The McCollough Effect Produces Increased fMRI Signal in the Fusiform and Lingual Gyri

NeuroImage ◽  
1998 ◽  
Vol 7 (4) ◽  
pp. S41 ◽  
Author(s):  
T.W. James ◽  
G.K. Humphrey ◽  
J.S. Gati ◽  
R.S. Menon ◽  
M.A. Goodale
2000 ◽  
Author(s):  
Lorraine G. Allan ◽  
Teresa A. Molino ◽  
Shepard Siegel

Author(s):  
Dennis H. Holding ◽  
Paul D. Jones
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan Baijot ◽  
Stijn Denissen ◽  
Lars Costers ◽  
Jeroen Gielen ◽  
Melissa Cambron ◽  
...  

AbstractGraph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.


NeuroImage ◽  
2003 ◽  
Vol 19 (2) ◽  
pp. 253-260 ◽  
Author(s):  
Vesa Kiviniemi ◽  
Juha-Heikki Kantola ◽  
Jukka Jauhiainen ◽  
Aapo Hyvärinen ◽  
Osmo Tervonen

NeuroImage ◽  
1998 ◽  
Vol 8 (4) ◽  
pp. 340-349 ◽  
Author(s):  
Xavier Descombes ◽  
Frithjof Kruggel ◽  
D.Yves von Cramon

2013 ◽  
Vol 25 (8) ◽  
pp. 1305-1314 ◽  
Author(s):  
Katherine S. Moore ◽  
Do-Joon Yi ◽  
Marvin Chun

Fundamental to our understanding of learning is the role of attention. We investigated how attention affects two fMRI measures of stimulus-specific memory: repetition suppression (RS) and pattern similarity (PS). RS refers to the decreased fMRI signal when a stimulus is repeated, and it is sensitive to manipulations of attention and task demands. In PS, region-wide voxel-level patterns of responses are evaluated for their similarity across repeated presentations of a stimulus. More similarity across presentations is related to better learning, but the role of attention on PS is not known. Here, we directly compared these measures during the visual repetition of scenes while manipulating attention. Consistent with previous findings, we observed RS in the scene-sensitive parahippocampal place area only when a scene was attended both at initial presentation and upon repetition in subsequent trials, indicating that attention is important for RS. Likewise, we observed greater PS in response to repeated pairs of scenes when both instances of the scene were attended than when either or both were ignored. However, RS and PS did not correlate on either a scene-by-scene or subject-by-subject basis, and PS measures revealed above-chance similarity even when stimuli were ignored. Thus, attention has different effects on RS and PS measures of perceptual repetition.


2017 ◽  
Vol 163 (4) ◽  
pp. 497-499 ◽  
Author(s):  
M. E. Mel’nikov ◽  
D. D. Bezmaternykh ◽  
E. D. Petrovskii ◽  
L. I. Kozlova ◽  
M. B. Shtark ◽  
...  

NeuroImage ◽  
2008 ◽  
Vol 42 (1) ◽  
pp. 196-206 ◽  
Author(s):  
Bram B. Zandbelt ◽  
Thomas E. Gladwin ◽  
Mathijs Raemaekers ◽  
Mariët van Buuren ◽  
Sebastiaan F. Neggers ◽  
...  

2020 ◽  
Vol 220 ◽  
pp. 281-282 ◽  
Author(s):  
Annabel Umeh ◽  
Jyothika Kumar ◽  
Susan T. Francis ◽  
Peter F. Liddle ◽  
Lena Palaniyappan
Keyword(s):  

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