Behavior modulates effective connectivity between cortex and striatum.

It has been notoriously difficult to understand interactions in the basal ganglia because of multiple recurrent loops. Another complication is that activity there is strongly dependent on behavior, suggesting that directional interactions, or effective connections, can dynamically change. A simplifying approach would be to examine just the direct, monosynaptic projections from cortex to striatum and contrast this with the polysynaptic feedback connections from striatum to cortex. Previous work by others on effective connectivity in this pathway indicated that activity in cortex could be used to predict activity in striatum, but that striatal activity could not predict cortical activity. However, this work was conducted in anesthetized or seizing animals, making it impossible to know how free behavior might influence effective connectivity. To address this issue, we applied Granger causality to local field potential signals from cortex and striatum in freely behaving rats. Consistent with previous results, we found that effective connectivity was largely unidirectional, from cortex to striatum, during anesthetized and resting states. Interestingly, we found that effective connectivity became bidirectional during free behaviors. These results are the first to our knowledge to show that striatal influence on cortex can be as strong as cortical influence on striatum. In addition, these findings highlight how behavioral states can affect basal ganglia interactions. Finally, we suggest that this approach may be useful for studies of Parkinson's or Huntington's diseases, in which effective connectivity may change during movement

Expression of histone deacetylase (HDAC) family members in bortezomib-refractory multiple myeloma and modulation by panobinostat

Aim: Multiple myeloma (MM) is a hematological malignancy of antibody-producing mature B cells or plasma cells. The proteasome inhibitor, bortezomib, was the first-in-class compound to be FDA approved for MM and is frequently utilized in induction therapy. However, bortezomib refractory disease is a major clinical concern, and the efficacy of the pan-histone deacetylase inhibitor (HDACi), panobinostat, in bortezomib refractory disease indicates that HDAC targeting is a viable strategy. Here, we utilized isogenic bortezomib resistant models to profile HDAC expression and define baseline and HDACi-induced expression patterns of individual HDAC family members in sensitive vs. resistant cells to better understanding the potential for targeting these enzymes.Methods: Gene expression of HDAC family members in two sets of isogenic bortezomib sensitive or resistant myeloma cell lines was examined. These cell lines were subsequently treated with HDAC inhibitors: panobinostat or vorinostat, and HDAC expression was evaluated. CRISPR/Cas9 knockdown and pharmacological inhibition of specific HDAC family members were conducted.Results: Interestingly, HDAC6 and HDAC7 were significantly upregulated and downregulated, respectively, in bortezomib-resistant cells. Panobinostat was effective at inducing cell death in these lines and modulated HDAC expression in cell lines and patient samples. Knockdown of HDAC7 inhibited cell growth while pharmacologically inhibiting HDAC6 augmented cell death by panobinostat.Conclusion: Our data revealed heterogeneous expression of individual HDACs in bortezomib sensitive vs. resistant isogenic cell lines and patient samples treated with panobinostat. Cumulatively our findings highlight distinct roles for HDAC6 and HDAC7 in regulating cell death in the context of bortezomib resistance

Non-parametric Granger causality during spontaneous behavior.

<p>Granger causality derived directly from the Fourier transform of LFPs gathered during alertness (A), exploration (B), and rearing (C). Note the substantial similarity between these estimates of Granger causality and those obtained using a regression model. The agreement between these measures lends credence to our analysis and rules out model fitting as a source of error.</p

Coherence and causality spectra during multiple behaviors.

<p>Coherence spectra (A–E) and Granger causality (F–J) based on data recorded during anesthesia (row 1), sleep (row 2), immobile alertness (row 3), exploration (row 4), and rearing (row 5). Gray shaded regions indicate a non-Gaussian approximation of +/−1 standard deviation based on resampling (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089443#s2" target="_blank">methods</a>). Bars above each peak indicates the range over frequency over which GC exceeds a 0.005 cutoff based upon permutation analysis; solid lines denote frequency regimes over which corticostriatal causality is above chance, and dashed lines denote regimes over which striatocortical causality is above chance. Note the dominance of cortical drive during both anesthesia and sleep, as well as the complete lack of causality peaks in the striatocortical direction during sleep. Also note the refined information revealed by Granger causality. The coherence spectra are similar among the three behaviors; however, the relative contribution of information flow in the corticostriatal and striatocortical directions varies substantially among behaviors.</p

Time-frequency content of LFPs recorded in M1 during anesthesia in all rats showing consistent spectral content in multiple signals recorded in multiple animals.

<p>This figure was generated by wavelet transforming each epoch using multiwavelet estimation and estimating the average squared amplitude. The resulting single-trial spectrograms were concatenated to form the image shown. The color bar gives the averaged squared amplitude of the wavelet coefficients at each point in time-frequency space. Note the concentration of power in the 1–3 Hz range across all epochs of anesthesia.</p

Peak Granger causality for each behavior (shown in bold) with the 95% intersubject confidence interval, determined by a bootstrapping procedure described in the methods.

<p>The second column shows the number of realizations used to generate the ensemble average, and the duration of each realization in parentheses. Model order refers to the number of parameters used to generate the regression model, using data from all animals engaged in a particular behavior.</p

A representative histology section.

<p>Asterisks indicate the location of electrode tips, as revealed by metal ion deposits marked by ferrocyanide.</p

f Power spectral densities generated using LFPs recorded in M1 (A–E) and dStr (F–J) during anesthesia (row 1), sleep (row 2), immobile alertness (row 3), exploration (row 4), and rearing (row 5).

<p>Power in both M1 (A) and dStr (F) was higher during anesthesia than during sleep (B,D). Quantile ranges for some data are too narrow to be discernable in this figure. Gray shaded regions indicate a non-Gaussian approximation of +/−1 standard deviation based on resampling (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089443#s2" target="_blank">methods</a>). Note the variation in contributions to total power from oscillations in the 1–5 Hz and 7–12 Hz ranges across the three spontaneous behaviors.</p