Keywords

1 Outline

The present chapter introduces for the first time computational modeling results on the effect of modafinil on electroencephalographic attention neural correlates based on the hypothesis that modafinil modulates neural activity in the locus coeruleus. Notably, such a modulation has been implicated with an enhancement in connectivity between the prefrontal cortex and subcortical structures as in the case of the thalamus and the limbic system. The organization of this chapter is as follows: Sect. 5.2 introduces the basics about modafinil and the emerging field of neural enhancement modeling; Sect. 5.3 presents our biologically-inspired model of modafinil effects on electroencephalographic correlates of selective attention; Sect. 5.4 introduces our numerical results; Sect. 5.5 presents our conclusions.

2 Introduction

2.1 Modafinil

Modafinil is a psychostimulant that has been utilized to enhance wakefulness, attention capacity, and vigilance (Franke and Lieb 2010). It has attracted considerable interest due to its pharmacological profile that differs from traditional neuroenhancers such as amphetamines and methylphenidate. Several research studies support that modafinil is a safe, well-tolerated drug, with long-lasting effects (Sugarman et al. 2011). In fact, modafinil has been tested in the treatment of Alzheimer’s disease (Bransfield 2004); depression (Frye et al. 2007); attention-deficit hyperactivity disorder (ADHD) (Keen and Hadijikoumi 2011); multiple sclerosis-induced fatigue (Téllez and Montalbán 2004; Nicholas and Chataway 2007); post-cognitive impairment in schizophrenia (Ballon and Feifel 2006; Henderson et al. 2005); spasticity associated with cerebral palsy (Ballon and Feifel 2006); age-related memory decline as a neuroprotective agent against the “dopamine-deficiency disorder” Parkinson’s disease (Knie et al. 2011; van Vliet et al. 2006) and the treatment of cancer-related fatigue (Morrow et al. 2005). The use of modafinil in the treatment of shift work sleep disorder and obstructive sleep apnea has also been emphasized (Ballon and Feifel 2006). Focusing on arousal enhancement, modafinil has gained widespread attention in popular press articles because of its increasing popularity among students at leading American colleges and military personnel.

In spite of the aforementioned potential curative benefits, concerns about possible side effects related to modafinil consumption have also been expressed by several authors, who advocate limitations on its daily use; these authors’ concerns are based on the fact that there is a lack of knowledge regarding modafinil’s neural mechanisms and the way it influences brain structures (Joos et al. 2010).

With regard to neural mechanisms, some authors hypothesized that modafinil promotes wakefulness via its effects in the anterior hypothalamus, whereas a dopamine-related action in the nucleus accumbens has been considered moderate (Scamell et al. 2000). With respect to alertness and partial wakefulness, it has been assumed that modafinil inhibits the reuptake of noradrenaline by the noradrenergic terminals on sleep-promoting neurons of the ventrolateral preoptic nucleus (VLPO) (Gallopin et al. 2004). More significant, perhaps, has been the report of modafinil’s ability to increase excitatory glutamatergic transmission in thalamic and limbic structures. This mechanism reduces local GABAergic transmission, thereby diminishing GABA (A) receptor signaling on the mesolimbic and thalamic dopamine terminals (Ferraro et al. 1997). With regard to attentional control, recent studies have stressed the importance of the locus coeruleus (LC), a portion of the brainstem. It has been hypothesized that the LC is involved in shifts from distractible to attentive states. By visualizing activity from functional magnetic resonance imaging (fMRI), researchers have shown that modafinil indeed alters the state of the LC (Minzenberg et al. 2008). Moreover, these authors were able to shift subjects into a more attentive state by administering modafinil, as reflected by enhanced coordinated brain activity and better performance on an attentional test. In particular, the reported experimental findings suggest that modafinil decreases activity in the LC while favoring subcortical connectivity with the prefrontal cortex.

2.2 Neural Enhancement Modeling

Modeling, in the most abstract sense, provides a conceptual structure by which assumptions are incorporated and hypotheses articulated. A mathematical model differs from a theoretical, descriptive model in that the assumptions are articulated via mathematical rules. In particular, mathematical modeling is intended not only to describe, explain and test hypotheses about real-world phenomena arising from fields such as engineering, physics, physiology ecology, neuroscience, wildlife management, chemistry, economics, etc. but, more importantly, to make predictions. In particular, neural modeling addresses the proposal and development of computational and mathematical schemes aimed to study the human brain function. Due to the brain’s high complexity (the brain involves a large number of interconnected neurons underlying responses at different temporal and spatial scales), a modeling approach represents a plausible methodology for describing and extracting relevant features of the brain’s functionality in a tractable and reduced manner.

In order to successfully model and elucidate the neurodynamics of prospective neuroenhancers such as modafinil, one should consider the multiscale nature of the human brain. Theoretically, neuroenhancement modeling is achieved by integrating responses of neural populations into large scale neural responses that are comparable to data normally obtained by way of experimental techniques. Such modeling formulations have already proven their capability for capturing details ranging from single neurons to neural networks and cortical column maps. Such models, might in principle, provide deeper insights into the influence of neuroenhancers at neural network and cortical levels. In order to test prospective functional mechanisms of pharmaceutical neuroenhancers, the multiscale modeling approaches enable the integration of spatial (fMRI data, optogenetics, voltage sensitive dye imaging) and temporal data (behavioral studies, electroencephalographic data and intracranial recordings) from current brain imaging technologies.

In addition, implementing an efficient and validated model for studying the dynamics of brain structures under the influence of a specific psychostimulant, could be useful not only for developing better cognitive enhancers, but also for supporting the development of diagnostic tools and treatments for people suffering from a variety of neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD), autism, schizophrenia, etc.

3 Computational Model

The proposed model is a biologically plausible, large-scale, computational model, whose architecture and connectivity adopt principles from brain cortical and subcortical structures (Trenado et al. 2009). In particular, the model incorporates excitatory and inhibitory neural population effects that correspond to neural networks at the cortex, the thalamus, and the limbic system. Modulatory effects of modafinil are thus incorporated by a “modafinil protocol” block that regulates activity in the locus coeruleus (LC) (see Fig. 5.1). The critical role of the interaction between the cortex, the thalamus and the limbic system in relation to control and gating of selective attention has already been addressed by several authors (Ledoux et al. 1991; Destexhe 2000); whereas, the involvement of the LC in relation to modafinil has just recently been addressed (Minzenberg et al. 2008).

Fig. 5.1
figure 1

Scheme of the brain structures considered in the model for studying modafinil effects on neural correlates of selective attention. The modafinil protocol block refers to a dose of modafinil being administered to a patient or subject; the locus coeruleus block (LC) represents the mean neural activity at that structure, which in turn influences the limbic system and corticothalamic connectivity

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Interestingly, the LC, a small nucleus located in the pons, represents the main source of noradrenaline in the forebrain. In addition, the LC is involved in what is known as the ascending reticular activating system, a neural path critical for arousal and wakefulness. Specifically, LC neurons possess wide projections and their activity varies according to the degree of arousal and specific cognitive processes. A crucial observation is that the variability in LC neural responses results in a release of noradrenaline that targets numerous brain areas including the human prefrontal cortex (Bouret and Sara 2005).

As suggested by some authors (Minzenberg et al. 2008), we assume that modafinil acts by decreasing the neural activity in the LC, while enhancing neural activity between cortical and prominent subcortical structures. In this regard, we propose a relationship between modafinil dose (D) (typical range of dose 0–400 mg) and the LC neural activity (LCN) as provided by

$$ LCN=\exp (-\theta D+K) $$
(5.1)

where θ denotes a parameter governing the degree of decay of LCN activity and K is a threshold determined by the minimum level of LCN activity.

With respect to thalamic modulation, we propose a relationship between corticothalamic gains G 1 and G 2 and the LCN activity as provided by,

$$ \begin{aligned}[b] \varPsi & = \exp (-{(LCN)^2}) \\ \varPsi & = {G_1}+{G_2} \end{aligned} $$
(5.2)

Here, the values of parameters G 1 and G 2 are selected in accordance with Trenado et al. (2009).

In our model, consideration of excitatory and inhibitory neural activities from both cortical and subcortical populations (Fig. 5.2) is achieved by means of a mean field equation of the form,

Fig. 5.2
figure 2

The figure shows schematically the interaction and integration of cortical and subcortical neural populations which are summarized by the term (P a )

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$$ {P_a}={N_{ae }}{s_e}{\phi_e}+{N_{ai }}{s_i}{\phi_i}+{N_{as }}{s_s}{\phi_s} $$

where ϕ e , ϕ i and ϕ s denote mean field excitatory, inhibitory and subcortical neural activities, and N ae , N ai , N as denote parameters related to excitatory, inhibitory and subcortical connectivity of neural populations.

The computation of the mean soma potential V a is performed by integrating neural activities from different neural populations as provided by the following equation,

$$ {V_a}(\vec{r},t)=\int\limits_{{-\infty}}^{\infty } L (t-{t}^{\prime}){P_a}(\vec{r},{t}^{\prime})d{t}^{\prime} $$

The term L accounts for low-pass dendritic effects. Here, the mean firing rate is defined by a sigmoidal type function; the propagation of action potentials is accounted for by a damped wave equation; whereas, corticothalamic modulation is modeled by means of a transfer function involving gains G 1, G 2 and G 3 (see Trenado et al. 2009 for details).

4 Numerical Results

By specifying a modafinil protocol (namely, by defining a relationship between modafinil doses administered to a subject and the degree of LC activity, Eq. (5.1)), we are able to carry out the modulation of mean neural activity of LC neurons. Subsequently, cortical and subcortical modulation is achieved by varying gains G 1 and G 2 under the influence of LC neuron activity as specified by Eq. (5.2). In particular, numerical simulation of neural correlates of selective attention as reflected by the degree of synchronization of relevant evoked response components N1 (occurring at ∼100 ms) and P2 (occurring at ∼200 ms), was performed by following the approach in Trenado et al. (2009). Figure 5.3 shows numerical results of the N1 component synchronization by varying LC neural activity. In agreement with our simulations and the results reported in Minzenberg et al. (2008), it was observed that a high synchronization of the N1 component corresponded to a low LC neural activity; whereas, a high activity of the LC led to a decrease in the synchronization of the N1 component. As suggested by Trenado et al. (2009) and Bollimunta (2011), varying parameters G 1 and G 2 reflects a modulation of corticothalamic circuits that has implications on a mechanism for selective attention. Thus, our simulations provide support for the hypothesis that an increase (decrease) in LC neural activity leads to a non-focal (focal) attention state. In light of previous studies on neural correlates of selective attention, an observed synchronization (desynchronization) of the N1 component has been interpreted as intensification (relaxation) of the activity of the thalamic relay nuclei that leads to an increasing (decreasing) allocation of brain resources towards a target stimulus.

Fig. 5.3
figure 3

Shows synchronization results of relevant evoked response components by varying LC neural activity. Here, a coherent synchronization corresponds to a state of focal attention, while a decrease in synchronization corresponds to a state of non-focal attention

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5 Conclusions

While the present study is preliminary, the obtained results showed the potential of applying neural computational models to test different hypotheses regarding the influence of neuroenhancers on humans. The present chapter addresses, for the first time, the effects of modafinil on a corticothalamic large-scale model of neural correlates of selective attention. In particular, the model is useful to test recent experimental findings regarding the involvement of the LC neural activity and its role in facilitating bottom-up projections thus enabling improvement in focal attention (Fig. 5.3). An interesting feature that is yet to be explored is the transition between states of focal and non-focal attention under the influence of modafinil. In particular, since fMRI possesses a good spatial resolution but lacks good temporal resolution, the applicability of a well-calibrated model promises to be instrumental in providing deeper insight into the influence of modafinil at time scales of milliseconds. In addition, the possibility of understanding the effect of modafinil on different cortical and subcortical brain structures might prove useful in elucidating treatments for disorders such as ADHD, schizophrenia, autism, among others. Needless to say, a deeper understanding of modafinil mechanisms in the human brain is instrumental in order to clarify the issue of its positive or negative effects. As a final note, it should be pointed out that biologically plausible models not only offer the possibility of incorporating findings from different brain technologies, but also the opportunity to test mechanisms that could be experimentally unfeasible.