Robust reproducible resting state networks in the awake rodent brain

Resting state networks (RSNs) have been studied extensively with functional MRI in humans in health and disease to reflect brain function in the un-stimulated state as well as reveal how the brain is altered with disease. Rodent models of disease have been used comprehensively to understand the biology of the disease as well as in the development of new therapies. RSN reported studies in rodents, however, are few, and most studies are performed with anesthetized rodents that might alter networks and differ from their non-anesthetized state. Acquiring RSN data in the awake rodent avoids the issues of anesthesia effects on brain function. Using high field fMRI we determined RSNs in awake rats using an independent component analysis (ICA) approach, however, ICA analysis can produce a large number of components, some with biological relevance (networks). We further have applied a novel method to determine networks that are robust and reproducible among all the components found with ICA. This analysis indicates that 7 networks are robust and reproducible in the rat and their putative role is discussed.     

Spatial constancy and the brain: insights from neural networks

To form an accurate internal representation of visual space, the brain must accurately account for movements of the eyes, head or body. Updating of internal representations in response to these movements is especially important when remembering spatial information, such as the location of an object, since the brain must rely on non-visual extra-retinal signals to compensate for self-generated movements. We investigated the computations underlying spatial updating by constructing a recurrent neural network model to store and update a spatial location based on a gaze shift signal, and to do so flexibly based on a contextual cue. We observed a striking similarity between the patterns of behaviour produced by the model and monkeys trained to perform the same task, as well as between the hidden units of the model and neurons in the lateral intraparietal area (LIP). In this report, we describe the similarities between the model and single unit physiology to illustrate the usefulness of neural networks as a tool for understanding specific computations performed by the brain.     

Energetic dynamics and anuran breeding phenology : insights from a dynamic game

We designed a dynamic optimization model to examine anuran-breeding phenologies. We evaluated the fitness consequences for malesadopting one of four alternative strategies : calling, satelliting,foraging, or hiding. Various factors potentially influencemale behavior, including energy reserves, predation risk, costof calling, probability of finding food, distribution of maleenergy states in the population, and probability of survivingto another breeding season. We manipulated these parametersto determine how strongly each affects breeding phenology andchorus structure. Manipulating parameters related to the energeticcosts and benefits of individual decisions, we generated thethree basic patterns of anuran breeding phenology : explosive,continuous, and prolonged breeding with episodic chorusing.Increasing the probability of successful foraging caused a shift from an explosive pattern to a prolonged, episodic chorusingpattern. Decreasing the calling cost resulted in continuouschorusing. Our model predicted that satelliting will be a rarestrategy adopted by individuals with relatively low energyreserves. Additionally, individuals adopting the satellitestrategy should alternate among satelliting, foraging, and calling as their energy reserves fluctuate. Our results suggest thatenergetic costs of reproduction and resource limitation maybe crucial factors influencing the phenology of anuran chorusing.We propose that under varying conditions of resource availability,male decisions are the consequence of two strategies : a starvationminimization strategy and an energy-state maximization strategy.     

Understanding autism: insights from mind and brain

Autism is a developmental disorder characterized by impaired social interaction and communication as well as repetitive behaviours and restricted interests. The consequences of this disorder for everyday life adaptation are extremely variable. The general public is now more aware of the high prevalence of this lifelong disorder, with ca. 0.6% of the population being affected. However, the signs and symptoms of autism are still puzzling. Since a biological basis of autism was accepted, approaches from developmental cognitive neuroscience have been applied to further our understanding of the autism spectrum. The study of the behavioural and underlying cognitive deficits in autism has advanced ahead of the study of the underlying brain abnormalities and of the putative genetic mechanisms. However, advances in these fields are expected as methodological difficulties are overcome. In this paper, recent developments in the field of autism are outlined. In particular, we review the findings of the three main neuro-cognitive theories of autism: theory-of-mind deficit, weak central coherence and executive dysfunction.     

Visual attention: insights from brain imaging

We are not passive recipients of the information that impinges on our retinae, but active participants in our own perceptual processes. Visual experience depends critically on attention. We select particular aspects of a visual scene for detailed analysis and control of subsequent behaviour, but ignore other aspects so completely that moments after they disappear from view we cannot report anything about them. Here we show that functional neuroimaging is revealing much more than where attention happens in the brain; it is beginning to answer some of the oldest and deepest questions about what visual attention is and how it works.     

Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study

Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.     

Biological networks: from physical principles to biological insights

A report on the Fourth Georgia Tech and UGA International Conference on Bioinformatics 'Biological Networks: From Genomics to Epidemiology', Atlanta, USA, 13-16 November 2003.     

The adolescent brain: insights from functional neuroimaging research

With the development of functional neuroimaging tools, the past two decades have witnessed an explosion of work examining functional brain maps, mostly in the adult brain. Against this backdrop of work in adults, developmental research begins to gather a substantial body of knowledge about brain maturation. The purpose of this review is to present some of these findings from the perspective of functional neuroimaging. First, a brief survey of available neuroimaging techniques (i.e., fMRI, MRS, MEG, PET, SPECT, and infrared techniques) is provided. Next, the key cognitive, emotional, and social changes taking place during adolescence are outlined. The third section gives examples of how these behavioral changes can be understood from a neuroscience perspective. The conclusion places this functional neuroimaging research in relation to clinical and molecular work, and shows how answers will ultimately come from the combined efforts of these disciplines.     

Invasive recordings from the human brain: clinical insights and beyond

Although non-invasive methods such as functional magnetic resonance imaging, electroencephalograms and magnetoencephalograms provide most of the current data about the human brain, their resolution is insufficient to show physiological processes at the cellular level. Clinical approaches sometimes allow invasive recordings to be taken from the human brain, mainly in patients with epilepsy or with movement disorders, and such recordings can sample neural activity at spatial scales ranging from single cells to distributed cell assemblies. In addition to their clinical relevance, these recordings can provide unique insights into brain functions such as movement control, perception, memory, language and even consciousness.     

The effect of space in plant–animal mutualistic networks: insights from a simulation study

The topology of plant–animal mutualistic networks has the potential to determine the ecological and evolutionary dynamics of interacting species. Many mechanisms have been proposed as explanations of observed network patterns; however, the fact that plant–animal interactions are inherently spatial has so far been ignored. Using a simulation model of frugivorous birds foraging in spatially explicit landscapes we evaluated how plant distribution and the scale of bird movement decisions influenced species interaction probabilities and the resulting network properties. Spatial aggregation and limited animal mobility restricted encounter probabilities, so that the distribution of animal visits per plant deviated strongly from the binomial distribution expected for a well-mixed system. Lack of mixing in turn resulted in a strong decrease in network connectance, a weak decrease in nestedness, stronger interactions, greater strength asymmetry and the unexpected presence/absence of some interactions. Our results suggest that spatial processes may contribute substantially to structure plant–animal mutualistic networks.     

Turbidostat culture of yeast during transition from a resting state to active growth

The age components of a Saccharomyces cerevisiae 14 culture and the kinetics of its growth were studied afer the quiescent state at the onset active growth. The following factors induced the quiescent state: the cessation of a chemostat flow for 24 h, growth inhibition with 2,4-dinitrophenol (DNP) for 24 h, the storage of a culture growing on agar in a refrigerator during 24 h. The process of transition from the point of growth activation to the maximum rate of growth was then studied in turbidostat. This process took the shortest time in a refrigerated culture as well as in a culture that had been limited with a phosphorus source and in a culture limited with a nitrogen source and grown in chemostat at D = 0.26 h-1. The process was longer in cultures that had been either limited with glucose or inhibited with DNP and longest in a chemostat culture limited with a nitrogen source at D = 0.15 and 0.05 h-1. The rate of initial mitosis phases in the yeast is presumed to exert the greatest effect on the duration of this transition process.     

Loading effect of fibroblast-myocyte coupling on resting potential, impulse propagation, and repolarization: insights from a microstructure model

The numerous nonmyocytes present within the myocardium may establish electrical connections with myocytes through gap junctions, formed naturally or as a result of a cell therapy. The strength of the coupling and its potential impact on action potential characteristics and conduction are not well understood. This study used computer simulation to investigate the load-induced electrophysiological consequences of the coupling of myocytes with fibroblasts, where the fibroblast resting potential, density, distribution, and coupling strength were varied. Conduction velocity (CV), upstroke velocity, and action potential duration (APD) were analyzed for longitudinal and transverse impulse propagation in a two-dimensional microstructure tissue model, developed to represent a monolayer culture of cardiac cells covered by a layer of fibroblasts. The results show that 1) at weak coupling (<0.25 nS), the myocyte resting potential was elevated, leading to CV up to 5% faster than control; 2) at intermediate coupling, the myocyte resting potential elevation saturated, whereas the current flowing from the myocyte to the fibroblast progressively slowed down both CV and upstroke velocity; 3) at strong couplings (>8 nS), all of the effects saturated; and 4) APD at 90% repolarization was usually prolonged by 0-20 ms (up to 60-80 ms for high fibroblast density and coupling) by the coupling to fibroblasts. The changes in APD depended on the fibroblast resting potential. This complex, coupling-dependent interaction of fibroblast and myocytes also has relevance to the integration of other nonmyocytes in the heart, such as those used in cellular therapies.     

Searching for a baseline: functional imaging and the resting human brain

Functional brain imaging in humans has revealed task-specific increases in brain activity that are associated with various mental activities. In the same studies, mysterious, task-independent decreases have also frequently been encountered, especially when the tasks of interest have been compared with a passive state, such as simple fixation or eyes closed. These decreases have raised the possibility that there might be a baseline or resting state of brain function involving a specific set of mental operations. We explore this possibility, including the manner in which we might define a baseline and the implications of such a baseline for our understanding of brain function.     

Reward representations and reward-related learning in the human brain: insights from neuroimaging

This review outlines recent findings from human neuroimaging concerning the role of a highly interconnected network of brain areas including orbital and medial prefrontal cortex, amygdala, striatum and dopaminergic mid-brain in reward processing. Distinct reward-related functions can be attributed to different components of this network. Orbitofrontal cortex is involved in coding stimulus reward value and in concert with the amygdala and ventral striatum is implicated in representing predicted future reward. Such representations can be used to guide action selection for reward, a process that depends, at least in part, on orbital and medial prefrontal cortex as well as dorsal striatum.     

Hearing and seeing meaning in speech and gesture: insights from brain and behaviour

As we speak, we use not only the arbitrary form–meaning mappings of the speech channel but also motivated form–meaning correspondences, i.e. iconic gestures that accompany speech (e.g. inverted V-shaped hand wiggling across gesture space to demonstrate walking). This article reviews what we know about processing of semantic information from speech and iconic gestures in spoken languages during comprehension of such composite utterances. Several studies have shown that comprehension of iconic gestures involves brain activations known to be involved in semantic processing of speech: i.e. modulation of the electrophysiological recording component N400, which is sensitive to the ease of semantic integration of a word to previous context, and recruitment of the left-lateralized frontal–posterior temporal network (left inferior frontal gyrus (IFG), medial temporal gyrus (MTG) and superior temporal gyrus/sulcus (STG/S)). Furthermore, we integrate the information coming from both channels recruiting brain areas such as left IFG, posterior superior temporal sulcus (STS)/MTG and even motor cortex. Finally, this integration is flexible: the temporal synchrony between the iconic gesture and the speech segment, as well as the perceived communicative intent of the speaker, modulate the integration process. Whether these findings are special to gestures or are shared with actions or other visual accompaniments to speech (e.g. lips) or other visual symbols such as pictures are discussed, as well as the implications for a multimodal view of language.     

Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity

Background

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.

Methodology/Principal Findings

In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.

Conclusions/Significance

The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.     

Musical training induces functional plasticity in perceptual and motor networks: insights from resting-state FMRI

A number of previous studies have examined music-related plasticity in terms of multi-sensory and motor integration but little is known about the functional and effective connectivity patterns of spontaneous intrinsic activity in these systems during the resting state in musicians. Using functional connectivity and Granger causal analysis, functional and effective connectivity among the motor and multi-sensory (visual, auditory and somatosensory) cortices were evaluated using resting-state functional magnetic resonance imaging (fMRI) in musicians and non-musicians. The results revealed that functional connectivity was significantly increased in the motor and multi-sensory cortices of musicians. Moreover, the Granger causality results demonstrated a significant increase outflow-inflow degree in the auditory cortex with the strongest causal outflow pattern of effective connectivity being found in musicians. These resting state fMRI findings indicate enhanced functional integration among the lower-level perceptual and motor networks in musicians, and may reflect functional consolidation (plasticity) resulting from long-term musical training, involving both multi-sensory and motor functional integration.