Quantitative assessment of visual cortex function with fMRI at 7 Tesla-test-retest variability

Multivariate patterns of brain-behavior associations across the adult lifespan

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20–73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks’ FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Brain responses to high frequencies (270 Hz-480 Hz) changes due to vibratory stimulation of human fingertips: An fMRI study

This fMRI study investigated the effects of vibratory stimulation on somatosensory areas during high-frequencies stimulation using a piezoelectric finger stimulation system during an fMRI scan. Twelve healthy right-handed subjects were stimulated at 270 Hz-480 Hz and the fMRI dataset was analysed to generate the activated regions due to the high-frequencies stimulation. The activated regions were identified and thresholded at Puncorrected<0.001 for multiple comparisons. The average effect of frequencies revealed significant activation in the left thalamus, right inferior parietal gyrus, right medial frontal gyrus, and right precuneus whereas the main effect of frequencies revealed significant activation in the left thalamus. The positive effect of frequencies displayed significant activation in the left pallidum, right amygdala, right superior temporal gyrus, right medial temporal gyrus. The vibratory stimulation at a frequency of 330 Hz and 360 Hz (330 Hz<360 Hz) revealed a significant difference in the left thalamus. Findings indicated the role of the secondary somatosensory areas processing and transporting sensory information to perform the perceptual and cognitive function.

Accurate autocorrelation modeling substantially improves fMRI reliability

Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorrelation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. SPM’s alternative pre-whitening method, FAST, performed better than SPM’s default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems.

There has been recent controversy over the validity of commonly-used software packages for functional MRI (fMRI) data analysis. Here, the authors compare the performance of three leading packages (AFNI, FSL, SPM) in terms of temporal autocorrelation modeling, a key statistical step in fMRI analysis.

Transorbital alternating current stimulation modifies BOLD activity in healthy subjects and in a stroke patient with hemianopia: A 7 Tesla fMRI feasibility study

BACKGROUND

Modifying brain activity using non-invasive, low intensity transcranial electrical brain stimulation (TES) has rapidly increased during the past 20 years. Alternating current stimulation (ACS), for example, has been shown to alter brain rhythm activities and modify neuronal functioning in the visual system. Daily application of transorbital ACS to patients with optic nerve damage induces functional connectivity reorganization, and partially restores vision. While ACS is thought to mainly modify neuronal mechanisms, e.g. changes in brain oscillations that can be detected by EEG, it is still an open question, whether and how it may alter BOLD activity.

OBJECTIVE

We evaluated whether transorbital ACS modulates BOLD activity in early visual cortex using high-resolution 7 Tesla functional magnetic resonance imaging (fMRI).

METHODS

In this feasibility study transorbital ACS in the alpha range and sham ACS was applied in a random block design in five healthy subjects for 20 min at 1 mA. Brain activation in the visual areas V1, V2 and V3 were measured using 7 Tesla fMRI-based retinotopic mapping at the time points before (baseline) and after stimulation. In addition, we collected data from one hemianopic stroke patient with visual cortex damage after ten daily sessions with 25-50 min stimulation duration.

RESULTS

In healthy subjects transorbital ACS increased the activated cortical surface area, decreased the fMRI response amplitude and increased coherence in the visual cortex, which was most prominent in the full field task. In the patient, stimulation improved contrast sensitivity in the central visual field. BOLD amplitudes and coherence values were increased in most early visual areas in both hemispheres, with the most pronounced activation detected during eccentricity testing in retinotopic mapping.

CONCLUSIONS

This feasibility study showed that transorbital ACS modifies BOLD activity to visual stimulation, which outlasts the duration of the AC stimulation. This is in line with earlier neurophysiological findings of increased power in EEG recordings and functional connectivity reorganization in patients with impaired vision. Accordingly, the larger BOLD response area after stimulation can be explained by more coherent activation and lower variability in the activation. Alternatively, increased neuronal activity can also be taken into account. Controlled trials are needed to systematically evaluate the potential of repetitive transorbital ACS to improve visual function after visual pathway stroke and to determine the cause-effect relationship between neural and BOLD activity changes.

Variance decomposition for single-subject task-based fMRI activity estimates across many sessions

Here we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across-session variance is the second largest contributor to total variance in occipital cortex, while across-runs variance is the second dominant source for the rest of the brain. Network-specific analysis revealed that across-block variance contributes more to total variance in higher-order cognitive networks than in somatosensory cortex. Moreover, in some higher-order cognitive networks across-block variance can exceed across-session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within-subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test-retest reliability even in the absence of residual artifacts and sufficiently high contrast-to-noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results.