Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR

Glover, Gary H.; Li, Tie‐Qiang; Ress, David

doi:10.1002/1522-2594(200007)44:1<162::aid-mrm23>3.0.co;2-e

Cited by 1,836 publications

(1,618 citation statements)

References 14 publications

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“…B0 field distortions due to susceptibility changes at tissue boundaries can cause signal dropouts and geometric distortions (Hutton, Andersson, Deichmann, & Weiskopf, 2013). This could be addressed using complimentary strategies together with PMC implementation (Glover, Li, & Ress, 2000; Lutti, Thomas, Hutton, & Weiskopf, 2013). …”

Section: Discussionmentioning

confidence: 99%

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Huang

Carlin

Alink

et al. 2018

Human Brain Mapping

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We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in‐bore optical camera to track an external marker attached to the participant via a custom‐molded mouthpiece. The study was conducted at two resolutions (1.5 mm vs 3 mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi‐voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist‐molded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.

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Section: Discussionmentioning

confidence: 99%

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Huang

Carlin

Alink

et al. 2018

Human Brain Mapping

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“…A set of 14 physiological regressors, generated using an in‐house developed Matlab toolbox (Hutton et al, 2011), were based on cardiac and respiratory traces recorded on Spike2 (Cambridge Electronic Design Limited, Cambridge, UK) with a respiration belt and pulse oximeter. Twelve regressors, based on a set of sine and cosine Fourier series components extending to the third harmonic, were built to model the cardiac and respiratory phase (Glover, Li, & Ress, 2000; Josephs, Howseman, Friston, & Turner, 1997). Two additional regressors were included to model the variation in respiratory volume (based on Birn et al, 2006, 2008) and heart rate (based on Chang & Glover, 2009) .…”

Section: Methodsmentioning

confidence: 99%

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Corbin

Todd

Friston

et al. 2018

Human Brain Mapping

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Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false‐positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first‐order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the “FAST” model implemented in SPM is used with a well‐controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal‐to‐noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t‐score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t‐score measure is directly related to the t‐score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.

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“…The effect of respiratory and cardiac confounds (corrected using RETROICOR) (Glover, Li, & Ress, 2000) and subsequently variations in breathing depth and heart rate interval (Birn, Diamond, Smith, & Bandettini, 2006; Chang, Cunningham, & Glover, 2009) were reduced using custom MATLAB code. Data were then preprocessed according to standard methodology prior to FC analysis (Fox et al., 2005).…”

Section: Methodsmentioning

confidence: 99%

Thalamic functional connectivity and its association with behavioral performance in older age

et al. 2018

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IntroductionDespite the thalamus’ dense connectivity with both cortical and subcortical structures, few studies have specifically investigated how thalamic connectivity changes with age and how such changes are associated with behavior. This study investigated the effect of age on thalamo‐cortical and thalamo‐hippocampal functional connectivity (FC) and the association between thalamic FC and visual–spatial memory and reaction time (RT) performance in older adults.MethodsResting‐state functional magnetic resonance images were obtained from younger (n = 20) and older (n = 20) adults. A seed‐based approach was used to assess the FC between the thalamus and (1) sensory resting‐state networks; (2) the hippocampus. Participants also completed visual–spatial memory and RT tasks, from the Cambridge Neuropsychological Test Automated Battery (CANTAB).ResultsOlder adults exhibited a loss of specificity in the FC between sensory thalamic subregions and corresponding sensory cortex. Greater thalamo‐motor FC in older adults was associated with faster RTs. Furthermore, older adults exhibited greater thalamo‐hippocampal FC compared to younger adults, which was greatest for those with the poorest visual–spatial memory performance.ConclusionAlthough older adults exhibited poorer visual–spatial memory and slower reaction times compared to younger adults, “good” and “poorer” older performers exhibited different patterns of thalamo‐cortical and thalamo‐hippocampal FC. These results highlight the potential role of thalamic connectivity in supporting reaction times and memory in aging. Furthermore, these results highlight the importance of including the thalamus in studies of aging to fully understand how brain changes with age may be associated with behavior.

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Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR

Cited by 1,836 publications

References 14 publications

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Thalamic functional connectivity and its association with behavioral performance in older age

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