BOLD Time Courses Research Articles

Altered functional connectivity and spatiotemporal dynamics in individuals with sleep disorders.

Idiopathic hypersomnia (IH) is a sleep disorder characterized by highly disruptive symptoms. Like narcolepsy type 1, a well-characterized sleep disorder, individuals with IH suffer from excessive daytime sleepiness, though there is little overlap in metabolic or neural biomarkers across these two disorders. This lack of common pathophysiology, combined with the clear overlap in symptoms presents an ideal paradigm for better understanding the impact of IH on an individual's functional activity and organization, and potentially, the underlying pathophysiology. This study examines the observed functional connectivity in patients with IH, and patients with narcolepsy type 1 (NT1) against healthy control individuals. Static functional connectivity is compared, as are quasi-periodic patterns, acquired from the BOLD timecourse, for all groups. In addition to baseline data comparison, the study also included a post-nap condition, where the individuals included in this analysis napped for at least 10 minutes prior to the scanning session, to explore why individuals with IH do not feel refreshed after a nap like individuals with NT1 do. Assessing the groups' spatiotemporal patterns revealed key differences across both disorders and conditions: static connectivity revealed at baseline higher subcortical connectivity in the NT1 group. There was also observably less connectivity in the IH group both at baseline and post-nap, though none of these static analyses survived multiple comparisons correction to reach significance. The QPP results however found significant differences in the IH group in key networks, particularly the DAN/FPCN correlation is significantly different at baseline vs. post-nap, a trend not observed in either the control or NT1 groups. The DAN and FPCN are drastically altered both at baseline and post-nap when compared to the other groups, and may likely be a disorder-specific result. This study demonstrates that key networks for arousal are more heavily disrupted in IH patients, who are less affected by a nap, confirmed through both subject reporting and functional evidence through spatiotemporal patterns.

Specialized late cingulo-opercular network activation elucidates the mechanisms underlying decisions about ambiguity

Cortical task control networks, including the cingulo-opercular (CO) network play a key role in decision-making across a variety of functional domains. In particular, the CO network functions in a performance reporting capacity that supports successful task performance, especially in response to errors and ambiguity. In two studies testing the contribution of the CO network to ambiguity processing, we presented a valence bias task in which masked clearly and ambiguously valenced emotional expressions were slowly revealed over several seconds. This slow reveal task design provides a window into the decision-making mechanisms as they unfold over the course of a trial. In the main study, the slow reveal task was administered to 32 young adults in the fMRI environment and BOLD time courses were extracted from regions of interest in three control networks. In a follow-up study, the task was administered to a larger, online sample (n = 81) using a more extended slow reveal design with additional unmasking frames. Positive judgments of surprised faces were uniquely accompanied by slower response times and strong, late activation in the CO network. These results support the initial negativity hypothesis, which posits that the default response to ambiguity is negative and positive judgments are associated with a more effortful controlled process, and additionally suggest that this controlled process is mediated by the CO network. Moreover, ambiguous trials were characterized by a second CO response at the end of the trial, firmly placing CO function late in the decision-making process.

Affective flexibility as a developmental building block of cognitive reappraisal: An fMRI study

Cognitive reappraisal is a form of emotion regulation that involves reinterpreting the meaning of a stimulus, often to downregulate one’s negative affect. Reappraisal typically recruits distributed regions of prefrontal and parietal cortex to generate new appraisals and downregulate the emotional response in the amygdala. In the current study, we compared reappraisal ability in an fMRI task with affective flexibility in a sample of children and adolescents (ages 6–17, N = 76). Affective flexibility was defined as variability in valence interpretations of ambiguous (surprised) facial expressions from a second behavioral task. Results demonstrated that age and affective flexibility predicted reappraisal ability, with an interaction indicating that flexibility in children (but not adolescents) supports reappraisal success. Using a region of interest-based analysis of participants’ BOLD time courses, we also found dissociable reappraisal-related brain mechanisms that support reappraisal success and affective flexibility. Specifically, late increases in middle prefrontal cortex activity supported reappraisal success and late decreases in amygdala activity supported flexibility. Together, these results suggest that our novel measure of affective flexibility – the ability to see multiple interpretations of an ambiguous emotional cue – may represent part of the developmental building blocks of cognitive reappraisal ability.

Connective profiles and antagonism between dynamic and static connectivity underlying generalized epilepsy.

This study aims to characterize the connective profiles and the coupling relationship between dynamic and static functional connectivity (dFC and sFC) in large-scale brain networks in patients with generalized epilepsy (GE). Functional, structural and diffuse MRI data were collected from 83 patients with GE and 106 matched healthy controls (HC). Resting-state BOLD time course was deconvolved to neural time course using a blind hemodynamic deconvolution method. Then, five connective profiles, including the structural connectivity (SC) and BOLD/neural time course-derived sFC/dFC networks, were constructed based on the proposed whole brain atlas. Network-level weighted correlation probability (NWCP) were proposed to evaluate the association between dFC and sFC. Both the BOLD signal and neural time course showed highly concordant findings and the present study emphasized the consistent findings between two functional approaches. The patients with GE showed hypervariability and enhancement of FC, and notably decreased SC in the subcortical network. Besides, increased dFC, weaker anatomic links, and complex alterations of sFC were observed in the default mode network of GE. Moreover, significantly increased SC and predominantly increased sFC were found in the frontoparietal network. Remarkably, antagonism between dFC and sFC was observed in large-scale networks in HC, while patients with GE showed significantly decreased antagonism in core epileptic networks. In sum, our study revealed distinct connective profiles in different epileptic networks and provided new clues to the brain network mechanism of epilepsy from the perspective of antagonism between dynamic and static functional connectivity.

Large-scale resculpting of cortical circuits in children\xa0after surgical resection

Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there is rather little research on the neural (re)organization that potentially subserves behavioral compensation. Here, we examined the post-surgical functional connectivity (FC) in children and adolescents who have undergone unilateral cortical resection and, yet, display remarkably normal behavior. Conventionally, FC has been investigated in terms of the mean correlation of the BOLD time courses extracted from different brain regions. Here, we demonstrated the value of segregating the voxel-wise relationships into mutually exclusive populations that were either positively or negatively correlated. While, relative to controls, the positive correlations were largely normal, negative correlations among networks were increased. Together, our results point to reorganization in the contralesional hemisphere, possibly suggesting competition for cortical territory due to the demand for representation of function. Conceivably, the ubiquitous negative correlations enable the differentiation of function in the reduced cortical volume following a unilateral resection.

Frontal, Parietal, and Temporal Brain Areas Are Differentially Activated When Disambiguating Potential Objects of Joint Attention.

Humans establish joint attention with others by following the other’s gaze. Previous work has suggested that a cortical patch (gaze-following patch, GFP) close to the posterior superior temporal sulcus (pSTS) may serve as a link between the extraction of the other’s gaze direction and the resulting shifts of attention, mediated by human lateral intraparietal area (hLIP). However, it is not clear how the brain copes with situations in which information on gaze direction alone is insufficient to identify the target object because more than one may lie along the gaze vector. In this fMRI study, we tested human subjects on a paradigm that allowed the identification of a target object based on the integration of the other’s gaze direction and information provided by an auditory cue on the relevant object category. Whereas the GFP activity turned out to be fully determined by the use of gaze direction, activity in hLIP reflected the total information needed to pinpoint the target. Moreover, in an exploratory analysis, we found that a region in the inferior frontal junction (IFJ) was sensitive to the total information on the target. An examination of the BOLD time courses in the three identified areas suggests functionally complementary roles. Although the GFP seems to primarily process directional information stemming from the other’s gaze, the IFJ may help to analyze the scene when gaze direction and auditory information are not sufficient to pinpoint the target. Finally, hLIP integrates both streams of information to shift attention to distinct spatial locations.

Intersubject Synchronization of Late Adolescent Brain Responses to Violent Movies: A Virtue-Ethics Approach.

Movies that involve violence increasingly attract large audiences, leading to concern that such entertainment will encourage imitation by youth, especially when the violence is seen as justified. To assess differences between brain responses to justified and unjustified film violence, we computed intersubject correlation (ISC) of fMRI BOLD time courses in a sample of late adolescents while they watched pairs of movie segments featuring violent characters prior to and during violent action. Based on a virtue-ethics approach that emphasizes motives in moral evaluation, we hypothesized significant ISC in lateral orbital frontal cortex (lOFC) and ventromedial prefrontal cortex (vmPFC) in response to unjustified and justified scenes of movie violence, respectively. Our predictions were confirmed. In addition, unjustified violence elicited greater intersubject synchrony in insular cortex, consistent with an empathic response to the pain experienced by victims of this kind of violence. The results provide evidence supporting the notion that lOFC and vmPFC play unique roles in moral evaluation of violence, with lOFC becoming more synchronous in response to unacceptable violence and vmPFC becoming more synchronous in response to virtuous forms of self-defense, thereby expanding the purview of current models that only focus on vmPFC. The results suggest that justified violence in popular movies is acceptable to youth who are accustomed to viewing such entertainment.

Processing pathways for emotional vocalizations.

Emotional sounds are processed within a large cortico-subcortical network, of which the auditory cortex, the voice area, and the amygdala are the core regions. Using 7T fMRI, we have compared the effect of emotional valence (positive, neutral, and negative) and the effect of the type of environmental sounds (human vocalizations and non-vocalizations) on neural activity within individual early stage auditory areas, the voice area, and the amygdala. A two-way ANOVA was applied to the BOLD time course within each ROI. In several early stage auditory areas, it yielded a significant main effect of vocalizations and of valence, but not a significant interaction. Significant interaction as well as significant main effects of vocalization and of valence were present in the voice area; the former was driven by a significant emotional modulation of vocalizations but not of other sounds. Within the amygdala, only the main effect of valence was significant. Post-hoc correlation analysis highlighted coupling between the voice area and early stage auditory areas during the presentation of any vocalizations, and between the voice area and the right amygdala during positive vocalizations. Thus, the voice area is selectively devoted to the encoding of the emotional valence of vocalizations; it shares with several early stage auditory areas encoding characteristics for vocalizations and with the amygdala for the emotional modulation of vocalizations. These results are indicative of a dual pathway, whereby the emotional modulation of vocalizations within the voice area integrates the input from the lateral early stage auditory areas and from the amygdala.

Functional anomaly mapping using BOLD time-courses reveals local and distant dysfunction caused by stroke (P5.6-002)

Functional anomaly mapping using BOLD time-courses reveals local and distant dysfunction caused by stroke (P5.6-002)

Mutual Information Better Quantifies Brain Network Architecture in Children with Epilepsy

Purpose Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ. Materials and Methods Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm2, resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition. Results Twenty-four patients met the inclusion criteria (age: 8–18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%). Conclusion Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation. Clinical Relevance Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers.

Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal

BackgroundfMRI provides spatial resolution that is unmatched by non-invasive neuroimaging techniques. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic signal. New MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed on single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect. ResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of mean BOLD amplitude differences. Using Monte-Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and single-subject levels, FWER was either at or significantly below 5%. We reached the desired power with 18 subjects and 12 trials for the group level, and with 14 trials in the single-subject scenario. Comparison with existing methodsWe compare the tMVPA statistical evaluation to that of a linear support vector machine (SVM). SVM outperformed tMVPA with large N and trial numbers. Conversely, tMVPA, leveraging on single trials analyses, outperformed SVM in low N and trials and in a single-subject scenario. ConclusionRecent evidence suggesting that the BOLD signal carries finer-grained temporal information than previously thought, advocates the need for analytical tools, such as tMVPA, tailored to investigate BOLD temporal dynamics. The comparable performance between tMVPA and SVM, a powerful and reliable tool for fMRI, supports the validity of our technique.

Thalamocortical connectivity in major depressive disorder

BackgroundMajor Depressive Disorder (MDD) is highly prevalent and potentially devastating, with widespread aberrations in brain activity. Thalamocortical networks are a potential candidate marker for psychopathology in MDD, but have not yet been thoroughly investigated. Here we examined functional connectivity between major cortical areas and thalamus. MethodResting-state fMRI from 54 MDD patients and 40 healthy controls were collected. The cortex was segmented into six regions of interest (ROIs) consisting of frontal, temporal, parietal, and occipital lobes and pre-central and post-central gyri. BOLD signal time courses were extracted from each ROI and correlated with voxels in thalamus, while removing signals from every other ROI. ResultsOur main findings showed that MDD patients had predominantly increased connectivity between medial thalamus and temporal areas, and between medial thalamus and somatosensory areas. Furthermore, a positive correlation was found between thalamo-temporal connectivity and severity of symptoms. LimitationsMost of the patients in this study were not medication naïve and therefore we cannot rule out possible long-term effects of antidepressant use on the findings. ConclusionThe abnormal connectivity between thalamus and temporal, and thalamus and somatosensory regions may represent impaired cortico-thalamo-cortical modulation underlying emotional, and sensory disturbances in MDD. In the context of similar abnormalities in thalamocortical systems across major psychiatric disorders, thalamocortical dysconnectivity could be a reliable transdiagnostic marker.

Structurofunctional resting-state networks correlate with motor function in chronic stroke

PurposeMotor function and recovery after stroke likely rely directly on the residual anatomical connections in the brain and its resting-state functional connectivity. Both structural and functional properties of cortical networks after stroke are revealed using multimodal magnetic resonance imaging (MRI). Specifically, functional connectivity MRI (fcMRI) can extract functional networks of the brain at rest, while structural connectivity can be estimated from white matter fiber orientations measured with high angular-resolution diffusion imaging (HARDI). A model that marries these two techniques may be the key to understanding functional recovery after stroke. In this study, a novel set of voxel-level measures of structurofunctional correlations (SFC) was developed and tested in a group of chronic stroke subjects.MethodsA fully automated method is presented for modeling the structure-function relationship of brain connectivity in individuals with stroke. Brains from ten chronic stroke subjects and nine age-matched controls were imaged with a structural T1-weighted scan, resting-state fMRI, and HARDI. Each subject's T1-weighted image was nonlinearly registered to a T1-weighted 152-brain MNI template using a local histogram-matching technique that alleviates distortions caused by brain lesions. Fractional anisotropy maps and mean BOLD images of each subject were separately registered to the individual's T1-weighted image using affine transformations. White matter fiber orientations within each voxel were estimated with the q-ball model, which approximates the orientation distribution function (ODF) from the diffusion-weighted measurements. Deterministic q-ball tractography was performed in order to obtain a set of fiber trajectories. The new structurofunctional correlation method assigns each voxel a new BOLD time course based on a summation of its structural connections with a common fiber length interval. Then, the voxel's original time-course was correlated with this fiber-distance BOLD signal to derive a novel structurofunctional correlation coefficient. These steps were repeated for eight fiber distance intervals, and the maximum of these correlations was used to define an intrinsic structurofunctional correlation (iSFC) index. A network-based SFC map (nSFC) was also developed in order to enhance resting-state functional networks derived from conventional functional connectivity analyses. iSFC and nSFC maps were individually compared between stroke subjects and controls using a voxel-based two-tailed Student's t-test (alpha = 0.01). A linear regression was also performed between the SFC metrics and the Box and Blocks Score, a clinical measure of arm motor function.ResultsSignificant decreases (p < 0.01) in iSFC were found in stroke subjects within functional hubs of the brain, including the precuneus, prefrontal cortex, posterior parietal cortex, and cingulate gyrus. Many of these differences were significantly correlated with the Box and Blocks Score. The nSFC maps of prefrontal networks in control subjects revealed localized increases within the cerebellum, and these enhancements were diminished in stroke subjects. This finding was further supported by a reduction in functional connectivity between the prefrontal cortex and cerebellum. Default-mode network nSFC maps were higher in the contralesional hemisphere of lower-functioning stroke subjects.ConclusionThe results demonstrate that changes after a stroke in both intrinsic and network-based structurofunctional correlations at rest are correlated with motor function, underscoring the importance of residual structural connectivity in cortical networks.

Functional Connectivity's Degenerate View of Brain Computation.

Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability.

Differentiating the functional contributions of resting connectivity networks to memory decision-making: fMRI support for multistage control processes.

Neural substrates of memory control are engaged when participants encounter unexpected mnemonic stimuli (e.g., a new word when told to expect an old word). The present fMRI study (n = 18) employed the likelihood cueing recognition task to elucidate the role of functional connectivity (fcMRI) networks in supporting memory control processes engaged by these unexpected events. Conventional task-evoked BOLD analyses recovered a memory control network similar to that previously reported, comprising medial prefrontal, lateral prefrontal, and inferior parietal regions. These were split by their differential affiliation to distinct fcMRI networks ("conflict detection" and "confirmatory retrieval" networks). Subsequent ROI analyses clarified the functional significance of this connectivity differentiation, with "conflict" network-affiliated regions specifically sensitive to cue strength, but not to response confidence, and "retrieval" network-affiliated regions showing the opposite pattern. BOLD time course analyses corroborated the segregation of memory control regions into "early" conflict detection and "late" retrieval analysis, with both processes underlying the allocation of memory control. Response specificity and time course findings were generalized beyond task-recruited ROIs to clusters within the large-scale fcMRI networks, suggesting that this connectivity architecture could underlie efficient processing of distinct processes within cognitive tasks. The findings raise important parallels between prevailing theories of memory and cognitive control.

Combining EPI and motion correction for fMRI human brain images with big motion.

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool. Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion.

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints.

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting-state FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks suggesting that they could be capturing consciousness-related processes such as mind wandering.

Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings.

IntroductionResponse inhibition is one of the executive functions impaired in attention-deficit/hyperactivity disorder (ADHD). Increasing evidence indicates that altered functional and structural neural connectivity are part of the neurobiological basis of ADHD. Here, we investigated if adolescents with ADHD show altered functional connectivity during response inhibition compared to their unaffected siblings and healthy controls.MethodsResponse inhibition was assessed using the stop signal paradigm. Functional connectivity was assessed using psycho-physiological interaction analyses applied to BOLD time courses from seed regions within inferior- and superior frontal nodes of the response inhibition network. Resulting networks were compared between adolescents with ADHD (N = 185), their unaffected siblings (N = 111), and controls (N = 125).ResultsControl subjects showed stronger functional connectivity than the other two groups within the response inhibition network, while subjects with ADHD showed relatively stronger connectivity between default mode network (DMN) nodes. Stronger connectivity within the response inhibition network was correlated with lower ADHD severity, while stronger connectivity with the DMN was correlated with increased ADHD severity. Siblings showed connectivity patterns similar to controls during successful inhibition and to ADHD subjects during failed inhibition. Additionally, siblings showed decreased connectivity with the primary motor areas as compared to both participants with ADHD and controls.DiscussionSubjects with ADHD fail to integrate activation within the response inhibition network and to inhibit connectivity with task-irrelevant regions. Unaffected siblings show similar alterations only during failed stop trials, as well as unique suppression of motor areas, suggesting compensatory strategies. These findings support the role of altered functional connectivity in understanding the neurobiology and familial transmission of ADHD.

Spin-glass model predicts metastable brain states that diminish in anesthesia.

Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states) reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm2 area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T = 0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep) and increased activation (hyper-activation) moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large-scale brain states was maximum, compatible with maximum information capacity—a presumed condition of consciousness.

Initiation of voluntary movements at free will and ongoing 0.1-Hz BOLD oscillations in the insula-a pilot study.

Recently we hypothesized that the intention to initiate a voluntary movement at free will may be related to the dynamics of hemodynamic variables, which may be supported by the intertwining of networks for the timing of voluntary movements and the control of cardiovascular variables in the insula. In the present study voluntary movements of 3 healthy subjects were analyzed using fMRI scans at 1.83-s intervals along with the time course of slow hemodynamic changes in sensorimotor networks. For the analyses of BOLD time courses the Wavelet transform coherence (WTC) and calculation of phase-locking values were used. Analyzed was the frequency band between 0.07 and 0.13 Hz in the supplementary motor area (SMA) and insula, two widely separated regions co-active in motor behavior. BOLD signals displayed slow fluctuations, concentrated around 0.1 Hz whereby the intrinsic oscillations in the insula preceded those in the SMA by 0.5–1 s. These preliminary results suggest that slow hemodynamic changes in SMA and insula may condition the initiation of a voluntary movement at free will.