Dynamic Statistical Mapping
Dynamic Statistical Mapping - Related Videos
11:52
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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2017
The segmentation clock drives oscillatory gene expression across the pre-somitic mesoderm (PSM). Dynamic Notch activity is key to this process. We use imaging and computational analyses to extract temporal dynamics from spatial expression data to demonstrate that Delta ligand and Notch receptor expression oscillate in the vertebrate PSM.
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.
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
11:03
Motor Maps
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex...
01:14
Probability in Statistics
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2023
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts. An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
FAQs
It evaluates neural measurements across both brain location and time, allowing statistical comparisons to identify spatially distinct effects and their temporal onset. This prevents a changing response from being reduced to a single overall value. The resulting analysis can show whether two conditions differ in the same regions at different times or produce different regional patterns altogether.
Neural measurements vary across observations, and that variability can obscure meaningful condition-related patterns. Statistical modeling helps separate structured differences from variation across measurements, making estimated effects more interpretable. This is especially important when comparing responses across complex datasets, because conclusions about regional activity or temporal evolution should reflect both the observed pattern and the variability surrounding it.
A changing pattern can indicate that neural responses are not fixed throughout an observation. Tracking how relationships among measured signals evolve helps identify functionally distinct states and connect transient activity with perception, cognition, or behavior. Rather than treating a response as one stable event, the analysis preserves information about transitions and changing organization across time.
The framework estimates where measured activity differs between conditions and when those differences appear. Such comparisons can reveal whether conditions produce distinct regional responses, different temporal profiles, or changes in both dimensions. This provides a more precise basis for interpreting neural effects than a summary that ignores either their location or their timing.
Dynamic Statistical Mapping can be applied to time-resolved neuroimaging or electrophysiological data, provided the measurements capture neural signals across time and can be related to brain regions or their measured locations. Statistical models then organize these observations into spatial and temporal patterns, supporting analysis of transient responses and their relationships across the recorded dataset.
The analysis can identify functionally distinct neural states, characterize how patterns change, and relate transient brain dynamics to perception, cognition, and behavior. It also supports investigation of disease-related neural changes. These outcomes help researchers interpret complex spatiotemporal datasets and compare neural responses with greater precision across conditions or relevant observations.