Brain Invader Data
Brain Invader Data - Related Videos
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Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
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Cited by 7 •
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We report a technique permitting live imaging of microtubule dynamics in glioblastoma (GBM) cells invading a vertebrate brain tissue. Coupling the orthotopic injection of fluorescently tagged GBM cells into a transparent zebrafish brain with high-resolution intravital imaging allows the measurement of cytoskeleton dynamics during in situ cancer invasion.
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Laser Microdissection: A Technique to Isolate Invading Colorectal Cancer cells from a 3D Organotypic Culture Model
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The video describes the laser dissection of the 3D organotypic co-culture model with micron-level precision. In 3D organotypic models, stromal constituents such as fibroblasts are 3-dimensionally co-cultured with cancer epithelial cells. The 3D model serves as an experimental tool that enables invasion and cancer-stroma interactions to be studied in near-physiological conditions.
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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Cited by 49 •
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Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.
01:30
Data Interpretation
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2026
Research papers often present findings in the form of numerical tables, statistical outputs, and graphical representations. However, numerical results alone do not convey meaning unless researchers examine and place them in context. Data interpretation is the systematic process through which researchers transform raw information into clear, meaningful insights. It begins only after researchers have collected data through experiments, surveys, or observational studies. In their unprocessed form,...
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How Data are Classified: Numerical Data
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2023
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data. Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
FAQs
The key contrast is whether EEG and behavioral measurements differ when a participant attends to a designated item versus ignores a distractor. Those differences provide evidence about target detection and attention. In the dataset, neural features can therefore be examined alongside task behavior rather than treating every visual event as equivalent, helping researchers relate stimulus status to cognitive processing.
The P300 provides a neural feature associated with detecting an attended target. Researchers can examine its event-related potential pattern to determine whether the EEG signal reflects the participant’s focus on the designated visual item. Its presence or variation is therefore relevant for studying attention, cognitive processing, and the neural basis of selection during the task.
Classification uses neural features extracted from the recorded EEG to distinguish responses linked to attended targets from responses linked to distractors. The resulting signal patterns can be related to the participant’s intended choice in the visual task. This makes the data useful for evaluating whether brain activity can provide reliable information about selection without relying only on observed behavior.
Behavioral measurements show how participants perform during the task, while neurophysiological measurements reveal associated brain responses. Considering both can connect observable task outcomes with EEG features related to attention and target detection. This combined perspective helps researchers evaluate cognitive processing and BCI performance more fully than either measurement type would provide alone.
Analysis begins with the behavioral records and EEG measurements collected during the visual game. Researchers distinguish events involving designated targets from those involving distractors, then examine event-related potentials and other signal features associated with those events. These features can subsequently support comparisons across participants or conditions and classification of the user’s intended selection.
Comparing Brain Invader data across participants, sessions, or experimental conditions shows how attention-related EEG features and task performance vary in different contexts. Such comparisons can help researchers evaluate the consistency of target-related responses and BCI performance. They also provide a framework for determining whether observed patterns are specific to one participant or persist across measurements.
These data support research on attention, cognitive processing, EEG-based signal analysis, and BCI performance. In neuroscience, investigators can relate P300 responses and other neural features to the detection of attended visual targets. In BCI research, the same measurements help assess whether brain signals can be classified to represent a user’s intended selection during the task.