The vertebrate nervous system coordinates an animal’s involuntary and voluntary actions, and is responsible for transmitting signals between different parts of the body. Two different cell types, glial cells and neurons, make up the nervous system: glial cells play a metabolic or structural role, whereas neurons are responsible for physically transmitting the signals. Signals are sent quickly and precisely between neurons in the form of electrochemical waves, and it is this electrical activity that allows researchers to measure and study the communication of signals throughout the body.

At the center of the nervous system is the brain. The brain receives and integrates information from all parts of the body, and coordinates appropriate responses to various stimuli. The outermost layer of the brain, which is known as the cerebral cortex, enables an organism to perceive and interact with the world in meaningful ways, and is also responsible for the body’s movement, as well as for thought and cognition. Composed of an estimated 30 billion neurons, the cerebral cortex generates a tremendous amount of electrical activity during sensory, motor or cognitive events.

Researchers interested in evaluating brain function or assessing the level of activity in the cerebral cortex often use a non-invasive technique called electroencephalography. This technique has provided insight into the regions of the brain that process sensory and motor events, but it has been difficult to work out where cognitive events are processed. To date, most studies have tried to identify the region of the cerebral cortex that is responsible for generating the electrical activity associated with cognitive events, ignoring the possibility that other regions of the brain could play a significant role in producing this activity that is observed in the cerebral cortex.

Now Nguyen and Lin have shown that the basal forebrain, a region of the brain located beneath the cerebral cortex, is responsible for generating some of the electrical activity that happens during cognitive events. The experiments involved making measurements on rats as they performed a cognitive task. Nguyen and Lin found that a form of electrical activity called an event-related potential occurred in the frontal lobe of the cerebral cortex at the same time as the activity of single neurons in the basal forebrain. Stimulating neurons in the basal forebrain also resulted in an event-related potential being measured within the frontal cortex. These findings raise the possibility that the impairment of these basal forebrain neurons is involved in conditions such as Schizophrenia, ADHD and Alzheimer’s disease, and also in normal cognitive aging.

Event-related potential (ERP) represents the stereotypical response of the electroencephalogram (EEG) activity to an internal or external stimulus, and reflects reproducible and highly coherent large-scale activity patterns in the underlying cerebral cortical network (Davis, 1939; Rohrbaugh et al., 1990; Luck, 2005a; Luck and Kappenman, 2012). ERPs have been widely used in both healthy and neuropsychiatric conditions as robust physiological indices of cognitive functions because the amplitudes of late ERP components are modulated by various cognitive functions including attention (Hillyard and Kutas, 1983; Näätänen, 1988; Schreiber et al., 1992; Iragui et al., 1993; Polich, 1997; Herrmann and Knight, 2001; Barry et al., 2003; Caravaglios et al., 2008; Luck and Kappenman, 2012).

Despite the broad applications of ERP in both basic and clinical research, there remains considerable debate regarding the source mechanisms that generate cognitive ERPs (Snyder, 1991; Miltner et al., 1994; Pascual-Marqui et al., 2002; Luck, 2005b; Nunez and Srinivasan, 2006; Cohen et al., 2009; Riera et al., 2012). It is commonly assumed that cognitive ERPs are generated by, and therefore reflect the functions of, local activity within the cerebral cortex. However, inferring the underlying sources based on the skull surface EEG pattern, generally referred to as the ‘inverse problem’, is difficult and lacks a unique mathematical solution because the same EEG pattern can be generated by many different configurations of underlying sources (Helmholtz, 1853; Luck, 2005b; Nunez and Srinivasan, 2006). For this reason, it has remained difficult to experimentally demonstrate how cognitive ERPs are generated.

While most studies have focused on identifying cortical activities responsible for generating cognitive ERPs, one neglected possibility is that subcortical inputs play a significant role. In this study, we explored an alternative hypothesis that ERPs are driven by subcortical inputs from the basal forebrain (BF), one of the major cortically-projecting neuromodulatory systems (Semba, 2000; Zaborszky, 2002; Jones, 2003). This hypothesis is motivated by recent findings that a subset of BF neurons is robustly activated by motivationally salient stimuli that attract animals’ attention (Richardson and Delong, 1991; Lin and Nicolelis, 2008; Avila and Lin, 2014), and that the phasic bursting response of these BF neurons is tightly coupled with LFP activity in the frontal cortex (Lin et al., 2006). These discoveries led us to the hypothesis that ERPs elicited by motivationally salient stimuli are generated by the bursting response of BF neurons.

To test this hypothesis, we trained rats to perform an auditory oddball task that is commonly used in human ERP studies and observed a robust ERP response in the frontal cortex that was coupled with behavioral performance. We simultaneously recorded skull surface EEG and BF single neuron activity, and investigated whether frontal ERP and BF bursting activity were correlated, in single trials, in terms of amplitude and timing. To better understand how the frontal ERP was generated, we recorded LFPs throughout all layers of frontal cortical regions, and identified the layer-specific LFP response that was coupled with the frontal ERP and BF bursting activity. Finally, we tested whether activating BF via electrical stimulation was sufficient to trigger the layer-specific LFP response in the frontal cortex. The results support that the frontal ERP is correlated with, and likely generated by, subcortical inputs from the basal forebrain.

We first developed a rat version of the auditory oddball task commonly used in human ERP studies, with the goal of reproducing characteristic ERP responses. In the auditory oddball task (Figure 1A), a standard tone (10 kHz) was presented once every 2 s, and occasionally once every 6–14 s a deviant oddball tone (6 kHz) was presented instead. Adult Long Evans rats (n = 8) were trained to discriminate the standard tone from the oddball tone that signaled the availability of liquid reward. After training, rats showed high hit rates toward the oddball tone and a low false alarm rate toward the standard tone (Figure 1B).

Figure 1 with 2 supplements see all

Download asset Open asset

We found a prominent negative ERP response in the frontal cortex when rats correctly responded to the oddball tone (hit) (Figure 1C–D). This ERP response started at 50 msec, peaked around 100 msec, and was absent when the same stimuli were not behaviorally relevant (Figure 1C). Furthermore, the amplitude of this frontal ERP reliably discriminated hit from miss responses to oddball tones (Figure 1E), and also discriminated oddball from standard tones (Figure 1—figure supplement 1). These results demonstrate that this frontal ERP is highly relevant for behavioral performance and does not simply reflect sensory processing.

In 120 BF neurons recorded simultaneously with frontal EEG activity, 55% (66/120) showed robust bursting response to the oddball tone in hit trials and were classified as BF bursting neurons (Figure 1—figure supplement 2; Table 1). These BF neurons showed stronger bursting responses in hit trials than in miss trials, and in oddball trials than in standard trials (Figure 1F), consistent with the encoding of motivational salience as previously reported (Lin and Nicolelis, 2008; Avila and Lin, 2014). Interestingly, the timing of the BF bursting response as well as the modulation of BF bursting amplitude between different trial types (Figure 1F,G) were highly similar to those of the frontal ERP, suggesting that BF bursting may be functionally coupled with the frontal ERP.

To determine whether the amplitudes of BF bursting and the frontal ERP were coupled in single trials, we first visualized their relationship by sorting oddball and standard trials based on the amplitude of BF population bursting response in the 50–200 msec window. The example depicted in Figure 2A illustrates the frequent observation that trials with strong BF bursting responses were accompanied by strong frontal ERPs, while trials with weak BF bursting were accompanied by weak frontal ERPs. Indeed, significant single trial amplitude correlation (p<0.001) was observed in 95% (63/66) of BF bursting neurons (Figure 2B). Furthermore, the amplitude coupling relationships between BF bursting and the frontal ERP were well described by a highly homogeneous linear scaling function (Figure 2B) despite the variable bursting amplitude among BF bursting neurons (Figure 1—figure supplement 2). This linear scaling relationship indicates that the amplitude coupling between BF bursting and the frontal ERP remains invariant in the face of different types of stimulus (oddball vs standard) or behavioral response (hit vs miss), and therefore likely reflects a true functional coupling between BF bursting and the frontal ERP.

Figure 2

Download asset Open asset

In contrast to the homogeneous amplitude coupling relationship in BF bursting neurons, only 20% (11/54) of non-bursting BF neurons showed significant single trial amplitude correlation with the frontal ERP, which consisted of heterogeneous coupling relationships (Figure 2C) that were less correlated with the frontal ERP compared to BF bursting neurons (Figure 2D). This finding is evidence that the frontal ERP is coupled specifically with the subset of BF neurons that encode motivational salience using the phasic bursting response (Lin and Nicolelis, 2008; Avila and Lin, 2014).

In addition to amplitude coupling, BF bursting and the frontal ERP were also coupled temporally. At the gross temporal scale, BF bursting and the frontal ERP always occurred in the same time window despite the variable latency of the frontal ERP responses between sessions and across trial types (Figure 1D–G). These coupling relationships are compatible with two concurrently plausible explanations: First, it may be that both the BF bursting and frontal ERP are controlled by common inputs from un-observed third brain region(s). A second possibility is that the frontal ERP is driven by BF activity with a very short delay.

To further examine these two possibilities, we determined the time lag that gave the maximal cross correlation between each BF bursting neuron and the frontal ERP (Figure 3, Figure 3—figure supplement 1). This analysis showed that, while the activity of many BF bursting neurons either preceded or followed the frontal ERP, the BF neurons that temporally led the frontal ERP were better correlated with the frontal ERP and showed stronger bursting responses. In other words, the contributions from BF bursting neurons that led or trailed the frontal ERP were not equal. These results support the hypothesis that the frontal ERP may be driven by the strongly bursting BF neurons that led the ERP by 5–10 msec through a fast circuit mechanism.

Figure 3 with 2 supplements see all

Download asset Open asset

To further understand how the frontal ERP was generated, we recorded LFP activity across all cortical layers in frontal cortical regions below the EEG electrode. Concomitant with the frontal ERP and the BF bursting response, we observed clear LFP responses in a subset of cortical layers (Figure 4A,B, Figure 4—figure supplement 1, Figure 4—figure supplement 2). The non-uniform distribution of LFP responses across cortical layers indicates that these LFP responses were not volume conducted from distant sources.

Figure 4 with 2 supplements see all

Download asset Open asset

Furthermore, we found that the layer profiles of LFP responses associated with BF bursting were highly similar between oddball and standard trials (Figure 4A–C, Figure 4—figure supplement 1, Figure 4—figure supplement 2). The amplitudes of LFP responses and BF bursting were significantly correlated in single trials (p<0.001) in 86% (66/77) of BF bursting neurons recorded simultaneously with frontal LFPs (Figure 4D). These results indicate that the LFP response amplitudes were linearly scaled between oddball and standard trials, and also linearly scaled with BF bursting amplitudes, similar to what we observed between BF bursting and the frontal ERP (Figure 2B). Therefore, the characteristic LFP layer profile associated with BF bursting response likely reflects the organization of local activity dipoles that generate the frontal ERP at the skull surface.

The most prominent feature of the LFP layer profile was a strong positive LFP response located within the deep cortical layers of the frontal cortex (Figure 4E, Figure 4—figure supplement 1, Figure 4—figure supplement 2), which are known to be the predominant target layers of cortical projections from the BF (Henny and Jones, 2008).

Finally, to further determine whether BF activity was sufficient to trigger the frontal ERP with a short delay, we investigated whether artificially inducing BF bursting activity can produce the characteristic LFP response in the oddball task. To reliably activate BF bursting neurons, electrical stimulation was chosen over more selective methods such as optogenetics because the neurochemical identity of BF bursting neurons needed for selective activation techniques remains to be established (Lin and Nicolelis, 2008).

We found that a single pulse of BF electrical stimulation in the absence of any auditory stimulus was able to elicit highly reliable LFP responses in frontal cortical regions, both under isoflurane anesthesia and during wakefulness (Figure 5, Figure 4—figure supplement 1, Figure 4—figure supplement 2). BF electrical stimulation generated positive LFP responses in the same deep cortical layers that showed strong positive LFP responses in the oddball task within 5–10 msec (Figure 5, Figure 4—figure supplement 1, Figure 4—figure supplement 2), thus recapitulating the signature features of oddball LFP responses—in terms of timing, layer profile and polarity of LFP responses. BF electrical stimulation also generated an earlier LFP response, possibly due to activation of other neuronal populations in the BF (Gritti et al., 1997, 2003) or from antidromic activation of frontal cortex projections to the BF (Zaborszky et al., 1997). Together, these results support the idea that the BF bursting in the oddball task is sufficient to generate and account for key features of the frontal ERP response.

Figure 5

Download asset Open asset

ERP is one of the oldest and also most widely used methods in neuroscience research (Davis, 1939; Rohrbaugh et al., 1990; Luck, 2005a; Luck and Kappenman, 2012). Contrary to the common belief that cognitive ERPs are generated by local activity within the cerebral cortex, we found that a frontal ERP response closely linked to behavioral performance in the auditory oddball task (Figure 1) is tightly coupled with BF bursting activity in single trials in terms of both amplitude (Figure 2) and timing (Figure 3). The coupling between BF bursting and the frontal ERP remains invariant in the face of different types of stimulus (oddball vs standard) and behavioral response (hit vs miss). The frontal ERP is likely generated by the underlying layer-specific LFP response, which is similarly coupled with BF bursting activity (Figure 4). BF electrical stimulation reproduces the key features of LFP responses in the oddball task (Figure 5), suggesting that the BF bursting activity is sufficient to generate and account for most of the frontal ERP response in the oddball task. The BF bursting activity therefore provides a novel neural correlate of the frontal ERP response. This is the first time, to the best of our knowledge, that a key component of cognitive ERPs has been linked to the activity of a well-defined neuronal population located outside of the cerebral cortex. These findings highlight the important and previously unrecognized role of long-range subcortical inputs from the BF in generating cognitive ERPs.

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.

Two key features of the coupling between BF bursting and the frontal ERP—the short temporal delay and the LFP layer profile concentrating in deep cortical layers—are consistent with the specific properties of BF projections to the frontal cortex. The average conduction latency of BF neurons to the frontal cortex, measured by antidromic stimulation, is less than 5 msec (Aston-Jones et al., 1985; Reiner et al., 1987). Moreover, the fast-conducting population of cortically-projecting BF neurons (latencies 1–4 msec) are most easily activated (with lowest activation threshold) when the stimulation electrode is placed in deep cortical layers of the frontal cortex (Aston-Jones et al., 1985; Reiner et al., 1987). Deep cortical layers in the frontal cortex (layer V–VI) are also the predominant target layers of cortical projections from the BF, which contain more than 70% of axonal varicosities from the BF (Henny and Jones, 2008). Among the BF axonal varicosities, the most abundant (∼50%) is the large GABAergic terminal, while cholinergic terminals from the BF are smaller and fewer (15–20%) (Freund and Meskenaite, 1992; Henny and Jones, 2008). These specific properties of BF projection to the frontal cortex support our hypothesis that the frontal ERP is generated by BF inputs to deep cortical layers through a fast circuit mechanism.

A likely candidate to mediate the fast circuit mechanism is the non-cholinergic BF inputs because of their anatomical abundance (Freund and Meskenaite, 1992; Gritti et al., 1997; Henny and Jones, 2008) and their fast ionotropic actions. While studies of the BF have traditionally focused on its cholinergic neurons (Everitt and Robbins, 1997), the majority of BF corticopetal projections are non-cholinergic neurons, consisting mostly of GABAergic neurons and a smaller subset of glutamatergic neurons (Gritti et al., 1997; Henny and Jones, 2008). The GABAergic BF neurons in particular preferentially innervate intracortical GABAergic interneurons and therefore may rapidly enhance cortical activity through disinhibition (Freund and Meskenaite, 1992; Henny and Jones, 2008). Consistent with such a suggestion, salience-encoding BF bursting neurons represent a physiologically homogeneous group of non-cholinergic BF neurons, which, unlike cholinergic BF neurons, do not change their mean firing rates between awake and sleep states (Lin et al., 2006; Lin and Nicolelis, 2008). The widespread spatial distribution of bursting neurons throughout multiple brain regions of the BF (Figure 1—figure supplement 2) is also consistent with the location of cortically-projecting BF neurons (Aston-Jones et al., 1985; Reiner et al., 1987; Gritti et al., 1997). Therefore, our results highlight the potential functional significance of the poorly understood non-cholinergic BF neurons (Sarter and Bruno, 2002; Lau and Salzman, 2008), and the importance in determining the neurochemical identity of salience-encoding BF bursting neurons. Future studies are needed to elucidate the precise nature of this ERP response, and test whether the frontal ERP reflects, as a result of BF modulation, the synchronous activation of pyramidal neuronal ensembles in the frontal cortex as commonly assumed (Snyder, 1991; Miltner et al., 1994; Pascual-Marqui et al., 2002; Luck, 2005b; Nunez and Srinivasan, 2006; Riera et al., 2012), or, alternatively, reflects the direct postsynaptic response of cortical neurons to non-cholinergic BF inputs.

The single trial coupling between BF bursting activity and the frontal ERP in the current study suggests that both neural signals are functionally homologous and reflect the motivational salience of attended stimuli (Lin and Nicolelis, 2008; Avila and Lin, 2014). In support of this view, behaviorally irrelevant stimuli presented passively to the animal elicit little response in both the BF neurons (Lin and Nicolelis, 2008) and the frontal ERP (Figure 1C). This observation indicates that both neural signals do not reflect passive sensory processing. Instead, the BF bursting activity, and likely the frontal ERP response, reflects motivational salience irrespective of sensory modality or stimulus identity (Lin and Nicolelis, 2008; Avila and Lin, 2014). This motivational salience signal is highly relevant for behavioral performance, which predicts successful detection of a near-threshold sound (Lin and Nicolelis, 2008) as well as performance in the oddball task (Figure 1E). It is important to point out that the relationship between BF activity and behavioral performance is more complex and involves more than the phasic bursting response that encodes motivational salience. In behavioral contexts that require inhibition, such as Nogo trials in the Go/Nogo task, BF neurons display an initial bursting response followed by a subsequent sustained inhibition, with the latter response better correlated with behavior (Lin and Nicolelis, 2008). Future studies will need to determine the contribution of BF inhibitory responses on behavioral performance when animals need to stop or cancel a behavioral response instead of making a response to motivationally salient stimuli. It will also be important to determine whether the BF inhibitory response leads to a corresponding frontal ERP signature.

Based on these observations, we propose that the main function of non-cholinergic BF neurons is to serve as a signal amplifying mechanism for motivationally salient stimuli. This amplification mechanism is needed because most sensory stimuli animals and humans encounter are behaviorally irrelevant and not processed further by the brain. For the subset of stimuli that are motivationally salient and predict important outcomes, the brain therefore requires an additional system to amplify its processing based on the motivational, but not perceptual, salience of the stimulus. The non-cholinergic BF neurons represent an ideal candidate for this amplification mechanism, which not only needs to encode the motivational salience of the attended stimulus but also needs to powerfully modulate and amplify cortical activity as fast as possible.

The current study supports this hypothesis by demonstrating that the motivational salience information encoded by BF bursting activity is transformed into the frontal ERP response with a minimum delay, which likely provides powerful modulation and amplification on the early stages of information processing. Future studies need to determine precisely how task-related single neuron activity in the frontal cortex is modulated by BF inputs and the resulting frontal ERP. Ultimately, this proposed amplification mechanism should lead to faster and better decision making. In support of this prediction, we recently showed that stronger BF motivational salience signal is quantitatively coupled with faster and more precise decision speed (Avila and Lin, 2014).

The findings in this study likely can be extended to primates because BF neurons with bursting responses to motivationally salient stimuli are similarly found in monkeys (DeLong, 1971; Wilson and Rolls, 1990; Richardson and Delong, 1991), and BF electrical stimulation in monkeys similarly triggers LFP responses in the frontal cortex (Richardson and Fetz, 2012). Despite the many differences in rodent and human ERP studies (such as species, the use of primary reward and punishment in rodents but not in humans, the choice of electrical reference, etc), the major ERP components are broadly similar in rodents and humans (Yamaguchi et al., 1993; Ehlers et al., 1994; Shinba, 1997; Sambeth et al., 2003). The rodent frontal ERP described in this study is most similar to the processing negativity or Nd ERP component in humans (Hansen and Hillyard, 1980; Näätänen, 1982; Näätänen et al., 1987) because both the rodent frontal ERP and the human processing negativity responses share several features—negative polarity of the ERP, early timing in the N1 ERP window, spatial distribution centered on the frontal cortex, as well as reflecting motivational salience or selective attention toward the stimulus. Further studies are needed to establish the functional homology between the rodent and human frontal ERP components, and determine the potential role of non-cholinergic BF neurons in selective attention in humans.

An important implication of this study is that rodent ERPs may serve as a unique translational platform to bridge the human ERP literature and the neurophysiology literature in animal models. Studies in rodent models may provide unique mechanistic insights that can inform human ERP studies. For example, localizing the sources of human ERP responses, that is solving the ‘inverse problem’, requires assumptions about the number and spatial distribution of underlying sources (Pascual-Marqui et al., 2002; Luck, 2005b). Our results suggest that, at least in the case of the frontal ERP component, the underlying source may reflect the anatomical projection pattern of the salience-encoding BF neurons, which project extensively to broad regions of the cerebral cortex (Gritti et al., 1997; Zaborszky, 2002; Henny and Jones, 2008; Chandler et al., 2013). Our results also raise the novel hypothesis that the decline of frontal ERP amplitude and associated cognitive functions in conditions such as schizophrenia, ADHD, Alzheimer’s disease and cognitive aging (Schreiber et al., 1992; Iragui et al., 1993; Polich, 1997; Barry et al., 2003; Caravaglios et al., 2008) may result from the functional impairment of the poorly understood non-cholinergic basal forebrain system or a dysfunctional BF-cortical interaction. These results also suggest that the increasing use of deep brain stimulation of the BF to ameliorate dementia (Hescham et al., 2013; Salma et al., 2014) may be mediated in part by activating non-cholinergic BF neurons and perhaps compensating for their functional impairment in these conditions.

1. 1. Aston-Jones G
   2. Shaver R
   3. Dinan TG

   (1985) Nucleus basalis neurons exhibit axonal branching with decreased impulse conduction velocity in rat cerebrocortex

   Brain Research 325:271–285.

   https://doi.org/10.1016/0006-8993(85)90323-3

   • Google Scholar
2. 1. Avila I
   2. Lin S-C

   (2014) Motivational salience signal in the basal forebrain is coupled with faster and more precise decision speed

   PLOS Biology 12:e1001811.

   https://doi.org/10.1371/journal.pbio.1001811

   • Google Scholar
3. 1. Barry RJ
   2. Johnstone SJ
   3. Clarke AR

   (2003) A review of electrophysiology in attention-deficit/hyperactivity disorder: II. Event-related potentials

   Clinical Neurophysiology 114:184–198.

   https://doi.org/10.1016/S1388-2457(02)00363-2

   • Google Scholar
4. 1. Caravaglios G
   2. Costanzo E
   3. Palermo F
   4. Muscoso EG

   (2008) Decreased amplitude of auditory event-related delta responses in Alzheimer’s disease

   International Journal of Psychophysiology 70:23–32.

   https://doi.org/10.1016/j.ijpsycho.2008.04.004

   • Google Scholar
5. 1. Chandler DJ
   2. Lamperski CS
   3. Waterhouse BD

   (2013) Identification and distribution of projections from monoaminergic and cholinergic nuclei to functionally differentiated subregions of prefrontal cortex

   Brain Research 1522:38–58.

   https://doi.org/10.1016/j.brainres.2013.04.057

   • Google Scholar
6. 1. Cohen J
   2. Heitz R
   3. Schall J
   4. Woodman G

   (2009) On the origin of event-related potentials indexing covert attentional selection during visual search

   Journal of Neurophysiology pp. 2375–2386.

   https://doi.org/10.1152/jn.00680.2009

   • Google Scholar
7. 1. Davis P

   (1939)

   Effects of acoustic stimuli on the waking human brain

   Journal of Neurophysiology 2:494–499.

   • Google Scholar
8. 1. DeLong MR

   (1971)

   Activity of pallidal neurons during movement

   Journal of Neurophysiology 34:414–427.

   • Google Scholar
9. 1. Ehlers CL
   2. Kaneko WM
   3. Robledo P
   4. Lopez AL

   (1994) Long-latency event-related potentials in rats: effects of task and stimulus parameters

   Neurosciences 62:759–769.

   https://doi.org/10.1016/0306-4522(94)90474-X

   • Google Scholar
10. 1. Everitt BJ
    2. Robbins TW

    (1997) Central cholinergic systems and cognition

    Annual Review of Psychology 48:649–684.

    https://doi.org/10.1146/annurev.psych.48.1.649

    • Google Scholar
11. 1. Freund TF
    2. Meskenaite V

    (1992) gamma-Aminobutyric acid-containing basal forebrain neurons innervate inhibitory interneurons in the neocortex

    Proceedings of the National Academy of Sciences of the United States of America 89:738–742.

    https://doi.org/10.1073/pnas.89.2.738

    • Google Scholar
12. Book

    1. Green DM
    2. Swets JA

    (1966)

    Signal detection theory and psychophysics

    Oxford, UK: John Wiley.

    • Google Scholar
13. 1. Gritti I
    2. Mainville L
    3. Mancia M
    4. Jones BE

    (1997) GABAergic and other noncholinergic basal forebrain neurons, together with cholinergic neurons, project to the mesocortex and isocortex in the rat

    The Journal of Comparative Neurology 383:163–177.

    https://doi.org/10.1002/(SICI)1096-9861(19970630)383:23.0.CO;2-Z

    • Google Scholar
14. 1. Gritti I
    2. Manns IDAND
    3. Mainville L
    4. Jones BE

    (2003) Parvalbumin, calbindin, or calretinin in cortically projecting and GABAergic, cholinergic, or glutamatergic basal forebrain neurons of the rat

    Journal of Comparative Neurology 458:11–31.

    https://doi.org/10.1002/cne.20087

    • Google Scholar
15. 1. Hansen JC
    2. Hillyard SA

    (1980) Endogenous brain potentials associated with selective auditory attention

    Electroencephalography and Clinical Neurophysiology 49:277–290.

    https://doi.org/10.1016/0013-4694(80)90222-9

    • Google Scholar
16. 1. Helmholtz H

    (1853) Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern mit Anwendung auf die thierisch-elektrischen Versuche

    Annalen Der Physik Und Chemie 165:211–233.

    https://doi.org/10.1002/andp.18531650603

    • Google Scholar
17. 1. Henny P
    2. Jones BE

    (2008) Projections from basal forebrain to prefrontal cortex comprise cholinergic, GABAergic and glutamatergic inputs to pyramidal cells or interneurons

    European Journal of Neuroscience 27:654–670.

    https://doi.org/10.1111/j.1460-9568.2008.06029.x

    • Google Scholar
18. 1. Herrmann CS
    2. Knight RT

    (2001) Mechanisms of human attention: event-related potentials and oscillations

    Neuroscience & Biobehavioral Reviews 25:465–476.

    https://doi.org/10.1016/S0149-7634(01)00027-6

    • Google Scholar
19. 1. Hescham S
    2. Lim LW
    3. Jahanshahi A
    4. Blokland A
    5. Temel Y

    (2013) Deep brain stimulation in dementia-related disorders

    Neuroscience & Biobehavioral Reviews 37:2666–2675.

    https://doi.org/10.1016/j.neubiorev.2013.09.002

    • Google Scholar
20. 1. Hillyard SA
    2. Kutas M

    (1983) Electrophysiology of cognitive processing

    Annual Review of Psychology 34:33–61.

    https://doi.org/10.1146/annurev.ps.34.020183.000341

    • Google Scholar
21. 1. Iragui VJ
    2. Kutas M
    3. Mitchiner MR
    4. Hillyard SA

    (1993) Effects of aging on event-related brain potentials and reaction times in an auditory oddball task

    Psychophysiology 30:10–22.

    https://doi.org/10.1111/j.1469-8986.1993.tb03200.x

    • Google Scholar
22. 1. Jones BE

    (2003) Arousal systems

    Frontiers in Bioscience 8:s438–s451.

    https://doi.org/10.2741/1074

    • Google Scholar
23. 1. Lau B
    2. Salzman CD

    (2008) Noncholinergic neurons in the basal forebrain: often neglected but motivationally salient

    Neuron 59:6–8.

    https://doi.org/10.1016/j.neuron.2008.06.017

    • Google Scholar
24. 1. Lin S-C
    2. Nicolelis MA

    (2008) Neuronal ensemble bursting in the basal forebrain encodes salience irrespective of valence

    Neuron 59:138–149.

    https://doi.org/10.1016/j.neuron.2008.04.031

    • Google Scholar
25. 1. Lin S-C
    2. Gervasoni D
    3. Nicolelis MAL

    (2006) Fast modulation of prefrontal cortex activity by basal forebrain noncholinergic neuronal ensembles

    Journal of Neurophysiology 96:3209–3219.

    https://doi.org/10.1152/jn.00524.2006

    • Google Scholar
26. Book

    1. Luck SJ

    (2005a)

    An introduction to the event-related potential technique

    Cambridge, MA: MIT Press.

    • Google Scholar
27. 1. Luck SJ

    (2005)

    An introduction to the event-related potential technique

    267–302, An introduction to the event-related potential technique, Cambridge, MA, MIT Press.

    • Google Scholar
28. Book

    1. Luck S
    2. Kappenman E

    (2012)

    The Oxford handbook of event-related potential components

    New York, NY: Oxford University Press.

    • Google Scholar
29. 1. Miltner W
    2. Braun C
    3. Johnson R
    4. Simpson GV
    5. Ruchkin DS

    (1994)

    A test of brain electrical source analysis (BESA): a simulation study

    Electroencephalography and Clinical Neurophysiology 91:295–310.

    • Google Scholar
30. 1. Näätänen R
    2. Picton T

    (1987) The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure

    Psychophysiology 24:375–425.

    https://doi.org/10.1111/j.1469-8986.1987.tb00311.x

    • Google Scholar
31. 1. Näätänen R

    (1982) Processing negativity: an evoked-potential reflection of selective attention

    Psychological Bulletin 92:605–640.

    https://doi.org/10.1037/0033-2909.92.3.605

    • Google Scholar
32. 1. Näätänen R

    (1988) Implications of ERP data for psychological theories of attention

    Biological Psychology 26:117–163.

    https://doi.org/10.1016/0301-0511(88)90017-8

    • Google Scholar
33. Book

    1. Nunez PL
    2. Srinivasan R

    (2006)

    Electric fields of the brain: the neurophysics of EEG

    New York, NY: Oxford University Press.

    • Google Scholar
34. 1. Pascual-Marqui RD
    2. Esslen M
    3. Kochi K
    4. Lehmann D

    (2002)

    Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review

    Methods and Findings in Experimental and Clinical Pharmacology 24:91–95.

    • Google Scholar
35. Book

    1. Paxinos G
    2. Watson C

    (2007)

    The rat brain in stereotaxic coordinates

    London, UK: Academic Press.

    • Google Scholar
36. 1. Polich J

    (1997) EEG and ERP assessment of normal aging

    Electroencephalography and Clinical Neurophysiology 104:244–256.

    https://doi.org/10.1016/S0168-5597(97)96139-6

    • Google Scholar
37. 1. Reiner P
    2. Semba K
    3. Fibiger H
    4. McGeer E

    (1987) Physiological evidence for subpopulations of cortically projecting basal forebrain neurons in the anesthetized rat

    Neurosciences 20:629–636.

    https://doi.org/10.1016/0306-4522(87)90115-1

    • Google Scholar
38. 1. Richardson AG
    2. Fetz EE

    (2012) Brain state-dependence of electrically evoked potentials monitored with head-mounted electronics

    IEEE Transactions on Neural Systems Rehabilitation Engineering 20:756–761.

    https://doi.org/10.1109/TNSRE.2012.2204902

    • Google Scholar
39. 1. Richardson RT
    2. Delong MR

    (1991) Electrophysiological studies of the functions of the nucleus basalis in primates

    Advances in Experimental Medicine and Biology 295:233–252.

    https://doi.org/10.1007/978-1-4757-0145-6_12

    • Google Scholar
40. 1. Riera JJ
    2. Ogawa T
    3. Goto T
    4. Sumiyoshi A
    5. Nonaka H
    6. Evans A
    7. Miyakawa H
    8. Kawashima R

    (2012) Pitfalls in the dipolar model for the neocortical EEG sources

    Journal of Neurophysiology 108:956–975.

    https://doi.org/10.1152/jn.00098.2011

    • Google Scholar
41. Book

    1. Rohrbaugh J
    2. Parasuraman R
    3. Johnson R

    (1990)

    Event-related brain potentials: basic issues and applications

    New York, NY: Oxford University Press.

    • Google Scholar
42. 1. Salma A
    2. Vasilakis M
    3. Tracy PT

    (2014) Deep Brain stimulation for cognitive disorders: insights on targeting nucleus basalis of meynert in Alzheimer’s dementia

    World Neurosurgery 81:e4–e5.

    https://doi.org/10.1016/j.wneu.2013.08.011

    • Google Scholar
43. 1. Sambeth A
    2. Maes JHR
    3. Van Luijtelaar G
    4. Molenkamp IBS
    5. Jongsma MLA
    6. Van Rijn CM

    (2003) Auditory event-related potentials in humans and rats: effects of task manipulation

    Psychophysiology 40:60–68.

    https://doi.org/10.1111/1469-8986.00007

    • Google Scholar
44. 1. Sarter M
    2. Bruno JP

    (2002) The neglected constituent of the basal forebrain corticopetal projection system: GABAergic projections

    European Journal of Neuroscience 15:1867–1873.

    https://doi.org/10.1046/j.1460-9568.2002.02004.x

    • Google Scholar
45. 1. Schreiber H
    2. Stolz-Born G
    3. Kornhuber HH
    4. Born J

    (1992) Event-related potential correlates of impaired selective attention in children at high risk for schizophrenia

    Biological Psychiatry 32:634–651.

    https://doi.org/10.1016/0006-3223(92)90294-A

    • Google Scholar
46. 1. Semba K

    (2000) Multiple output pathways of the basal forebrain: organization, chemical heterogeneity, and roles in vigilance

    Behavioural Brain Research 115:117–141.

    https://doi.org/10.1016/S0166-4328(00)00254-0

    • Google Scholar
47. 1. Shinba T

    (1997) Event-related potentials of the rat during active and passive auditory oddball paradigms

    Electroencephalography and Clinical Neurophysiology 104:447–452.

    https://doi.org/10.1016/S0168-5597(97)00047-6

    • Google Scholar
48. 1. Snyder AZ

    (1991) Dipole source localization in the study of EP generators: a critique

    Electroencephalography and Clinical Neurophysiology 80:321–325.

    https://doi.org/10.1016/0168-5597(91)90116-F

    • Google Scholar
49. 1. Wilson FA
    2. Rolls ET

    (1990)

    Learning and memory is reflected in the responses of reinforcement-related neurons in the primate basal forebrain

    The Journal of Neuroscience 10:1254–1267.

    • Google Scholar
50. 1. Yamaguchi S
    2. Globus H
    3. Knight RT

    (1993) P3-like potential in rats

    Electroencephalography and Clinical Neurophysiology 88:151–154.

    https://doi.org/10.1016/0168-5597(93)90066-X

    • Google Scholar
51. 1. Zaborszky L

    (2002) The modular organization of brain systems. Basal forebrain: the last frontier

    Progress in Brain Research 136:359–372.

    https://doi.org/10.1016/S0079-6123(02)36030-8

    • Google Scholar
52. 1. Zaborszky L
    2. Gaykema RP
    3. Swanson DJ
    4. Cullinan WE

    (1997) Cortical input to the basal forebrain

    Neurosciences 79:1051–1078.

    https://doi.org/10.1016/S0306-4522(97)00049-3

    • Google Scholar

Author details

1. David P Nguyen

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

   Present address

   Department of Pharmacokinetics, Pharmacodynamics and Metabolism, Worldwide Research and Development, Pfizer Inc., Cambridge, United States

   Contribution

   DPN, Designed the experiment jointly with S-CL, Performed experiments, Collected data, Pre-processed data

   Competing interests

   The authors declare that no competing interests exist.

2. Shih-Chieh Lin

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

   Contribution

   S-CL, Conceived the study, Designed the experiment jointly with DN, Interpreted data, Performed statistical analysis, Wrote the manuscript

   For correspondence

   shih-chieh.lin@nih.gov

   Competing interests

   The authors declare that no competing interests exist.

• 2,295

  views

• 193

  downloads

• 32

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.