The contribution of gamma bursting to spontaneous gamma activity in schizophrenia

Increased spontaneous gamma (30–100 Hz) activity (SGA) has been reported in the auditory cortex in schizophrenia. This phenomenon has been correlated with psychotic symptoms such as auditory hallucinations and could reflect the dysfunction of NMDA receptors on parvalbumin-expressing inhibitory interneurons. Previous findings are from time-averaged spectra, so it is unknown whether increased spontaneous gamma occurs at a constant level, or rather in bursts. To better understand the dynamical nature of spontaneous gamma activity in schizophrenia, here we examined the contribution of gamma bursting and the slope of the EEG spectrum to this phenomenon. The main results from this data set were previously reported. Participants were 24 healthy control participants (HC) and 24 matched participants with schizophrenia (SZ). The data were from EEG recordings during auditory steady-state stimulation, which were localized to bilateral pairs of dipoles in auditory cortex. Time-frequency analysis was performed using Morlet wavelets. Oscillation bursts in the gamma range were defined as periods during which power exceeded 2 standard deviations above the trial-wide average value for at least one cycle. We extracted the burst parameters power, count, and area, as well as non-burst trial power and spectral slope. Gamma burst power and non-burst trial power were greater in SZ than HC, but burst count and area did not differ. Spectral slope was less negative in SZ than HC. Regression modeling found that gamma burst power alone best predicted SGA for both HC and SZ (> = 90% of variance), while spectral slope made a small contribution and non-burst trial power did not influence SGA. Increased SGA in the auditory cortex in schizophrenia is accounted for by increased power within gamma bursts, rather than a tonic increase in gamma-range activity, or a shift in spectral slope. Further research will be necessary to determine if these measures reflect different network mechanisms. We propose that increased gamma burst power is the main component of increased SGA in SZ and could reflect abnormally increased plasticity in cortical circuits due to enhanced plasticity of synapses on parvalbumin-expressing inhibitory interneurons. Thus, increased gamma burst power may be involved in producing psychotic symptoms and cognitive dysfunction

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

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

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

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

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