies investigating regionally synchronous BOLD fluctuations have revealed a developing functional organization of the brain from a local into a more distributed architecture. However, interpretation of these results is confounded by the fact that the BOLD signal is directly related to blood oxygenation driven by changes in blood flow and only indirectly related to neuronal activity, and may thus be affected by changing neuronal–vascular coupling. BOLD signal and cerebral blood flow (CBF) were measured simultaneously in a cohort of 113 typically developing awake participants ages 3–18 performing a narrative comprehension task. Using a novel voxelwise wild bootstrap analysis technique, an increased ratio of BOLD signal to relative CBF signal change with age (indicative of increased neuronal–vascular coupling) was seen in the middle temporal gyri and the left inferior frontal gyrus. Additionally, evidence of decreased relative oxygen metabolism (indicative of decreased neuronal activity) with age was found in the same regions. These findings raise concern that results of developmental BOLD studies cannot be unambiguously attributed to neuronal activity. Astrocytes and astrocytic processes may significantly affect the maturing functional architecture of the brain, consistent with recent research demonstrating a key role for astrocytes in mediating increased CBF following neuronal activity and for astrocyte processes in modulating synaptic connectivity. Hum Brain Mapp, 36:1–15, 2015. © 2014 Wiley Periodicals, Inc .     

Localization of the hand motor area by arterial spin labeling and blood oxygen level‐dependent functional magnetic resonance imaging

The new clinically available arterial spin labeling (ASL) perfusion imaging sequences present some advantages relatively to the commonly used blood oxygen level‐dependent (BOLD) method for functional brain studies using magnetic resonance imaging (MRI). In particular, regional cerebral blood flow (CBF) changes are thought to be more directly related with neuronal activation. In this study, we aimed to investigate the accuracy of the functional localization of the hand motor area obtained by simultaneous CBF and BOLD contrasts provided by ASL functional MRI (fMRI) and compare it with a standard BOLD fMRI protocol. For this purpose, we measured the distance between the center of gravity of the activation clusters obtained with each contrast (CBF, BOLD_ASL, and Standard BOLD) and 11 positions defined on a well‐established anatomical landmark of the hand motor area (the omega in the axial plane of the precentral gyrus). We found that CBF measurements were significantly closer to the anatomical landmark than the ones obtained using either simultaneous BOLD_ASL or standard BOLD contrasts. Moreover, we also observed reduced intersubject variability of the functional localization, as well as percent signal change, for CBF relative to both BOLD contrast measurements. In conclusion, our results add further evidence in support to the notion that CBF provides a more accurate localization of motor activation than BOLD contrast, indicating that ASL may be an appropriate technique for clinical fMRI studies. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.     

Functional MRI registration with tissue‐specific patch‐based functional correlation tensors

Population studies of brain function with resting‐state functional magnetic resonance imaging (rs‐fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high‐resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs‐fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole‐brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white‐matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level‐dependent (BOLD) signals using tissue‐specific patch‐based functional correlation tensors (ts‐PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi‐channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods.     

Realistic models of apparent dynamic changes in resting‐state connectivity in somatosensory cortex

Variations over time in resting‐state correlations in blood oxygenation level‐dependent (BOLD) signals from different cortical areas may indicate changes in brain functional connectivity. However, apparent variations over time may also arise from stationary signals when the sample duration is finite. Recently, a vector autoregressive (VAR) null model has been proposed to simulate real functional magnetic resonance imaging (fMRI) data, which provides a robust stationary model for identifying possible temporal dynamic changes in functional connectivity. In this work, we propose a simpler model that uses a filtered stationary dataset. The filtered stationary model generates statistically stationary time series from random data with a single prescribed correlation coefficient that is calculated as the average over the entire time series. In addition, we propose a dynamic model, which is better able to replicate real fMRI connectivity, estimated from monkey brain studies, than the two stationary models. We compare simulated results using these three models with the behavior of primary somatosensory cortex (S1) networks in anesthetized squirrel monkeys at high field (9.4 T), using a sliding window correlation analysis. We found that at short window sizes, both stationary models reproduced the distribution of correlations of real signals well, but at longer window sizes, a dynamic model reproduced the distribution of correlations of real signals better than the stationary models. While stationary models replicate several features of real data, a close representation of the behavior of resting‐state data acquired from somatosensory cortex of non‐human primates is obtained only when a dynamic correlation is introduced, suggesting dynamic variations in connectivity are real. Hum Brain Mapp 37:3897–3910, 2016. © 2016 Wiley Periodicals, Inc .     

Altered resting‐state connectivity in Huntington's Disease

Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. Using resting‐state fMRI (rs‐fMRI) we investigated the functional integrity of resting‐state networks (RSN) in HD. 17 HD and 19 matched control participants were examined at a 3 Tesla MR scanner. After controlling for structural degeneration by means of voxel‐based morphometry, task‐free rs‐fMRI data were analyzed using Independent Component Analysis (ICA) and a dual‐regression approach in the context of genetic and clinical parameters. Further, we evaluated HD‐related differences in interregional connectivity between networks. RSN analysis showed a significant increase in intrinsic functional connectivity in the HD sample compared with controls, including the thalamus, striatum, prefrontal, premotor, and parietal maps. A subset of the Default Mode Network (DMN) was also affected. In the HD cohort, motor impairment correlated with higher network connectivity in mainly motor and parietal cortices. Deteriorating total functional capacity was additionally associated with higher connectivity in the striatum, thalamus, insular and frontal areas. This pattern of increased activity in intrinsic functional networks might suggest a reduced ability of intra‐network differentiation with clinical disease progression in HD. Finally, results showed reduced long‐range connectivity between parietal ICA components in HD compared to controls, indicating impaired functional coupling between interregional networks in HD. Our data demonstrates that functional connectivity is profoundly altered in HD, both within and between RSN. Rs‐fMRI analysis may provide additional valuable insights into neuronal dysfunctions beyond HD‐related structural degeneration and disruptions of functional circuits in HD. Hum Brain Mapp 35:2582–2593, 2014. © 2013 Wiley Periodicals, Inc .     

A comparison of physiologic modulators of fMRI signals

One of the main obstacles in quantitative interpretation of functional magnetic resonance imaging (fMRI) signal is that this signal is influenced by non‐neural factors such as vascular properties of the brain, which effectively increases signal variability. One approach to account for non‐neural components is to identify and measure these confounding factors and to include them as covariates in data analysis or interpretation. Previously, several research groups have independently identified four potential physiologic modulators of fMRI signals, including baseline venous oxygenation (Y_v), cerebrovascular reactivity (CVR), resting state BOLD fluctuation amplitude (RSFA), and baseline cerebral blood flow (CBF). This study sought to directly compare the modulation effects of these indices in the same fMRI session. The physiologic parameters were measured with techniques comparable with those used in the previous studies except for CBF, which was determined globally with a velocity‐based phase‐contrast MRI (instead of arterial‐spin‐labeling MRI). Using an event‐related, scene‐categorization fMRI task, we showed that the fMRI signal amplitude was positively correlated with CVR (P < 0.0001) and RSFA (P = 0.002), while negatively correlated with baseline Y_v (P < 0.0001). The fMRI‐CBF correlation did not reach significance, although the (negative) sign of the correlation was consistent with the earlier study. Furthermore, among the physiologic modulators themselves, significant correlations were observed between baseline Y_v and baseline CBF (P = 0.01), and between CVR and RSFA (P = 0.05), suggesting that some of the modulators may partly be of similar physiologic origins. These observations as well as findings in recent literature suggest that additional measurement of physiologic modulator(s) in an fMRI session may provide a practical approach to control for inter‐subject variations and to improve the ability of fMRI in detecting disease or medication related differences. Hum Brain Mapp 34:2078–2088, 2013. © 2011 Wiley Periodicals, Inc.     

Functional localization in the human brain: Gradient‐echo,spin‐echo,and arterial spin‐labeling fMRI compared with neuronavigated TMS

A spatial mismatch of up to 14 mm between optimal transcranial magnetic stimulation (TMS) site and functional magnetic resonance imaging (fMRI) signal has consistently been reported for the primary motor cortex. The underlying cause might be the effect of magnetic susceptibility around large draining veins in Gradient‐Echo blood oxygenation level‐dependent (GRE‐BOLD) fMRI. We tested whether alternative fMRI sequences such as Spin‐Echo (SE‐BOLD) or Arterial Spin‐Labeling (ASL) assessing cerebral blood flow (ASL‐CBF) may localize neural activity closer to optimal TMS positions and primary motor cortex than GRE‐BOLD. GRE‐BOLD, SE‐BOLD, and ASL‐CBF signal changes during right thumb abductions were obtained from 15 healthy subjects at 3 Tesla. In 12 subjects, tissue at fMRI maxima was stimulated with neuronavigated TMS to compare motor‐evoked potentials (MEPs). Euclidean distances between the fMRI center‐of‐gravity (CoG) and the TMS motor mapping CoG were calculated. Highest SE‐BOLD and ASL‐CBF signal changes were located in the anterior wall of the central sulcus [Brodmann Area 4 (BA4)], whereas highest GRE‐BOLD signal changes were significantly closer to the gyral surface. TMS at GRE‐BOLD maxima resulted in higher MEPs which might be attributed to significantly higher electric field strengths. TMS‐CoGs were significantly anterior to fMRI‐CoGs but distances were not statistically different across sequences. Our findings imply that spatial differences between fMRI and TMS are unlikely to be caused by spatial unspecificity of GRE‐BOLD fMRI but might be attributed to other factors, e.g., interactions between TMS‐induced electric field and neural tissue. Differences between techniques should be kept in mind when using fMRI coordinates as TMS (intervention) targets. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.     

Synchronization of intrinsic 0.1‐Hz blood‐oxygen‐level‐dependent oscillations in amygdala and prefrontal cortex in subjects with increased state anxiety

Low‐frequency oscillations with a dominant frequency at 0.1 Hz are one of the most influential intrinsic blood‐oxygen‐level‐dependent (BOLD) signals. This raises the question if vascular BOLD oscillations (originating from blood flow in the brain) and intrinsic slow neural activity fluctuations (neural BOLD oscillations) can be differentiated. In this study, we report on two different approaches: first, on computing the phase‐locking value in the frequency band 0.07–0.13 Hz between heart beat‐to‐beat interval (RRI) and BOLD oscillations and second, between multiple BOLD oscillations (functional connectivity) in four resting states in 23 scanner‐naïve, anxious healthy subjects. The first method revealed that vascular 0.1‐Hz BOLD oscillations preceded those in RRI signals by 1.7 ± 0.6 s and neural BOLD oscillations lagged RRI oscillations by 0.8 ± 0.5 s. Together, vascular BOLD oscillations preceded neural BOLD oscillations by ~90° or ~2.5 s. To verify this discrimination, connectivity patterns of neural and vascular 0.1‐Hz BOLD oscillations were compared in 26 regions involved in processing of emotions. Neural BOLD oscillations revealed significant phase‐coupling between amygdala and medial frontal cortex, while vascular BOLD oscillations showed highly significant phase‐coupling between amygdala and multiple regions in the supply areas of the anterior and medial cerebral arteries. This suggests that not only slow neural and vascular BOLD oscillations can be dissociated but also that two strategies may exist to optimize regulation of anxiety, that is increased functional connectivity between amygdala and medial frontal cortex, and increased cerebral blood flow in amygdala and related structures.     

Disruption of cortical integration during midazolam‐induced light sedation

This work examines the effect of midazolam‐induced light sedation on intrinsic functional connectivity of human brain, using a randomized, double‐blind, placebo‐controlled, cross‐over, within‐subject design. Fourteen healthy young subjects were enrolled and midazolam (0.03 mg/kg of the participant's body mass, to a maximum of 2.5 mg) or saline were administrated with an interval of one week. Resting‐state fMRI was conducted before and after administration for each subject. We focus on two types of networks: sensory related lower‐level functional networks and higher‐order functions related ones. Independent component analysis (ICA) was used to identify these resting‐state functional networks. We hypothesize that the sensory (visual, auditory, and sensorimotor) related networks will be intact under midazolam‐induced light sedation while the higher‐order (default mode, executive control, salience networks, etc.) networks will be functionally disconnected. It was found that the functional integrity of the lower‐level networks was maintained, while that of the higher‐level networks was significantly disrupted by light sedation. The within‐network connectivity of the two types of networks was differently affected in terms of direction and extent. These findings provide direct evidence that higher‐order cognitive functions including memory, attention, executive function, and language were impaired prior to lower‐level sensory responses during sedation. Our result also lends support to the information integration model of consciousness. Hum Brain Mapp 36:4247–4261, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc .     

Functional connectivity associated with gait velocity during walking and walking‐while‐talking in aging: A resting‐state fMRI study

Gait decline is common among older adults and is a risk factor for adverse outcomes. Poor gait performance in dual‐task conditions, such as walking while performing a secondary cognitive interference task, is associated with increased risk of frailty, disability, and death. Yet, the functional neural substrates that support locomotion are not well established. We examined the functional connectivity associated with gait velocity in single‐ (normal pace walking) and dual‐task (walking while talking) conditions using resting‐state functional Magnetic Resonance Imaging (fMRI). We acquired 6 minutes of resting‐state fMRI data in 30 cognitively healthy older adults. Independent components analyses were performed to separate resting‐state fMRI data into group‐level statistically independent spatial components that correlated with gait velocity in single‐ and dual‐task conditions. Gait velocity in both task conditions was associated with similar functional connectivity in sensorimotor, visual, vestibular, and left fronto‐parietal cortical areas. Compared to gait velocity in the single‐task condition, the networks associated with gait velocity in the dual‐task condition were associated with greater functional connectivity in supplementary motor and prefrontal regions. Our findings show that there are partially overlapping functional networks associated with single‐ and dual‐task walking conditions. These initial findings encourage the future use of resting‐state fMRI as tool in developing a comprehensive understanding of age‐related mobility impairments. Hum Brain Mapp 36:1484–1493, 2015. © 2014 Wiley Periodicals, Inc.     

Association between serotonin denervation and resting‐state functional connectivity in mild cognitive impairment

Resting‐state functional connectivity alterations have been demonstrated in Alzheimer's disease (AD) and mild cognitive impairment (MCI) before the observation of AD neuropathology, but mechanisms driving these changes are not well understood. Serotonin neurodegeneration has been observed in MCI and AD and is associated with cognitive deficits and neuropsychiatric symptoms, but the role of the serotonin system in relation to brain network dysfunction has not been a major focus of investigation. The current study investigated the relationship between serotonin transporter availability (SERT; measured using positron emission tomography) and brain network functional connectivity (measured using resting‐state functional MRI) in 20 participants with MCI and 21 healthy controls. Two SERT regions of interest were selected for the analysis: the Dorsal Raphe Nuclei (DRN) and the precuneus which represent the cell bodies of origin and a cortical target of projections of the serotonin system, respectively. Both regions show decreased SERT in MCI compared to controls and are the site of early AD pathology. Average resting‐state functional connectivity did not differ between MCI and controls. Decreased SERT in DRN was associated with lower hippocampal resting‐state connectivity in MCI participants compared to controls. Decreased SERT in the right precuneus was also associated with lower resting‐state connectivity of the retrosplenial cortex to the dorsal lateral prefrontal cortex and higher resting‐state connectivity of the retrosplenial cortex to the posterior cingulate and in patients with MCI but not in controls. These results suggest that a serotonergic mechanism may underlie changes in brain functional connectivity in MCI. Hum Brain Mapp 38:3391–3401, 2017. © 2017 Wiley Periodicals, Inc.