Neuroimaging and neurofeedback as future tools in mental health therapeutics

Can we look forward to portable fNIRS-driven neurofeedback?

I believe that the use of biofeedback and neurofeedback techniques has a role to play in future therapeutic pathways for the treatment (and diagnostics) of mental health conditions. The below is a somewhat rambling essay on what I see as the background and current state of the art in the study of working memory as a biomarker in stress and other mental health issues and disorders. Shout if you know of other studies.

The use of FMRI in the study of working memory

The concept of working memory (WM) originally developed in order to extend the idea of short-term memory and to illustrate the kinds of processes that are involved when task or perceptual information is retained over longer period of time than short-term (several seconds or minutes). In order to distinguish between short-term and WM, some make the distinction that WM represents a ‘limited-capacity store for retaining information over the short term’ (Gazzaniga, Ivry, & Mangun, 2002, p. 317). This is akin to RAM in a computer, whereby the information is temporarily held in store, and then processed. WM can hold information from sensory inputs and memory or could be retrieved from longer-term storage (to follow the computer analogy, this would be the hard drive). This information can be acted on and processed whether it came from sensory or long-term memory.

The initial formulation of WM by Baddeley and Hitch (1974) described a three-part system whereby a central executive mechanism controls two subordinate systems, which then process either phonological or visuospatial information. The central executive mechanism, again in a computing analogy, is similar to the central bus system, which brokers the different kinds of information back and forth from memory to processing units: it itself isn’t specific to one sensory modality.

The two subsystems it controls (the phonological loop and the visuospatial sketch-pad) have been hypothesized as mechanisms in studies initially looking at recall of particular sounds made in speech (consonants), where evidence showed that participants stored the sound (and not the shape) of letters when asked to replace them. These acoustic codes are paralleled in the visuospatial sketch-pad by visual codes, which have been found to be separate to acoustic ones. Initial evidence for the distinct nature of these subsystems and their anatomical components in the brain came from studies examining WM after specific brain lesions, in study designs whereby participants performed spatial versus verbal WM tasks (Awh et al., 1996), where changes in local cerebral blood flow were measured using positron emission tomography. Activation (increasing blood flow matching increased neural activity) was found in left hemisphere regions for the the verbal tasks, and right hemisphere activation for the spatial tasks. More recent evidence suggests that in adults at least, there is consistently greater activation to verbal WM tasks in the left frontal and temporal lobes, and greater activation to spatial WM in right frontal, parietal, and occipital cortices (Nagel, Herting, Maxwell, Bruno, & Fair, 2013; Thomason et al., 2009).

Working memory in studies of mood disorders

FMRI has made it possible to begin working towards a better understanding of the neuroanatomical substrates in working memory; as WM is linked to the voluntary allocation of attentional resources, over the last two decades studies have revealed how the lateral prefrontal, premotor, posterior parietal cortices activate during performance of WM tasks. The clear utility of FMRI in being able to offer distinct activation of brain regions depending on the content and nature of tasks has meant that very fine-grained explanations as to how processes and different types of stimuli are dealt with by the brain. Meta-analyses of FMRI-based neuroimaging studies of WM have show that Our WM representations in the frontal cortex are organized by process, rather than by material type, for example, and that executive functions and demand are largely confined in activation to the frontal cortex (but also in the superior parietal cortex) (Wager & Smith, 2003). More recent studies have shown that lateralization of brain function changes with age (Nagel et al., 2013), leading to studies whose results suggesting that changes in gray matter volume in particular brain sites are related to free recall WM deficits in early stage Parkinon’s disease (Ellfolk et al., 2013), and others suggesting that adults come to rely less on particular neural networks in response to working WM tasks that are more prevalent in childhood (Vogan, Morgan, Powell, Smith, & Taylor, 2016).

Just as WM plays an important role in the development of other cognitive skills such as social ability (Dennis, Agostino, Roncadin, & Levin, 2009) and learning (Gathercole & Alloway, 2008), WM dysfunction or reduced capacity has been linked to various neurodevelopmental disorders such as Autism Spectrum Disorder (ASD; (Southwick et al., 2011) and Attention Deficit Hyperactivity Disorder (ADHD; (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005), and also in the study of mood disorders, whereby persistent systematic changes in neural networks have been suspected in cases of major depressive disorder, as cingulate function (important for cognitive but also emotional processing) was still abnormal after mood was restored, indicating a long-lasting disturbance of the typical WM network (Schöning et al., 2009). In other recent studies, not targeting individuals suffering from mood or attentional disorders but looking at healthy individuals, the focus has been on using WM as a therapeutic pathway: improving WM by training showed reduced anger, fatigue and depression and reduced activity in the left posterior insula, leading to a potential for WM training to reduce levels of anger by increasing the ability to manage emotional stimuli at the cognitive level (Nouchi et al., 2013; Takeuchi et al., 2014).


This, more recent, use of FMRI to better understand not only the perceptual and processing logistics of WM within the brain, but its role as a potential biomarker in mental health disorders, is leading to potential mechanisms through which the identification of mental disorders could be brought forward from its current form, whereby individuals present symptoms to their doctor (by which time a disorder might be full blown). Portable neuroimaging techniques such as EEG (and consumer products by such companies as TMSi, ANT neuro, gtec, Muse, etc.) and NIRS (near-infrared spectroscopy) and Functional Near-Infrared Spectroscopy (fNIR or fNIRS), which is the use NIRS for functional neuroimaging. Using fNIRS, we can measure brain activity hemodynamic responses associated with neuron behaviour in a similar way to FMRI, but portable headsets make it possible to measure during the course of an individual’s working day. Although not quite seamless and unintrusive, the pace of development in this area is rapid, and will no doubt offer products soon that will simply look like normal caps or scarves.

Already, the combination of EEG and fNIRS for the objective assessment of stress has been proposed (Al-Shargie et al., 2016), and its effect on prefrontal cortical activity assessed (Al-Shargie, Tang, & Kiguchi, 2017). Stress is obviously of great interest in the biomedical engineering community, in terms of its relationship to mental and physical health, and very recent work sets out to understand how new techniques, such as fNIRS, will help confirm the validity of previous neuroimaging data obtained from stress-inducing procedures and studies by looking at cortical activation in paradigms such as the Trier Social Stress test (TSST) (Rosenbaum, Hilsendegen, et al., 2018). This type of validation can then enable work that targets particular constructs, such as rumination, in the context of cortical activity during TSST, using fNIRS to illustrate that during such a test, there is impairment in the activation of the right inferior frontal gyrus (IFG), for participants who classify as high ruminators, possibly indicating that high ruminators might be less resilient to adverse events. Since previous studies looking at IFG lesions have shown that subjects with IFG damage find it difficult to suppress material from memory retrieval (Conway & Fthenaki, 2003), impairment of IFG in high ruminators would mean that they would generally find it more difficult to inhibit stress-related emotional and cognitive responses, and would also change stte rumination in post-stress testing (Rosenbaum, Thomas, et al., 2018), which could effectively be understood as an inability to prevent difficult thoughts from entering working memory. Future systems that could measure and understand how this inhibitory system should work might suggest to individuals, in real-time, perhaps through the use of Augmented Reality mechanisms, ways in which to train the same brain systems to recognize and deal with stressors differently, more efficiently, and more importantly how to deal with the aftermath of intrusive thoughts, not necessarily as a form of neurofeedback.

fNIRS-based neurofeedback (NF) concepts have in fact recently been investigated in the assessment of learning and the effects on performance in a working memory task (n-back), whereby participants underwent NF training sessions to ‘voluntarily up-regulate hemodynamic activity in prefrontal areas’ (Barth, Strehl, Fallgatter, & Ehlis, 2016), finding that NF appeared to induce more focused and specific brain activation that pre-training measurements indicated, pointing at its potential in therapeutic treatment for patients exhibiting pathological deviations in prefrontal function. In another NF study, Hudak et al. (Hudak et al., 2017) found that, in an NF intervention designed to act as a neurocognitive treatment for ADHD, after NF training, there was a significant reduction in commission errors on the no-go task, with simultaneous increase in prefrontal blood flow for the experimental group, potentially showing the potential of NF-directed learning feedback control in moderating behavioural outcomes, in that Hudak et al.’s intervention appears to have reduced impulsive behaviour, possibly through strengthening frontal lobe function.


  • Al-Shargie, F., Kiguchi, M., Badruddin, N., Dass, S. C., Hani, A. F. M., & Tang, T. B. (2016). Mental stress assessment using simultaneous measurement of EEG and fNIRS. Biomedical Optics Express, 7(10), 3882–3898.

  • Al-Shargie, F., Tang, T. B., & Kiguchi, M. (2017). Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study. Biomedical Optics Express, 8(5), 2583–2598.

  • Awh, E., Jonides, J., Smith, E. E., Schumacher, E. H., Koeppe, R. A., & Katz, S. (1996). Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence From Positron Emission Tomography. Psychological Science, 7(1), 25–31. Baddeley, A. D., & Hitch, G. (1974). Working Memory. In G. H. Bower (Ed.), Psychology of Learning and Motivation (Vol. 8, pp. 47–89). Academic Press.

  • Barth, B., Strehl, U., Fallgatter, A. J., & Ehlis, A.-C. (2016). Near-Infrared Spectroscopy based Neurofeedback of Prefrontal Cortex Activity: A Proof-of-Concept Study. Frontiers in Human Neuroscience, 10, 633.

  • Conway, M. A., & Fthenaki, A. (2003). Disruption of inhibitory control of memory following lesions to the frontal and temporal lobes. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 39(4-5), 667–686.

  • Dennis, M., Agostino, A., Roncadin, C., & Levin, H. (2009). Theory of mind depends on domain-general executive functions of working memory and cognitive inhibition in children with traumatic brain injury. Journal of Clinical and Experimental Neuropsychology, 31(7), 835–847.

  • Ellfolk, U., Joutsa, J., Rinne, J. O., Parkkola, R., Jokinen, P., & Karrasch, M. (2013). Brain volumetric correlates of memory in early Parkinson’s disease. Journal of Parkinson’s Disease, 3(4), 593–601.

  • Gathercole, S., & Alloway, T. P. (2008). Working Memory and Learning: A Practical Guide for Teachers. SAGE.

  • Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (2002). Cognitive Neuroscience, New York: W. W. Norton & Company.

  • Hudak, J., Blume, F., Dresler, T., Haeussinger, F. B., Renner, T. J., Fallgatter, A. J., … Ehlis, A.-C. (2017).

  • Near-Infrared Spectroscopy-Based Frontal Lobe Neurofeedback Integrated in Virtual Reality Modulates Brain and Behavior in Highly Impulsive Adults. Frontiers in Human Neuroscience, 11, 425.

  • Martinussen, R., Hayden, J., Hogg-Johnson, S., & Tannock, R. (2005). A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 44(4), 377–384.

  • Nagel, B. J., Herting, M. M., Maxwell, E. C., Bruno, R., & Fair, D. (2013). Hemispheric lateralization of verbal and spatial working memory during adolescence. Brain and Cognition, 82(1), 58–68.

  • Nouchi, R., Taki, Y., Takeuchi, H., Hashizume, H., Nozawa, T., Kambara, T., … Kawashima, R. (2013). Brain training game boosts executive functions, working memory and processing speed in the young adults: a randomized controlled trial. PloS One, 8(2), e55518.

  • Rosenbaum, D., Hilsendegen, P., Thomas, M., Haeussinger, F. B., Metzger, F. G., Nuerk, H.-C., … Ehlis, A.-C. (2018).

  • Cortical hemodynamic changes during the Trier Social Stress Test: An fNIRS study. NeuroImage, 171, 107–115.

  • Rosenbaum, D., Thomas, M., Hilsendegen, P., Metzger, F. G., Haeussinger, F. B., Nuerk, H.-C., … Ehlis, A.-C. (2018).

  • Stress-related dysfunction of the right inferior frontal cortex in high ruminators: An fNIRS study. NeuroImage: Clinical, 18, 510–517.

  • Schöning, S., Zwitserlood, P., Engelien, A., Behnken, A., Kugel, H., Schiffbauer, H., … Konrad, C. (2009). Working-memory fMRI reveals cingulate hyperactivation in euthymic major depression. Human Brain Mapping, 30(9), 2746–2756.

  • Southwick, J. S., Bigler, E. D., Froehlich, A., Dubray, M. B., Alexander, A. L., Lange, N., & Lainhart, J. E. (2011).

  • Memory functioning in children and adolescents with autism. Neuropsychology, 25(6), 702–710.

  • Takeuchi, H., Taki, Y., Nouchi, R., Hashizume, H., Sekiguchi, A., Kotozaki, Y., … Kawashima, R. (2014). Working memory training improves emotional states of healthy individuals. Frontiers in Systems Neuroscience, 8, 200.

  • Thomason, M. E., Race, E., Burrows, B., Whitfield-Gabrieli, S., Glover, G. H., & Gabrieli, J. D. E. (2009). Development of spatial and verbal working memory capacity in the human brain. Journal of Cognitive Neuroscience, 21(2), 316–332.

  • Vogan, V. M., Morgan, B. R., Powell, T. L., Smith, M. L., & Taylor, M. J. (2016). The neurodevelopmental differences of increasing verbal working memory demand in children and adults. Developmental Cognitive Neuroscience, 17, 19–27.

  • Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working memory: Cognitive, Affective & Behavioral Neuroscience, 3(4), 255–274.