Task-dependent organization of brain regions active during rest

Increased local synchronization of resting-state fMRI signal after episodic memory encoding reflects off-line memory consolidation

The changes of spontaneous activity from before and after a memory or learning task had been considered to be related to off-line memory consolidation process in human brain by using resting-state functional connectivity (RSFC) MRI (fMRI). However, RSFC reflects temporal synchronization of timecourses of spatially distinct brain regions and therefore could not determine which specific brain region is involved in the memory consolidation process. Here we used regional homogeneity (ReHo), a method for measuring local synchronization, to link the local spontaneous activity change to off-line episodic memory consolidation. We hypothesized that the spontaneous activity change would be different between people with better memory performance and those with worse performance in memory-related regions. All participants completed two resting-state sessions, that is, before (REST-1) and after (REST-2) an episodic memory encoding task (picture indoor or outdoor judgment). Then, based on the d' of a later surprise memory retrieval test, a high-performance group and a low-performance group, each consisting of 16 participants, were chosen from whole 58 participants. We defined a ReHo ratio, that is, ReHo of REST-2 divided by ReHo of REST-1, as a change induced by memory consolidation. The high-performance group showed a significant higher ReHo ratio than low-performance group in medial temporal lobe (MTL) including parahippocampal and anterior temporal regions. The current results provide neuroimaging evidence supporting that the MTL is involved in off-line memory consolidation of episodic memory. Moreover, this study may provide a paradigm for understanding of episodic memory deficit in Alzheimer's disease.

Functional and developmental significance of amplitude variance asymmetry in the BOLD resting-state signal

It is known that the brain's resting-state activity (RSA) is organized in low frequency oscillations that drive network connectivity. Recent research has also shown that elements of RSA described by high-frequency and nonoscillatory properties are non-random and functionally relevant. Motivated by this research, we investigated nonoscillatory aspects of the blood-oxygen-level-dependent (BOLD) RSA using a novel method for characterizing subtle fluctuation dynamics. The metric that we develop quantifies the relative variance of the amplitude of local-maxima and local-minima in a BOLD time course (amplitude variance asymmetry; AVA). This metric reveals new properties of RSA activity, without relying on connectivity as a descriptive tool. We applied the AVA analysis to data from 3 different participant groups (2 adults, 1 child) collected from 3 different centers. The analyses show that AVA patterns a) identify 3 types of RSA profiles in adults' sensory systems b) differ in topology and pattern of dynamics in adults and children, and c) are stable across magnetic resonance scanners. Furthermore, children with higher IQ demonstrated more adult-like AVA patterns. These findings indicate that AVA reflects important and novel dimensions of brain development and RSA.

Working memory-related changes in functional connectivity persist beyond task disengagement

We examined whether altered connectivity in functional networks during working memory performance persists following conclusion of that performance, into a subsequent resting state. We conducted functional magnetic resonance imaging (fMRI) in 50 young adults during an initial resting state, followed by an N-back working memory task and a subsequent resting state, in order to examine changes in functional connectivity within and between the default-mode network (DMN) and the task-positive network (TPN) across the three states. We found that alterations in connectivity observed during the N-back task persisted into the subsequent resting state within the TPN and between the DMN and TPN, but not within the DMN. Further, both speed of working memory performance and TPN connectivity strength during the N-back task predicted connectivity strength in the subsequent resting state. Finally, DMN connectivity measured before and during the N-back task predicted individual differences in self-reported inattentiveness, but this association was not found during the post-task resting state. Together, these findings have important implications for models of how the brain recovers following effortful cognition, as well as for experimental designs using resting and task scans.

In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention

Despite growing recognition that attention fluctuates from moment-to-moment during sustained performance, prevailing analysis strategies involve averaging data across multiple trials or time points, treating these fluctuations as noise. Here, using alternative approaches, we clarify the relationship between ongoing brain activity and performance fluctuations during sustained attention. We introduce a novel task (the gradual onset continuous performance task), along with innovative analysis procedures that probe the relationships between reaction time (RT) variability, attention lapses, and intrinsic brain activity. Our results highlight 2 attentional states-a stable, less error-prone state ("in the zone"), characterized by higher default mode network (DMN) activity but during which subjects are at risk of erring if DMN activity rises beyond intermediate levels, and a more effortful mode of processing ("out of the zone"), that is less optimal for sustained performance and relies on activity in dorsal attention network (DAN) regions. These findings motivate a new view of DMN and DAN functioning capable of integrating seemingly disparate reports of their role in goal-directed behavior. Further, they hold potential to reconcile conflicting theories of sustained attention, and represent an important step forward in linking intrinsic brain activity to behavioral phenomena.

Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually

A better understanding of how behavioral performance emerges from interacting brain systems may come from analysis of functional networks using functional magnetic resonance imaging. Recent studies comparing such networks with human behavior have begun to identify these relationships, but few have used a time scale small enough to relate their findings to variation within a single individual's behavior. In the present experiment we examined the relationship between a psychomotor vigilance task and the interacting default mode and task positive networks. Two time-localized comparative metrics were calculated: difference between the two networks' signals at various time points around each instance of the stimulus (peristimulus times) and correlation within a 12.3-s window centered at each peristimulus time. Correlation between networks was also calculated within entire resting-state functional imaging runs from the same individuals. These metrics were compared with response speed on both an intraindividual and an interindividual basis. In most cases, a greater difference or more anticorrelation between networks was significantly related to faster performance. While interindividual analysis showed this result generally, using intraindividual analysis it was isolated to peristimulus times 4 to 8 s before the detected target. Within that peristimulus time span, the effect was stronger for individuals who tended to have faster response times. These results suggest that the relationship between functional networks and behavior can be better understood by using shorter time windows and also by considering both intraindividual and interindividual variability.

Functional network connectivity during rest and task conditions: a comparative study

Functional connectivity (FC) examines temporal statistical dependencies among distant brain regions by means of seed-based analysis or independent component analysis (ICA). Spatial ICA also makes it possible to investigate FC at the network level, termed functional network connectivity (FNC). The dynamics of each network (ICA component), which may consist of several remote regions is described by the ICA time-course of that network; hence, FNC studies statistical dependencies among ICA time-courses. In this article, we compare comprehensively FNC in the resting state and during performance of an auditory oddball (AOD) task in 28 healthy subjects on relevant (nonartifactual) brain networks. The results show global FNC decrease during the performance of the task. In addition, we show that specific networks enlarge and/or demonstrate higher activity during the performance of the task. The results suggest that performing an active task like AOD may be facilitated by recruiting more neurons and higher activation of related networks rather than collaboration among different brain networks. We also evaluated the impact of temporal filtering on FNC analyses. Results showed that the results are not significantly affected by filtering.

The blue-collar brain

Much effort has gone into elucidating control of the body by the brain, less so the role of the body in controlling the brain. This essay develops the idea that the brain does a great deal of work in the service of behavior that is controlled by the body, a blue-collar role compared to the white-collar control exercised by the body. The argument that supports a blue-collar role for the brain is also consistent with recent discoveries clarifying the white-collar role of synergies across the body's tensegrity structure, and the evidence of critical phenomena in brain and behavior.

Memory stabilization with targeted reactivation during human slow-wave sleep

It is believed that neural representations of recent experiences become reactivated during sleep, and that this process serves to stabilize associated memories in long-term memory. Here, we initiated this reactivation process for specific memories during slow-wave sleep. Participants studied 50 object-location associations with object-related sounds presented concurrently. For half of the associations, the related sounds were re-presented during subsequent slow-wave sleep while participants underwent functional MRI. Compared with control sounds, related sounds were associated with increased activation of right parahippocampal cortex. Postsleep memory accuracy was positively correlated with sound-related activation during sleep in various brain regions, including the thalamus, bilateral medial temporal lobe, and cerebellum. In addition, postsleep memory accuracy was also positively correlated with pre- to postsleep changes in parahippocampal-medial prefrontal connectivity during retrieval of reactivated associations. Our results suggest that the brain is differentially activated by studied and unstudied sounds during deep sleep and that the thalamus and medial temporal lobe are involved in establishing the mnemonic consequences of externally triggered reactivation of associative memories.

The effects of sustained cognitive task performance on subsequent resting state functional connectivity in healthy young and middle-aged male schoolteachers

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.

Changes in the brain intrinsic organization in both on-task state and post-task resting state

The dynamic and robust characteristics of intrinsic functional connectivity of coherent spontaneous activity are critical for the brain functional stability and flexibility. Studies have demonstrated modulation of intrinsic connectivity within local spatial patterns during or after task performance, such as the default mode network (DMN) and task-specific networks. Moreover, recent studies have compared the global spatial pattern in different tasks or over time. However, it is still unclear how the large-scale intrinsic connectivity varies during and after a task. To better understand this issue, we conducted a functional MRI experiment over three sequential periods: an active semantic-matching task period and two rest periods, before and after the task respectively (namely, on-task state and pre-/post-task resting states), to detect task-driven effect on the dynamic large-scale intrinsic organization in both on-task state and post-task resting state. Three hierarchical levels were investigated, including (a) the whole brain small-world topology, (b) the whole pairwise functional connectivity patterns both within the DMN and between the DMN and other regions (i.e., the global/full DMN topography), and (c) the DMN nodal graph properties. The major findings are: (1) The large-scale small-world configuration of brain functional organization is robust, regardless of the behavioral state changing, while it varies adaptively with significantly higher local efficiency and lower global efficiency during the on-task state (P<0.05, Monte-Carlo corrected); (2) The DMN may be essentially engaged during both task and post-task processes with adaptively varied spatial patterns and nodal graph properties. The present study provides further insights into the robustness and plasticity of the brain intrinsic organization over states, which may be the basis of memory and learning in the brain.

Resting-state functional connectivity between fronto-parietal and default mode networks in obsessive-compulsive disorder

Background

Obsessive-compulsive disorder (OCD) is characterized by an excessive focus on upsetting or disturbing thoughts, feelings, and images that are internally-generated. Internally-focused thought processes are subserved by the “default mode network" (DMN), which has been found to be hyperactive in OCD during cognitive tasks. In healthy individuals, disengagement from internally-focused thought processes may rely on interactions between DMN and a fronto-parietal network (FPN) associated with external attention and task execution. Altered connectivity between FPN and DMN may contribute to the dysfunctional behavior and brain activity found in OCD.

Methods

The current study examined interactions between FPN and DMN during rest in 30 patients with OCD (17 unmedicated) and 32 control subjects (17 unmedicated). Timecourses from seven fronto-parietal seeds were correlated across the whole brain and compared between groups.

Results

OCD patients exhibited altered connectivity between FPN seeds (primarily anterior insula) and several regions of DMN including posterior cingulate cortex, medial frontal cortex, posterior inferior parietal lobule, and parahippocampus. These differences were driven largely by a reduction of negative correlations among patients compared to controls. Patients also showed greater positive connectivity between FPN and regions outside DMN, including thalamus, lateral frontal cortex, and somatosensory/motor regions.

Conclusions

OCD is associated with abnormal intrinsic functional connectivity between large-scale brain networks. Alteration of interactions between FPN and DMN at rest may contribute to aspects of the OCD phenotype, such as patients' inability to disengage from internally-generated scenarios and thoughts when performing everyday tasks requiring external attention.

Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI

This study aimed to identify subunits of the basal ganglia and thalamus and to investigate the functional connectivity among these anatomically segregated subdivisions and the cerebral cortex in healthy subjects. For this purpose, we introduced multilevel independent component analysis (ICA) of the resting-state functional magnetic resonance imaging (fMRI). After applying ICA to the whole brain gray matter, we applied second-level ICA restrictively to the basal ganglia and the thalamus area to identify discrete functional subunits of those regions. As a result, the basal ganglia and the thalamus were parcelled into 31 functional subdivisions according to their temporal activity patterns. The extracted parcels showed functional network connectivity between hemispheres, between subdivisions of the basal ganglia and thalamus, and between the extracted subdivisions and cerebral functional components. Grossly, these findings correspond to cortico-striato-thalamo-cortical circuits in the brain. This study also showed the utility of multilevel ICA of resting state fMRI in brain network research.

Default mode network activity and connectivity in psychopathology

Neuropsychiatric disorders are associated with abnormal function of the default mode network (DMN), a distributed network of brain regions more active during rest than during performance of many attention-demanding tasks and characterized by a high degree of functional connectivity (i.e., temporal correlations between brain regions). Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of the DMN is associated with better performance on attention-demanding tasks. In schizophrenia and depression, the DMN is often found to be hyperactivated and hyperconnected. In schizophrenia this may relate to overly intensive self-reference and impairments in attention and working memory. In depression, DMN hyperactivity may be related to negative rumination. These findings are considered in terms of what is known about psychological functions supported by the DMN, and alteration of the DMN in other neuropsychiatric disorders.

Maturation of task-induced brain activation and long range functional connectivity in adolescence revealed by multivariate pattern classification

The present study uses multivariate pattern classification analysis to examine maturation in task-induced brain activation and in functional connectivity during adolescence. The multivariate approach allowed accurate discrimination of adolescent boys of respectively 13, 17 and 21years old based on brain activation during a gonogo task, whereas the univariate statistical analyses showed no or only very few, small age-related clusters. Developmental differences in task activation were spatially distributed throughout the brain, indicating differences in the responsiveness of a wide range of task-related and default mode regions. Moreover, these distributed age-distinctive patterns generalized from a simple gonogo task to a cognitively and motivationally very different gambling task, and vice versa. This suggests that functional brain maturation in adolescence is driven by common processes across cognitive tasks as opposed to task-specific processes. Although we confirmed previous reports of age-related differences in functional connectivity, particularly for long range connections (>60mm), these differences were not specific to brain regions that showed maturation of task-induced responsiveness. Together with the task-independency of brain activation maturation, this result suggests that brain connectivity changes in the course of adolescence affect brain functionality at a basic level. This basic change is manifest in a range of tasks, from the simplest gonogo task to a complex gambling task.

Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction

The insular cortex is one of the brain regions that show consistent abnormalities in both structural and functional neuroimaging studies of schizophrenia. In healthy individuals, the insula has been implicated in a myriad of physiologic functions. The anterior cingulate cortex (ACC) and insula together constitute the salience network, an intrinsic large-scale network showing strong functional connectivity. Considering the insula as a functional unit along with the ACC provides an integrated understanding of the role of the insula in information processing. In this review, we bring together evidence from imaging studies to understand the role of the salience network in schizophrenia and propose a model of insular dysfunction in psychosis.

Neural Encoding of Objects Relevant for Navigation and Resting State Correlations with Navigational Ability

Objects along a route can help us to successfully navigate through our surroundings. Previous neuroimaging research has shown that the parahippocampal gyrus (PHG) distinguishes between objects that were previously encountered at navigationally relevant locations (decision points) and irrelevant locations (nondecision points) during simple object recognition. This study aimed at unraveling how this neural marking of objects relevant for navigation is established during learning and postlearning rest. Twenty-four participants were scanned using fMRI while they were viewing a route through a virtual environment. Eye movements were measured, and brain responses were time-locked to viewing each object. The PHG showed increased responses to decision point objects compared with nondecision point objects during route learning. We compared functional connectivity between the PHG and the rest of the brain in a resting state scan postlearning with such a scan prelearning. Results show that functional connectivity between the PHG and the hippocampus is positively related to participants' self-reported navigational ability. On the other hand, connectivity with the caudate nucleus correlated negatively with navigational ability. These results are in line with a distinction between egocentric and allocentric spatial representations in the caudate nucleus and the hippocampus, respectively. Our results thus suggest a relation between navigational ability and a neural preference for a specific type of spatial representation. Together, these results show that the PHG is immediately involved in the encoding of navigationally relevant object information. Furthermore, they provide insight into the neural correlates of individual differences in spatial ability.

Hemispheric asymmetry of visual scene processing in the human brain: evidence from repetition priming and intrinsic activity

Asymmetrical specialization of cognitive processes across the cerebral hemispheres is a hallmark of healthy brain development and an important evolutionary trait underlying higher cognition in humans. While previous research, including studies of priming, divided visual field presentation, and split-brain patients, demonstrates a general pattern of right/left asymmetry of form-specific versus form-abstract visual processing, little is known about brain organization underlying this dissociation. Here, using repetition priming of complex visual scenes and high-resolution functional magnetic resonance imaging (MRI), we demonstrate asymmetrical form specificity of visual processing between the right and left hemispheres within a region known to be critical for processing of visual spatial scenes (parahippocampal place area [PPA]). Next, we use resting-state functional connectivity MRI analyses to demonstrate that this functional asymmetry is associated with differential intrinsic activity correlations of the right versus left PPA with regions critically involved in perceptual versus conceptual processing, respectively. Our results demonstrate that the PPA comprises lateralized subregions across the cerebral hemispheres that are engaged in functionally dissociable yet complementary components of visual scene analysis. Furthermore, this functional asymmetry is associated with differential intrinsic functional connectivity of the PPA with distinct brain areas known to mediate dissociable cognitive processes.

Motor response selection in overt sentence production: a functional MRI study

Many different cortical areas are thought to be involved in the process of selecting motor responses, from the inferior frontal gyrus, to the lateral and medial parts of the premotor cortex. The objective of the present study was to examine the neural underpinnings of motor response selection in a set of overt language production tasks. To this aim, we compared a sentence repetition task (externally constrained selection task) with a sentence generation task (volitional selection task) in a group of healthy adults. In general, the results clarify the contribution of the pre-SMA, cingulate areas, PMv, and pars triangularis to the process of selecting motor responses in the context of sentence production, and shed light on the manner in which this network is modulated by selection mode. Further, the present study suggests that response selection in sentence production engages neural resources similar to those engaged in the production of isolated words and oral motor gestures.

Functional differentiation of posterior superior temporal sulcus in autism: a functional connectivity magnetic resonance imaging study

BACKGROUND

Socio-communicative impairments are salient features of autism spectrum disorder (ASD). Abnormal development of posterior superior temporal sulcus (pSTS)--a key processing area for language, biological motion, and social context--could play a role in these deficits.

METHODS

Functional connectivity magnetic resonance imaging was used to examine the synchronization of low-frequency blood oxygen level-dependent fluctuations during continuous performance on a visual search task. Twenty-one children and adolescents with ASD and 26 typically developing individuals-matched on age and IQ-participated in the study. Three subregions of pSTS were delineated with a data-driven approach, and differentiation of pSTS was examined by comparing the connectivity of each subregion.

RESULTS

In typically developing individuals, differentiation of networks was positively associated with age and anatomical maturation (cortical thinning in pSTS, greater white matter volume). In the ASD group, differentiation of pSTS connectivity was significantly reduced, and correlations with anatomical measures were weak or absent. Moreover, pSTS differentiation was inversely correlated with autism symptom severity.

CONCLUSIONS

Atypical maturation of pSTS suggests altered trajectories for functional segregation and integration of networks in ASD, potentially related to impaired cognitive and sensorimotor development. Furthermore, our findings provide a novel explanation for atypically increased connectivity in ASD that has been observed in some functional connectivity magnetic resonance imaging studies.

Can resting-state functional MRI serve as a complement to task-based mapping of sensorimotor function? A test-retest reliability study in healthy volunteers

PURPOSE

To investigate if resting-state functional MRI (fMRI) reliably can serve as a complement to task-based fMRI for presurgical mapping of the sensorimotor cortex.

MATERIALS AND METHODS

Functional data were obtained in 10 healthy volunteers using a 3 Tesla MRI system. Each subject performed five bilateral finger tapping experiments interleaved with five resting-state experiments. Following preprocessing, data from eight volunteers were further analyzed with the general linear model (finger tapping data) and independent component analysis (rest data). Test-retest reliability estimates (hit rate and false alarm rate) for resting-state fMRI activation of the sensorimotor network were compared with the reliability estimates for task-evoked activation of the sensorimotor cortex. The reliability estimates constituted a receiver operating characteristics curve from which the area under the curve (AUC) was calculated. Statistical testing was performed to compare the two groups with respect to reliability.

RESULTS

The AUC was generally higher for the task experiments, although median AUC was not significantly different on a group level. Also, the two groups showed comparable levels of within-group variance.

CONCLUSION

Test-retest reliability was comparable between resting-state measurements and task-based fMRI, suggesting that presurgical mapping of functional networks can be a supplement to task-based fMRI in cases where patient status excludes task-based fMRI.

Concepts and principles in the analysis of brain networks

The brain is a large-scale network, operating at multiple levels of information processing ranging from neurons, to local circuits, to systems of brain areas. Recent advances in the mathematics of graph theory have provided tools with which to study networks. These tools can be employed to understand how the brain's behavioral repertoire is mediated by the interactions of objects of information processing. Within the graph-theoretic framework, networks are defined by independent objects (nodes) and the relationships shared between them (edges). Importantly, the accurate incorporation of graph theory into the study of brain networks mandates careful consideration of the assumptions, constraints, and principles of both the mathematics and the underlying neurobiology. This review focuses on understanding these principles and how they guide what constitutes a brain network and its elements, specifically focusing on resting-state correlations in humans. We argue that approaches that fail to take the principles of graph theory into consideration and do not reflect the underlying neurobiological properties of the brain will likely mischaracterize brain network structure and function.

Category-selective background connectivity in ventral visual cortex

Ventral visual cortex contains specialized regions for particular object categories, but little is known about how these regions interact during object recognition. Here we examine how the face-selective fusiform gyrus (FG) and the scene-selective parahippocampal cortex (PHC) interact with each other and with the rest of the brain during different visual tasks. To assess these interactions, we developed a novel approach for identifying patterns of connectivity associated with specific task sets, independent of stimulus-evoked responses. We tested whether this "background connectivity" between the FG and PHC was modulated when subjects engaged in face and scene processing tasks. In contrast to what would be predicted from biased competition or intrinsic activity accounts, we found that the strength of FG-PHC background connectivity depended on which category was task relevant: connectivity increased when subjects attended to scenes (irrespective of whether a competing face was present) and decreased when subjects attended to faces (irrespective of competing scenes). We further discovered that posterior occipital cortex was correlated selectively with the FG during face tasks and the PHC during scene tasks. These results suggest that category specificity exists not only in which regions respond most strongly but also in how these and other regions interact.

Resting-state brain networks: literature review and clinical applications

This review focuses on resting-state functional connectivity, a functional MRI technique which allows the study of spontaneous brain activity generated under resting conditions. This approach is useful to explore the brain's functional organization and to examine if it is altered in neurological or psychiatric diseases. Resting-state functional connectivity has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity. In this review, we examine the behavioral, physiological and neurological evidences relevant to this coherent brain activity and, in particular, to each network. The investigation of functional connectivity appears promising from a clinical perspective, considering the amount of evidence regarding the importance of spontaneous activity and that resting-state paradigms are inherently simple to implement. We also discuss some examples of existing clinical applications, such as in Alzheimer's disease, and emerging possibilities such as in pre-operative mapping and disorders of consciousness.

Functional connectivity during light sleep is correlated with memory performance for face-location associations

The consolidation of declarative memories benefits from sleep. The neural mechanisms involved in sleep-dependent consolidation, however, are largely unknown. Here, we used a combination of functional magnetic resonance imaging, polysomnography and a face-location associative memory task to target neural connectivity of a face sensitive area during an afternoon nap. Fusiform connectivity was substantially greater during sleep stage 1 than in wake in a network extending from early visual areas bilaterally to the fusiform gyrus, ventrally and into the posterior parietal cortices, dorsally. In sleep stage 2, fusiform connectivity was found to be larger in the precuneus, bilateral middle temporal gyrus and medial prefrontal cortex. Specific functional connectivity increases observed during light sleep were positively correlated with memory performance for face-location associations. A distinction could be made between fusiform-medial prefrontal connectivity during sleep stage 1 and 2 that was positively correlated with retention of associations learned prior to sleep and fusiform-hippocampal connectivity during sleep stage 1 that was correlated with better acquisition of new associations learned after sleep. Our results suggest that fusiform-medial prefrontal connectivity during sleep has a stabilizing effect on recently learned associative memories, possibly due to the existence of a task-related schema that allows rapid consolidation of related information. Our data further indicate that sleep-dependent connectivity between the fusiform gyrus and hippocampus correlated with new learning after sleep. Thus, our study provides correlational evidence for the behavioral relevance of specific medial prefrontal and hippocampal interactions with the fusiform gyrus during light sleep.

Interpretation-mediated changes in neural activity during language comprehension

Using functional magnetic resonance imaging (fMRI), we identified cortical regions mediating interpretive processes that take place during language comprehension. We manipulated participants' interpretation of texts by asking them to focus on action-, space-, or time-related features while listening to identical short stories. We identify several cortical regions where activity varied significantly in response to this attention manipulation, even though the content being processed was exactly the same. Activity in the posterior and anterior sections of the left inferior frontal gyrus (IFG), which are thought to have different sensitivities to high-level language processing, was modulated by the listeners' attentional focus, but in ways that were quite different. The posterior left IFG (Pars Opercularis) showed different activity levels for the three conditions. However, a population coding analysis demonstrated similar distributions of activity across conditions. This suggests that while the gain of the response in the Pars Opercularis was modulated, its core organization was relatively invariant across the experimental conditions. In the anterior left IFG (Pars Triangularis), the analysis of population codes revealed different activity patterns between conditions: there was little similarity between activity during time-attention and action- and space-attention, however there were similar activity patterns while attending to space and action information. In addition, both the left superior temporal gyrus and sulcus showed greater activity in the space and action attention conditions when contrasted with time attention. We discuss these findings in light of work on the role of left IFG in processing semantic information in language, and in light of theories suggesting that temporal information in language is processed in the brain using similar mechanisms as spatial information. Our findings suggest that a substantial source of variance in neural activity during language comprehension emerges from the internally-driven, information-seeking preferences of listeners rather than the syntactic or semantic properties of a text.

Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect?

The simultaneous recording of the activity of many neurons poses challenges for multivariate data analysis. Here, we propose a general scheme of reconstruction of the functional network from spike train recordings. Effective, causal interactions are estimated by fitting generalized linear models on the neural responses, incorporating effects of the neurons’ self-history, of input from other neurons in the recorded network and of modulation by an external stimulus. The coupling terms arising from synaptic input can be transformed by thresholding into a binary connectivity matrix which is directed. Each link between two neurons represents a causal influence from one neuron to the other, given the observation of all other neurons from the population. The resulting graph is analyzed with respect to small-world and scale-free properties using quantitative measures for directed networks. Such graph-theoretic analyses have been performed on many complex dynamic networks, including the connectivity structure between different brain areas. Only few studies have attempted to look at the structure of cortical neural networks on the level of individual neurons. Here, using multi-electrode recordings from the visual system of the awake monkey, we find that cortical networks lack scale-free behavior, but show a small, but significant small-world structure. Assuming a simple distance-dependent probabilistic wiring between neurons, we find that this connectivity structure can account for all of the networks’ observed small-world ness. Moreover, for multi-electrode recordings the sampling of neurons is not uniform across the population. We show that the small-world-ness obtained by such a localized sub-sampling overestimates the strength of the true small-world structure of the network. This bias is likely to be present in all previous experiments based on multi-electrode recordings.

Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state

Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages ("rBeta") can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas.

A neuronal basis for task-negative responses in the human brain

Neuroimaging studies have revealed a number of brain regions that show a reduced blood oxygenation level–dependent (BOLD) signal during externally directed tasks compared with a resting baseline. These regions constitute a network whose operation has become known as the default mode. The source of functional magnetic resonance imaging (fMRI) signal reductions in the default mode during task performance has not been resolved, however. It may be attributable to neuronal effects (neuronal firing), physiological effects (e.g., task vs. rest differences in respiration rate), or even increases in neuronal activity with an atypical blood response. To establish the source of signal decreases in the default mode, we used the calibrated fMRI method to quantify changes in the cerebral metabolic rate of oxygen (CMRO₂) and cerebral blood flow (CBF) in those regions that typically show reductions in BOLD signal during a demanding cognitive task. CBF:CMRO₂ coupling during task-negative responses were linear, with a coupling constant similar to that in task-positive regions, indicating a neuronal source for signal reductions in multiple brain areas. We also identify, for the first time, two modes of neuronal activity in this network; one in which greater deactivation (characterized by metabolic rate reductions) is associated with more effort and one where it is associated with less effort.

The relation of ongoing brain activity, evoked neural responses, and cognition

Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a "segregationist" view on ongoing activity, both in time and space, which would selectively associate certain frequency bands or levels of spatial organization with specific functional roles. Instead, we emphasize the functional importance of the full range, from differentiation to integration, of intrinsic activity within a hierarchical spatiotemporal structure. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which - applied to human brain function - provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behavior.

Atypical network connectivity for imitation in autism spectrum disorder

Imitation has been considered as one of the precursors for sociocommunicative development. Impairments of imitation in autism spectrum disorder (ASD) could be indicative of dysfunctional underlying neural processes. Neuroimaging studies have found reduced activation in areas associated with imitation, but a functional connectivity MRI network perspective of these regions in autism is unavailable. Functional and effective connectivity was examined in 14 male participants with ASD and 14 matched typically developing (TD) participants. We analyzed intrinsic, low-frequency blood oxygen level dependent (BOLD) fluctuations of three regions in literature found to be associated with imitation (inferior frontal gyrus [IFG], inferior parietal lobule [IPL], superior temporal sulcus [STS]). Direct group comparisons did not show significantly reduced functional connectivity within the imitation network in ASD. Conversely, we observed greater connectivity with frontal regions, particularly superior frontal and anterior cingulate gyri, in the ASD compared to TD group. Structural equation modeling of effective connectivity revealed a significantly reduced effect of IPL on IFG together with an increased influence of a region in dorsal prefrontal cortex (dPFC) on IFG in the ASD group. Our results suggest atypical connectivity of the imitation network with an enhanced role of dPFC, which may relate to behavioral impairments.

What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders?

PURPOSE OF REVIEW

Resting-state functional MRI (rs-fMRI) is an increasingly popular technique for studying brain dysfunction in psychiatric patients, and is widely assumed to measure intrinsic properties of functional brain organization. Here, we review rs-fMRI studies of psychiatric populations and consider how recent evidence concerning the neuronal basis, behavioural relevance, and the stability of rs-fMRI measures can inform and constrain interpretation of findings obtained using case-control designs.

RECENT FINDINGS

A range of rs-fMRI measures have been applied to different patient groups, although the findings have not always been consistent. The large-scale organization of rs-fMRI networks is robust and reproducible, and rs-fMRI measures show correlations with behavioural phenotypes relevant to psychiatry. However, evidence that such measures are also influenced by preceding psychological states and contexts, as well as individual variations in physiological arousal, may help to explain inconsistent findings in case-control comparisons.

SUMMARY

rs-fMRI measures show both stable and dynamic properties, the nature of which are only beginning to be uncovered. As such, interpreting significant differences between patients and controls on rs-fMRI measures as evidence for alterations in intrinsic functional brain organization should be done cautiously. Better understanding of the relationship between stable and transient aspects of spontaneous brain dynamics will be necessary to constrain interpretation of case-control studies and inform pathophysiological models.

Regional homogeneity of fMRI time series in autism spectrum disorders

Functional magnetic resonance imaging (fMRI) and functional connectivity MRI (fcMRI) studies of autism spectrum disorders (ASD) have suggested atypical patterns of activation and long-distance connectivity for diverse tasks and networks in ASD. We explored the regional homogeneity (ReHo) approach in ASD, which is analogous to conventional fcMRI, but focuses on local connectivity. FMRI data of 26 children with ASD and 29 typically developing (TD) children were acquired during continuous task performance (visual search). Effects of motion and task were removed and Kendall's coefficient of concordance (KCC) was computed, based on the correlation of the blood oxygen level dependent (BOLD) time series for each voxel and its six nearest neighbors. ReHo was lower in the ASD than the TD group in superior parietal and anterior prefrontal regions. Inverse effects of greater ReHo in the ASD group were detected in lateral and medial temporal regions, predominantly in the right hemisphere. Our findings suggest that ReHo is a sensitive measure for detecting cortical abnormalities in autism. However, impact of methodological factors (such as spatial resolution) on ReHo require further investigation.

Advances and pitfalls in the analysis and interpretation of resting-state FMRI data

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

Persistent schema-dependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans

The hippocampus is thought to promote gradual incorporation of novel information into long-term memory by binding, reactivating, and strengthening distributed cortical-cortical connections. Recent studies implicate a key role in this process for hippocampally driven crosstalk with the (ventro)medial prefrontal cortex (vmPFC), which is proposed to become a central node in such representational networks over time. The existence of a relevant prior associative network, or schema, may moreover facilitate this process. Thus, hippocampal-vmPFC crosstalk may support integration of new memories, particularly in the absence of a relevant prior schema. To address this issue, we used functional magnetic resonance imaging (fMRI) and prior schema manipulation to track hippocampal-vmPFC connectivity during encoding and postencoding rest. We manipulated prior schema knowledge by exposing 30 participants to the first part of a movie that was temporally scrambled for 15 participants. The next day, participants underwent fMRI while encoding the movie's final 15 min in original order and, subsequently, while resting. Schema knowledge and item recognition performance show that prior schema was successfully and selectively manipulated. Intersubject synchronization (ISS) and interregional partial correlation analyses furthermore show that stronger prior schema was associated with more vmPFC ISS and less hippocampal-vmPFC interregional connectivity during encoding. Notably, this connectivity pattern persisted during postencoding rest. These findings suggest that additional crosstalk between hippocampus and vmPFC is required to compensate for difficulty integrating novel information during encoding and provide tentative support for the notion that functionally relevant hippocampal-neocortical crosstalk persists during off-line periods after learning.

Enhanced brain correlations during rest are related to memory for recent experiences

Long-term storage of episodic memories is hypothesized to result from the off-line transfer of information from the hippocampus to neocortex, allowing a hippocampal-independent cortical representation to emerge. However, off-line hippocampal-cortical interactions have not been demonstrated to be linked with long-term memory. Here, using functional magnetic resonance imaging, we examined if hippocampal-cortical BOLD correlations during rest following an associative encoding task are related to later associative memory performance. Our data show enhanced functional connectivity between the hippocampus and a portion of the lateral occipital complex (LO) during rest following a task with high subsequent memory compared to pretask baseline resting connectivity. This effect is not seen during rest following a task with poor subsequent memory. Furthermore, the magnitude of hippocampal-LO correlations during posttask rest predicts individual differences in later associative memory. These results demonstrate the importance of postexperience resting brain correlations for memory for recent experiences.

Intrinsic connectivity between the hippocampus and posteromedial cortex predicts memory performance in cognitively intact older individuals

Coherent fluctuations of spontaneous brain activity are present in distinct functional-anatomic brain systems during undirected wakefulness. However, the behavioral significance of this spontaneous activity has only begun to be investigated. Our previous studies have demonstrated that successful memory formation requires coordinated neural activity in a distributed memory network including the hippocampus and posteromedial cortices, specifically the precuneus and posterior cingulate (PPC), thought to be integral nodes of the default network. In this study, we examined whether intrinsic connectivity during the resting state between the hippocampus and PPC can predict individual differences in the performance of an associative memory task among cognitively intact older individuals. The intrinsic connectivity, between regions within the hippocampus and PPC that were maximally engaged during a subsequent memory fMRI task, was measured during a period of rest prior to the performance of the memory paradigm. Stronger connectivity between the hippocampal and posteromedial regions during rest predicted better performance on the memory task. Furthermore, hippocampal-PPC intrinsic connectivity was also significantly correlated with episodic memory measures on neuropsychological tests, but not with performance in non-memory domains. Whole-brain exploratory analyses further confirmed the spatial specificity of the relationship between hippocampal-default network posteromedial cortical connectivity and memory performance in older subjects. Our findings provide support for the hypothesis that one of the functions of this large-scale brain network is to subserve episodic memory processes. Research is ongoing to determine if impaired connectivity between these regions may serve as a predictor of memory decline related to early Alzheimer's disease.

Reading networks at rest

Resting-state functional connectivity (RSFC) approaches offer a novel tool to delineate distinct functional networks in the brain. In the present functional magnetic resonance imaging (fMRI) study, we elucidated patterns of RSFC associated with 6 regions of interest selected primarily from a meta-analysis on word reading (Bolger DJ, Perfetti CA, Schneider W. 2005. Cross-cultural effect on the brain revisited: universal structures plus writing system variation. Hum Brain Mapp. 25: 92-104). In 25 native adult readers of English, patterns of positive RSFC were consistent with patterns of task-based activity and functional connectivity associated with word reading. Moreover, conjunction analyses highlighted the posterior left inferior frontal gyrus and the posterior left middle temporal gyrus (post-LMTG) as potentially important loci of functional interaction among 5 of the 6 reading networks. The significance of the post-LMTG has typically been unappreciated in task-based studies on unimpaired readers but is frequently reported to be a locus of hypoactivity in dyslexic readers and exhibits intervention-induced changes of activity in dyslexic children. Finally, patterns of negative RSFC included not only regions of the so-called default mode network but also regions involved in effortful controlled processes, which may not be required once reading becomes automatized. In conclusion, the current study supports the utility of resting-state fMRI for investigating reading networks and has direct relevance for the understanding of reading disorders such as dyslexia.

Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity

The resting brain exhibits coherent patterns of spontaneous low-frequency BOLD fluctuations. These so-called resting-state functional connectivity (RSFC) networks are posited to reflect intrinsic representations of functional systems commonly implicated in cognitive function. Yet, the direct relationship between RSFC and the BOLD response induced by task performance remains unclear. Here we examine the relationship between a region's pattern of RSFC across participants and that same region's level of BOLD activation during an Eriksen Flanker task. To achieve this goal we employed a voxel-matched regression method, which assessed whether the magnitude of task-induced activity at each brain voxel could be predicted by measures of RSFC strength for the same voxel, across 26 healthy adults. We examined relationships between task-induced activation and RSFC strength for six different seed regions [Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673-9678.], as well as the "default mode" and "task-positive" resting-state networks in their entirety. Our results indicate that, for a number of brain regions, inter-individual differences in task-induced BOLD activity were predicted by one of two resting-state properties: (1) the region's positive connectivity strength with the task-positive network, or (2) its negative connectivity with the default mode network. Strikingly, most of the regions exhibiting a significant relationship between their RSFC properties and task-induced BOLD activity were located in transition zones between the default mode and task-positive networks. These results suggest that a common mechanism governs many brain regions' neural activity during rest and its neural activity during task performance.

Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.

Correlated low-frequency BOLD fluctuations in the resting human brain are modulated by recent experience in category-preferential visual regions

The resting brain is associated with significant intrinsic activity fluctuations, such as the correlated low-frequency (LF) blood oxygen level-dependent (BOLD) fluctuations measured by functional magnetic resonance imaging. Despite a recent expansion of studies investigating resting-state LF-BOLD correlations, their nature and function are poorly understood. A major constraint on LF-BOLD correlations appears to be stable properties of anatomic connectivity. There is also evidence that coupling can be modulated by recent or ongoing task performance, suggesting that certain components of correlated dynamics are malleable on short timescales. Here, we compared activity during extended periods of rest following performance of 2 distinct cognitive tasks using different categories of visual stimuli-faces and complex scenes. Prolonged exposure to these distinct categories of visual information caused frontal networks to couple differentially with posterior category-preferential visual regions during subsequent periods of rest. In addition, we report preliminary evidence suggesting that conditions exist in which the degree of modulation of LF-BOLD correlations predicts subsequent memory. The finding that resting-state LF-BOLD correlations are modulated by recent experience in functionally specific brain regions engaged during prior task performance clarifies their role as a dynamic phenomenon which may be involved in mnemonic processes.

Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders

Autism spectrum disorders (ASD) are associated with disturbances of neural connectivity. Functional connectivity between neural structures is typically examined within the context of a cognitive task, but also exists in the absence of a task (i.e., "rest"). Connectivity during rest is particularly active in a set of structures called the default network, which includes the posterior cingulate cortex (PCC), retrosplenial cortex, lateral parietal cortex/angular gyrus, medial prefrontal cortex, superior frontal gyrus, temporal lobe, and parahippocampal gyrus. We previously reported that adults with ASD relative to controls show areas of stronger and weaker connectivity within the default network. The objective of the present study was to examine the default network in adolescents with ASD. Sixteen adolescents with ASD and 15 controls participated in a functional MRI study. Functional connectivity was examined between a PCC seed and other areas of the default network. Both groups showed connectivity in the default network. Relative to controls, adolescents with ASD showed widespread weaker connectivity in nine of the eleven areas of the default network. Moreover, an analysis of symptom severity indicated that poorer social skills and increases in restricted and repetitive behaviors and interests correlated with weaker connectivity, whereas poorer verbal and non-verbal communication correlated with stronger connectivity in multiple areas of the default network. These findings indicate that adolescents with ASD show weaker connectivity in the default network than previously reported in adults with ASD. The findings also show that weaker connectivity within the default network is associated with specific impairments in ASD.

Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework

Functional magnetic resonance imaging (fMRI) has led to an enormous growth in the study of cognitive neuroanatomy, and combined with advances in high-field electrophysiology (and other methods), has led to a fast-growing field of human neuroscience. Technological advances in both hardware and software will lead to an ever more promising future for fMRI. We have developed a new computational framework that facilitates fMRI experimentation and analysis, and which has led to some rethinking of the nature of experimental design and analysis. The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data. By using CNARI, it is possible to perform naturalistic, network-based, statistically valid experiments in systems neuroscience on a very large scale, with ease of data manipulation and analysis, within reasonable computational time scales. In this article, we describe this infrastructure and then illustrate its use on a number of actual examples in both cognitive neuroscience and neurological research. We believe that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.