The association between resting‐state functional magnetic resonance imaging and aortic pulse‐wave velocity in healthy adults

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is frequently used to study brain function; but, it is unclear whether BOLD‐signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular‐health factors are reflected in rs‐fMRI metrics in the healthy population. As arterial stiffening is a known age‐related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse‐wave velocity [PWV]) and rs‐fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole‐body vascular stiffness), as well as dual‐echo pseudo‐continuous arterial‐spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting‐state BOLD signal; (2) higher PWV is also associated with lower BOLD‐based resting‐state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions‐of‐interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age‐related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs‐fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age‐related rs‐fMRI differences may be driven by vascular effects rather than directly by brain function.     

Exploring the advantages of multiband fMRI with simultaneous EEG to investigate coupling between gamma frequency neural activity and the BOLD response in humans

We established an optimal combination of EEG recording during sparse multiband (MB) fMRI that preserves high‐resolution, whole‐brain fMRI coverage while enabling broad‐band EEG recordings which are uncorrupted by MRI gradient artefacts (GAs). We first determined the safety of simultaneous EEG recording during MB fMRI. Application of MB factor = 4 produced <1°C peak heating of electrode/hardware during 20 min of GE‐EPI data acquisition. However, higher SAR sequences require specific safety testing, with greater heating observed using PCASL with MB factor = 4. Heating was greatest in the electrocardiogram channel, likely due to it possessing longest lead length. We investigated the effect of MB factor on the temporal signal‐to‐noise ratio for a range of GE‐EPI sequences (varying MB factor and temporal interval between slice acquisitions). We found that, for our experimental purpose, the optimal acquisition was achieved with MB factor = 3, 3mm isotropic voxels, and 33 slices providing whole head coverage. This sequence afforded a 2.25 s duration quiet period (without GAs) in every 3 s TR. Using this sequence, we demonstrated the ability to record gamma frequency (55–80 Hz) EEG oscillations, in response to right index finger abduction, that are usually obscured by GAs during continuous fMRI data acquisition. In this novel application of EEG‐MB fMRI to a motor task, we observed a positive correlation between gamma and BOLD responses in bilateral motor regions. These findings support and extend previous work regarding coupling between neural and hemodynamic measures of brain activity in humans and showcase the utility of EEG‐MB fMRI for future investigations.     

Distortion correction for diffusion‐weighted MRI tractography and fMRI in the temporal lobes

Single shot echo‐planar imaging (EPI) sequences are currently the most commonly used sequences for diffusion‐weighted imaging (DWI) and functional magnetic resonance imaging (fMRI) as they allow relatively high signal to noise with rapid acquisition time. A major drawback of EPI is the substantial geometric distortion and signal loss that can occur due to magnetic field inhomogeneities close to air‐tissue boundaries. If DWI‐based tractography and fMRI are to be applied to these regions, then the distortions must be accurately corrected to achieve meaningful results. We describe robust acquisition and processing methods for correcting such distortions in spin echo (SE) EPI using a variant of the reversed direction k space traversal method with a number of novel additions. We demonstrate that dual direction k space traversal with maintained diffusion‐encoding gradient strength and direction results in correction of the great majority of eddy current‐associated distortions in DWI, in addition to those created by variations in magnetic susceptibility. We also provide examples to demonstrate that the presence of severe distortions cannot be ignored if meaningful tractography results are desired. The distortion correction routine was applied to SE‐EPI fMRI acquisitions and allowed detection of activation in the temporal lobe that had been previously found using PET but not conventional fMRI. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Frequency‐dependent tACS modulation of BOLD signal during rhythmic visual stimulation

Transcranial alternating current stimulation (tACS) has emerged as a promising tool for modulating cortical oscillations. In previous electroencephalogram (EEG) studies, tACS has been found to modulate brain oscillatory activity in a frequency‐specific manner. However, the spatial distribution and hemodynamic response for this modulation remains poorly understood. Functional magnetic resonance imaging (fMRI) has the advantage of measuring neuronal activity in regions not only below the tACS electrodes but also across the whole brain with high spatial resolution. Here, we measured fMRI signal while applying tACS to modulate rhythmic visual activity. During fMRI acquisition, tACS at different frequencies (4, 8, 16, and 32 Hz) was applied along with visual flicker stimulation at 8 and 16 Hz. We analyzed the blood‐oxygen‐level‐dependent (BOLD) signal difference between tACS‐ON vs tACS‐OFF, and different frequency combinations (e.g., 4 Hz tACS, 8 Hz flicker vs 8 Hz tACS, 8 Hz flicker). We observed significant tACS modulation effects on BOLD responses when the tACS frequency matched the visual flicker frequency or the second harmonic frequency. The main effects were predominantly seen in regions that were activated by the visual task and targeted by the tACS current distribution. These findings bridge different scientific domains of tACS research and demonstrate that fMRI could localize the tACS effect on stimulus‐induced brain rhythms, which could lead to a new approach for understanding the high‐level cognitive process shaped by the ongoing oscillatory signal.     

Modulating functional connectivity between medial frontopolar cortex and amygdala by inhibitory and excitatory transcranial magnetic stimulation

The prefrontal‐limbic network in the human brain plays a major role in social cognition, especially cognitive control of emotion. The medial frontopolar cortex (mFP; Brodmann Area 10) and the amygdala are part of this network and display correlated neuronal activity in time, as measured by functional magnetic resonance imaging (fMRI). This functional connectivity is dynamic, sensitive to training, and affected in mental disorders. However, the effects of neurostimulation on functional connectivity within this network have not yet been systematically investigated. Here, we investigate the effects of both low‐ and high‐frequency repetitive transcranial magnetic stimulation (rTMS) to the right mFP on functional connectivity between mFP and amygdala, as measured with resting state fMRI (rsfMRI). Three groups of healthy participants received either low‐frequency rTMS (1 Hz; N = 18), sham TMS (1 Hz, subthreshold; N = 18) or high‐frequency rTMS (20 Hz; N = 19). rsfMRI was acquired before and after (separate days). We hypothesized a modulation of functional connectivity in opposite directions compared to sham TMS through adjustment of the stimulation frequency. Groups differed in functional connectivity between mFP and amygdala after stimulation compared to before stimulation (low‐frequency: decrease, high‐frequency: increase). Motion or induced changes in neuronal activity were excluded as confounders. Results show that rTMS is effective for increasing and decreasing functional coherence between prefrontal and limbic regions. This finding is relevant for social and affective neuroscience as well as novel treatment approaches in psychiatry.     

Wavelet‐based regularity analysis reveals recurrent spatiotemporal behavior in resting‐state fMRI

One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large‐scale networks. In resting state fMRI (rs‐fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs‐fMRI signal. Here, we develop a wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs‐fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of “lagged” subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an “objective” estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs‐fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet‐based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs‐fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs‐fMRI as well as other biological signals. Hum Brain Mapp 36:3603–3620, 2015. © 2015 Wiley Periodicals, Inc.     

Frequency dependence of the functional MRI response after electrical median nerve stimulation

Localizing sensorimotor areas with high resolution functional MRI is of considerable interest for a wide range of medical applications from the preoperative planning of neurosurgical interventions to determining the course of neuroplastic reorganisation after brain lesions. We examined the effect of the stimulation frequency on the blood oxygen level dependent (BOLD) fMRI response and on perfusion weighted fMRI using electrical median nerve stimulation at 5, 15, 40, and 100 Hz. BOLD fMRI was performed using a single shot gradient echo EPI sequence to acquire 15 contiguous slices. For the qualitative flow sensitive studies, a single slice inversion recovery prepared spin echo echoplanar sequence (IR-SE EPI) was used. In the primary sensorimotor cortex, a linear increase of the fMRI-BOLD response, affecting both the number of activated pixels and the amplitude of the signal changes, was seen with increasing stimulation frequencies. The qualitative in-flow sensitive studies, using the IR-SE EPI sequence, indicate that the tissue perfusion also increases over the same range of frequencies. This implicates that larger fMRI responses can be obtained if electrical median nerve stimulation is performed at higher frequencies. The results are compared with electrophysiological data, which show a decrease of the early somatosensory evoked potentials at higher frequencies.     

Pathological uncoupling between amplitude and connectivity of brain fluctuations in epilepsy

Amplitude and functional connectivity are two fundamental parameters for describing the spontaneous brain fluctuations. These two parameters present close coupling in physiological state, and present different alteration patterns in epilepsy revealed by functional MRI (fMRI). We hypothesized that the alteration of coupling between these two imaging parameters may be underpinned by specific pathological factors of epilepsy, and can be employed to improve the capability for epileptic focus detection. Forty‐seven patients (26 left‐ and 21 right‐sided) with mesial temporal lobe epilepsy (mTLE) and 32 healthy controls underwent resting‐state fMRI scans. All patients were detected to have interictal epileptic discharges on simultaneous electroencephalograph (EEG) recordings. Amplitude‐connectivity coupling was calculated by correlating amplitude and functional connectivity density of low‐frequency brain fluctuations. We observed reduced amplitude‐connectivity coupling associated with epileptic discharges in the mesial temporal regions in both groups of patients, and increased coupling associated with epilepsy durations in the posterior regions of the default‐mode network in the right‐sided patients. Moreover, we proposed a new index of amplitude subtracting connectivity, which elevated imaging contrast for differentiating the patients from the controls. The findings indicated that epileptic discharges and chronic damaging effect of epilepsy might both contribute to alterations of amplitude‐connectivity coupling in different pivotal regions in mTLE. Investigation on imaging coupling provides synergistic approach for describing brain functional changing features in epilepsy. Hum Brain Mapp 36:2756–2766, 2015. © 2015 Wiley Periodicals, Inc.     

Effects of galvanic vestibular stimulation on resting state brain activity in patients with bilateral vestibulopathy

We examined the effect of galvanic vestibular stimulation (GVS) on resting state brain activity using fMRI (rs‐fMRI) in patients with bilateral vestibulopathy. Based on our previous findings, we hypothesized that GVS, which excites the vestibular nerve fibers, (a) increases functional connectivity in temporoparietal regions processing vestibular signals, and (b) alleviates abnormal visual–vestibular interaction. Rs‐fMRI of 26 patients and 26 age‐matched healthy control subjects was compared before and after GVS. The stimulation elicited a motion percept in all participants. Using different analyses (degree centrality, DC; fractional amplitude of low frequency fluctuations [fALFF] and seed‐based functional connectivity, FC), group comparisons revealed smaller rs‐fMRI in the right Rolandic operculum of patients. After GVS, rs‐fMRI increased in the right Rolandic operculum in both groups and in the patients' cerebellar Crus 1 which was related to vestibular hypofunction. GVS elicited a fALFF increase in the visual cortex of patients that was inversely correlated with the patients' rating of perceived dizziness. After GVS, FC between parietoinsular cortex and higher visual areas increased in healthy controls but not in patients. In conclusion, short‐term GVS is able to modulate rs‐fMRI in healthy controls and BV patients. GVS elicits an increase of the reduced rs‐fMRI in the patients' right Rolandic operculum, which may be an important contribution to restore the disturbed visual–vestibular interaction. The GVS‐induced changes in the cerebellum and the visual cortex were associated with lower dizziness‐related handicaps in patients, possibly reflecting beneficial neural plasticity that might subserve visual–vestibular compensation of deficient self‐motion perception.     

Functional connectivity corresponding to the tonotopic differentiation of the human auditory cortex

Recent research has demonstrated that resting‐state functional connectivity (RS‐FC) within the human auditory cortex (HAC) is frequency‐selective, but whether RS‐FC between the HAC and other brain areas is differentiated by frequency remains unclear. Three types of data were collected in this study, including resting‐state functional magnetic resonance imaging (fMRI) data, task‐based fMRI data using six pure tone stimuli (200, 400, 800, 1,600, 3,200, and 6,400 Hz), and structural imaging data. We first used task‐based fMRI to identify frequency‐selective cortical regions in the HAC. Six regions of interest (ROIs) were defined based on the responses of 50 participants to the six pure tone stimuli. Then, these ROIs were used as seeds to determine RS‐FC between the HAC and other brain regions. The results showed that there was RS‐FC between the HAC and brain regions that included the superior temporal gyrus, dorsolateral prefrontal cortex (DL‐PFC), parietal cortex, occipital lobe, and subcortical structures. Importantly, significant differences in FC were observed among most of the brain regions that showed RS‐FC with the HAC. Specifically, there was stronger RS‐FC between (1) low‐frequency (200 and 400 Hz) regions and brain regions including the premotor cortex, somatosensory/‐association cortex, and DL‐PFC; (2) intermediate‐frequency (800 and 1,600 Hz) regions and brain regions including the anterior/posterior superior temporal sulcus, supramarginal gyrus, and inferior frontal cortex; (3) intermediate/low‐frequency regions and vision‐related regions; (4) high‐frequency (3,200 and 6,400 Hz) regions and the anterior cingulate cortex or left DL‐PFC. These findings demonstrate that RS‐FC between the HAC and other brain areas is frequency selective.     

A Glutamate Transporter EAAT1 Gene Variant Influences Amygdala Functional Connectivity in Bipolar Disorder

Bipolar disorder (BD) is a severe illness characterized by recurrent depressive and manic episodes and by emotional dysregulation. Altered cortico-limbic connectivity could account for typical symptoms of the disorder such as mood instability, emotional dysregulation, and cognitive deficits. Functional connectivity positively associated with glutamatergic neurotransmission. The inactivation of glutamate is handled by a series of glutamate transporters, among them, the excitatory amino acid transporter 1 (EAAT1) which is modulated by a SNP rs2731880 (C/T) where the C allele leads to increased EAAT1 expression and glutamate uptake. We hypothesized that rs2731880 would affect cortico-limbic functional connectivity during an implicit affective processing task. Sixty-eight BD patients underwent fMRI scanning during implicit processing of fearful and angry faces. We explored the effect of rs2731880 on the strength of functional connectivity from the amygdalae to the whole brain. A significant activation in response to emotional processing was observed in two main clusters encompassing the right and left amygdala. Amygdalae to whole-brain functional connectivity analyses revealed a significant interaction between rs2731880 and the task (emotional stimuli vs geometric shapes) for the functional connections between the right amygdala and right subgenual anterior cingulate cortex. Post-hoc analyses revealed that T/T patients showed a significant negative connectivity between the amygdala and anterior cingulate cortex compared to C carriers. T/T subjects also performed significantly better in the face-matching task than rs2731880*C carriers. Our findings reveal an EAAT1 genotype-associated difference in cortico-limbic connectivity during affective regulation, possibly identifying a neurobiological underpinning of emotional dysfunction in BD.     

Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI

To spatially cluster resting state‐functional magnetic resonance imaging (rs‐fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio‐temporally homogeneous but irreproducible across subjects. Alternatively, extraction of a small number of clusters creates spatially large networks that are temporally heterogeneous but spatially reproducible across subjects. We propose a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility. Subsequently, the large networks are iteratively subdivided to create spatially reproducible subnetworks until the overall within‐network homogeneity does not increase substantially. The proposed approach discovers a rich network hierarchy in the brain while simultaneously optimizing spatial reproducibility of networks across subjects and individual network homogeneity. We also propose a novel metric to measure the connectivity of brain regions, and in a simulation study show that our connectivity metric and framework perform well in the face of low signal to noise and initial segmentation errors. Experimental results generated using real fMRI data show that the proposed metric improves stability of network clusters across subjects, and generates a meaningful pattern for spatially hierarchical structure of the brain. Hum Brain Mapp 36:3303–3322, 2015. © 2015 Wiley Periodicals, Inc .     

Different pre‐scanning instructions induce distinct psychological and resting brain states during functional magnetic resonance imaging

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is widely used to investigate functional brain network connectivity during rest or when the subject is not performing an explicit task. In the standard procedure, subjects are instructed to ‘let your mind wander’ or ‘think of nothing’. While these instructions appear appropriate to induce a ‘resting‐state’, they could induce distinct psychological and physiological states during the scan. In this study, we investigated whether different instructions affect mental state and functional connectivity (FC) (i.e. induce distinct ‘resting states’) during rs‐fMRI scanning. Thirty healthy subjects were subjected to two rs‐fMRI scans differing only in pre‐scan instructions: think of nothing (TN) and mind‐wandering (MW) conditions. Self‐reports confirmed that subjects spent the majority of the scanning time in the appropriate mental state. Independent component analysis extracted 19 independent components (ICs) of interest and functional network connectivity analyses indicated several conditional differences in FCs among those ICs, especially characterised by stronger FC in the MW condition than in the TN condition, between default mode network and salience/visual/frontal network. Complementary correlation analysis indicated that some of the network FCs were significantly correlated with their self‐reported data on how often they had the TN condition during the scans. The present results provide evidence that the pre‐scan instruction has a significant influence on resting‐state FC and its relationship with mental activities.     

Disrupted resting‐state functional connectivity and its changing trend in migraine suffers

Chronic pain has been linked with learning and memory processes and functional changes in brain plasticity in its development and maintenance via neuroimaging studies. However, the principle of reorganization of the migraine brain network as the brain progresses into chronic pain remain poorly understood. Here, using resting‐state functional magnetic resonance imaging (rs‐fMRI) and graph theory approaches, we aimed to investigate the dynamic dysfunctional connectivity in 108 patients with migraine without aura (MWoA) and 30 gender‐matched healthy controls (HC). All patients were divided into 40 groups using a sliding boxcar grouping of subjects in disease duration order. As compared with HC, nonparametric permutation tests were applied for between‐group comparisons of functional connectivity strength in each patient group. We focused only on the between‐group differences of functional connections in MWoA, and the situation how these different connections were organized along with the changing trend. As the disease duration increased, the presence of chronic headache altered the functional connectivity from the local central nervous system (CNS) to a disruption in the whole‐brain networks. These dysfunctional connections integrated into a connected component in relatively longer migraine duration groups, suggesting an abnormal integrated network configuration with ongoing central changes for long‐term migraine. Within these between‐group differences of the connected component, there were contained a small number of brain regions that had disproportionately numerous connections. Moreover, these brain regions exhibited a tendency to link to each other were organized into a strongly interconnected community. These interconnected brain regions were mainly located in the sensory‐discriminative brain areas. Our results exhibited a working model of the central mechanisms of migraine where the brain functional connectivity was altered from the local central nervous system to a densely interconnected center, which may extend our understanding of the role of learning mechanisms which are likely involved in maintenance of chronic pain. Hum Brain Mapp 36:1892–1907, 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 subject‐specific resting‐state network detection and extraction with fast fMRI

Resting‐state networks have become an important tool for the study of brain function. An ultra‐fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo‐planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting‐state networks. Healthy controls underwent two consecutive resting‐state scans, one with EPI and the other with MREG. Subject‐level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817–830, 2017. © 2016 Wiley Periodicals, Inc.     

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.     

Beyond BOLD: Optimizing functional imaging in stroke populations

Blood oxygenation level‐dependent (BOLD) signal changes are often assumed to directly reflect neural activity changes. Yet the real relationship is indirect, reliant on numerous assumptions, and subject to several sources of noise. Deviations from the core assumptions of BOLD contrast functional magnetic resonance imaging (fMRI), and their implications, have been well characterized in healthy populations, but are frequently neglected in stroke populations. In addition to conspicuous local structural and vascular changes after stroke, there are many less obvious challenges in the imaging of stroke populations. Perilesional ischemic changes, remodeling in regions distant to lesion sites, and diffuse perfusion changes all complicate interpretation of BOLD signal changes in standard fMRI protocols. Most stroke patients are also older than the young populations on which assumptions of neurovascular coupling and the typical analysis pipelines are based. We present a review of the evidence to show that the basic assumption of neurovascular coupling on which BOLD‐fMRI relies does not capture the complex changes arising from stroke, both pathological and recovery related. As a result, estimating neural activity using the canonical hemodynamic response function is inappropriate in a number of contexts. We review methods designed to better estimate neural activity in stroke populations. One promising alternative to event‐related fMRI is a resting‐state‐derived functional connectivity approach. Resting‐state fMRI is well suited to stroke populations because it makes no performance demands on patients and is capable of revealing network‐based pathology beyond the lesion site. Hum Brain Mapp 36:1620–1636, 2015. © 2014 Wiley Periodicals, Inc .     

Intrinsic connectivity between the hippocampus,nucleus accumbens,and ventral tegmental area in humans

Recent studies suggest that memory formation in the hippocampus is modulated by the motivational significance of events, allowing past experience to adaptively guide behavior. The effects of motivation on memory are thought to depend on interactions between the hippocampus, the ventral tegmental area (VTA), and the nucleus accumbens (NAcc). Indeed, animal studies reveal anatomical pathways for circuit‐level interaction between these regions. However, a homologue circuit connectivity in humans remains to be shown. We characterized this circuitry in humans by exploiting spontaneous low‐frequency modulations in the fMRI signal (termed resting‐state functional connectivity), which are thought to reflect functionally related regions and their organization into functional networks in the brain. We examined connectivity in this network across two datasets (hi‐resolution, n = 100; standard resolution, n = 894). Results reveal convergent connectivity between the hippocampus, and both the NAcc and the VTA centered on ventral regions in the body of the hippocampus. Additionally, we found individual differences in the strength of connectivity within this network. Together, these results provide a novel task‐independent characterization of circuitry underlying interactions between the hippocampus, NAcc, and VTA and provide a framework with which to understand how connectivity might reflect and constrain the effects of motivation on memory. © 2012 Wiley Periodicals, Inc.     

Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity

fMRI studies of brain activity at rest study slow (<0.1 Hz) intrinsic fluctuations in the blood‐oxygenation‐level‐dependent (BOLD) signal that are observed in a temporal scale of several minutes. The origin of these fluctuations is not clear but has previously been associated with slow changes in rhythmic neuronal activity resulting from changes in cortical excitability or neuronal synchronization. In this work, we show that individual spontaneous BOLD events occur during rest, in addition to slow fluctuations. Individual spontaneous BOLD events were identified by deconvolving the hemodynamic impulse response function for each time point in the fMRI time series, thus requiring no information on timing or a‐priori spatial information of events. The patterns of activation detected were related to the motor, visual, default‐mode, and dorsal attention networks. The correspondence between spontaneous events and slow fluctuations in these networks was assessed using a sliding window, seed‐correlation analysis, where seed regions were selected based on the individual spontaneous event BOLD activity maps. We showed that the correlation varied considerably over time, peaking at the time of spontaneous events in these networks. By regressing spontaneous events out of the fMRI signal, we showed that both the correlation strength and the power in spectral frequencies <0.1 Hz decreased, indicating that spontaneous activation events contribute to low‐frequency fluctuations observed in resting state networks with fMRI. This work provides new insights into the origin of signals detected in fMRI studies of functional connectivity. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.