Showing papers in "Brain connectivity in 2012"
TL;DR: The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fc MRI measures.
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...
3,388 citations
TL;DR: A problem that is still treated lightly despite its significant impact on RS-FMRI inferences is discussed; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness.
Abstract: Resting-state functional magnetic resonance imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but is not easy to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated that GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem that GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter interregional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true interregional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network's regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.
834 citations
TL;DR: It is shown that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity, and these metrics can be applied both in studies with complex naturalistic stimuli and more controlled paradigms.
Abstract: Functional brain activity and connectivity have been studied by calculating intersubject and seed-based correlations of hemodynamic data acquired with functional magnetic resonance imaging (fMRI). To inspect temporal dynamics, these correlation measures have been calculated over sliding time windows with necessary restrictions on the length of the temporal window that compromises the temporal resolution. Here, we show that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity. We applied PS on an fMRI dataset obtained while 12 healthy volunteers watched a feature film. Narrow frequency band (0.04–0.07 Hz) was used in the PS analysis to avoid artifactual results. We defined three metrics for computing time-varying functional connectivity and time-varying intersubject reliability based on estimation of instantaneous PS across the subjects: (1) seed-based PS, (2) intersubject PS, and (3) intersu...
282 citations
TL;DR: The prime candidates for functional networks of the forebrain that play a critical role in maintaining the state of consciousness are those based on the posterior parietal-cingulate-precuneus region and the nonspecific thalamus.
Abstract: General anesthesia consists of amnesia, hypnosis, analgesia, and areflexia Of these, the mechanism of hypnosis, or loss of consciousness, has been the most elusive, yet a fascinating problem How anesthetic agents suppress human consciousness has been investigated with neuroimaging for two decades Anesthetics substantially reduce the global cerebral metabolic rate and blood flow with a degree of regional heterogeneity characteristic to the anesthetic agent The thalamus appears to be a common site of modulation by several anesthetics, but this may be secondary to cortical effects Stimulus-dependent brain activation is preserved in primary sensory areas, suggesting that unconsciousness cannot be explained by cortical deafferentation or a diminution of cortical sensory reactivity The effect of general anesthetics in functional and effective connectivity is varied depending on the agent, dose, and network studied At an anesthetic depth characterized by the subjects' unresponsiveness, a partial,
226 citations
TL;DR: The results show that power fluctuations in posterior alpha oscillations result in local and long-range neural connectivity changes, and illustrate an enhanced functional inhibition during a higher alpha activity.
Abstract: In the past decade, the fast and transient coupling and uncoupling of functionally related brain regions into networks has received much attention in cognitive neuroscience. Empirical tools to study network coupling include functional magnetic resonance imaging (fMRI)-based functional and/or effective connectivity, and electroencephalography (EEG)/magnetoencephalography-based measures of neuronal synchronization. Here we use simultaneously recorded EEG and fMRI to assess whether fMRI-based connectivity and frequency-specific EEG power are related. Using data collected during resting state, we studied whether posterior EEG alpha power fluctuations are correlated with connectivity within the visual network and between the visual cortex and the rest of the brain. The results show that when alpha power increases, BOLD connectivity between the primary visual cortex and occipital brain regions decreases and that the negative relation of the visual cortex with the anterior/medial thalamus decreases and the ventral-medial prefrontal cortex is reduced in strength. These effects were specific for the alpha band, and not observed in other frequency bands. The decreased connectivity within the visual system may indicate an enhanced functional inhibition during a higher alpha activity. This higher inhibition level also attenuates long-range intrinsic functional antagonism between the visual cortex and the other thalamic and cortical regions. Together, these results illustrate that power fluctuations in posterior alpha oscillations result in local and long-range neural connectivity changes.
161 citations
TL;DR: Preliminary illustration is provided, suggesting that the human brain metabolism pertains to organized covariance patterns that might partially reflect functional connectivity as revealed by resting-state blood oxygen level dependent (BOLD).
Abstract: The human brain is inherently organized as separate networks, as has been widely revealed by resting-state functional magnetic resonance imaging (fMRI) Although the large-scale functional connectivity can be partially explained by the underlying white-matter structural connectivity, the question of whether the underlying functional connectivity is related to brain metabolic factors is still largely unanswered The present study investigated the presence of metabolic covariant networks across subjects using a set of fluorodeoxyglucose (18F, FDG) positron-emission tomography (PET) images Spatial-independent component analysis was performed on the subject series of FDG-PET images A number of networks that were mainly homotopic regions could be identified, including visual, auditory, motor, cerebellar, and subcortical networks However, the anterior-posterior networks such as the default-mode and left frontoparietal networks could not be observed Region-of-interest-based correlation analysis conf
130 citations
TL;DR: It is argued that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word in relation to Granger causality analysis.
Abstract: Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence—synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis—hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of Granger causality analysis. Finally, we discuss our previous work on the less-appreciated interactions between instantaneous and causal relationships and the utility and interpretation of Granger causality results obtained from task versus resting state (e.g., ability of causal relationships to provide a mode of connectivity between regions that are instantaneously dissociated in resting state). We conclude by discussing future directions in this area.
121 citations
TL;DR: Fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series, achieves high accelerations for computing voxels-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware.
Abstract: Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterize connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional magnetic resonance imaging (fMRI) is the cost of computing and storing the connectivity matrix. This article presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical gold standard, and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D dataset whose volumes consist of separate regions with known intra- and inter-regional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real-versus-sham repetitive transcranial magnetic stimulation show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multimodality and high-resolution functional neuroimaging data.
103 citations
TL;DR: The pros and cons of various connectivity methods as potential diagnostic tools in brain-damaged patients with DOC are reviewed and the relevance of the study of the level versus the contents of consciousness in this context is discussed.
Abstract: The last 10 years witnessed a considerable increase in our knowledge of brain function in survivors to severe brain injuries with disorders of consciousness (DOC). At the same time, a growing interest developed for the use of functional neuroimaging as a new diagnostic tool in these patients. In this context, particular attention has been devoted to connectivity studies-as these, more than measures of brain metabolism, may be more appropriate to capture the dynamics of large populations of neurons. Here, we will review the pros and cons of various connectivity methods as potential diagnostic tools in brain-damaged patients with DOC. We will also discuss the relevance of the study of the level versus the contents of consciousness in this context.
89 citations
TL;DR: Findings indicate that badminton expertise is associated with not only plastic structural changes in terms of enlarged gray matter density in the cerebellum, but also functional alterations in fronto-parietal connectivity.
Abstract: Neuroimaging studies of professional athletic or musical training have demonstrated considerable practice-dependent plasticity in various brain structures, which may reflect distinct training demands. In the present study, structural and functional brain alterations were examined in professional badminton players and compared with healthy controls using magnetic resonance imaging (MRI) and resting-state functional MRI. Gray matter concentration (GMC) was assessed using voxel-based morphometry (VBM), and resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity. Results showed that the athlete group had greater GMC and ALFF in the right and medial cerebellar regions, respectively. The athlete group also demonstrated smaller ALFF in the left superior parietal lobule and altered functional connectivity between the left superior parietal and frontal regions. These findings indicate that badminton expertise is associated with not only plastic structural changes in terms of enlarged gray matter density in the cerebellum, but also functional alterations in fronto-parietal connectivity. Such structural and functional alterations may reflect specific experiences of badminton training and practice, including high-capacity visuo-spatial processing and hand-eye coordination in addition to refined motor skills.
86 citations
TL;DR: Graph spectral analysis (GSA) was applied to magnetoencephalography data to explore functional network integrity in AD and implies that GSA is valuable for the purpose of studying altered brain network topology and dynamics in AD.
Abstract: In Alzheimer’s disease (AD), structural and functional brain network organization is disturbed. However, many of the present network analysis measures require a priori assumptions and methodological choices that influence outcomes and interpretations. Graph spectral analysis (GSA) is a more direct algebraic method that describes network properties, which might lead to more reliable results. In this study, GSA was applied to magnetoencephalography (MEG) data to explore functional network integrity in AD. Sensor-level resting-state MEG was performed in 18 Alzheimer patients (age 67 – 9, 6 women) and 18 healthy controls (age 66 – 9, 11 women). Weighted, undirected graphs were constructed based on functional connectivity analysis using the Synchronization likelihood, and GSA was performed with a focus on network connectivity, synchronizability, and node centrality. The main outcomes were a global loss of network connectivity and altered synchronizability in most frequency bands. Eigenvector centrality mapping confirmed the hub status of the parietal areas, and demonstrated a low centrality of the left temporal region in the theta band in AD patients that was strongly related to the mini mental state examination (global cognitive function test) score (r = 0.67, p = 0.001). Summarizing, GSA is a theoretically solid approach that is able to detect the disruption of functional network topology in AD. In addition to the previously reported overall connectivity losses and parietal area hub status, impaired network synchronizability and a clinically relevant left temporal centrality loss were found in AD patients. Our findings imply that GSA is valuable for the purpose of studying altered brain network topology and dynamics in AD.
TL;DR: Repeated measures were obtained across two imaging protocols allowing intra-subject and inter-site variability to be assessed, and regional measures within white matter were obtained for standard rotationally invariant measures.
Abstract: A number of studies are now collecting diffusion tensor imaging (DTI) data across sites. While the reliability of anatomical images has been established by a number of groups, the reliability of DTI data has not been studied as extensively. In this study, five healthy controls were recruited and imaged at eight imaging centers. Repeated measures were obtained across two imaging protocols allowing intra-subject and inter-site variability to be assessed. Regional measures within white matter were obtained for standard rotationally invariant measures: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. Intra-subject coefficient of variation (CV) was typically <1% for all scalars and regions. Inter-site CV increased to ∼1%–3%. Inter-vendor variation was similar to inter-site variability. This variability includes differences in the actual implementation of the sequence.
TL;DR: Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults, and are worse in those genetically predisposed to brain degeneration.
Abstract: Antiretroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population shows some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of noninfected people. Here we combined magnetic resonance imaging-based cortical parcellations with high angular resolution diffusion tensor imaging tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60–80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the apolipoprotein E4 allele (ApoE4) genotype—which puts them at even great...
TL;DR: The variable bandwidth method of filtering an amplitude modulated signal is proposed to preserve amplitude modulation and enable accurate CFC measurements and comparisons between filtering methods indicate that the variable bandwidth approach presented in this article is preferred when examining amplitude modulations above the theta band.
Abstract: There is an increasing interest in examining cross-frequency coupling (CFC) between groups of oscillating neurons. Most CFC studies examine how the phase of lower-frequency brain activity modulates the amplitude of higher-frequency brain activity. This study focuses on the signal filtering that is required to isolate the higher-frequency neuronal activity which is hypothesized to be amplitude modulated. In particular, previous publications have used a filter bandwidth fixed to a constant for all assessed modulation frequencies. The present article demonstrates that fixed bandwidth filtering can destroy amplitude modulation and create false-negative CFC measures. To overcome this limitation, this study presents a variable bandwidth filter that ensures preservation of the amplitude modulation. Simulated time series data were created with theta-gamma, alpha-gamma, and beta-gamma phase-amplitude coupling. Comparisons between filtering methods indicate that the variable bandwidth approach presented in this article is preferred when examining amplitude modulations above the theta band. The variable bandwidth method of filtering an amplitude modulated signal is proposed to preserve amplitude modulation and enable accurate CFC measurements.
TL;DR: In patients, depression severity was correlated with reduced perfusion in the DMN in the posterior cingulate cortex and the right inferior parietal lobe, supporting a role of theDMN in depression pathobiology and the interpretation of BOLD functional magnetic resonance imaging data in MDD.
Abstract: The default-mode network (DMN) was shown to have aberrant blood oxygenation-level-dependent (BOLD) activity in major depressive disorder (MDD). While BOLD is a relative measure of neural activity, cerebral blood flow (CBF) is an absolute measure. Resting-state CBF alterations have been reported in MDD. However, the association of baseline CBF and CBF fluctuations is unclear in MDD. Therefore, the aim was to investigate the CBF within the DMN in MDD, applying a strictly data-driven approach. In 22 MDD patients and 22 matched healthy controls, CBF was acquired using arterial spin labeling (ASL) at rest. A concatenated independent component analysis was performed to identify the DMN within the ASL data. The perfusion of the DMN and its nodes was quantified and compared between groups. The DMN was identified in both groups with high spatial similarity. Absolute CBF values within the DMN were reduced in MDD patients (p<0.001). However, after controlling for whole-brain gray matter CBF and age, the group difference vanished. In patients, depression severity was correlated with reduced perfusion in the DMN in the posterior cingulate cortex and the right inferior parietal lobe. Hypoperfusion within the DMN in MDD is not specific to the DMN. Still, depression severity was linked to DMN node perfusion, supporting a role of the DMN in depression pathobiology. The finding has implications for the interpretation of BOLD functional magnetic resonance imaging data in MDD.
TL;DR: These progressive patients showed a differential profile of functional connectivity values compared with those patients who remained stable over time, and there were higher synchronization values over the parieto-occipital region in α and β frequency bands.
Abstract: It is now widely accepted that Alzheimer's disease is characterized by a functional disconnection between brain regions. The disease appears to begin up to decades prior to clinical diagnosis. Therefore, in the present study, we combined magnetoencephalography, a memory task, and functional connectivity analysis in mild cognitive impairment subjects in order to identify functional connectivity patterns that could characterize subjects who would eventually go on to develop the disease. We monitored 19 subjects and finally 5 of them developed Alzheimer's disease. These progressive patients showed a differential profile of functional connectivity values compared with those patients who remained stable over time. Specifically there were higher synchronization values over the parieto-occipital region in α and β frequency bands. The involvement of this brain region in amyloid-β accumulation and its possible association with hyper-synchronization are also discussed.
TL;DR: This study investigates a new approach for examining the separation of the brain into resting- state networks (RSNs) on a group level using resting-state parameters (amplitude of low-frequency fluctuation [ALFF], fractional ALFF], the Hurst exponent, and signal standard deviation) and suggests that the LFFs and RSFC networks have neurophysiological origins.
Abstract: In this study, we investigate a new approach for examining the separation of the brain into resting-state networks (RSNs) on a group level using resting-state parameters (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [fALFF], the Hurst exponent, and signal standard deviation). Spatial independent component analysis is used to reveal covariance patterns of the relevant resting-state parameters (not the time series) across subjects that are shown to be related to known, standard RSNs. As part of the analysis, nonresting state parameters are also investigated, such as mean of the blood oxygen level-dependent time series and gray matter volume from anatomical scans. We hypothesize that meaningful RSNs will primarily be elucidated by analysis of the resting-state functional connectivity (RSFC) parameters and not by non-RSFC parameters. First, this shows the presence of a common influence underlying individual RSFC networks revealed through low-frequency fluctation (LFF) parameter prop...
TL;DR: Small intraindividual temporal IAF fluctuations were correlated to increased blood oxygenation level-dependent signal in brain areas associated to working memory functions and to the modulation of attention, and a consistent functionally and structurally connected network related to IAF was observed.
Abstract: Structural and functional connectivity are intrinsic properties of the human brain and represent the amount of cognitive capacities of individual subjects. These connections are modulated due to development, learning, and disease. Momentary adaptations in functional connectivity alter the structural connections, which in turn affect the functional connectivity. Thus, structural and functional connectivity interact on a broad timescale. In this study, we aimed to explore distinct measures of connectivity assessed by functional magnetic resonance imaging and diffusion tensor imaging and their association to the dominant electroencephalogram oscillatory property at rest: the individual alpha frequency (IAF). We found that in 21 healthy young subjects, small intraindividual temporal IAF fluctuations were correlated to increased blood oxygenation level-dependent signal in brain areas associated to working memory functions and to the modulation of attention. These areas colocalized with functionally connected networks supporting the respective functions. Furthermore, subjects with higher IAF show increased fractional anisotropy values in fascicles connecting the above-mentioned areas and networks. Hence, due to a multimodal approach a consistent functionally and structurally connected network related to IAF was observed.
TL;DR: Dysfunction in network engagement in PBD patients illustrates that they are expending greater effort in face emotion evaluation, while being less able to engage affect regulation regions.
Abstract: This study examined whether adolescents with pediatric bipolar disorder (PBD) have abnormal regional functional connectivity in distributed brain networks during an affective working memory task. Adolescents with PBD (n=41) and healthy controls (HC; n=16) performed a two-back functional magnetic resonance imaging working memory task with blocks of either angry or neutral faces. Independent component analysis methodology identified two temporally independent and functionally connected brain networks that showed differential functional connectivity in PBD and HC. Within a network for “affect evaluation and regulation,” PBD showed decreased functional connectivity relative to HC in regions involved in emotion processing such as the right amygdala, and in emotion regulation regions such as the right ventrolateral prefrontal cortex (VLPFC), while functional connectivity was increased in emotion evaluation regions such as the bilateral medial PFC. Furthermore, in an “Affective Working Memory Network,” ...
TL;DR: Dynamic sleep-related dissociations and reconnections between sleep/wake conditions might provide the key to understanding cognitive modulations in sleep.
Abstract: The function of sleep in humans has been investigated using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging recordings to provide accurate sleep scores with spatial precision. Recent studies have demonstrated that spontaneous brain oscillations and functional connectivity dissociate during nonrapid eye movement (NREM) sleep; this leads to spontaneous cognitive processes, such as memory consolidation and emotional modulation. However, variations in network connectivity across the sleep stages or between sleep/wake transitions require further elucidation. We observed changes in the connectivity of the sensorimotor and default-mode networks (DMN) mediated by midnight sleep among 18 healthy participants. The results indicated that (1) functional connectivity in both networks showed increasing dissociation as NREM sleep deepened, whereas hyperconnectivity occurred during rapid eye movement (REM) sleep; and (2) compared with connectivity before sleep, the DMN presented a comparable connectivity pattern immediately after awakening, whereas the connectivity of the sensorimotor network remained disrupted. These findings showed that connectivity patterns dissociate and reconnect coherently in both cortical networks during NREM and REM sleep, respectively. After the person awakened, the DMN connectivity was re-established before the sensorimotor reconnection. These dynamic sleep-related dissociations and reconnections between sleep/wake conditions might provide the key to understanding cognitive modulations in sleep. If so, connectivity changes might serve as an alternative indicator beyond the EEG signature to unveil the spontaneous processes that occur during sleep.
TL;DR: Previous models of visual search processes are extended to include specific frontal-occipital neuronal interactions during a natural and complex search task to test the hypothesis that high-level frontal regions interact with visual regions associated with object recognition during visual search.
Abstract: Although expectation- and attention-related interactions between ventral and medial prefrontal cortex and stimulus category-selective visual regions have been identified during visual detection and discrimination, it is not known if similar neural mechanisms apply to other tasks such as visual search. The current work tested the hypothesis that high-level frontal regions, previously implicated in expectation and visual imagery of object categories, interact with visual regions associated with object recognition during visual search. Using functional magnetic resonance imaging, subjects searched for a specific object that varied in size and location within a complex natural scene. A model-free, spatial-independent component analysis isolated multiple task-related components, one of which included visual cortex, as well as a cluster within ventromedial prefrontal cortex (vmPFC), consistent with the engagement of both top-down and bottom-up processes. Analyses of psychophysiological interactions sho...
TL;DR: The findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network that is additionally applicable to a wide variety of problems identifying structural networks from seed regions.
Abstract: We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to a structural ...
TL;DR: It is found that IP led to higher functional connectivity between the right and left dorsal lateral prefrontal cortex (DLPFC) and between the dorsal premotor cortex (PMd) and inferior parietal lobule (IPL) in older adults and increased connectivity between these regions was significantly associated with the learning benefits of IP.
Abstract: We recently demonstrated that older adults can benefit as much as younger adults from learning skills in an interleaved manner. Here we investigate whether optimized learning through interleaved practice (IP) is associated with changes in inter-regional brain connectivity and whether younger and older adults differ in such brain–behavior correlations. Younger and older adults practiced a set of three 4-element motor sequences in a repetitive or in an interleaved order for 2 consecutive days. Retention of the practiced sequences was evaluated 3 days after practice with functional images acquired simultaneously. A within-subject design was used so that subjects practiced sequences in the other condition (repetitive or interleaved) 2–4 weeks later. Using the psychophysiological interaction (PPI) analysis approach, we found that IP led to higher functional connectivity between the right and left dorsal lateral prefrontal cortex (DLPFC) and between the dorsal premotor cortex (PMd) and inferior parieta...
TL;DR: This approach uses Bayesian Model Selection to find a best fitting model and then uses a bootstrapping technique to provide an estimate of the parameter variance, which will be a useful guide for Dynamic Causal Modeling studies.
Abstract: Functional magnetic resonance imaging (fMRI) has proved to be useful for analyzing the effects of illness and pharmacological agents on brain activation. Many fMRI studies now incorporate effective connectivity analyses on data to assess the networks recruited during task performance. The assessment of the sample size that is necessary for carrying out such calculations would be useful if these techniques are to be confidently applied. Here, we present a method of estimating the sample size that is required for a study to have sufficient power. Our approach uses Bayesian Model Selection to find a best fitting model and then uses a bootstrapping technique to provide an estimate of the parameter variance. As illustrative examples, we apply this technique to two different tasks and show that for our data, ~20 volunteers per group is sufficient. Due to variability between task, volunteers, scanner, and acquisition parameters, this would need to be evaluated on individual datasets. This approach will be a useful guide for Dynamic Causal Modeling studies.
TL;DR: An age-related increase in the density and size of the networks and loss of small-worldness was observed, related to an expanded distribution of brain activity during both memory demands in seniors, and a more specific and localized activity in young subjects.
Abstract: The aim of the study was to investigate age-related differences in large-scale functional connectivity networks during episodic and working memory challenge. A graph theoretical approach was used providing an exhaustive set of topological measures to quantify age-related differences in the network structure on various scales. In a single session, 10 young (22–30 years) and 10 senior (62–77 years) subjects performed an episodic and a working memory task during functional magnetic resonance imaging. Networks of functional connectivity were constructed by correlating the blood oxygenation level-dependent (BOLD) signal for every pair of voxels. Statistical network parameters yield a global characterization of the network topology, the quantification of the importance of specific regions, and shifts in local connectivity. An age-related increase in the density and size of the networks and loss of small-worldness was observed, related to an expanded distribution of brain activity during both memory demands in seniors, and a more specific and localized activity in young subjects. In addition, we found highly symmetrical neural networks in young subjects accompanied by a strong coupling between parietal and occipital regions. In contrast, seniors showed pronounced left-hemispheric asymmetry with decreased connectivity within occipital areas, but increased connectivity within parietal areas. Moreover, seniors engaged an additional frontal network strongly connected to parietal areas. In contrast to young subjects, seniors showed an almost identical structure of network connectivity during both memory tasks. The chosen network approach is explorative and hypothesis-free. Our results extend seed-based and BOLD-signal intensity focused studies, and support present hypotheses like compensation and dedifferentiation.
TL;DR: The overall effects of single administrations of oxazepam and L-dopa on resting-state connectivity were small both in strength and in spatial extent, and were on par with placebo effects as revealed by comparing the two placebo groups.
Abstract: Pharmacological functional brain imaging has traditionally focused on neuropharmacological modulations of event-related responses. The current study is a randomized, cross-sectional resting-state functional magnetic resonance imaging study where a single dose of commonly prescribed amounts of either benzodiazepine (oxazepam), L-dopa, or placebo was given to 81 healthy subjects. It was hypothesized that the connectivity in resting-state networks would be altered, and that the strength of connectivity in areas rich in target receptors would be particularly affected. Additionally, based on known anxiolytic mechanisms of benzodiazepines, modulated amygdala (Am) connectivity was predicted. To test this, seed region-based correlational analysis was performed using seven seeds placed in well-characterized resting-state networks, in regions with above-average densities of GABA-A or dopamine receptors and in Am. To alleviate the anatomical bias introduced by the a priori selected seed regions, whole-brain exploratory analysis of regional homogeneity and fractional amplitude of low-frequency fluctuations (fALFF) was also carried out. Oxazepam increased functional connectivity between midline regions of the default-mode network (DMN) and the prefrontal, parietal, and cerebellar areas, but decreased connectivity between, for example, the Am and temporal cortex. L-dopa mainly decreased connectivity between the Am and bilateral inferior frontal gyri and between midline regions of the DMN. The fALFF analysis revealed that L-dopa decreased low-frequency fluctuations in the cerebellum. It was concluded that the overall effects of single administrations of oxazepam and L-dopa on resting-state connectivity were small both in strength and in spatial extent, and were on par with placebo effects as revealed by comparing the two placebo groups.
TL;DR: The results suggest that effects of age on RS FC are already present at middle age, and sustained cognitive performance increased RS FC between task-positive networks and other brain regions, although a change in RS FC within the networks was not found.
Abstract: Previous studies showed that functional connectivity (FC) within resting state (RS) networks is modulated by previous experience. In this study the effects of sustained cognitive performance on subsequent RS FC were investigated in healthy young (25-30 years; n=15) and middle-aged (50-60 years; n=14) male schoolteachers. Participants were scanned (functional magnetic resonance imaging [MRI]) after a cognitively demanding and a control intervention (randomized tester-blind within-subject design). Independent component analysis (ICA) was used to decompose the data into spatially independent networks. This study focused on the executive control (ExN), the left and right frontoparietal (FPN), and the default mode network (DMN). The effects of cognitive performance and age were calculated with a full-factorial analysis of variance (ANOVA). A main effect of age was found in the left inferior frontal gyrus for the ExN and in the middle frontal gyrus for the DMN with middle-aged teachers having reduced RS FC. Sustained cognitive performance increased subsequent RS FC between the ExN and a lingual/parahippocampal cluster, and between the left FPN and a right calcarine/precuneus cluster. In these clusters, FC strength correlated positively with the perceived amount of effort during the intervention. Further, sustained cognitive performance affected subsequent RS FC between the ExN and the right temporal superior gyrus differently in young and middle-aged men. The results suggest that effects of age on RS FC are already present at middle age. Sustained cognitive performance increased RS FC between task-positive networks and other brain regions, although a change in RS FC within the networks was not found.
TL;DR: Results from psychophysiological interaction analyses revealed that significantly altered, inverse coupling occurs between the aMCC and the ventral striatum when OCD patients anticipate potential punishment, and support the importance of applying connectivity methods to study corticostriatal networks in OCD.
Abstract: Neurobiological models of obsessive-compulsive disorder (OCD) assume abnormalities in corticostriatal networks involving cingulate and orbitofrontal cortices, but the connectivity within these systems is rarely addressed in experimental imaging studies in this patient group. Using an established monetary reinforcement paradigm known to involve the cingulate cortex and the ventral striatum, the present study sought to test for altered corticostriatal coupling in OCD patients anticipating potential punishment. The anterior midcingulate cortex (aMCC), a region integrating negative emotion, pain, and cognitive control, was chosen as a seed region due to its particular relevance in OCD, representing the neurosurgical target for cingulotomy, and showing increased responses to errors in OCD patients. Results from psychophysiological interaction analyses revealed that significantly altered, inverse coupling occurs between the aMCC and the ventral striatum when OCD patients anticipate potential punishment. This abnormality links the two major contemporary neurosurgical OCD target sites, and provides direct experimental evidence of altered corticostriatal connectivity in OCD. Noteworthy, an abnormal aMCC coupling with cortical areas outside of traditional corticostriatal circuitry was identified besides the alteration in the cingulostriatal pathway. In conclusion, these findings support the importance of applying connectivity methods to study corticostriatal networks in OCD, and favor the application of effective connectivity methods to study corticostriatal abnormalities in OCD patients performing tasks that involve symptom provocation and reinforcement learning.
TL;DR: The results suggest that measures of structure and function are not necessarily complementary, and highlight the utility of spTMS-fMRI, a method that directly and causally probes effective connectivity, as a tool for studying brain-based disorders.
Abstract: Schizophrenia is a severe mental illness with neurobiological bases that remain elusive. One hypothesis emphasizes disordered thalamic function. We previously used concurrent single pulse transcranial magnetic stimulation (spTMS) and functional magnetic resonance imaging (fMRI) to show that individuals with schizophrenia have a decreased spTMS-evoked response in the thalamus, and decreased effective connectivity between thalamus and insula and thalamus and superior frontal gyrus. To better understand the factors that may accompany or account for these findings, we investigated, in the same participants, resting state functional connectivity, white matter structural connectivity, and grey matter integrity. Patients with schizophrenia did not differ from healthy control subjects in resting state functional- or white matter structural connectivity, although they did show decreased measures of grey matter integrity in the insula. However, in this region, the spTMS-evoked response did not differ betwe...
TL;DR: It is demonstrated that when ICA is applied to localized fMRI data, it can be used to distinguish resting-state LFFs associated with specific motor functions in the primary motor cortex, and these ICA components generated from localized data can then be used as functional regions of interest to map whole-brain connectivity.
Abstract: Recent studies have shown that blood oxygen level-dependent low-frequency (<0.1 Hz) fluctuations (LFFs) during a resting-state exhibit a high degree of correlation with other regions that share cognitive function. Initial studies of resting-state network mapping have focused primarily on major networks such as the default mode network, primary motor, somatosensory, visual, and auditory networks. However, more specific or subnetworks, including those associated with specific motor functions, have yet to be properly addressed. We performed independent component analysis (ICA) in a specific target region of the brain, a process we name, “localized ICA.” We demonstrated that when ICA is applied to localized fMRI data, it can be used to distinguish resting-state LFFs associated with specific motor functions (e.g., finger tapping, foot movement, or bilateral lip pulsing) in the primary motor cortex. These ICA components generated from localized data can then be used as functional regions of interest to...