Neurofeedback training research for the aging brain, Alzheimer’s (AD) and dementia

The connection between attention functions and memory performance

Attention is a central component of cognitive ability. The ability to focus attention, encode and maintain information are among the brain’s most important cognitive functions. Since Working Memory and attention share common neural mechanisms (e.g., Gazzaley and Nobre, 2012), enhancement of attention improves encoding, maintenance and retrieval of items held in WM for online usage (Karbach and Verhaeghen, 2014).

Unlike ADHD in children, prominent cognitive deficits in aging brain occur in the switching and division of attention, whereas phasic arousal and focused attention to stimulus features are only minimally affected in the early stages of Alzheimer’s Disease. For instance, selective attention deficit is one of the first cognitive indicators of neocortical dysfunction in early AD (Parasuraman and Haxby, 1993).

Cognitive decline symptoms and the way they are experienced

Complaints about declined attention and memory are common in healthy and cognitively intact older adults during brain aging. Neural mechanisms underlying short-term memory (e.g., Working Memory) and attention undergo a significant early change in aging (Lawson et al., 2007).

The most common early symptoms of Alzheimer’s are problems with short-term memory (Reiman et al., 2011).

Since there is no effective drug treatment thus far to stop cognitive decline, attention training has become an increasingly attractive option.

Brain training Neurofeedback for the aging brain

Neurofeedback training combines cognitive training with operant conditioning of the associated neural substrate, e.g., attention or memory recall (Sitaram et al., 2017).

Preliminary studies with healthy participants and different clinical populations suggest that neurofeedback training may be effective to improve brain function, treat cognitive as well as affective symptoms and induce brain plasticity (Arns et al., 2017; Sitaram et al., 2017; Thibault et al., 2018). Therefore, neurofeedback training has been suggested as a potential complementary treatment for patients suffering from dementia.

Work by Angelakis et al. (2007) applied EEG Neurofeeback in the older population and showed improved processing speed and executive functions. Additional success has been reported using EEG-based NF for attention training and Working Memory in older dementia patients (e.g., Surmeli et al., 2016).

Training neural networks; for early EEG detection for Alzheimer's signs, as well for training to boost cognitive performance

One of the networks trained by Neurofeedback is the default-mode network (DMN). This is one of the brain’s connectivity networks which has been found to be able to serve as a neuro-marker for the detection of early Alzheimer’s signs. When mapped as part of a QEEG assessment, functional connectivity changes in dementia brains can be identified in EEG recordings (McBride et al., 2013, 2014, 2015; Sargolzaei et al., 2015).

The DMN can show low connectivity which may be caused in older brains by Aß peptides disrupt neural activity at the synaptic level (Palop and Mucke, 2010). Those peptides are one aspect of the physiological basis for Alzheimer’s.

As brain ages, Aβ plaques form within distinct regions of the brain’s default-mode network (Buckner and Vincent, 2007).

Since patterns of functional brain connectivity in humans are highly predictive of cognitive performance (Hachinski et al., 2006; Finn et al., 2015), the DMN functionality can support improvements in performance under cognitive decline.

It was indeed demonstrated by fMRI imaging that the connectivity within neural networks was correlated with the Alzheimer’s biomarkers found in the CSF (brain fluids) during resting state and cognitive tasks (Jiang et al., 2016).

Research findings for Neurofeedback training in healthy aging brains, Dementia and Mild Cognitive Impaired (MCI) brains

Points to keep in mind when screening studies in the field:

1. Difference in behavioural/cognitive changes across studies might be due to the heterogeneity of training protocols. For instance, two studies employed participant-specific designs.

2. All other EEG-based protocols included different channels or frequency ranges in their protocols, limiting comparisons across studies.

3. The challenge of attentional training in older adults is that measurement of cognitive training is often confounded with multiple factors, such as individual differences that tend to increase with age.

These factors include individual differences in brain aging associated with visual attention (Monge et al., 2016), attention capture to rewarding objects (Donohue et al., 2016), Working Memory and performance (Parasuraman and Jiang, 2012), learning transfer beyond trained tasks (Greenwood and Parasuraman, 2016), and placebo effects where performance of older adults is simply improved by participating in Cognitive Training (Foroughi et al., 2016).


  1. Fabienne Marlats. Theta Neurofeedback Training Improves Cognitive Performance and EEG Activity in Elderly With Mild Cognitive Impairment: A Pilot Study. READ

  2. Suwicha Jirayucharoensak. Game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment. READ

  3. Xin Li. Neurofeedback Training for Brain Functional Connectivity Improvement in Mild Cognitive Impairment. READ

  4. Yotam Lavy. Neurofeedback Improves Memory and Peak Alpha Frequency in Individuals with Mild Cognitive Impairment. READ

  5. Jung-Hee Jang. Beta wave enhancement neurofeedback improves cognitive functions in patients with mild cognitive impairment: A preliminary pilot study. READ

  6. Robin E Luijmes. The effectiveness of neurofeedback on cognitive functioning in patients with Alzheimer's disease: Preliminary results. READ

  7. Tanju Surmeli. Quantitative EEG Neurometric Analysis-Guided Neurofeedback Treatment in Dementia: 20 Cases. How Neurometric Analysis Is Important for the Treatment of Dementia and as a Biomarker? READ

  8. Tomas Ros. Mind over chatter: Plastic up-regulation of the fMRI salience network directly after EEG neurofeedback. READ

  9. Benedikt Zoefel. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. READ

  10. S.M. Staufenbiel. Effect of beta and gamma neurofeedback on memory and intelligence in the elderly. READ