Cortical Source Estimates
Cortical Source Estimates - Related Videos
09:32
Cortical Source Analysis of High-Density EEG Recordings in Children
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Cited by 6 •
2014
In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.
08:45
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Cited by 17 •
2012
We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.
09:14
Preparing a Celadonite Electron Source and Estimating Its Brightness
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2019
The article presents a protocol to prepare a celadonite source and estimate its brightness for use in a long-range imaging low-energy electron point-source projection microscope.
08:25
BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams
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Cited by 2 •
2019
We present a simple and cost-effective method to build an open-source datalogger that measures the conductance of nonvascular cryptogams together with the environmental temperature and humidity. We describe the hardware design of the datalogger and provide step-by-step assembly instructions, the list of required open-source logging software, the code to run the datalogger, and a calibration protocol.
09:34
A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
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Cited by 5 •
2017
In this protocol, the femur surface strains are estimated during fracture testing using the digital image correlation technique. The novelty of the method involves application of a high-contrast stochastic speckle pattern on the femur surface, carefully specified illumination, high speed video capture, and digital image correlation analysis for strain calculations.
FAQs
A forward model predicts how activity at candidate brain locations would appear in scalp recordings. Cortical source estimates use that prediction as the signal-propagation link between hypothesized neural generators and measured EEG or MEG. Without it, the inverse calculation would lack a principled way to relate anatomical activity to the patterns observed at the sensors.
The inverse problem is difficult because several neural sources can contribute to the same scalp measurement after volume conduction. Consequently, source estimation identifies likely generators rather than providing a direct, uniquely observed readout of one region. Interpreting the result therefore requires attention to the head model, forward model, and assumptions built into the inverse method.
Volume conduction means that activity from multiple sources contributes to measurements at the scalp, making a sensor signal difficult to attribute to a single cortical region. Source estimation addresses this mixing computationally by evaluating plausible cortical generators. This helps shift interpretation from where activity was recorded at the scalp toward which anatomically meaningful regions may have produced it.
Cortical Source Estimates complement sensor-level analyses by adding an anatomical perspective to the same recorded activity. Sensor-level results describe patterns at EEG or MEG measurement locations, whereas source estimates help associate those patterns with cortical regions and neural activity. Using both views can connect the measured signal with functional brain organization without treating either representation as sufficient alone.
A basic workflow begins with recorded EEG or MEG data, then uses a head model and forward model to represent how candidate cortical activity could propagate to the scalp. An inverse method is subsequently applied to infer likely generators. The resulting estimates can then be examined by cortical region and in relation to the recorded measurements.
Researchers can use Cortical Source Estimates when a study asks where functional activity may arise during sensory processing or cognition. They provide a way to characterize activity in cortical regions rather than limiting analysis to sensor locations. This regional perspective can help relate measured signals to the neural systems engaged by the process under investigation.
In studies of neurological disorders, source estimates can help researchers examine abnormal network dynamics in relation to cortical regions. This adds anatomical context to unusual patterns observed in EEG or MEG recordings. The approach therefore supports comparisons between measured signals and regional functional activity, while preserving the distinction between computationally inferred generators and directly recorded scalp measurements.