[Show abstract][Hide abstract] ABSTRACT: Several neuroimaging studies reported that a common set of regions are recruited during action observation and execution and it has been proposed that the modulation of the μ rhythm, in terms of oscillations in the alpha and beta bands might represent the electrophysiological correlate of the underlying brain mechanisms. However, the specific functional role of these bands within the μ rhythm is still unclear. Here, we used magnetoencephalography (MEG) to analyze the spectral and temporal properties of the alpha and beta bands in healthy subjects during an action observation and execution task. We associated the modulation of the alpha and beta power to a broad action observation network comprising several parieto-frontal areas previously detected in fMRI studies. Of note, we observed a dissociation between alpha and beta bands with a slow-down of beta oscillations compared to alpha during action observation. We hypothesize that this segregation is linked to a different sequence of information processing and we interpret these modulations in terms of internal models (forward and inverse). In fact, these processes showed opposite temporal sequences of occurrence: anterior-posterior during action (both in alpha and beta bands) and roughly posterior-anterior during observation (in the alpha band). The observed differentiation between alpha and beta suggests that these two bands might pursue different functions in the action observation and execution processes.
[Show abstract][Hide abstract] ABSTRACT: Spontaneous fMRI fluctuations are organized in large-scale spatiotemporal structures, or resting-state networks (RSN). However, it is unknown how task performance affects RSN dynamics. We use MEG to measure slow (∼0.1 Hz) coherent fluctuations of band-limited power (BLP), a robust correlate of RSN, during rest and movie observation and compare them to fMRI-RSN. BLP correlation, especially in α band, dropped in multiple RSN during movie although overall topography was maintained. Variability of power correlation increased in visual occipital cortex, and transient decrements corresponded to scenes perceived as "event boundaries." Additionally, stronger task-dependent interactions developed between vision and language networks in θ and β bands, and default and language networks in γ band. The topography of fMRI connectivity and relative changes induced by the movie were well matched to MEG. We conclude that resting-state and task network interactions are clearly different in the frequency domain despite maintenance of underlying network topography.
[Show abstract][Hide abstract] ABSTRACT: The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently have studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.
[Show abstract][Hide abstract] ABSTRACT: Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally < 10(-1) Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 10(0) - 10(2) Hz. We examined phase-shifted interactions in the delta (2-3.5 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the Multivariate Interaction Measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band. These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs.
[Show abstract][Hide abstract] ABSTRACT: The principles of functional specialization and integration in the resting brain are implemented in a complex system of specialized networks that share some degree of interaction. Recent studies have identified wider functional modules compared to previously defined networks and reported a small-world architecture of brain activity in which central nodes balance the pressure to evolve segregated pathways with the integration of local systems. The accurate identification of such central nodes is crucial but might be challenging for several reasons, e.g. inter-subject variability and physiological/pathological network plasticity, and recent works reported partially inconsistent results concerning the properties of these cortical hubs. Here, we applied a whole-brain data-driven approach to extract cortical functional cores and examined their connectivity from a resting state fMRI experiment on healthy subjects. Two main statistically significant cores, centered on the Posterior Cingulate Cortex and the Supplementary Motor Area, were extracted and their functional connectivity maps, thresholded at three statistical levels, revealed the presence of two complex systems. One system is consistent with the Default Mode Network (DMN) and gradually connects to visual regions, the other centered on motor regions and gradually connects to more sensory-specific portions of cortex. These two large scale networks eventually converged to regions belonging to the medial aspect of the DMN, potentially allowing inter-network interactions.
[Show abstract][Hide abstract] ABSTRACT: We used magneto-encephalography to study the temporal dynamics of band-limited power correlation at rest within and across six brain networks previously defined by prior functional magnetic resonance imaging (fMRI) studies. Epochs of transiently high within-network band limited power (BLP) correlation were identified and correlation of BLP time-series across networks was assessed in these epochs. These analyses demonstrate that functional networks are not equivalent with respect to cross-network interactions. The default-mode network and the posterior cingulate cortex, in particular, exhibit the highest degree of transient BLP correlation with other networks especially in the 14-25 Hz (β band) frequency range. Our results indicate that the previously demonstrated neuroanatomical centrality of the PCC and DMN has a physiological counterpart in the temporal dynamics of network interaction at behaviorally relevant timescales. This interaction involved subsets of nodes from other networks during periods in which their internal correlation was low.
[Show abstract][Hide abstract] ABSTRACT: Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering.
[Show abstract][Hide abstract] ABSTRACT: We recently suggested that serotonin 7 (5-Ht7) receptors may play a role in ADHD-like symptoms, at least in animal models. A mixed 5-Ht(1a/7) agonist, 8-OH-DPAT, counteracted the augmented levels of basal impulsivity, observed after treatment with a selective 5-Ht7 antagonist, SB269970 (Leo et al., 2009). In the present study, these serotonergic compounds were investigated by pharmacological magnetic resonance imaging (phMRI) at 4.7 T in adult isoflurane-anaesthetized rats. Axial echo-planar images were collected from the prefrontal cortex (PFC), ventral (nucleus accumbens, NAcc) and dorsal (dStr) striata, the hippocampus and the thalamus. After consecutive image collection for 30 min (50 baseline images), adult rats received either SB269970 (3mg/kg), 8-OH-DPAT (0.06 mg/kg) or saline intra-peritoneally (i.p.) via a remote cannula; the images were then collected for further 30 min (50 post-treatment images). Data were analysed 1) through an activation map generated on brain templates, obtained by using animals from each experimental group; 2) by a two-way ANOVA for the evaluation of temporal profiles, extracted within selected ROIs of each animal. Both compounds increased the BOLD signal in the areas of interest: SB269970, the selective 5-Ht7 antagonist, induced a significant effect in the PFC, particularly the orbital (oPFC) region, and in the NAcc. This effect started 6 to 12 min after drug administration, reached a maximum (+2.8%/+2.3%) at 12 to 18 min, and then moved to the dorsal thalamic nuclei. In contrast, the effects of 8-OH-DPAT were first observed in midline thalamic nuclei, and later appeared in forebrain regions: its effects were modest and transient within the NAcc and oPFC (+1.7% at 18 to 24 min after injection), whereas they were higher and long-lasting in the dStr and PFC, specifically the medial (mPFC) region (+3.1%/+4.0% from 15 min after drug administration onwards). The brain BOLD changes, reported as a consequence of selective 5-Ht7 antagonist administration, seemed restricted to the oPFC, NAcc and dorso-thalamic circuits, whereas the non-selective blockade of serotonergic receptors affected the mPFC, dStr and mid(line)-thalamic circuitry. The present findings revealed two differential serotonergic sub-pathways, as evidenced by the detection of physiological vascular feedback and/or neuronal activation.
[Show abstract][Hide abstract] ABSTRACT: To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the channel maps associated with the latter ones are projected into the source space and the resulting voxel-wise weights are used to linearly combine the IC time courses. An extensive description of the proposed pipeline is provided here, along with an assessment of its performances with respect to alternative approaches. The following investigations were carried out: (1) ICA decomposition algorithm. Synthetic data are used to assess the sensitivity of the ICA results to the decomposition algorithm, by testing FastICA, INFOMAX, and SOBI. FastICA with deflation approach, a standard solution, provides the best decomposition. (2) Recombination of brain ICs versus subtraction of artifactual ICs (at the channel level). Both the recombination of the brain ICs in the sensor space and the classical procedure of subtracting the artifactual ICs from the recordings provide a suitable reconstruction, with a lower distortion using the latter approach. (3) Recombination of brain ICs after localization versus localization of artifact-corrected recordings. The brain IC recombination after source localization, as implemented in the proposed pipeline, provides a lower source-level signal distortion. (4) Detection of RSNs. The accuracy in source-level reconstruction by the proposed pipeline is confirmed by an improved specificity in the retrieval of RSNs from experimental data.
[Show abstract][Hide abstract] ABSTRACT: Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.
Proceedings of the National Academy of Sciences 03/2010; 107(13):6040-5. · 9.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the
outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure
for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications.
In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data.
Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure
deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed
objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure
in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian
Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs
consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity
of a shape.
[Show abstract][Hide abstract] ABSTRACT: Adolescent rodents differ markedly from adults in several neuro-behavioural parameters. Moreover, 'paradoxical' responses to psychostimulants have been reported at this age.
Thus, we investigated the responses of adolescent (post-natal day, PND, 34 to 43) and adult (PND >60) Sprague-Dawley male rats to the psychostimulant drug methylphenidate (MPH). We used pharmacological magnetic resonance imaging (phMRI) performed at 4.7 T under isoflurane anaesthesia. Following anatomical MRI, axial gradient echo images were collected continuously. After baseline recording (32 min), animals received MPH (0 or 4 mg/kg i.p.) and were recorded for further 32 min.
Region-specific changes in the blood-oxygenation level dependent (BOLD) signal were evident as a function of age. As expected, among adults MPH induced an increase of BOLD signal in nucleus accumbens (NAcc) and prefrontal cortex (PFC), with no effects in the hippocampus (Hip). Notably, among adolescents, MPH induced a marked and generalised decrease of BOLD signal, which occurred earlier in NAcc and PFC whilst being delayed in the Hip. Any bias in BOLD responses was excluded by the measurement of physiological parameters.
The present findings highlight the utility of phMRI in animal models. The peculiar negative BOLD effect found in adolescent rats may be suggestive of a reduced cerebro-vascular feedback and/or an increased MPH-induced neuronal activation. Data are relevant for a better understanding of brain/behavioural regulation during adolescent development. Moreover, a greater understanding of the differences between adult and adolescent drug responses will aid in the development of a more appropriate age-specific treatment strategy.
[Show abstract][Hide abstract] ABSTRACT: In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is proposed. The Bayesian framework is appealing since complex models can be adopted in the analysis both for the image and noise model. Here, the noise autocorrelation is taken into account by adopting an AutoRegressive model of order one and a versatile non-linear model is assumed for the task-related activation. Model parameters include the noise variance and autocorrelation, activation amplitudes and the hemodynamic response function parameters. These are estimated at each voxel from samples of the Posterior Distribution. Prior information is included by means of a 4D spatio-temporal model for the interaction between neighbouring voxels in space and time. The results show that this model can provide smooth estimates from low SNR data while important spatial structures in the data can be preserved. A simulation study is presented in which the accuracy and bias of the estimates are addressed. Furthermore, some results on convergence diagnostic of the adopted algorithm are presented. To validate the proposed approach a comparison of the results with those from a standard GLM analysis, spatial filtering techniques and a Variational Bayes approach is provided. This comparison shows that our approach outperforms the classical analysis and is consistent with other Bayesian techniques. This is investigated further by means of the Bayes Factors and the analysis of the residuals. The proposed approach applied to Blocked Design and Event Related datasets produced reliable maps of activation.
[Show abstract][Hide abstract] ABSTRACT: Ferrous-sulphate infused gels, or 'Fricke gels', encounter great interest in the field of radiation dosimetry, due to their potential for 3D radiation dose mapping. Typically, magnetic resonance (MR) relaxation rates are determined in these systems in order to derive the absorbed dose. However, when large concentration gradients are present, diffusion effects before and during the MR imaging may not be negligible. In these cases, optical techniques may represent a viable alternative. This paper describes research aimed at measuring 3D dose distributions in a Fricke-xylenol orange gel by measuring optical density with a CCD camera. This method is inexpensive and fast. A series of early experiments is described, in which optical density profiles were measured with a commercial microdensitometer for film dosimetry. The light box of the device was modified to work at 567 nm, close to the maximum absorbance of the ferric ion-xylenol orange complex. Under these conditions, the gel shows linearity with dose and high sensitivity.
[Show abstract][Hide abstract] ABSTRACT: In Fricke-agarose gels, an accurate determination of the spatial dose distribution is hindered by the diffusion of ferric ions. In this work, a model was developed to describe the diffusion process within gel samples of finite length and, thus, permit the reconstruction of the initial spatial distribution of the ferric ions. The temporal evolution of the ion concentration as a function of the initial concentration is derived by solving Fick's second law of diffusion in two dimensions with boundary reflections. The model was applied to magnetic resonance imaging data acquired at high spatial resolution (0.3 mm) and was found to describe accurately the observed diffusion effects.