scholarly journals fMRI single trial discovery of spatio-temporal brain activity patterns

2016 ◽  
Vol 38 (3) ◽  
pp. 1421-1437 ◽  
Author(s):  
Michele Allegra ◽  
Shima Seyed-Allaei ◽  
Fabrizio Pizzagalli ◽  
Fahimeh Baftizadeh ◽  
Marta Maieron ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Single Trial ◽  
Spatio Temporal

scholarly journals The structured flow on the brain's resting state manifold

2022 ◽  
Author(s):  
Jan Fousek ◽  
Giovanni Rabuffo ◽  
Kashyap Gudibanda ◽  
Hiba Sheheitli ◽  
Viktor Jirsa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Activity Patterns ◽  
Predictive Coding ◽  
Network Connectivity ◽  
High Amplitude ◽  
Characteristic Functional ◽  
Spatio Temporal ◽  
Generative Mechanism ◽  
Task Conditions

Spontaneously fluctuating brain activity patterns emerge at rest and relate to brain functional networks involved in task conditions. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a complete mechanistic description in terms of the constituent entities and the productive relation of their causal activities leading to the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major empirical data features including spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding.

scholarly journals On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG

2019 ◽  
Author(s):  
Lau M. Andersen ◽  
Christoph Pfeiffer ◽  
Silvia Ruffieux ◽  
Bushra Riaz ◽  
Dag Winkler ◽  
...  
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Signal Strength ◽  
Classification Performance ◽  
Discrimination Accuracy ◽  
Sensor Coverage ◽  
Non Invasive ◽  
Spatio Temporal ◽  
Sensor Density ◽  
The Right

AbstractMagnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20-40 mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system.We compared spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges.We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (15-30 ms). This corresponded to electroencephalographic (EEG) response components N16 and P23, and was an unexpected observation as these components are usually not observed in conventional MEG. They indicate that on-scalp MEG opens up for a richer registration of the cortical signal, allowing for sensitivity to what are potentially sources in the thalamo-cortical radiation and to quasi-radial sources.We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60-75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We believe this may be due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We discuss how sensor density and coverage as well as between-phalange source field dissimilarities may influence our hypothesis testing, which we believe to be useful for future benchmarking measurements.

scholarly journals Методика пространственно-временного анализа электрической активности головного мозга

2020 ◽  
Vol 46 (11) ◽  
pp. 39
Author(s):  
А.Е. Руннова ◽  
М.О. Журавлев ◽  
А.Р. Киселёв ◽  
А.О. Сельский
Keyword(s):  
Wavelet Transform ◽  
Electrical Activity ◽  
Continuous Wavelet Transform ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Activity Patterns ◽  
Spatial Dynamics ◽  
Continuous Wavelet ◽  
Brain Electrical Activity ◽  
Spatio Temporal

In the framework of this work a new method based on continuous wavelet transform was proposed for analyzing the spatio-temporal dynamics of brain activity patterns. We described the example of this method application for the analysis of brain electrical activity signals. It is shown that this method has the ability to visually detect the occurrence and spatial dynamics of frequency patterns.

scholarly journals Neural alignment predicts learning outcomes in students taking an introduction to computer science course

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Brain Activity ◽  
Activity Patterns ◽  
Real Life ◽  
Final Exam ◽  
Neural Representations ◽  
Real Life Setting ◽  
The Right ◽  
Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

scholarly journals Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 226
Author(s):  
Lisa-Marie Vortmann ◽  
Leonid Schwenke ◽  
Felix Putze
Keyword(s):  
Augmented Reality ◽  
Eye Tracking ◽  
Brain Activity ◽  
Activity Patterns ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Neural Net ◽  
Dependent Manner ◽  
Tracking Data ◽  
Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

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