Alison L. Barth
159C Mellon Institute
Department of Biological Sciences
Carnegie Mellon University
4400 Fifth Avenue
Pittsburgh, PA 15213
Ph.D., University of California, Berkeley
Postdoctoral Appointment, Stanford University School of Medicine
ResearchHow does experience shape the brain? Research in the Barth lab is focused on understanding how experience assembles and alters the properties of neural circuits in the cerebral cortex, in both normal and disease states. The lab has a specific focus on somatosensation in the mouse model system, where specific types of sensory input from the skin are used to drive neural activity to change the strength of synaptic connections and the firing output of cortical neurons. This neural plasticity can result in enhanced perceptual capabilities and influence subsequent learning. A detailed examination of how synapses are changed by experience is revealing fundamental principles about both perception and learning across many neural systems. In addition, researchers in the lab are using electrophysiological recordings, electron microscopy, and computational modeling to understand how functional networks are constructed and optimized in the neocortex. Experiments take advantage of transgenic mice to manipulate gene expression and label defined neural subsets and whole-cell recording and imaging to quantitate the electrical properties of cortical neurons. Ongoing projects include:
- Experience-dependent plasticity in somatosensory cortex
FosGFP transgenic mice were developed in the Barth lab more than 10 years ago to identify neurons that have been activated (and changed) by in vivo experience. We are using fos-driven expression of fluorescent reporter genes as well as channelrhodopsin to identify and record from specific neocortical areas and neurons that have been altered by experience. This approach has revealed molecular details about the mechanisms behind synaptic potentiation, and has tested models about how different cells capture synaptic change. Recent research in the lab has established that experience-dependent plasticity undergoes multiple phases before it is finally consolidated, a finding that has implications for many other forms of learning across the brain.
- Development of neocortical networks for somatosensation
Somatosensation is a complex sense that is constructed from a multitude of specialized receptor neurons in the skin, each tuned to specific stimulus properties. Research in the lab has traditionally focused on the sense of touch mediated through specialized hairs on the mouse face. We have begun to investigate other neocortical maps that are specialized for other skin sensations, such as pain and temperature. These modalities appear to be differently encoded in the cerebral cortex, and offer exciting approaches for precise stimulus control to assemble and modulate circuit function during development and in adult animals.
- Activity-dependent changes in ion channel function
Neurons respond to experience not only by changing the synaptic strength, but also by adjusting their excitable properties. We have identified specific ion channels that are regulated by activity, both during normal experience and also after epileptic seizures. The molecular mechanisms that regulate channel currents over both short and long time scales offer the potential to control neural excitability and treat seizure disorders. Real-time, live cell imaging of ion channel distribution has revealed new principles for channel regulation.
- Network assembly and optimization using principles of neural design
The cerebral cortex has expanded enormously through phylogeny, but recent analysis indicates that many neurons are virtually silent. Why have so many cells, if they aren’t firing? We have been using expression of the fosGFP transgene to identify neural ensembles that may be functionally linked, and are studying how these ensembles are created during development and by experience-dependent plasticity. In addition, members of the lab are modeling competitive processes for establishing neural ensembles to determine what principles optimize efficiency, cost, and robustness in information transfer.
Glazewski S, Barth AL. Stimulus intensity determines experience-dependent modifications in neocortical neuron firing rates. Eur J Neurosci. 2014 Dec 26. doi: 10.1111/ejn.12805.
Czajkowski R, Jayaprakash B, Wiltgen B, Rogerson T, Guzman-Karlsson MC, Barth AL, Trachtenberg JT, Silva AJ. Encoding and storage of spatial information in the retrosplenial cortex. Proc Natl Acad Sci U S A. 2014 May 27. pii: 201313222.
Wen JA and Barth AL. Initiation, labile, and stabilization phases of experience-dependent plasticity at neocortical synapses. Journal of Neuroscience 33(19):8483-93, 2013.
Navlakha S, Suhan J, Barth AL, and Bar-Joseph Z. A high-throughput machine learning framework to detect synapses in electron microscopy images. Bioinformatics. 2013 Jul 1;29(13):i9-i17 and Proc. of the 21st Intl. Conf. on Intelligent Systems for Molecular Biology and 12th European Conf. on Computational Biology (ISMB/ECCB).
Benedetti BL*, Takashima Y*, Wen JA, Barth AL. Asymmetric wiring of layer 2/3 neurons drives sparse and reliable firing during neocortical development. Cerebral Cortex Aug 23, 2012. [Epub ahead of print] *These authors contributed equally to this work.
Poulet JFA and Barth AL. Experimental evidence for sparse firing in the neocortex. Trends in Neurosciences, Jun;35(6):345-55.
Wen JA and Barth AL. Synaptic lability after experience-dependent plasticity is not mediated by calcium-permeable AMPARs. Frontiers in Molecular Neuroscience 2012;5:15, 2012. epub 2012 Feb 29.
Shruti S, Urban-Ciecko J, Fitzpatrick J, Brenner R, Bruchez M and Barth AL. The brain-specific beta 4 subunit downregulates BK channel cell surface expression. PlosOne 2012;7(3):e33429, 2012. epub Mar 16.
Wen JA and Barth AL. Input-specific critical periods for experience-dependent plasticity in layer 2/3 pyramidal neurons. Journal of Neuroscience 23;31(12):4456-65, 2011.
Yassin L, Benedetti BL, Jouhanneau JS, Wen JA, Poulet JF, Barth AL. An embedded subnetwork of highly active neurons in the neocortex. Neuron. 2010 Dec 22;68(6):1043-50
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