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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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What is fMRI ?

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Dr. Rishabh Pathak

 


Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique that measures brain activity by detecting changes in blood flow and oxygen levels in response to neural activity. fMRI is widely used in neuroscience and cognitive psychology to study brain function and connectivity during various tasks, behaviors, and resting states.

Key features of fMRI include:


1.     Principle of fMRI:

o    fMRI is based on the principle that changes in neural activity are accompanied by changes in blood flow and oxygenation levels in the brain.

o    When a specific brain region becomes active, it requires more oxygenated blood to support the increased metabolic demands of neural activity.

o    The fMRI scanner detects these changes in blood oxygen level-dependent (BOLD) signals, providing a measure of brain activity in different regions.

2.     Task-Based fMRI:

o    In task-based fMRI studies, participants perform specific cognitive tasks or stimuli while inside the MRI scanner.

o    By comparing brain activity during task performance to baseline activity, researchers can identify brain regions involved in task processing and cognitive functions.

3.     Resting-State fMRI:

o    Resting-state fMRI involves measuring spontaneous brain activity while the participant is at rest and not engaged in any specific task.

o    Resting-state fMRI is used to study functional connectivity between different brain regions and identify intrinsic brain networks that are synchronized in their activity.

4.     Spatial and Temporal Resolution:

o    fMRI provides high spatial resolution, allowing researchers to localize brain activity to specific regions or structures.

o    The temporal resolution of fMRI is relatively slow compared to other neuroimaging techniques like EEG, with changes in brain activity measured over seconds to minutes.

5.     Data Analysis:

o    fMRI data is processed and analyzed using specialized software to identify regions of brain activation, create statistical maps, and study functional connectivity.

o    Common analysis methods include general linear modeling, region of interest analysis, independent component analysis, and seed-based correlation analysis.

6.     Applications:

o    fMRI is used in a wide range of research areas, including cognitive neuroscience, psychology, neurology, and psychiatry.

o    Applications of fMRI include studying language processing, memory, emotion regulation, sensory perception, motor function, and clinical conditions such as Alzheimer's disease, schizophrenia, and depression.

Overall, fMRI is a powerful tool for studying brain function and connectivity in both healthy and clinical populations, providing valuable insights into the neural mechanisms underlying cognition, behavior, and brain disorders.


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