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At least 55 records · Page 3Linked to original sources
PubMed2004

Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations.

The human brain spontaneously generates large-scale network oscillations at around 10 and 20 Hz. The amplitude envelope of these oscillations fluctuates intermittently and was recently reported to exhibit power-law decay of the autocorrelation for hundreds of seconds. This indicates that the underlying networks are in a dynamic state resembling the self-organized critical state known to exist in many complex systems. Based on the mechanism of how correlations emerge in these systems, we hypothesized that the physiological basis of long-range power-law correlations is the buildup of a memory of past activity by a continuous modification of the network's functional connectivity by the ongoing oscillations. In this framework, exogenous perturbations of ongoing oscillations would degrade or abolish this dynamic network memory. We investigated the sensitivity of the temporal correlations in sensorimotor 10- and 20-Hz oscillations to median nerve stimulation that is known to have immediate effects on ongoing oscillations. Our results show that the amplitude fluctuations of these oscillations were effectively modulated by the somatosensory stimuli but still exhibited long-range temporal correlations and power-law scaling behaviour. The magnitude of the temporal correlations was, however, attenuated and the power-law exponents were decreased. This implies that the stimuli indeed degraded the network's memory of its past.

Adult↗
PubMed2006

Functional and evolutionary inference in gene networks: does topology matter?

The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest. It has been suggested that most, if not all, biological networks are 'scale free.' That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa. The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions. These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology. One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes. For example, more highly connected proteins are more likely to be lethal when knocked out. However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect. Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations. Evolutionary simulations of gene-regulatory networks, presented here, suggest that such weak or non-existent correlations are to be expected, and are likely not due to inadequacy of experimental data. We argue that 'top-down' inferences of biological properties based on simple measures of network topology are of limited utility, and we present simulation results suggesting that much more detailed information about a gene's location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions. Specifically, we find in our simulations that: (1) the relationship between a gene's connectivity and its fitness effect upon knockout depends on its equilibrium expression level; (2) correlation between connectivity and genetic variation is virtually non-existent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and (3) certain genes show low polymorphism yet high divergence among independent evolutionary runs. This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral coevolution of regulatory inputs to the same gene.

Algorithms↗
PubMed2004

Functional connectivity patterns of human magnetoencephalographic recordings: a 'small-world' network?

EEG and MEG (magnetoencephalography) are widely used to study functional connectivity between different brain regions. We address the question whether such connectivity patterns display an optimal organization for information processing. MEG recordings of five healthy human subjects were converted to sparsely connected graphs (N=126; k=15) by applying a suitable threshold to the N * N matrix of synchronization strengths. For intermediate frequencies (8-30 Hz) the synchronization patterns were similar to those of an ordered graph with a consistent drop of synchronization strength as a function of distance. For low (<8 Hz) and high (>30 Hz) frequency bands the synchronization patterns displayed the features of a so-called 'small-world' network. This might reflect an optimal organization pattern for information processing, connecting any two brain area by only a small number of intermediate steps.

Action Potentials↗
PubMed1995

Artificial neural network and classical least-squares methods for neurotransmitter mixture analysis.

Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets. and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.

Acetylcholine↗
PubMed2001

Neural network analysis of the pattern of functional connectivity between cerebral areas in schizophrenia.

Recent evidence suggests that the core abnormality of cerebral function in schizophrenia is a disruption of functional connectivity between diverse cerebral sites. Functional connectivity is defined as the correlation between neuronal activity at remote sites. It can be measured using functional imaging techniques such as positron emission tomography (PET). This paper reports an analysis using a neural network to discriminate between the patterns of functional connectivity in schizophrenic patients and healthy subjects. The data was derived from a PET study of regional cerebral blood flow during word generation in 6 healthy subjects and 16 schizophrenic patients with established illness, in whom the clinical diagnosis could be made with confidence. After training on data from two healthy subjects and seven schizophrenic patients, the neural network successfully assigned all members of a test set of four healthy subjects and nine schizophrenic patients to the correct diagnostic category. While this result should be interpreted with caution on account of the small sample size, it indicates that neural network analysis is potentially of value in the diagnosis of schizophrenia.

Case-Control Studies↗
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↗
PubMed2000

Modeling of spontaneous activity in developing spinal cord using activity-dependent depression in an excitatory network.

Spontaneous episodic activity is a general feature of developing neural networks. In the chick spinal cord, the activity comprises episodes of rhythmic discharge (duration 5-90 sec; cycle rate 0.1-2 Hz) that recur every 2-30 min. The activity does not depend on specialized connectivity or intrinsic bursting neurons and is generated by a network of functionally excitatory connections. Here, we develop an idealized, qualitative model of a homogeneous, excitatory recurrent network that could account for the multiple time-scale spontaneous activity in the embryonic chick spinal cord. We show that cycling can arise from the interplay between excitatory connectivity and fast synaptic depression. The slow episodic behavior is attributable to a slow activity-dependent network depression that is modeled either as a modulation of cellular excitability or as synaptic depression. Although the two descriptions share many features, the model with a slow synaptic depression accounts better for the experimental observations during blockade of excitatory synapses.

Animals↗
PubMed1997

Functional impact of cerebral connections.

Cerebral networks are complex sets of connections that resemble a ladder-like web of multiple parallel feedforward, lateral, and feedback connections. This static anatomical description has been pivotal in guiding our understanding of signal processing within cerebral networks. However, measures on both magnitude and functional significance of connections are extremely limited. Here, we compare the anatomically defined strengths of a set of cerebral pathways emerging from the visual middle suprasylvian (MS) cortex of the cat with measures of the functional impact the same region has over distant sites. These functional measures were obtained by analyzing the local and distant effects of MS cooling deactivation on deoxyglucose uptake. Relative to major efferent projections from MS cortex that have a strong influence, projections to early visual processing stages have weaker functional influences than predicted from the anatomy. For higher processing stages, the converse holds: projections from MS cortex have stronger functional influence than predicted from the anatomy. We conclude that these and future functional measures, obtained using the same combination of techniques, will furnish fundamental, new information that complements and extends current models of static cerebral networks, and lead to more realistic models of cerebral network function and component interactions.

Animals↗
PubMed2001

Functional connectivity and working memory in schizophrenia: an EEG study.

A leading hypothesis suggests that schizophrenic patients suffer from a disconnection syndrome. A failure in functional connectivity curtails the cortical integration and network activation needed to perform working memory tasks. Simulations with neural network models also indicate that connectivity is crucial for simulation of working memory asks. Multichannel EEG correlation-coefficient estimations are considered as a reliable measurement of connectivity patterns among cortical regions. In this study EEG samples are obtained selectively at the delay epochs of a delayed response working memory task. Results of correlation-coefficient estimations indicate a lack of statistically significant changes between non-task and task conditions in frontal, certain parietal, temporal and central channels. These findings propose that schizophrenics probably "fail" to activate the neural networks of the fronto-temporal regions. These are the networks involved in computation of the working memory task. Interestingly also good performers schizophrenics failed to activate these networks suggesting that the connectivity function is more relevant to the disorder than to task performance. If distinct deficits in cortical network activations would correlate with mental disorders it would be relevant to diagnosis and treatment of psychiatric disorders.

Brain↗
PubMed2026

Dual Transcranial Direct Current Stimulation Modulates Hierarchical Functional Network Organization in Post-Stroke Cognitive Impairment: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the clinical efficacy of dual transcranial direct current stimulation (tDCS) in patients with post-stroke cognitive impairment (PSCI) and to explore the effects on the hierarchical organization of functional brain networks, ranging from regional synchronization to inter-regional connectivity and global network topology. METHODS: In this randomized, double-blind, sham-controlled trial, 74 PSCI patients received conventional therapy alongside either active dual-tDCS (n&#x2009;=&#x2009;38) or sham stimulation (n&#x2009;=&#x2009;36). Active tDCS targeted the dorsolateral prefrontal cortex (DLPFC) via anodal-left/cathodal-right nodes (2.0&#x2009;mA, 20&#x2009;min/day, 20 sessions). The primary outcome was the Montreal Cognitive Assessment (MoCA). Secondary outcomes included the Mini-Mental Status Examination (MMSE), Stroop Test (ST), Trail Making Test (TMT), Wechsler Memory Scale (WMS), and Barthel Index (BI). A subgroup of 36 participants (18 per group) underwent resting-state functional magnetic resonance imaging (rs-fMRI) to analyze regional homogeneity (ReHo), functional connectivity (FC), and network topology. Partial correlations assessed the association between neuroimaging alterations and clinical improvements. RESULTS: The tDCS group showed significantly greater improvements in MoCA scores (tDCS: 5.74&#x2009;&#xb1;&#x2009;2.76 vs. sham: 2.69&#x2009;&#xb1;&#x2009;2.69; t&#x2009;=&#x2009;4.799, p&#x2009;<&#x2009;0.001) as well as in attention and memory domains compared to the sham group. The rs-fMRI changes included increased ReHo in the right middle temporal gyrus (MTG) and the left inferior frontal gyrus (IFG), and reduced FC between the right MTG-left superior frontal gyrus and left IFG-cerebellum (p&#x2009;<&#x2009;0.05, FWE-corrected). Additionally, small-worldness and global efficiency increased (p&#x2009;<&#x2009;0.05) with these alterations correlating with clinical recovery. Adverse events were rare and self-limiting. CONCLUSION: Dual-tDCS over bilateral DLPFC safely improves cognitive recovery in PSCI. These clinical gains are associated with rs-fMRI alterations, specifically in regional synchronization, inter-regional connectivity, and global topology, which suggest a potential biomarker for monitoring tDCS efficacy, offering a rationale for precision neuromodulation in stroke rehabilitation.

Humans↗
PubMed2000

Information processing in large-scale cerebral networks: the causal connectivity approach.

Today, cognitive functions are considered to be the offspring of the activity of large-scale networks of functionally interconnected cerebral regions. The interpretation of cerebral activation data provided by functional imaging has therefore recently moved to the search for the effective connectivity of activated regions, which aims at understanding the role of anatomical links in the activation propagation. Our assumption is that only causal connectivity can offer a real understanding of the links between brain and mind. Causal connectivity is based on the anatomical connection pattern, the information processing within cerebral regions and the causal influences that connected regions exert on each other. In our approach, the information processing within a region is implemented by a causal network of functional primitives, which are the interpretation of integrated biological properties. Our choice of a qualitative representation of information reflects the fact that cerebral activation data are only the approximate view, provided by imaging techniques, of the real cerebral activity. This explicit modeling approach allows the formulation and the simulation of functional and physiological assumptions about activation data. Two alternative models explaining results of the striate cortex activation described by Fox and Raichle (Fox PT, Raichle ME (1984) J. Neurophysiol 51:1109-1120; Fox PT, Raichle ME (1985) Ann Neurol 17:303-305) are provided as an example of our approach.

Animals↗
PubMed2005

Altered functional connectivity related to white matter changes inside the working memory network at the very early stage of MS.

Functional magnetic resonance imaging (fMRI) using paced auditory serial addition test (PASAT) as paradigm was used to study the functional connectivity in 18 patients at the very early stage of multiple sclerosis (MS) compared with 18 controls, to determine the existence of circuitry disturbance inside the working memory network and its relationship with white matter abnormalities assessed by conventional MRI and magnetization transfer ratio (MTR) imaging. The left BA 45/46 was selected as the seed region to compute correlation maps with other brain regions. After obtaining the correlation map for each subject, between-group comparisons were performed using random effect procedure. Compared with controls, patients did not show any greater functional connectivity between left BA 45/46 and other regions during PASAT. In contrast, decrease in functional connectivity was observed in patients between left BA 45/46 and left BA 9, right BA 3, and the anterior cingulate cortex (BA 24). In patients, no correlations were found between altered functional connectivity and clinical data. However, functional connectivity observed between left BA 45/46 and BA 24 in patients was correlated with the MTR of normal appearing white matter, and with brain T(2) lesion load. Altered functional connectivity is present inside the working memory network of patients at the very early stage of MS and is related to the extent of diffuse white matter changes.

Acoustic Stimulation↗
PubMed1997

The functional matrix hypothesis revisited. 2. The role of an osseous connected cellular network.

Intercellular gap junctions permit bone cells to intercellularly transmit, and subsequently process, periosteal functional matrix information, after its initial intracellular mechanotransduction. In addition, gap junctions, as electrical synapses, underlie the organization of bone tissue as a connected cellular network, and the fact that all bone adaptation processes are multicellular. The structural and operational characteristics of such biologic networks are outlined and their specific bone cell attributes described. Specifically, bone is "tuned" to the precise frequencies of skeletal muscle activity. The inclusion of the concepts and databases that are related to the intracellular and intercellular bone cell mechanisms and processes of mechanotransduction and the organization of bone as a biologic connected cellular network permit revision of the functional matrix hypothesis, which offers an explanatory chain, extending from the epigenetic event of muscle contraction hierarchically downward to the regulation of the bone cell genome.

Adaptation, Physiological↗
PubMed2005

Excitatory cortical neurons form fine-scale functional networks.

The specificity of cortical neuron connections creates columns of functionally similar neurons spanning from the pia to the white matter. Here we investigate whether there is an additional, finer level of specificity that creates subnetworks of excitatory neurons within functional columns. We tested for fine-scale specificity of connections to cortical layer 2/3 pyramidal neurons in rat visual cortex by using cross-correlation analyses of synaptic currents evoked by photostimulation. Recording simultaneously from adjacent layer 2/3 pyramidal cells, we find that when they are connected to each other (20% of all recorded pairs) they share common input from layer 4 and within layer 2/3. When adjacent layer 2/3 neurons are not connected to each other, they share very little (if any) common excitatory input from layers 4 and 2/3. In contrast, all layer 2/3 neurons share common excitatory input from layer 5 and inhibitory input from layers 2/3 and 4, regardless of whether they are connected to each other. Thus, excitatory connections from layer 4 to layer 2/3 and within layer 2/3 form fine-scale assemblies of selectively interconnected neurons; inhibitory connections and excitatory connections from layer 5 link neurons across these fine-scale subnetworks. Relatively independent subnetworks of excitatory neurons are therefore embedded within the larger-scale functional architecture; this allows neighbouring neurons to convey information more independently than suggested by previous descriptions of cortical circuitry.

Action Potentials↗
PubMed2006

Growth of cortical neuronal network in vitro: modeling and analysis.

We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed.

Action Potentials↗
PubMed2005

Synaptic plasticity in micropatterned neuronal networks.

Synaptic plasticity is thought to be of central importance for information processing by the nervous system. Additionally, specific neuronal connectivity patterns in the brain are implicated to play a role in the perception, processing and storage of incoming signals. Experimental control over connectivity within functional neuronal networks is therefore a promising approach in research on signal transduction and processing by the nervous system. A cell culture system is presented that allows experimental determination of neuronal connectivity patterns in an in vitro network. Rat embryonic cortical neurons were grown on patterns of extracellular matrix proteins applied to polystyrene substrates by microcontact printing. Cells comply well with the pattern and form synaptic connections along the experimentally defined pathways. Chemical synapses identified by double patch-clamp measurement showed paired pulse depression as well as frequency-dependent depression in response to trains of stimuli. This type of short-term plasticity has similarly been reported by others in brain slices. Thus, the system reproduces features central for neuronal information processing while the architecture of the network is experimentally manipulable. The ability to tailor the geometry of functional neuronal networks offers a valuable tool both for fundamental questions in neuroscientific research and a wide range of biotechnological applications.

Action Potentials↗
PubMed2005

Causal connectivity of evolved neural networks during behavior.

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.

Aging↗