Related Experiment Video
Updated: Nov 10, 2025
08:51
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
5.8K
Methodological Considerations for Incorporating Clinical Data Into a Network Model of Retrieval Failures
1Department of Communicative Disorders and Sciences, University at Buffalo.
Topics in Cognitive Science
|April 5, 2021
Summary
Neurogenic communication disorders like aphasia and dementia cause word retrieval failures. Network science offers a robust method to model these failures, overcoming limitations of traditional clinical data analysis.
Area of Science:
- Cognitive Neuroscience
- Computational Linguistics
- Network Science
Background:
- Word retrieval difficulties are common in neurogenic communication disorders such as aphasia and dementia.
- Existing theoretical models often use clinical data, which has limitations regarding the locus of failure, individual heterogeneity, and disease progression.
Purpose of the Study:
- To review recent advancements in using multiplex lexical networks to model word retrieval failures.
- To highlight the advantages of network science in addressing the limitations of clinical data for theoretical modeling.
Main Methods:
- Review of research employing multiplex lexical network models.
- Application of network science techniques to analyze word retrieval failures.
Main Results:
- Network science techniques can effectively handle the complexities and limitations inherent in clinical data.
- Multiplex lexical networks provide a framework for understanding word retrieval failures.
Conclusions:
- Network science offers a powerful approach to modeling word retrieval deficits in neurogenic communication disorders.
- The development of theoretically sound network models is crucial for potential clinical applications and improving patient outcomes.
Related Concept Videos
01:24Kaplan-Meier Approach
352
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
352
01:16Clinical Trials
9.9K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
There are four phases in a clinical trial. A phase one...
9.9K
01:12Retrieval
252
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
252
01:03Data Validation
6.0K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Nursing assessment guides are generally based on holistic models rather than medical...
6.0K
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
304
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
304
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
155
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
155