Resting state networks and memory consolidation

3T vs. 7T fMRI: capturing early human memory consolidation after motor task utilizing the observed higher functional specificity of 7T

Objective

Functional magnetic resonance imaging (fMRI) visualizes brain structures at increasingly higher resolution and better signal-to-noise ratio (SNR) as field strength increases. Yet, mapping the blood oxygen level dependent (BOLD) response to distinct neuronal processes continues to be challenging. Here, we investigated the characteristics of 7 T-fMRI compared to 3 T-fMRI in the human brain beyond the effect of increased SNR and verified the benefits of 7 T-fMRI in the detection of tiny, highly specific modulations of functional connectivity in the resting state following a motor task.

Methods

18 healthy volunteers underwent two resting state and a stimulus driven measurement using a finger tapping motor task at 3 and 7 T, respectively. The SNR for each field strength was adjusted by targeted voxel size variation to minimize the effect of SNR on the field strength specific outcome. Spatial and temporal characteristics of resting state ICA, network graphs, and motor task related activated areas were compared. Finally, a graph theoretical approach was used to detect resting state modulation subsequent to a simple motor task.

Results

Spatial extensions of resting state ICA and motor task related activated areas were consistent between field strengths, but temporal characteristics varied, indicating that 7 T achieved a higher functional specificity of the BOLD response than 3 T-fMRI. Following the motor task, only 7 T-fMRI enabled the detection of highly specific connectivity modulations representing an "offline replay" of previous motor activation. Modulated connections of the motor cortex were directly linked to brain regions associated with memory consolidation.

Conclusion

These findings reveal how memory processing is initiated even after simple motor tasks, and that it begins earlier than previously shown. Thus, the superior capability of 7 T-fMRI to detect subtle functional dynamics promises to improve diagnostics and therapeutic assessment of neurological diseases.

Heksor: The CNS substrate of an adaptive behavior

Over the past half‐century, the largely hardwired central nervous system (CNS) of 1970 has become the ubiquitously plastic CNS of today, in which change is the rule not the exception. This transformation complicates a central question in neuroscience: how are adaptive behaviours – behaviours that serve the needs of the individual – acquired and maintained through life? It poses a more basic question: how do many adaptive behaviours share the ubiquitously plastic CNS? This question compels neuroscience to adopt a new paradigm. The core of this paradigm is a CNS entity with unique properties, here given the name heksor from the Greek hexis. A heksor is a distributed network of neurons and synapses that changes itself as needed to maintain the key features of an adaptive behaviour, the features that make the behaviour satisfactory. Through their concurrent changes, the numerous heksors that share the CNS negotiate the properties of the neurons and synapses that they all use. Heksors keep the CNS in a state of negotiated equilibrium that enables each heksor to maintain the key features of its behaviour. The new paradigm based on heksors and the negotiated equilibrium they create is supported by animal and human studies of interactions among new and old adaptive behaviours, explains otherwise inexplicable results, and underlies promising new approaches to restoring behaviours impaired by injury or disease. Furthermore, the paradigm offers new and potentially important answers to extant questions, such as the generation and function of spontaneous neuronal activity, the aetiology of muscle synergies, and the control of homeostatic plasticity.

Resting-state functional connectivity predicts recovery from visually induced motion sickness

Movies depicting certain types of motion often provoke uncomfortable symptoms similar to motion sickness, termed visually induced motion sickness (VIMS). VIMS generally evolves slowly during the viewing of a motion stimulus and, when the stimulus is removed, the recovery proceeds over time. Recent human neuroimaging studies have provided new insights into the neural bases of the evolution of VIMS. In contrast, no study has investigated the neural correlates of the recovery from VIMS. Study of the recovery process is critical for the development of a way to promote recovery and could provide further clues for understanding the mechanisms of VIMS. We thus investigated brain activity during the recovery from VIMS with functional connectivity magnetic resonance imaging. We found enhanced recovery-related functional connectivity patterns involving brain areas such as the insular, cingulate and visual cortical regions, which have been suggested to play important roles in the emergence of VIMS. These regions also constituted large interactive networks. Furthermore, the increase in functional connectivity was correlated with the subjective awareness of recovery for the following five pairs of brain regions: insula-superior temporal gyrus, claustrum-left and right inferior parietal lobules, claustrum-superior temporal gyrus and superior frontal gyrus-lentiform nucleus. Considering the previous findings on the functions of these regions and the present results, it is suggested that the increase in FC may reflect brain processes such as enhanced interoceptive awareness to one's own bodily state, a neuroplastic change in visual-processing circuits and/or the maintenance of visual spatial memory.

How to optimize knowledge construction in the brain

Well-structured knowledge allows us to quickly understand the world around us and make informed decisions to adequately control behavior. Knowledge structures, or schemas, are presumed to aid memory encoding and consolidation of new experiences so we cannot only remember the past, but also guide behavior in the present and predict the future. However, very strong schemas can also lead to unwanted side effects such as false memories and misconceptions. To overcome this overreliance on a schema, we should aim to create robust schemas that are on the one hand strong enough to help to remember and predict, but also malleable enough to avoid such undesirable side effects. This raises the question as to whether there are ways to deliberately influence knowledge construction processes, with the goal to reach such optimally balanced schemas. Here, we will discuss how the mnemonic processes in our brains build long-term knowledge and, more specifically, how different phases of memory formation (encoding, consolidation, retrieval, and reconsolidation) contribute to this schema build-up. We finally provide ways how to best keep a balance between generalized semantic and detailed episodic memories, which can prove very useful in, e.g., educational settings.

Exploring resting-state functional connectivity invariants across the lifespan in healthy people by means of a recently proposed graph theoretical model

In this paper we investigate the changes in the functional connectivity intensity, and some related properties, in healthy people, across the life span and at resting state. For the explicit computation of the functional connectivity we exploit a recently proposed model, that bases not only on the correlations data provided by the acquisition equipment, but also on different parameters, such as the anatomical distances between nodes and their degrees. The leading purpose of the paper is to show that the proposed approach is able to recover the main aspects of resting state condition known from the available literature, as well as to suggest new insights, perspectives and speculations from a neurobiological point of view. Our study involves 133 subjects, both males and females of different ages, with no evidence of neurological diseases or systemic disorders. First, we show how the model applies to the sample, where the subjects are grouped into 28 different groups (14 of males and 14 of females), according to their age. This leads to the construction of two graphs (one for males and one for females), that can be realistically interpreted as representative of the neural network during the resting state. Second, following the idea that the brain network is better understood by focusing on specific nodes having a kind of centrality, we refine the two output graphs by introducing a new metric that favours the selection of nodes having higher degrees. As a third step, we extensively comment and discuss the obtained results. In particular, it is remarkable that, despite a great overlapping exists between the outcomes concerning males and females, some intriguing differences appear. This motivates a deeper local investigation, which represents the fourth part of the paper, carried out through a thorough statistical analysis. As a result, we are enabled to support that, for two special age groups, a few links contribute in differentiating the behaviour of males and females. In addition, we performed an average-based comparison between the proposed model and the traditional statistical correlation-based approach, then discussing and commenting the main outlined discrepancies.

The negotiated equilibrium model of spinal cord function

The belief that the spinal cord is hardwired is no longer tenable. Like the rest of the CNS, the spinal cord changes during growth and ageing, when new motor behaviours are acquired, and in response to trauma and disease. This paper describes a new model of spinal cord function that reconciles its recently appreciated plasticity with its long-recognized reliability as the final common pathway for behaviour. According to this model, the substrate of each motor behaviour comprises brain and spinal plasticity: the plasticity in the brain induces and maintains the plasticity in the spinal cord. Each time a behaviour occurs, the spinal cord provides the brain with performance information that guides changes in the substrate of the behaviour. All the behaviours in the repertoire undergo this process concurrently; each repeatedly induces plasticity to preserve its key features despite the plasticity induced by other behaviours. The aggregate process is a negotiation among the behaviours: they negotiate the properties of the spinal neurons and synapses that they all use. The ongoing negotiation maintains the spinal cord in an equilibrium - a negotiated equilibrium - that serves all the behaviours. This new model of spinal cord function is supported by laboratory and clinical data, makes predictions borne out by experiment, and underlies a new approach to restoring function to people with neuromuscular disorders. Further studies are needed to test its generality, to determine whether it may apply to other CNS areas such as the cerebral cortex, and to develop its therapeutic implications.

Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation

Increasing contextual interference (CI) during practice benefits learning, making it a desirable difficulty. For example, interleaved practice (IP) of motor sequences is generally more difficult than repetitive practice (RP) during practice but leads to better learning. Here we investigated whether CI in practice modulated resting-state functional connectivity during consolidation. 26 healthy adults (11 men/15 women, age = 23.3 ± 1.3 years) practiced two sets of three sequences in an IP or RP condition over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, functional magnetic resonance imaging (fMRI) data were acquired during practice and also in a resting state immediately after practice. The resting-state fMRI data were processed using independent component analysis (ICA) followed by functional connectivity analysis, showing that IP on Day 1 led to greater resting connectivity than RP between the left premotor cortex and left dorsolateral prefrontal cortex (DLPFC), bilateral posterior cingulate cortices, and bilateral inferior parietal lobules. Moreover, greater resting connectivity after IP than RP on Day 1, between the left premotor cortex and the hippocampus, amygdala, putamen, and thalamus on the right, and the cerebellum, was associated with better learning following IP. Mediation analysis further showed that the association between enhanced resting premotor-hippocampal connectivity on Day 1 and better retention performance following IP was mediated by greater task-related functional activation during IP on Day 2. Our findings suggest that the benefit of CI to motor learning is likely through enhanced resting premotor connectivity during the early phase of consolidation.

The Default Mode Network Supports Episodic Memory in Cognitively Unimpaired Elderly Individuals: Different Contributions to Immediate Recall and Delayed Recall

While the neural correlates of age-related decline in episodic memory have been the subject of much interest, the spontaneous functional architecture of the brain for various memory processes in elderly adults, such as immediate recall (IR) and delayed recall (DR), remains unclear. The present study thus examined the neural correlates of age-related decline of various memory processes. A total of 66 cognitively normal older adults (aged 60–80 years) participated in this study. Memory processes were measured using the Auditory Verbal Learning Test as well as resting-state brain images, which were analyzed using both regional homogeneity (ReHo) and correlation-based functional connectivity (FC) approaches. We found that both IR and DR were significantly correlated with the ReHo of these critical regions, all within the default mode network (DMN), including the parahippocampal gyrus, posterior cingulate cortex/precuneus, inferior parietal lobule, and medial prefrontal cortex. In addition, DR was also related to the FC between these DMN regions. These results suggest that the DMN plays different roles in memory retrieval across different retention intervals, and connections between the DMN regions contribute to memory consolidation of past events in healthy older people.

The Lag Structure of Intrinsic Activity is Focally Altered in High Functioning Adults with Autism

The behaviors that define autism spectrum disorders (ASDs) have been hypothesized to result from disordered communication within brain networks. Several groups have investigated this question using resting-state functional magnetic resonance imaging (RS-fMRI). However, the published findings to date have been inconsistent across laboratories. Prior RS-fMRI studies of ASD have employed conventional analysis techniques based on the assumption that intrinsic brain activity is exactly synchronous over widely separated parts of the brain. By relaxing the assumption of synchronicity and focusing, instead, on lags between time series, we have recently demonstrated highly reproducible patterns of temporally lagged activity in normal human adults. We refer to this analysis technique as resting-state lag analysis (RS-LA). Here, we report RS-LA as well as conventional analyses of RS-fMRI in adults with ASD and demographically matched controls. RS-LA analyses demonstrated significant group differences in rs-fMRI lag structure in frontopolar cortex, occipital cortex, and putamen. Moreover, the degree of abnormality in individuals was highly correlated with behavioral measures relevant to the diagnosis of ASD. In this sample, no significant group differences were observed using conventional RS-fMRI analysis techniques. Our results suggest that altered propagation of intrinsic activity may contribute to abnormal brain function in ASD.

Formation of Long-Term Locomotor Memories Is Associated with Functional Connectivity Changes in the Cerebellar-Thalamic-Cortical Network

Although motor adaptation is typically rapid, accumulating evidence shows that it is also associated with long-lasting behavioral and neuronal changes. Two processes were suggested to explain the formation of long-term motor memories: recall, reflecting a retrieval of previous motor actions, and faster relearning, reflecting an increased sensitivity to errors. Although these manifestations of motor memories were initially demonstrated in the context of adaptation experiments in reaching, indications of long-term motor memories were also demonstrated recently in other kinds of adaptation such as in locomotor adaptation. Little is known about the neural processes that underlie these distinct aspects of memory. We hypothesize that recall and faster relearning reflect different learning processes that operate at the same time and depend on different neuronal networks. Seventeen subjects performed a multisession locomotor adaptation experiment in the laboratory, together with resting-state and localizer fMRI scans, after the baseline and the locomotor adaptation sessions. We report a modulation of the cerebellar-thalamic-cortical and cerebellar-basal ganglia networks after locomotor adaptation. Interestingly, whereas thalamic-cortical baseline connectivity was correlated with recall, cerebellar-thalamic baseline connectivity was correlated with faster relearning. Our results suggest that separate neuronal networks underlie error sensitivity and retrieval components. Individual differences in baseline resting-state connectivity can predict idiosyncratic combination of these components.

SIGNIFICANCE STATEMENT

The ability to shape our motor behavior rapidly in everyday activity, such as when walking on sand, suggests the existence of long-term motor memories. It was suggested recently that this ability is achieved by the retrieval of previous motor actions and by enhanced relearning capacity. Little is known about the neural mechanisms that underlie these memory processes. We studied the modularity in long-term motor memories in the context of locomotor adaptation using resting-state fMRI. We show that retrieval and relearning effects are associated with separate locomotor control networks and that intersubject variability in learning and in the generation of motor memories could be predicted from baseline resting-state connectivity in locomotor-related networks.

Resting-state brain networks revealed by granger causal connectivity in frogs

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli.

Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces

Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different brain states. These neural states, e.g., self-regulated brain activity vs. rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy (CA) have been introduced to evaluate the performance of these algorithms. Interestingly enough, precisely these measures are often used to estimate the subject's ability to perform brain self-regulation. This is surprising, given that the goal of improving the tool that differentiates between brain states is different from the aim of optimizing NFB for the subject performing brain self-regulation. For the latter, knowledge about mental resources and work load is essential in order to adapt the difficulty of the intervention accordingly. In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development (ZPD) as a measure of a subject's cognitive resources and the instructional efficacy of NFB. This approach is based on a reconsideration of item-response theory (IRT) and cognitive load theory for instructional design, and combines them with the CA curve to provide a measure of BCI performance.

Brain network adaptability across task states

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.

The human brain is a complex system in which the interactions of billions of neurons give rise to a fascinating range of behaviors. In response to its changing environment—for example, across situations involving rest, memory, focused attention, or learning—the brain dynamically switches between distinct patterns of activation. Despite the wealth of neuroimaging data available, the immense complexity of the brain makes the identification of fundamental principles guiding this task-based organization of neural activity a distinct challenge. We apply new techniques from dynamic network theory to describe the functional interactions between brain regions as an evolving network, allowing us to understand these time-dependent interactions in terms of organizing characteristics of the whole network. We examine patterns of neural activity during rest, an attention-demanding task, and two memory-demanding tasks. Using network science techniques, we identify groups of brain region interactions that change cohesively together over time, both across tasks and within individual tasks. By developing tools to analyze the size and spatial distributions of these groups, we quantify significant differences between brain network dynamics in different tasks. These tools provide a promising method for investigating how the changing brain network properties of individuals correspond to task performance.

Resting-State Network Complexity and Magnitude Are Reduced in Prematurely Born Infants

Premature birth is associated with high rates of motor and cognitive disability. Investigations have described resting-state functional magnetic resonance imaging (rs-fMRI) correlates of prematurity in older children, but comparable data in the neonatal period remain scarce. We studied 25 term-born control infants within the first week of life and 25 very preterm infants (born at gestational ages ranging from 23 to 29 weeks) without evident structural injury at term equivalent postmenstrual age. Conventional resting-state network (RSN) mapping revealed only modest differences between the term and prematurely born infants, in accordance with previous work. However, clear group differences were observed in quantitative analyses based on correlation and covariance matrices representing the functional MRI time series extracted from 31 regions of interest in 7 RSNs. In addition, the maximum likelihood dimensionality estimates of the group-averaged covariance matrices in the term and preterm infants were 5 and 3, respectively, indicating that prematurity leads to a reduction in the complexity of rs-fMRI covariance structure. These findings highlight the importance of quantitative analyses of rs-fMRI data and suggest a more sensitive method for delineating the effects of preterm birth in infants without evident structural injury.

Differential lateralization of hippocampal connectivity reflects features of recent context and ongoing demands: an examination of immediate post-task activity

Neuroimaging studies have shown that task demands affect connectivity patterns in the human brain not only during task performance but also during subsequent rest periods. Our goal was to determine whether ongoing connectivity patterns during rest contain information about both the current rest state, as well as the recently terminated task. Our experimental design consisted of two types of active tasks that were followed by two types of low-demand rest states. Using this design, we examined whether hippocampal functional connectivity during wakeful rest reflects both features of a recently terminated task and those of the current resting-state condition. We identified four types of networks: (i) one whose connectivity with the hippocampus was determined only by features of a recently terminated task, (ii) one whose connectivity was determined only by features of the current resting-state, (iii) one whose connectivity reflected aspects of both the recently terminated task and ongoing resting-state features, and (iv) one whose connectivity with the hippocampus was strong, but not affected by any external factor. The left and right hippocampi played distinct roles in these networks. These findings suggest that ongoing hippocampal connectivity networks mediate information integration across multiple temporal scales, with hippocampal laterality moderating these connectivity patterns.

Effective connectivity among the working memory regions during preparation for and during performance of the n-back task

Recent neuroimaging studies have shown that working memory (WM) task difficulty can be decoded from patterns of brain activation in the WM network during preparation to perform those tasks. The inter-regional connectivity among the WM regions during task preparation has not yet been investigated. We examined this question using the graph modeling methods IMaGES and LOFS, applied to the previously published fMRI data of Manelis and Reder (2013). In that study, subjects performed 1-, 2-, and 3-back tasks. Each block of n-back was preceded by a preparation period and followed by a rest period. The analyses of task-related brain activity identified a network of 18 regions that increased in activation from 1- to 3-back (Increase network) and a network of 17 regions that decreased in activation from 1- to 3-back (Decrease network). The graph analyses revealed two types of connectivity sub-networks within the Increase and Decrease networks: “default” and “preparation-related.” The “default” connectivity was present not only during task performance, but also during task preparation and during rest. We propose that this sub-network may serve as a core system that allows one to quickly activate cognitive, perceptual and motor systems in response to the relevant stimuli. The “preparation-related” connectivity was present during task preparation and task performance, but not at rest, and depended on the n-back condition. The role of this sub-network may be to pre-activate a connectivity “road map” in order to establish a top-down and bottom-up regulation of attention prior to performance on WM tasks.

Being in two minds: the neural basis of experiencing action crises in personal long-term goals

Although the successful pursuit of long-term goals constitutes an essential prerequisite to personal development, health, and well-being, little research has been devoted to the understanding of its underlying neural processes. A critical phase in the pursuit of long-term goals is defined as an action crisis, conceptualized as the intra-psychic conflict between further goal pursuit and disengagement from the goal. In the present research, we applied an interdisciplinary (cognitive and neural) approach to the analysis of processes underlying the experience of an action crisis. In Study 1, a longitudinal field study, action crises in personal goals gave rise to an increased and unbiased (re)evaluation of the costs and benefits (i.e., rewards) of the goal. Study 2 was a magnetic resonance imaging study examining resting-state functional connectivity. The extent of experienced action crises was associated with enhanced fronto-accumbal connectivity signifying increased reward-related impact on prefrontal action control. Action crises, furthermore, mediated the relationship between a dispositional measure of effective goal pursuit (action orientation) and fronto-accumbal connectivity. The converging and complementary results from two methodologically different approaches advance the understanding of the neurobiology of personal long-term goals, especially with respect to the role of rewards in the context of goal-related conflicts.

Neurocognitive enhancement in older adults: comparison of three cognitive training tasks to test a hypothesis of training transfer in brain connectivity

The ultimate goal of cognitive enhancement as an intervention for age-related cognitive decline is transfer to everyday cognitive functioning. Development of training methods that transfer broadly to untrained cognitive tasks (far transfer) requires understanding of the neural bases of training and far transfer effects. We used cognitive training to test the hypothesis that far transfer is associated with altered attentional control demands mediated by the dorsal attention network and trained sensory cortex. In an exploratory study, we randomly assigned 42 healthy older adults to six weeks of training on Brain Fitness (BF-auditory perception), Space Fortress (SF-visuomotor/working memory), or Rise of Nations (RON-strategic reasoning). Before and after training, cognitive performance, diffusion-derived white matter integrity, and functional connectivity of the superior parietal cortex (SPC) were assessed. We found the strongest effects from BF training, which transferred to everyday problem solving and reasoning and selectively changed integrity of occipito-temporal white matter associated with improvement on untrained everyday problem solving. These results show that cognitive gain from auditory perception training depends on heightened white matter integrity in the ventral attention network. In BF and SF (which also transferred positively), a decrease in functional connectivity between SPC and inferior temporal lobe (ITL) was observed compared to RON-which did not transfer to untrained cognitive function. These findings highlight the importance for cognitive training of top-down control of sensory processing by the dorsal attention network. Altered brain connectivity - observed in the two training tasks that showed far transfer effects - may be a marker for training success.

The encoding/retrieval flip: interactions between memory performance and memory stage and relationship to intrinsic cortical networks

fMRI studies have linked the posteromedial cortex to episodic learning (encoding) and remembering (retrieval) processes. The posteromedial cortex is considered part of the default network and tends to deactivate during encoding but activate during retrieval, a pattern known as the encoding/retrieval flip. Yet, the exact relationship between the neural correlates of memory performance (hit/miss) and memory stage (encoding/retrieval) and the extent of overlap with intrinsic cortical networks remains to be elucidated. Using task-based fMRI, we isolated the pattern of activity associated with memory performance, memory stage, and the interaction between both. Using resting-state fMRI, we identified which intrinsic large-scale functional networks overlapped with regions showing task-induced effects. Our results demonstrated an effect of successful memory performance in regions associated with the control network and an effect of unsuccessful memory performance in the ventral attention network. We found an effect of memory retrieval in brain regions that span the default and control networks. Finally, we found an interaction between memory performance and memory stage in brain regions associated with the default network, including the posteromedial cortex, posterior parietal cortex, and parahippocampal cortex. We discuss these findings in relation to the encoding/retrieval flip. In general, the findings demonstrate that task-induced effects cut across intrinsic cortical networks. Furthermore, regions within the default network display functional dissociations, and this may have implications for the neural underpinnings of age-related memory disorders.

Changes occur in resting state network of motor system during 4 weeks of motor skill learning

We tested whether the resting state functional connectivity of the motor system changed during 4 weeks of motor skill learning using functional magnetic resonance imaging (fMRI). Ten healthy volunteers learned to produce a sequential finger movement by daily practice of the task over a 4 week period. Changes in the resting state motor network were examined before training (Week 0), two weeks after the onset of training (Week 2), and immediately at the end of the training (Week 4). The resting state motor system was analyzed using group independent component analysis (ICA). Statistical Parametric Mapping (SPM) second-level analysis was conducted on independent z-maps generated by the group ICA. Three regions, namely right postcentral gyrus, and bilateral supramarginal gyri were found to be sensitive to the training duration. Specifically, the strength of resting state functional connectivity in the right postcentral gyrus and right supramarginal gyrus increased from Week 0 to Week 2, during which the behavioral performance improved significantly, and decreased from Week 2 to Week 4, during which there was no more significant improvement in behavioral performance. The strength of resting state functional connectivity in left supramarginal gyrus increased throughout the training. These results confirm changes in the resting state network during slow-learning stage of motor skill learning, and support the premise that the resting state networks play a role in improving performance.

Disruption of functional connectivity of the default-mode network in alcoholism

The default mode network (DMN) comprises brain structures maximally active at rest. Disturbance of network nodes or their connections occurs with some neuropsychiatric conditions and may underlie associated dysfunction. DMN connectivity has not been examined in alcoholism, which is marked by compromised DMN nodes and impaired spatial working memory. To test whether performance would be related to DMN integrity, we examined DMN functional connectivity using functional magnetic resonance imaging (fMRI) data and graph theory analysis. We assumed that disruption of short paths between network nodes would attenuate processing efficiency. Alcoholics and controls were scanned at rest and during a spatial working memory task. At rest, the spontaneous slow fluctuations of fMRI signals in the posterior cingulate and cerebellar regions in alcoholics were less synchronized than in controls, indicative of compromised functional connectivity. Graph theory analysis indicated that during rest, alcoholics had significantly lower efficiency indices than controls between the posterior cingulate seed and multiple cerebellar sites. Greater efficiency in several connections correlated with longer sobriety in alcoholics. During the task, on which alcoholics performed on par with controls, connectivity between the left posterior cingulate seed and left cerebellar regions was more robust in alcoholics than controls and suggests compensatory networking to achieve normal performance.