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At least 667 records · Page 37Linked to original sources
PubMed2003

BDNF regulates spontaneous correlated activity at early developmental stages by increasing synaptogenesis and expression of the K+/Cl- co-transporter KCC2.

Spontaneous neural activity is a basic property of the developing brain, which regulates key developmental processes, including migration, neural differentiation and formation and refinement of connections. The mechanisms regulating spontaneous activity are not known. By using transgenic embryos that overexpress BDNF under the control of the nestin promoter, we show here that BDNF controls the emergence and robustness of spontaneous activity in embryonic hippocampal slices. Further, BDNF dramatically increases spontaneous co-active network activity, which is believed to synchronize gene expression and synaptogenesis in vast numbers of neurons. In fact, BDNF raises the spontaneous activity of E18 hippocampal neurons to levels that are typical of postnatal slices. We also show that BDNF overexpression increases the number of synapses at much earlier stages (E18) than those reported previously. Most of these synapses were GABAergic, and GABAergic interneurons showed hypertrophy and a 3-fold increase in GAD expression. Interestingly, whereas BDNF does not alter the expression of GABA and glutamate ionotropic receptors, it does raise the expression of the recently cloned K(+)/Cl(-) KCC2 co-transporter, which is responsible for the conversion of GABA responses from depolarizing to inhibitory, through the control of the Cl(-) potential. Together, results indicate that both the presynaptic and postsynaptic machineries of GABAergic circuits may be essential targets of BDNF actions to control spontaneous activity. The data indicate that BDNF is a potent regulator of spontaneous activity and co-active networks, which is a new level of regulation of neurotrophins. Given that BDNF itself is regulated by neuronal activity, we suggest that BDNF acts as a homeostatic factor controlling the emergence, complexity and networking properties of spontaneous networks.

Animals↗
PubMed1997

A hierarchical neuronal network for planning behavior.

Planning a goal-directed sequence of behavior is a higher function of the human brain that relies on the integrity of prefrontal cortical areas. In the Tower of London test, a puzzle in which beads sliding on pegs must be moved to match a designated goal configuration, patients with lesioned prefrontal cortex show deficits in planning a goal-directed sequence of moves. We propose a neuronal network model of sequence planning that passes this test and, when lesioned, fails in a way that mimics prefrontal patients' behavior. Our model comprises a descending planning system with hierarchically organized plan, operation, and gesture levels, and an ascending evaluative system that analyzes the problem and computes internal reward signals that index the correct/erroneous status of the plan. Multiple parallel pathways connecting the evaluative and planning systems amend the plan and adapt it to the current problem. The model illustrates how specialized hierarchically organized neuronal assemblies may collectively emulate central executive or supervisory functions of the human brain.

Behavior↗
PubMed1991

Speech recognition by an artificial neural network using findings on the afferent auditory system.

An artificial neural network which uses anatomical and physiological findings on the afferent pathway from the ear to the cortex is presented and the roles of the constituent functions in recognition of continuous speech are examined. The network deals with successive spectra of speech sounds by a cascade of several neural layers: lateral excitation layer (LEL), lateral inhibition layer (LIL), and a pile of feature detection layers (FDL's). These layers are shown to be effective for recognizing spoken words. Namely, first, LEL reduces the distortion of sound spectrum caused by the pitch of speech sounds. Next, LIL emphasizes the major energy peaks of sound spectrum, the formants. Last, FDL's detect syllables and words in successive formants, where two functions, time-delay and strong adaptation, play important roles: time-delay makes it possible to retain the pattern of formant changes for a period to detect spoken words successively; strong adaptation contributes to removing the time-warp of formant changes. Digital computer simulations show that the network detect isolated syllables, isolated words, and connected words in continuous speech, while reproducing the fundamental responses found in the auditory system such as ON, OFF, ON-OFF, and SUSTAINED patterns.

Acoustic Stimulation↗
PubMed2001

Network activity evoked by neocortical stimulation in area 36 of the guinea pig perirhinal cortex.

The perirhinal cortex is a key structure involved in memory consolidation and retrieval. In spite of the extensive anatomical studies that describe the intrinsic and extrinsic associative connections of the perirhinal cortex, the activity generated within such a network has been poorly investigated. We describe here the pattern of synaptic interactions that subtend the responses evoked in area 36 of the perirhinal cortex by neocortical and local stimulation. The experiments were carried out in the in vitro isolated guinea pig brain. The synaptic perirhinal circuit was reconstructed by integrating results obtained during intracellular recordings from layer II-III neurons with simultaneous current source density analysis of laminar profiles performed with 16-channel silicon probes. Both neocortical and local stimulation of area 36 determined a brief monosynaptic excitatory potential in layer II-III neurons, followed by a biphasic synaptic inhibitory potential possibly mediated by a feed-forward inhibitory circuit at sites close to the stimulation electrode and a late excitatory postsynaptic potential (EPSP) that propagated at distance within area 36 along the rhinal sulcus. During a paired-pulse stimulation test, the inhibitory postsynaptic potential (IPSP) and the late EPSP were abolished in the second conditioned response, suggesting that they are generated by poli-synaptic circuits. Current source density analysis of the field responses demonstrated that 1) the monosynaptic activity was generated in layers II-III and 2) the sink associated to the disynaptic responses was localized within the superficial layer of area 36. We conclude that the neocortical input induces a brief monosynaptic excitation in area 36 of the perirhinal cortex, that is curtailed by a prominent inhibition and generates a recurrent excitatory associative response that travels at distance within area 36 itself. The results suggest that the perirhinal cortex network has the potentials to integrate multimodal incoming neocortical information on its way to the hippocampus.

Animals↗
PubMed2004

Neuronal bases of perceptual learning revealed by a synaptic balance scheme.

Our ability to perceive external sensory stimuli improves as we experience the same stimulus repeatedly. This perceptual enhancement, called perceptual learning, has been demonstrated for various sensory systems, such as vision, audition, and somatosensation. I investigated the contribution of lateral excitatory and inhibitory synaptic balance to perceptual learning. I constructed a simple associative neural network model in which sensory features were expressed by the activities of specific cell assemblies. Each neuron is sensitive to a specific sensory feature, and the neurons belonging to the same cell assembly are sensitive to the same feature. During perceptual learning processes, the network was presented repeatedly with a stimulus that was composed of a sensory feature and noise, and the lateral excitatory and inhibitory synaptic connection strengths between neurons were modified according to a pulse-timing-based Hebbian rule. Perceptual learning enhanced the cognitive performance of the network, increasing the signal-to-noise ratio of neuronal activity. I suggest here that the alteration of the synaptic balance may be essential for perceptual learning, especially when the brain tries to adopt the most suitable strategy--signal enhancement, noise reduction, or both--for a given perceptual task.

Action Potentials↗
PubMed1999

Memory retrieval under the control of the prefrontal cortex.

Memory retrieval is a process wherein a distributed neural network reactivates the brain's representation of past experiences. Sensory long-term memory is represented among a population of neurones in the modality-specific posterior association cortex. The coded representation of memory can be retrieved by interactions of hierarchically different cortical areas along bottom-up and top-down anatomical connections. We examined the function of the prefrontal cortex in memory retrieval by two different approaches. Firstly, a meta-analysis of brain imaging studies revealed that the prefrontal cortex is reliably activated by memory retrieval in humans. Secondly, in order to determine the causal relationship between the prefrontal activations and memory retrieval, we designed a new experimental paradigm using posterior-split-brain monkeys. Following section of the splenium of the corpus callosum and the anterior commissure, visual stimulus-stimulus association learning within one hemisphere did not transfer to the other. Nevertheless, when a visual cue was presented to one hemisphere, the prefrontal cortex could instruct the contralateral hemisphere to retrieve the correct stimulus specified by the cue. These findings suggest that the prefrontal cortex can regulate the recall of long-term memory in the absence of bottom-up sensory inputs.

Animals↗
PubMed2002

Residual cerebral activity and behavioural fragments can remain in the persistently vegetative brain.

This report identifies evidence of partially functional cerebral regions in catastrophically injured brains. To study five patients in a persistent vegetative state (PVS) with different behavioural features, we employed [(18)F]fluorodeoxyglucose-positron emission tomography (FDG-PET), MRI and magnetoencephalographic (MEG) responses to sensory stimulation. Each patient's brain expressed a unique metabolic pattern. In three of the five patients, co-registered PET/MRI correlate islands of relatively preserved brain metabolism with isolated fragments of behaviour. Two patients had suffered anoxic injuries and demonstrated marked decreases in overall cerebral metabolism to 30-40% of normal. Two other patients with non-anoxic, multifocal brain injuries demonstrated several isolated brain regions with relatively higher metabolic rates, that ranged up to 50-80% of normal. Nevertheless, their global metabolic rates remained <50% of normal. MEG recordings from three PVS patients provide clear evidence for the absence, abnormality or reduction of evoked responses. Despite major abnormalities, however, these data also provide evidence for localized residual activity at the cortical level. Each patient partially preserved restricted sensory representations, as evidenced by slow evoked magnetic fields and gamma band activity. In two patients, these activations correlate with isolated behavioural patterns and metabolic activity. Remaining active regions identified in the three PVS patients with behavioural fragments appear to consist of segregated corticothalamic networks that retain connectivity and partial functional integrity. A single patient who suffered severe injury to the tegmental mesencephalon and paramedian thalamus showed widely preserved cortical metabolism, and a global average metabolic rate of 65% of normal. The relatively high preservation of cortical metabolism in this patient defines the first functional correlate of clinical- pathological reports associating permanent unconsciousness with structural damage to these regions. The specific patterns of preserved metabolic activity identified in these patients do not appear to represent random survivals of a few neuronal islands; rather they reflect novel evidence of the modular nature of individual functional networks that underlie conscious brain function. The variations in cerebral metabolism in chronic PVS patients indicate that some cerebral regions can retain partial function in catastrophically injured brains.

Adult↗
PubMed2006

Prenatal programming of the female reproductive neuroendocrine system by androgens.

It has been clear for several decades that the areas of the brain that control reproductive function are sexually dimorphic and that the 'programming actions' of the male gonadal steroids are responsible for sex-specific release of the gonadotrophins from the pituitary gland. The administration of exogenous steroids to fetal/neonatal animals has pinpointed windows of time in an animals' development when the reproductive neuroendocrine axis is responsive to the organisational influences of androgens. These 'critical' periods for sexual differentiation of the brain are trait- and species-specific. The neural network regulating the activity of the gonadotrophin releasing hormone (GnRH) neurones is vital to the control of reproductive function. It appears that early exposure to androgens does not influence the migratory pathway of the GnRH neurone from the olfactory placode or the size of the population of neurones that colonise the postnatal hypothalamus. However, androgens do influence the number and the nature of connections that these neurones make with other neural phenotypes. Gonadal steroid hormones play key roles in the regulation of GnRH release acting largely via steroid-sensitive intermediary neurones that impinge on the GnRH cells. Certain populations of hormonally responsive neurones have been identified that are sexually dimorphic and project from hypothalamic areas known to be involved in the regulation of GnRH release. These neurones are excellent candidates for the programming actions of male hormones in the reproductive neuroendocrine axis of the developing female.

Androgens↗
PubMed2003

Glial neuronal signaling in the central nervous system.

Glial cells are known to interact extensively with neuronal elements in the brain, influencing their activity. Astrocytes associated with synapses integrate neuronal inputs and release transmitters that modulate synaptic sensitivity. Glial cells participate in formation and rebuilding of synapses and play a prominent role in protection and repair of nervous tissue after damage. For glial cells to take an active part in plastic alterations under physiological conditions and pathological disturbances, extensive specific signaling, both within single cells and between cells, is required. In recent years, intensive research has led to our first insight into this signaling. We know there are active connections between astrocytes in the form of networks promoting Ca2+ and ATP signaling; we also know there is intense signaling between astrocytes, microglia, oligodendrocytes, and neurons, with an array of molecules acting as signaling substances. The cells must be functionally integrated to facilitate the enormous dynamics of and capacity for reconstruction within the nervous system. In this paper, we summarize some basic data on glial neuronal signaling to provide insight into synaptic modulation and reconstruction in physiology and protection and repair after damage.

Animals↗
PubMed2001

Motion processing in the macaque: revisited with functional magnetic resonance imaging.

A great deal is known about the response properties of single neurons processing sensory information. In contrast, less is understood about the collective characteristics of networks of neurons that may underlie sensory capacities of animals. We used functional magnetic resonance imaging to study the emergent properties of populations of neurons processing motion across different brain areas. Using a visual adaptation paradigm, we localized a distributed network of visual areas that process information about the direction of motion as expected from single-cell recording studies. However, we found an apparent discrepancy between the directional signals in certain visual areas as measured with blood oxygenation level-dependent imaging compared with an estimate based on the spiking of single neurons. We propose a hypothesis that may account for this difference based on the postulate that neuronal selectivity is a function of the state of adaptation. Consequently, neurons classically thought to lack information about certain attributes of the visual scene may nevertheless receive and process this information. We further hypothesize that this adaptation-dependent selectivity may arise from intra- or inter-area cellular connections, such as feedback from higher areas. This network property may be a universal principle the computational goal of which is to enhance the ability of neurons in earlier visual areas to adapt to statistical regularities of the input and therefore increase their sensitivity to detect changes along these stimulus dimensions.

Adaptation, Ocular↗
PubMed1995

Compensatory mechanisms in an attractor neural network model of schizophrenia.

We investigate the effect of synaptic compensation on the dynamic behavior of an attractor neural network receiving its input stimuli as external fields projecting on the network. It is shown how, in the face of weakened inputs, memory performance may be preserved by strengthening internal synaptic connections and increasing the noise level. Yet, these compensatory changes necessarily have adverse side effects, leading to spontaneous, stimulus-independent retrieval of stored patterns. These results can support Stevens' recent hypothesis that the onset of schizophrenia is associated with frontal synaptic regeneration, occurring subsequent to the degeneration of temporal neurons projecting on these areas.

Brain↗
PubMed2007

Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data.

The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.

Attention↗
PubMed2001

A neural network approach to the acoustic startle reflex and prepulse inhibition.

Prepulse inhibition (PPI) is the normal suppression of the startle reflex when an intense stimulus is preceded by a weak non-startling prestimulus. PPI is widely used as a model for sensorimotor gating processes and has been shown to be impaired in various neuropsychiatric disorders, including schizophrenia. We have reproduced startle-like behavior and basic PPI modifications with a neural network. The network design was constrained by the attempt (1) to use as few connections as possible and (2) to relate neuroanatomical structures to the simulated network. Performance of the network was evaluated by the behavior of the simulated motor neurons in response to prepulse and pulse stimuli presented with various lead intervals and prepulse intensities. A delayed inhibitory pathway via the pedunculopontine nucleus (PPTg) to the caudal pontine reticular nucleus was found to be a necessary but insufficient requirement to reproduce basic PPI output patterns. Additional requirements included (a) a low threshold at or below the caudal pontine reticular formation, (b) signal amplification in the inhibitory pathway and (c) prolongation of activity in the inhibitory pathway. On the grounds of the most appropriate output patterns of the simulations, we propose a mechanism of sustained activation in the PPTg due to recursive connections. Relations between stimuli, behavior (motor output) and the underlying architecture are discussed. Potentially, this modeling technique can be extended to investigate the impact of drugs and higher brain regions on PPI.

Animals↗
PubMed1995

Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system.

Neuroanatomists have established that the various gross structures of the brain are divided into a large number of different processing regions and have catalogued a large number of connections between these regions. The connectional data derived from neuroanatomical studies are complex, and reliable conclusions about the organization of brain systems cannot be drawn from considering them without some supporting analysis. Recognition of this problem has recently led to the application of a variety of techniques to the analysis of connection data. One of the techniques that we previously employed, non-metric multidimensional scaling (NMDS), appears to have revealed important aspects of the organization of the central nervous system, such as the gross organization of the whole cortical network in two species. We present here a detailed treatment of methodological aspects of the application of NMDS to connection data. We first examine in detail the particular properties of neuroanatomical connection data. Second, we consider the details of NMDS and discuss the propriety of different possible NMDS approaches. Third, we present results of the analyses of connection data from the primate visual system, and discuss their interpretation. Fourth, we study independent analyses of the organization of the visual system, and examine the relation between the results of these analyses and those from NMDS. Fifth, we investigate quantitatively the performance of a number of data transformation and conditioning procedures, as well as tied and untied NMDS analysis of untransformed low-level data, to determine how well NMDS can recover known metric parameters from artificial data. We then re-analyse real connectivity data with the most successful methods at removing the effects of sparsity, to ensure that this aspect of data structure does not obscure others. Finally, we summarize the evidence on the connectional organization of the primate visual system, and discuss the reliability of NMDS analyses of neuroanatomical connection data.

Animals↗
PubMed2006

Disrupting the brain to guide plasticity and improve behavior.

Neurones may be highly stable and nonplastic cellular structures, but they are engaged in dynamically changing, intrinsically plastic neural networks that provide a most energy efficient, spatially compact, and precise means to process input signals and generate adaptable responses to a changing environment. Neural plasticity is evolution's invention to enable the nervous system to escape the restrictions of its own genome (and its highly specialized cellular specification) and thus adapt to environmental pressures, physiologic changes, and experiences. At neural system level two steps of plasticity can be identified: unmasking existing connections that may be followed by establishment of new ones possibly even with integration of new neural structures and neurons. In any case, plastic changes may not necessarily represent a behavioral gain for a given subject, as they represent the mechanism for development and learning, as much as a cause of pathology and disease. The challenge is to learn enough about the mechanisms of plasticity to be able to guide them, suppressing changes that may lead to undesirable behaviors while accelerating or enhancing those that result in a behavioral benefit for the subject or patient. Neurostimulation, including noninvasive brain stimulation techniques, provide an opportunity to modulate brain plasticity in a controlled and specific manner. Such interventions to guide behavior or treat pathological symptomatology might be more immediate in their behavioral repercussion and thus more effective than approaches intent on addressing underlying genetic predispositions.

Animals↗
PubMed2005

Dendritic release of retrograde messengers controls synaptic transmission in local neocortical networks.

The contribution of retrograde signaling to information processing in the brain has been contemplated for a long time, especially with respect to central nervous system development and long-term synaptic plasticity. During the past few years, however, the concept of retrograde signaling has been expanding to include short-term modifications of synaptic efficacy. The classic point of view on synaptic transmission represents it as a unidirectional transfer of information from presynaptic to postsynaptic sites. This paradigm has, however, been questioned in several experimental studies of neurons in different brain regions. These results suggest that a fast retrograde signal, which provides feedback, exists in active synaptic contacts. In particular, it was found that the dendritic release of retrograde messengers controls the efficacy of synaptic transmission in both excitatory and inhibitory connections between neocortical pyramidal cells and interneurons. The present review discusses these findings and the mechanisms underlying synaptic retrograde signaling.

Animals↗
PubMed2004

Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI.

Recent functional imaging studies have revealed coactivation in a distributed network of cortical regions that characterizes the resting state, or default mode, of the human brain. Among the brain regions implicated in this network, several, including the posterior cingulate cortex and inferior parietal lobes, have also shown decreased metabolism early in the course of Alzheimer's disease (AD). We reasoned that default-mode network activity might therefore be abnormal in AD. To test this hypothesis, we used independent component analysis to isolate the network in a group of 13 subjects with mild AD and in a group of 13 age-matched elderly controls as they performed a simple sensory-motor processing task. Three important findings are reported. Prominent coactivation of the hippocampus, detected in all groups, suggests that the default-mode network is closely involved with episodic memory processing. The AD group showed decreased resting-state activity in the posterior cingulate and hippocampus, suggesting that disrupted connectivity between these two regions accounts for the posterior cingulate hypometabolism commonly detected in positron emission tomography studies of early AD. Finally, a goodness-of-fit analysis applied at the individual subject level suggests that activity in the default-mode network may ultimately prove a sensitive and specific biomarker for incipient AD.

Adult↗
PubMed2006

Model-based analysis of excitatory lateral connections in the visual cortex.

Excitatory lateral connections within the primary visual cortex are thought to link neurons with similar receptive field properties. Here we studied whether this rule can predict the distribution of excitatory connections in relation to cortical location and orientation preference in the cat visual cortex. To this end, we obtained orientation maps of areas 17 or 18 using optical imaging and injected anatomical tracers into these regions. The distribution of labeled axonal boutons originating from large populations of excitatory neurons was then analyzed and compared with that of individual pyramidal or spiny stellate cells. We demonstrate that the connection patterns of populations of nearby neurons can be reasonably predicted by Gaussian and von Mises distributions as a function of cortical location and orientation, respectively. The connections were best described by superposition of two components: a spatially extended, orientation-specific and a local, orientation-invariant component. We then fitted the same model to the connections of single cells. The composite pattern of nine excitatory neurons (obtained from seven different animals) was consistent with the assumptions of the model. However, model fits to single cell axonal connections were often poorer and their estimated spatial and orientation tuning functions were highly variable. We conclude that the intrinsic excitatory network is biased to similar cortical locations and orientations but it is composed of neurons showing significant deviations from the population connectivity rule.

Animals↗