Estrogen alters behavior and forebrain c-fos expression in ovariectomized rats subjected to the forced swim test

Estrogen has been implicated in brain functions related to affective state, including hormone-related affective disorders in women. Although some reports suggest that estrogen appears to decrease vulnerability to affective disorders in certain cases, the mechanisms involved are unknown. We used the forced swim test (FST), a paradigm used to test the efficacy of antidepressants, and addressed the hypotheses that estrogen alters behavior of ovariectomized rats in the FST and the FST-induced expression of c-fos, a marker for neuronal activity, in the rat forebrain. The behaviors displayed included struggling, swimming, and immobility. One hour after the beginning of the test on day 2, the animals were perfused, and the brains were processed for c-fos immunocytochemistry. On day 1, the estradiol benzoate-treated animals spent significantly less time struggling and virtually no time in immobility and spent most of the time swimming. Control rats spent significantly more time struggling or being immobile during a comparable period. On day 2, similar behavioral patterns with still more pronounced differences were observed between estradiol benzoate and ovariectomized control groups in struggling, immobility, and swimming. Analysis of the mean number of c-fos immunoreactive cell nuclei showed a significant reduction in the estradiol benzoate versus control groups in areas of the forebrain relating to sensory, contextual, and integrative processing. Our results suggest that estrogen-induced neurochemical changes in forebrain neurons may translate into an altered behavioral output in the affective domain.

Control of time-dependent biological processes by temporally patterned input

Temporal patterning of biological variables, in the form of oscillations and rhythms on many time scales, is ubiquitous. Altering the temporal pattern of an input variable greatly affects the output of many biological processes. We develop here a conceptual framework for a quantitative understanding of such pattern dependence, focusing particularly on nonlinear, saturable, time-dependent processes that abound in biophysics, biochemistry, and physiology. We show theoretically that pattern dependence is governed by the nonlinearity of the input–output transformation as well as its time constant. As a result, only patterns on certain time scales permit the expression of pattern dependence, and processes with different time constants can respond preferentially to different patterns. This has implications for temporal coding and decoding, and allows differential control of processes through pattern. We show how pattern dependence can be quantitatively predicted using only information from steady, unpatterned input. To apply our ideas, we analyze, in an experimental example, how muscle contraction depends on the pattern of motorneuron firing.