Cortically Constrained Minimum Norm
Cortically Constrained Minimum Norm - Related Videos
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 •
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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.
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A Minority of One: Conformity to Group Norms
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2023
Source: William Brady & Jay Van Bavel—New York University It is obvious that we are influenced by those around us, but in the early to mid 1900's, psychologists began to study how potent social influence can be on our thoughts and behaviors. Motivated in part by attempts to explain the behaviors of Nazi soldiers in World War II, one topic of considerable interest at the time in psychology was conformity, the phenomenon in which people match their attitudes, behaviors, or beliefs to group...
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Cortical Source Analysis of High-Density EEG Recordings in Children
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Cited by 6 •
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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.
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Local Maximum and Minimum Values
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2026
In multivariable calculus, a function of two variables can exhibit local maximum or minimum values at certain points on its surface. A local maximum occurs when the function's value at a point is greater than at all nearby points, while a local minimum occurs when the function’s value is less than at all nearby locations. These points are referred to as local extrema and are of central importance in optimization problems.Local extrema are found at critical points, where the surface becomes...
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A Novel Cell Injection Method with Minimum Invasion
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2023
This method eliminates any major invasion during cell injections caused by the cell suspension solution.
FAQs
The method first uses a forward model to predict how candidate cortical electrical sources would appear at EEG or MEG sensors. It then compares those predicted signals with the recorded observations and adjusts the source pattern to reduce the mismatch. This forward-to-inverse sequence links sensor-level measurements with a distributed cortical estimate rather than treating each sensor as a direct anatomical location.
The minimum-norm criterion favors the source pattern with the smallest overall amplitude among patterns that explain the recorded data. Because the inverse problem can admit multiple source configurations, this preference supplies a principled way to select one estimate. Regularization further limits sensitivity to noise, helping prevent unstable source maps from reflecting measurement imperfections rather than neural activity.
Restricting candidate sources to the cerebral cortex narrows the set of locations considered during estimation. This constraint makes the resulting activity pattern easier to relate to cortical anatomy than an estimate that permits sources throughout the brain. It also supports interpretation of noninvasive EEG or MEG recordings in terms of activity distributed across the cortical surface.
An application begins with EEG or MEG observations and a model describing how cortical sources generate signals at the sensors. The estimation then searches for a cortical source pattern that explains those observations while minimizing overall amplitude, with regularization used to control noise sensitivity. The resulting distribution can be examined as a map of estimated cortical activity.
Researchers can use the estimates to investigate sensory processing, cognitive activity, epilepsy, and brain-network dynamics. Their value comes from connecting noninvasive sensor recordings with the cortical locations that may contribute to those signals. This connection allows activity recorded outside the brain to be considered alongside cortical structure when studying how neural processes are organized.
It provides an anatomically interpretable estimate of where activity is distributed across the cerebral cortex, rather than only describing signals at recording sensors. Researchers can therefore examine cortical patterns and relate them to structural anatomy, sensory or cognitive processes, epilepsy, or network dynamics. These maps remain estimates produced by an inverse solution, not direct measurements from individual cortical sites.