scholarly journals fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Ranganatha Sitaram ◽  
Andrea Caria ◽  
Ralf Veit ◽  
Tilman Gaber ◽  
Giuseppina Rota ◽  
...  
Keyword(s):  
Region Of Interest ◽  
High Specificity ◽  
Neural Substrates ◽  
Technological Advancement ◽  
Noninvasive Technique ◽  
Computer Interfaces ◽  
Modify Activity ◽  
Independent Variable ◽  
Brain Fmri ◽  
The Brain

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.

Lessons From the Neural Bases of Speech and Voice

2011 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Christy L. Ludlow
Keyword(s):  
Brain Networks ◽  
Neural Substrates ◽  
Voice Behavior ◽  
Therapeutic Approaches ◽  
Neural Bases ◽  
Sound Foundation ◽  
The Brain ◽  
Normal Speech

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data

2021 ◽  
Vol 226 (4) ◽  
pp. 1155-1167 ◽  
Author(s):  
Anne C. Trutti ◽  
Laura Fontanesi ◽  
Martijn J. Mulder ◽  
Pierre-Louis Bazin ◽  
Bernhard Hommel ◽  
...  
Keyword(s):  
Ventral Tegmental Area ◽  
Region Of Interest ◽  
Reward Processing ◽  
7 Tesla ◽  
Subcortical Structures ◽  
7 Tesla Mri ◽  
Subcortical Nuclei ◽  
Ventral Tegmental ◽  
The Brain

AbstractFunctional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Neural correlates of motion processing through echolocation, source hearing, and vision in blind echolocation experts and sighted echolocation novices

2014 ◽  
Vol 111 (1) ◽  
pp. 112-127 ◽  
Author(s):  
L. Thaler ◽  
J. L. Milne ◽  
S. R. Arnott ◽  
D. Kish ◽  
M. A. Goodale
Keyword(s):  
Visual Motion ◽  
Brain Activity ◽  
Region Of Interest ◽  
Occipital Cortex ◽  
Neural Substrates ◽  
Motion Processing ◽  
Planum Temporale ◽  
Show Evidence ◽  
Visual Cortical ◽  
Source Motion

We have shown in previous research (Thaler L, Arnott SR, Goodale MA. PLoS One 6: e20162, 2011) that motion processing through echolocation activates temporal-occipital cortex in blind echolocation experts. Here we investigated how neural substrates of echo-motion are related to neural substrates of auditory source-motion and visual-motion. Three blind echolocation experts and twelve sighted echolocation novices underwent functional MRI scanning while they listened to binaural recordings of moving or stationary echolocation or auditory source sounds located either in left or right space. Sighted participants' brain activity was also measured while they viewed moving or stationary visual stimuli. For each of the three modalities separately (echo, source, vision), we then identified motion-sensitive areas in temporal-occipital cortex and in the planum temporale. We then used a region of interest (ROI) analysis to investigate cross-modal responses, as well as laterality effects. In both sighted novices and blind experts, we found that temporal-occipital source-motion ROIs did not respond to echo-motion, and echo-motion ROIs did not respond to source-motion. This double-dissociation was absent in planum temporale ROIs. Furthermore, temporal-occipital echo-motion ROIs in blind, but not sighted, participants showed evidence for contralateral motion preference. Temporal-occipital source-motion ROIs did not show evidence for contralateral preference in either blind or sighted participants. Our data suggest a functional segregation of processing of auditory source-motion and echo-motion in human temporal-occipital cortex. Furthermore, the data suggest that the echo-motion response in blind experts may represent a reorganization rather than exaggeration of response observed in sighted novices. There is the possibility that this reorganization involves the recruitment of “visual” cortical areas.

scholarly journals Action video gaming and the brain: fMRI effects without behavioral effects in visual and verbal cognitive tasks

2017 ◽  
Vol 8 (1) ◽  
pp. e00877 ◽  
Author(s):  
Fabio Richlan ◽  
Juliane Schubert ◽  
Rebecca Mayer ◽  
Florian Hutzler ◽  
Martin Kronbichler
Keyword(s):  
Behavioral Effects ◽  
Cognitive Tasks ◽  
Video Gaming ◽  
Brain Fmri ◽  
The Brain

Shape Memory Alloy Actuatable Woven Neural Probes

2018 ◽  
Author(s):  
Gabriella Shull ◽  
Jay Jia Hu ◽  
Justin Buschnyj ◽  
Henry Koon ◽  
Julianna Abel ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Neural Activity ◽  
Inflammatory Responses ◽  
Neural Probes ◽  
Thermally Induced ◽  
Tissue Phantom ◽  
Computer Interfaces ◽  
Ordered Array ◽  
The Brain

The ability to sense neural activity using electrodes has allowed scientists to use this information to temporarily restore movement in paralyzed individuals using brain-computer interfaces (BCI). However, current electrodes do not provide chronic recording of the brain due to the inflammatory response of the immune system caused by the large (∼ 20–80 μm) size of the shanks, and the mechanical mismatch of the shanks relative to the brain. Electrode designs are evolving to use small (< 15 μm) flexible neural probes to minimize inflammatory responses and enable chronic use. However, their flexibility limits the scalability — it is challenging to assemble 3D arrays of such electrodes, to insert the arrays of flexible neural probes into the brain without buckling, and to uniformly distribute them into large areas of the brain. Thus, we created Shape Memory Alloy (SMA) actuated Woven Neural Probes (WNPs). A linear array of 32 flexible insulated microwires were interwoven with SMA wires resulting in an ordered array of parallel electrodes. SMA WNPs were shaped to an initial constricted profile for reliable insertion into a tissue phantom. Following insertion, the SMA wires were used as actuators to unravel the constricted WNP to distribute electrodes across large volumes. We demonstrated that the WNPs could be inserted into the brain without buckling and record neural activity. In separate experiments, we showed that the SMA could mechanically distribute the WNPs via thermally induced actuation. This work thus highlights the potential of actuatable WNPs to be used as a platform for neural recording.

scholarly journals Mapping the common and distinct neural correlates of visual, rule and motor conflict

2020 ◽  
Author(s):  
Bryony Goulding Mew ◽  
Darije Custovic ◽  
Eyal Soreq ◽  
Romy Lorenz ◽  
Ines Violante ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Neural Correlates ◽  
Brain Regions ◽  
Control Mechanisms ◽  
Activation Patterns ◽  
The Common ◽  
Full Factorial ◽  
The Brain ◽  
Categorical Scale ◽  
Conflict Types

AbstractFlexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals The current state of electrocorticography-based brain–computer interfaces

2020 ◽  
Vol 49 (1) ◽  
pp. E2 ◽  
Author(s):  
Kai J. Miller ◽  
Dora Hermes ◽  
Nathan P. Staff
Keyword(s):  
Functional Limitations ◽  
Brain Computer Interfaces ◽  
Brain Surface ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Temporal And Spatial ◽  
Direct Feedback ◽  
The Brain ◽  
Lateral Sclerosis

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

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