Altered cortical thickness and structural covariance networks in upper limb amputees: A graph theoretical analysis

Background

The extensive functional and structural remodeling that occurs in the brain after amputation often results in phantom limb pain (PLP). These closely related phenomena are still not fully understood.

Methods

Using magnetic resonance imaging (MRI) and graph theoretical analysis (GTA), we explored how alterations in brain cortical thickness (CTh) and structural covariance networks (SCNs) in upper limb amputees (ULAs) relate to PLP. In all, 45 ULAs and 45 healthy controls (HCs) underwent structural MRI. Regional network properties, including nodal degree, betweenness centrality (BC), and node efficiency, were analyzed with GTA. Similarly, global network properties, including global efficiency (Eglob), local efficiency (Eloc), clustering coefficient (Cp), characteristic path length (Lp), and the small-worldness index, were evaluated.

Results

Compared with HCs, ULAs had reduced CThs in the postcentral and precentral gyri contralateral to the amputated limb; this decrease in CTh was negatively correlated with PLP intensity in ULAs. ULAs showed varying degrees of change in node efficiency in regional network properties compared to HCs (p < 0.005). There were no group differences in Eglob, Eloc, Cp, and Lp properties (all p > 0.05). The real-worldness SCN of ULAs showed a small-world topology ranging from 2% to 34%, and the area under the curve of the small-worldness index in ULAs was significantly different compared to HCs (p < 0.001).

Conclusion

These results suggest that the topological organization of human CNS functional networks is altered after amputation of the upper limb, providing further support for the cortical remapping theory of PLP.     

Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder

In this study, we investigated the changes in topological architectures of brain functional networks in attention-deficit/hyperactivity disorder (ADHD). Functional magnetic resonance images (fMRI) were obtained from 19 children with ADHD and 20 healthy controls during resting state. Brain functional networks were constructed by thresholding the correlation matrix between 90 cortical and subcortical regions and further analyzed by applying graph theoretical approaches. Experimental results showed that, although brain networks of both groups exhibited economical small-world topology, altered functional networks were demonstrated in the brain of ADHD when compared with the normal controls. In particular, increased local efficiencies combined with a decreasing tendency in global efficiencies found in ADHD suggested a disorder-related shift of the topology toward regular networks. Additionally, significant alterations in nodal efficiency were also found in ADHD, involving prefrontal, temporal, and occipital cortex regions, which were compatible with previous ADHD studies. The present study provided the first evidence for brain dysfunction in ADHD from the viewpoint of global organization of brain functional networks by using resting-state fMRI.     

Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depression

ObjectiveTo assess whether pretreatment theta current density in the rostral anterior cingulate (rACC) and medial orbitofrontal cortex (mOFC) differentiates responders from non-responders to antidepressant medication or placebo in a double-blinded study.MethodsPretreatment EEGs were collected from 72 subjects with Major Depressive Disorder (MDD) who participated in one of three placebo-controlled trials. Subjects were randomized to receive treatment with fluoxetine, venlafaxine, or placebo. Low-resolution brain electromagnetic tomography (LORETA) was used to assess theta current density in the rACC and mOFC.ResultsMedication responders showed elevated rACC and mOFC theta current density compared to medication non-responders (rACC: p = 0.042; mOFC: p = 0.039). There was no significant difference in either brain region between placebo responders and placebo non-responders.ConclusionsTheta current density in the rACC and mOFC may be useful as a biomarker for prediction of response to antidepressant medication.SignificanceThis is the first double-blinded treatment study to examine pretreatment rACC and mOFC theta current density in relation to antidepressant response and placebo response. Results support the potential clinical utility of this approach for predicting clinical outcome to antidepressant treatments in MDD.     

Negative functional brain networks

The anticorrelations in fMRI measurements are still not well characterized, but some new evidences point to a possible physiological role. We explored the topology of functional brain networks characterized by negative edgess and their possible alterations in schizophrenia, using functional images of 8 healthy subjects and 8 schizophrenic patients in a resting state condition. In order to minimize the insertion of artifactual negative correlations, the preprocessing of images was carried out by the CompCorr procedure, and the results compared with the Global Signal Regression (GSR) procedure. The degree distribution, the centrality, the efficiency and the rich-club behavior were used to characterize the functional brain network with negative links of healthy controls in comparison with schizophrenic patients. The results show that functional brain networks with both positive and negative values have a truncated power-law degree distribution. Moreover, although functional brain networks characterized by negative values have not small-world topology, they show a specific disassortative configuration: the more connected nodes tend to have fewer connections between them. This feature is lost using the GSR procedure. Finally, the comparison with schizophrenic patients showed a decreased (local and global) efficiency associated to a decreased connectivity among central nodes. As a conclusion, functional brain networks characterized by negative values, despite lacking a well defined topology, show specific features, different from random, and indicate an implication in the alterations associated to schizophrenia.     

Small-world brain networks.

Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.     

Small-world networks in neuronal populations: A computational perspective

The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors.In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution.Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level.     

Rostral anterior cingulate cortex activity and early symptom improvement during treatment for major depressive disorder

In treatment trials for major depressive disorder (MDD), early symptom improvement is predictive of eventual clinical response. Clinical response may also be predicted by elevated pretreatment theta (4-7 Hz) current density in the rostral anterior cingulate (rACC) and medial orbitofrontal cortex (mOFC). We investigated the relationship between pretreatment EEG and early improvement in predicting clinical outcome in 72 MDD subjects across three placebo-controlled treatment trials. Subjects were randomized to receive fluoxetine, venlafaxine, or placebo. Theta current density in the rACC and mOFC was computed with Low-Resolution Brain Electromagnetic Tomography (LORETA). An analysis of covariance examining week-8 Hamilton Depression Rating Scale (HamD) percent change, showed a significant effect of week-2 HamD percent change, and a significant three-way interaction of week-2 HamD percent change×treatment × rACC. Medication subjects with robust early improvement showed almost no relationship between rACC theta current density and final clinical outcome. However, in subjects with little early improvement, rACC activity showed a strong relationship with clinical outcome. The model examining the mOFC showed a trend in the three-way interaction. A combination of pretreatment rACC activity and early symptom improvement may be useful for predicting treatment response.     

The obese brain: association of body mass index and insulin sensitivity with resting state network functional connectivity

Obesity is a key risk factor for the development of insulin resistance, Type 2 diabetes and associated diseases; thus, it has become a major public health concern. In this context, a detailed understanding of brain networks regulating food intake, including hormonal modulation, is crucial. At present, little is known about potential alterations of cerebral networks regulating ingestive behavior. We used "resting state" functional magnetic resonance imaging to investigate the functional connectivity integrity of resting state networks (RSNs) related to food intake in lean and obese subjects using independent component analysis. Our results showed altered functional connectivity strength in obese compared to lean subjects in the default mode network (DMN) and temporal lobe network. In the DMN, obese subjects showed in the precuneus bilaterally increased and in the right anterior cingulate decreased functional connectivity strength. Furthermore, in the temporal lobe network, obese subjects showed decreased functional connectivity strength in the left insular cortex. The functional connectivity magnitude significantly correlated with body mass index (BMI). Two further RSNs, including brain regions associated with food and reward processing, did not show BMI, but insulin associated functional connectivity strength. Here, the left orbitofrontal cortex and right putamen functional connectivity strength was positively correlated with fasting insulin levels and negatively correlated with insulin sensitivity index. Taken together, these results complement and expand previous functional neuroimaging findings by demonstrating that obesity and insulin levels influence brain function during rest in networks supporting reward and food regulation.     

Apolipoprotein E ε4 modulates functional brain connectome in Alzheimer's disease

The apolipoprotein E (APOE) ?4 allele is a well‐established genetic risk factor for Alzheimer's disease (AD). Recent research has demonstrated an APOE ?4‐mediated modulation of intrinsic functional brain networks in cognitively normal individuals. However, it remains largely unknown whether and how APOE ?4 affects the brain's functional network architecture in patients with AD. Using resting‐state functional MRI and graph‐theory approaches, we systematically investigated the topological organization of whole‐brain functional networks in 16 APOE ?4 carriers and 26 matched noncarriers with AD at three levels: global whole‐brain, intermediate module, and regional node/connection. Neuropsychological analysis showed that the APOE ?4 carriers performed worse on delayed memory but better on a late item generation of a verbal fluency task (associated with executive function) than noncarriers. Whole‐brain graph analyses revealed that APOE ?4 significantly disrupted whole‐brain topological organization as characterized by (i) reduced parallel information transformation efficiency; (ii) decreased intramodular connectivity within the posterior default mode network (pDMN) and intermodular connectivity of the pDMN and executive control network (ECN) with other neuroanatomical systems; and (iii) impaired functional hubs and their rich‐club connectivities that primarily involve the pDMN, ECN, and sensorimotor systems. Further simulation analysis indicated that these altered connectivity profiles of the pDMN and ECN largely accounted for the abnormal global network topology. Finally, the changes in network topology exhibited significant correlations with the patients' cognitive performances. Together, our findings suggest that the APOE genotype modulates large‐scale brain networks in AD and shed new light on the gene‐connectome interaction in this disease. Hum Brain Mapp 36:1828–1846, 2015. © 2015 Wiley Periodicals, Inc .     

The effects of working memory training on functional brain network efficiency

The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM.     

Graph theoretical analysis of organization of functional brain networks in ADHD

This article presents a new methodology for investigation of the organization of the overall and hemispheric brain network of patients with attention-deficit hyperactivity disorder (ADHD) using theoretical analysis of a weighted graph with the goal of discovering how the brain topology is affected in such patients. The synchronization measure used is the nonlinear fuzzy synchronization likelihood (FSL) developed by the authors recently. Recent evidence indicates a normal neocortex has a small-world (SW) network with a balance between local structure and global structure characteristics. Such a network results in optimal balance between segregation and integration which is essential for high synchronizabilty and fast information transmission in a complex network. The SW network is characterized by the coexistence of dense clustering of connections (C) and short path lengths (L) among the network units. The results of investigation of C show the local structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is recognizable in the delta electroencephalograph (EEG) sub-band. Also, the results of investigation for L show the global structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is observable in the delta EEG sub-band. It is concluded that the changes in left-hemisphere brain's structure of ADHD from that of the non-ADHD are so much that L and C can distinguish the ADHD brain from the non-ADHD brain in the delta EEG sub-band.     

Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury

This study investigated the characteristics of the small-world brain network architecture of patients with mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7 days after injury respectively. After preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 templates. The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these parameters between the MTBI and control group. Significantly different parameters were investigated for correlations with SAC scores; those that correlated were chosen for further curve fitting. The clustering coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local networks, and strength of mean connectivity. These features may be associated with brain function compensation. The network parameters significantly correlated with SAC total and attention scores.     

Reorganization of brain functional small-world networks during finger movements

A functional measure of brain organization is the efficiency of functional connectivity. The degree of functional connectivity can differ during a task compared to the rest, and to study this issue, we investigated the functional connectivity networks in healthy subjects during a simple, right-handed, sequential finger-tapping task using graph theoretic measures. EEGs were recorded from 58 channels in 15 healthy subjects at rest and during a motor task. We estimated mutual information values of wavelet coefficients to create an association matrix between EEG electrodes and produced a series of adjacency matrices or graphs, A, by thresholding with network cost. These graphs are called small-world networks, and we assessed their efficiency measures. We found economical small-world properties in brain functional connectivity networks in the alpha and beta band networks. The efficiency of the brain networks was enhanced during the task in the beta band networks, but not in the alpha band networks. A regional efficiency analysis during the task showed that the bilateral primary motor and left sensory areas showed increased nodal efficiency, Enodal, whereas decreased Enodal was found over the posterior parietal areas. The present study provides evidence for the reorganization of brain functional connectivity networks in a motor task with the greatest increase in Enodal in motor executive areas.     

Baseline functional connectivity may predict placebo responses to accelerated rTMS treatment in major depression

Although in theory sham repetitive transcranial magnetic stimulation (rTMS) has no inherent therapeutic value, nonetheless, such placebo stimulations may have relevant therapeutic effects in clinically depressed patients. On the other hand, antidepressant responses to sham rTMS are quite heterogeneous across individuals and its neural underpinnings have not been explored yet. The current brain imaging study aims to detect baseline neural fingerprints resulting in clinically beneficial placebo rTMS treatment responses. We collected resting‐state functional magnetic resonance imaging data prior to a registered randomized clinical trial of accelerated placebo stimulation protocol in patients documented with treatment‐resistant depression ( http://clinicaltrials.gov/show/NCT01832805 ). In addition to global brain connectivity and rostral anterior cingulate cortex (rACC) seed‐based functional connectivity (FC), elastic‐net regression and cross‐validation procedures were used to identify baseline intrinsic brain connectivity biomarkers for sham‐rTMS responses. Placebo responses to accelerated sham rTMS were correlated with baseline global brain connectivity in the rACC/ventral medial prefrontal cortex (vmPFC). Concerning the rACC seed‐based FC analysis, the placebo response was associated positively with the precuneus/posterior cingulate (PCun/PCC) cortex and negatively with the middle frontal gyrus. Our findings provide first brain imaging evidence for placebo responses to sham stimulation being predictable from rACC rsFC profiles, especially in brain areas implicated in (re)appraisal and self‐focus processes.     

Disrupted brain connectivity networks in acute ischemic stroke patients

Neuroimaging studies have shown that local brain lesions could result in abnormal information transfer far from the lesion site in acute ischemic stroke (AIS) patients; yet, little is known about alternations of the topological organization of whole-brain networks in AIS. By using resting state functional magnetic resonance imaging (MRI) and graph theory analysis, we systematically investigated the topological properties of the functional brain networks of 28 healthy controls (HC, age: 56.9?±?0.45 years) and 29 AIS (age: 57.6?±?0.21 years) with proximal anterior circulation occlusion within 12 h of symptom onset. In our results, both the AIS and HC groups exhibited small-world network organization, suggesting a functional balance between local specialization and global integration. However, compared with the HC, the AIS patients had a lower shortest path length and higher global efficiency, indicating a tendency of randomization in patients’ functional brain networks. The AIS patients had an increased nodal degree in the precuneus (PCUN), middle frontal gyrus (MFG), medial part of the superior frontal gyrus (SFGmed), orbital part of the middle frontal gyrus, and the opercular part of the inferior frontal gyrus, and increased nodal efficiency in the PUCN, MFG, SFGmed, and the angular gyrus. The decreased nodal degree in AIS was found in the heschl gyrus (HES), and no significant decreased nodal efficiency was observed. The dysfunctional connections were mainly concentrated in the HES and prefrontal cortex. Furthermore, the altered nodal centrality of the MFG and abnormal functional connectivity in AIS were associated with patients’ Mini-Mental State Examination scores. These results suggested that interrupted functional connectivity in language system organization after focal brain lesions could also result in disruptions in the topological organization of other brain circuits, and this may contribute to disturbances in cognition in AIS patients.     

Heritability of "small-world" networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity

Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic features of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and average path length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small-world organization are viable markers of genetic differences in brain organization.     

Altered sleep brain functional connectivity in acutely depressed patients

Recent evidence suggests that problems in information processing within neural networks may underlie depressive disease. In this study, we investigated whether sleep functional brain networks are abnormally organized during a major depressive episode (MDE). We characterized spatial patterns of functional connectivity by computing the "synchronization likelihood" (SL) of 19 sleep EEG channels in 11 acutely depressed patients [42 (20-51) years] and 14 healthy controls [32.9 (27-42) years]. To test whether disrupting an optimal pattern ["small-world network" (SWN)] of functional brain connectivity underlies MDE, graph theoretical measures were then applied to the resulting synchronization matrices, and a clustering coefficient (C, measure of local connectedness) and a shortest path length (L, measure of overall network integration) were determined. In the depressed group, the mean SL was lower in the delta, theta and sigma frequency bands. Acutely depressed patients showed a significantly lower path length in the theta and delta frequency bands, whereas the cluster coefficient showed no significant changes. The present study provides further support that sleep functional brain networks exhibit "small-world" properties. Sleep neuronal functional networks in depressed patients are characterized by a functional reorganization with a lower mean level of global synchronization and loss of SWN characteristics. These results argue for considering an MDE as a problem of neuronal network organization and a problem of information processing.     

Small-world network organization of functional connectivity of EEG slow-wave activity during sleep.

OBJECTIVE: To analyze the functional connectivity patterns of the EEG slow-wave activity during the different sleep stages and Cyclic Alternating Pattern (CAP) conditions, using concepts derived from Graph Theory. METHODS: We evaluated spatial patterns of EEG slow-wave synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during the different sleep stages and CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s). RESULTS: Cp and Cp/Cp-s increased significantly from wakefulness to sleep while Lp and Lp/Lp-s did not show changes. Cp/Cp-s was higher for A1 phases, compared to B phases of CAP. CONCLUSIONS: The network organization of the EEG slow-wave synchronization during sleep shows features characteristic of small-world networks (high Cp combined with low Lp); this type of organization is slightly but significantly more evident during the CAP A1 subtypes. SIGNIFICANCE: Our results show feasibility of using graph theoretical measures to characterize the complexity of brain networks during sleep and might indicate sleep, and the A1 phases of CAP in particular, as a period during which slow-wave synchronization shows optimal network organization for information processing.     

Age-related changes in topological organization of structural brain networks in healthy individuals

The aim of this study was to examine structural brain networks using regional gray matter volume, as well as to investigate changes in small-world and modular organization with normal aging. We constructed structural brain networks composed of 90 regions in young, middle, and old age groups. We randomly selected 350 healthy subjects for each group from a Japanese magnetic resonance image database. Structural brain networks in three age groups showed economical small-world properties, providing high global and local efficiency for parallel information processing at low connection cost. The small-world efficiency and node betweenness varied significantly and revealed a U- or inverted U-curve model tendency among three age groups. Results also demonstrated that structural brain networks exhibited a modular organization in which the connections between regions are much denser within modules than between them. The modular organization of structural brain networks was similar between the young and middle age groups, but quite different from the old group. In particular, the old group showed a notable decrease in the connector ratio and the intermodule connections. Combining the results of small-world efficiency, node betweenness and modular organization, we concluded that the brain network changed slightly, developing into a more distributed organization from young to middle age. The organization eventually altered greatly, shifting to a more localized organization in old age. Our findings provided quantitative insights into topological principles of structural brain networks and changes related to normal aging.     

The functional connectivity of different EEG bands moves towards small-world network organization during sleep.

OBJECTIVE: To analyze the functional connectivity patterns of the different EEG bands during wakefulness and sleep (different sleep stages and cyclic alternating pattern (CAP) conditions), using concepts derived from Graph Theory. METHODS: We evaluated spatial patterns of EEG band synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during wakefulness and the different sleep stages/CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s). RESULTS: We found values of Cp and Lp compatible with a small-world network organization in all sleep stages and for all EEG bands. All bands below 15Hz showed an increase of these features during sleep (and during CAP-A phases in particular), compared to wakefulness. CONCLUSIONS: The results of this study seem to confirm our initial hypothesis that during sleep there exists a clear trend for the functional connectivity of the EEG to move forward to an organization more similar to that of a small-world network, at least for the frequency bands lower than 15Hz. SIGNIFICANCE: Sleep network "reconfiguration" might be one of the key mechanisms for the understanding of the "global" and "local" neural plasticity taking place during sleep.