Non-invasive assessment of white-matter functionality in the nervous system would be a valuable basic neuroscience and clinical diagnostic tool. Using standard MRI techniques, a visual-stimulus-induced 27% decrease in the apparent diffusion coefficient of water perpendicular to the axonal fibers (ADC⊥) is demonstrated for C57BL/6 mouse optic nerve in vivo. No change in ADC|| (diffusion parallel to the optic nerve fibers) was observed during visual stimulation. The stimulus-induced changes are completely reversible. A possible vascular contribution was sought by carrying out the ADC⊥ measurements in hypercapnic mice with and without visual stimulus. Similar effects were seen in room-air-breathing and hypercapnic animals. The in vivo stimulus-induced ADC⊥ decreases are roughly similar to literature reports for ex vivo rat optic nerve preparations under conditions of osmotic swelling. The experimental results strongly suggest that osmotic after-effects of nerve impulses through the axonal fibers are responsible for the observed ADC decrease.
Lateralization of function is a fundamental feature of the human brain as exemplified by the left hemisphere dominance of language. Despite the prominence of lateralization in the lesion, split-brain and task-based fMRI literature, surprisingly little asymmetry has been revealed in the increasingly popular functional imaging studies of spontaneous fluctuations in the fMRI BOLD signal (so-called resting-state fMRI). Here, we show the global signal, an often discarded component of the BOLD signal in resting-state studies, reveals a leftward asymmetry that maps onto regions preferential for semantic processing in left frontal and temporal cortex and the right cerebellum and a rightward asymmetry that maps onto putative attention-related regions in right frontal, temporoparietal, and parietal cortex. Hemispheric asymmetries in the global signal resulted from amplitude modulation of the spontaneous fluctuations. To confirm these findings obtained from normal, healthy, right-handed subjects in the resting-state, we had them perform 2 semantic processing tasks: synonym and numerical magnitude judgment and sentence comprehension. In addition to establishing a new technique for studying lateralization through functional imaging of the resting-state, our findings shed new light on the physiology of the global brain signal.
One of the most difficult category learning problems for humans is learning nonnative speech categories. While feedback-based category training can enhance speech learning, the mechanisms underlying these benefits are unclear. In this functional magnetic resonance imaging study, we investigated neural and computational mechanisms underlying feedback-dependent speech category learning in adults. Positive feedback activated a large corticostriatal network including the dorsolateral prefrontal cortex, inferior parietal lobule, middle temporal gyrus, caudate, putamen, and the ventral striatum. Successful learning was contingent upon the activity of domain-general category learning systems: the fast-learning reflective system, involving the dorsolateral prefrontal cortex that develops and tests explicit rules based on the feedback content, and the slow-learning reflexive system, involving the putamen in which the stimuli are implicitly associated with category responses based on the reward value in feedback. Computational modeling of response strategies revealed significant use of reflective strategies early in training and greater use of reflexive strategies later in training. Reflexive strategy use was associated with increased activation in the putamen. Our results demonstrate a critical role for the reflexive corticostriatal learning system as a function of response strategy and proficiency during speech category learning.
Most labs have plastic goggles for correcting a subject's vision in the MRI...
These goggles work pretty well for a standard sized head coil, such as the 12-channel TIM coil on my Siemens Trio. But for a tighter fitting coil, such as the 32-channel head coil, there is simply no way to cram a subject wearing goggles into the space available. For a start the goggles' frame prevents the subject's nose from penetrating the appropriate gap in the front of the coil.
A simple solution is to relocate the corrective lenses on the outer surface of the head coil. All that's required is a different way to hold the lenses in place
The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.
We propose a marker for core AC based directly on functional magnetic resonance imaging (fMRI) data and pattern classification. We show that a portion of AC in Heschl's gyrus classifies sound frequency more accurately than other regions in AC. Using fMRI data from macaques, we validate that the region where frequency classification performance is significantly above chance overlaps core auditory fields, predominantly A1. Within this region, we measure tonotopic gradients and estimate the locations of the human homologues of the core auditory subfields A1 and R.
Here we scanned 37 children aged 7–13 and 19 young adults who performed a well-normed picture-naming task with 3 levels of difficulty. While neural organization for naming was largely similar in childhood and adulthood, adults had greater activation in all naming conditions over inferior temporal gyri and superior temporal gyri/supramarginal gyri. Manipulating naming complexity affected adults and children quite differently: neural activation, especially over the dorsolateral prefrontal cortex, showed complexity-dependent increases in adults, but complexity-dependent decreases in children. These represent fundamentally different responses to the linguistic and conceptual challenges of a simple naming task that makes no demands on literacy or metalinguistics.
In our task, spatial cues indicating the relevant item in a WM array occurred either before the memory array or during the maintenance period, providing a direct comparison between prospective and retrospective control of WM. We found that in both cases a frontoparietal network activated following the cue, but following retrocues this activation was transient and was succeeded by a cingulo-opercular network activation.
The aim of this study was to use intelligible and unintelligible (spectrally rotated) sentences to determine if the vATL could be detected during a passive speech comprehension task using a dual-echo acquisition. A whole brain analysis for an intelligibility contrast showed bilateral superior temporal lobe activations and a cluster of activation within the left vATL.
Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6–C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain–spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. T
Very nice and important discussion of regressor orthogonalization.
The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.
Important paper from Tsvetanov, Cam-CAN et al. on vascular effects in aging in a sample of 335 subjects.
The scaling analysis revealed that much of the effects of age on task-based activation studies with fMRI do not survive correction for changes in vascular reactivity, and are likely to have been overestimated in previous fMRI studies of ageing.
The Enigma machine and de Bruijn sequences inform neuroscience analyses, including quotes from Josh Gold and Geoff Aguirre.
The Penn scientists have taken their cues from a 73-year-old algorithm that British code-breaker Alan Turing used to read secret German messages during World War II, and a mathematical sequence more famously used to break into digital keypad locks on cars.