SEARCH · PubMed Health

Results for “functional network connectivity”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15Linked to original sources

PubMed2004

[Changes in the emotional processing in depressive patients: a study with functional magnetoresonance tomography under the employment of pictures with affective contents].

OBJECTIVE: The influence of emotional contents on brain activation was examined using functional magnetic resonance tomography (fMRT). METHOD: Aquiring 100 EPI-measurements of the whole brain, series of positive and negative pictures from the International Affective Picture System (IAPS) were shown to eight depressed males and to healthy controls. RESULTS: In depression, there was an overactivation in hippocampal area during positive stimulation, whereas an overactivation of amygdala, orbital and prefrontal regions was seen during negative stimulation. CONCLUSIONS: These results emphasize the importance of functional interaction between the neural network associated with emotion. We could show that depression is connected to dysregulation and disturbed functional connectivity during emotional processing.

Adult↗

PubMed2004

Selective amplification of neocortical neuronal output by fast prepotentials in vivo.

Neocortical cells integrate inputs from thousands of presynaptic neurons distributed along their dendritic arbors. Propagation of postsynaptic potentials to the soma is crucial in determining neuronal output. Using intracellular recordings in anesthetized and non-anesthetized, naturally awake and sleeping cats, we found evidence for generation of fast, all-or-none events recorded at the soma in about 20% of regular-spiking and intrinsically-bursting neurons. These events, termed fast prepotentials (FPPs), were suppressed by hyperpolarizing the neurons or by inhibiting synaptic transmission with perfusion of Ca2+-free artificial cerebrospinal fluid. FPPs could be evoked by activation of specific cortical inputs and allowed neurons to fire at more hyperpolarized levels of membrane potentials. Thus, FPPs represent a powerful mechanism to boost the output of neocortical neurons in response to given inputs. We further found evidence for modulation of FPPs generation across the waking-sleep cycle, indicating important changes in the integrative properties of neocortical neurons in different states of vigilance. We suggest that FPPs represent attenuated spikes generated in hot spots of the dendritic arbor and constitute a powerful mechanism to reinforce the functional connections between specific elements of the cortical networks.

Animals↗

PubMed2006

Effects of healthy aging on hippocampal and rhinal memory functions: an event-related fMRI study.

Event-related functional magnetic resonance imaging was used to study the effects of healthy aging on hippocampal and rhinal memory functions. Memory for past events can be based on retrieval accompanied by specific contextual details (recollection) or on the feeling that an event is old or new without the recovery of contextual details (familiarity). There is evidence that recollection is more dependent on hippocampus, whereas familiarity is more dependent on the rhinal cortex, and that healthy aging has greater effects on recollection than on familiarity. However, little evidence is available about the neural correlates of these effects. Here, we isolated activity associated with recollection and familiarity by distinguishing between linear and quasi-exponential "perceived oldness" functions derived from recognition confidence levels. The main finding was a double dissociation within the medial temporal lobes between recollection-related activity in hippocampus, which was reduced by aging, and familiarity-related activity in rhinal cortex, which was increased by aging. In addition, age dissociations were found within parietal and posterior midline regions. Finally, aging reduced functional connectivity within a hippocampal-retrosplenial/parietotemporal network but increased connectivity within a rhinal-frontal network. These findings indicate that older adults compensate for hippocampal deficits by relying more on rhinal cortex, possibly through a top-down frontal modulation. This finding has important clinical implications because early Alzheimer's disease impairs both hippocampus and rhinal cortex.

Adult↗

PubMed2006

A plaidoyer for 'systems immunology'.

A complete understanding of the immune system will ultimately require an integrated perspective on how genetic and epigenetic entities work together to produce the range of physiologic and pathologic behaviors characteristic of immune function. The immune network encompasses all of the connections and regulatory associations between individual cells and the sum of interactions between gene products within a cell. With 30,000+ protein-coding genes in a mammalian genome, further compounded by microRNAs and yet unrecognized layers of genetic controls, connecting the dots of this network is a monumental task. Over the past few years, high-throughput techniques have allowed a genome-scale view on cell states and cell- or system-level responses to perturbations. Here, we observe that after an early burst of enthusiasm, there has developed a distinct resistance to placing a high value on global genomic or proteomic analyses. Such reluctance has affected both the practice and the publication of immunological science, resulting in a substantial impediment to the advances in our understanding that such large-scale studies could potentially provide. We propose that distinct standards are needed for validation, evaluation, and visualization of global analyses, such that in-depth descriptions of cellular responses may complement the gene/factor-centric approaches currently in favor.

Allergy and Immunology↗

PubMed1997

A neural network based artificial vision system for licence plate recognition.

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

Algorithms↗

PubMed2005

Is the cerebellum relevant in the circuitry of neuropsychiatric disorders?

Contemporary mechanistic models of several psychiatric disorders propose abnormalities in the structure and function of distinct neural networks. The cerebellum has both anatomic and functional connections to the prefrontal cortex, the subcortical limbic structures and monoamine-producing brainstem nuclei. Conspicuously, however, the cerebellum has been underemphasized in neuropsychiatric research. A growing confluence of scientific data indicate that the cerebellum may not be irrelevant, which suggests that an integrated model of neuropsychiatric disorders should include a role for the cerebellum and its relevant neural connections. This review summarizes the published data describing and characterizing the putative role of the cerebellum in normal and abnormal mood regulation, with specific attention to states of psychosis, depression and mania. The available evidence suggests that a functional role for the cerebellum should be considered in future neuropsychiatric studies.

Bipolar Disorder↗

PubMed1984

Selective networks and recognition automata.

The results we have presented demonstrate that a network based on a selective principle can function in the absence of forced learning or an a priori program to give recognition, classification, generalization, and association. While Darwin II is not a model of any actual nervous system, it does set out to solve one of the same problems that evolution had to solve--the need to form categories in a bottom-up manner from information in the environment, without incorporating the assumptions of any particular observer. The key features of the model that make this possible are (1) Darwin II incorporates selective networks whose initial specificities enable them to respond without instruction to unfamiliar stimuli; (2) degeneracy provides multiple possibilities of response to any one stimulus, at the same time providing functional redundancy against component failure; (3) the output of Darwin II is a pattern of response, making use of the simultaneous responses of multiple degenerate groups to avoid the need for very high specificity and the combinatorial disaster that would imply; (4) reentry within individual networks vitiates the limitations described by Minsky and Papert for a class of perceptual automata lacking such connections; and (5) reentry between intercommunicating networks with different functions gives rise to new functions, such as association, that either one alone could not display. The two kinds of network are roughly analogous to the two kinds of category formation that people use: Darwin, corresponding to the exemplar description of categories, and Wallace, corresponding to the probabilistic matching description of categories. These principles lead to a new class of pattern-recognizing machine of which Darwin II is just an example. There are a number of obvious extensions to this work that we are pursuing. These include giving Darwin II the capability to deal with stimuli that are in motion, an ability that probably precedes the ability of biological organisms to deal with stationary stimuli, giving it the capability to deal with multiple stimulus objects through some form of attentional mechanism, and giving it a means to respond directly and to receive feedback from the world so that it can learn conventionally. Already, however, we have shown that a working pattern-recognition automaton can be built based on a selective principle. This development promises ultimately to show us how to build recognizing machines without programs and to provide a sound basis for the study of both natural and artificial intelligence.

Automation↗

PubMed1994

Average case analysis of an Hebb-type rule that finds the network connectivity.

We describe an Hebb-type algorithm for learning unions of nonoverlapping perceptrons with binary weights. Two perceptrons are said to be nonoverlapping if they do not share any input variables. The learning algorithm is able to find both the network architecture and the weight values necessary to represent the target function. Moreover, the algorithm is local, homogeneous, and simple enough to be biologically plausible. We investigate the average behavior of this algorithm as a function of the size of the training set. We find that, as the size of the training set increases, the hypothesis network built by the algorithm "converges" to the target network, both in terms of the number of perceptrons and the connectivity. Moreover, the generalization rate converges exponentially to perfect generalization as a function of the number of training examples. The analytic expressions are in excellent agreement with the numerical simulations. To our knowledge, this is the first average case analysis of an algorithm that finds both the weight values and the network connectivity.

Algorithms↗

PubMed2004

Molecular evolution in the yeast transcriptional regulation network.

We analyze the structure of the yeast transcriptional regulation network, as revealed by chromatin immunoprecipitation experiments, and characterize the molecular evolution of both its transcriptional regulators and their target (regulated) genes. We test the hypothesis that highly connected genes are more important to the function of gene networks. Three lines of evidence-the rate of molecular evolution of network genes, the rate at which network genes undergo gene duplication, and the effects of synthetic null mutation in network genes-provide no strong support for this hypothesis. In addition, we ask how network genes diverge in their transcriptional regulation after duplication. Both loss (subfunctionalization) and gain (neofunctionalization) of transcription factor binding play a role in this divergence, which is often rapid. On the one hand, gene duplicates experience a net loss in the number of transcription factors binding to them, indicating the importance of losing transcription factor binding sites after gene duplication. On the other hand, the number of transcription factors that bind to highly diverged duplicates is significantly greater than would be expected if loss of binding played the only role in the divergence of duplicate genes.

Binding Sites↗

PubMed2003

Sleep networks and the anatomic and physiologic connections with respiratory control.

A central neuronal network regulates airway functions from the nares to the bronchioles and is an integral component of a regulatory system for brain control of breathing and airway patency during wakefulness and sleep. This network, components of which include sleep generating sites and monoaminergic neurons in particular, is characterized by reciprocal interconnections, parallel organization, and state-dependent activity patterns, which can be influenced by both genes and environment. Sleep generating neurons are interconnected with the monoaminergic containing cells to the extent that sleep-related changes in upper and lower airway patency could be due to inhibitory influences of sleep-activated neurons on serotonergic and noradrenergic producing cells. Neurochemical studies and physiologic experiments show that serotonergic and noradrenergic producing cells can make parallel pathways, directly innervating the hypoglossal motor cells regulating upper airway dilating muscles, and vagal preganglionic neurons providing cholinergic outflow to the airways. Activation of serotonergic and noradrenergic cell groups preferentially increases activity of the genioglossus muscle, but diminishes cholinergic outflow to the airways. Hence, inhibition of monoaminergic neurons during sleep may lead to a decrease in upper airway dilating forces and an elevation of cholinergic outflow to the airways. Qualitatively different responses of hypoglossal and airway-related vagal preganglionic neurons (AVPNs) occur in response to endogenously released serotonin or norepinephrine and could be related to its simultaneous action on different serotonin or norepinephrine receptor subtypes. Dysfunction of monaminergic cell groups during sleep may predispose to upper airway occlusion as well as bronchoconstriction. Pharmacological corrections of alterations of these transmitter specific converging systems might be an avenue for treatment of sleep related airway disorders such as sleep apnea and worsening of asthma.

Animals↗

PubMedDate not supplied

[Plastic changes of the network properties of neurons during learning in the cat].

Functional connections between neurones of various types in microareas and between microareas of the motor cortex in cats have been studied during elaboration of an electro-defensive reflex to sound. A difference has been shown between neighbouring neurones in formation of their contacts with nearby neurones located within an area of 500 mc. Neurones generating spikes of high amplitude had more active "outputs" to neurones situated at different distances, while neurones generating spikes of low amplitude had more active "inputs" to them. On the other hand, as a result of conditioning "inputs" to distant neurones underwent more significant changes in the first type of neurones, and in the second type--the "outputs" changed more markedly.

Animals↗

PubMed1977

The connections of the trigeminal and facial motor nuclei in the brain of the carp (Cyprinus carpio L.) as revealed by anterograde and retrograde transport of horseradish peroxidase.

The connections of the rostral and caudal parts of the trigeminal and facial motor nuclei in the carp were studied with the horseradish peroxidase technique. Following ionophoretic peroxidase injections in these motor nuclei, retrogradely labeled cells were observed together with anterogradely labeled motor cell processes. Several cellular areas in thalamus, cerebellum and medulla oblongata were shown to project to the V and VII motor nuclei. Labeled cells were found in the inferior lobe and the glomerular complex of the thalamus. In the medulla oblongata, cells in the descending trigeminal nucleus, reticular nuclei and motor nuclei other than those injected were labeled. Besides these conspicuous projections several smaller connections were also found. These findings are discussed on their significance to respiratory function. Anterogradely labeled cellular processes constitute a relatively simple network of fiber connections between the various motor nuclei and the reticular nuclei of the brainstem. This apparently dendritic system of the bulbar motor complex shows a certain degree of similarity to the structure of the motor system in the spinal cord, and might play a role in the coordinated control of the muscular system.

Animals↗

PubMed1994

A biologically motivated and analytically soluble model of collective oscillations in the cortex. II. Application to binding and pattern segmentation.

Feature linking and pattern separation are shown to be performed as simultaneous processes by a highly connected auto-associative network of spiking neurons (spike response model). In principle, many (e.g., with nine) patterns can be separated, but with a biological set of parameters the number is limited to four. The patterns have been learned by an asymmetric hebbian rule that can handle a low activity which may vary from pattern to pattern (in a range between 4% and 7%). Spikes are generated by a threshold process and--with some delay--transmitted to postsynaptic neurons. There they evoke an excitatory or inhibitory postsynaptic potential (EPSP or IPSP). Spike emission is followed by an absolute refractory period (1 ms) and activates an inhibitory delay loop that prevents continuous firing. Three different network topologies are discussed, i.e., a structureless fully connected system, a network composed of two 'hemispheres', and finally a hierarchical network with four subsystems that represent different 'functions' and interact via feedforward and feedback connections. Functional feedback turns out to be essential for context-sensitive binding. The coherence between the two hemispheres is dependent on the interhemispheric delays. If these are on average too large, the two hemispheres oscillate coherently by themselves but phase-shifted by half a period with respect to each other.

Animals↗

PubMed2007

An interagency network perspective on HIV prevention.

OBJECTIVE: The objective of this study was to describe the network structures of agencies and individuals engaged in HIV prevention in a North Carolina county. STUDY DESIGN: The authors conducted a cross-sectional study of interconnected agencies and individuals identified through snowball sampling. Participants were interviewed face to face with a structured questionnaire about interactions with others in the network. Network characteristics such as density and centralization were computed with UCINET software. RESULTS: The network of 11 agencies had an information exchange density of 14%. Exchanges of clients and funds were even more rare. The network of 17 individuals influential in HIV prevention was better connected with a density of 42%. CONCLUSIONS: The HIV prevention agencies in the county were not functioning as a network and thus were not benefiting the community to their full potential. Any connectedness between the agencies may be attributable to the better connections between influential individuals, several of whom worked for the agencies in the agency network.

Community Networks↗

PubMed1990

Microfibrillar meshwork of the synovial lining and associated broad banded collagen: a clue to identity.

The surface layer of synovial interstitium lining the rabbit knee was studied by transmission electron microscopy. Over a distance of 2-3 microns normal to the surface the interstitium contained a network of fine microfibrils (diameter 9.3 (0.7) nm, mean (SEM] which was quite dense in places (fractional area of projection 0.189 (0.023], and stained with ruthenium red. Periodic collagen fibrils were relatively scanty and fine (diameter 32 (2) nm) in this surface layer. Broad cross-striated bundles occurred in association with the microfibrils and B cells. These fibrous long spacing bundles (FLS) had a single period of 92.8 (2.8) nm with a broad dark band (37.6) (1.8) nm--so called 'zebra collagen'. Both the periodicity of the FLS and the morphological characteristics of the microfibrils are typical of type VI collagen, a widespread constituent of soft connective tissues. The functional importance of the inner microfibril network is likely to be mechanical, biochemical (glycosaminoglycan and glycoprotein entrapment), and to a very minor degree hydraulic resistance.

Actin Cytoskeleton↗

PubMed2006

Associative learning in hierarchical self-organizing learning arrays.

In this paper, we introduce feedback-based associative learning in self-organized learning arrays (SOLAR). SOLAR structures are hierarchically organized networks of sparsely connected neurons that define their own functions and select their interconnections locally. This paper provides a description of neuron self-organization and signal processing. Feedforward processing is used to make necessary correlations and learn the input patterns. Discovered associations between neuron inputs are used to generate feedback signals. These feedback signals, when propagated to the primary inputs, can establish the expected input values. This can be used for heteroassociative (HA) and autoassociative (AA) learning and pattern recognition. Example applications in HA learning are given.

Algorithms↗

PubMed2006

The functional nature of synaptic circuitry is altered in area CA3 of the hippocampus in a mouse model of Down's syndrome.

Down's syndrome (DS) is the most common cause of mental retardation, and memory impairments are more severe in DS than in most if not all other causes of mental retardation. The Ts65Dn mouse, a genetic model of DS, exhibits phenotypes of DS, including memory impairments indicative of hippocampal dysfunction. We examined functional synaptic connectivity in area CA3 of the hippocampus of Ts65Dn mice using organotypic slice cultures as a model. We found reductions in multiple measures of synaptic function in both excitatory and inhibitory inputs to pyramidal neurons in CA3 of the Ts65Dn hippocampus. However, associational synaptic connections between pyramidal neurons were more abundant and more likely to be active rather than silent in the Ts65Dn hippocampus. Synaptic potentiation was normal in these associational connections. Decreased overall functional synaptic input onto pyramidal neurons expressed along with the specific hyperconnectivity of associational connections between pyramidal neurons will result in predictable alterations of CA3 network function, which may contribute to the memory impairments seen in DS.

Animals↗

PubMed2003

Learning rules and network repair in spike-timing-based computation networks.

Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of a network. Ongoing network self-repair is thus necessary. We describe a method to derive spike-timing-dependent plasticity rules for self-repair, based on the firing patterns of a functioning network. These plasticity rules for self-repair also provide the basis for unsupervised learning of new tasks. The particular plasticity rule derived for a network depends on the network and task. Here, self-repair is illustrated for a model of the mammalian olfactory system in which the computational task is that of odor recognition. In this olfactory example, the derived rule has qualitative similarity with experimental results seen in spike-timing-dependent plasticity. Unsupervised learning of new tasks by using the derived self-repair rule is demonstrated by learning to recognize new odors.

Action Potentials↗