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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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Multilayer Biophysical Network Models

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Dr. Rishabh Pathak

Multilayer Biophysical Network Models are advanced computational frameworks that integrate functional and structural connectivity patterns of the brain across multiple layers or levels. 

 

1. Integration of Functional and Structural Connectivity:

   - These models combine information from functional imaging techniques like functional magnetic resonance imaging (fMRI) and structural imaging techniques like diffusion MRI (dMRI) to create a comprehensive understanding of brain network organization.

   - By incorporating both functional and structural connectivity data, multilayer models can capture the dynamic interactions and relationships between different brain regions and networks.

 

2. Study of Network Configurations:

   - Multilayer biophysical network models allow researchers to study the interactions between different network configurations within the brain.

   - These models can provide insights into how changes in network connectivity at the microscale (e.g., neuronal activity) influence network dynamics at the macroscale (e.g., functional connectivity patterns).

 

3. Reliable Diagnostic Markers:

   - By leveraging multilayer biophysical network models, researchers aim to identify more reliable markers for informing diagnostics and personalized treatment targets in neuropsychiatric disorders like depression.

   - These models can help uncover subtle alterations in brain network connectivity that may underlie specific symptoms or subtypes of depression, leading to more targeted and effective interventions.

 

4. Clinical Applications:

   - Multilayer biophysical network models have the potential to enhance our understanding of the neurobiological mechanisms underlying psychiatric disorders and guide the development of novel treatment strategies.

   - By simulating the complex interactions between different brain regions and networks, these models can offer valuable insights into the pathophysiology of conditions like depression and inform the design of personalized therapeutic interventions.

 

5. Future Directions:

   - Continued advancements in multilayer biophysical network modeling, along with the integration of large-scale neuroimaging datasets and machine learning techniques, hold promise for identifying reproducible and interpretable connectome-based subtypes of depression.

   - These models may ultimately be translated into clinical practice to improve diagnostic accuracy, treatment selection, and outcomes for individuals with depression and other neuropsychiatric disorders.

 

In summary, multilayer biophysical network models represent a cutting-edge approach to studying the complex interactions within the brain and their relevance to neuropsychiatric conditions like depression. By integrating functional and structural connectivity data across multiple layers, these models offer a comprehensive framework for understanding brain network dynamics and developing personalized treatment strategies.

 

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