Browsing by Author "Sitaram, Ranganatha"

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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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    A subject-independent pattern-based Brain-Computer Interface
    (2015) Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos Núñez, Cristián Andrés; Zamorano, Francisco; Ruiz Poblete, Sergio Marcelo; Birbaumer, Niels; Aboitiz, Francisco
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    Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach
    (2013) Ruiz, S.; Birbaumer, N.; Sitaram, Ranganatha
    Considering that single locations of structural and functional abnormalities are insufficient to explain the diverse psychopathology of schizophrenia, new models have postulated that the impairments associated with the disease arise from a failure to integrate the activity of local and distributed neural circuits: the “abnormal neural connectivity hypothesis.” In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI) are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia. The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem.
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    Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia
    (2013) Ruiz Poblete, Sergio Marcelo; Lee, Sangkyun; Soekadar, Surjo R.; Caria, Andrea; Veit, Ralf; Kircher, Tilo; Birbaumer, Niels; Sitaram, Ranganatha
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    Brain-Machine Interface Induced Morpho-Functional Remodeling of the Neural Motor System in Severe Chronic Stroke
    (2020) Caria, Andrea; da Rocha, Josue Luiz Dalboni; Gallitto, Giuseppe; Birbaumer, Niels; Sitaram, Ranganatha; Murguialday, Ander Ramos
    Brain-machine interfaces (BMI) permit bypass motor system disruption by coupling contingent neuroelectric signals related to motor activity with prosthetic devices that enhance afferent and proprioceptive feedback to the somatosensory cortex. In this study, we investigated neural plasticity in the motor network of severely impaired chronic stroke patients after an EEG-BMI-based treatment reinforcing sensorimotor contingency of ipsilesional motor commands. Our structural connectivity analysis revealed decreased fractional anisotropy in the splenium and body of the corpus callosum, and in the contralesional hemisphere in the posterior limb of the internal capsule, the posterior thalamic radiation, and the superior corona radiata. Functional connectivity analysis showed decreased negative interhemispheric coupling between contralesional and ipsilesional sensorimotor regions, and decreased positive intrahemispheric coupling among contralesional sensorimotor regions. These findings indicate that BMI reinforcing ipsilesional brain activity and enhancing proprioceptive function of the affected hand elicits reorganization of contralesional and ipsilesional somatosensory and motor-assemblies as well as afferent and efferent connection-related motor circuits that support the partial re-establishment of the original neurophysiology of the motor system even in severe chronic stroke.
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    Closed-loop brain training : the science of neurofeedback
    (2017) Sitaram, Ranganatha; Ros, Tomas; Stoeckel, Luke; Haller, Sven; Scharnowski, Frank
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    Comparison of LED- and LASER-based fNIRS technologies to record the human peri-spinal cord neurovascular response
    (2024) Caulier-Cisterna, Raill; Appelgren-Gonzales, Juan -Pablo; Oyarzun, Juan -Esteban; Valenzuela, Felipe; Sitaram, Ranganatha; Eblen-Zajjur, Antonio; Uribe, Sergio
    Recently, functional Near-Infrared Spectroscopy (fNIRS) was applied to obtain, non-invasively, the human peri-spinal Neuro-Vascular Response (NVR) under a non-noxious electrical stimulation of a peripheral nerve. This method allowed the measurements of changes in the concentration of oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb) from the peri-spinal vascular network. However, there is a lack of clarity about the potential differences in perispinal NVR recorded by the different fNIRS technologies currently available. In this work, the two main noninvasive fNIRS technologies were compared, i.e., LED and LASER-based. The recording of the human peri-spinal NVR induced by non-noxious electrical stimulation of a peripheral nerve was recorded simultaneously at C7 and T10 vertebral levels. The amplitude, rise time, and full width at half maximum duration of the perispinal NVRs were characterized in healthy volunteers and compared between both systems. The main difference was that the LED-based system shows about one order of magnitude higher values of amplitude than the LASER-based system. No statistical differences were found for rise time and for duration parameters (at thoracic level). The comparison of point-to-point wave patterns did not show significant differences between both systems. In conclusion, the peri-spinal NRV response obtained by different fNIRS technologies was reproducible, and only the amplitude showed differences, probably due to the power of the system which should be considered when assessing the human peri-spinal vascular network.
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    Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)
    (2020) Ros, T.; Enriquez Geppert, S.; Zotev, V.; Young, K. D.; Wood, G.; Whitfield-Gabrieli, S.; Wan, F.; Vuilleumier, P.; Vialatte, F.; Sitaram, Ranganatha; Van de Ville, D.; Todder, D.; Surmeli, T.; Sulzer, J. S.; Strehl, U.; Sterman, M. B.; Steiner, N. J.; Sorger, B.; Soekadar, S. R.; Sherlin, L. H.; Schonenberg, M.; Scharnowski, F.; Schabus, M.; Rubia, K.; Rosa, A.; Reinel Pineda, Mahaira Catalina; Pineda, J. A.; Paret, C.; Ossadtchi, A.; Nicholson, A. A.; Nan, W. Y.; Minguez, J.; Micoulaud-Franchi, J. A.; Mehler, D. M. A.; Luhrs, M.; Lubar, J.; Lotte, F.; Linden, D. E. J.; Lewis-Peacock, J. A.; Lebedev, M. A.; Lanius, R. A.; Kubler, A.; Kranczioch, C.; Koush, Y.; Konicar, L.; Kohl, S. H.; Kober, S. E.; Klados, M. A.; Jeunet, C.; Janssen, T. W. P; Huster, R. J.; Hoedlmoser, K.; Hirshberg, L. M.; Heunis, S.
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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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    Detection of electrophysiological patterns of declarative memory reactivations during sleep
    (2021) Sánchez Corzo, Andrea del Pilar; Sitaram, Ranganatha; Pontificia Universidad Católica de Chile. Escuela de Medicina
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    Differential frontal alpha oscillations and mechanisms underlying loss of consciousness : a comparison between slow and fast propofol infusion rates
    (2020) Sepúlveda, P.; Cortinez, L. I.; Irani, M.; Egaña, J. I.; Contreras, V.; Sánchez Corzo, A.; Acosta, I.; Sitaram, Ranganatha
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    Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images
    (2017) Ebadi, A.; Da Rocha, J.; Nagaraju, D.; Tovar, F.; Bramati, I.; Coutinho, G.; Sitaram, Ranganatha; Rashidi, P.
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    Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI
    (2021) Ravindran, Aniruddh; Rieke, Jake D.; Zapata, Jose Daniel Alcantara; White, Keith D.; Matarasso, Avi; Yusufali, M. Minhal; Rana, Mohit; Gunduz, Aysegul; Modarres, Mo; Sitaram, Ranganatha; Daly, Janis J.
    Objective
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    Fractional Anisotropy changes in Parahippocampal Cingulum due to Alzheimer's Disease
    (2020) da Rocha, Josue Luiz Dalboni; Bramati, Ivanei; Coutinho, Gabriel; Moll, Fernanda Tovar; Sitaram, Ranganatha
    Current treatments for Alzheimer's disease are only symptomatic and limited to reduce the progression rate of the mental deterioration. Mild Cognitive Impairment, a transitional stage in which the patient is not cognitively normal but do not meet the criteria for specific dementia, is associated with high risk for development of Alzheimer's disease. Thus, non-invasive techniques to predict the individual's risk to develop Alzheimer's disease can be very helpful, considering the possibility of early treatment. Diffusion Tensor Imaging, as an indicator of cerebral white matter integrity, may detect and track earlier evidence of white matter abnormalities in patients developing Alzheimer's disease. Here we performed a voxel-based analysis of fractional anisotropy in three classes of subjects: Alzheimer's disease patients, Mild Cognitive Impairment patients, and healthy controls. We performed Support Vector Machine classification between the three groups, using Fisher Score feature selection and Leave-one-out cross-validation. Bilateral intersection of hippocampal cingulum and parahippocampal gyrus (referred as parahippocampal cingulum) is the region that best discriminates Alzheimer's disease fractional anisotropy values, resulting in an accuracy of 93% for discriminating between Alzheimer's disease and controls, and 90% between Alzheimer's disease and Mild Cognitive Impairment. These results suggest that pattern classification of Diffusion Tensor Imaging can help diagnosis of Alzheimer's disease, specially when focusing on the parahippocampal cingulum.
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    How feedback, motor imagery, and reward influence brain self‐regulation using real‐time fMRI
    (2016) Sepulveda, P.; Sitaram, Ranganatha; Rana, M.; Montalba, C.; Tejos Núñez, Cristián Andrés; Ruiz Poblete, Sergio Marcelo
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    Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI. Connectivity-Based Neurofeedback
    (2016) Liew, S.; Rana, M.; Cornelsen, S.; De Barros, M.; Birbaumer, N.; Sitaram, Ranganatha; Cohen, L.; Soekadar, S.
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    Involvement of top-down networks in the perception of facial emotions : A magnetoencephalographic investigation
    (2020) Kajal, D. S.; Fioravanti, C.; Elshahabi, A.; Ruíz Poblete, Sergio Marcelo; Sitaram, Ranganatha; Braun, C.
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    Learned Control of Inter-Hemispheric Connectivity: Effects on Bimanual Motor Performance
    (2017) Kajal, D.; Braun, C.; Mellinger, J.; Sacchet, M.; Ruiz Poblete, Sergio Marcelo; Fetz, E.; Birbaumer, N.; Sitaram, Ranganatha