Skip to main content

Transcranial Current Stimulation (TCS)

Transcranial Current Stimulation (TCS) is a non-invasive neuromodulation technique that involves applying low-intensity electrical currents to the scalp to modulate brain activity. There are two main types of Transcranial Current Stimulation: Transcranial Direct Current Stimulation (tDCS) and Transcranial Alternating Current Stimulation (tACS). Here is an overview of Transcranial Current Stimulation (TCS):


1.      Transcranial Direct Current Stimulation (tDCS):

otDCS involves delivering a constant, low-intensity electrical current (typically between 1-2 mA) through electrodes placed on the scalp. The current flows from anode to cathode and can modulate neuronal excitability in the underlying brain regions.

otDCS is known for its ability to induce polarity-dependent effects on cortical excitability. Anodal stimulation is generally associated with increased excitability, while cathodal stimulation is linked to decreased excitability.

otDCS has been studied for its potential therapeutic applications in various neurological and psychiatric conditions, including depression, chronic pain, stroke rehabilitation, and cognitive enhancement.

2.     Transcranial Alternating Current Stimulation (tACS):

otACS involves delivering alternating current at specific frequencies to the brain through scalp electrodes. By entraining neural oscillations, tACS can influence brain rhythms and synchronization in targeted regions.

otACS is used to modulate endogenous brain oscillations and has been investigated for its effects on cognitive functions, sensory processing, motor control, and sleep regulation.

oDifferent frequencies of tACS (e.g., theta, alpha, beta, gamma) can be applied to match the natural oscillatory patterns of the brain and potentially enhance neural network activity.

3.     Mechanisms of Action:

oThe mechanisms underlying the effects of TCS are complex and involve changes in neuronal membrane potentials, synaptic plasticity, neurotransmitter release, and network connectivity.

oTCS is thought to influence neuronal firing rates, cortical excitability, and functional connectivity within distributed brain networks, leading to alterations in behavior and cognition.

4.    Safety and Considerations:

o TCS is generally considered safe when administered within established guidelines and safety protocols. Adverse effects are typically mild and transient, including tingling sensations, itching, or mild discomfort at the electrode sites.

oIndividual variability in response to TCS, optimal stimulation parameters, and long-term effects are areas of ongoing research and consideration.

In summary, Transcranial Current Stimulation (TCS), including tDCS and tACS, is a non-invasive neuromodulation technique that can modulate brain activity by applying electrical currents to the scalp. These methods have shown promise in research and clinical applications for studying brain function, enhancing cognitive abilities, and potentially treating various neurological and psychiatric disorders.

 

Comments

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....

Linear Models

1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...

Predicting Probabilities

1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...

Maximum Stimulator Output (MSO)

Maximum Stimulator Output (MSO) refers to the highest intensity level that a transcranial magnetic stimulation (TMS) device can deliver. MSO is an important parameter in TMS procedures as it determines the maximum strength of the magnetic field generated by the TMS coil. Here is an overview of MSO in the context of TMS: 1.   Definition : o   MSO is typically expressed as a percentage of the maximum output capacity of the TMS device. For example, if a TMS device has an MSO of 100%, it means that it is operating at its maximum output level. 2.    Significance : o    Safety : Setting the stimulation intensity below the MSO ensures that the TMS procedure remains within safe limits to prevent adverse effects or discomfort to the individual undergoing the stimulation. o Standardization : Establishing the MSO allows researchers and clinicians to control and report the intensity of TMS stimulation consistently across studies and clinical applications. o   Indi...

Supervised Learning

What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...