Antenas Cassegrain

Prior studies have been replicated noninvasively in human subjects with the help of PET neuroimaging methods, whereby a selective release of dopamine in the striatum was observed following high frequency rTMS of the primary motor cortex and dorsolateral prefrontal cortex (Strafella, et al., 2001; Strafella, et al., 2003). Similarly, the clinical efficacy of electroacupuncture (EA) and moxibustion, is dependent on functional alterations in cerebral dopaminergic and serotonergic neurons, especially because of their anti-stress and psychosomatic actions (Yano, et al., 2004). Furthermore, long-term high-frequency EA has also been demonstrated to be effective in halting the degeneration of dopaminergic neurons in the substantia nigra and in up-regulating the levels of brain-derived neurotrophic factor (BDNF) mRNA in the subfields of the ventral midbrain (Liang, et al., 2002; Liang, et al., 2003). The activation of endogenous neurotrophins by EA may be involved in the

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regeneration of the injured dopaminergic neurons, which may explain the effectiveness of EA in the treatment of PD (Liang, et al., 2003). EA might regulate the biosynthesis of DA by altering the tyrosine hydroxylase (TH) gene transcription (Liang, et al., 2002; Wang, et al., 1999).

Evidently, the dopamine precursor, Levodopa, is able to induce a significant boost in the performance of a serial reaction-time task for stroke patients (Rösser et al., 2008). Hence, the combination of rTMS or EA intervention with the usual rehabilitative treatments could improve the outcome of neurorehabilitation in real-life situations. A simple explanation may be that sustained rTMS or EA gives rise to a cumulative release of neuromodulators (e.g., serotonin, dopamine), which then modulate cortical excitability and practice-dependent plasticity that is necessary for learning. The neuro-anatomical correlates of successful reaction-time task performance implicate the basal ganglia as a key structure that seems to be necessary, as well as being sufficient for procedural learning, which indicates that dopamine is crucial to motor sequence learning and synaptic plasticity in the primary motor cortex (Eckart, et al., 2010; Molina-Luna, et al., 2009).

However, there is a possibility that neurofeedback training (NFT) may also upregulate dopaminergic tone in the motor cortex and/or basal ganglia. An animal study that examined the EEG dynamics, using the spectral power densities (SPDs) of the alpha and theta rhythms, found that the spiking frequency of supposedly dopaminergic (DA) neurons from the ventral tegmentum directed changes in the EEG characteristics, in the course of neurofeedback sessions (Kulichenko, et al., 2009). While the animals learned to correlate changes in the intensity of the sound signal and power of the EEG rhythms and to control the latter, in a conditioned-reflex mode, the

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α/θ ratio changed, in the course of neurofeedback sessions, due to an increase in the SPD of the alpha EEG component and a noticeable drop in the SPD of the theta oscillations. Meanwhile, in a similar manner, augmentation of the spike activity of DA neurons was observed, which indicates the probable mechanisms for the involvement of the cerebral DA system in the results of neurofeedback sessions (Kulichenko, et al., 2009). Thus the method of combining and integrating NFT and stimulation strategies may enhance learning and performance, based on the likely system for neuromodulation. Intriguingly, the combination of feedback techniques and stimulation strategies may facilitate neurofeedback training (Hirshberg, et al., 2005; Ros, et al., 2010). Electroacupuncture (EA) stimulation has also been found to enhance alpha power, a non-specific change, or to inhibit theta rhythmic activity, during high frequency EA stimulation (Chen, et al., 2006), and to enhance attention levels (Chen, et al., 2011). The real-time emergent pattern of the EEG may be assisted by other successive non-invasive brain stimulation techniques, such as rTMS or EA, resulting in enhanced learning and performance (e.g., stimulation to enhance rhythmic activity), which implies that these combined stimulation and feedback approaches may be more effective than either alone (Hirshberg, et al., 2005; Keck, et al., 2002).

The close relationship between the basic modulation of the nervous system, the DA system and an associated improvement in attention, reward and learning has been extensively covered in the previous paragraphs. In addition, the probable mechanism that describes the effect of NFT on the cerebral DA system is based on the premise that neurofeedback sessions which direct change in the EEG characteristics may cause up-regulation of dopaminergic tone, because of the observed augmentation of the spike activity of DA neurons (Kulichenko, et al. 2009). It is therefore pertinent to

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review the literature pertaining to EEG oscillation, which is relevant to particular enhancement and inhibition of EEG rhythms due to neurofeedback that produces an improvement in attention (details in the chapter 5). Most neurofeedback research to date has concentrated on the improvement of cognitive functions, such as attentional skills, and mood.

1.7.4 Attention and vigilance

Attention refers to the ability to focus on a specific thing, without becoming distracted, and also to a more focused activation of the cerebral cortex that enhances information processing (Mesulam, 1990; Oken, et al., 2006; Posner, 1989). Attention is different from simply being alert, because alertness refers to basic arousal, which refers to the state of simply being awake. For example, an alert but inattentive patient is attracted to any novel stimulus, but cannot screen out irrelevant stimuli in the environment (Oken, et al., 2006). However, one state, termed sustained attention, is synonymous with the most common usage of vigilance (Parasuraman, et al., 1998). Although there are several activation states of the cerebral cortex that impact the ability to process information globally or locally, no terms which have been used to describe these states of arousal, alertness, vigilance, or attention perfectly describe these states of cortical activation, since most terms are used broadly, with various associations, and there are no perfect physiological markers. In particular, the term, vigilance, has been used in many different ways by different groups of scientists. For example, psychologists and cognitive neuroscientists use the term specifically to describe an ability to sustain attention during a task and a performance that requires attention for a period of time (Davies and Parasuraman, 1982; Mackworth, 1964; Parasuraman, et al., 1998). However, clinical neurophysiologists use the term,

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vigilance level, as well as arousal level, in the sleep–wake spectrum, without reference to cognition or behavioural responsiveness, because of an EEG’s great sensitivity to the activity of the corticothalamic networks that are fundamental to the sleep–wake dimension (Steriade, 1999, 2000).

Vigilance is a term that has various definitions, but the most common scientific usage is to define a state of sustained attention or tonic alertness (Oken, et al., 2006). Vigilance implies both a degree of arousal in the sleep–wake cycle and in the level of cognitive performance over time. The EEG is the most common physiological measure of vigilance, and various measures of eye movement and of autonomic nervous system activity have also been used (Oken, et al., 2006). Attention tasks can be made progressively more complicated, but the evaluation of more complex functions requires vigilance (Gillig and Sanders, 2011). Problems with vigilance are indicated by the omission of a letter, or by signalling when the letter is not presented, which is called a commission error. The GNG test is also a popular format for testing vigilance (Gillig and Sanders, 2011; Sander, 2010).

Interestingly, subjects who are uninterested in the environment are not as vigilant as those people with high motivation. In other words, the underlying brain system that impacts sustained attention is motivation. The motivational system includes much of the dopamine system and portions of the frontal lobes (e.g., anterior cingulate), as well as the limbic and subcortical structures (striatum, nucleus accumbens and amygdala) (Robbins and Everitt, 1996). The dopamine system may be related to reward (Schultz, 2002). Conceptually, effort (Kahneman, 1973) and motivation are related to sustained attention.

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