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At least 307 records · Page 17Linked to original sources
PubMed2005

Brain-immune interactions and disease susceptibility.

Many studies have established the routes by which the immune and central nervous (CNS) systems communicate. This network of connections permits the CNS to regulate the immune system through both neuroendocrine and neuronal pathways. In turn, the immune system signals the CNS through neuronal and humoral routes, via immune mediators and cytokines. This regulatory system between the immune system and CNS plays an important role in susceptibility and resistance to autoimmune, inflammatory, infectious and allergic diseases. This review focuses on the regulation of the immune system via the neuroendocrine system, and underlines the link between neuroendocrine dysregulation and development of major depressive disorders, autoimmune diseases and osteoporosis.

Autoimmune Diseases↗
PubMed2002

Resonant spatiotemporal learning in large random recurrent networks.

Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present some hypotheses on the computational role of feedback connections.

Brain↗
PubMed1982

Brain transglutaminase: in vitro crosslinking of human neurofilament proteins into insoluble polymers.

The accumulation in aged human neurons of insoluble, high molecular weight filamentous polymers apparently linked by nondisulfide covalent bonds led us to examine human brain for the presence of transglutaminase (EC 2.3.2.13) and endogenous protein substrates for this crosslinking enzyme. We demonstrate the presence in brain of a transamidating enzyme that can covalently crosslink brain proteins into insoluble polymers in vitro by forming gamma-glutamyl-epsilon-lysine intermolecular bridges. Brain transglutaminase is Ca2+ dependent, has an electrophoretic mobility similar to that of erythrocyte transglutaminase, and is active in human postmortem brain from aged normal individuals and patients with Alzheimer disease (senile dementia). Brain neurofilament fractions incubated in the presence of transglutaminase, Ca2+, and the fluorescent amine dansylcadaverine form a fluorescent, nondisulfide-bonded insoluble polymer; this process is associated with a decrease in the amount of soluble neurofilament polypeptides in the preparation. Electron microscopy of the polymeric material reveals an extensive network of connecting filaments, which can be immunostained with various neurofilament antisera. Cystamine, an inhibitor of transglutaminase, prevents the neurofilament crosslinking. Glial filaments and myelin basic protein can also serve as substrates of brain transglutaminase in vitro. Although Alzheimer disease-type paired helical filaments are not formed under the specific in vitro conditions employed, the data suggest one possible mechanism for the covalent crosslinking of filaments into insoluble polymers during human neuronal aging.

Acyltransferases↗
PubMed1998

Functional impact of cerebral projection systems.

Typically, anatomical connections have been traced by injecting pathway-tracing chemicals into restricted portions of the brain. After a few days, the brains are fixed and the transported chemicals identified in histological sections. Orthograde tracers move forward along axons from cell body to axon terminals and retrograde tracers move backwards along axons from terminals to parent cell body. The use of both types of tracers has revealed origins and terminations of pathways and a massively complex network of connections between numerous functionally and anatomically distinct cerebral and subcortical regions. In the monkey visual system alone more than 300 connections have been described between the 32 visual cortical areas. Even so, in the network descriptions neither anatomical strengths nor functional impacts of individual connections are identified. Yet, there is no doubt that knowledge about both aspects of connectivity is essential for developing accurate descriptions of network operations. We describe a new combination of a metabolic mapping and a reversible deactivation technique in an animal model to assess the functional impact of cerebral connections.

Animals↗
PubMed2005

Functional connectivity in elderly controls and AD patients using resting state fMRI: a pilot study.

Conventionally, Alzheimer's disease (AD) and other dementias are diagnosed using clinical assessment, neuropsychology and also structural neuroimaging, showing neuronal degeneration starting in the hippocampal regions. However, there is an increasing need for a new method that is more sensitive to early AD identification than currently possible. A new promising technique that may be used for this is to measure local brain activation using functional magnetic resonance imaging (fMRI), since functional loss predates structural loss of brain tissue. A new method to apply fMRI is to study connectivity between brain regions during a resting state without application of a task. Recent data suggest that connectivity within memory systems during such a resting state is associated with the level of memory function. Here we explain how we will study healthy elderly controls, patients with a mild cognitive impairment (MCI, considered to be a transitional stage between normal condition and AD), and AD patients using resting state connectivity fMRI. If resting state connectivity is sensitive to cognitive decline, this will be of great importance for noninvasive dementia research, offering a tool to easily study functional networks in the brain without the requirement of a memory task, and perhaps offering a tool sensitive for early diagnostics.

Aged↗
PubMed2006

Independent component model of the default-mode brain function: Assessing the impact of active thinking.

The "default-mode" network is an ensemble of cortical regions, which are typically deactivated during demanding cognitive tasks in functional magnetic resonance imaging (fMRI) studies. Using functional connectivity, this network can be conceptualized and studied as a "stand-alone" function or system. Regardless of the task, independent component analysis (ICA) produces a picture of the "default-mode" function even when the subject is performing a simple sensori-motor task or just resting in the scanner. This has boosted the use of default-mode fMRI for non-invasive research in brain disorders. Here, we studied the effect of cognitive load modulation of fMRI responses on the ICA-based pictures of the default-mode function. In a standard graded working memory study based on the n-back task, we used group-level ICA to explore the variability of the default-mode network related to the engagement in the task, in 10 healthy volunteers. The analysis of the default-mode components highlighted similarities and differences in the layout under three different cognitive loads. We found a load-related general increase of deactivation in the cortical network. Nonetheless, a variable recruitment of the cingulate regions was evident, with greater extension of the anterior and lesser extension of the posterior clusters when switching from lower to higher working memory loads. A co-activation of the hippocampus was only found under no working memory load. As a generalization of our results, the variability of the default-mode pattern may link the default-mode system as a whole to cognition and may more directly support use of the ICA model for evaluating cognitive decline in brain disorders.

Adult↗
PubMed1999

Analytical description of the evolution of neural networks: learning rules and complexity.

We present the foundation of a physical formalism that allows us to characterize the dynamics of the evolution of neural networks both in regard to the network configuration and to network performance. Model runs were performed on a simple network consisting of six neurons, allowing complete analytical description of the network's behaviour. Order parameters are characterized that allow an analytical description of critical periods in network evolution. Thus, correlations of the local dynamics and the system's global behaviour could be computed. It is shown that local learning rules are sufficient to model complex dynamical aspects of the evolution of networks. It is demonstrated in how far novel statistical formalisms, e.g. neural complexity, can be employed to evaluate the system's dynamics. The introduction of order parameters allows an analytical characterization of transient phases in the network's behaviour, correlating network connectivity with neuronal firing patterns. The relevance of this approach for the interpretation of physiological data is discussed.

Animals↗
PubMed2006

The cognit: a network model of cortical representation.

The prevalent concept in modular models is that there are discrete cortical domains dedicated more or less exclusively to such cognitive functions as visual discrimination, language, spatial attention, face recognition, motor programming, memory retrieval, and working memory. Most of these models have failed or languished for lack of conclusive evidence. In their stead, network models are emerging as more suitable and productive alternatives. Network models are predicated on the basic tenet that cognitive representations consist of widely distributed networks of cortical neurons. Cognitive functions, namely perception, attention, memory, language, and intelligence, consist of neural transactions within and between these networks. The present model postulates that memory and knowledge are represented by distributed, interactive, and overlapping networks of neurons in association cortex. Such networks, named cognits, constitute the basic units of memory or knowledge. The association cortex of posterior-post-rolandic-regions contains perceptual cognits: cognitive networks made of neurons associated by information acquired through the senses. Conversely, frontal association cortex contains executive cognits, made of neurons associated by information related to action. In both posterior and frontal cortex, cognits are hierarchically organized. At the bottom of that organization-that is, in parasensory and premotor cortex-cognits are small and relatively simple, representing simple percepts or motor acts. At the top of the organization-in temporo-parietal and prefrontal cortex-cognits are wider and represent complex and abstract information of perceptual or executive character. Posterior and frontal networks are associated by long reciprocal cortico-cortical connections. These connections support the dynamics of the perception-action cycle in sequential behavior, speech, and reasoning.

Animals↗
PubMed2005

Neural networks approach to clustering of activity in fMRI data.

Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity.

Algorithms↗
PubMed1992

Thyroid hormone effects on neuronal differentiation during brain development.

Neurite outgrowth and acquisition of neuronal polarity depend on microtubule assembly and this process is impaired when hypothyroidism is established at late fetal stages in the rat. Taking in account these observations the effects of thyroid hormone deficiency in the developing cerebellum were studied with probes for different tubulin isoforms and for two microtubule-associated proteins, tau and MAP2, which are specific for the axons and the dendrites, respectively. The results showed that thyroid hormone deficiency: 1) desynchronizes the spatio-temporal program of axonal and dendritic differentiation in the cerebellum. 2) Modifies the developmental pattern of expression of various tubulin isoforms. 3) Delays replacement of the immature tau variants by the mature forms. The adult variants of tau proteins specify adult and stable axons whereas the juvenile forms are expressed when axons are growing actively. According to these criteria the hypothyroid brain remains immature at stages when proper connectivity is normally established. Thyroid hormone appears therefore as an epigenic signal that synchronizes axonal and dendritic outgrowth, two major parameters of the construction of the neuronal network.

Animals↗
PubMed2003

Enhanced cholinergic suppression of previously strengthened synapses enables the formation of self-organized representations in olfactory cortex.

Computational modeling assists in analyzing the specific functional role of the cellular effects of acetylcholine within cortical structures. In particular, acetylcholine may regulate the dynamics of encoding and retrieval of information by regulating the magnitude of synaptic transmission at excitatory recurrent connections. Many abstract models of associative memory function ignore the influence of changes in synaptic strength during the storage process and apply the effect of these changes only during a so-called recall-phase. Efforts to ensure stable activity with more realistic, continuous updating of the synaptic strength during the storage process have shown that the memory capacity of a realistic cortical network can be greatly enhanced if cholinergic modulation blocks transmission at synaptic connections of the association fibers during the learning process. We here present experimental data from an olfactory cortex brain slice preparation showing that previously potentiated fibers show significantly greater suppression (presynaptic inhibition) by the cholinergic agonist carbachol than unpotentiated fibers. We conclude that low suppression of non-potentiated fibers during the learning process ensures the formation of self-organized representations in the neural network while the higher suppression of previously potentiated fibers minimizes interference between overlapping patterns. We show in a computational model of olfactory cortex, that, together, these two phenomena reduce the overlap between patterns that are stored within the same neural network structure. These results further demonstrate the contribution of acetylcholine to mechanisms of cortical plasticity. The results are consistent with the extensive evidence supporting a role for acetylcholine in encoding of new memories and enhancement of response to salient sensory stimuli.

Carbachol↗
PubMed1999

Matching the modules: cortical maps and long-range intrinsic connections in visual cortex during development.

Visual cortical neurons exhibit a high degree of response selectivity and are grouped into small columns according to their response preferences. The columns are located at regularly spaced intervals covering the whole cortical representation of the visual field with a modular system of feature-selective neurons. The selectivity of these cells and their modular arrangement is thought to emerge from interactions in the network of specific intracortical and thalamocortical connections. Understanding the ontogenesis of this complex structure and contributions of intrinsic and extrinsic, experience-dependent mechanisms during cortical development can provide new insights into the way the visual cortex processes information about the environment. Available data about the development of connections and response properties in the visual cortex suggest that maturation proceeds in two distinct steps. In the first phase, mechanisms inherent to the cortex establish a crude framework of interconnected neural modules which exhibit the basic but still immature traits of the adult state. Relevant mechanisms in this phase are assumed to consist of molecular cues and patterns of spontaneous neural activity in cortical and corticothalamic interconnections. In a second phase, the primordial layout becomes refined under the control of visual experience establishing a fine-tuned network of connections and mature response properties.

Animals↗
PubMed2003

Learning and inference in the brain.

This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in article are: (i). hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii). The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii). Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically.

Bayes Theorem↗
PubMed1992

Stimulus configuration, classical conditioning, and hippocampal function.

Hippocampal participation in classical conditioning is described in terms of a multilayer network that portrays stimulus configuration. The network (a) describes behavior in real time, (b) incorporates a layer of "hidden" units positioned between input and output units, (c) includes inputs that are connected to the output directly as well as indirectly through the hidden-unit layer, and (d) uses a biologically plausible backpropagation procedure to train the hidden-unit layer. Nodes and connections in the neural network are mapped onto regional cerebellar, cortical, and hippocampal circuits, and the effect of lesions of different brain regions is formally studied. Computer simulations of the following classical conditioning paradigms are presented: acquisition of delay and trace conditioning, extinction, acquisition-extinction series of delay conditioning, blocking, over-shadowing, discrimination acquisition, discrimination reversal, feature-positive discrimination, conditioned inhibition, negative patterning, positive patterning, and generalization. The model correctly describes the effect of hippocampal and cortical lesions in many of these paradigms, as well as neural activity in hippocampus and medial septum during classical conditioning. Some of these results might be extended to the description of anterograde amnesia in human patients.

Animals↗
PubMed2004

Role of serotonin in the pathophysiology of the irritable bowel syndrome.

The irritable bowel syndrome (IBS) is a complex disorder that is associated with altered gastrointestinal motility, secretion, and sensation. Serotonin (5-HT) is an important neurotransmitter and paracrine signalling molecule in the gastrointestinal tract. 5-HT release from enterochromaffin (EC) cells initiates peristaltic, secretory, vasodilatory, vagal and nociceptive reflexes. The enteric nervous system (ENS) comprises a semiautonomous effector system that is connected to the central autonomic network. Parasympathetic and sympathetic nerves modulate the ENS via afferent and efferent communications. Ongoing, bidirectional brain-gut interactions involving 5-HT pathways occur that significantly influence the effector systems. Altered 5-HT signalling may lead to both intestinal and extraintestinal symptoms in IBS. 5-HT directly and indirectly affects intestinal motor and secretory function and abnormalities may lead to either constipation or diarrhea. 5-HT modulates sensation and perception of visceral stimulation at peripheral and central sites. Therapeutic agents targeting altered 5-HT signalling may provide new, effective treatments for patients with IBS.

Animals↗
PubMed2003

Executive functions and their disorders.

The term executive function defines complex cognitive processing requiring the co-ordination of several subprocesses to achieve a particular goal. Neuropsychological evidence suggests that executive processing is intimately connected with the intact function of the frontal cortices. Executive dysfunction has been associated with a range of disorders, and is generally attributed to structural or functional frontal pathology. Neuroimaging, with PET and fMRI, has confirmed the relationship; however, attempts to link specific aspects of executive functioning to discrete prefrontal foci have been inconclusive. Instead, the emerging view suggests that executive function is mediated by dynamic and flexible networks, that can be characterised using functional integration and effective connectivity analyses. This view is compatible with the clinical presentation of executive dysfunction associated with a range of pathologies, and also with evidence that recovery of executive function can occur after traumatic brain injury, perhaps due to functional reorganisation within executive networks.

Brain Injuries↗
PubMed1997

Modifiable neuronal connections: an overview for psychiatrists.

Synaptic plasticity is currently the target of much neurobiological research, because it is thought to play an important role in brain function (particularly memory formation). However, it has attracted little attention from psychiatrists to date despite accumulating evidence that links it to various clinical syndromes, including amnesia and possibly psychosis. The purpose of this article is to present an overview of the two major arms of synaptic plasticity research-theoretical (the field of neural network modeling) and neurobiological (long-term potentiation). Artificial neural networks are a class of theoretical model that has been developed with the aim of understanding how information could, in principle, be represented by large numbers of interconnected and relatively simple units. Over the past few decades, several theoretical accounts of information-processing mechanisms have been developed, and these are briefly reviewed. The principle common to representation formation in nearly all neural networks is that of "associability"-the idea that streams of information are combined by forming, strengthening, or pruning connections between them to form new representations that can later be retrieved. Associability also lies at the heart of psychological theories of information storage in the brain. Research into associability has directed the attention of many experimenters toward the possible biological correlates of such mechanisms. Of particular interest is the recent discovery that some neurons appear to possess connections of modifiable strength. The implications of this finding for psychiatry are discussed in relation to representational disorders such as delusions and amnesia.

Amnesia↗