Introduction: Task-based language fMRI is a non-invasive method of identifying brain regions subserving language that is used to plan neurosurgical resections which potentially encroach on eloquent regions. The use of unstructured fMRI paradigms, such as naturalistic fMRI, to map language is of increasing interest. Their analysis necessitates the use of alternative methods such as machine learning (ML) and deep learning (DL) because task regressors may be difficult to define in these paradigms.
Methods: Using task-based language fMRI as a starting point, this study investigates the use of different categories of ML and DL algorithms to identify brain regions subserving language. Data comprising of seven task-based language fMRI paradigms were collected from 26 individuals, and ML and DL models were trained to classify voxel-wise fMRI time series.
Results: The general machine learning and the interval-based methods were the most promising in identifying language areas using fMRI time series classification. The geneal machine learning method achieved a mean whole-brain Area Under the Receiver Operating Characteristic Curve (AUC) of , mean Dice coefficient of and mean Euclidean distance of mm between activation peaks across the evaluated regions of interest. The interval-based method achieved a mean whole-brain AUC of , mean Dice coefficient of and mean Euclidean distance of mm between activation peaks across the evaluated regions of interest.
Discussion: This study demonstrates the utility of different ML and DL methods in classifying task-based language fMRI time series. A potential application of these methods is the identification of language activation from unstructured paradigms.
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http://dx.doi.org/10.3389/fradi.2024.1495181 | DOI Listing |
Annu Rev Chem Biomol Eng
January 2025
1Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; email:
Understanding the molecular, cellular, and physiological components of neurodegenerative diseases (NDs) is paramount for developing accurate diagnostics and efficacious therapies. However, the complexity of ND pathology and the limitations associated with conventional analytical methods undermine research. Fortunately, microfluidic technology can facilitate discoveries through improved biomarker quantification, brain organoid culture, and small animal model manipulation.
View Article and Find Full Text PDFJ Bone Joint Surg Am
November 2024
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
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PLoS One
January 2025
Department of Computer Science, University of Jaén, Jaén, Spain.
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
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