Upper Alpha Based Neurofeedback Training in Chronic Stroke: Brain Plasticity Processes and Cognitive Effects

Whatever the fuck upper alpha is?
http://link.springer.com/article/10.1007/s10484-017-9353-5

• Authors
• Authors and affiliations

• Silvia Erika KoberEmail author
• Daniela Schweiger
• Johanna Louise Reichert
• Christa Neuper
• Guilherme Wood

• Silvia Erika Kober
  • 1
  • 2
  Email author
• Daniela Schweiger
  • 1
• Johanna Louise Reichert
  • 1
• Christa Neuper
  • 1
  • 2
  • 3
• Guilherme Wood
  • 1
  • 2

1. 1.Department of PsychologyUniversity of GrazGrazAustria
2. 2.BioTechMed-GrazGrazAustria
3. 3.Institute of Neural Engineering, Laboratory of Brain-Computer InterfacesGraz University of TechnologyGrazAustria

Open AccessArticle

First Online:

14 February 2017

DOI: 10.1007/s10484-017-9353-5

Cite this article as:

Kober, S.E., Schweiger, D., Reichert, J.L. et al. Appl Psychophysiol Biofeedback (2017). doi:10.1007/s10484-017-9353-5

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Abstract

In the present study, we investigated the effects of upper alpha based neurofeedback (NF) training on electrical brain activity and cognitive functions in stroke survivors. Therefore, two single chronic stroke patients with memory deficits (subject A with a bilateral subarachnoid hemorrhage; subject B with an ischemic stroke in the left arteria cerebri media) and a healthy elderly control group (N  = 24) received up to ten NF training sessions. To evaluate NF training effects, all participants performed multichannel electroencephalogram (EEG) resting measurements and a neuropsychological test battery assessing different cognitive functions before and after NF training. Stroke patients showed improvements in memory functions after successful NF training compared to the pre-assessment. Subject B had a pathological delta (0.5–4 Hz) and upper alpha (10–12 Hz) power maximum over the unaffected hemisphere before NF training. After NF training, he showed a more bilateral and “normalized” topographical distribution of these EEG frequencies. Healthy participants as well as subject A did not show any abnormalities in EEG topography before the start of NF training. Consequently, no changes in the topographical distribution of EEG activity were observed in these participants when comparing the pre- and post-assessment. Hence, our results show that upper alpha based NF training had on the one hand positive effects on memory functions, and on the other hand led to cortical “normalization” in a stroke patient with pathological brain activation patterns, which underlines the potential usefulness of NF as neurological rehabilitation tool.

Keywords

Cortical reorganizationNeurofeedbackMemoryStroke recovery

Background

Following stroke, changes in electrical brain activity as well as cognitive impairment are often evident (Melkas et al. 2014; Jordan 2004; Kaplan and Rossetti 2011; Finnigan and van Putten 2013; Niedermeyer 2005). In this context, EEG based neurofeedback (NF) might be a useful rehabilitation tool. There is evidence that NF training can lead to changes in electrical brain activity which goes along with cognitive improvements (Kober et al. 2015a, b; Reichert et al. 2016; Kropotov 2009; Gruzelier 2014). Using NF, participants can learn to voluntarily modulate their electrical brain activity. Specific parameters of the EEG, such as power values in specific frequency bands, can be extracted and analyzed in real-time and fed back to the participants via auditory and/or visual feedback. Hence, with the method of NF, the electrical activity of the brain is modulated directly and, therefore, the cortical substrates of cognitive functions. This direct access to neural activity by means of NF may alter or accelerate functional reorganization in the brain following stroke. NF might speed up functional recovery or even enable functional recovery that otherwise would not have occurred (Nelson 2007). Therefore, the aim of the present study was to evaluate the effects of EEG based NF training on brain plasticity processes and cognitive functions in stroke survivors.

It has been demonstrated that the electroencephalogram (EEG) is a highly sensitive measure to detect cerebral ischemic or hemorrhagic stroke. Stroke patients show EEG abnormalities compared to healthy people, which change over the course of the disease. The quantitative EEG during the acute and sub-acute state has a high prognostic value concerning the outcome from stroke (Finnigan et al. 2007; Finnigan and van Putten 2013; Tecchio et al. 2007; Sheorajpanday et al. 2011). In this context, slow wave EEG activity in the delta range (0.5–4 Hz) as well as faster oscillatory activity in the alpha range (8–12 Hz) turned out to play an essential role (Niedermeyer 2005). Delta power was found to be negatively correlated with regional cerebral blood flow (rCBF) while alpha power showed a relatively strong positive correlation with rCBF (Tolonen and Sulg 1981; Finnigan and van Putten 2013). In stroke patients with unilateral cerebral infarction delta activity is typically most pronounced over the affected hemisphere in the acute state (Finnigan and van Putten 2013; Jordan 2004; Tecchio et al. 2006). Across a few hours during the acute stroke period, the scalp topography of delta activity shifts from a maximum over the affected hemisphere to a maximum over the healthy, unaffected hemisphere. This interhemispheric shift of scalp delta power maxima is associated with worsening of cerebral pathophysiology and clinical state in stroke patients (Finnigan et al. 2008; Tecchio et al. 2007; Zappasodi et al. 2007; Niedermeyer 2005; Rossini et al. 2003). There is evidence that pathological asymmetry in EEG delta power decreases after thrombolytic therapy, which in turn leads to improvements in clinical symptoms (Finnigan et al. 2006; de Vos et al. 2008). Alpha amplitude attenuation is also generally indicative for cortical injury (Finnigan and van Putten 2013; Finnigan et al. 2007; Klimesch 1999). Alpha power in the acute state is negatively related to the severity of stroke symptoms in patients with unilateral lesions in the arteria cerebri media (ACM) (Finnigan et al. 2007). In stroke patients with acute subarachnoid hemorrhage (SAH), EEG delta activity is increased and alpha activity is reduced, too (Vespa et al. 1997; Claassen et al. 2004; Niedermeyer 2005; Labar et al. 1991). Some EEG studies also investigated changes in EEG activity in the post-acute and chronic stage (Mattia et al. 2003). These studies showed that the greatest improvement in EEG activity occurred during the first 3 months after stroke (Giaquinto et al. 1994; de Weerd et al. 1988; Jonkman et al. 1984). Stroke patients with a unilateral insult in the ACM showed decreases in delta power and increases in alpha power levels over the injured hemisphere during this time period. Alpha power levels also increased over the healthy hemisphere. An overall increase in alpha power was also partially associated with improvements in motor functions and activities in daily living (Giaquinto et al. 1994). Furthermore, delta and alpha power became more symmetrically distributed over both hemispheres with clinical recovery, which might be an indicator of “normalization” of electrical brain activity (Giaquinto et al. 1994; Tecchio et al. 2006).

In the present investigation, we evaluated whether EEG based NF training can be used as therapeutic tool to evoke changes in electrical brain activation patterns in chronic stroke patients, which may be accompanied by cognitive improvements. In NF training paradigms, participants can learn to voluntarily increase or decrease the amplitude of specific EEG frequencies. There is some empirical evidence that voluntary modulation of EEG amplitudes determines other aspects of electrical brain activity in healthy people, which are responsible for improved cognitive performance (Egner et al. 2004; Egner and Gruzelier 2004; Kropotov et al. 2005; Kober et al. 2015a; Reichert et al. 2016). A few single-case studies in stroke patients reported heterogeneous results. Some found positive effects of NF training on cognitive functions as well as a EEG normalization after NF training (Rozelle and Budzynski 1995; Bearden et al. 2003; Laibow et al. 2002; Putman 2002; Hofer et al. 2014), others could not find any significant effects (Doppelmayr et al. 2007). However, the generalizability of these prior findings is limited due to the incomplete description of training-specific EEG signal changes as well as the absence of control groups. The majority of NF training studies examined the effects of NF only on the behavioral level (see Gruzelier 2014 for a review). Generally, successful modulation of EEG band power is associated with cognitive and behavioral improvements (Kropotov 2009; Gruzelier 2014; Kober et al. 2015a, b; Hofer et al. 2014; Reichert et al. 2016). For instance, voluntary up-regulation of the upper alpha frequency band (UA, about 10–12 Hz) generally leads to improvements in working memory (WM) and short-term memory performance (Escolano et al. 2011, 2012, 2013, 2014; Angelakis et al. 2007; Nan et al. 2012). It is assumed that alpha activity inhibits unnecessary or conflicting processes to the task being performed, thus facilitating attention and memory by actively suppressing distracting stimuli (Klimesch et al. 2007). Beside voluntary modulation of the magnitude of EEG amplitudes, NF can be also used to change the topographical distribution of EEG activity. For instance, NF is used to tread depressive symptoms by changing hemispheric asymmetry in alpha band power (8–12 Hz) in prefrontal brain areas (Kropotov 2009).

Summing up, we aimed at investigated the effects of NF training on (i) electrical brain activity, such as power in different EEG frequencies and the topographical distribution of EEG activity, and (ii) cognitive functions in chronic stroke patients. Therefore, we present two cases, a stroke patient with a unilateral middle cerebral artery (ACM) stroke and a stroke patient with a bilateral subarachnoid hemorrhage (SAH). We used an UA based NF training, since the two chronic stroke patients showed deficits in memory functions prior to the NF training. Based on the literature, UA based NF training should have specific positive effects on memory functions (Escolano et al. 2011, 2012, 2013, 2014; Angelakis et al. 2007; Nan et al. 2012). We compared the results of the two stroke patients to the results of a healthy, elderly control group. We expected that pathological EEG patterns in stroke patients would change due to UA based NF training, which should be associated with cognitive recovery.