Limits...
Network Mechanisms Generating Abnormal and Normal Hippocampal High-Frequency Oscillations: A Computational Analysis.

Fink CG, Gliske S, Catoni N, Stacey WC - eNeuro (2015 May-Jun)

Bottom Line: Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing.Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner.These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Physics & Astronomy and Neuroscience Program, Ohio Wesleyan University, Delaware, OH, USA.

ABSTRACT

High-frequency oscillations (HFOs) are an intriguing potential biomarker for epilepsy, typically categorized according to peak frequency as either ripples (100-250 Hz) or fast ripples (>250 Hz). In the hippocampus, fast ripples were originally thought to be more specific to epileptic tissue, but it is still very di cult to distinguish which HFOs are caused by normal versus pathological brain activity. In this study we use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by di erent mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples.

No MeSH data available.


Related in: MedlinePlus

HFOs resulting from noisy input to pyramidal cell population. A, Increased noise intensity to activated pyramidal cells stimulated increases in the two highest-power frequencies observed in the LFP PSDs, which were generally bimodal (see insets). Note that the network was incapable of generating rhythms faster than 250 Hz, in contrast with simulations in which basket cells received direct input (Fig. 2A). B, High-frequency and low-frequency spectral bumps corresponded to the mean firing rates of basket cells and pyramidal cells, respectively. C, Relative dominance between the two spectral bumps varied with noise intensity, as shown in this plot of the ratio of the maximum power of the high-frequency peak to the maximum power of the low-frequency peak. Ratios >1 (demarcated by black horizontal line) imply that the high-frequency peak dominated the low-frequency peak. D, Total LFP power >30 Hz tended to decrease somewhat with increasing noise intensity, though not nearly as dramatically as when basket cells received noisy input (Fig. 2D). All error bars represent SEM over 10 simulations. Note that the pyramidal cells in this figure require higher levels of noise input to fire than the basket cells in Figure 2, as a result of their different input impedance. This disparity was also recently shown experimentally (Karlócai et al., 2014).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4487885&req=5

Figure 3: HFOs resulting from noisy input to pyramidal cell population. A, Increased noise intensity to activated pyramidal cells stimulated increases in the two highest-power frequencies observed in the LFP PSDs, which were generally bimodal (see insets). Note that the network was incapable of generating rhythms faster than 250 Hz, in contrast with simulations in which basket cells received direct input (Fig. 2A). B, High-frequency and low-frequency spectral bumps corresponded to the mean firing rates of basket cells and pyramidal cells, respectively. C, Relative dominance between the two spectral bumps varied with noise intensity, as shown in this plot of the ratio of the maximum power of the high-frequency peak to the maximum power of the low-frequency peak. Ratios >1 (demarcated by black horizontal line) imply that the high-frequency peak dominated the low-frequency peak. D, Total LFP power >30 Hz tended to decrease somewhat with increasing noise intensity, though not nearly as dramatically as when basket cells received noisy input (Fig. 2D). All error bars represent SEM over 10 simulations. Note that the pyramidal cells in this figure require higher levels of noise input to fire than the basket cells in Figure 2, as a result of their different input impedance. This disparity was also recently shown experimentally (Karlócai et al., 2014).

Mentions: Network activity was distinctly different when noisy input was delivered to pyramidal cells rather than basket cells (Fig. 3A). The spectral content was bimodal due to the different firing rates of these two classes of cells. The lower frequency peak was due to pyramidal cell firing (Fig. 3A,B, compare the lower lines), which dominated the LFP at low noise intensity (Fig. 3C), and the higher-frequency peak was due to basket cell firing (Fig. 3A,B, compare the upper lines), which dominated the output for high noise intensities. Thus, when the pyramidal cells were driven by varying levels of synaptic activity, they produced a range of strong oscillations from 60–250 Hz. It is important to note that no level of noise intensity was capable of eliciting a network rhythm faster than about 250 Hz. The pyramidal cells reached firing rates of over 100 Hz, a level that would only be expected in highly active conditions such as epilepsy (see Discussion). Thus, this model shows the transition from what are likely normal to epileptic HFOs. However, with this configuration (all connections intact) it was impossible to elicit fast ripples for two reasons: (1) the basket cell inhibition effectively limited the peak frequency of pyramidal cell firing, and (2) pyramidal cells were synchronized when inhibitory feedback was intact.


Network Mechanisms Generating Abnormal and Normal Hippocampal High-Frequency Oscillations: A Computational Analysis.

Fink CG, Gliske S, Catoni N, Stacey WC - eNeuro (2015 May-Jun)

HFOs resulting from noisy input to pyramidal cell population. A, Increased noise intensity to activated pyramidal cells stimulated increases in the two highest-power frequencies observed in the LFP PSDs, which were generally bimodal (see insets). Note that the network was incapable of generating rhythms faster than 250 Hz, in contrast with simulations in which basket cells received direct input (Fig. 2A). B, High-frequency and low-frequency spectral bumps corresponded to the mean firing rates of basket cells and pyramidal cells, respectively. C, Relative dominance between the two spectral bumps varied with noise intensity, as shown in this plot of the ratio of the maximum power of the high-frequency peak to the maximum power of the low-frequency peak. Ratios >1 (demarcated by black horizontal line) imply that the high-frequency peak dominated the low-frequency peak. D, Total LFP power >30 Hz tended to decrease somewhat with increasing noise intensity, though not nearly as dramatically as when basket cells received noisy input (Fig. 2D). All error bars represent SEM over 10 simulations. Note that the pyramidal cells in this figure require higher levels of noise input to fire than the basket cells in Figure 2, as a result of their different input impedance. This disparity was also recently shown experimentally (Karlócai et al., 2014).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487885&req=5

Figure 3: HFOs resulting from noisy input to pyramidal cell population. A, Increased noise intensity to activated pyramidal cells stimulated increases in the two highest-power frequencies observed in the LFP PSDs, which were generally bimodal (see insets). Note that the network was incapable of generating rhythms faster than 250 Hz, in contrast with simulations in which basket cells received direct input (Fig. 2A). B, High-frequency and low-frequency spectral bumps corresponded to the mean firing rates of basket cells and pyramidal cells, respectively. C, Relative dominance between the two spectral bumps varied with noise intensity, as shown in this plot of the ratio of the maximum power of the high-frequency peak to the maximum power of the low-frequency peak. Ratios >1 (demarcated by black horizontal line) imply that the high-frequency peak dominated the low-frequency peak. D, Total LFP power >30 Hz tended to decrease somewhat with increasing noise intensity, though not nearly as dramatically as when basket cells received noisy input (Fig. 2D). All error bars represent SEM over 10 simulations. Note that the pyramidal cells in this figure require higher levels of noise input to fire than the basket cells in Figure 2, as a result of their different input impedance. This disparity was also recently shown experimentally (Karlócai et al., 2014).
Mentions: Network activity was distinctly different when noisy input was delivered to pyramidal cells rather than basket cells (Fig. 3A). The spectral content was bimodal due to the different firing rates of these two classes of cells. The lower frequency peak was due to pyramidal cell firing (Fig. 3A,B, compare the lower lines), which dominated the LFP at low noise intensity (Fig. 3C), and the higher-frequency peak was due to basket cell firing (Fig. 3A,B, compare the upper lines), which dominated the output for high noise intensities. Thus, when the pyramidal cells were driven by varying levels of synaptic activity, they produced a range of strong oscillations from 60–250 Hz. It is important to note that no level of noise intensity was capable of eliciting a network rhythm faster than about 250 Hz. The pyramidal cells reached firing rates of over 100 Hz, a level that would only be expected in highly active conditions such as epilepsy (see Discussion). Thus, this model shows the transition from what are likely normal to epileptic HFOs. However, with this configuration (all connections intact) it was impossible to elicit fast ripples for two reasons: (1) the basket cell inhibition effectively limited the peak frequency of pyramidal cell firing, and (2) pyramidal cells were synchronized when inhibitory feedback was intact.

Bottom Line: Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing.Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner.These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Physics & Astronomy and Neuroscience Program, Ohio Wesleyan University, Delaware, OH, USA.

ABSTRACT

High-frequency oscillations (HFOs) are an intriguing potential biomarker for epilepsy, typically categorized according to peak frequency as either ripples (100-250 Hz) or fast ripples (>250 Hz). In the hippocampus, fast ripples were originally thought to be more specific to epileptic tissue, but it is still very di cult to distinguish which HFOs are caused by normal versus pathological brain activity. In this study we use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by di erent mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples.

No MeSH data available.


Related in: MedlinePlus