Sound-Induced Flash Illusion is Resistant to Feedback Training

A single flash accompanied by two auditory beeps tends to be perceived as two flashes (Shams et al. Nature 408:788, 2000, Cogn Brain Res 14:147–152, 2002). This phenomenon is known as ‘sound-induced flash illusion.’ Previous neuroimaging studies have shown that this illusion is correlated with modulation of activity in early visual cortical areas (Arden et al. Vision Res 43(23):2469–2478, 2003; Bhattacharya et al. NeuroReport 13:1727–1730, 2002; Shams et al. NeuroReport 12(17):3849–3852, 2001, Neurosci Lett 378(2):76–81, 2005; Watkins et al. Neuroimage 31:1247–1256, 2006, Neuroimage 37:572–578, 2007; Mishra et al. J Neurosci 27(15):4120–4131, 2007). We examined how robust the illusion is by testing whether the frequency of the illusion can be reduced by providing feedback. We found that the sound-induced flash illusion was resistant to feedback training, except when the amount of monetary reward was made dependent on accuracy in performance. However, even in the latter case the participants reported that they still perceived illusory two flashes even though they correctly reported single flash. Moreover, the feedback training effect seemed to disappear once the participants were no longer provided with feedback suggesting a short-lived refinement of discrimination between illusory and physical double flashes rather than vanishing of the illusory percept. These findings indicate that the effect of sound on the perceptual representation of visual stimuli is strong and robust to feedback training, and provide further evidence against decision factors accounting for the sound-induced flash illusion

Ten simple rules for reporting voxel-based morphometry studies

Voxel-based morphometry [Ashburner, J. and Friston, K.J., 2000. Voxel-based morphometry—the methods. NeuroImage 11(6 Pt 1), 805–821] is a commonly used tool for studying patterns of brain change in development or disease and neuroanatomical correlates of subject characteristics. In performing a VBM study, many methodological options are available; if the study is to be easily interpretable and repeatable, the processing steps and decisions must be clearly described. Similarly, unusual methods and parameter choices should be justified in order to aid readers in judging the importance of such options or in comparing the work with other studies. This editorial suggests core principles that should be followed and information that should be included when reporting a VBM study in order to make it transparent, replicable and useful

Serial Correlations in Single-Subject fMRI with Sub-Second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences

Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making

Prefrontal involvement in imitation learning of hand actions : effects of practice and expertise.

In this event-related fMRI study, we demonstrate the effects of a single session of practising configural hand actions (guitar chords) on cortical activations during observation, motor preparation, and imitative execution. During the observation of non-practised actions, the mirror neuron system (MNS), consisting of inferior parietal and ventral premotor areas, was more strongly activated than for the practised actions. This finding indicates a strong role of the MNS in the early stages of imitation learning. In addition, the dorsolateral prefrontal cortex (DLPFC) was selectively involved during observation and motor preparation of the non-practised chords. This finding confirms Buccino et al.’s (2004a) model of imitation learning: for actions that are not yet part of the observer’s motor repertoire, DLPFC engages in operations of selection and combination of existing, elementary representations in the MNS. The pattern of prefrontal activations further supports Shallice’s (2004) proposal of a dominant role of the left DLPFC in modulating lower-level systems, and of a dominant role of the right DLPFC in monitoring operations

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Visual feedback alters force control and functional activity in the visuomotor network after stroke.

Modulating visual feedback may be a viable option to improve motor function after stroke, but the neurophysiological basis for this improvement is not clear. Visual gain can be manipulated by increasing or decreasing the spatial amplitude of an error signal. Here, we combined a unilateral visually guided grip force task with functional MRI to understand how changes in the gain of visual feedback alter brain activity in the chronic phase after stroke. Analyses focused on brain activation when force was produced by the most impaired hand of the stroke group as compared to the non-dominant hand of the control group. Our experiment produced three novel results. First, gain-related improvements in force control were associated with an increase in activity in many regions within the visuomotor network in both the stroke and control groups. These regions include the extrastriate visual cortex, inferior parietal lobule, ventral premotor cortex, cerebellum, and supplementary motor area. Second, the stroke group showed gain-related increases in activity in additional regions of lobules VI and VIIb of the ipsilateral cerebellum. Third, relative to the control group, the stroke group showed increased activity in the ipsilateral primary motor cortex, and activity in this region did not vary as a function of visual feedback gain. The visuomotor network, cerebellum, and ipsilateral primary motor cortex have each been targeted in rehabilitation interventions after stroke. Our observations provide new insight into the role these regions play in processing visual gain during a precisely controlled visuomotor task in the chronic phase after stroke