Network analysis of resting state EEG in the developing young brain: structure comes with maturation

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.

Empirical evaluations of slice-timing, smoothing, and normalization effects in seed-based, resting-state functional magnetic resonance imaging analyses

Recently, functional connectivity analyses using spontaneous functional magnetic resonance imaging (fMRI) fluctuations have been applied in the context of neurological and psychiatry diseases. In the analyses procedure, preprocessing steps are commonly utilized in exploring functional connectivity, the same strategy as what was conducted in the fMRI process. However, the effectiveness of these preprocessing steps on resting-state fMRI (rs-fMRI) was rarely investigated, and the significance of preprocessing steps on rs-fMRI needs to be studied. Therefore, the main purpose of the current study was to evaluate the effects of multiple preprocessing procedures, including slice-timing correction, smoothing, and spatial normalization, on rs-fMRI signal. Through a seed-based correlation analysis on the motor network, we empirically estimated three indices of spontaneous fMRI fluctuations induced: correlation coefficients (CC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF (fALFF), with different strategies of three preprocessing steps. Multiple repetition times (TRs = 2, 3, 4 sec) were also compared to address the issue of temporal mismatch. In the temporal preprocess, we found that the use of slice-timing correction and different TRs had minimal effects on CC and fALFF. However, ALFF was significantly affected using different TR but not affected by slice-timing correction as well. In the spatial preprocess, fALFF was insensitive to both smoothing and normalization. Smoothing consistently increased spatial extents and CC, but suppressed ALFF values. Performing normalization before index calculations provided better spatial sensitivity with larger variability in ALFF, whereas performing normalization after index calculations might preserve the ALFF level as in the unnormalized data. Conclusively, the effects of choosing preprocessing parameters and strategies were presented in the current study, providing practical considerations when conducting rs-fMRI analyses.

Scale-free modulation of resting-state neuronal oscillations reflects prolonged brain maturation in humans

Human neuronal circuits undergo life-long functional reorganization with profound effects on cognition and behavior. Well documented prolonged development of anatomical brain structures includes white and gray matter changes that continue into the third decade of life. We investigated resting-state EEG oscillations in 1433 subjects from 5 to 71 years. Neuronal oscillations exhibit scale-free amplitude modulation as reflected in power-law decay of autocorrelations--also known as long-range temporal correlations (LRTC)--which was assessed by detrended fluctuation analysis. We observed pronounced increases in LRTC from childhood to adolescence, during adolescence, and even into early adulthood (∼25 years of age) after which the temporal structure stabilized. A principal component analysis of the spatial distribution of LRTC revealed increasingly uniform scores across the scalp. Together, these findings indicate that the scale-free modulation of resting-state oscillations reflects brain maturation, and suggests that scaling analysis may prove useful as a biomarker of pathophysiology in neurodevelopmental disorders such as attention deficit hyperactivity disorder and schizophrenia.

Functional embedding predicts the variability of neural activity

Neural activity is irregular and unpredictable, yet little is known about why this is the case and how this property relates to the functional architecture of the brain. Here we show that the variability of a region’s activity systematically varies according to its topological role in functional networks. We recorded the resting-state electroencephalogram (EEG) and constructed undirected graphs of functional networks. We measured the centrality of each node in terms of the number of connections it makes (degree), the ease with which the node can be reached from other nodes in the network (efficiency) and the tendency of the node to occupy a position on the shortest paths between other pairs of nodes in the network (betweenness). As a proxy for variability, we estimated the information content of neural activity using multiscale entropy analysis. We found that the rate at which information was generated was largely predicted by centrality. Namely, nodes with greater degree, betweenness, and efficiency were more likely to have high information content, while peripheral nodes had relatively low information content. These results suggest that the variability of regional activity reflects functional embedding.

Characterizing brain cortical plasticity and network dynamics across the age-span in health and disease with TMS-EEG and TMS-fMRI

Brain plasticity can be conceptualized as nature's invention to overcome limitations of the genome and adapt to a rapidly changing environment. As such, plasticity is an intrinsic property of the brain across the lifespan. However, mechanisms of plasticity may vary with age. The combination of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) enables clinicians and researchers to directly study local and network cortical plasticity, in humans in vivo, and characterize their changes across the age-span. Parallel, translational studies in animals can provide mechanistic insights. Here, we argue that, for each individual, the efficiency of neuronal plasticity declines throughout the age-span and may do so more or less prominently depending on variable 'starting-points' and different 'slopes of change' defined by genetic, biological, and environmental factors. Furthermore, aberrant, excessive, insufficient, or mistimed plasticity may represent the proximal pathogenic cause of neurodevelopmental and neurodegenerative disorders such as autism spectrum disorders or Alzheimer's disease.

Functional roles of alpha-band phase synchronization in local and large-scale cortical networks

Alpha-frequency band (8-14 Hz) oscillations are among the most salient phenomena in human electroencephalography (EEG) recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging and trans-cranial magnetic stimulation studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses.

Functional roles of alpha-band phase synchronization in local and large-scale cortical networks

Alpha-frequency band (8–14 Hz) oscillations are among the most salient phenomena in human electroencephalography (EEG) recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging and trans-cranial magnetic stimulation studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses.

Sex steroids and connectivity in the human brain: a review of neuroimaging studies

Our brain operates by the way of interconnected networks. Connections between brain regions have been extensively studied at a functional and structural level, and impaired connectivity has been postulated as an important pathophysiological mechanism underlying several neuropsychiatric disorders. Yet the neurobiological mechanisms contributing to the development of functional and structural brain connections remain to be poorly understood. Interestingly, animal research has convincingly shown that sex steroid hormones (estrogens, progesterone and testosterone) are critically involved in myelination, forming the basis of white matter connectivity in the central nervous system. To get insights, we reviewed studies into the relation between sex steroid hormones, white matter and functional connectivity in the human brain, measured with neuroimaging. Results suggest that sex hormones organize structural connections, and activate the brain areas they connect. These processes could underlie a better integration of structural and functional communication between brain regions with age. Specifically, ovarian hormones (estradiol and progesterone) may enhance both cortico-cortical and subcortico-cortical functional connectivity, whereas androgens (testosterone) may decrease subcortico-cortical functional connectivity but increase functional connectivity between subcortical brain areas. Therefore, when examining healthy brain development and aging or when investigating possible biological mechanisms of 'brain connectivity' diseases, the contribution of sex steroids should not be ignored.

Network analysis: applications for the developing brain

Development of the human brain follows a complex trajectory of age-specific anatomical and physiological changes. The application of network analysis provides an illuminating perspective on the dynamic interregional and global properties of this intricate and complex system. Here, we provide a critical synopsis of methods of network analysis with a focus on developing brain networks. After discussing basic concepts and approaches to network analysis, we explore the primary events of anatomical cortical development from gestation through adolescence. Upon this framework, we describe early work revealing the evolution of age-specific functional brain networks in normal neurodevelopment. Finally, we review how these relationships can be altered in disease and perhaps even rectified with treatment. While this method of description and inquiry remains in early form, there is already substantial evidence that the application of network models and analysis to understanding normal and abnormal human neural development holds tremendous promise for future discovery.