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Ren Z, Xiao L, Xie Y, Huang Z, Lin S, Si L, Wang G. Effects of testosterone dose on depression-like behavior among castrated adult male rats. Psychoneuroendocrinology 2024; 165:107046. [PMID: 38626557 DOI: 10.1016/j.psyneuen.2024.107046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 04/02/2024] [Accepted: 04/08/2024] [Indexed: 04/18/2024]
Abstract
Previous research has shown a decrease in serum testosterone levels in male patients with depression. In recent years, the results of testosterone replacement therapy (TRT) to improve depression have been mixed. Using the classic CUMS model, we induced depressive-like behaviors in rats and observed a decrease in their serum testosterone levels along with an increase in androgen receptor expression in the hippocampus. We then performed castration and sham surgery on male rats and found that testosterone deprivation led to the manifestation of depressive-like behavior that could be ameliorated by TRT. Through a repeated measures experiment consisting of five blocks over a period of 25 days, we discovered that the reduction in depressive-like behavior in testosterone-deprived rats began 22 days after drug administration (0.5 and 0.25 mg/rat). Furthermore, rats in 0.5mgT group showed the most significant improvements. Subsequently, this dose was used in CUMS rats and reduced the occurrence of depressive-like behaviors. Our study has demonstrated the complex interplay between depression and testosterone, as well as the intricate dose-response relationship between TRT and reduction in depression. Our research supports the use of TRT to alleviate depression, but dosage and duration of treatment are critical factors in determining efficacy.
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Affiliation(s)
- Zhongyu Ren
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China
| | - Ling Xiao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China; Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China
| | - Yinping Xie
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China; Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China
| | - Zhengyuan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China
| | - Shanshan Lin
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China
| | - Lujia Si
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China; Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People's Republic of China; Taikang center for life and medical sciences, Wuhan University, Wuhan, Hubei 430060, People's Republic of China.
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AL-Quraishi MS, Tan WH, Elamvazuthi I, Ooi CP, Saad NM, Al-Hiyali MI, Karim H, Azhar Ali SS. Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities. Heliyon 2024; 10:e30406. [PMID: 38726180 PMCID: PMC11079093 DOI: 10.1016/j.heliyon.2024.e30406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/17/2024] [Accepted: 04/25/2024] [Indexed: 05/12/2024] Open
Abstract
Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features-fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy-were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.
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Affiliation(s)
- Maged S. AL-Quraishi
- Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia
| | - Wooi Haw Tan
- Center of Digital Home, Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia
| | - Irraivan Elamvazuthi
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 36210, Perak, Malaysia
| | - Chee Pun Ooi
- Center of Digital Home, Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia
| | - Naufal M. Saad
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 36210, Perak, Malaysia
| | - Mohammed Isam Al-Hiyali
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 36210, Perak, Malaysia
| | - H.A. Karim
- Center of Digital Home, Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia
| | - Syed Saad Azhar Ali
- Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia
- Aerospace Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, 31261, Saudi Arabia
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Alizadeh Darbandi SS, Fornito A, Ghasemi A. The impact of input node placement in the controllability of structural brain networks. Sci Rep 2024; 14:6902. [PMID: 38519624 PMCID: PMC10960045 DOI: 10.1038/s41598-024-57181-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.
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Affiliation(s)
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Abdorasoul Ghasemi
- Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
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Khan MA, Fares H, Ghayvat H, Brunner IC, Puthusserypady S, Razavi B, Lansberg M, Poon A, Meador KJ. A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery. Front Neurol 2023; 14:1272992. [PMID: 38145118 PMCID: PMC10739305 DOI: 10.3389/fneur.2023.1272992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Background Stroke is one of the most common neurological conditions that often leads to upper limb motor impairments, significantly affecting individuals' quality of life. Rehabilitation strategies are crucial in facilitating post-stroke recovery and improving functional independence. Functional Electrical Stimulation (FES) systems have emerged as promising upper limb rehabilitation tools, offering innovative neuromuscular reeducation approaches. Objective The main objective of this paper is to provide a comprehensive systematic review of the start-of-the-art functional electrical stimulation (FES) systems for upper limb neurorehabilitation in post-stroke therapy. More specifically, this paper aims to review different types of FES systems, their feasibility testing, or randomized control trials (RCT) studies. Methods The FES systems classification is based on the involvement of patient feedback within the FES control, which mainly includes "Open-Loop FES Systems" (manually controlled) and "Closed-Loop FES Systems" (brain-computer interface-BCI and electromyography-EMG controlled). Thus, valuable insights are presented into the technological advantages and effectiveness of Manual FES, EEG-FES, and EMG-FES systems. Results and discussion The review analyzed 25 studies and found that the use of FES-based rehabilitation systems resulted in favorable outcomes for the stroke recovery of upper limb functional movements, as measured by the FMA (Fugl-Meyer Assessment) (Manually controlled FES: mean difference = 5.6, 95% CI (3.77, 7.5), P < 0.001; BCI-controlled FES: mean difference = 5.37, 95% CI (4.2, 6.6), P < 0.001; EMG-controlled FES: mean difference = 14.14, 95% CI (11.72, 16.6), P < 0.001) and ARAT (Action Research Arm Test) (EMG-controlled FES: mean difference = 11.9, 95% CI (8.8, 14.9), P < 0.001) scores. Furthermore, the shortcomings, clinical considerations, comparison to non-FES systems, design improvements, and possible future implications are also discussed for improving stroke rehabilitation systems and advancing post-stroke recovery. Thus, summarizing the existing literature, this review paper can help researchers identify areas for further investigation. This can lead to formulating research questions and developing new studies aimed at improving FES systems and their outcomes in upper limb rehabilitation.
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Affiliation(s)
- Muhammad Ahmed Khan
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Hoda Fares
- Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| | - Hemant Ghayvat
- Department of Computer Science, Linnaeus University, Växjö, Sweden
| | | | | | - Babak Razavi
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
| | - Maarten Lansberg
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
| | - Ada Poon
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States
| | - Kimford Jay Meador
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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Kerick SE, Asbee J, Spangler DP, Brooks JB, Garcia JO, Parsons TD, Bannerjee N, Robucci R. Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study. PLoS One 2023; 18:e0283418. [PMID: 36952490 PMCID: PMC10035884 DOI: 10.1371/journal.pone.0283418] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/08/2023] [Indexed: 03/25/2023] Open
Abstract
Previous neurofeedback research has shown training-related frontal theta increases and performance improvements on some executive tasks in real feedback versus sham control groups. However, typical sham control groups receive false or non-contingent feedback, making it difficult to know whether observed differences between groups are associated with accurate contingent feedback or other cognitive mechanisms (motivation, control strategies, attentional engagement, fatigue, etc.). To address this question, we investigated differences between two frontal theta training groups, each receiving accurate contingent feedback, but with different top-down goals: (1) increase and (2) alternate increase/decrease. We hypothesized that the increase group would exhibit greater increases in frontal theta compared to the alternate group, which would exhibit lower frontal theta during down- versus up-modulation blocks over sessions. We also hypothesized that the alternate group would exhibit greater performance improvements on a Go-NoGo shooting task requiring alterations in behavioral activation and inhibition, as the alternate group would be trained with greater task specificity, suggesting that receiving accurate contingent feedback may be the more salient learning mechanism underlying frontal theta neurofeedback training gains. Thirty young healthy volunteers were randomly assigned to increase or alternate groups. Training consisted of an orientation session, five neurofeedback training sessions (six blocks of six 30-s trials of FCz theta modulation (4-7 Hz) separated by 10-s rest intervals), and six Go-NoGo testing sessions (four blocks of 90 trials in both Low and High time-stress conditions). Multilevel modeling revealed greater frontal theta increases in the alternate group over training sessions. Further, Go-NoGo task performance increased at a greater rate in the increase group (accuracy and reaction time, but not commission errors). Overall, these results reject our hypotheses and suggest that changes in frontal theta and performance outcomes were not explained by reinforcement learning afforded by accurate contingent feedback. We discuss our findings in terms of alternative conceptual and methodological considerations, as well as limitations of this research.
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Affiliation(s)
- Scott E Kerick
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Justin Asbee
- The Institute for Integrative & Innovative Research, University of Arkansas, Fayetteville, AR, United States of America
| | - Derek P Spangler
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
- Department of Biobehavioral Health, Penn State University, University Park, PA, United States of America
| | - Justin B Brooks
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
- D-Prime, Washington, DC, United States of America
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, United States of America
| | - Javier O Garcia
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Thomas D Parsons
- Computational Neuropsychology and Simulation (CNS) Laboratory, Edson College, Arizona State University, Phoenix, AZ, United States of America
| | - Nilanjan Bannerjee
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, United States of America
| | - Ryan Robucci
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, United States of America
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Shah-Basak P, Boukrina O, Li XR, Jebahi F, Kielar A. Targeted neurorehabilitation strategies in post-stroke aphasia. Restor Neurol Neurosci 2023; 41:129-191. [PMID: 37980575 PMCID: PMC10741339 DOI: 10.3233/rnn-231344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
BACKGROUND Aphasia is a debilitating language impairment, affecting millions of people worldwide. About 40% of stroke survivors develop chronic aphasia, resulting in life-long disability. OBJECTIVE This review examines extrinsic and intrinsic neuromodulation techniques, aimed at enhancing the effects of speech and language therapies in stroke survivors with aphasia. METHODS We discuss the available evidence supporting the use of transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation, and functional MRI (fMRI) real-time neurofeedback in aphasia rehabilitation. RESULTS This review systematically evaluates studies focusing on efficacy and implementation of specialized methods for post-treatment outcome optimization and transfer to functional skills. It considers stimulation target determination and various targeting approaches. The translation of neuromodulation interventions to clinical practice is explored, emphasizing generalization and functional communication. The review also covers real-time fMRI neurofeedback, discussing current evidence for efficacy and essential implementation parameters. Finally, we address future directions for neuromodulation research in aphasia. CONCLUSIONS This comprehensive review aims to serve as a resource for a broad audience of researchers and clinicians interested in incorporating neuromodulation for advancing aphasia care.
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Affiliation(s)
| | - Olga Boukrina
- Kessler Foundation, Center for Stroke Rehabilitation Research, West Orange, NJ, USA
| | - Xin Ran Li
- School of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Fatima Jebahi
- Department of Speech, Languageand Hearing Sciences, University of Arizona, Tucson, AZ, USA
| | - Aneta Kielar
- Department of Speech, Languageand Hearing Sciences, University of Arizona, Tucson, AZ, USA
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8
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Nikolic M, Pezzoli P, Jaworska N, Seto MC. Brain responses in aggression-prone individuals: A systematic review and meta-analysis of functional magnetic resonance imaging (fMRI) studies of anger- and aggression-eliciting tasks. Prog Neuropsychopharmacol Biol Psychiatry 2022; 119:110596. [PMID: 35803398 DOI: 10.1016/j.pnpbp.2022.110596] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 11/24/2022]
Abstract
Reactive aggression in response to perceived threat or provocation is part of humans' adaptive behavioral repertoire. However, high levels of aggression can lead to the violation of social and legal norms. Understanding brain function in individuals with high levels of aggression as they process anger- and aggression-eliciting stimuli is critical for refining explanatory models of aggression and thereby improving interventions. Three neurobiological models of reactive aggression - the limbic hyperactivity, prefrontal hypoactivity, and dysregulated limbic-prefrontal connectivity models - have been proposed. However, these models are based on neuroimaging studies involving mainly non-aggressive individuals, leaving it unclear which model best describes brain function in those with a history of aggression. We conducted a systematic literature search (PubMed and Psycinfo) and Multilevel Kernel Density meta-analysis (MKDA) of nine functional magnetic resonance imaging (fMRI) studies (eight included in the between-group analysis [i.e., aggression vs. control groups], five in the within-group analysis). Studies examined brain responses to tasks putatively eliciting anger and aggression in individuals with a history of aggression alone and relative to controls. Individuals with a history of aggression exhibited greater activity in the superior temporal gyrus and in regions comprising the cognitive control and default mode networks (right posterior cingulate cortex, precentral gyrus, precuneus, right inferior frontal gyrus) during reactive aggression relative to baseline conditions. Compared to controls, individuals with a history of aggression exhibited increased activity in limbic regions (left hippocampus, left amygdala, left parahippocampal gyrus) and temporal regions (superior, middle, inferior temporal gyrus), and reduced activity in occipital regions (left occipital cortex, left calcarine cortex). These findings lend support to the limbic hyperactivity model in individuals with a history of aggression, and further indicate altered temporal and occipital activity in anger- and aggression-eliciting conditions involving face and speech processing.
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Affiliation(s)
- Maja Nikolic
- McGill University, Montreal, QC, Canada; McMaster University, Hamilton, ON, Canada.
| | - Patrizia Pezzoli
- University College London, London, United Kingdom; University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.
| | - Natalia Jaworska
- University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, ON, Canada; Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.
| | - Michael C Seto
- University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.
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Pereira JA, Ray A, Rana M, Silva C, Salinas C, Zamorano F, Irani M, Opazo P, Sitaram R, Ruiz S. 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. Front Hum Neurosci 2022; 16:933559. [PMID: 36092645 PMCID: PMC9452730 DOI: 10.3389/fnhum.2022.933559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
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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Affiliation(s)
- Jaime A. Pereira
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andreas Ray
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Claudio Silva
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Cesar Salinas
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Francisco Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Martin Irani
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Patricia Opazo
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
- *Correspondence: Ranganatha Sitaram
| | - Sergio Ruiz
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Sergio Ruiz
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Orth L, Meeh J, Gur RC, Neuner I, Sarkheil P. Frontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review. Front Hum Neurosci 2022; 16:933718. [PMID: 36092647 PMCID: PMC9449529 DOI: 10.3389/fnhum.2022.933718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/08/2022] [Indexed: 11/19/2022] Open
Abstract
Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the “limbic,” the “associative,” and the “motor” subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies (n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases (n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.
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Affiliation(s)
- Linda Orth
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- *Correspondence: Linda Orth
| | - Johanna Meeh
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany
| | - Pegah Sarkheil
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
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Tartt AN, Mariani MB, Hen R, Mann JJ, Boldrini M. Dysregulation of adult hippocampal neuroplasticity in major depression: pathogenesis and therapeutic implications. Mol Psychiatry 2022; 27:2689-2699. [PMID: 35354926 PMCID: PMC9167750 DOI: 10.1038/s41380-022-01520-y] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 02/22/2022] [Accepted: 03/09/2022] [Indexed: 02/07/2023]
Abstract
Major depressive disorder (MDD) was previously hypothesized to be a disease of monoamine deficiency in which low levels of monoamines in the synaptic cleft were believed to underlie depressive symptoms. More recently, however, there has been a paradigm shift toward a neuroplasticity hypothesis of depression in which downstream effects of antidepressants, such as increased neurogenesis, contribute to improvements in cognition and mood. This review takes a top-down approach to assess how changes in behavior and hippocampal-dependent circuits may be attributed to abnormalities at the molecular, structural, and synaptic level. We conclude with a discussion of how antidepressant treatments share a common effect in modulating neuroplasticity and consider outstanding questions and future perspectives.
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Affiliation(s)
| | | | - Rene Hen
- Department of Psychiatry, Columbia University, New York, NY 10032, USA,Department of Neuroscience, Columbia University, New York, NY 10032, USA,Department of Pharmacology, Columbia University, New York, NY 10032, USA,Areas of Integrative Neuroscience, NYS Psychiatric Institute, New York, NY 10032, USA
| | - J. John Mann
- Department of Psychiatry, Columbia University, New York, NY 10032, USA,Molecular Imaging and Neuropathology, NYS Psychiatric Institute, New York, NY 10032, USA
| | - Maura Boldrini
- Departments of Psychiatry, Columbia University, New York, NY, USA. .,Molecular Imaging and Neuropathology, NYS Psychiatric Institute, New York, NY, USA.
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12
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Cognitive Training with Neurofeedback Using fNIRS Improves Cognitive Function in Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095531. [PMID: 35564926 PMCID: PMC9104766 DOI: 10.3390/ijerph19095531] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 12/10/2022]
Abstract
This study examined the effects of a 4-week cognitive training program with neurofeedback (CT-NF) among 86 healthy adults (M = 66.34 years, range 54-84) randomized to either a treatment (app-based ABC games) or control (Tetris) group. Participants completed seven cognitive assessments, pre- and post-intervention, and measured their cortical brain activity using a XB-01 functional near-infrared spectroscopy (fNIRS) brain sensor, while engaging in CT-NF. The treatment (ABC) group showed significant (pre/post-intervention) improvements in memory (MEM), verbal memory (VBM), and composite cognitive function, while the control group did not. However, both groups showed significant improvements in processing speed (PS) and executive function (EF). In line with other studies, we found that strength of cortical brain activity (measured during CT-NF) was associated with both cognitive (pre and post) and game performance. In sum, our findings suggest that CT-NF and specifically ABC exercises, confer improved cognition in the domains of MEM, VBM, PS, and EF.
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Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9935192. [PMID: 35252458 PMCID: PMC8896931 DOI: 10.1155/2022/9935192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions. This technique has been widely used in the rehabilitation of stroke patients in recent years and appears to promote motor function recovery after stroke. At present, the application of BCI in poststroke cognitive impairment is increasing, which is a common complication that also affects the rehabilitation process. This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.
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Nouchi R, Nouchi H, Dinet J, Kawashima R. Cognitive Training with Neurofeedback Using NIRS Improved Cognitive Functions in Young Adults: Evidence from a Randomized Controlled Trial. Brain Sci 2021; 12:brainsci12010005. [PMID: 35053748 PMCID: PMC8774006 DOI: 10.3390/brainsci12010005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/16/2021] [Accepted: 12/16/2021] [Indexed: 02/07/2023] Open
Abstract
(1) Background: A previous study has shown that cognitive training with neurofeedback (CT-NF) using down-regulation improves cognitive functions in young adults. Neurofeedback has two strategies for manipulating brain activity (down-regulation and upregulation). However, the benefit of CT-NF with the upregulation of cognitive functions is still unknown. In this study, we investigated whether the upregulation of CT-NF improves a wide range of cognitive functions compared to cognitive training alone. (2) Methods: In this double-blinded randomized control trial (RCT), 60 young adults were randomly assigned to one of three groups: CT-NF group, CT alone group, and an active control (ACT) group who played a puzzle game. Participants in the three groups used the same device (tablet PC and 2ch NIRS (near-infrared spectroscopy)) and performed the training game for 20 min every day for four weeks. We measured brain activity during training in all groups, but only CT-NFs received NF. We also measured a wide range of cognitive functions before and after the intervention period. (3) Results: The CT-NF groups showed superior beneficial effects on episodic memory, working memory, and attention compared to the CT alone and ACT groups. In addition, the CT-NF group showed an increase in brain activity during CT, which was associated with improvements in cognitive function. (4) Discussion: This study first demonstrated that the CT-NF using the upregulation strategy has beneficial effects on cognitive functions compared to the CT alone. Our results suggest that greater brain activities during CT would enhance a benefit from CT.
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Affiliation(s)
- Rui Nouchi
- Department of Cognitive Health Science, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai 980-8575, Japan;
- Smart Aging Research Center (S.A.R.C.), Tohoku University, Seiryo-Machi 4-1, Sendai 980-8575, Japan;
- Correspondence:
| | - Haruka Nouchi
- Department of Cognitive Health Science, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai 980-8575, Japan;
| | - Jerome Dinet
- Department of Psychology, Université de Lorraine, F-54000 Nancy, France;
- Lorraine Research Laboratory in Computer Science and Its Applications (LORIA), Université de Lorraine, CNRS, INRIA, F-54000 Nancy, France
| | - Ryuta Kawashima
- Smart Aging Research Center (S.A.R.C.), Tohoku University, Seiryo-Machi 4-1, Sendai 980-8575, Japan;
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai 980-8575, Japan
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Farkhondeh Tale Navi F, Heysieattalab S, Ramanathan DS, Raoufy MR, Nazari MA. Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs. Neuroscience 2021; 483:104-126. [PMID: 34902494 DOI: 10.1016/j.neuroscience.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 11/27/2022]
Abstract
Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.
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Affiliation(s)
- Farhad Farkhondeh Tale Navi
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | - Soomaayeh Heysieattalab
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | | | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Ali Nazari
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
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Khan MA, Saibene M, Das R, Brunner IC, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng 2021; 18. [PMID: 34736239 DOI: 10.1088/1741-2552/ac36aa] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/04/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities. APPROACH For stroke applications, FT mainly includes the "flexible/stretchable electronics", "e-textile (electronic textile)" and "soft robotics". Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application. MAIN RESULTS In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the "biosignal acquisition unit", "rehabilitation devices" and "assistive systems". In terms of biosignals acquisition, electroencephalography (EEG) and electromyography (EMG) are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation (FES) and robotics units (exoskeleton, orthosis, etc.) have been explained. SIGNIFICANCE This is the first review article that compiles the different studies regarding flexible technology based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.
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Affiliation(s)
- Muhammad Ahmed Khan
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 215, Lyngby, 2800, DENMARK
| | - Matteo Saibene
- Technical University of Denmark, Ørsteds Plads, Building 345C, Lyngby, 2800, DENMARK
| | - Rig Das
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 214, Lyngby, 2800, DENMARK
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Neurofeedback for cognitive enhancement and intervention and brain plasticity. Rev Neurol (Paris) 2021; 177:1133-1144. [PMID: 34674879 DOI: 10.1016/j.neurol.2021.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/27/2021] [Indexed: 12/18/2022]
Abstract
In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.
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18
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A Multivariate Randomized Controlled Experiment about the Effects of Mindfulness Priming on EEG Neurofeedback Self-Regulation Serious Games. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Neurofeedback training (NFT) is a technique often proposed to train brain activity SR with promising results. However, some criticism has been raised due to the lack of evaluation, reliability, and validation of its learning effects. The current work evaluates the hypothesis that SR learning may be improved by priming the subject before NFT with guided mindfulness meditation (MM). The proposed framework was tested in a two-way parallel-group randomized controlled intervention with a single session alpha NFT, in a simplistic serious game design. Sixty-two healthy naïve subjects, aged between 18 and 43 years, were divided into MM priming and no-priming groups. Although both the EG and CG successfully attained the up-regulation of alpha rhythms (F(1,59) = 20.67, p < 0.001, ηp2 = 0.26), the EG showed a significantly enhanced ability (t(29) = 4.38, p < 0.001) to control brain activity, compared to the CG (t(29) = 1.18, p > 0.1). Furthermore, EG superior performance on NFT seems to be explained by the subject’s lack of awareness at pre-intervention, less vigour at post-intervention, increased task engagement, and a relaxed non-judgemental attitude towards the NFT tasks. This study is a preliminary validation of the proposed assisted priming framework, advancing some implicit and explicit metrics about its efficacy on NFT performance, and a promising tool for improving naïve “users” self-regulation ability.
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Kim DY, Tegethoff M, Meinlschmidt G, Yoo SS, Lee JH. Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: empirical evidence from functional MRI data. Neuroreport 2021; 32:762-770. [PMID: 33901056 DOI: 10.1097/wnr.0000000000001653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. OBJECTIVES The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training. METHODS Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs). RESULTS Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs. CONCLUSION Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
| | - Marion Tegethoff
- Institute of Psychology, RWTH Aachen, Jägerstrasse, Aachen, Germany
- Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Gunther Meinlschmidt
- Division of Clinical Psychology and Cognitive Behavioral Therapy, International Psychoanalytic University, Stromstrasse, Berlin, Germany
- Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse, Basel, Switzerland
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Seung-Schik Yoo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
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20
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Gao X, Wang Y, Chen X, Gao S. Interface, interaction, and intelligence in generalized brain-computer interfaces. Trends Cogn Sci 2021; 25:671-684. [PMID: 34116918 DOI: 10.1016/j.tics.2021.04.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/07/2021] [Accepted: 04/05/2021] [Indexed: 11/16/2022]
Abstract
A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.
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Affiliation(s)
- Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yijun Wang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Tianjin, China
| | - Shangkai Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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21
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Zhang X, Yao L, Wang X, Monaghan JJM, Mcalpine D, Zhang Y. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng 2020; 18. [PMID: 33171452 DOI: 10.1088/1741-2552/abc902] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/10/2020] [Indexed: 12/25/2022]
Abstract
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
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Affiliation(s)
- Xiang Zhang
- Harvard University, Cambridge, Massachusetts, UNITED STATES
| | - Lina Yao
- University of New South Wales, Sydney, New South Wales, AUSTRALIA
| | - Xianzhi Wang
- Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo, Sydney, New South Wales, 2007, AUSTRALIA
| | | | - David Mcalpine
- Macquarie University, Sydney, New South Wales, AUSTRALIA
| | - Yu Zhang
- Stanford University, Stanford, California, 94305-6104, UNITED STATES
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22
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Pamplona GS, Heldner J, Langner R, Koush Y, Michels L, Ionta S, Scharnowski F, Salmon CE. Network-based fMRI-neurofeedback training of sustained attention. Neuroimage 2020; 221:117194. [DOI: 10.1016/j.neuroimage.2020.117194] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 07/07/2020] [Accepted: 07/20/2020] [Indexed: 11/29/2022] Open
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Vu H, Kim HC, Jung M, Lee JH. fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations. Neuroimage 2020; 223:117328. [PMID: 32896633 DOI: 10.1016/j.neuroimage.2020.117328] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 07/16/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.
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Affiliation(s)
- Hanh Vu
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Minyoung Jung
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.
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Pandria N, Athanasiou A, Konstantara L, Karagianni M, Bamidis PD. Advances in biofeedback and neurofeedback studies on smoking. Neuroimage Clin 2020; 28:102397. [PMID: 32947225 PMCID: PMC7502375 DOI: 10.1016/j.nicl.2020.102397] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/02/2020] [Accepted: 08/19/2020] [Indexed: 11/19/2022]
Abstract
Smoking is a leading cause of morbidity and premature death constituting a global health challenge. Although, pharmacological and behavioral approaches comprise the mainstay of smoking cessation interventions, the efficacy and safety of pharmacotherapy is not demonstrated for some populations. Non-pharmacological approaches, such as biofeedback (BF) and neurofeedback (NF) could facilitate self-regulation of predisposing factors of relapse such as craving and stress. The current review aims to aggregate the existing evidence regarding the effects of BF and NF training on smokers. Relevant studies were identified through searching in Scopus, PubMed and Cochrane Library, and through hand-searching the references of screened articles. Peer-reviewed controlled and uncontrolled studies, where BF and/or NF training was administered, were included and evaluated according to PICOS framework. Narrative qualitative synthesis of ten eligible studies was performed, aggregated into three categories according to training provided. BF outcomes seem to be affected by smoking behavior prior to training; individualized EEG NF training holds promise for modulating craving-related response while minimizing the required number of sessions. Real-time fMRI NF studies concluded that nicotine-dependent individuals could modulate craving-related brain responses, while mixed results were revealed regarding smokers' ability to modulate brain responses related to resistance towards the urge to smoke. BF and NF training seem to facilitate modulation of autonomous and/or central nervous system activity while also transferring this learned self-regulation to behavioral outcomes. BF and NF training should a) address remaining issues on specificity and scientific validity, b) target diverse demographics, and c) produce robust reproducible methodologies and clinical guidelines for relevant health care providers, in order to be considered as viable complementary tools to standard smoking cessation care.
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Affiliation(s)
- N Pandria
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece; Northern Greece Neurofeedback Center, Thessaloniki, Greece.
| | - A Athanasiou
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
| | - L Konstantara
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
| | - M Karagianni
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
| | - P D Bamidis
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
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25
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Liu N, Yao L, Zhao X. Evaluating the amygdala network induced by neurofeedback training for emotion regulation using hierarchical clustering. Brain Res 2020; 1740:146853. [PMID: 32339500 DOI: 10.1016/j.brainres.2020.146853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 04/08/2020] [Accepted: 04/22/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Previous studies have shown that regulating the target region by real-time fMRI-based neurofeedback training can influence the activation of other regions and the functional connectivity between them. However, it is not clear whether the training effect of neurofeedback, especially in emotion regulation, is manifested in local network specialization or global network integration. In the current study, we chose the left amygdala (LA) as the target region to regulate positive emotion through real-time fMRI training. Average-linkage hierarchical clustering was employed to cluster the fMRI data recorded during the training to construct whole-brain networks and the LA network to which the LA belongs. RESULTS The activation in the LA and those in some other regions were significantly up-regulated during the training. The clustering analysis at group level showed that the feedback training did not affect the number of networks in the whole brain but altered the distribution and functional connectivity in the LA network. CONCLUSION These findings suggested that the feedback training effects in emotion regulation pattern reflected by the activity of the target brain network and the connections within the network were robustly embodied in local network specialization instead of in global network integration.
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Affiliation(s)
- Ning Liu
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xiaojie Zhao
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
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Sorinas J, Ferrández JM, Fernandez E. Brain and Body Emotional Responses: Multimodal Approximation for Valence Classification. SENSORS 2020; 20:s20010313. [PMID: 31935909 PMCID: PMC6982758 DOI: 10.3390/s20010313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 01/02/2020] [Accepted: 01/03/2020] [Indexed: 11/16/2022]
Abstract
In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work, we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and peripheral responses in the processing of emotions by separating the sample by sex; though, the differences between men and women were only notable at the peripheral nervous system level.
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Affiliation(s)
- Jennifer Sorinas
- The Institute of Bioengineering, University Miguel Hernandez, 03202 Elche, Spain
- Department of Electronics and Computer Technology, University of Cartagena, 30202 Cartagena, Spain;
- Correspondence: (J.S.); (E.F.)
| | - Jose Manuel Ferrández
- Department of Electronics and Computer Technology, University of Cartagena, 30202 Cartagena, Spain;
| | - Eduardo Fernandez
- The Institute of Bioengineering, University Miguel Hernandez, 03202 Elche, Spain
- Correspondence: (J.S.); (E.F.)
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Pichiorri F, Mattia D. Brain-computer interfaces in neurologic rehabilitation practice. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:101-116. [PMID: 32164846 DOI: 10.1016/b978-0-444-63934-9.00009-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.
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Affiliation(s)
- Floriana Pichiorri
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - Donatella Mattia
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
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28
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Pereira JA, Sepulveda P, Rana M, Montalba C, Tejos C, Torres R, Sitaram R, Ruiz S. Self-Regulation of the Fusiform Face Area in Autism Spectrum: A Feasibility Study With Real-Time fMRI Neurofeedback. Front Hum Neurosci 2019; 13:446. [PMID: 31920602 PMCID: PMC6933482 DOI: 10.3389/fnhum.2019.00446] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 12/04/2019] [Indexed: 12/27/2022] Open
Abstract
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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Affiliation(s)
- Jaime A. Pereira
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
| | - Pradyumna Sepulveda
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Mohit Rana
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Cristian Montalba
- Biomedical Imaging Center, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
| | - Cristian Tejos
- Biomedical Imaging Center, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
| | - Rafael Torres
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Faculty of Engineering, Pontifical Catholic University of Chile, Santiago, Chile
| | - Sergio Ruiz
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
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Pereira J, Direito B, Sayal A, Ferreira C, Castelo-Branco M. Self-Modulation of Premotor Cortex Interhemispheric Connectivity in a Real-Time Functional Magnetic Resonance Imaging Neurofeedback Study Using an Adaptive Approach. Brain Connect 2019; 9:662-672. [DOI: 10.1089/brain.2019.0697] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- João Pereira
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Bruno Direito
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Alexandre Sayal
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Carlos Ferreira
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
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30
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Zioga I, Hassan R, Luft CDB. Success, but not failure feedback guides learning during neurofeedback: An ERP study. Neuroimage 2019; 200:26-37. [DOI: 10.1016/j.neuroimage.2019.06.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/30/2019] [Accepted: 06/02/2019] [Indexed: 10/26/2022] Open
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Zhu Y, Gao H, Tong L, Li Z, Wang L, Zhang C, Yang Q, Yan B. Emotion Regulation of Hippocampus Using Real-Time fMRI Neurofeedback in Healthy Human. Front Hum Neurosci 2019; 13:242. [PMID: 31379539 PMCID: PMC6660260 DOI: 10.3389/fnhum.2019.00242] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 06/28/2019] [Indexed: 01/12/2023] Open
Abstract
Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is a prospective tool to enhance the emotion regulation capability of participants and to alleviate their emotional disorders. The hippocampus is a key brain region in the emotional brain network and plays a significant role in social cognition and emotion processing in the brain. However, few studies have focused on the emotion NF of the hippocampus. This study investigated the feasibility of NF training of healthy participants to self-regulate the activation of the hippocampus and assessed the effect of rtfMRI-NF on the hippocampus before and after training. Twenty-six right-handed healthy volunteers were randomly assigned to the experimental group receiving hippocampal rtfMRI-NF (n = 13) and the control group (CG) receiving rtfMRI-NF from the intraparietal sulcus rtfMRI-NF (n = 13) and completed a total of four NF runs. The hippocampus and the intraparietal sulcus were defined based on the Montreal Neurological Institute (MNI) standard template, and NF signal was measured as a percent signal change relative to the baseline obtained by averaging the fMRI signal for the preceding 20 s long rest block. NF signal (percent signal change) was updated every 2 s and was displayed on the screen. The amplitude of low-frequency fluctuation and regional homogeneity values was calculated to evaluate the effects of NF on spontaneous neural activity in resting-state fMRI. A standard general linear model (GLM) analysis was separately conducted for each fMRI NF run. Results showed that the activation of hippocampus increased after four NF training runs. The hippocampal activity of the experiment group participants was higher than that of the CG. They also showed elevated hippocampal activity and the greater amygdala–hippocampus connectivity. The anterior temporal lobe, parahippocampal gyrus, hippocampus, and amygdala of brain regions associated with emotional processing were activated during training. We presented a proof-of-concept study using rtfMRI-NF for hippocampus up-regulation in the recall of positive autobiographical memories. The current study may provide a new method to regulate our emotions and can potentially be applied to the clinical treatment of emotional disorders.
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Affiliation(s)
- Yashuo Zhu
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
| | - Hui Gao
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
| | - Li Tong
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
| | - ZhongLin Li
- Department of Radiology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China
| | - Linyuan Wang
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
| | - Chi Zhang
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
| | - Qiang Yang
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
| | - Bin Yan
- PLA Strategy Support Force Information Engineering University, Communication Engineering College, Zhengzhou, China
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32
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Oblak EF, Sulzer JS, Lewis-Peacock JA. A simulation-based approach to improve decoded neurofeedback performance. Neuroimage 2019; 195:300-310. [DOI: 10.1016/j.neuroimage.2019.03.062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 02/21/2019] [Accepted: 03/27/2019] [Indexed: 12/13/2022] Open
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Kim HC, Tegethoff M, Meinlschmidt G, Stalujanis E, Belardi A, Jo S, Lee J, Kim DY, Yoo SS, Lee JH. Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback. Neuroimage 2019; 195:409-432. [DOI: 10.1016/j.neuroimage.2019.03.066] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 03/05/2019] [Accepted: 03/27/2019] [Indexed: 12/13/2022] Open
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34
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Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 2019; 193:35-45. [PMID: 30831310 PMCID: PMC6521850 DOI: 10.1016/j.neuroimage.2019.02.057] [Citation(s) in RCA: 211] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 01/28/2019] [Accepted: 02/21/2019] [Indexed: 11/24/2022] Open
Abstract
Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.
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35
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Ramot M, Gonzalez-Castillo J. A framework for offline evaluation and optimization of real-time algorithms for use in neurofeedback, demonstrated on an instantaneous proxy for correlations. Neuroimage 2019; 188:322-334. [PMID: 30553044 PMCID: PMC11103676 DOI: 10.1016/j.neuroimage.2018.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 11/14/2018] [Accepted: 12/03/2018] [Indexed: 01/09/2023] Open
Abstract
Interest in real-time fMRI neurofeedback has grown exponentially over the past few years, both for use as a basic science research tool, and as part of the search for novel clinical interventions for neurological and psychiatric illnesses. In order to expand the range of questions which can be addressed with this tool however, new neurofeedback methods must be developed, going beyond feedback of activations in a single region. These new methods, several of which have already been proposed, are by their nature complex, involving many possible parameters. Here we suggest a framework for evaluating and optimizing algorithms for use in a real-time setting, before beginning the neurofeedback experiment, by offline simulations of algorithm output using a previously collected dataset. We demonstrate the application of this framework on the instantaneous proxy for correlations which we developed for training connectivity between different network nodes, identify the optimal parameters for use with this algorithm, and compare it to more traditional correlation methods. We also examine the effects of advanced imaging techniques, such as multi-echo acquisition, and the integration of these into the real-time processing stream.
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Affiliation(s)
- Michal Ramot
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
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36
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Direito B, Lima J, Simões M, Sayal A, Sousa T, Lührs M, Ferreira C, Castelo-Branco M. Targeting dynamic facial processing mechanisms in superior temporal sulcus using a novel fMRI neurofeedback target. Neuroscience 2019; 406:97-108. [PMID: 30825583 DOI: 10.1016/j.neuroscience.2019.02.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 02/16/2019] [Accepted: 02/18/2019] [Indexed: 10/27/2022]
Abstract
The superior temporal sulcus (STS) encompasses a complex set of regions involved in a wide range of cognitive functions. To understand its functional properties, neuromodulation approaches such brain stimulation or neurofeedback can be used. We investigated whether the posterior STS (pSTS), a core region in the face perception and imagery network, could be specifically identified based on the presence of dynamic facial expressions (and not just on simple motion or static face signals), and probed with neurofeedback. Recognition of facial expressions is critically impaired in autism spectrum disorder, making this region a relevant target for future clinical neurofeedback studies. We used a stringent localizer approach based on the contrast of dynamic facial expressions against static neutral faces plus moving dots. The target region had to be specifically responsive to dynamic facial expressions instead of mere motion and/or the presence of a static face. The localizer was successful in selecting this region across subjects. Neurofeedback was then performed, using this region as a target, with two novel feedback rules (mean or derivative-based, using visual or auditory interfaces). Our results provide evidence that a facial expression-selective cluster in pSTS can be identified and may represent a suitable target for neurofeedback approaches, aiming at social and emotional cognition. These findings highlight the presence of a highly selective region in STS encoding dynamic aspects of facial expressions. Future studies should elucidate its role as a mechanistic target for neurofeedback strategies in clinical disorders of social cognition such as autism.
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Affiliation(s)
- Bruno Direito
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra Institute for Biomedical Imaging and Life Sciences (CIBIT), University of Coimbra, Coimbra, Portugal
| | - João Lima
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Marco Simões
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Alexandre Sayal
- Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra Institute for Biomedical Imaging and Life Sciences (CIBIT), University of Coimbra, Coimbra, Portugal
| | - Teresa Sousa
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra Institute for Biomedical Imaging and Life Sciences (CIBIT), University of Coimbra, Coimbra, Portugal; Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal
| | - Michael Lührs
- Maastricht University, Department of Cognitive Neuroscience, Maastricht, Netherlands
| | - Carlos Ferreira
- Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra Institute for Biomedical Imaging and Life Sciences (CIBIT), University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Institute of Nuclear Sciences Applied to Health (ICNAS), Coimbra Institute for Biomedical Imaging and Life Sciences (CIBIT), University of Coimbra, Coimbra, Portugal.
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37
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Papo D. Neurofeedback: Principles, appraisal, and outstanding issues. Eur J Neurosci 2019; 49:1454-1469. [PMID: 30570194 DOI: 10.1111/ejn.14312] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 11/21/2018] [Accepted: 11/27/2018] [Indexed: 12/16/2022]
Abstract
Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.
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Affiliation(s)
- David Papo
- SCALab, CNRS, Université de Lille, Villeneuve d'Ascq, France
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38
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A conceptual space for EEG-based brain-computer interfaces. PLoS One 2019; 14:e0210145. [PMID: 30605482 PMCID: PMC6317819 DOI: 10.1371/journal.pone.0210145] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 11/29/2018] [Indexed: 12/11/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) have become more and more popular these last years. Researchers use this technology for several types of applications, including attention and workload measures but also for the direct control of objects by the means of BCIs. In this work we present a first, multidimensional feature space for EEG-based BCI applications to help practitioners to characterize, compare and design systems, which use EEG-based BCIs. Our feature space contains 4 axes and 9 sub-axes and consists of 41 options in total as well as their different combinations. We presented the axes of our feature space and we positioned our feature space regarding the existing BCI and HCI taxonomies and we showed how our work integrates the past works, and/or complements them.
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39
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Luo J, Feng Z, Lu N. Spatio-temporal discrepancy feature for classification of motor imageries. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.07.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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40
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Hong KS, Zafar A. Existence of Initial Dip for BCI: An Illusion or Reality. Front Neurorobot 2018; 12:69. [PMID: 30416440 PMCID: PMC6212489 DOI: 10.3389/fnbot.2018.00069] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 10/03/2018] [Indexed: 01/21/2023] Open
Abstract
A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.
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Affiliation(s)
- Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
| | - Amad Zafar
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
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41
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Ehlis AC, Barth B, Hudak J, Storchak H, Weber L, Kimmig ACS, Kreifelts B, Dresler T, Fallgatter AJ. Near-Infrared Spectroscopy as a New Tool for Neurofeedback Training: Applications in Psychiatry and Methodological Considerations. JAPANESE PSYCHOLOGICAL RESEARCH 2018. [DOI: 10.1111/jpr.12225] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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42
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Leong SL, Vanneste S, Lim J, Smith M, Manning P, De Ridder D. A randomised, double-blind, placebo-controlled parallel trial of closed-loop infraslow brain training in food addiction. Sci Rep 2018; 8:11659. [PMID: 30076365 PMCID: PMC6076277 DOI: 10.1038/s41598-018-30181-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 07/25/2018] [Indexed: 12/20/2022] Open
Abstract
The posterior cingulate cortex (PCC) is involved in food craving in obese food addicted individuals. This randomised, double-blind, placebo-controlled parallel study explored the potential therapeutic effects of infraslow neurofeedback (ISF-NF) on food craving targeting the PCC in obese women with symptoms of food addiction. Participants received six sessions of either ISF-NF (n = 11) or placebo (n = 10) over a three-week period. There were no reported adverse effects. Electrophysiologically, there were significant increases in infraslow activity (p = 0.0002) and infraslow/beta nesting (p < 0.001) in the PCC in the ISF-NF group (mean r = 0.004 ± 0.002) compared to placebo (mean r = 0.02 ± 0.002) two days after the last intervention. Also, there was a significant decrease in different dimensions of state food craving compared to baseline and to placebo. Findings suggest that source localized IFS-NF results in electrophysiological changes and may be associated with reduced food craving. This trial is registered at www.anzctr.org.au , identifier, ACTRN12617000601336. This study was funded by the Otago Medical Research Grant: CT375.
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Affiliation(s)
- Sook Ling Leong
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Sven Vanneste
- School of Behavioral and Brain Sciences, University of Texas, Dallas, USA
| | - Joyce Lim
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Mark Smith
- Neurofeedback Therapy Services of New York, New York, USA
| | - Patrick Manning
- Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
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43
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Trainability of hemodynamic parameters: A near-infrared spectroscopy based neurofeedback study. Biol Psychol 2018; 136:168-180. [DOI: 10.1016/j.biopsycho.2018.05.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 01/17/2018] [Accepted: 05/16/2018] [Indexed: 11/22/2022]
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44
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Liu N, Yu X, Yao L, Zhao X. Mapping the Cortical Network Arising From Up-Regulated Amygdaloidal Activation Using -Louvain Algorithm. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1169-1177. [PMID: 29877841 DOI: 10.1109/tnsre.2018.2838075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The amygdala plays an important role in emotion processing. Several studies have proved that its activation can be regulated by real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback training. However, although studies have found brain regions that are functionally closely connected to the amygdala in the cortex, it is not clear whether these brain regions and the amygdala are structurally closely connected, and if they show the same training effect as the amygdala in the process of emotional regulation. In this paper, we instructed subjects to up-regulate the activation of the left amygdala (LA) through rtfMRI-based neurofeedback training. In order to fuse multimodal imaging data, we introduced a network analysis method called the -Louvain clustering algorithm. This method was used to integrate multimodal data from the training experiment and construct an LA-cortical network. Correlation analysis and main-effect analysis were conducted to determine the signal covariance associated with the activation of the target area; ultimately, we identified the left temporal pole superior as the amygdaloidal-cortical network region. As a deep nucleus in the brain, the treatment and stimulation of the amygdala remains challenging. Our results provide new insights for the regulation of activation in a deep nucleus using more neurofeedback techniques.
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45
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Auer T, Dewiputri WI, Frahm J, Schweizer R. Higher-order Brain Areas Associated with Real-time Functional MRI Neurofeedback Training of the Somato-motor Cortex. Neuroscience 2018; 378:22-33. [PMID: 27133575 PMCID: PMC5953411 DOI: 10.1016/j.neuroscience.2016.04.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/09/2016] [Accepted: 04/22/2016] [Indexed: 01/22/2023]
Abstract
Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings.
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Affiliation(s)
- Tibor Auer
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany; MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
| | - Wan Ilma Dewiputri
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany; Department of Neuroscience, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia; Pusat PERMATApintar Negara, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia
| | - Jens Frahm
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany
| | - Renate Schweizer
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany
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46
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Ihssen N, Sokunbi MO, Lawrence AD, Lawrence NS, Linden DEJ. Neurofeedback of visual food cue reactivity: a potential avenue to alter incentive sensitization and craving. Brain Imaging Behav 2018; 11:915-924. [PMID: 27233784 PMCID: PMC5486584 DOI: 10.1007/s11682-016-9558-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
FMRI-based neurofeedback transforms functional brain activation in real-time into sensory stimuli that participants can use to self-regulate brain responses, which can aid the modification of mental states and behavior. Emerging evidence supports the clinical utility of neurofeedback-guided up-regulation of hypoactive networks. In contrast, down-regulation of hyperactive neural circuits appears more difficult to achieve. There are conditions though, in which down-regulation would be clinically useful, including dysfunctional motivational states elicited by salient reward cues, such as food or drug craving. In this proof-of-concept study, 10 healthy females (mean age = 21.40 years, mean BMI = 23.53) who had fasted for 4 h underwent a novel 'motivational neurofeedback' training in which they learned to down-regulate brain activation during exposure to appetitive food pictures. FMRI feedback was given from individually determined target areas and through decreases/increases in food picture size, thus providing salient motivational consequences in terms of cue approach/avoidance. Our preliminary findings suggest that motivational neurofeedback is associated with functionally specific activation decreases in diverse cortical/subcortical regions, including key motivational areas. There was also preliminary evidence for a reduction of hunger after neurofeedback and an association between down-regulation success and the degree of hunger reduction. Decreasing neural cue responses by motivational neurofeedback may provide a useful extension of existing behavioral methods that aim to modulate cue reactivity. Our pilot findings indicate that reduction of neural cue reactivity is not achieved by top-down regulation but arises in a bottom-up manner, possibly through implicit operant shaping of target area activity.
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Affiliation(s)
- Niklas Ihssen
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK. .,Department of Psychology, Durham University, Queen's Campus, Stockton-on-Tees, TS17 6BH, UK.
| | - Moses O Sokunbi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK.,Cognitive Neuroscience Sector, International School for Advanced Studies (SISSA), Trieste, 34136, Italy
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK
| | | | - David E J Linden
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
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47
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Framework for Virtual Cognitive Experiment in Virtual Geographic Environments. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7010036] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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48
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Pattnaik PK, Sarraf J. Brain Computer Interface issues on hand movement. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2018. [DOI: 10.1016/j.jksuci.2016.09.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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49
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Jacob Y, Or-Borichev A, Jackont G, Lubianiker N, Hendler T. Network Based fMRI Neuro-Feedback for Emotion Regulation; Proof-of-Concept. COMPLEX NETWORKS & THEIR APPLICATIONS VI 2018. [DOI: 10.1007/978-3-319-72150-7_101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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50
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Wang T, Mantini D, Gillebert CR. The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review. Cortex 2017; 107:148-165. [PMID: 28992948 PMCID: PMC6182108 DOI: 10.1016/j.cortex.2017.09.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 08/02/2017] [Accepted: 09/07/2017] [Indexed: 12/17/2022]
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.
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Affiliation(s)
- Tianlu Wang
- Department of Brain & Cognition, University of Leuven, Leuven, Belgium
| | - Dante Mantini
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom; Research Center for Movement Control and Neuroplasticity, University of Leuven, Leuven, Belgium; Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Celine R Gillebert
- Department of Brain & Cognition, University of Leuven, Leuven, Belgium; Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
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