The development of human functional brain networks

Graph-based network analysis in schizophrenia

Over the last few years, many studies have been published using modern network analysis of the brain. Researchers and practical doctors alike should understand this method and its results on the brain evaluation at rest, during activation and in brain disease. The studies are noninvasive and usually performed with elecroencephalographic, magnetoencephalographic, magnetic resonance imaging and diffusion tensor imaging brain recordings. Different tools for analysis have been developed, although the methods are in their early stages. The results of these analyses are of special value. Studies of these tools in schizophrenia are important because widespread and local network disturbances can be evaluated by assessing integration, segregation and several structural and functional properties. With the help of network analyses, the main findings in schizophrenia are lower optimum network organization, less efficiently wired networks, less local clustering, less hierarchical organization and signs of disconnection. There are only about twenty five relevant papers on the subject today. Only a few years of study of these methods have produced interesting results and it appears promising that the development of these methods will present important knowledge for both the preclinical signs of schizophrenia and the methods’ therapeutic effects.

Enhanced efficiency of the executive attention network after training in preschool children: immediate changes and effects after two months

Executive attention is involved in the regulation of thoughts, emotions and responses. This function experiences major development during preschool years and is associated to a neural network involving the anterior cingulate cortex and prefrontal structures. Recently, there have been some attempts to improve attention and other executive functions through training. In the current study, a group of 5 years old children (n=37) were assigned to either a training-group who performed ten sessions of computerized training of attention or a non-trained control group. Assessment of performance in a range of tasks, targeting attention, intelligence and regulation of affect was carried out in three occasions: (1) before, (2) after, and (3) two months after completion of training. Also, brain function was examined with a high-density electroencephalogram system. Results demonstrate that trained children activate the executive attention network faster and more efficiently than untrained children, an effect that was still observed two months after without further training. Also, evidence of transfer of attention training to fluid intelligence and, to a lesser degree, to regulation of affect was observed. Results show that efficiency of the brain system underlying self-regulation can be enhanced by experience during development, providing opportunities for curricular improvement.

Brain imaging and hepatic encephalopathy

Novel imaging techniques allow the investigation of structural and functional neuropathology of hepatic encephalopathy in greater detail, but limited techniques are applicable to the clinic. Computed tomography and magnetic resonance imaging (MRI) can rule out other diagnoses and, in MRI, give diagnostic features in widely available sequences. An internationally accepted diagnostic framework that includes an objective imaging test to replace or augment psychometry remains elusive. Quantitative MRI is likely to be the best candidate to become this test. The utility of MR and nuclear medical techniques to the clinic and results from recent research are described in this article.

Functional network organization of the human brain

Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.

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.

The relationship of developmental changes in white matter to the onset of psychosis

Schizophrenia is a disorder with a pronounced developmental component. Accordingly, there is a growing interest in characterizing developmental changes in the period leading up to disease onset, in an effort to develop effective preventative interventions. One of the ongoing neurodevelopmental changes known to occur in the late adolescent period that often overlaps with the prodromal phase and time of onset is white matter development and myelination. In this critical review, a disruption in the normal trajectory of white matter development could potentially play an important role in the onset of psychosis. We seek to summarize the existing state of research on white matter development in prodromal subjects, with a particular focus on diffusion tensor imaging (DTI) measures. First, we describe the physiological basis of developmental white matter changes and myelination. Next, we characterize the pattern of white matter changes associated with typical development across adolescence as measured with DTI. Then, we discuss white matter changes observed in adult patients with schizophrenia and in individuals seen in genetic and clinical high risk states. Finally, we discuss the implications of these findings for future research directions and for potential therapeutic interventions.

Large-scale brain systems in ADHD: beyond the prefrontal-striatal model

Attention-deficit/hyperactivity disorder (ADHD) has long been thought to reflect dysfunction of prefrontal-striatal circuitry, with involvement of other circuits largely ignored. Recent advances in systems neuroscience-based approaches to brain dysfunction have facilitated the development of models of ADHD pathophysiology that encompass a number of different large-scale resting-state networks. Here we review progress in delineating large-scale neural systems and illustrate their relevance to ADHD. We relate frontoparietal, dorsal attentional, motor, visual and default networks to the ADHD functional and structural literature. Insights emerging from mapping intrinsic brain connectivity networks provide a potentially mechanistic framework for an understanding of aspects of ADHD such as neuropsychological and behavioral inconsistency, and the possible role of primary visual cortex in attentional dysfunction in the disorder.

Broadband neurophysiological abnormalities in the medial prefrontal region of the default-mode network in adults with ADHD

Previous investigations of the default-mode network (DMN) in persons with attention-deficit/hyperactivity disorder (ADHD) have shown reduced functional connectivity between the anterior and posterior aspects. This finding was originally demonstrated in adults with ADHD, then in youth with ADHD, and has been tentatively linked to ultra low frequency oscillations within the DMN. The current study evaluates the specificity of DMN abnormalities to neuronal oscillations in the ultra low frequency range, and examines the regional specificity of these DMN aberrations in medicated and unmedicated adults with, and those without ADHD. An individually matched sample of adults with and without ADHD completed 6-minute sessions of resting-state magnetoencephalography (MEG). Participants with ADHD were known responders to stimulant medications and completed two sessions (predrug/postdrug). MEG data were coregistered to the participant's MRI, corrected for head motion, fitted to a regional-level source model, and subjected to spectral analyses to extract neuronal population activity in regions of the DMN. The unmedicated adults with ADHD exhibited broadband deficits in medial prefrontal cortices (MPFC), but not other DMN regions compared to adults without ADHD. Unmedicated patients also showed abnormal cross-frequency coupling in the gamma range between the MPFC and posterior cingulate areas, and disturbed balance within the DMN as activity in posterior regions was stronger than frontal regions at beta and lower frequencies, which dissipated at higher γ-frequencies. Administration of pharmacotherapy significantly increased prefrontal alpha activity (8-14 Hz) in adults with ADHD, and decreased the cross-frequency gamma coupling. These results indicate that neurophysiological aberrations in the DMN of patients with ADHD are not limited to ultra slow oscillations, and that they may be primarily attributable to abnormal broadband activity in the MPFC.

Watching the fetal brain at 'rest'

Functional magnetic resonance imaging (fMRI) has allowed insights into the spatiotemporal distribution of human brain networks. According to the neurophysiological property of the fetal brain to generate spontaneous activity, we aimed to determine the feasibility of investigating the maturation of intrinsic networks, beginning at gestational week 20 in healthy human fetuses by combining resting-state fMRI and an analytical approach, independent component analysis (ICA). In this study, functional images of 16 fetuses with morphologically normal brain development, from 20 to 36 gestational weeks of age, were acquired on a 1.5T unit (Philips Medical Systems, Best, The Netherlands) using single-shot, gradient-recalled echo-planar imaging. After preprocessing (motion correction, brain extraction), images were analyzed using single-subject ICA. We visualized a bilateral occipital network and medial and lateral prefrontal activity pattern that involved the future Brodmann areas 9-11. Furthermore, there was one either predominantly right (3/7 cases) or left (4/7 cases) hemispheric lateralized network that involved the superior temporal cortical regions (Brodmann areas 22 and 39). Frequency oscillations were in the range of 0.01-0.06Hz for all networks. This study shows that resting-state networks (RSNs) are shaped and are detectable in utero. Further investigations of resting-state measurements in the fetus may therefore allow developmental brain activity monitoring and may provide insights into early brain function.

Ongoing physiological processes in the cerebral cortex

Functional magnetic resonance imaging (fMRI) has revealed that the human brain undergoes prominent, regional hemodynamic fluctuations when a subject is at rest. These ongoing fluctuations exhibit distinct patterns of spatiotemporal synchronization that have been dubbed "resting state functional connectivity", and which currently serve as a principal tool to investigate neural networks in the normal and pathological human brain. Despite the wide application of this approach in human neuroscience, the neural mechanisms that give rise to spontaneous fMRI correlations are largely unknown. Here we review results of recent electrophysiological studies in the cerebral cortex of humans and nonhuman primates that link neural activity to ongoing fMRI fluctuations. We begin by describing results obtained with simultaneous fMRI and electrophysiological measurements that allow for the identification of direct neural correlates of resting state functional connectivity. We next highlight experiments that investigate the correlational structure of spontaneous neural signals, including the spatial variation of signal coherence over the cortical surface, across cortical laminae, and between the two hemispheres. In the final section we speculate on the origins and potential consequences of ongoing signals for normal brain function, and point out inherent limitations of the fMRI correlation approach.

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

Network centrality in the human functional connectome

The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel insights into connectivity within the whole-brain functional network (i.e., the functional connectome). We first assemble and visualize the voxel-wise (4 mm) functional connectome as a functional network. We then demonstrate that each centrality measure captures different aspects of connectivity, highlighting the importance of considering both global and local connectivity properties of the functional connectome. Beyond "detecting functional hubs," we treat centrality as measures of functional connectivity within the brain connectome and demonstrate their reliability and phenotypic correlates (i.e., age and sex). Specifically, our analyses reveal age-related decreases in degree centrality, but not eigenvector centrality, within precuneus and posterior cingulate regions. This implies that while local or (direct) connectivity decreases with age, connections with hub-like regions within the brain remain stable with age at a global level. In sum, these findings demonstrate the nonredundancy of various centrality measures and raise questions regarding their underlying physiological mechanisms that may be relevant to the study of neurodegenerative and psychiatric disorders.

Developmental changes in adolescents’ neural response to challenge

Adolescents often fail to adaptively regulate their emotions and behaviors. This is most clearly demonstrated by the marked increase during this period in fatalities that are attributable to preventable causes. Using functional magnetic resonance methodology, this study explored whether adolescents and adults differed in their engagement of prefrontal circuitry in response to a cognitive and emotional challenge. Twenty-four adolescents and twenty-three adults were scanned while they solved difficult math problems with induced failure and negative social evaluation. Data is reported from 23 adolescents and 23 adults. Adult and adolescent participants showed similar increases in heart rate when responding to the experimental challenge. Despite the similarity of the autonomic response, adolescents recruited a more restricted network of prefrontal regions as compared to adults. Both adolescents and adults recruited the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex, however adults additionally recruited the anterior insula. Functional connectivity between the anterior insula and other prefrontal regions was stronger in adults as compared to adolescents. Further, for adults, the magnitude of activity in the insula predicted lower autonomic activity in response to the challenge. Differences between adolescents and adults engagement of prefrontal networks may relate to adolescents’ poor behavioral and emotional regulation.

Alteration and reorganization of functional networks: a new perspective in brain injury study

Plasticity is the mechanism underlying the brain’s potential capability to compensate injury. Recently several studies have shown how functional connections among the brain areas are severely altered by brain injury and plasticity leading to a reorganization of the networks. This new approach studies the impact of brain injury by means of alteration of functional interactions. The concept of functional connectivity refers to the statistical interdependencies between physiological time series simultaneously recorded in various areas of the brain and it could be an essential tool for brain functional studies, being its deviation from healthy reference an indicator for damage. In this article, we review studies investigating functional connectivity changes after brain injury and subsequent recovery, providing an accessible introduction to common mathematical methods to infer functional connectivity, exploring their capabilities, future perspectives, and clinical uses in brain injury studies.

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

Functional network disruption in the degenerative dementias

Despite advances towards understanding the molecular pathophysiology of the neurodegenerative dementias, the mechanisms linking molecular changes to neuropathology and neuropathological changes to clinical symptoms remain largely obscure. Connectivity is a distinctive feature of the brain and the integrity of functional network dynamics is crucial for normal functioning. A better understanding of network disruption in the neurodegenerative dementias might help bridge the gap between molecular changes, pathological changes, and symptoms. Recent findings on functional network disruption as assessed with resting-state or intrinsic connectivity functional MRI and electroencephalography and magnetoencephalography have shown distinct patterns of network disruption across the major neurodegenerative diseases. These network abnormalities are somewhat specific to the clinical syndromes and, in Alzheimer's disease and frontotemporal dementia, network disruption tracks the pattern of pathological changes. These findings might have practical implications for diagnostic accuracy, allowing earlier detection of neurodegenerative diseases even at the presymptomatic stage, and tracking of disease progression.

Resting-state functional connectivity indexes reading competence in children and adults

Task-based neuroimaging studies face the challenge of developing tasks capable of equivalently probing reading networks across different age groups. Resting-state fMRI, which requires no specific task, circumvents these difficulties. Here, in 25 children (8-14 years) and 25 adults (21-46 years), we examined the extent to which individual differences in reading competence can be related to resting-state functional connectivity (RSFC) of regions implicated in reading. In both age groups, reading standard scores correlated positively with RSFC between the left precentral gyrus and other motor regions, and between Broca's and Wernicke's areas. This suggests that, regardless of age group, stronger coupling among motor regions, as well as between language/speech regions, subserves better reading, presumably reflecting automatized articulation. We also observed divergent RSFC-behavior relationships in children and adults, particularly those anchored in the left fusiform gyrus (FFG) (the visual word form area). In adults, but not children, better reading performance was associated with stronger positive correlations between FFG and phonology-related regions (Broca's area and the left inferior parietal lobule), and with stronger negative relationships between FFG and regions of the "task-negative" default network. These results suggest that both positive RSFC (functional coupling) between reading regions and negative RSFC (functional segregation) between a reading region and default network regions are important for automatized reading, characteristic of adult readers. Together, our task-independent RSFC findings highlight the importance of appreciating developmental changes in the neural correlates of reading competence, and suggest that RSFC may serve to facilitate the identification of reading disorders in different age groups.

Connectivity and the corpus callosum in autism spectrum conditions: insights from comparison of autism and callosal agenesis

Neural models of autism spectrum disorders (ASDs) have moved, in recent years, from a lesion model to a focus on abnormal connectivity. In this chapter, we review this work and summarize findings from our recent research comparing autism and agenesis of the corpus callosum (AgCC). We discuss our findings in the context of the "fractionable triad" account and highlight three main points. First, the social aspects of autism can be found in isolation, not accompanied by the nonsocial features of this disorder, supporting a view of autism as a "compound," rather than "monolithic," condition. Second, many young people with callosal agenesis show theory of mind- and emotion-processing deficits akin to those seen in autism. Diagnostic overshadowing may mean these people do not receive interventions that have proven beneficial in ASD. Last, study of AgCC shows that it is possible, in some cases, to develop good social cognitive skills in the absence of the corpus callosum, presenting a challenge to future connectivity models of autism.

Developmental aspects of respiratory chain from fetus to infancy

Reviewing the recent literature on the role of mitochondria during fetal development paradoxically reveals two features: the importance of mitochondria in these early developmental phases, and the scarcity of information available for humans. Indeed, most of the available information on the role of mitochondria during development comes from studies of animal models that do not necessarily strictly apply to humans. In this paper, we attempted to collect information existing on humans, together with data from animal studies essentially presented as corroboration. This makes clear that a complex interacting network of energetic, genetic and epigenetic factors governs the impact of mitochondrial function on early development in humans. This complexity presumably also accounts for our poor understanding of the consequences of impaired mitochondrial function on prenatal development, or conversely, of the impact of development on the expression of such deficiencies.

Parcellation in left lateral parietal cortex is similar in adults and children

A key question in developmental neuroscience involves understanding how and when the cerebral cortex is partitioned into distinct functional areas. The present study used functional connectivity MRI mapping and graph theory to identify putative cortical areas and generate a parcellation scheme of left lateral parietal cortex (LLPC) in 7 to 10-year-old children and adults. Results indicated that a majority of putative LLPC areas could be matched across groups (mean distance between matched areas across age: 3.15 mm). Furthermore, the boundaries of children's putative LLPC areas respected the boundaries generated from the adults' parcellation scheme for a majority of children's areas (13/15). Consistent with prior research, matched LLPC areas showed age-related differences in functional connectivity strength with other brain regions. These results suggest that LLPC cortical parcellation and functional connectivity mature along different developmental trajectories, with adult-like boundaries between LLPC areas established in school-age children prior to adult-like functional connectivity.

Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors

BACKGROUND

Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established.

METHODOLOGY/PRINCIPAL FINDINGS

To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI) Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution.

CONCLUSIONS/SIGNIFICANCE

Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

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.

Decoding continuous variables from neuroimaging data: basic and clinical applications

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.

Long-term effects of motor training on resting-state networks and underlying brain structure

Acquired motor skills are coded in fronto-parietal brain networks, but how these networks evolve through motor training is unclear. On the one hand, increased functional connectivity has been shown immediately after a training session; on the other hand, training-induced structural changes are visible only after several weeks. Based on known associations between functional and structural network development during human ontogeny, we hypothesised that learning a challenging motor task leads to long-lasting changes in functional resting-state networks and the corresponding cortical and sub-cortical brain structures. Using longitudinal functional and structural MRI at multiple time points, we demonstrate increased fronto-parietal network connectivity one week after two brief motor training sessions in a dynamic balancing task, although subjects were engaged in their regular daily activities during the week. Repeated training sessions over six consecutive weeks progressively modulate these changes in accordance with individual performance improvements. Multimodal correlation analyses showed an association between structural grey matter alterations and functional connectivity changes in prefrontal and supplementary-motor areas. These coincident changes were most prominent in the first three weeks of training. In contrast, changes in fronto-parietal functional connectivity and the underlying white matter fibre structure developed gradually during the six weeks. Our results demonstrate a tight correlation between training-induced functional and structural brain plasticity on the systems level and suggest a functional relevance of intrinsic brain activity for morphological adaptation in the human brain.

The development of multisensory speech perception continues into the late childhood years

Observing a speaker's articulations substantially improves the intelligibility of spoken speech, especially under noisy listening conditions. This multisensory integration of speech inputs is crucial to effective communication. Appropriate development of this ability has major implications for children in classroom and social settings, and deficits in it have been linked to a number of neurodevelopmental disorders, especially autism. It is clear from structural imaging studies that there is a prolonged maturational course within regions of the perisylvian cortex that persists into late childhood, and these regions have been firmly established as being crucial to speech and language functions. Given this protracted maturational timeframe, we reasoned that multisensory speech processing might well show a similarly protracted developmental course. Previous work in adults has shown that audiovisual enhancement in word recognition is most apparent within a restricted range of signal-to-noise ratios (SNRs). Here, we investigated when these properties emerge during childhood by testing multisensory speech recognition abilities in typically developing children aged between 5 and 14 years, and comparing them with those of adults. By parametrically varying SNRs, we found that children benefited significantly less from observing visual articulations, displaying considerably less audiovisual enhancement. The findings suggest that improvement in the ability to recognize speech-in-noise and in audiovisual integration during speech perception continues quite late into the childhood years. The implication is that a considerable amount of multisensory learning remains to be achieved during the later schooling years, and that explicit efforts to accommodate this learning may well be warranted.

The development of multisensory speech perception continues into the late childhood years

Observing a speaker's articulations substantially improves the intelligibility of spoken speech, especially under noisy listening conditions. This multisensory integration of speech inputs is crucial to effective communication. Appropriate development of this ability has major implications for children in classroom and social settings, and deficits in it have been linked to a number of neurodevelopmental disorders, especially autism. It is clear from structural imaging studies that there is a prolonged maturational course within regions of the perisylvian cortex that persists into late childhood, and these regions have been firmly established as being crucial to speech and language functions. Given this protracted maturational timeframe, we reasoned that multisensory speech processing might well show a similarly protracted developmental course. Previous work in adults has shown that audiovisual enhancement in word recognition is most apparent within a restricted range of signal-to-noise ratios (SNRs). Here, we investigated when these properties emerge during childhood by testing multisensory speech recognition abilities in typically developing children aged between 5 and 14 years, and comparing them with those of adults. By parametrically varying SNRs, we found that children benefited significantly less from observing visual articulations, displaying considerably less audiovisual enhancement. The findings suggest that improvement in the ability to recognize speech-in-noise and in audiovisual integration during speech perception continues quite late into the childhood years. The implication is that a considerable amount of multisensory learning remains to be achieved during the later schooling years, and that explicit efforts to accommodate this learning may well be warranted.

Balancing the brain: resting state networks and deep brain stimulation

Over the last three decades, large numbers of patients with otherwise treatment-resistant disorders have been helped by deep brain stimulation (DBS), yet a full scientific understanding of the underlying neural mechanisms is still missing. We have previously proposed that efficacious DBS works by restoring the balance of the brain's resting state networks. Here, we extend this proposal by reviewing how detailed investigations of the highly coherent functional and structural brain networks in health and disease (such as Parkinson's) have the potential not only to increase our understanding of fundamental brain function but of how best to modulate the balance. In particular, some of the newly identified hubs and connectors within and between resting state networks could become important new targets for DBS, including potentially in neuropsychiatric disorders. At the same time, it is of essence to consider the ethical implications of this perspective.

Modulation of the brain's functional network architecture in the transition from wake to sleep

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.

Development of the brain's functional network architecture

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

Structural MRI of pediatric brain development: what have we learned and where are we going?

Magnetic resonance imaging (MRI) allows unprecedented access to the anatomy and physiology of the developing brain without the use of ionizing radiation. Over the past two decades, thousands of brain MRI scans from healthy youth and those with neuropsychiatric illness have been acquired and analyzed with respect to diagnosis, sex, genetics, and/or psychological variables such as IQ. Initial reports comparing size differences of various brain components averaged across large age spans have given rise to longitudinal studies examining trajectories of development over time and evaluations of neural circuitry as opposed to structures in isolation. Although MRI is still not of routine diagnostic utility for evaluation of pediatric neuropsychiatric disorders, patterns of typical versus atypical development have emerged that may elucidate pathologic mechanisms and suggest targets for intervention. In this review we summarize general contributions of structural MRI to our understanding of neurodevelopment in health and illness.