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 the basket cell population. A, Peak network frequency (defined as peak frequency of the LFP power spectral density) increased as the intensity of noisy synaptic input to basket cells increased. Insets, Two example PSD functions. Note the difference in scale between the vertical axes of the two insets, indicating the extreme diminution of oscillation amplitude as frequency increased. Individual PSDs were obtained from 1000 ms of simulation data. B, Mean basket cell firing frequency very closely tracked peak network frequency for a given level of synaptic input. C, Total LFP power >30 Hz decreased dramatically as noisy intensity (and peak network frequency) increased. Therefore, although it was possible for noisy input to basket cells to elicit rhythms with fast ripple frequencies, such rhythms exhibited very low amplitude. All error bars represent SEM over 10 simulations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: HFOs resulting from noisy input to the basket cell population. A, Peak network frequency (defined as peak frequency of the LFP power spectral density) increased as the intensity of noisy synaptic input to basket cells increased. Insets, Two example PSD functions. Note the difference in scale between the vertical axes of the two insets, indicating the extreme diminution of oscillation amplitude as frequency increased. Individual PSDs were obtained from 1000 ms of simulation data. B, Mean basket cell firing frequency very closely tracked peak network frequency for a given level of synaptic input. C, Total LFP power >30 Hz decreased dramatically as noisy intensity (and peak network frequency) increased. Therefore, although it was possible for noisy input to basket cells to elicit rhythms with fast ripple frequencies, such rhythms exhibited very low amplitude. All error bars represent SEM over 10 simulations.

Mentions: We first used the biophysical model to determine the parameters necessary to produce the full range of HFOs. We found that HFOs could be elicited through two distinct mechanisms: coherent firing of the 20 basket cells (which sent GABAergic connections to all 3080 pyramidal cells) or the 80 activated pyramidal cells (Fig. 1). In both cases, coherent LFP oscillations were generated by uncorrelated, noisy synaptic input to basket and/or pyramidal cells. Figure 2 shows the results of elevated input to the basket cell population. As depicted in Figure 2A, power spectral density (PSD) plots from the LFP were generally unimodal, and peak network frequency increased monotonically with increasing intensity of noisy synaptic input, spanning a range from gamma oscillations to fast ripples. A fast ripple was defined as a waveform in which the peak power >250 Hz exceeded peak power in the 100–250 Hz band. Peak network frequency (Fig. 2A) closely matched mean basket cell firing rate (Fig. 2B), indicating that the LFP resulted from IPSPs induced in pyramidal cells due to basket cell firing. Basket cell AP waveforms were present but contributed very little to the LFP, due to basket cells’ small size. Although the peak frequency did reach the fast ripple range, it is crucial to point out that the amplitude of network oscillations decreased substantially as peak network frequency increased (Fig. 2C). The total power was much higher in gamma (<100 Hz) frequencies, and reached very small levels >200 Hz (Fig. 2C). Such small-amplitude oscillations would be unlikely to resolve above background noise levels in a live recording.


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 the basket cell population. A, Peak network frequency (defined as peak frequency of the LFP power spectral density) increased as the intensity of noisy synaptic input to basket cells increased. Insets, Two example PSD functions. Note the difference in scale between the vertical axes of the two insets, indicating the extreme diminution of oscillation amplitude as frequency increased. Individual PSDs were obtained from 1000 ms of simulation data. B, Mean basket cell firing frequency very closely tracked peak network frequency for a given level of synaptic input. C, Total LFP power >30 Hz decreased dramatically as noisy intensity (and peak network frequency) increased. Therefore, although it was possible for noisy input to basket cells to elicit rhythms with fast ripple frequencies, such rhythms exhibited very low amplitude. All error bars represent SEM over 10 simulations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: HFOs resulting from noisy input to the basket cell population. A, Peak network frequency (defined as peak frequency of the LFP power spectral density) increased as the intensity of noisy synaptic input to basket cells increased. Insets, Two example PSD functions. Note the difference in scale between the vertical axes of the two insets, indicating the extreme diminution of oscillation amplitude as frequency increased. Individual PSDs were obtained from 1000 ms of simulation data. B, Mean basket cell firing frequency very closely tracked peak network frequency for a given level of synaptic input. C, Total LFP power >30 Hz decreased dramatically as noisy intensity (and peak network frequency) increased. Therefore, although it was possible for noisy input to basket cells to elicit rhythms with fast ripple frequencies, such rhythms exhibited very low amplitude. All error bars represent SEM over 10 simulations.
Mentions: We first used the biophysical model to determine the parameters necessary to produce the full range of HFOs. We found that HFOs could be elicited through two distinct mechanisms: coherent firing of the 20 basket cells (which sent GABAergic connections to all 3080 pyramidal cells) or the 80 activated pyramidal cells (Fig. 1). In both cases, coherent LFP oscillations were generated by uncorrelated, noisy synaptic input to basket and/or pyramidal cells. Figure 2 shows the results of elevated input to the basket cell population. As depicted in Figure 2A, power spectral density (PSD) plots from the LFP were generally unimodal, and peak network frequency increased monotonically with increasing intensity of noisy synaptic input, spanning a range from gamma oscillations to fast ripples. A fast ripple was defined as a waveform in which the peak power >250 Hz exceeded peak power in the 100–250 Hz band. Peak network frequency (Fig. 2A) closely matched mean basket cell firing rate (Fig. 2B), indicating that the LFP resulted from IPSPs induced in pyramidal cells due to basket cell firing. Basket cell AP waveforms were present but contributed very little to the LFP, due to basket cells’ small size. Although the peak frequency did reach the fast ripple range, it is crucial to point out that the amplitude of network oscillations decreased substantially as peak network frequency increased (Fig. 2C). The total power was much higher in gamma (<100 Hz) frequencies, and reached very small levels >200 Hz (Fig. 2C). Such small-amplitude oscillations would be unlikely to resolve above background noise levels in a live recording.

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