http://www.nitrc.org/forum/forum.php?forum_id=6229 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Quantifying learning-dependent changes in the brain: Single-trial multivoxel pattern analysis requires slow event-related fMRI.</b></p> <p>Psychophysiology. 2016 May 6;</p> <p>Authors: Visser RM, de Haan MI, Beemsterboer T, Haver P, Kindt M, Scholte HS</p> <p>Abstract<br/> Single-trial analysis is particularly useful for assessing cognitive processes that are intrinsically dynamic, such as learning. Studying these processes with fMRI is problematic, as the low signal-to-noise ratio of fMRI requires the averaging over multiple trials, obscuring trial-by-trial changes in neural activation. The superior sensitivity of multivoxel pattern analysis over univariate analyses has opened up new possibilities for single-trial analysis, but this may require different fMRI designs. Here, we measured fMRI and pupil dilation responses during discriminant aversive conditioning, to assess associative learning in a trial-by-trial manner. The impact of design choices was examined by varying trial spacing and trial order in a series of five experiments (total n = 66), while keeping stimulus duration constant (4.5 s). Our outcome measure was the change in similarity between neural response patterns related to two consecutive presentations of the same stimulus (within-stimulus) and between patterns related to pairs of different stimuli (between-stimulus) that shared a specific outcome (electric stimulation vs. no consequence). This trial-by-trial similarity analysis revealed clear single-trial learning curves in conditions with intermediate (8.1-12.6 s) and long (16.5-18.4 s) intervals, with effects being strongest in designs with long intervals and counterbalanced stimulus presentation. No learning curves were observed in designs with shorter intervals (1.6-6.1 s), indicating that rapid event-related designs-at present, the most common designs in fMRI research-are not suited for single-trial pattern analysis. These findings emphasize the importance of deciding on the type of analysis prior to data collection.<br/> </p><p>PMID: 27153295 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Tue, 05 May 2026 4:33:34 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator