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A frontal cortex event-related potential driven by the basal forebrain.

Nguyen DP, Lin SC - Elife (2014)

Bottom Line: Event-related potentials (ERPs) are widely used in both healthy and neuropsychiatric conditions as physiological indices of cognitive functions.Contrary to the common belief that cognitive ERPs are generated by local activity within the cerebral cortex, here we show that an attention-related ERP in the frontal cortex is correlated with, and likely generated by, subcortical inputs from the basal forebrain (BF).These results highlight the important and previously unrecognized role of long-range subcortical inputs from the BF in the generation of cognitive ERPs.

View Article: PubMed Central - PubMed

Affiliation: Neural Circuits and Cognition Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, United States.

ABSTRACT
Event-related potentials (ERPs) are widely used in both healthy and neuropsychiatric conditions as physiological indices of cognitive functions. Contrary to the common belief that cognitive ERPs are generated by local activity within the cerebral cortex, here we show that an attention-related ERP in the frontal cortex is correlated with, and likely generated by, subcortical inputs from the basal forebrain (BF). In rats performing an auditory oddball task, both the amplitude and timing of the frontal ERP were coupled with BF neuronal activity in single trials. The local field potentials (LFPs) associated with the frontal ERP, concentrated in deep cortical layers corresponding to the zone of BF input, were similarly coupled with BF activity and consistently triggered by BF electrical stimulation within 5-10 msec. These results highlight the important and previously unrecognized role of long-range subcortical inputs from the BF in the generation of cognitive ERPs. DOI: http://dx.doi.org/10.7554/eLife.02148.001.

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A model of how BF bursting activity generates the frontal ERP.Schematic of one possible model of the BF-ERP interaction that is consistent with our findings. This model consists of 10 BF bursting neurons (green to red traces show the PSTHs), where the ones with stronger bursting also have earlier onset latencies. Specifically, we set the peak bursting amplitudes to range from 100 to 20 spikes per second, and the onset latency to stagger by 5 msec. The population average is shown in black. In this model, we assume that all BF bursting neurons contribute to the generation of the frontal ERP with a fixed delay of 5 msec. The contribution of individual BF bursting neurons to the frontal ERP, indicated by dashed traces, cannot be directly observed and only the summed ERP response (black) can be experimentally observed. Two example trials are shown to illustrate how the BF bursting amplitude linearly scales with the frontal ERP (Figure 2): one with bursting amplitude set at 100% (left) to resemble a hit trial, and the other with bursting amplitude set at 50% (right) to resemble a miss or standard trial. The relative amplitude and relative timing between BF bursting neurons, and also between BF and the frontal ERP, are unaffected by the amplitude scaling. Raster plots for neuron#1 and #10 are shown below the two example trials (30 repeats each). Baseline tonic activity of BF bursting neurons is omitted for clarity. In this model, the bursting amplitudes of all BF neurons are linearly scaled with the frontal ERP amplitude. The subset of BF neurons with stronger bursting responses temporally leads the frontal ERP, while the activity of the majority of BF neurons trails the frontal ERP, even though all BF neurons causally contribute to ERP generation. This model further predicts higher magnitudes of BF-ERP cross correlation coefficient in BF neurons with stronger bursting responses, even though each of the smoothed PSTHs should be perfectly and equally correlated with the frontal ERP. This prediction arises because the higher firing rate in strongly bursting BF neurons allows the underlying spike trains to better approximate the smoothed PSTH function in single trials (e.g., neuron #1), while the stochastic spike trains provide a poor approximation of the smoothed PSTH especially when the BF bursting rate is low (e.g., neuron #10), partly because no spike was generated in a significant proportion of trials. Clearly, this model is only one of many possible models compatible with our findings. The purpose of this model is to show that our results are fully compatible with the scenario that all BF neurons causally contributes to the frontal ERP, even for the BF bursting neurons whose activity trails the frontal ERP in the cross correlation analysis. Alternatively, our results are compatible with the model that the subset of BF neurons trailing the frontal ERP may instead be driven by inputs from the frontal cortex.DOI:http://dx.doi.org/10.7554/eLife.02148.010
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fig3s2: A model of how BF bursting activity generates the frontal ERP.Schematic of one possible model of the BF-ERP interaction that is consistent with our findings. This model consists of 10 BF bursting neurons (green to red traces show the PSTHs), where the ones with stronger bursting also have earlier onset latencies. Specifically, we set the peak bursting amplitudes to range from 100 to 20 spikes per second, and the onset latency to stagger by 5 msec. The population average is shown in black. In this model, we assume that all BF bursting neurons contribute to the generation of the frontal ERP with a fixed delay of 5 msec. The contribution of individual BF bursting neurons to the frontal ERP, indicated by dashed traces, cannot be directly observed and only the summed ERP response (black) can be experimentally observed. Two example trials are shown to illustrate how the BF bursting amplitude linearly scales with the frontal ERP (Figure 2): one with bursting amplitude set at 100% (left) to resemble a hit trial, and the other with bursting amplitude set at 50% (right) to resemble a miss or standard trial. The relative amplitude and relative timing between BF bursting neurons, and also between BF and the frontal ERP, are unaffected by the amplitude scaling. Raster plots for neuron#1 and #10 are shown below the two example trials (30 repeats each). Baseline tonic activity of BF bursting neurons is omitted for clarity. In this model, the bursting amplitudes of all BF neurons are linearly scaled with the frontal ERP amplitude. The subset of BF neurons with stronger bursting responses temporally leads the frontal ERP, while the activity of the majority of BF neurons trails the frontal ERP, even though all BF neurons causally contribute to ERP generation. This model further predicts higher magnitudes of BF-ERP cross correlation coefficient in BF neurons with stronger bursting responses, even though each of the smoothed PSTHs should be perfectly and equally correlated with the frontal ERP. This prediction arises because the higher firing rate in strongly bursting BF neurons allows the underlying spike trains to better approximate the smoothed PSTH function in single trials (e.g., neuron #1), while the stochastic spike trains provide a poor approximation of the smoothed PSTH especially when the BF bursting rate is low (e.g., neuron #10), partly because no spike was generated in a significant proportion of trials. Clearly, this model is only one of many possible models compatible with our findings. The purpose of this model is to show that our results are fully compatible with the scenario that all BF neurons causally contributes to the frontal ERP, even for the BF bursting neurons whose activity trails the frontal ERP in the cross correlation analysis. Alternatively, our results are compatible with the model that the subset of BF neurons trailing the frontal ERP may instead be driven by inputs from the frontal cortex.DOI:http://dx.doi.org/10.7554/eLife.02148.010

Mentions: While the strong correlation between the frontal ERP and BF bursting activity does not rule out the possibility that the observed correlation might be generated by common inputs from other un-sampled structures, our finding that the activity of strongly bursting BF neurons temporally precedes the frontal ERP by 5–10 msec (Figure 3) is more compatible with BF driving the frontal ERP, and less compatible with the common input hypothesis. We suggest that even the weakly bursting BF neurons whose activity trails the frontal ERP may causally contribute to the frontal ERP with a short delay (Figure 3—figure supplement 2). Another possibility is that the subset of BF bursting neurons trailing the frontal ERP may be driven by inputs from the frontal cortex (Zaborszky et al., 1997). The hypothesis that BF bursting activity generates the frontal ERP is further supported by our finding that BF electrical stimulation triggers the same layer profile of oddball LFP responses within 5–10 msec (Figure 5), the same temporal delay found in the correlation analysis (Figure 3). Given the complex anatomical connection network associated with the BF region (Semba, 2000; Zaborszky, 2002; Jones, 2003), the goal of this study was not to fully dissect the causal contributions of various connection pathways. Rather, our goal was to test the specific hypothesis that the BF bursting activity alone, through the prominent projection to the frontal cortex, is sufficient to trigger and account for the oddball ERP response. While our results do not rule out the possible contribution of common inputs, our findings support the conclusion that the BF bursting activity alone is sufficient to trigger and account for most of the frontal ERP in the auditory oddball task.


A frontal cortex event-related potential driven by the basal forebrain.

Nguyen DP, Lin SC - Elife (2014)

A model of how BF bursting activity generates the frontal ERP.Schematic of one possible model of the BF-ERP interaction that is consistent with our findings. This model consists of 10 BF bursting neurons (green to red traces show the PSTHs), where the ones with stronger bursting also have earlier onset latencies. Specifically, we set the peak bursting amplitudes to range from 100 to 20 spikes per second, and the onset latency to stagger by 5 msec. The population average is shown in black. In this model, we assume that all BF bursting neurons contribute to the generation of the frontal ERP with a fixed delay of 5 msec. The contribution of individual BF bursting neurons to the frontal ERP, indicated by dashed traces, cannot be directly observed and only the summed ERP response (black) can be experimentally observed. Two example trials are shown to illustrate how the BF bursting amplitude linearly scales with the frontal ERP (Figure 2): one with bursting amplitude set at 100% (left) to resemble a hit trial, and the other with bursting amplitude set at 50% (right) to resemble a miss or standard trial. The relative amplitude and relative timing between BF bursting neurons, and also between BF and the frontal ERP, are unaffected by the amplitude scaling. Raster plots for neuron#1 and #10 are shown below the two example trials (30 repeats each). Baseline tonic activity of BF bursting neurons is omitted for clarity. In this model, the bursting amplitudes of all BF neurons are linearly scaled with the frontal ERP amplitude. The subset of BF neurons with stronger bursting responses temporally leads the frontal ERP, while the activity of the majority of BF neurons trails the frontal ERP, even though all BF neurons causally contribute to ERP generation. This model further predicts higher magnitudes of BF-ERP cross correlation coefficient in BF neurons with stronger bursting responses, even though each of the smoothed PSTHs should be perfectly and equally correlated with the frontal ERP. This prediction arises because the higher firing rate in strongly bursting BF neurons allows the underlying spike trains to better approximate the smoothed PSTH function in single trials (e.g., neuron #1), while the stochastic spike trains provide a poor approximation of the smoothed PSTH especially when the BF bursting rate is low (e.g., neuron #10), partly because no spike was generated in a significant proportion of trials. Clearly, this model is only one of many possible models compatible with our findings. The purpose of this model is to show that our results are fully compatible with the scenario that all BF neurons causally contributes to the frontal ERP, even for the BF bursting neurons whose activity trails the frontal ERP in the cross correlation analysis. Alternatively, our results are compatible with the model that the subset of BF neurons trailing the frontal ERP may instead be driven by inputs from the frontal cortex.DOI:http://dx.doi.org/10.7554/eLife.02148.010
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3974155&req=5

fig3s2: A model of how BF bursting activity generates the frontal ERP.Schematic of one possible model of the BF-ERP interaction that is consistent with our findings. This model consists of 10 BF bursting neurons (green to red traces show the PSTHs), where the ones with stronger bursting also have earlier onset latencies. Specifically, we set the peak bursting amplitudes to range from 100 to 20 spikes per second, and the onset latency to stagger by 5 msec. The population average is shown in black. In this model, we assume that all BF bursting neurons contribute to the generation of the frontal ERP with a fixed delay of 5 msec. The contribution of individual BF bursting neurons to the frontal ERP, indicated by dashed traces, cannot be directly observed and only the summed ERP response (black) can be experimentally observed. Two example trials are shown to illustrate how the BF bursting amplitude linearly scales with the frontal ERP (Figure 2): one with bursting amplitude set at 100% (left) to resemble a hit trial, and the other with bursting amplitude set at 50% (right) to resemble a miss or standard trial. The relative amplitude and relative timing between BF bursting neurons, and also between BF and the frontal ERP, are unaffected by the amplitude scaling. Raster plots for neuron#1 and #10 are shown below the two example trials (30 repeats each). Baseline tonic activity of BF bursting neurons is omitted for clarity. In this model, the bursting amplitudes of all BF neurons are linearly scaled with the frontal ERP amplitude. The subset of BF neurons with stronger bursting responses temporally leads the frontal ERP, while the activity of the majority of BF neurons trails the frontal ERP, even though all BF neurons causally contribute to ERP generation. This model further predicts higher magnitudes of BF-ERP cross correlation coefficient in BF neurons with stronger bursting responses, even though each of the smoothed PSTHs should be perfectly and equally correlated with the frontal ERP. This prediction arises because the higher firing rate in strongly bursting BF neurons allows the underlying spike trains to better approximate the smoothed PSTH function in single trials (e.g., neuron #1), while the stochastic spike trains provide a poor approximation of the smoothed PSTH especially when the BF bursting rate is low (e.g., neuron #10), partly because no spike was generated in a significant proportion of trials. Clearly, this model is only one of many possible models compatible with our findings. The purpose of this model is to show that our results are fully compatible with the scenario that all BF neurons causally contributes to the frontal ERP, even for the BF bursting neurons whose activity trails the frontal ERP in the cross correlation analysis. Alternatively, our results are compatible with the model that the subset of BF neurons trailing the frontal ERP may instead be driven by inputs from the frontal cortex.DOI:http://dx.doi.org/10.7554/eLife.02148.010
Mentions: While the strong correlation between the frontal ERP and BF bursting activity does not rule out the possibility that the observed correlation might be generated by common inputs from other un-sampled structures, our finding that the activity of strongly bursting BF neurons temporally precedes the frontal ERP by 5–10 msec (Figure 3) is more compatible with BF driving the frontal ERP, and less compatible with the common input hypothesis. We suggest that even the weakly bursting BF neurons whose activity trails the frontal ERP may causally contribute to the frontal ERP with a short delay (Figure 3—figure supplement 2). Another possibility is that the subset of BF bursting neurons trailing the frontal ERP may be driven by inputs from the frontal cortex (Zaborszky et al., 1997). The hypothesis that BF bursting activity generates the frontal ERP is further supported by our finding that BF electrical stimulation triggers the same layer profile of oddball LFP responses within 5–10 msec (Figure 5), the same temporal delay found in the correlation analysis (Figure 3). Given the complex anatomical connection network associated with the BF region (Semba, 2000; Zaborszky, 2002; Jones, 2003), the goal of this study was not to fully dissect the causal contributions of various connection pathways. Rather, our goal was to test the specific hypothesis that the BF bursting activity alone, through the prominent projection to the frontal cortex, is sufficient to trigger and account for the oddball ERP response. While our results do not rule out the possible contribution of common inputs, our findings support the conclusion that the BF bursting activity alone is sufficient to trigger and account for most of the frontal ERP in the auditory oddball task.

Bottom Line: Event-related potentials (ERPs) are widely used in both healthy and neuropsychiatric conditions as physiological indices of cognitive functions.Contrary to the common belief that cognitive ERPs are generated by local activity within the cerebral cortex, here we show that an attention-related ERP in the frontal cortex is correlated with, and likely generated by, subcortical inputs from the basal forebrain (BF).These results highlight the important and previously unrecognized role of long-range subcortical inputs from the BF in the generation of cognitive ERPs.

View Article: PubMed Central - PubMed

Affiliation: Neural Circuits and Cognition Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, United States.

ABSTRACT
Event-related potentials (ERPs) are widely used in both healthy and neuropsychiatric conditions as physiological indices of cognitive functions. Contrary to the common belief that cognitive ERPs are generated by local activity within the cerebral cortex, here we show that an attention-related ERP in the frontal cortex is correlated with, and likely generated by, subcortical inputs from the basal forebrain (BF). In rats performing an auditory oddball task, both the amplitude and timing of the frontal ERP were coupled with BF neuronal activity in single trials. The local field potentials (LFPs) associated with the frontal ERP, concentrated in deep cortical layers corresponding to the zone of BF input, were similarly coupled with BF activity and consistently triggered by BF electrical stimulation within 5-10 msec. These results highlight the important and previously unrecognized role of long-range subcortical inputs from the BF in the generation of cognitive ERPs. DOI: http://dx.doi.org/10.7554/eLife.02148.001.

Show MeSH