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Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

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Introduction: Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. Methods: We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Results: Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. Discussion: The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

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Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf) training is an emerging intervention for neurorehabilitation. However, its translation into clinical use on participants with clinical depression is unclear, the effect estimates from randomized control trials and the certainty of the supporting evidence on the effect estimates are unknown. As the number of studies on neurofeedback increases every year, and better quality evidence becomes available, we evaluate the evidence of all randomized control trials available on the clinical application of rt-fMRI-nf training on participants with clinical depression. We performed electronic searches in Pubmed, Embase, CENTRAL, rtFIN database, Epistemonikos, trial registers, reference lists, other systematic reviews, conference abstracts, and cross-citation in Google Scholar. Reviewers independently selected studies, extracted data and evaluated the risk of bias. The certainty of the evidence was judged using the GRADE framework. This review complies with PRISMA guidelines and was submitted to PROSPERO registration. We found 435 results. After the selection process, we included 11 reports corresponding to four RCTs. The effect of rt-fMRI-nf on improving the severity of clinical depression scores demonstrated a tendency to favor the intervention; however, the general effect was not significant. At end of treatment, SMD (standardized mean difference): -0.32 (95% CI -0.73 to 0.10). At follow-up, SMD: -0.33 (95% CI -0.91, 1.25). All the studies showed changes in BOLD fMRI activation after training; however, only one study confirmed regulation success during a transfer run. Whole-brain analyses suggests that rt-fMRI nf may alter activity patterns in brain networks. More studies are needed to evaluate quality of life, acceptability, adverse effects, cognitive tasks, and physiology measures. We conclude that the current evidence on the effect of rt-fMRI-nf training for decision-making outcomes in patients with clinical depression is still based on low certainty of the evidence.

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Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the "happiness emotional brain state" of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4-1 Median = 6.563%; Range = 4.10-27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1-15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.

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Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.

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In this article, the affiliation for Mohit Rana was incorrectly listed as the Institute for Biological and Medical Engineering, Department of Psychiatry, and Section of Neuroscience, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860 Hernán Briones, piso 2, Macul 782-0436, Santiago, Chile. The listed affiliation should have been the following: Departamento de Psiquiatría, Escuela de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile; and the Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile. An acknowledgement to Mohit Rana's funding source was also missing. The following sentence should have been included in the acknowledgments section: M.R. is supported by a Fondecyt postdoctoral fellowship (project no. 3100648).

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One of the most important and early impairments in autism spectrum disorder (ASD) is the abnormal visual processing of human faces. This deficit has been associated with hypoactivation of the fusiform face area (FFA), one of the main hubs of the face-processing network. Neurofeedback based on real-time fMRI (rtfMRI-NF) is a technique that allows the self-regulation of circumscribed brain regions, leading to specific neural modulation and behavioral changes. The aim of the present study was to train participants with ASD to achieve up-regulation of the FFA using rtfMRI-NF, to investigate the neural effects of FFA up-regulation in ASD. For this purpose, three groups of volunteers with normal I.Q. and fluent language were recruited to participate in a rtfMRI-NF protocol of eight training runs in 2 days. Five subjects with ASD participated as part of the experimental group and received contingent feedback to up-regulate bilateral FFA. Two control groups, each one with three participants with typical development (TD), underwent the same protocol: one group with contingent feedback and the other with sham feedback. Whole-brain and functional connectivity analysis using each fusiform gyrus as independent seeds were carried out. The results show that individuals with TD and ASD can achieve FFA up-regulation with contingent feedback. RtfMRI-NF in ASD produced more numerous and stronger short-range connections among brain areas of the ventral visual stream and an absence of the long-range connections to insula and inferior frontal gyrus, as observed in TD subjects. Recruitment of inferior frontal gyrus was observed in both groups during FAA up-regulation. However, insula and caudate nucleus were only recruited in subjects with TD. These results could be explained from a neurodevelopment perspective as a lack of the normal specialization of visual processing areas, and a compensatory mechanism to process visual information of faces. RtfMRI-NF emerges as a potential tool to study visual processing network in ASD, and to explore its clinical potential.

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Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ±â€¯2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ±â€¯5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.

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Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain-machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.

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Cognitive decline is a major concern in the aging population. It is normative to experience some deterioration in cognitive abilities with advanced age such as related to memory performance, attention distraction to interference, task switching, and processing speed. However, intact cognitive functioning in old age is important for leading an independent day-to-day life. Thus, studying ways to counteract or delay the onset of cognitive decline in aging is crucial. The literature offers various explanations for the decline in cognitive performance in aging; among those are age-related gray and white matter atrophy, synaptic degeneration, blood flow reduction, neurochemical alterations, and change in connectivity patterns with advanced age. An emerging literature on neurofeedback and Brain Computer Interface (BCI) reports exciting results supporting the benefits of volitional modulation of brain activity on cognition and behavior. Neurofeedback studies based on real-time functional magnetic resonance imaging (rtfMRI) have shown behavioral changes in schizophrenia and behavioral benefits in nicotine addiction. This article integrates research on cognitive and brain aging with evidence of brain and behavioral modification due to rtfMRI neurofeedback. We offer a state-of-the-art description of the rtfMRI technique with an eye towards its application in aging. We present preliminary results of a feasibility study exploring the possibility of using rtfMRI to train older adults to volitionally control brain activity. Based on these first findings, we discuss possible implementations of rtfMRI neurofeedback as a novel technique to study and alleviate cognitive decline in healthy and pathological aging.