CHAPTER 3 DISCUSSION AND CONCLUSIONS
2. Alpha Rhythm Coherence
2.1. Electrical signature of behavior
2. Alpha Rhythm Coherence
2.1. Electrical signature of behavior
An invariable discovery throughout our studies was the resting-state oscillation coherence in the alpha band as a reliable electrophysiological marker of human behavior. Alpha rhythm coherence is correlated in a linear fashion with motor and cognitive performance in stroke patients and patients with Alzheimer‟s disease, as well as in healthy young and elderly participants (Dubovik et al., 2012;
Dubovik et al., 2013; Rizk, accepted). In patients, brain regions that are normally responsible for deficient functions show reduced alpha-band coherence with the rest of the brain, and the magnitude of this reduction is associated with the severity of neurological deficits. For instance, stroke patients with pronounced Broca‟s aphasia are found to have decreased alpha-band connectivity between the left inferior frontal gyrus (IFG) and the rest of the brain. Patients with right hand paresis display a significant decrease of alpha band coherence in the left primary motor cortex (Figure 2). Linear correlations were found in a range of functions, including spatial attention, language, and motor function in stroke patients as well as in language and memory in Alzheimer‟s disease patients. In elderly adults, high levels of connectivity in the hippocampal area and in Broca‟s area were associated with good performance in memory and language tests, respectively (Dubovik et al., 2013). In young adults, the magnitude of alpha-band coherence in the right temporo-parietal junction (TPJ) was linearly related to their tendency to explore the left halves of symmetric photography (Rizk, accepted). Overall, the association was consistent across 4 different populations. Supplementary tests performed on the data of stroke patients confirmed that these observed correlations were not driven by local anatomical lesions or amplitude of neural activity (Dubovik et al., 2012). Hence, decreased alpha-band coherence, as observed in patients, does not merely reflect a loss of local neural tissue, but a network dysfunction. We concluded that alpha-band network connectivity at rest linearly translates into behavioral performance during tasks.
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Example of resting-state functional connectivity maps in 2 patients with ischemic stroke in the territory of the left middle cerebral artery. A, B. The ischemic lesion is marked in dark gray. Nodes showing significant decreases in FC with the rest of the brain as compared to a healthy control population are marked in blue, significant relative increases in red (p<0.05, corrected with 1% FDR). The topography of disconnected nodes reflects the profile of neurological deficits of each patient (C, D). E, F. FC reductions in affected areas (blue lines) as compared to the mean (±SD) of the entire cortex of each patient (gray lines) were most prominent in the alpha frequency range (7–13 Hz).
Our findings are congruent with a growing body of evidence that shows how alpha rhythm is directly involved in the neuronal regulation of mental activities, such as attention, memory and sensory awareness (Palva & Palva, 2007). Recent studies have confirmed that alpha oscillations are strengthened by internal tasks such as mental calculation (Palva et al., 2005a) and mental imagery (Cooper et al., 2006). Other studies have shown that the alpha oscillations have a direct involvement in top-down modulation of attention (Fries et al., 2001). In addition, it was shown that alpha frequency can synchronize with other frequency oscillations in response to cognitive demands (Palva et al., 2005a). This kind of interaction is likely to be essential for the coordination and communication in networks oscillating at different frequencies (Osipova et al., 2008). It is also important to recognize that alpha band oscillation has the ability to phase lock between widely separated cortical regions (Buzsaki
& Draguhn, 2004). This capacity might be crucial for the formation of large-scale functional networks.
The correlations of behavioral performance with alpha band coherence observed in our studies support these findings and suggest that alpha rhythm could serve an important role in interregional information transmission and processing.
Why alpha?
Since the first recording of a human EEG by Hans Berger (1929), the alpha band has attracted a lot of attention. It is the most common component of the human brain‟s electrical activity. Compared with other EEG rhythms, alpha activity has the best re-test reliability and has the quality of an individually stable trait (Gasser et al., 1985). It has even been proposed that alpha activity may provide further insight into the relationship between neurophysiology and consciousness (Fingelkurts, 2010).
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Despite the paucity of the existing knowledge on the alpha-band physiology, the crucial role of the thalamocortical circuitry and intracortical mechanisms in its generation is considered today as unequivocal (Palva & Palva, 2007). First observations suggested that cortical alpha oscillations are generated by the thalamus and paced by the thalamo-cortical neurons exhibiting intrinsic burst firing at alpha frequencies. The key role in alpha generation was assigned to lateral geniculate nucleus which is also known as a key thalamic region in the transmission of visual information to the cortex (Basar et al., 1997). However, later studies contested this view by showing that thalamic alpha-band burst firing is not always synchronized with alpha-band oscillations observed in cortical EEG (da Silva et al., 1973;Lopes da Silva et al., 1980). At least partial involvement of the thalamus in cortical alpha band generation remains, however, undeniable. Alpha-like responses were also found in the reticular formation (RF) and the hippocampus suggesting the existence of distributed alpha systems in the brain (Basar et al., 1997). At the cortical level, recordings in animals (Wiest & Nicolelis, 2003) as well as in humans (Linkenkaer-Hansen et al., 2004; Bollimunta et al., 2011) showed that alpha oscillations can be generated in high-order association cortices and play an active and task-relevant role. In overall consideration, alpha activity seems to be supported by topologically distinct generators (Palva
& Palva, 2011).
Despite considerable research efforts, the role of alpha oscillations has remained controversial. On the one hand, alpha band was described as an “idling rhythm” and was repeatedly found to increase during rest with eyes closed and to disappear as soon as the person was engaged in a cognitive activity (Pfurtscheller, 1992). The activation of a region was reported to result in suppression of alpha power, the so-called event-related desynchronization (ERD). Several studies showed both anticipatory and post-stimulus alpha-band amplitude decreases from baseline levels in task-relevant regions and increases in the presumably task-irrelevant regions according to perceptual, attention, and working memory task demands (Palva & Palva, 2011). Furthermore, a negative correlation was found between alpha-band power and the heamodynamic signal (BOLD) in the occipital cortex (Laufs et al., 2003), as well as between alpha-band power and fMRI activations (Logothetis et al., 2001; Niessing et al., 2005). On the other hand, alpha power was shown to be positively associated with short reaction times, and negatively associated with subjective levels of fatigue, indicating its active involvement in a general state of preparation for when it could be needed to react to a stimuli (Sadaghiani et al., 2010).
Also, new research showed that throughout several cognitive tasks, alpha band oscillations respond with an increase in amplitude (event-related synchronization (ERS)) in frontal, parietal and sensory regions known to be high in the processing hierarchy (Klimesch et al., 2007). For example, an enhancement of alpha band amplitude in these regions was reported during the short-term and working memory retention period. The amplitude increase returned to its baseline as soon as the activity had finished (Palva & Palva, 2007). Thus, it has been proposed that instead of a pure inhibitory function, alpha oscillations would participate actively, particularly in internal and working memory tasks (Klimesch et al., 2007; Mathewson et al., 2009).
Studies analysing alpha-band connectivity, in particular alpha-band phase synchrony, support this idea that the alpha-band phase dynamics are actively engaged in cognition and in the processing of
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information. In contrast to power and ERS/ERD values, phase synchrony contains timing aspects of neural information transmission (Klimesch et al., 2007). This is an important component to consider in the formation of transient neural assemblies, since communication between neurons and their integration in large-scale networks occurs with a time delay (Palva & Palva, 2007). Indeed, numerous studies reported an increased alpha band phase synchrony during internal tasks, working memory, and attention (Kolev et al., 2001; Jensen et al., 2002; Cooper et al., 2003). Enhanced frontoparietal alpha- and beta-band phase synchrony in MEG recordings was associated with mental calculation (Palva et al., 2005a). Moreover, phase locking in alpha band has predicted behavioral performance for perceptual discrimination (Hanslmayr et al., 2005; Haegens et al., 2011), conscious perception (Mima et al., 2001; Palva et al., 2005b), and long-term memory formation (Meeuwissen et al., 2011).Together, these findings provide evidence for an active role of alpha oscillation synchrony (Palva &
Palva, 2007).
Our results are in line with the reported data, implying that inter-regional alpha phase synchronization supports attention, executive and contextual functions. Moreover, these results contribute to our existing knowledge by showing that alpha band coherence does not depend on a particular behavior, but contains integrative information on the functional state of several cognitive and motor networks.
Other frequency bands
In our studies, only alpha band coherence was consistently found to correlate linearly with behavior.
However, the lack of correlations with other traditional frequency bands does not indicate that there is an absence of behavioral relevance in these bands. Since our analyses were conducted at rest, it is possible that other important EEG frequencies that are put into use during tasks could not be identified. In fact, relevant interactions have been observed in beta band coherence during motor task performance (Gerloff et al., 2006). Gamma power, which is supposed to reflect neural computation, was shown to be phase-locked to alpha-oscillations (Palva & Palva, 2007; Osipova et al., 2008).
Network interactions in the theta frequency band may play an important role during development and for long-term memory progress (Dubovik et al., 2013). In summation, alpha rhythms are likely to have a primarily integrative role consisting of synchronization and structuring of information within distributed functional networks at rest. The implication of other bands in cognitive processes might be more specific and task-dependent.