Browsing by Author "Ruiz, Sergio"

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    A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study
    (2022) Pereira, Jaime A.; Ray, Andreas; Rana, Mohit; Silva, Claudio; Salinas, Cesar; Zamorano, Francisco; Irani, Martin; Opazo, Patricia; Sitaram, Ranganatha; Ruiz, Sergio
    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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    Detection of Cerebral Reorganization Induced by Real-Time fMRI Feedback Training of Insula Activation: A Multivariate Investigation
    (2011) Lee, Sangkyun; Ruiz, Sergio; Caria, Andrea; Veit, Ralf; Birbaumer, Niels; Sitaram, Ranganatha
    Background. Studies with real-time functional magnetic resonance imaging (fMRI) demonstrate that humans volitionally regulate hemodynamic signals from circumscribed regions of the brain, leading to area-specific behavioral consequences. Methods to better determine the nature of dynamic functional interactions between different brain regions and plasticity due to self-regulation training are still in development. Objective. The authors investigated changes in brain states while training 6 healthy participants to self-regulate insular cortex by real-time fMRI feedback. Methods. The authors used multivariate pattern analysis to observe spatial pattern changes and a multivariate Granger causality model to show changes in temporal interactions in multiple brain areas over the course of 5 repeated scans per subject during positive and negative emotional imagery with feedback about the level of insular activation. Results. Feedback training leads to more spatially focused recruitment of areas relevant for learning and emotion. Effective connectivity analysis reveals that initial training is associated with an increase in network density; further training "prunes" presumably redundant connections and "strengthens" relevant connections. Conclusions. The authors demonstrate the application of multivariate methods for assessing cerebral reorganization during the learning of volitional control of local brain activity. The findings provide insight into mechanisms of training-induced learning techniques for rehabilitation. The authors anticipate that future studies, specifically designed with this hypothesis in mind, may be able to construct a universal index of cerebral reorganization during skill learning based on multiple similar criteria across various skilled tasks. These techniques may be able to discern recovery from compensation, dose-response curves related to training, and ways to determine whether rehabilitation training is actively engaging necessary networks.
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    Geophysical Characterization of the Chilean Seismological Stations : First Results
    (2018) Leyton, Felipe; Leopold, Alexander; Hurtado, G.; Pasten, Cesar; Ruiz, Sergio; Montalva, Gonzalo; Sáez Robert, Esteban
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    Motor Intentions Decoded from fMRI Signals
    (2024) Ruiz, Sergio; Lee, Sangkyun; Dalboni da Rocha, Josue Luiz; Ramos, Ander; Pasqualotto, Emanuele; Soares, Ernesto; García, Eliana; Fetz, Eberhard; Birbaumer, Niels; Sitaram Ranganatha
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    Neurorights in the Constitution: from neurotechnology to ethics and politics
    (2024) Ruiz, Sergio; Valera, Luca; Ramos Vergara, Paulina; Sitaram, Ranganatha
    Neuroimaging technologies such as brain-computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.
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    Real-time support vector classification and feedback of multiple emotional brain states
    (2011) Sitaram, Ranganatha; Lee, Sangkyun; Ruiz, Sergio; Rana, Mohit; Veit, Ralf; Birbaumer, Niels
    An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing and feature selection based on a new method called Effect mapping. The classifier also showed robust prediction rates in decoding three discrete emotional states (happiness, disgust and sadness) in an extended group of participants. Subjective reports ascertained that participants performed emotion imagery and that the online classifier decoded emotions and not arbitrary states of the brain. Offline whole brain classification as well as region-of-interest classification in 24 brain areas previously implicated in emotion processing revealed that the frontal cortex was critically involved in emotion induction by imagery. We also demonstrate an fMRI-BCI based on real-time classification of BOLD signals from multiple brain regions, for each repetition time (TR) of scanning, providing visual feedback of emotional states to the participant for potential applications in the clinical treatment of dysfunctional affect. (C) 2010 Elsevier Inc. All rights reserved.
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    Self-regulation of anterior insula with real-time fMRI and its behavioral effects in obsessive-compulsive disorder : a feasibility study
    (2015) Buyukturkoglu, Korhan; Roettgers, Hans Sommer; Jens, Rana; Mohit, Dietzsch; Leonie, Arikan; Ezgi Belkis; Veit, Ralf; Malekshahi, Rahim; Kircher, Tilo; Ruiz, Sergio; Sitaram, Ranganatha; Birbaumer, Niels
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    Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG
    (2020) Bhutada, Abhishek S.; Sepulveda, Pradyumna; Torres, Rafael; Ossandon, Tomas; Ruiz, Sergio; Sitaram, Ranganatha
    Electroencephalography (EEG) source reconstruction estimates spatial information from the brain's electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode's distance profile in relation to other electrodes. We then compare the subject's electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects' data with progressive improvements in accuracy: with similar to 58% accuracy based on a single EEG-template, with similar to 71% accuracy based on 3 EEG-templates, and with similar to 76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.
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    Use of Real-Time Functional Magnetic Resonance Imaging-Based Neurofeedback to Downregulate Insular Cortex in Nicotine-Addicted Smokers
    (2020) Rana, Mohit; Ruiz, Sergio; Sanchez Corzo, Andrea; Muehleck, Axel; Eck, Sandra; Salinas, Cesar; Zamorano, Francisco; Silva, Claudio; Rea, Massimiliano; Batra, Anil; Birbaumer, Niels; Sitaram, Ranganatha
    It has been more than a decade since the first functional magnetic resonance imaging (fMRI)-based neurofeedback approach was successfully implemented. Since then, various studies have demonstrated that participants can learn to voluntarily control a circumscribed brain region. Consequently, real-time fMRI (rtfMRI) provided a novel opportunity to study modifications of behavior due to manipulation of brain activity. Hence, reports of rtfMRI applications to train self-regulation of brain activity and the concomitant modifications in behavioral and clinical conditions such as neurological and psychiatric disorders [e.g., schizophrenia, obsessive compulsive Disorder (OCD), stroke] have rapidly increased.