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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 Brain Network Modulation?

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Dr. Rishabh Pathak

Brain network modulation refers to the process of influencing or altering the connectivity and activity patterns within the brain's functional networks. 

1. Definition:

   - Brain network modulation involves interventions or treatments that target specific brain regions or networks to induce changes in their functional connectivity, activity levels, or communication patterns.

   - The goal of brain network modulation is to restore or optimize the balance and coordination of neural activity within and between different brain regions, ultimately leading to improved cognitive or behavioral outcomes.

 

2. Therapeutic Interventions:

   - Various therapeutic interventions, such as pharmacotherapy, psychotherapy, neuromodulation techniques (e.g., transcranial magnetic stimulation, deep brain stimulation), and lifestyle interventions (e.g., exercise, mindfulness practices), can modulate brain networks in individuals with neuropsychiatric disorders like depression.

   - These interventions aim to target specific brain regions or networks that are implicated in the pathophysiology of the disorder and normalize their activity to alleviate symptoms and improve overall brain function.

 

3. Effects on Connectivity:

   - Brain network modulation can lead to changes in functional connectivity within and between resting-state networks (RSNs) in the brain.

   - For example, antidepressant medications have been shown to modulate connectivity patterns within the Default Mode Network (DMN) and other RSNs, leading to improvements in depressive symptoms.

 

4. Symptom-Specific Effects:

   - Different therapeutic modalities may have distinct effects on specific brain networks or subnetworks, depending on the targeted symptoms or cognitive functions.

   - For instance, treatments like transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS) tend to modulate connectivity in more specific RSNs compared to pharmacotherapy, which may have broader effects on distributed brain networks.

 

5. Personalized Treatment:

   - Understanding how different interventions modulate brain networks can inform the development of personalized and targeted treatment approaches for individuals with neuropsychiatric disorders.

   - By identifying the specific network abnormalities associated with an individual's symptoms and tailoring interventions to address those abnormalities, clinicians can optimize treatment outcomes and enhance therapeutic efficacy.

 

In summary, brain network modulation involves the targeted manipulation of brain network connectivity and activity patterns through various therapeutic interventions to improve cognitive function, alleviate symptoms of neuropsychiatric disorders, and enhance overall brain health. By modulating specific brain networks associated with a particular condition, clinicians can develop more effective and personalized treatment strategies for individuals with diverse neurological and psychiatric challenges.

 

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