Conditional deletion of TrkB alters adult hippocampal neurogenesis and anxiety-related behavior

Abstract

Brain-derived neurotrophic factor (BDNF) is a member of the neurotrophin family, which has been reported to regulate neurogenesis in the dentate gyrus, but the molecular control over this process remains unclear. We demonstrated that by activating TrkB receptor tyrosine kinase, BDNF controls the size of the surviving pool of newborn neurons at the time of connectivity. The TrkB-dependent decision regarding survival in these newborn neurons takes place just when they are integrated into the existing neural network at approximately 4 weeks of age. Before newborn neurons start to die they exhibit a drastic reduction in dendritic complexity and spine density, which may reflect a failure of these cells to integrate appropriately. Both the failure to become integrated, and subsequent dying, leads to impaired neurogenesis-dependent plasticity and increased anxiety-like behavior in mice lacking a functional TrkB receptor in newborn neurons. Thus, our data demonstrate the importance of BDNF/TrkB signaling for the survival and integration of newborn neurons in the adult hippocampus and suggest a critical function of these neurons in regulating the anxiety state of the animal.

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Acknowledgements

This work was partly funded by a National Science Foundation Graduate Fellowship (to Timothy F. Brady), a National Defense Science and Engineering Graduate Fellowship (to Talia Konkle), a National Research Service Award Fellowship F32-EY016982 (to George A. Alvarez), and a National Science Foundation (NSF) Career Award IIS-0546262 and NSF Grant IIS-0705677 (to Aude Oliva).

Figures and Tables

Figure 1 A screen initially displayed the duration the objects would be present for. This was followed by a brief blank, and then the six objects were presented. Afterwards, there was another brief blank and then a test object. Observers had to say whether the test object was the same or different than the object previously presented at that location.

Figure 2 Percent correct for objects in the three change-type conditions (novel, exemplar-level change, state-level change) as a function of encoding durations (1.2 s, 6 s and 18 s). There were six objects presented, so the encoding durations correspond to 200 ms/object, 1 sec/object and 3 sec/object, respectively.

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