AIMC Topic: Brain Mapping
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Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network.
NeuroImage
Dec 26, 2021
Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior locali...
A CNN-transformer hybrid approach for decoding visual neural activity into text.
Computer methods and programs in biomedicine
Dec 14, 2021
BACKGROUND AND OBJECTIVE: Most studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the o...
Functional selectivity for social interaction perception in the human superior temporal sulcus during natural viewing.
NeuroImage
Nov 17, 2021
Recognizing others' social interactions is a crucial human ability. Using simple stimuli, previous studies have shown that social interactions are selectively processed in the superior temporal sulcus (STS), but prior work with movies has suggested t...
Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations.
NeuroImage
Nov 12, 2021
In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To ...
QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.
Magnetic resonance in medicine
Oct 31, 2021
PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).
Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis.
Sensors (Basel, Switzerland)
Oct 25, 2021
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult ...
Brain oscillatory correlates of visuomotor adaptive learning.
NeuroImage
Oct 21, 2021
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learn...
Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
NeuroImage
Oct 19, 2021
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological proces...
Exploring self-generated thoughts in a resting state with natural language processing.
Behavior research methods
Oct 13, 2021
The present study seeks to examine individuals' stream of thought in real time. Specifically, we asked participants to speak their thoughts freely out loud during a typical resting-state condition. We first examined the feasibility and reliability of...
MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.
Nature communications
Oct 13, 2021
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires...
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