Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback.

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

A framework for disentangling the hyperbolic truth of neurofeedback: Comment on Thibault and Raz (2017).

In this comment, we propose a theoretical framework for disentangling the potentially multiple elements driving the effects of electroencephalogram (EEG)-neurofeedback (EEG-nf) to clarify the roadmap for research in the field. Three questions are identified: (a) Do EEG-nf effects originate from a placebo effect related to the technological environment of a neurofeedback session? (b) Do EEG-nf effects originate from a nonspecific effect of cognitive brain training during neurofeedback? If so, a cognitive training would be underpinned by the brain activity regulation loop but this training would not be specifically related to the neurophysiological biomarker chosen. (c) Do EEG-nf effects originate from a specific effect of cognitive brain training? If so, the effects of EEG-nf would be explained by the training of the specific neurophysiological biomarker chosen, depending on the pathophysiological mechanism(s) of the disorder. The proposed framework might thus allow to understand to what degree each of these level contribute to the effects of EEG-nf on the brain and behavior in view of the psychosocial variables involved. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback.

Current Issues in the Use of fMRI-Based Neurofeedback to Relieve Psychiatric Symptoms.

fMRI-based neurofeedback (fMRI-NF) is a non-invasive technique that allows participants to achieve control of their own brain activity using the real-time feedback of the activity (measured indirectly based on the BOLD signal) of a particular brain region or network. The feasibility of fMRI-NF in healthy subjects has been documented for a variety of brain areas and neural systems, and this technique has also been proposed for the treatment of psychiatric disorders in recent years. Through a systematic review of the scientific literature this paper probes the rationale and expected applications of fMRI-NF in psychiatry, discusses issues that must be addressed in the use of this technique to treat mental disorders. Six relevant references and five ongoing studies were identified according to our inclusion criteria. These studies show that in most psychiatric disorders (major depressive disorder, schizophrenia, personality disorders, addiction), patients are able to learn voluntary control of the neuronal activity of the targeted brain region(s). Interestingly, in some cases, this learning is associated with clinical improvement, showing that fMRI-NF can potentially be developed into a therapeutic tool. However, only low-level evidence is available to support the use of this relatively new technique in clinical practice. Notably, no randomized, controlled trial is currently available in this field of research. Finally, methodological issues and clinical perspectives (especially the potential use of pattern recognition in fMRI-NF protocols) are discussed.