<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0185-3325</journal-id>
<journal-title><![CDATA[Salud mental]]></journal-title>
<abbrev-journal-title><![CDATA[Salud Ment]]></abbrev-journal-title>
<issn>0185-3325</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0185-33252007000200011</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Las bases celulares de las oscilaciones neuronales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Treviño]]></surname>
<given-names><![CDATA[Mario]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="A01">
<institution><![CDATA[,UNAM Instituto de Fisiología Celular ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="A02">
<institution><![CDATA[,CINVESTAV Departamento de Fisiología, Biofísica y Neurociencias ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2007</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2007</year>
</pub-date>
<volume>30</volume>
<numero>2</numero>
<fpage>11</fpage>
<lpage>18</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0185-33252007000200011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0185-33252007000200011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0185-33252007000200011&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Una de las características de los circuitos neuronales es que sus componentes, las neuronas, pueden presentar actividad eléctrica sincrónica y, gracias a ésta, generar actividad oscilatoria. Esta actividad se ha asociado a diversas funciones fisiológicas como el procesamiento de información sensorial, la memoria, el ciclo vigiliasueño y la conciencia. La actividad oscilatoria de un circuito neuronal está mediada por i) las propiedades intrínsecas de sus células, ii) la arquitectura de sus conexiones y iii) la dinámica de sus interacciones sinápticas. A nivel celular, las señales sinápticas pueden generar oscilaciones subumbrales del potencial de membrana y regular la frecuencia de disparo. Estas oscilaciones modulan la respuesta celular a las entradas sinápticas que ocurren en frecuencias funcionalmente relevantes y que, finalmente, producen los diferentes ritmos cerebrales. A nivel estructural, el conjunto de proyecciones axonales de corto, mediano y largo alcance, permiten que módulos discretos, pero interconectados, oscilen en sincronía a lo largo de amplias regiones cerebrales. En esta revisión, resaltamos la importancia que tienen las interacciones sinápticas, excitadora e inhibidora, en la regulación y mantenimiento de la sincronía del disparo en grupos neuronales. Abordamos también el impacto de la plasticidad de corto y largo plazo de la transmisión GABAérgica en la modulación de las propiedades sinápticas, las oscilaciones neuronales y su participación en la generación de ritmos hiper-sincrónicos, asociados con las descargas epilépticas y las alteraciones que éstas últimas producen en los mecanismos de inhibición sináptica. Por ejemplo, después de una crisis convulsiva generalizada o de inducir un estado de hiperexcitación, las células granulares glutamatérgicas del giro dentado del hipocampo, presentan modificaciones que les permiten sintetizar y liberar GABA, que actúa sobre receptores pre- y postsinápticos. En este escenario, el GABA liberado espontáneamente de las células granulares, inhibe las oscilaciones de ~20 Hz en la zona CA3 del hipocampo. Se ha propuesto que este mecanismo, activado por crisis convulsivas, podría servir para limitar la actividad de la red neuronal en respuesta a incrementos de excitabilidad. De igual forma, su presencia se ha asociado con los efectos deletéreos que tienen las crisis convulsivas sobre el aprendizaje y la consolidación de memoria, particularmente durante la fase post-ictal. Finalmente discutimos el posible papel computacional que las oscilaciones neuronales tienen en la representación de información sensorial.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary Neuronal oscillations emerge as a consequence of the interaction of large groups of neurons, which can synchronize their activity to generate a rhythmic field behavior. They occur in different brain areas and have been associated to relevant physiological and pathological processes such as sensory processing, memory, epilepsy and consciousness. Neuronal oscillations are mediated and shaped by i) the intrinsic properties of the cell membrane, ii) the architecture of synaptic connections between the neurons confined in a network, and iii) the dynamics of the synaptic currents. The firing properties of the neurons depend on the ionic channels that they possess but nonlinear interactions between different families of ionic currents, may produce small subthreshold membrane oscillations (SMOs). Because the probability to generate an action potential rises during the depolarizing phase of the SMOs, this activity can regulate the neuron&#8217;s firing frequency. Consequently, SMOs influence the responsiveness of the neuron to synaptic inputs that occur at particular frequencies and which, finally, produce a broad range of brain rhythms. In addition to the intrinsic properties of the neuronal membrane, the firing frequency of single neurons depends on the synaptic inputs from other neurons within the network. Indeed, neurons can produce responses in their neighbors by means of electrical and chemical synapses. In this sense, two or more neurons are in synchrony if each fires action potentials, within a small time window before or after the other. This could be explained if both neurons share the same synaptic input or if they interact with each other. Hence, network synchrony is reflected by the current flow between the extracellular and intracellular compartments which can be recorded as a field potential in the extracellular space.  Thus, this field potential reflects both synchronic subthreshold events and action potentials generated by the cells contained in the recorded field. Therefore, the electric potential produced by the synchronized activity of cortical neurons can be recorded over the scalp (the electroencephalogram or EEG). This activity is characterized by its morphology, its frequency and the experimental context in which it is recorded. The cortical brain rhythms are classified in frequency bands, and they are associated with different brain states. They compete and interact with each other and can coexist in the same or in different structures. Because field oscillations can be spontaneously generated in vitro, their generating and sustaining mechanisms can be thoroughly studied. For instance, blockade of presynaptic or postsynaptic receptors is a common tool used to isolate the specific contributions of different synaptic components that generate the field oscillations. We also discuss the role of short and long-term GABAergic plasticity and its involvement in neuronal oscillations and in the generation of hyper-synchronic rhythms that underlie epileptic discharges. Indeed, excitatory and inhibitory synaptic interactions regulate and sustain the firing synchrony in neural networks. For instance, diverse sets of GABAergic interneurons contribute with different firing frequencies that finally inhibit their postsynaptic targets: excitatory principal cells and other interneurons. In other words, inhibitory synapses, as a whole, generate different synchronic neuronal events and restrain the network excitability. Hence, it is relevant to study which parameters do modify the GABAergic transmission. For instance, a feature of GABAergic synapses is that prolonged GABAA-R activation may lead to a switch from a hyperpolarizing to a depolarizing postsynaptic response. This is partly due to a positive shift on the GABAA-R reversal potential (EGABA) because of a GABA-induced-chloride (Cl-) accumulation in the postsynaptic neurons. Recent studies suggest that the activity-dependent EGABA shift may have important implications in the mechanisms involved in the generation of &#947; (~40 Hz) oscillations and seizure-like discharges. The study of how intracellular Cl- dynamics shape network oscillations may bring insights into the mechanisms of physiological and pathological brain rhythms. Moreover, Cl- dynamics have also prominent functional implications during development. Another relevant example of GABAergic plasticity is observed in the glutamatergic hippocampal granule cells (GCs). In response to an increment in network excitability, GCs are able to synthesize and release GABA for fast neurotransmission. Several experimental results have compellingly shown that GABAergic signaling from these cells activates presynaptic and postsynaptic GABAergic receptors. Therefore, it is plausible that after seizures, the GCs could spontaneously release GABA that would, in turn, change the spontaneous field activity that naturally emerges from the postsynaptic targets that comprise the CA3 intrinsic network oscillator. And this is indeed the case. GABA released from CGs inhibits &#946;/&#947; oscillations (~20 Hz) in the CA3 area, where principal cells and interneurons are impinged by CGs. Thus, this mechanism could be used to limit network excitability after seizures. The emergence of the GABAergic phenotype in CGs could also be involved in the deleterious effects on learning and memory consolidation that have been observed after seizures. Finally, we briefly discuss the computational role that network oscillations may have to represent sensory information.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Neuronal oscillations]]></kwd>
<kwd lng="en"><![CDATA[subthreshold oscillations]]></kwd>
<kwd lng="en"><![CDATA[synchrony]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
<kwd lng="en"><![CDATA[GABAergic plasticity]]></kwd>
<kwd lng="es"><![CDATA[Oscilaciones neuronales]]></kwd>
<kwd lng="es"><![CDATA[oscilaciones subumbrales]]></kwd>
<kwd lng="es"><![CDATA[sincronía]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[plasticidad GABAérgica]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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