Past events

Abstract: Past neurobiological models of mood disorders have not considered etiology or a developmental perspective. Recently enough data regarding candidate genes and the impact of adverse early experience has been published that the beginnings of a plausible and heuristically useful hypothetical causal model can be proposed. This talk will integrate known effects of susceptibility genes and childhood adversity in explaining the psychopathology and biological phenotype of major depression including data from postmortem studies and in vivo brain imaging.

The last twenty years of intense research have provided convincing evidence for a role of regulation of gene expression in control of long-term neuronal plasticity, including learning and memory. Starting from our discovery–in late eighties–of c-fos activation in those phenomena, we have focused on correlating the expression of c-fos mRNA and c-Fos protein in various cognition-related brain structures with neuronal plasticity, learning and memory. The major conclusion from our studies, as well as those by the others, is that c-Fos and its functional form, AP-1 transcription factor, is the best correlate of learning processes, especially of a novelty of the behavioral information, whose processing constitutes the very foundation of the learning phenomenon. However, our understanding of exact biological function(s) of c-Fos/AP-1 still remains largely missing. Recently, an extracellular proteolytic system, composed of tissue inhibitor of matrix metalloproteinases, TIMP-1 and matrix metalloproteinase-9, MMP-9, has emerged as a major AP-1 target in hippocampal neurons responding to enhanced neuronal activity. Structural remodeling of the dendritic spines and synapses is essential for synaptic plasticity, underlying learning and memory. Matrix metalloproteinases are pivotal for tissue remodeling throughout the body, especially during development. Matrix metalloproteinase 9 (MMP-9) is an extracellularly operating enzyme that have recently been implicated in dendritic remodeling, synaptic plasticity, learning and memory (Szklarczyk et al., J. Neurosci., 2002; Nagy et al., J. Neurosci., 2006; Okulski et al., Biol. Psych., 2007). Furthermore, we have recently identified MMP-9 as a being produced, expressed and active at the synaptic contacts (Konopacki et al., Neuroscience, 2007; Michaluk et al., J. Biol. Chem., 2007; Wilczynski et al., J. Cell Biol. in press). Most recently, we have also found that MMP-9 plays a key pathogenic role in two animal models of temporal lobe epilepsy (TLE): kainate-evoked-epilepsy and pentylenetetrazole (PTZ) kindling-induced epilepsy. TLE is a devastating disease in which aberrant synaptic plasticity plays a major role Notably, we show that the sensitivity to PTZ-epileptogenesis is decreased in MMP-9 KO mice, but is increased in novel strain of transgenic rats, we have produced to overexpress MMP-9 selectively in neurons. Immunoelectron microscopy has revealed that MMP-9 associates with hippocampal dendritic spines bearing asymmetric (excitatory) synapses, where both the MMP-9 protein levels and enzymatic activity become strongly increased upon seizures. Further, we find that MMP-9-deficiency diminishes seizure-evoked pruning of dendritic spines and decreases aberrant synaptogenesis following mossy-fibers sprouting. The latter observation provides a possible mechanistic basis for the effect of MMP-9 on epileptogenesis. Our work suggests that a synaptic pool of MMP-9 is critical for the sequence of events that underlie the development of seizures in animal models of TLE.

Large, strongly coupled neural networks tend to produce chaotic spontaneous activity. This might appear to make them unsuitable for generating reliable sensory responses or repeatable motor patterns. However, this is not the case. Inputs can induce a phase transition, leading to responses uncontaminated by chaotic "noise". Likewise, appropriately trained feedback units can control the chaos, resulting in a wide variety of repeatable output patterns. These issues will be discussed accompanied by examples and demonstrations.

It is usually assumed that the brain learns by changing neural circuits that are otherwise stable. However, recent experiments in monkeys show that the neural representation of movement in motor cortex is continually changing even without learning, when practicing a familiar task. We set to investigate the reason for these changes. We analyzed the empirical data and found that the changes are slow and random. We constructed a theoretical model of a cortical network that learns a motor skill by changing synaptic strengths. Our model explains how the network can change its synaptic strengths, and neural activity, without changing the motor behavior. Additionally, our model replicates the observed changes when synaptic learning is assumed highly noisy. We speculate that this noise serves to explore different synaptic configurations during learning.

Real-world events unfold at different time scales, and therefore cognitive and neuronal processes must likewise occur at different time scales. In the talk I will present a novel procedure that identifies brain regions responsive to the preceding sequence of events (past time) over different time scales. The fMRI activity was measured while observers viewed silent films presented forward, backward, or piecewise-scrambled in time. The results demonstrate that responses in different brain areas are affected by information that has been accumulated over different time scales, with a hierarchy of temporal receptive windows spanning from short (~4 s) to intermediate (~12 s) and long (~ 36 s). Thus, although we adopted an open-ended experimental protocol (free viewing of complex stimuli), we found that parametric manipulation of the temporal structure of a complex movie sequence produced lawful changes in cortical activity across different brain regions. In addition to the reliable cortical response patterns, I will also show that films exerted considerable control over the subjects' behavior (i.e., eye movements or galvanic skin responses). Finally, I will present few applications of this method for studying the neuronal correlates of complex human behaviors under more natural settings.