Some researchers take an extra step with fMRI data, using sets of correlations to make predictions. Last year, he and his colleagues suggested that fMRI-based work could predict which dyslexic students would improve in reading performance over the course of two and a half years more accurately than a battery of typical education tests.
The realm of prediction is also where we encounter mind-reading, untangling what someone is thinking based on brain activity. Already, researchers at the University of California Berkeley and San Francisco have had some success in reconstructing words being heard by a subject through another neuroscience tool, electroencephalography. Neuroscientist Jack Gallant and colleagues at the University of California Berkeley have demonstrated the ability to decode brain activity and reconstruct what a subject is seeing using fMRI.
So how do we go from illuminated voxels to what Gallant calls brain-reading? First, you need to model how activity in the brain's voxels corresponds to what a subject is seeing. Gallant will ask a subject to watch hours of movies while sitting in an fMRI. The researchers then map out the movies' visual stimuli to the pattern of voxel activation. This process is part of model making, in this case making 'encoding models,' which combine information from the stimulus movies and the cortex, to explain how the subject experiences watching the movie within the brain.
For each voxel, researchers must actually create dozens of models, test them, and ultimately select those that seem to build a clear and comprehensive description of what happens in the brain. Next you have to reconstruct what a viewer watched using only fMRI data.
This puts the discoveries made during encoding to the test, reversing the process and decoding brain activity to recreate the video. What's more, Gallant believes his decoders incorporate enough data that they do generalize. VBM deals with brain volume or density ; an alternate way to investigate similar questions is using cortical thickness measurements. Multiple statistical models can be run on the same set of preprocessed data. This refers generally to variations on a summary statistics approach in which data for each subject are analyzed independently, and then the results from these analysis i.
Results from this analysis would tell you whether, for this subject, a region showed significant activation for a given paradigm. A 2nd level analysis is equivalent to a group study, in which linear contrasts of parameter estimates are fit with a statistical model. Results from a 2nd level analysis would tell you whether a pattern of activation is significant across a group and thus likely to generalize across a population.
For structural MRI analyses, most often there is a single image per subject to begin with because there is no timeseries to analyze , and thus most analyses are 2nd level group analyses. Nifti files can be a single file with an. In both cases, there are two sets of information about each image:. Among many other issues, standardization to the Nifti format means that the orientation of MRI images is unambiguous; i. However, in older datasets, or with software that is not Nifti-compliant, this may not be true.
Although sometimes it can refer to the actual millimeter-by-millimeter alignment proposed in this atlas, it can also refer to this general system of alignment, which is how I intend it here. Schematic of stereotaxic brain coordinates on axial left , coronal middle , and sagittal right slices. In neurological convention , the left of an MRI image as you view it is the left side of the brain.
The T1-weighted images were uploaded and automatically segmented in cerebral cortex, subcortical grey matter, lobar white matter, brainstem and cerebellar compartments. From these parcellation maps, we derived different regions associated with the neuronal pathway of finger movements, such as the precentral gyrus, the postcentral gyrus, frontal operculum, and cerebellum.
We arbitrarily defined the functional region known as the supplementary motor area SMA by combining the paracentral lobule and the frontal superior gyrus. Secondly, the temporal delay on a voxel-wise basis between CO 2 and BOLD time course was calculated for improved alignment. After preprocessing, t-values were determined using a first level analysis using a mass-univariate general linear model within SPM Head movement correction in 6 directions was included as a covariate to decrease motion artefacts.
The number of significant voxels for every individual ROI was then evaluated. Statistical analysis was performed using SPSS The Shapiro-Wilk test was used to assess normality of distribution.
Individual differences were determined using a paired student t-test a p-value of 0. Moreover, the mean of all variables between both groups were compared using repeated measured ANOVA to test the variability of the means of each variable between both CO 2 conditions in the population. For both test, a Bonferroni correction was used to adjust for multiple comparisons. Statistical significance of the regression was evaluated based on Student t-test, with Bonferroni correction.
Request to have access to the raw data files can be made to the corresponding author. Subject demographics and group averages between both CO 2 conditions i. Seventeen subjects all but one right-handed were included. None of the subjects had a medical history of neurological intracranial disease, neurological symptoms or was taking medications at the time of scanning.
None of the subjects had to be excluded due to excessive head motion. All subjects completed the scanning protocol and no adverse events were reported. Mean CVR of the whole brain averaged from all scans was 0. Table 2 depicts the number of activated voxels during each of the CO 2 conditions. The number of significant voxels, exceeding a t-map threshold of 3.
The statistical results can be found in Table 1 and Fig 3. Each red and blue point represents the average value of a ROI of a single subject during either normocpanie blue or hypercapnia red.
Black line: least square error linear fit of each scatterplot. All correlations are highly significant. Here it is clear that for all anatomical regions during hypercapnia, fMRI t-values drop below the threshold resulting in false negative activation. CVR response and corresponding t-value comparison of an illustrative subject during both CO 2 conditions is shown in Fig 5.
The t-values does not seem to surpass a certain CVR threshold, indicated by the upper distribution of the voxels presented in the graph.
This threshold seems to differ only slightly between subjects. Such a pattern is not only seen in Fig 5 , but can also be seen in the scatter plots in Fig 2. For each ROI, only the voxels which were significantly activated in at least one of the two finger-tapping fMRI data are plotted. The drop in t-values from normo- to hyper-capnia below the threshold here 3. In some voxels, the t-values in response to a fingertapping paradigm are clearly not maximal. Single microvessels can be imaged in vivo , pinpointing the exact location of vasoconstriction and dilation and the signaling pathways involved, as well as flow in microvessels Kleinfeld et al.
The latter covers the entire cortical thickness in the mouse Mittmann et al. Optical coherence tomography OCT is a promising technique that is able to reach a depth of up to 2 mm, although its spatial resolution is diminished compared to other optical methods Huang et al. Neurovascular coupling research benefits from the comparison of optical imaging and high-resolution fMRI.
However, optical methods' lack of resolving power in the depth dimension also complicates the comparison. This comparison is only valid when the responses of the deeper layers are the same as those of the surface layers. Laminar fMRI can provide additional information by virtue of its ability to separate surface- and deep voxels and can thereby improve the comparison with optical imaging. The ability to resolve single arteries and venules with fMRI Yu et al. These methods provide opportunities to compare neural and hemodynamic activity by increasing the electrophysiological sampling density and by more accurate electrode localization.
Aside from its superior temporal resolution, the local field potentials LFP recorded with electrophysiology can be decomposed in different frequency bands that are engaged in different cognitive processes Belitski et al. The neural responses recorded with multisite electrodes depth-dependent or area-dependent can be compared with high-resolution fMRI to provide information about laminar circuits Maier et al.
The ability to record different locations simultaneously, such as different layers, or areas with positive and negative BOLD, would improve our understanding of neurovascular coupling. As fMRI increases its resolution and electrophysiology increases its coverage, this promises major advances in determining cortical circuit function in vivo.
Further exciting developments are the imaging and modeling of entire vascular and neural networks, which allows building increasingly sophisticated models of the cortical anatomy and blood flow in the cortical network Guibert et al. Such multidisciplinary research and collaborations will allow us to study neurovascular coupling at increasingly fine spatial detail and build comprehensive models of neural circuit activity and the subsequent hemodynamic responses.
In this paper we discussed the properties of high-resolution fMRI and factors that need to be taken into account when extending current macroscopic fMRI models to the mesoscopic scale that becomes accessible with high-resolution fMRI. Those factors are biological, for instance laminar differences in neurovascular coupling that become visible at high-resolution, and methodological, like the differences between GE- and SE-BOLD acquisitions.
For modeling of BOLD-responses at the mesoscopic scale this means that additional compartments and variables need to be included, needing more elaborate models and judicious choice of the models' assumptions. Aside from this complication, high-resolution fMRI provides an opportunity to improve BOLD-models, since more information is available.
An obvious example is the information about different cortical layers that becomes available, but high-resolution fMRI can also separate the responses in vessels from the responses in parenchyma and reduce partial volume effects.
The extension of fMRI models to the mesoscopic scale holds the promise of better understanding of the BOLD signal, neurovascular coupling, and the possibility of elucidating neural circuitry in vivo.
All authors edited and approved the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Many thanks to Daniel Zaldivar for collecting some of the data and to Dr. Kevin Whittingstall for comments on an earlier version of the manuscript. Supported by the Max Planck Society. Attwell, D. Glial and neuronal control of brain blood flow. Nature , — Barbier, E. Imaging cortical anatomy by high-resolution MR at 3. Belitski, A. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.
Large-scale, high-density up to channels recording of local circuits in behaving animals. Bianciardi, M. Blood Flow Metab. Blinder, P. The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow.
Boas, D. A vascular anatomical network model of the spatio-temporal response to brain activation. Neuroimage 40, — Bok, S. Gesamte Neurol. Boorman, L. Negative blood oxygen level dependence in the rat: a model for investigating the role of suppression in neurovascular coupling. Boxerman, J. MR contrast due to intravascular magnetic susceptibility perturbations.
Buxton, R. Modeling the hemodynamic response to brain activation. Neuroimage 23 Suppl. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Chang, E. Towards large-scale, human-based, mesoscopic neurotechnologies. Neuron 86, 68— Chen, L. High-resolution maps of real and illusory tactile activation in primary somatosensory cortex in individual monkeys with functional magnetic resonance imaging and optical imaging.
Chen-Bee, C. The triphasic intrinsic signal: implications for functional imaging. Cheng, K. Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron 32, — De Martino, F. Frequency preference and attention effects across cortical depths in the human primary auditory cortex.
Neuroimage , — Devor, A. Stimulus-induced changes in blood flow and 2-deoxyglucose uptake dissociate in ipsilateral somatosensory cortex.
Suppressed neuronal activity and concurrent arteriolar vasoconstriction may explain negative blood oxygenation level-dependent signal. Doty, F. Radio frequency coil technology for small-animal MRI. NMR Biomed. Duong, T. Localized cerebral blood flow response at submillimeter columnar resolution. Duvernoy, H. Cortical blood vessels of the human brain. Brain Res. Einevoll, G. Modelling and analysis of local field potentials for studying the function of cortical circuits.
Ernst, T. Observation of a fast response in functional MR. Errico, C. Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging. Fantini, S. Dynamic model for the tissue concentration and oxygen saturation of hemoglobin in relation to blood volume, flow velocity, and oxygen consumption: Implications for functional neuroimaging and coherent hemodynamics spectroscopy CHS.
Neuroimage 85, — Felleman, D. Distributed hierarchical processing in the primate cerebral cortex. Cortex 1, 1— Fleisher, A. Cerebral perfusion and oxygenation differences in Alzheimer's disease risk. Aging 30, — Fonta, C. Vascularization in the primate visual cortex during development. Cortex 12, — Goense, J. Flexible, phase-matched, linear receive arrays for high-field MRI in monkeys.
Neuron 76, — High-resolution fMRI of macaque V1. Grubb, R. Effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time. Stroke 5, — Guibert, R. Cerebral blood flow modeling in primate cortex. Hafkenschiel, J. Google Scholar. Hall, C. Capillary pericytes regulate cerebral blood flow in health and disease. Nature , 55— Hamel, E. Perivascular nerves and the regulation of cerebrovascular tone.
Cellular level brain imaging in behaving mammals: an engineering approach. Neuron 86, — Harel, N.
0コメント