Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment

Dementia describes a set of symptoms that occur in neurodegenerative disorders and that is characterized by gradual loss of cognitive and behavioral functions. Recently, non-invasive neurofeedback training has been explored as a potential complementary treatment for patients suffering from dementia or mild cognitive impairment. Here we systematically reviewed studies that explored neurofeedback training protocols based on electroencephalography or functional magnetic resonance imaging for these groups of patients. From a total of 1,912 screened studies, 10 were included in our final sample (N = 208 independent participants in experimental and N = 81 in the control groups completing the primary endpoint). We compared the clinical efficacy across studies, and evaluated their experimental designs and reporting quality. In most studies, patients showed improved scores in different cognitive tests. However, data from randomized controlled trials remains scarce, and clinical evidence based on standardized metrics is still inconclusive. In light of recent meta-research developments in the neurofeedback field and beyond, quality and reporting practices of individual studies are reviewed. We conclude with recommendations on best practices for future studies that investigate the effects of neurofeedback training in dementia and cognitive impairment

Subject-independent decoding of affective states using functional near-infrared spectroscopy.

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65±3.23 years) and then tested in a completely independent one (20 participants, 24.00±3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 ± 12.03%, p<0.01) and negative vs. neutral (68.25 ± 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 ± 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features

Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices

Major depressive disorder (MDD) is the leading cause of disability worldwide. Neurofeedback training has been suggested as a potential additional treatment option for MDD patients not reaching remission from standard care (i.e., psychopharmacology and psychotherapy). Here we systematically reviewed neurofeedback studies employing electroencephalography, or functional magnetic resonance-based protocols in depressive patients. Of 585 initially screened studies, 24 were included in our final sample (N = 480 patients in experimental and N = 194 in the control groups completing the primary endpoint). We evaluated the clinical efficacy across studies and attempted to group studies according to the control condition categories currently used in the field that affect clinical outcomes in group comparisons. In most studies, MDD patients showed symptom improvement superior to the control group(s). However, most articles did not comply with the most stringent study quality and reporting practices. We conclude with recommendations on best practices for experimental designs and reporting standards for neurofeedback training

Current brain activity is a predictor of longitudinal motor imagery performance

This study aimed to evaluate whether current electroencephalographic spectral measures can predict participant's performance during future sessions of a motor imagery task. By investigating this point, we hope to understand which spectral components are related to MI "literacy". Twelve healthy subjects performed a neurofeedback task whereby a cursor was moved to one of two targets (left and right) using only motor imagery of the corresponding hands. To evaluate the effect of aptitude, we measured the Mahalanobis' distances between whole-scalp spectral patterns in four frequency bands (theta, alpha, beta, and gamma) during the first session of left and right motor imagery. Later, we used these features as inputs in a Support Vector Regressor to predict performance during the following two sessions. The performance was measured as the percentage of trials where the cursor correctly reached the target. Since our sample was balanced, this approach predicted performance on sessions two and three with mean absolute errors of 15.07±12.94% and 11.98±11.40%, respectively. The most relevant feature in both cases was the Mahalanobis' distance in alpha. These results suggest that participants who can not evoke different patterns of alpha power during left- and right-hand motor imagery during the first session, also are less likely to improve during the following training sessions. The investigation of whole-scalp differences allows a holistic comprehension of the neural basis of motor imagery. This method also characterizes a potential predictor of performance for future applications of MI-based neurofeedback and brain-computer interfaces

On-task theta power is correlated to motor imagery performance

This study aimed to evaluate on-task electroencephalographic spectral measures and its correlation to performance during a motor imagery (MI) task. By investigating this aspect, we hope to understand what makes some individuals MI "illliterates". Eighteen healthy subjects performed an experimental task whereby a cursor was moved to one of two targets (left and right) using only MI of the left and right hands. To evaluate the effect of aptitude, performance was measured as percentage of correct movement to target, and Mahalanobis distances were calculated between whole-scalp spectral patterns during left and right motor imagery. Then the correlation between performance and Mahalanobis distance was investigated for central, and whole-head topographies using Spearman's correlations. In central topographies, distances on alpha band were positively correlated with performance (ρ=0.562, p=0.032), while distances on theta band were negatively correlated to performance (ρ--0.648, p=0.018) in whole-head maps. The investigation of on-task whole-scalp differences allows a holistic comprehension of the neural basis of motor imagery, as well as how this leads to performance variations

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity

Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e., table tennis), playing a musical instrument (i.e., piano and violin) alone or in duo and performing daily activities for many hours (i.e., continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain hemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience

Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging

Open science initiatives are creating opportunities to increase research coordination and impact in nonhuman primate (NHP) imaging. The PRIMatE Data and Resource Exchange community recently developed a collaboration-based strategic plan to advance NHP imaging as an integrative approach for multiscale neuroscience