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Nine mongrel adult dogs underwent temporalis muscle transposition on the surface of the left hemisphere, the arachnoid having been removed. Histological confirmation of vessel networks connecting muscle and brain was achieved after 90 days.
The social brain hypothesis is a useful heuristic for understanding schizophrenia. It focuses attention on the core Bleulerian concept of autistic alienation and is consistent with well-replicated findings of social brain dysfunction in schizophrenia as well as contemporary theories of human cognitive and brain evolution. The contributions of Heidegger, Merleau-Ponty and Wittgenstein allow us to arrive at a new "philosophy of interpersonal relatedness", which better reflects the "embodied mind" and signifies the end of Cartesian dualistic thinking. In this paper I review the evolution, development and neurobiology of the social brain - the anatomical and functional substrate for adaptive social behaviour and cognition. Functional imaging identifies fronto-temporal and fronto-parietal cortical networks as comprising the social brain, while the discovery of "mirror neurons" provides an understanding of social cognition at a cellular level. Patients with schizophrenia display abnormalities in a wide range of social cognition tasks such as emotion recognition, theory of mind and affective responsiveness. Furthermore, recent research indicates that schizophrenia is a disorder of functional and structural connectivity of social brain networks. These findings lend support to the claim that schizophrenia represents a costly by-product of social brain evolution in Homo sapiens. Individuals with this disorder find themselves seriously disadvantaged in the social arena and vulnerable to the stresses of their complex social environments. This state of "disembodiment" and interpersonal alienation is the core phenomenon of schizophrenia and the root cause of intolerable suffering in the lives of those affected.
We propose a novel method using Bayesian networks to learn the structure of effective connectivity among brain regions involved in a functional MR experiment. The approach is exploratory in the sense that it does not require an a priori model as in the earlier approaches, such as the Structural Equation Modeling or Dynamic Causal Modeling, which can only affirm or refute the connectivity of a previously known anatomical model or a hypothesized model. The conditional probabilities that render the interactions among brain regions in Bayesian networks represent the connectivity in the complete statistical sense. The present method is applicable even when the number of regions involved in the cognitive network is large or unknown. We demonstrate the present approach by using synthetic data and fMRI data collected in silent word reading and counting Stroop tasks.
How does the brain cause positive affective reactions to sensory pleasure? An answer to pleasure causation requires knowing not only which brain systems are activated by pleasant stimuli, but also which systems actually cause their positive affective properties. This paper focuses on brain causation of behavioral positive affective reactions to pleasant sensations, such as sweet tastes. Its goal is to understand how brain systems generate 'liking,' the core process that underlies sensory pleasure and causes positive affective reactions. Evidence suggests activity in a subcortical network involving portions of the nucleus accumbens shell, ventral pallidum, and brainstem causes 'liking' and positive affective reactions to sweet tastes. Lesions of ventral pallidum also impair normal sensory pleasure. Recent findings regarding this subcortical network's causation of core 'liking' reactions help clarify how the essence of a pleasure gloss gets added to mere sensation. The same subcortical 'liking' network, via connection to brain systems involved in explicit cognitive representations, may also in turn cause conscious experiences of sensory pleasure.
We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band-filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of "small-world" features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.
Obesity is today one of the commonest of life-threatening diseases in developed countries and generally results from an imbalance between energy intake and energy expenditure. Although there is increasing evidence for a genetic basis of obesity in some clinical syndromes, this seems to be the cause only in a limited number of patients and obesity is far from being considered as a gene-related disease. Eating is a complex and multifactorial process involving autonomous pathways that transfer sensory and motor information between the entire length of the digestive tract and the central nervous system. Modulation of the amount of energy that we take in as food involves several mechanisms and networks that connect the brain with the gut, this process being key to the regulation of body weight over time, as well as to the modification of long-term eating behaviors. Furthermore, this axis is closely coupled to other systems that are involved in energy homeostasis, namely, food preference, energy expenditure, and lifestyle. The identification of several neuropeptides that modulate eating behavior in various ways, along with studies performed in animal models, have focused attention on the role of these molecules and their clinical implications in the development of obesity in humans.
Latency to sleep and the amount of sensory stimulation required to awaken an animal are measures of arousal threshold, which are ultimately modulated by an arousal regulation system involving many brain areas. Among these brain areas and network connections are wake-promoting nuclei of the brainstem and their corresponding neurotransmitters, including norepinephrine (NE). In this study, we used mice that are unable to produce NE to study its role in regulating sleep latency after a variety of interventions, and to study arousal from sleep after sleep deprivation (SD). Sleep latency was measured after gentle awakening or after injections of saline, caffeine or modafinil. Sleep latency was also measured before and after partial restoration of NE pharmacologically. Arousal threshold was measured by recording the number of decibels of white noise required to wake each mouse from NREM sleep after 0, 3 and 3 + 3 h SD (3 h SD followed by < 2 min sleep, followed by an additional 3 h SD). Results showed that when mice were awakened without being touched, there were no differences in sleep latency between the genotypes. However, after an injection of saline, the control mice increased their sleep latency, whereas the NE-deficient mice did not. There were no group differences in sleep latency after treatment with either stimulant. The sleep latency difference between the genotypes was ameliorated by partial restoration of NE. The arousal threshold experiments revealed that significantly more noise was required to wake the NE-deficient mice after 3 and 3 + 3 h of SD. These findings show that mice lacking NE fall asleep more rapidly only after a mild stressor, such as an intraperitoneal injection. NE-deficient mice are also more difficult to wake up using audio stimulation after SD. The results presented here suggest that NE promotes wakefulness during transitions between sleep and wake under conditions involving mild stress and SD, but not under baseline circumstances.
The various components which together make up the complex state of consciousness require neural support involving a connected network of many brain areas at differing levels. At the lowest level is non-aware processing, of which there is not direct awareness. There are also modules involved in processing with awareness but without focussed attention. Finally there must be a set of modules involved in directing attention in a controlled manner. We expect to be able to dissociate the various components of the three-stage network by using different levels of attention. The results of an auditory experiment performed under three different levels of awareness and attention are analysed to show support for the three-stage model of awareness. The relevant auditory areas are delineated.
The precise number and pattern of axonal connections generated during brain development regulates animal behavior. Therefore, understanding how developmental signals interact to regulate axonal extension and retraction to achieve precise neuronal connectivity is a fundamental goal of neurobiology. We investigated this question in the developing adult brain of Drosophila and find that it is regulated by crosstalk between Wnt, fibroblast growth factor (FGF) receptor, and Jun N-terminal kinase (JNK) signaling, but independent of neuronal activity. The Rac1 GTPase integrates a Wnt-Frizzled-Disheveled axon-stabilizing signal and a Branchless (FGF)-Breathless (FGF receptor) axon-retracting signal to modulate JNK activity. JNK activity is necessary and sufficient for axon extension, whereas the antagonistic Wnt and FGF signals act to balance the extension and retraction required for the generation of the precise wiring pattern.
Noradrenaline is implicated in the modulation of attention and arousal, but the neuroanatomical basis of this effect in humans is unknown. A previous functional neuroimaging study failed to find clear effects of clonidine (alpha2 adrenoceptor agonist) on activity of brain regions implicated in attention. Therefore, we now investigate whether clonidine affects the functional integration of a neuroanatomical attentional network, by modulating connectivity between brain regions rather than activity within discrete regions. Following infusion of either clonidine or placebo, positron emission tomography measurements of brain activity were collected in 13 normal subjects while they were either resting or performing an attentional task. Effective connectivity analysis showed that during rest, clonidine decreased the functional strength of connections both from frontal cortex to thalamus and in pathways to and from visual cortex. Conversely, during the attentional task, functional integration generally increased, with changes being centered on parietal cortex (increased connectivity from locus coeruleus to parietal cortex and from parietal cortex to thalamus and frontal cortex). A drug-induced increase in the modulatory effects of frontal cortex on projections from locus coeruleus to parietal cortex was also observed. Collectively, these results highlight cognitively dissociable effects of clonidine on interactions among functionally integrated brain regions and implicate the noradrenergic system in mediating the functional integration of attentional brain systems. The context-sensitive nature of the changes are consistent with observations that noradrenergic drugs have differential effects on brain processes depending on subjects' underlying arousal levels. More generally, the results illustrate the dynamic plasticity of cognitive brain systems following neurochemical challenge.
Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called 'small-world' and 'scale-free' networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans. Here, we extend this work to a specific neural circuit of the vertebrate brain--the medial reticular formation (RF) of the brainstem--and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement.
Most cognitive processes are supported by large networks of brain regions. To describe the operation of these networks, it is critical to understand how individual areas are functionally connected. Here, we establish a statistical framework for studying effective and functional brain connectivity, using data obtained with a relatively new neuroimaging method, the event-related optical signal (EROS). The novelty of our approach is the use of timing information (in the form of lagged cross-correlations) in interpreting the connections between areas. Interpretation of lagged cross-correlations exploits the combination of spatial and temporal resolution provided by EROS. In this paper, we apply dynamic factor analysis as a method for testing various structural models on the lagged covariance matrices derived from the EROS data. We first illustrate the approach by testing a simple path model of neural activity propagation from area V1 to V3 in a visual stimulation task. We then build more complex structural equation models with latent variables, describing both within-hemisphere integrity, and interactions between the two hemispheres, to interpret data from a second task involving inter-hemispheric competition. The results demonstrate how the integrity of anatomical connections between the two hemispheres explains different patterns of cross-hemispheric interactions. This approach allows for fitting brain imaging data to complex models that capture dynamic cognitive processes as they rapidly evolve over time.
Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0-0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the "default" network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253-258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain.
What features of brain processing and neural development support linguistic and cognitive development in young children? To what extent are the profile and timing of development in young children determined by a preordained genetic programme? Does the environment play a crucial role in determining the patterns of change observed in children growing up? These questions have been of central concern to developmental psychologists for well over a century. Yet none of them have received answers that are generally accepted by the profession. This article reviews some recent computational modelling of developmental change in children that promises to contribute to a deeper understanding of the issues behind these questions. The modelling work exploits artificial neural networks that mimic some of the basic properties of neural processing in the brain. These networks involve densely connected webs of simple processing units that propagate and transform complex patterns of activity. When exposed to a training environment, they undergo a process of self-organisation, yielding information processing systems that support new forms of behaviour. The study of the dynamics of these systems and their learning capabilities promises to provide us with important clues as to the nature of the mechanisms underlying development in infants and young children.
Bayesian network connects graph theory with statistics, being an important research direction in data mining. Compared with other approaches used for data mining, Bayesian network can combine prior knowledge with observed data. Besides that, it can handle incomplete data sets. This paper applies Bayesian network to predict the malignant degree of brain glioma. Totally 280 cases are collected, and some of them contain missing values. Preprocessing is taken to make them applicable to the algorithms. Unlike MLP network, both Bayesian network and decision tree use attribute-value pairs to represent diagnostic knowledge derived from treated cases. These could improve both the understandability and applicability of their results. Results of all these algorithms can achieve accuracy rate over 80%, which satisfies the requirement of neuroradiologists.
OBJECTIVES: To illuminate the current methodological and conceptual pitfalls inherent in conducting functional magnetic resonance imaging (fMRI) research with individuals who have sustained traumatic brain injury (TBI) and to discuss appropriate remedies. The aim is describe fMRI research, its limitations, and how to best use this technology to examine TBI. DISCUSSION: The topics discussed in this article include issues regarding signal detection, brain activation measurement, head movement, and sources of signal artifact. Issues surrounding data interpretation and the importance of analyzing the brain as a connected neural network is also discussed. Finally, problems with spatial normalization when examining individuals with TBI are reviewed. CONCLUSIONS: To date, there is a scarcity of research applying fMRI technology to the study of TBI. However, because it is a noninvasive procedure with high availability in hospital settings across the country, the next decade of TBI research will likely include a proliferation of this form of investigation. At this time, much work is needed to better understand how to optimally use this technology to examine the effects of TBI on behavior. For fMRI to enhance TBI research it will be imperative to establish valid research protocols and reliable methods of data interpretation.
1. The forebrain is a major organizer of the complex behavioural, physiological and neuroendocrine responses to environmental challenges of a stressful nature. 2. Combined physiological and neuroanatomical studies suggest that a specific forebrain-brain stem network, composed of connections between the central nucleus of the amygdala, the paraventricular nucleus of the hypothalamus, the mesencephalic cuneiform nucleus, the parabrachial nucleus and the dorsal motor nucleus of the vagus nerve, may be important for integrating behavioural and physiological responses. 3. Based on studies using bilateral electrolytic lesions of the central nucleus of the amygdala, it has become clear that the central nucleus of the amygdala is one of the key structures involved in unconditioned responses to inescapable footshock. These responses include freezing behaviour, tachycardia and the release of adrenaline, noradrenaline, prolactin and corticosterone. However, this nucleus is involved only in the freezing behaviour and bradycardiac responses to conditioned emotional stress or to social defeat. 4. Both peptidergic (corticotropin releasing hormone and vasopressin/oxytocin) and aminergic (noradrenaline and dopamine) mechanisms in the central amygdala are involved in the regulation of integrated behavioural, physiological and neuroendocrine stress responses. This is indicated by studies with an infusion of an agonist and/or antagonist of the peptides or neurotransmitters into the central amygdala of freely moving rats. Sympathetic cardiac control is intensified by corticotropin releasing hormone and oxytocin, probably by inhibiting vagal output. In contrast, vagal activity is facilitated by vasopressin, noradrenaline and dopamine.
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