Current Status of Neurofeedback for Post-traumatic Stress Disorder: A Systematic Review and the Possibility of Decoded Neurofeedback

Frontiers in Human Neuroscience, Год журнала: 2019, Номер 13

Опубликована: Июль 17, 2019

Background: Post-traumatic stress disorder (PTSD) is a neuropsychiatric affective that can develop after traumatic life-events. Exposure-based therapy currently one of the most effective treatments for PTSD. However, exposure to stimuli so aversive significant number patients drop-out during course treatment. Among various attempts novel therapies bypass such aversiveness, neurofeedback appears promising. With neurofeedback, unconsciously self-regulate brain activity via real-time monitoring and feedback EEG or fMRI signals. conventional methods, however, it difficult induce neural representation related specific trauma because based on signals averaged within areas. To overcome this difficulty, approaches as Decoded Neurofeedback (DecNef) might prove helpful. Instead average BOLD signals, DecNef allows implicitly regulate multivariate voxel patterns with feared stimuli. As such, effects are postulated derive either from counter-conditioning, some combination both. Although exact mechanism not yet fully understood. has been successfully applied reduce fear responses induced by fear-conditioned phobic among non-clinical participants. Methods: Follows Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, systematic review was conducted compare effect those EEG/fMRI-based PTSD amelioration. elucidate possible mechanisms reduction, we mathematically modeled exposure-based counter conditioning separately data obtained past studies. Finally, four patients. Here, recent advances in application treatments, including DecNef. This intended be informative neuroscientists general well practitioners planning use therapeutic strategy Results: Our mathematical model suggested key component Following reduction severity observed. comparable reported approach. Conclusions: much larger participants will needed future, could promising bypasses unpleasantness conscious associated disorders,

Язык: Английский

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Decoding and mapping task states of the human brain via deep learning

Human Brain Mapping, Год журнала: 2019, Номер 41(6), С. 1505 - 1519

Опубликована: Дек. 9, 2019

Support vector machine (SVM) based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, SVM-MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly multiple brain from fMRI signals without any burden handcrafts. We trained and tested DNN classifier using data Human Connectome Project's S1200 dataset (N=1034). tests verify its performance, proposed classification method identified seven tasks with an average accuracy 93.7%. also showed general applicability transfer learning small datasets (N=43), situation encountered typical neuroscience research. The achieved 89.0% 94.7% working memory motor task, respectively, higher than 69.2% 68.6% obtained by SVM-MVPA. A visualization that automatically detected features areas related each task. Without incurring handcrafting features, can classify highly accurately, is powerful tool researchers.

Язык: Английский

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Process-based framework for precise neuromodulation

Nature Human Behaviour, Год журнала: 2019, Номер 3(5), С. 436 - 445

Опубликована: Апрель 15, 2019

Язык: Английский

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Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices

Neuroscience & Biobehavioral Reviews, Год журнала: 2021, Номер 125, С. 33 - 56

Опубликована: Фев. 15, 2021

Язык: Английский

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Neurofeedback for cognitive enhancement and intervention and brain plasticity

Revue Neurologique, Год журнала: 2021, Номер 177(9), С. 1133 - 1144

Опубликована: Окт. 19, 2021

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 activity and them control it, initiating directional changes. The overarching hypothesis behind this method that results in an enhancement of the abilities associated activity, triggers specific structural functional changes brain, promoted by learning neuronal plasticity effects. Here, we review general methodological principles describe its behavioural benefits experimental contexts. We non-specific effects reinforcement striato-frontal networks well more cortical which exerted. Last, analyse current challenges faces studies, including quantification temporal dynamics effects, generalisation outcomes everyday life situations, design appropriate controls disambiguate placebo from true development advanced signal processing achieve finer-grained real-time modelling functions.

Язык: Английский

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Transforming Minds AI and Machine Learning Applications in Cognitive Neuroscience

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 107 - 128

Опубликована: Янв. 3, 2025

Through their unique capabilities in analysing mental and behavioral data AI ML transform the field of cognitive neuroscience. MRI fMRI rely on to reliably identify memory troubles also boost early recognition neurodegenerative disorders like Alzheimer's Parkinson's. rehabilitation programs that utilize improve therapy results by responding instantly individual needs. By using power, them interfaces allow neural patterns link with outside tools offer fresh treatment methods for those who are mentally challenged. Psychological evaluations treatments upgrade reliability as a result AI's method analyze emotional states behaviours. Machine learning changing neuroscience improving diagnostics many disorders.

Язык: Английский

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Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback

Frontiers in Neurology, Год журнала: 2018, Номер 9

Опубликована: Июль 24, 2018

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion cognitive processes, their underlying neurobiology. It remains unclear whether NFB can be used aid sustain complex emotions, ecological validity implications. We provide a technical proof concept novel real-time functional magnetic resonance imaging (rtfMRI) procedure. Using rtfMRI-NFB, we enabled participants voluntarily own neural activity while they experienced emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted virtual environment as computer interface (BCI) musical excerpts induce two emotions (tenderness anguish), aided by participants' preferred personalized strategies maximize intensity these Eight from experimental sites performed on consecutive days in counterbalanced design. On one day, delivered using region interest (ROI) method, other day support vector machine (SVM) classifier. Our multimodal VR/NFB approach technically feasible robust method for measurement correlates emotional states modulation. Guided color changes BCI during successfully increased real time, septo-hypothalamic area amygdala ROI based evoked distributed patterns classified tenderness anguish SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that conditions, recruited regions ascribed social affiliative (medial frontal / temporal pole precuneus). During dorsolateral prefrontal additional associated negative affect. These findings were demonstrable at individual subject level, reflected self-reported being observed both SVM methods across sites. VR/rtfMRI-NFB protocol provides an engaging tool brain-based interventions healthy subjects may find clinical conditions anxiety, stress impaired empathy among others.

Язык: Английский

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Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Wellcome Open Research, Год журнала: 2018, Номер 3, С. 19 - 19

Опубликована: Окт. 10, 2018

Background. Chronic pain is a common, often disabling condition thought to involve combination of peripheral and central neurobiological factors. However, the extent nature changes in brain poorly understood. Methods. We investigated network architecture using resting-state fMRI data chronic back patients UK Japan (41 patients, 56 controls), as well open from USA. applied machine learning deep (conditional variational autoencoder architecture) methods explore classification patients/controls based on connectivity. then studied topology data, developed multislice modularity method look for consensus evidence modular reorganisation pain. Results. Machine allowed reliable third, independent set with an accuracy 63%, 68% cross validation all data. identified robust hub disruption pain, most consistently respect clustering coefficient betweenness centrality. found pattern involving extensive, bilateral regions sensorimotor cortex, characterised primarily by negative - tendency cortex nodes be less inclined form pairwise links other nodes. Furthermore, these were display increased connectivity pregenual anterior cingulate region known involved endogenous control. In contrast, intraparietal sulcus displayed propensity towards positive reorganisation, suggesting that it might have role forming modules associated state. Conclusion. The results provide consistent characteristic extensive cortex.

Язык: Английский

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A systematic review of fMRI neurofeedback reporting and effects in clinical populations

NeuroImage Clinical, Год журнала: 2020, Номер 28, С. 102496 - 102496

Опубликована: Янв. 1, 2020

Real-time fMRI-based neurofeedback is a relatively young field with potential to impact the currently available treatments of various disorders. In order evaluate evidence clinical benefits and investigate how consistently studies report their methods results, an exhaustive search fMRI in populations was performed. Reporting evaluated using limited number Consensus on reporting experimental design cognitive-behavioral (CRED-NF checklist) items, which was, together statistical power sensitivity calculation, used also existing measures. The 62 found investigated regulation abilities and/or wide range disorders, but small sample sizes were therefore unable detect effects. Most points from CRED-NF checklist adequately reported by majority studies, some improvements are suggested for group comparisons relations between success benefits. To establish as tool, more emphasis should be placed future larger determined through priori calculations standardization procedures reporting.

Язык: Английский

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Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Frontiers in Neural Circuits, Год журнала: 2021, Номер 14

Опубликована: Янв. 21, 2021

Background Alzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), dementia (middle-stage), severe (late-stage). Recent studies showed changes functional network connectivity obtained from resting-state magnetic resonance imaging (rs-fMRI) during transition healthy aging to AD. By assuming that brain interaction static scanning time, prior are focused on or (sFNC). Dynamic (dFNC) explores temporal patterns of provides additional information its counterpart. Method We used longitudinal rs-fMRI 1385 scans (from 910 subjects) at stages AD normal very vmAD). group-independent component analysis (group-ICA) extracted 53 maximally independent components (ICs) for whole brain. Next, we a sliding-window approach estimate dFNC ICs, then group them into 3 states using clustering method. Then, estimated hidden Markov model (HMM) occupancy rate (OCR) each subject. Finally, investigated link between clinical subject with state-specific FNC, OCR, HMM. Results All significant disruption progression vmAD one. Specifically, found subcortical network, auditory visual sensorimotor cerebellar decrease compared those also reorganized (i.e., both increases decreases) control default mode by dementia. Similarly, pattern between-network when transits However, decreases spends more time state higher networks. Conclusion Our results spatial whole-brain FNC differentiates form suggested substantial disruptions across multiple dynamic states. In detail, our sensory affected than other one last networks get addition, abnormal were identified early stage AD, some abnormalities correlated score.

Язык: Английский

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