Standard fMRI Research Articles

Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data.

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.

The GridCAT: A Toolbox for Automated Analysis of Human Grid Cell Codes in fMRI.

Human functional magnetic resonance imaging (fMRI) studies examining the putative firing of grid cells (i.e., the grid code) suggest that this cellular mechanism supports not only spatial navigation, but also more abstract cognitive processes. Despite increased interest in this research, there remain relatively few human grid code studies, perhaps due to the complex analysis methods, which are not included in standard fMRI analysis packages. To overcome this, we have developed the Matlab-based open-source Grid Code Analysis Toolbox (GridCAT), which performs all analyses, from the estimation and fitting of the grid code in the general linear model (GLM), to the generation of grid code metrics and plots. The GridCAT, therefore, opens up this cutting-edge research area by allowing users to analyze data by means of a simple and user-friendly graphical user interface (GUI). Researchers confident with programming can edit the open-source code and use example scripts accompanying the GridCAT to implement their own analysis pipelines. Here, we review the current literature in the field of fMRI grid code research with particular focus on the different analysis options that have been implemented, which we describe in detail. Key features of the GridCAT are demonstrated via analysis of an example dataset, which is also provided online together with a detailed manual, so that users can replicate the results presented here, and explore the GridCAT’s functionality. By making the GridCAT available to the wider neuroscience community, we believe that it will prove invaluable in elucidating the role of grid codes in higher-order cognitive processes.

Omega-3 Supplementation and the Neural Correlates of Negative Affect and Impulsivity: A Double-Blind, Randomized, Placebo-Controlled Trial in Midlife Adults.

In clinical trials, omega-3 fatty acid supplementation improves symptoms in psychiatric disorders involving dysregulated mood and impulse control, yet it is unclear whether in healthy adults, omega-3 fatty acid supplementation affects mood, impulse control, and the brain systems supporting these processes. Accordingly, this study tested the hypotheses that eciosapentaenoic (EPA) and docosahexaenoic (DHA) acid supplementation reduces negative affect and impulsive behaviors in healthy adults and that these changes correspond to alterations in corticolimbic and corticostriatal brain systems, which support affective and impulsive processes. Healthy volunteers (N = 272) consuming 300 mg/d or less of EPA and DHA were enrolled in a double-blind, randomized, placebo controlled clinical trial. The participants received either capsules providing 1000 mg of EPA and 400 mg of DHA versus identical appearing soybean oil capsules per day for 18 weeks. Negative affect and impulsivity were measured by questionnaire and ecological momentary assessment, as well as functional alterations in corticolimbic and corticostriatal brain systems evoked by standardized functional magnetic resonance imaging tasks. There were no group by time interactions for any questionnaire or ecological momentary assessment measures of mood and impulsivity. Likewise, no group by time interactions were observed for functional magnetic resonance imaging responses evoked within corticolimbic and corticostriatal systems. In healthy adults with low intake of omega-3 fatty acids, moderate-dose supplementation for 18 weeks did not alter affect or impulsive behaviors nor alter corticolimbic and corticostriatal brain functionality. Trial number NCT00663871.

Analysis and correction of field fluctuations in fMRI data using field monitoring

This work investigates the role of magnetic field fluctuations as a confound in fMRI. In standard fMRI experiments with single-shot EPI acquisition at 3 Tesla the uniform and gradient components of the magnetic field were recorded with NMR field sensors. By principal component analysis it is found that differences of field evolution between the EPI readouts are explainable by few components relating to slow and within-shot field dynamics of hardware and physiological origin. The impact of fluctuating field components is studied by selective data correction and assessment of its influence on image fluctuation and SFNR.Physiological field fluctuations, attributed to breathing, were found to be small relative to those of hardware origin. The dominant confounds were hardware-related and attributable to magnet drift and thermal changes. In raw image time series, field fluctuation caused significant SFNR loss, reflected by a 67% gain upon correction. Large part of this correction can be accomplished by traditional image realignment, which addresses slow and spatially uniform field changes. With realignment, explicit field correction increased the SFNR on the order of 6%.In conclusion, field fluctuations are a relevant confound in fMRI and can be addressed effectively by retrospective data correction. Based on the physics involved it is anticipated that the advantage of full field correction increases with field strength, with non-Cartesian readouts, and upon phase-sensitive BOLD analysis.

Deep grey matter iron accumulation in alcohol use disorder

PurposeEvaluate brain iron accumulation in alcohol use disorder (AUD) patients compared to controls using quantitative susceptibility mapping (QSM). MethodsQSM was performed retrospectively by using phase images from resting state functional magnetic resonance imaging (fMRI). 20 male AUD patients and 15 matched healthy controls were examined. Susceptibility values were manually traced in deep grey matter regions including caudate nucleus, combined putamen and globus pallidus, combined substantia nigra and red nucleus, dentate nucleus, and a reference white matter region in the internal capsule. Average susceptibility values from each region were compared between the patients and controls. The relationship between age and susceptibility was also explored. ResultsThe AUD group exhibited increased susceptibility in caudate nucleus (+8.5%, p=0.034), combined putamen and globus pallidus (+10.8%, p=0.006), and dentate nucleus (+14.9%, p=0.022). Susceptibility increased with age in two of the four measured regions - combined putamen and globus pallidus (p=0.013) and combined substantia nigra and red nucleus (p=0.041). AUD did not significantly modulate the rate of susceptibility increase with age in our data. ConclusionRetrospective QSM computed from standard fMRI datasets provides new opportunities for brain iron studies in psychiatry. Substantially elevated brain iron was found in AUD subjects in the basal ganglia and dentate nucleus. This was the first human AUD brain iron study and the first retrospective clinical fMRI QSM study.

Improving language mapping in clinical fMRI through assessment of grammar.

IntroductionBrain surgery in the language dominant hemisphere remains challenging due to unintended post-surgical language deficits, despite using pre-surgical functional magnetic resonance (fMRI) and intraoperative cortical stimulation. Moreover, patients are often recommended not to undergo surgery if the accompanying risk to language appears to be too high. While standard fMRI language mapping protocols may have relatively good predictive value at the group level, they remain sub-optimal on an individual level. The standard tests used typically assess lexico-semantic aspects of language, and they do not accurately reflect the complexity of language either in comprehension or production at the sentence level. Among patients who had left hemisphere language dominance we assessed which tests are best at activating language areas in the brain.MethodWe compared grammar tests (items testing word order in actives and passives, wh-subject and object questions, relativized subject and object clauses and past tense marking) with standard tests (object naming, auditory and visual responsive naming), using pre-operative fMRI. Twenty-five surgical candidates (13 females) participated in this study. Sixteen patients presented with a brain tumor, and nine with epilepsy. All participants underwent two pre-operative fMRI protocols: one including CYCLE-N grammar tests (items testing word order in actives and passives, wh-subject and object questions, relativized subject and object clauses and past tense marking); and a second one with standard fMRI tests (object naming, auditory and visual responsive naming). fMRI activations during performance in both protocols were compared at the group level, as well as in individual candidates.ResultsThe grammar tests generated more volume of activation in the left hemisphere (left/right angular gyrus, right anterior/posterior superior temporal gyrus) and identified additional language regions not shown by the standard tests (e.g., left anterior/posterior supramarginal gyrus). The standard tests produced more activation in left BA 47. Ten participants had more robust activations in the left hemisphere in the grammar tests and two in the standard tests. The grammar tests also elicited substantial activations in the right hemisphere and thus turned out to be superior at identifying both right and left hemisphere contribution to language processing.ConclusionThe grammar tests may be an important addition to the standard pre-operative fMRI testing.

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc.

Structural and functional quantitative susceptibility mapping from standard fMRI studies.

Standard functional MRI (fMRI), which includes resting-state or paradigm-driven designs, is widely used in studies of brain function, aging, and disease. These fMRI studies typically use two-dimensional gradient echo-planar imaging, which inherently contains phase data that enables quantitative susceptibility mapping (QSM). This work focuses on the dual value of QSM within fMRI studies, by providing both a localized analysis of functional changes in activated tissue, and iron-sensitive structural maps in deep grey matter (DGM). Using a visual paradigm fMRI study on healthy volunteers at clinical (1.5 T) and high field strength (4.7 T), we perform functional analysis of magnitude and QSM time series, and at the same time harness structural QSM of iron-rich DGM, including globus pallidus, putamen, caudate head, substantia nigra, and red nucleus. The effects of fMRI spatial resolution and time series variation on structural DGM QSM are investigated. Our results indicate that structural DGM QSM is feasible within existing fMRI studies, provided that the voxel dimensions are equal to or less than 3 mm, with higher resolutions preferred. The mean DGM QSM values were about 40 to 220 ppb, while the interquartile ranges of the DGM QSM time series varied from about 3 to 9 ppb, depending on structure and resolution. In contrast, the peak voxel functional QSM (fQSM) changes in activated visual cortex ranged from about -10 to -30 ppb, and functional clusters were consistently smaller on QSM than magnitude fMRI. Mean-level DGM QSM of the time series was successfully extracted in all cases, while fQSM results were more prone to residual background fields and showed less functional change compared with standard magnitude fMRI. Under the conditions prescribed, standard fMRI studies may be used for robust mean-level DGM QSM, enabling study of DGM iron accumulation, in addition to functional analysis. Copyright © 2016 John Wiley & Sons, Ltd.

SNR dependence on hardware installation and patient immobilization in fMRI examinations. MR imaging protocol optimization

Introduction The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast imaging. In BOLD fMRI imaging, the signal-to-noise ratio (SNR) is extremely low and statistical methods are used to extract quantitative data. SNR of the images obtained has to be maintained as high as possible. Purpose The aim of this study is to estimate the SNR dependence on the video and audio device hardware as well as the patient immobilization in event related functional MRI examinations. Materials and methods A head mimicking phantom printed on a commercial 3D printer was scanned on a clinical 1.5 T whole-body clinical scanner utilizing a standard fMRI sequence protocol. Both anatomical and functional MR sequences were used for data acquisition. SNR parametric maps were produced and were fused on both anatomical and functional MR images obtained. The phantom was scanned before and after the installation of all the fMRI stimulus devices (visual and audio). Further examination conditions referring to immobilization of the phantom and cable looping were also studied separately. Results SNR is reduced down to 30% when the hardware cables were looped. Immobilization of the head mimicking phantom affected SNR by reducing its value to a level of 10%. The whole fMRI hardware installation also affected SNR with discrepancies at a level of 10%. Conclusion The fMRI hardware pre-installation and patient immobilization are important factors that have to be taken into consideration before performing an fMRI examination, as they can both affect the already extremely low SNR of the obtained images.

Probing neuronal activation by functional quantitative susceptibility mapping under a visual paradigm: A group level comparison with BOLD fMRI and PET

Dynamic changes of brain-tissue magnetic susceptibility provide the basis for functional MR imaging (fMRI) via T2*-weighted signal-intensity modulations. Promising initial work on a detection of neuronal activity via quantitative susceptibility mapping (fQSM) has been published but consistently reported on ill-understood positive and negative activation patterns (Balla et al., 2014; Chen and Calhoun, 2015a). We set out to (i) demonstrate that fQSM can exploit established fMRI data acquisition and processing methods and to (ii) better describe aspects of the apparent activation patterns using fMRI and PET as standards of reference. Under a standardized visual-stimulation paradigm PET and 3-T gradient-echo EPI-based fQSM, fMRI data from 9 healthy volunteers were acquired and analyzed by means of Independent Component Analysis (ICA) at subject level and, for the first time, at group level. Numbers of activated (z-score>2.0) voxels were counted and their mean z-scores calculated in volumes of interest (occipital lobe (Nocc_lobe), segmented occipital gray-matter (NGM_occ_lobe), large veins (Nveins)), and in occipital-lobe voxels commonly activated in fQSM and fMRI component maps. Common but not entirely congruent regions of apparent activation were found in the occipital lobe in z-score maps from all modalities, fQSM, fMRI and PET, with distinct BOLD-negatively correlated regions in fQSM data. At subject-level, Nocc_lobe, NGM_occ_lobe and their mean z-scores were significantly smaller in fQSM than in fMRI, but their ratio, NGM_occ_lobe/Nocc_lobe, was comparable. Nveins did not statistically differ and the ratio Nveins/NGM_occ_lobe as well as the mean z-scores were higher for fQSM than for fMRI. In veins and immediate vicinity, z-score maps derived from both phase and fQSM-data showed positive and negative lobes resembling dipole shapes in simulated field and phase maps with no correlate in fMRI or PET data. Our results show that standard fMRI tools can directly be used for fQSM processing, and suggest that fQSM may have the potential to detect gray-matter activation distant from large veins, to improve detection of veins with stimulus-induced venous oxygen saturation (SvO2) variations, and to better localize areas of activation. However, our results seem to clearly expose issues that phenomenologically resemble an incomplete dipolar inversion and that need to be subject to further investigation.

The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI

Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals.

Identification of Voxels Confounded by Venous Signals Using Resting-State fMRI Functional Connectivity Graph Community Identification.

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6±5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remaining differences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needing any additional data beyond what is usually acquired (and exported) in standard fMRI experiments.

The quest for the best: The impact of different EPI sequences on the sensitivity of random effect fMRI group analyses

We compared the sensitivity of standard single-shot 2D echo planar imaging (EPI) to three advanced EPI sequences, i.e., 2D multi-echo EPI, 3D high resolution EPI and 3D dual-echo fast EPI in fixed effect and random effects group level fMRI analyses at 3T. The study focused on how well the variance reduction in fixed effect analyses achieved by advanced EPI sequences translates into increased sensitivity in the random effects group level analysis. The sensitivity was estimated in a functional MRI experiment of an emotional learning and a reward based learning tasks in a group of 24 volunteers. Each experiment was acquired with the four different sequences. The task-related response amplitude, contrast level and respective t-value were proxies for the functional sensitivity across the brain. All three advanced EPI methods increased the sensitivity in the fixed effects analyses, but standard single-shot 2D EPI provided a comparable performance in random effects group analysis when whole brain coverage and moderate resolution are required. In this experiment inter-subject variability determined the sensitivity of the random effects analysis for most brain regions, making the impact of EPI pulse sequence improvements less relevant or even negligible for random effects analyses. An exception concerns the optimization of EPI reducing susceptibility-related signal loss that translates into an enhanced sensitivity e.g. in the orbitofrontal cortex for multi-echo EPI. Thus, future optimization strategies may best aim at reducing inter-subject variability for higher sensitivity in standard fMRI group studies at moderate spatial resolution.

Pre-surgical fMRI Data Analysis Using a Spatially Adaptive Conditionally Autoregressive Model

Spatial smoothing is an essential step in the analysis of functional magnetic resonance imaging (fMRI) data. One standard smoothing method is to convolve the image data with a three-dimensional Gaussian kernel that applies a fixed amount of smoothing to the entire image. In pre-surgical brain image analysis where spatial accuracy is paramount, this method, however, is not reasonable as it can blur the boundaries between activated and deactivated regions of the brain. Moreover, while in a standard fMRI analysis strict false positive control is desired, for pre-surgical planning false negatives are of greater concern. To this end, we propose a novel spatially adaptive conditionally autoregressive model with variances in the full conditional of the means that are proportional to error variances, allowing the degree of smoothing to vary across the brain. Additionally, we present a new loss function that allows for the asymmetric treatment of false positives and false negatives. We compare our proposed model with two existing spatially adaptive conditionally autoregressive models. Simulation studies show that our model outperforms these other models; as a real model application, we apply the proposed model to the pre-surgical fMRI data of two patients to assess peri- and intra-tumoral brain activity.

Creating 3D visualizations of MRI data: A brief guide

While magnetic resonance imaging (MRI) data is itself 3D, it is often difficult to adequately present the results papers and slides in 3D. As a result, findings of MRI studies are often presented in 2D instead. A solution is to create figures that include perspective and can convey 3D information; such figures can sometimes be produced by standard functional magnetic resonance imaging (fMRI) analysis packages and related specialty programs. However, many options cannot provide functionality such as visualizing activation clusters that are both cortical and subcortical (i.e., a 3D glass brain), the production of several statistical maps with an identical perspective in the 3D rendering, or animated renderings. Here I detail an approach for creating 3D visualizations of MRI data that satisfies all of these criteria. Though a 3D 'glass brain' rendering can sometimes be difficult to interpret, they are useful in showing a more overall representation of the results, whereas the traditional slices show a more local view. Combined, presenting both 2D and 3D representations of MR images can provide a more comprehensive view of the study's findings.

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability.

Triptan-induced disruption of trigemino-cortical connectivity.

The 5-HT1B/D agonists (triptans) are specific headache medications that have no effect on pain as such. Although they are routinely used in the treatment of acute migraine attacks, the underlying mechanisms of action are still a matter of debate. Forty-three healthy participants underwent fMRI while receiving trigemino-nociceptive stimulation and control stimuli in a standardized fMRI paradigm. Using a crossover, double-blind, placebo-controlled design, 21 participants (10 women, mean age 26.9, range 20-37 years) received sumatriptan and 22 participants (11 women, mean age 25.5, range 22-32 years) received acetylsalicylic acid (ASA). Administration of medication and saline was randomized between participants of each group resulting in half of the participants receiving saline and the other half receiving the respective medication during the first fMRI data acquisition. While mean pain intensity ratings did not differ significantly between control and medication nor between medications, we found a significant blood oxygen level-dependent signal increase in the trigeminal nuclei and the thalamus after sumatriptan treatment compared with placebo or ASA. In addition, we specifically looked for the pharmacologic modulation of functional coupling between trigeminal nuclei and higher brain areas, i.e., trigemino-cortical pathways, and found a strong coupling during the saline condition, which was altered by sumatriptan but not after ASA administration. These data suggest that a specific functional inhibition of trigemino-cortical projections is one of the reasons that triptans, unlike pain killers, act highly specifically on headache and migraine but not pain as such.

A survey of patient motion in disorders of consciousness and optimization of its retrospective correction

Functional magnetic resonance imaging (fMRI) can be seriously impaired by patient motion. The purpose of this study was to characterize the typical motion in a clinical population of patients in disorders of consciousness and compare the performance of retrospective correction with rigid-body realignment as implemented in widely used software packages. 63 subjects were scanned with an fMRI visual checkerboard paradigm using a 3T scanner. Time series were corrected for motion, and the resulting transformations were used to calculate a motion score. SPM, FSL, AFNI and AIR were evaluated by comparing the motion obtained by re-running the tool on the corrected data. A publicly available sample fMRI dataset was modified with the motion detected in each patient with each tool. The performance of each tool was measured by comparing the number of supra-threshold voxels after standard fMRI analysis, both in the sample dataset and in simulated fMRI data. We assessed the effect of user-changeable parameters on motion correction in SPM. We found the equivalent motion in the patient population to be 1.4mm on average. There was no significant difference in performance between SPM, FSL and AFNI. AIR was considerably worse, and took more time to run. We found that in SPM the quality factor and interpolation method have no effect on the cluster size, while higher separation and smoothing reduce it. We showed that the main packages SPM, FSL and AFNI are equally suitable for retrospective motion correction of fMRI time series. We show that typically only 80% of activated voxels are recovered by retrospective motion correction.

Beyond BOLD: optimizing functional imaging in stroke populations.

Blood oxygenation level-dependent (BOLD) signal changes are often assumed to directly reflect neural activity changes. Yet the real relationship is indirect, reliant on numerous assumptions, and subject to several sources of noise. Deviations from the core assumptions of BOLD contrast functional magnetic resonance imaging (fMRI), and their implications, have been well characterized in healthy populations, but are frequently neglected in stroke populations. In addition to conspicuous local structural and vascular changes after stroke, there are many less obvious challenges in the imaging of stroke populations. Perilesional ischemic changes, remodeling in regions distant to lesion sites, and diffuse perfusion changes all complicate interpretation of BOLD signal changes in standard fMRI protocols. Most stroke patients are also older than the young populations on which assumptions of neurovascular coupling and the typical analysis pipelines are based. We present a review of the evidence to show that the basic assumption of neurovascular coupling on which BOLD-fMRI relies does not capture the complex changes arising from stroke, both pathological and recovery related. As a result, estimating neural activity using the canonical hemodynamic response function is inappropriate in a number of contexts. We review methods designed to better estimate neural activity in stroke populations. One promising alternative to event-related fMRI is a resting-state-derived functional connectivity approach. Resting-state fMRI is well suited to stroke populations because it makes no performance demands on patients and is capable of revealing network-based pathology beyond the lesion site.

Probabilistic Maps of Visual Topography in Human Cortex

The human visual system contains an array of topographically organized regions. Identifying these regions in individual subjects is a powerful approach to group-level statistical analysis, but this is not always feasible. We addressed this limitation by generating probabilistic maps of visual topographic areas in 2 standardized spaces suitable for use with adult human brains. Using standard fMRI paradigms, we identified 25 topographic maps in a large population of individual subjects (N = 53) and transformed them into either a surface- or volume-based standardized space. Here, we provide a quantitative characterization of the inter-subject variability within and across visual regions, including the likelihood that a given point would be classified as a part of any region (full probability map) and the most probable region for any given point (maximum probability map). By evaluating the topographic organization across the whole of visual cortex, we provide new information about the organization of individual visual field maps and large-scale biases in visual field coverage. Finally, we validate each atlas for use with independent subjects. Overall, the probabilistic atlases quantify the variability of topographic representations in human cortex and provide a useful reference for comparing data across studies that can be transformed into these standard spaces.