Neurofeedback as a Neurorehabilitation Tool for Memory Deficits

A Phase 0 Clinical Trial

Katia Andrade

1,2,3

, Nesma Houmani

, Bruno Dubois

and François Vialatte

1,2

ESPCI Paris, PSL Research University, Paris, France

BCI team, Brain Plasticity Laboratory, CNRS UMR, Paris, France

Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Paris, France

SAMOVAR, Télécom SudParis, CNRS, Université Paris-Saclay, 91011 EVRY Cedex, France

1 OBJECTIVES

Although underexplored, the idea of using brain

computer interfaces (BCI) for behavioral and

cognitive rehabilitation is based on recent evidence

suggesting that not only self-regulated brain signals,

but also involuntary brain signals may provide

useful information about the BCI user. These BCI

systems, also called passive BCIs, acquire brain

waves from an electroencephalographic (EEG)

amplifier and then utilize the biomarkers derived

from the brain signal and adapt to the user’s

performance without the purpose of voluntary

control of the system (Zander & Kothe, 2011).The

aim is to apply neuro-physiological regulation to

foster cortical reorganization and compensatory

cerebral activation by targeting brain-wave

correlates of functional deficits, thus promoting

Central Nervous System (CNS) plasticity (Duffau,

2006). Critically, CNS plasticity has been observed

in early-stages of dementia, thus constituting a great

challenge for the development of “cognitive BCIs”

focused on the rehabilitation of brain functions in

neurological patients (Hill et al., 2011). The main

goal of this project is to promote CNS plasticity, and

therefore cognitive reserve, through neurofeedback

training in subjects with Subjective Memory

Complaints (SMC) related to attentional deficits. It

is a Phase 0 clinical trial.

2 METHODS

Several EEG markers were developed in the

literature for Alzheimer’s disease (AD) detection

and their efficiency was largely proven in the state-

of-the-art (Cibils, 2002; Babiloni et al., 2004; Ilh et

al., 1996; Vialatte et al., 2011; Houmani et al.,

2015). In this project, we will transpose these

biomarkers to the framework of our BCI-system and

apply them in subjects with Subjective Memory

Complaints (SMC). Such markers can be reduced to

small sets of EEG channels: we conducted

simulations, and obtained stable classifications

results using a set of four EEG channels.

Experiments will involve 40 SMC subjects, recruited

at the Institut de la Mémoire et de la Maladie

d’Alzheimer, in the Salpêtrière’s hospital, in Paris.

Subjects will be assigned randomly to either the

neurofeedback or the sham task. The procedure will

be double-blinded. Subjects will participate in 20

(neurofeedback or sham) sessions, twice per week

over a period of maximally 10 weeks. At the end of

each neurofeedback/sham session, the state of the

patients will be assessed in order to evaluate for any

adverse effect. In case such effects were to be

observed, the protocol would be interrupted. Each

session of 30 minutes will start and end with a

recording of 1 minute of rest EEG with eyes opened.

In addition, subjects will be administered a pre-trial

and a post-trial standardized neuropsychological

battery, lasting 1 hour. The individual results (n=40)

will be analyzed with a reliable change index (RCI;

Jacobson & Truax, 1991). Additional analyses

between neurofeedback and sham groups will be

performed. The training protocol will be

personalized. This is critical, since each subject has

his/her own EEG pattern. Moreover, the use of one

standard protocol could be ineffective or even

adverse.

3 RESULTS

We expect the development of a cognitive BCI that

allows 1) an electrophysiological reorganization of

subjects’ brain activity, directly correlated with 2)

subjective and objective improvement of subject’s

memory and attentional functions, as measured by a

previously validated Memory Complaints

Questionnaire and specific neuropsychological tests,

all administered to each subject pre- and post-trial.

Andrade K., Houmani N., Dubois B. and Vialatte F.

Neurofeedback as a Neurorehabilitation Tool for Memory Deﬁcits - A Phase 0 Clinical Trial.

In NEUROTECHNIX 2017 - Extended Abstracts (NEUROTECHNIX 2017), pages 14-16

4 DISCUSSION

Subjective Memory Complaints (SMC) are reports

of problems with, or changes in, memory, being

often a source of distress among older adults (Yates

et al., 2017). Indeed, although the subjective decline

lies within the normal limits of cognitive ageing, it

negatively influences everyday functioning.

However, attentional resources have been found to

be critical for subjects’ perception of everyday

memory functioning, which seems related to the role

of prefrontal attention systems for memory retrieval

(Davidson et al., 2006). Furthermore, it has been

demonstrated that depression or anxiety may also

influence the expression of SMC (Balash et al.,

2013). Therefore, the examination of memory

efficiency in older subjects requires not only

memory tasks, but additional measures of cognitive

function (focusing on attention), as well as mood

examination. Criticaly, recent evidence from both

neuroimaging and behavioral outcomes research

supports the ability of the brain to adapt, modify,

and learn throughout, at a minimum, the early stages

of dementia. For instance, evidence from functional

neuroimaging has shown that AD patients can use

additional neural resources in the prefrontal cortex to

compensate for losses attributable to the

degenerative process of the disease (Grady et al.,

2003). Moreover, neurofeedback (NFB) training has

been found to improve attention abilities in elderly

people (Angelakis et al., 2007; Wang & Hsieh,

2013). Taken together, these findings suggest that

NFB may have a place in the treatment of

individuals with Subjective Memory Complaints, as

well as in patients in very mild stages of

Alzheimer’s disease. Importantly, Alzheimer’s

disease (AD) is a chronic neurodegenerative

disorder that leads to progressive decline of

cognitive functions, along with behavioral

disturbances and insidious loss of autonomy in daily

living activities (Dubois et al., 2014). Its incidence

increases exponentially with age, and doubles every

5 years after the age of 65 (Kukull et al., 2002; Qiu

et al., 2009; Corrada et al., 2010), being the most

common cause of dementia in late adult life.

Accordingly, and because of the unprecedented level

of aging in developed countries, the health care costs

associated with AD are exceptional high, imposing a

tremendous burden on modern societies. Currently,

two classes of drugs, cholinesterase inhibitors [ChE-

I] and N-metil-D-aspartate [NMDA] receptor

antagonist, are recommended for the symptomatic

treatment of AD, each targeting a different

neurochemical component thought to underlie the

condition (Cummings, 2000). Unfortunately, none of

the available treatments is able to stop or reverse the

disease progression, and their cost-effectiveness has

been questioned (Loveman et al., 2006). Thus,

continuing efforts are required, with an urgent need

for the development of novel therapeutic strategies,

envisaging not only pharmacological but also non-

pharmacological interventions. This project

represents a first step on this path, even though

considerable development and controlled clinical

trials will be required before these BCI interventions

earn a place in our standard of clinical care.

ACKNOWLEDGEMENTS

This research project is supported by UrgoTech.

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