Reproducibility of Activation Maps for Longitudinal Studies of Visual Function by Functional Magnetic Resonance Imaging

Ming, Jian; Thulborn, Keith R.; Szlyk, Janet P.

doi:10.1167/iovs.11-8375

Cited by 6 publications

(7 citation statements)

References 51 publications

(53 reference statements)

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“…It was also noted from Tables V and VI that ICC values computed using maximum and mean CNR of the first four sessions are usually greater than those calculated using all eight sessions. If we compare these ICC values to those from short-term studies (Ming et al, 2012, Kristo et al, 2014, similar observations can be made. This is reasonable considering the longterm and short-term factors described in the introduction.…”

Section: Resultssupporting

confidence: 66%

“…A variety of studies have been conducted to examine fMRI reproducibility within a session (Tegeler et al, ), across multiple sessions for a single subject (McGonigle et al, ), and across multiple subjects and sessions (Rombouts et al, ; Specht et al, ; Wei et al, , Ming et al, , Kristo et al, ). Some studies were carried out during a relatively short period of time with two or more repeated sessions separated by a couple of hours, a few days, or up to 2 months (Rombouts et al, ; McGonigle et al, ; Specht et al, ; Ming et al, , Kristo et al, ). A small number of studies were performed to examine the long‐term fMRI reproducibility over 1 year, which is comparable to the time duration of clinical trials (Wei et al, ; Yoo et al, ).…”

Section: Introductionmentioning

confidence: 99%

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A study of long‐term fMRI reproducibility using data‐driven analysis methods

Song

Panych

Chou

et al. 2014

Int J Imaging Syst Tech

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The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI-based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques. Particularly, the assessment of reproducibility is complicated by a fact that fMRI results may depend on data analysis techniques used in reproducibility studies. In this work, the long-term fMRI reproducibility was investigated with a focus on the data analysis methods. Two spatial smoothing techniques, including a wavelet-domain Bayesian method and the Gaussian smoothing, were evaluated in terms of their effects on the long-term reproducibility. A multivariate support vector machine (SVM)-based method was used to identify active voxels, and compared to a widely used general linear model (GLM)-based method at the group level. The reproducibility study was performed using multisession fMRI data acquired from eight healthy adults over 1.5 years’ period of time. Three regions-of-interest (ROI) related to a motor task were defined based upon which the long-term reproducibility were examined. Experimental results indicate that different spatial smoothing techniques may lead to different reproducibility measures, and the wavelet-based spatial smoothing and SVM-based activation detection is a good combination for reproducibility studies. On the basis of the ROIs and multiple numerical criteria, we observed a moderate to substantial within-subject long-term reproducibility. A reasonable long-term reproducibility was also observed from the inter-subject study. It was found that the short-term reproducibility is usually higher than the long-term reproducibility. Furthermore, the results indicate that brain regions with high contrast-to-noise ratio do not necessarily exhibit high reproducibility. These findings may provide supportive information for optimal design/implementation of fMRI studies and data interpretation.

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Section: Resultssupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

A study of long‐term fMRI reproducibility using data‐driven analysis methods

Song

Panych

Chou

et al. 2014

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Similar results are reported for resting-state fMRI studies (Blautzik et al, 2012;Meindl et al, 2010;Shehzad et al, 2009;Song et al, 2012). A recent eye movement study of the reliability of a saccadic task showed an interclass correlation coefficient of > 0.5 in 75% of the subjects studied (Ming et al, 2012). Further, studies report that reliability is improved when movement artifacts are small (Lund et al, 2005) and that 3-T scanners have better repeatability compared with 1.5-T scanners (Zou et al, 2005).…”

Section: Reliability Of Fmrisupporting

confidence: 79%

Task-Modulated Coactivation of Vergence Neural Substrates

et al. 2014

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While functional magnetic resonance imaging (fMRI) has identified which regions of interests (ROIs) are functionally active during a vergence movement (inward or outward eye rotation), task-modulated coactivation between ROIs is less understood. This study tested the following hypotheses: (1) significant task-modulated coactivation would be observed between the frontal eye fields (FEFs), the posterior parietal cortex (PPC), and the cerebellar vermis (CV); (2) significantly more functional activity and task-modulated coactivation would be observed in binocularly normal controls (BNCs) compared with convergence insufficiency (CI) subjects; and (3) after vergence training, the functional activity and task-modulated coactivation would increase in CIs compared with their baseline measurements. A block design of sustained fixation versus vergence eye movements stimulated activity in the FEFs, PPC, and CV. fMRI data from four CI subjects before and after vergence training were compared with seven BNCs. Functional activity was assessed using the blood oxygenation level dependent (BOLD) percent signal change. Task-modulated coactivation was assessed using an ROI-based taskmodulated coactivation analysis that revealed significant correlation between the FEF, PPC, and CV ROIs. Prior to vergence training, the CIs had a reduced BOLD percent signal change compared with BNCs for the CV ( p < 0.05), FEFs, and PPC ( p < 0.01). The BOLD percent signal change increased within the CV, FEF, and PPC ROIs ( p < 0.001) as did the task-modulated coactivation between the FEFs and CV as well as the PPC and CV ( p < 0.05) when comparing the CI pre-and post-training datasets. Results from the Convergence Insufficiency Symptom Survey were correlated to the percent BOLD signal change from the FEFs and CV ( p < 0.05).

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“…Anatomic imaging included nonvolumetric FLAIR imaging and volumetric MPRAGE sequences slices with slice gap of 0 mm) were acquired using clinical fMRI equipment (MRIv Technologies, Bannockburn, IL, USA) 15 and software to present a visually guided saccade for 30 seconds interleaved with 30 seconds of rest (repeated four times). 16 Diffusion and fMRI data sets then underwent spatial normalization to the MNI152 standard brain, 17 using a 12parameter affine coregistration algorithm (MINC tools; McConnell Brain Imaging Centre, http://www.bic.mni.mcgill.ca/ ServicesSoftware/MINC, available in the public domain), 18 and all datasets underwent rigorous quality assurance by an MR physicist (B.A.M.). A previously established and validated automatic diffusion tractography algorithm 10 was then used to identify and segment the OR.…”

Section: Methodsmentioning

confidence: 99%