maandag 18 juni 2012

An Article from PLoS: The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements

Jonas Kubilius has sent you an open-access article from PLoS ONE.

The sender added this:

This is a rather crazy paper stating that your fMRI analysis results can depend on your computer and operating system. Not sure what to think about that...

Read the open-access, full-text article here:
http://dx.plos.org/10.1371/journal.pone.0038234

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The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements

Abstract:

FreeSurfer is a popular software package to measure cortical thickness and volume of neuroanatomical structures. However, little if any is known about measurement reliability across various data processing conditions. Using a set of 30 anatomical T1-weighted 3T MRI scans, we investigated the effects of data processing variables such as FreeSurfer version (v4.3.1, v4.5.0, and v5.0.0), workstation (Macintosh and Hewlett-Packard), and Macintosh operating system version (OSX 10.5 and OSX 10.6). Significant differences were revealed between FreeSurfer version v5.0.0 and the two earlier versions. These differences were on average 8.8±6.6% (range 1.3–64.0%) (volume) and 2.8±1.3% (1.1–7.7%) (cortical thickness). About a factor two smaller differences were detected between Macintosh and Hewlett-Packard workstations and between OSX 10.5 and OSX 10.6. The observed differences are similar in magnitude as effect sizes reported in accuracy evaluations and neurodegenerative studies.

The main conclusion is that in the context of an ongoing study, users are discouraged to update to a new major release of either FreeSurfer or operating system or to switch to a different type of workstation without repeating the analysis; results thus give a quantitative support to successive recommendations stated by FreeSurfer developers over the years. Moreover, in view of the large and significant cross-version differences, it is concluded that formal assessment of the accuracy of FreeSurfer is desirable.

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maandag 19 maart 2012

Fwd: Similar spatial patterns of neural coding of category selectivity in FFA and VWFA under different attention conditions



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From: "ScienceDirect Publication: Neuropsychologia" <>
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Cc:
Subject: Similar spatial patterns of neural coding of category selectivity in FFA and VWFA under different attention conditions
Date: Mon, 19 Mar 2012 10:01:34 +0100

Publication year: 2012
Source:Neuropsychologia, Volume 50, Issue 5

Guifang Xu, Yi Jiang, Lifei Ma, Zhi Yang, Xuchu Weng

It has long been debated whether attention alters the categorical selectivity in regions such as the fusiform face area (FFA) and the visual word form area (VWFA). We addressed this issue by examining whether the spatial pattern of neural representations for certain stimulus categories in these regions would change under different attention conditions. Faces, Chinese characters, and textures were presented in a block design fMRI experiment where participants in different runs attended to the stimuli under different conditions of attention. After localizing regions of interest (ROIs) in FFA and VWFA using general linear models, we performed spatial pattern analyses to examine both within- and cross-condition classification in these ROIs. The within-condition results replicated previous findings showing significant classification accuracy reduction when there was less attention compared with more attention. Critically, cross-condition classification in both FFA and VWFA revealed significantly above-chance accuracy for all stimulus categories, suggesting similar spatial neural representations across different attention conditions. Further strengthening this conclusion, when the contrast-to-noise ratio (CNR) of the signals was adjusted to increase signal strength, cross-condition classification accuracy for faces in FFA and for Chinese characters in VWFA improved significantly, even approaching within-condition accuracy. This indicates that attention does not modulate the spatial pattern of neural representations involved in category selectivity, but only changes the signal strength relative to the noise level.




Full article



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Suggested reading by Kathleen
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maandag 12 maart 2012

Fwd: Computational advances towards linking BOLD and behavior



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From: "ScienceDirect Publication: Neuropsychologia" <>
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Subject: Computational advances towards linking BOLD and behavior
Date: Mon, 12 Mar 2012 11:48:19 +0100

Publication year: 2012
Source:Neuropsychologia, Volume 50, Issue 4

John T. Serences, Sameer Saproo

Traditionally, fMRI studies have focused on analyzing the mean response amplitude within a cortical area. However, the mean response is blind to many important patterns of cortical modulation, which severely limits the formulation and evaluation of linking hypotheses between neural activity, BOLD responses, and behavior. More recently, multivariate pattern classification analysis (MVPA) has been applied to fMRI data to evaluate the information content of spatially distributed activation patterns. This approach has been remarkably successful at detecting the presence of specific information in targeted brain regions, and provides an extremely flexible means of extracting that information without a precise generative model for the underlying neural activity. However, this flexibility comes at a cost: since MVPA relies on pooling information across voxels that are selective for many different stimulus attributes, it is difficult to infer how specific sub-sets of tuned neurons are modulated by an experimental manipulation. In contrast, recently developed encoding models can produce more precise estimates of feature-selective tuning functions, and can support the creation of explicit linking hypotheses between neural activity and behavior. Although these encoding models depend on strong – and often untested – assumptions about the response properties of underlying neural generators, they also provide a unique opportunity to evaluate population-level computational theories of perception and cognition that have previously been difficult to assess using either single-unit recording or conventional neuroimaging techniques.




Full article



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Suggested reading by Kathleen
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