Neural Efficiency
Neural Efficiency - Related Videos
06:57
Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
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Cited by 10 •
2016
This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.
10:32
Enumeration of Neural Stem Cells Using Clonal Assays
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Cited by 5 •
2016
Neural stem cells (NSCs) refer to cells which can self-renew and differentiate into the three neural lineages. Here, we describe a protocol to determine NSC frequency in a given cell population using neurosphere formation and differentiation under clonal conditions.
12:48
Isolation and Culture of Neural Crest Cells from Embryonic Murine Neural Tube
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Cited by 26 •
2012
Isolation of embryonic neural crest from the neural tube facilitates the use of in vitro methods for studying migration, self-renewal, and multipotency of neural crest.
14:46
Efficient Generation of hiPSC Neural Lineage Specific Knockin Reporters Using the CRISPR/Cas9 and Cas9 Double Nickase System
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Cited by 5 •
2015
Genome editing tools such as the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas (CRISPR-associated) system have greatly improved gene targeting efficiency in human induced pluripotent stem cells (hiPSCs). This manuscript describes a protocol for generating lineage specific hiPSC reporter using CRISPR/Cas system assisted homologous recombination.
11:17
Efficient Neural Differentiation using Single-Cell Culture of Human Embryonic Stem Cells
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Cited by 4 •
2020
Presented here is a protocol for the generation of a single-cell culture of human embryonic stem cells and their subsequent differentiation into neural progenitor cells. The protocol is simple, robust, scalable, and suitable for drug screening and regenerative medicine applications.
FAQs
Neural efficiency can appear as stronger recruitment of task-relevant networks, reduced activity in competing regions, or better communication among brain areas. These patterns suggest that the brain is allocating processing toward information needed for the task rather than broadly activating unrelated systems. The relevant pattern may therefore involve both what becomes active and what becomes less engaged.
A smaller activation signal does not by itself establish better processing. Researchers interpret neural measures alongside accuracy, reaction time, and task difficulty. A person may show limited activation because a task is easy, or because performance is poor. Combining brain and behavioral evidence helps distinguish effective resource use from insufficient task engagement.
As skills develop, processing may become more efficient through improved communication among brain areas and more focused recruitment of relevant networks. This change can help explain why expertise is associated with different patterns of brain activity than less practiced performance. In psychology, the comparison is useful for studying learning and how practice changes cognitive processing.
Studies commonly pair functional neuroimaging or electrophysiological measures with behavioral outcomes. Brain data indicate where or when processing changes, while accuracy and reaction time show whether those changes support task performance. Researchers can also consider task difficulty, allowing neural responses to be interpreted in relation to the demands placed on the participant rather than in isolation.
A typical comparison examines brain activation during a cognitive task and relates it to performance measures such as accuracy and reaction time. Researchers may also vary or account for task difficulty, then assess whether activity reflects focused network recruitment, reduced engagement of competing regions, or improved communication. This framework supports comparisons across individuals, skills, and conditions.
It helps researchers investigate individual differences in attention, learning, expertise, intelligence, and cognitive aging. The same framework can inform studies of training and education by examining whether performance changes coincide with altered neural organization. It is also relevant to neurological-condition research, where brain activity and behavior can be considered together to characterize cognitive functioning.