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DIVERGENT SCALING of MINIATURE EXCITATORY POST-SYNAPTIC CURRENT AMPLITUDES in HOMEOSTATIC PLASTICITY a Dissertation Submitted In

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DIVERGENT SCALING of MINIATURE EXCITATORY POST-SYNAPTIC CURRENT AMPLITUDES in HOMEOSTATIC PLASTICITY a Dissertation Submitted In DIVERGENT SCALING OF MINIATURE EXCITATORY POST-SYNAPTIC CURRENT AMPLITUDES IN HOMEOSTATIC PLASTICITY A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy by AMANDA L. HANES M.S., Wright State University, 2009 B.S.C.S., Wright State University, 2006 2018 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 12, 2018 I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SUPERVISION BY Amanda L. Hanes ENTITLED Divergent scaling of miniature post-synaptic current amplitudes in homeostatic plasticity BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. ________________________________ Kathrin L. Engisch, Ph.D Dissertation Director ________________________________ Mill W. Miller, Ph.D. Director, Biomedical Sciences PhD Program ________________________________ Barry Milligan, Ph.D. Interim Dean of the Graduate School Committee on Final Examination: ________________________________ Mark Rich, M.D./Ph.D. ________________________________ David Ladle, Ph.D. ________________________________ Michael Raymer, Ph.D. ________________________________ Courtney Sulentic, Ph.D. ABSTRACT Hanes, Amanda L. Ph.D. Biomedical Sciences PhD Program, Wright State University, 2018. Divergent scaling of miniature excitatory post-synaptic current amplitudes in homeostatic plasticity Synaptic plasticity, the ability of neurons to modulate their inputs in response to changing stimuli, occurs in two forms which have opposing effects on synaptic physiology. Hebbian plasticity induces rapid, persistent changes at individual synapses in a positive feedback manner. Homeostatic plasticity is a negative feedback effect that responds to chronic changes in network activity by inducing opposing, network-wide changes in synaptic strength and restoring activity to its original level. The changes in synaptic strength can be measured as changes in the amplitudes of miniature post- synaptic excitatory currents (mEPSCs). Together, the two forms of plasticity underpin nervous system functions such as movement, learning and memory, and perception, while preventing pathological states of hyper- or hypoactivity that could occur if network activity were not maintained. The current hypothesis of homeostatic plasticity states that mEPSC amplitudes exhibit uniform multiplicative scaling, a transformation in which the amplitudes are scaled up or down globally by a multiplicative factor. This hypothesis constrains the possible mechanism of homeostatic plasticity, which remains unknown despite intensive study. iii Here, we compare an experimental data set previously collected in our laboratory to the results of an empirical simulation of uniform multiplicative scaling and conclude that the homeostatic increase in mEPSC amplitudes in our data is not uniform. We develop and validate a novel method, comparative standardization, for calculating the scaling transformation between treated and untreated mEPSC amplitudes and identifying the transformation as either uniform, divergent, or convergent. When applied to our experimental data, comparative standardization finds divergent scaling, in which the homeostatic effect increases with synaptic strength, causing the control and treated mEPSC amplitude distributions to diverge. The divergent scaling transformation computed by comparative standardization is also more accurate than the transformations computed by existing methods. Finally, we generalize our findings by applying our approach to several additional homeostatic plasticity data sets obtained from our collaborators: All additional data exhibit divergent scaling, and comparative standardization consistently outperforms both existing methods for computing the homeostatic scaling transformation. iv TABLE OF CONTENTS I. Introduction ............................................................................................................................. 1 II. Methods ................................................................................................................................. 10 III. Specific Aim 1 ......................................................................................................................... 17 Rationale .................................................................................................................................... 17 Approach .................................................................................................................................... 18 Results ........................................................................................................................................ 19 Discussion ................................................................................................................................... 29 IV. Specific Aim 2 ..................................................................................................................... 32 Rationale .................................................................................................................................... 32 Approach .................................................................................................................................... 33 Results ........................................................................................................................................ 34 Discussion ................................................................................................................................... 41 V. Specific Aim 3 ......................................................................................................................... 45 Rationale .................................................................................................................................... 45 Approach .................................................................................................................................... 46 Results ........................................................................................................................................ 47 Discussion ................................................................................................................................... 53 VI. Dissertation summary ........................................................................................................ 58 Conclusions by specific aim ........................................................................................................ 58 Discussion ................................................................................................................................... 59 VII. Figures ................................................................................................................................ 62 VIII. Tables ................................................................................................................................. 94 IX. Equations ............................................................................................................................... 95 X. Bibliography ........................................................................................................................... 98 v LIST OF FIGURES Figure 1. Diagram of the homeostatic plasticity effect in terms of network activity and mEPSC amplitude ....................................................................................................................................... 62 Figure 2. Flow diagram of a bootstrapping procedure for validating the rank-order method. .... 63 Figure 3. The amplitude of miniature excitatory post synaptic currents (mEPSCs) is increased in data previously recorded in dissociated cultures of mouse cortical neurons treated with 500 nM tetrodotoxin (TTX) for 48 hours ..................................................................................................... 64 Figure 4. Experimental data differ from a simulation of perfect uniform multiplicative scaling .. 65 Figure 5. Empirical simulations of uniform, multiplicative scaling in variable data ...................... 66 Figure 6. The scaling transformation computed by the rank order method produces a nearly perfect fit between the downscaled TTX and control data from empirical simulations with no or a moderate detection threshold, but not in data with a high detection threshold ...................... 67 Figure 7. The results of the rank order method on experimental data differ from the results on the uniformly scaled simulation data. ........................................................................................... 68 Figure 8. The scaling transformation computed by the iterative method produces a nearly perfect fit between the downscaled TTX and control data from the empirical simulation with a detection threshold of 7 pA. .......................................................................................................... 69 Figure 9. The scaling transformation computed by the iterative method failed to produce a close match between downscaled TTX and control distributions in experimental data ........................ 70 Figure 10. Bootstrap validation and estimation of the test statistic distribution confirm that the null hypothesis of uniform scaling is rejected in experimental data ............................................. 71 Figure 11. The ratio of TTX to control amplitudes is approximately uniform in simulation data, with minor deviations caused by detection thresholds,
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