fMRI and its interpretations: an illustration on directional selectivity in area V5/MT

Dissociating representations of affect and motion in visual cortices

While a delicious dessert being presented to us may elicit strong feelings of happiness and excitement, the same treat falling slowly away can lead to sadness and disappointment. Our emotional response to the item depends on its visual motion direction. Despite this importance, it remains unclear whether (and how) cortical areas devoted to decoding motion direction represents or integrates emotion with perceived motion direction. Motion-selective visual area V5/MT+ sits, both functionally and anatomically, at the nexus of dorsal and ventral visual streams. These pathways, however, differ in how they are modulated by emotional cues. The current study was designed to disentangle how emotion and motion perception interact, as well as use emotion-dependent modulation of visual cortices to understand the relation of V5/MT+ to canonical processing streams. During functional magnetic resonance imaging (fMRI), approaching, receding, or static motion after-effects (MAEs) were induced on stationary positive, negative, and neutral stimuli. An independent localizer scan was conducted to identify the visual-motion area V5/MT+. Through univariate and multivariate analyses, we demonstrated that emotion representations in V5/MT+ share a more similar response profile to that observed in ventral visual than dorsal, visual structures. Specifically, V5/MT+ and ventral structures were sensitive to the emotional content of visual stimuli, whereas dorsal visual structures were not. Overall, this work highlights the critical role of V5/MT+ in the representation and processing of visually acquired emotional content. It further suggests a role for this region in utilizing affectively salient visual information to augment motion perception of biologically relevant stimuli.

Advancing brain network models to reconcile functional neuroimaging and clinical research

Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.

Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning

Identifying the circuits responsible for cognition and understanding their embedded computations is a challenge for neuroscience. We establish here a hierarchical cross-scale approach, from behavioral modeling and fMRI in task-performing mice to cellular recordings, in order to disentangle local network contributions to olfactory reinforcement learning. At mesoscale, fMRI identifies a functional olfactory-striatal network interacting dynamically with higher-order cortices. While primary olfactory cortices respectively contribute only some value components, the downstream olfactory tubercle of the ventral striatum expresses comprehensively reward prediction, its dynamic updating, and prediction error components. In the tubercle, recordings reveal two underlying neuronal populations with non-redundant reward prediction coding schemes. One population collectively produces stabilized predictions as distributed activity across neurons; in the other, neurons encode value individually and dynamically integrate the recent history of uncertain outcomes. These findings validate a cross-scale approach to mechanistic investigations of higher cognitive functions in rodents.

Where and how the brain learns from experience is not fully understood. Here the authors use a hierarchical approach from behavioural modelling to systems fMRI to cellular coding reveals brain mechanisms for history informed updating of future predictions.

Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex

Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake nonhuman primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF models based on the fMRI blood-oxygen-level-dependent (BOLD) signal, multi-unit spiking activity (MUA), and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. fMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF size with increasing eccentricity, as well as a retinotopically specific deactivation of default mode network nodes similar to previous observations in humans.

Examining cross-modal fMRI adaptation for observed and executed actions in the monkey brain

While mirror neurons have been found in several monkey brain regions, their existence in the human brain is still largely inferred from indirect non-invasive measurements like functional MRI. It has been proposed that, beyond showing overlapping brain responses during action observation and execution tasks, candidate mirror neuron regions should demonstrate cross-modal action specificity, in line with a defining physiological characteristic of these neurons in the monkey brain. Although cross-modal fMRI adaptation has been put forward as a suited method to test this key feature of cross-modal action specificity in the human brain, so far, the overall usefulness of this technique to demonstrate mirror neuron activity remains unclear. To date, it has never been tested to what extent monkey brain regions known to house mirror neurons, would yield uni- and/or cross-modal fMRI adaptation effects. We therefore performed an fMRI adaptation experiment while male rhesus macaques either performed or observed two different goal-directed hand actions. Executing grasp/lift or touch/press actions in the dark, as well as observing videos of these monkey hand actions, yielded robust responses throughout the brain, including overlapping fMRI responses in parietal and premotor mirror neuron regions. Uni-modal adaptation effects were mostly restricted to the visual modality and the early visual cortices. Both frequentist and Bayesian statistical analyses however suggested no evidence for cross-modal fMRI adaptation effects in monkey parietal and premotor mirror neuron regions. Overall, these findings suggest monkey mirror neuron activity does not readily translate into cross-modal repetition suppression effects that can be detected by fMRI.

The dual nature of the BOLD signal: Responses in visual area hMT+ reflect both input properties and perceptual decision

Neuroimaging studies have suggested that hMT+ encodes global motion interpretation, but this contradicts the notion that BOLD activity mainly reflects neuronal input. While measuring fMRI responses at 7 Tesla, we used an ambiguous moving stimulus, yielding the perception of two incoherently moving surfaces—component motion—or only one coherently moving surface—pattern motion, to induce perceptual fluctuations and identify perceptual organization size‐matched domains in hMT+. Then, moving gratings, exactly matching either the direction of component or pattern motion percepts of the ambiguous stimulus, were shown to the participants to investigate whether response properties reflect the input or decision. If hMT+ responses reflect the input, component motion domains (selective to incoherent percept) should show grating direction stimulus‐dependent changes, unlike pattern motion domains (selective to the coherent percept). This hypothesis is based on the known direction‐selective nature of inputs in component motion perceptual domains versus non‐selectivity in pattern motion perceptual domains. The response amplitude of pattern motion domains did not change with grating direction (consistently with their non‐selective input), in contrast to what happened for the component motion domains (consistently with their selective input). However, when we analyzed relative ratio measures they mirrored perceptual interpretation. These findings are consistent with the notion that patterns of BOLD responses reflect both sensory input and perceptual read‐out.

Localized Prediction of Glutamate from Whole-Brain Functional Connectivity of the Pregenual Anterior Cingulate Cortex

Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity in psychiatric disorders. However, noninvasive neuroimaging techniques such as magnetic resonance spectroscopy (MRS) may cover anatomically and functionally distinct areas, such as p32 and p24 of the pregenual anterior cingulate cortex (pgACC). Here, we aimed to overcome this low spatial specificity of MRS by predicting local glutamate and GABA based on functional characteristics and neuroanatomy in a sample of 88 human participants (35 females), using complementary machine learning approaches. Functional connectivity profiles of pgACC area p32 predicted pgACC glutamate better than chance (R2 = 0.324) and explained more variance compared with area p24 using both elastic net and partial least-squares regression. In contrast, GABA could not be robustly predicted. To summarize, machine learning helps exploit the high resolution of fMRI to improve the interpretation of local neurometabolism. Our augmented multimodal imaging analysis can deliver novel insights into neurobiology by using complementary information.SIGNIFICANCE STATEMENT Magnetic resonance spectroscopy (MRS) measures local glutamate and GABA noninvasively. However, conventional MRS requires large voxels compared with fMRI, because of its inherently low signal-to-noise ratio. Consequently, a single MRS voxel may cover areas with distinct cytoarchitecture. In the largest multimodal 7 tesla machine learning study to date, we overcome this limitation by capitalizing on the spatial resolution of fMRI to predict local neurotransmitters in the PFC. Critically, we found that prefrontal glutamate could be robustly and exclusively predicted from the functional connectivity fingerprint of one of two anatomically and functionally defined areas that form the pregenual anterior cingulate cortex. Our approach provides greater spatial specificity on neurotransmitter levels, potentially improving the understanding of altered functional connectivity in mental disorders.

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning

Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty‐four healthy subjects participated in 2 × 2 matrix‐based sequential‐move games. Searchlight‐based multivoxel patterns were used as input for a support vector machine using nested cross‐validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.

Model-based whole-brain effective connectivity to study distributed cognition in health and disease

Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.

Smooth Pursuit and Saccades after Sport-Related Concussion

Smooth pursuit eye movements (SPEMs) and saccadic eye movements are both commonly impaired following sport-related concussion (SRC). Typical oculomotor assessments measure individual eye movements in a series of restrictive tests designed to isolate features such as response times. These measures lack ecological validity for athletes because athletes are adept at simple tasks designed for the general population. Yet, because eye movement metrics are sensitive and well-characterized neuroanatomically, it would be valuable to test whether athletes exhibit abnormal eye movements with more challenging tasks. To address this gap in knowledge, we collected eye-tracking data during a sport-like task to gain insight on gaze behavior during active self-motion. SPEMs and saccadic eye movements were recorded during a sport-like visual task within 24-48 h following SRC. Thirty-six Division I student-athletes were divided into SRC and control (CON) groups. All participants completed two blocks of the Wii Fit© soccer heading game (WF) while wearing a monocular infrared eye tracker. Eye movement classification systems quantified saccadic amplitude (SA), velocity (SV), and count (SC); as well as SPEM velocity (SPV) and amplitude (SPA). Separate Mann-Whitney U tests evaluated SPA and SC and found no significant effects (SPA, p = 0.11; SC, p = 0.10). A multi-variate analysis of variance (MANOVA) for remaining variables revealed SPV was significantly greater in CON (p < 0.05), but the SRC group had greater SA and SV (p < 0.05). These findings suggest that during a sport-like task, to maintain foveation SRC subjects used larger amplitude, faster saccades, but exhibited slower SPEMs. Measuring oculomotor function during ecologically valid, sport-like tasks may serve as a concussion biomarker and provide insights into eye movement control after SRC.

Gradient theories of brain activation: A novel application to studying the parental brain

PURPOSE OF REVIEW

Parental brain research primarily employs general-linear-model-based (GLM-based) analyses to assess blood-oxygenation-level-dependent responses to infant auditory and visual cues, reporting common responses in shared cortical and subcortical structures. However, this approach does not reveal intermixed neural substrates related to different sensory modalities. We consider this notion in studying the parental brain.

RECENT FINDINGS

Spatial independent component analysis (sICA) has been used to separate mixed source signals from overlapping functional networks. We explore relative differences between GLM-based analysis and sICA as applied to an fMRI dataset acquired from women while they listened to infant cries or viewed infant sad faces.

SUMMARY

There is growing appreciation for the value of moving beyond GLM-based analyses to consider brain functional organization as continuous, distributive, and overlapping gradients of neural substrates related to different sensory modalities. Preliminary findings suggest sICA can be applied to the study of the parental brain.

The left lateral occipital cortex exhibits decreased thickness in children with sensorineural hearing loss

Patients with sensorineural hearing loss (SNHL) tend to show language delay, executive functioning deficits, and visual cognitive impairment, even after intervention with hearing amplification and cochlear implants, which suggest altered brain structures and functions in SNHL patients. In this study, we investigated structural brain MRI in 30 children with SNHL (18 mild to moderate [M-M] SNHL and 12 moderately severe to profound [M-P] SNHL) by comparing gender- and age-matched normal controls (NC). Region-based analyses did not show statistically significant differences in volumes of the cerebrum, basal ganglia, cerebellum, and the ventricles between SNHL and NC. On surface-based analyses, the global and lobar cortical surface area, thickness, and volumes were not statistically significantly different between SNHL and NC participants. Regional surface areas, cortical thicknesses, and cortical volumes were statistically significantly smaller in M-P SNHL compared to NC in the left middle occipital cortex, and left inferior occipital cortex after a correction for multiple comparisons using random field theory (p < 0.02). These regions were identified as areas known to be related to high level visual cognition including the human middle temporal area, lateral occipital area, occipital face area, and V8. The observed regional decreased thickness in M-P SNHL may be associated with dysfunctions of visual cognition in SNHL detectable in a clinical setting.

Continuous and discrete representations of feature-based attentional priority in human frontoparietal network

Previous studies suggest that human frontoparietal network represents feature-based attentional priority, yet the precise nature of the priority signals remains unclear. Here, we examined whether priority signals vary continuously or discretely as a function of feature similarity. In an fMRI experiment, we presented two superimposed dot fields moving along two linear directions (leftward and rightward) while varying the angular separation between the two directions. Subjects were cued to attend to one of the two dot fields and respond to a possible speed-up in the cued direction. We used multivariate analysis to evaluate how priority representation of the attended direction changes with feature similarity. We found that in early visual areas as well as posterior intraparietal sulcus and inferior frontal junction, the patterns of neural activity became more different as the feature similarity decreased, indicating a continuous representation of the attended feature. In contrast, patterns of neural activity in anterior intraparietal sulcus and frontal eye field remained invariant to changes in feature similarity, indicating a discrete representation of the attended feature. Such distinct neural coding of attentional priority across the frontoparietal network may make complementary contributions to enable flexible attentional control.

Columnar clusters in the human motion complex reflect consciously perceived motion axis

Existing knowledge of how cortical responses link to conscious content in humans is either inferred from animal models or from human studies limited by lower spatial resolution. While previous studies could relate distinct categorical percepts (faces vs. places) to signal differences across brain areas, measuring responses at submillimeter resolution allowed us to link subcategory conscious percepts (vertical vs. horizontal motion) to amplitude changes of separate populations within the same brain area. Furthermore, preferences for horizontal and vertical motion were organized into columnar clusters. We pave the way for future studies investigating if columnar clusters represent subcategorical distinctions in conscious content different from motion or in high-level perceptual and cognitive phenomena.

The specific contents of human consciousness rely on the activity of specialized neurons in cerebral cortex. We hypothesized that the conscious experience of a specific visual motion axis is reflected in response amplitudes of direction-selective clusters in the human motion complex. Using submillimeter fMRI at ultrahigh field (7 T) we identified fine-grained clusters that were tuned to either horizontal or vertical motion presented in an unambiguous motion display. We then recorded their responses while human observers reported the perceived axis of motion for an ambiguous apparent motion display. Although retinal stimulation remained constant, subjects reported recurring changes between horizontal and vertical motion percepts every 7 to 13 s. We found that these perceptual states were dissociatively reflected in the response amplitudes of the identified horizontal and vertical clusters. We also found that responses to unambiguous motion were organized in a columnar fashion such that motion preferences were stable in the direction of cortical depth and changed when moving along the cortical surface. We suggest that activity in these specialized clusters is involved in tracking the distinct conscious experience of a particular motion axis.

Representation of Auditory Motion Directions and Sound Source Locations in the Human Planum Temporale

The ability to compute the location and direction of sounds is a crucial perceptual skill to efficiently interact with dynamic environments. How the human brain implements spatial hearing is, however, poorly understood. In our study, we used fMRI to characterize the brain activity of male and female humans listening to sounds moving left, right, up, and down as well as static sounds. Whole-brain univariate results contrasting moving and static sounds varying in their location revealed a robust functional preference for auditory motion in bilateral human planum temporale (hPT). Using independently localized hPT, we show that this region contains information about auditory motion directions and, to a lesser extent, sound source locations. Moreover, hPT showed an axis of motion organization reminiscent of the functional organization of the middle-temporal cortex (hMT+/V5) for vision. Importantly, whereas motion direction and location rely on partially shared pattern geometries in hPT, as demonstrated by successful cross-condition decoding, the responses elicited by static and moving sounds were, however, significantly distinct. Altogether, our results demonstrate that the hPT codes for auditory motion and location but that the underlying neural computation linked to motion processing is more reliable and partially distinct from the one supporting sound source location.SIGNIFICANCE STATEMENT Compared with what we know about visual motion, little is known about how the brain implements spatial hearing. Our study reveals that motion directions and sound source locations can be reliably decoded in the human planum temporale (hPT) and that they rely on partially shared pattern geometries. Our study, therefore, sheds important new light on how computing the location or direction of sounds is implemented in the human auditory cortex by showing that those two computations rely on partially shared neural codes. Furthermore, our results show that the neural representation of moving sounds in hPT follows a "preferred axis of motion" organization, reminiscent of the coding mechanisms typically observed in the occipital middle-temporal cortex (hMT+/V5) region for computing visual motion.

Real-motion signals in human early visual cortex

Eye movements induce visual motion that can complicate the stable perception of the world. The visual system compensates for such self-induced visual motion by integrating visual input with efference copies of eye movement commands. This mechanism is central as it does not only support perceptual stability but also mediates reliable perception of world-centered objective motion. In humans, it remains elusive whether visual motion responses in early retinotopic cortex are driven by objective motion or by retinal motion associated with it. To address this question, we used fMRI to examine functional responses of sixteen visual areas to combinations of planar objective motion and pursuit eye movements. Observers were exposed to objective motion that was faster, matched or slower relative to pursuit, allowing us to compare conditions that differed in objective motion velocity while retinal motion and eye movement signals were matched. Our results show that not only higher level motion regions such as V3A and V6, but also early visual areas signaled the velocity of objective motion, hence the product of integrating retinal with non-retinal signals. These results shed new light on mechanisms that mediate perceptual stability and real-motion perception, and show that extra-retinal signals related to pursuit eye movements influence processing in human early visual cortex.

Connectivity Reveals Sources of Predictive Coding Signals in Early Visual Cortex During Processing of Visual Optic Flow

Superimposed on the visual feed-forward pathway, feedback connections convey higher level information to cortical areas lower in the hierarchy. A prominent framework for these connections is the theory of predictive coding where high-level areas send stimulus interpretations to lower level areas that compare them with sensory input. Along these lines, a growing body of neuroimaging studies shows that predictable stimuli lead to reduced blood oxygen level-dependent (BOLD) responses compared with matched nonpredictable counterparts, especially in early visual cortex (EVC) including areas V1-V3. The sources of these modulatory feedback signals are largely unknown. Here, we re-examined the robust finding of relative BOLD suppression in EVC evident during processing of coherent compared with random motion. Using functional connectivity analysis, we show an optic flow-dependent increase of functional connectivity between BOLD suppressed EVC and a network of visual motion areas including MST, V3A, V6, the cingulate sulcus visual area (CSv), and precuneus (Pc). Connectivity decreased between EVC and 2 areas known to encode heading direction: entorhinal cortex (EC) and retrosplenial cortex (RSC). Our results provide first evidence that BOLD suppression in EVC for predictable stimuli is indeed mediated by specific high-level areas, in accord with the theory of predictive coding.

Repetition suppression: a means to index neural representations using BOLD?

Understanding how the human brain gives rise to complex cognitive processes remains one of the biggest challenges of contemporary neuroscience. While invasive recording in animal models can provide insight into neural processes that are conserved across species, our understanding of cognition more broadly relies upon investigation of the human brain itself. There is therefore an imperative to establish non-invasive tools that allow human brain activity to be measured at high spatial and temporal resolution. In recent years, various attempts have been made to refine the coarse signal available in functional magnetic resonance imaging (fMRI), providing a means to investigate neural activity at the meso-scale, i.e. at the level of neural populations. The most widely used techniques include repetition suppression and multivariate pattern analysis. Human neuroscience can now use these techniques to investigate how representations are encoded across neural populations and transformed by relevant computations. Here, we review the physiological basis, applications and limitations of fMRI repetition suppression with a brief comparison to multivariate techniques. By doing so, we show how fMRI repetition suppression holds promise as a tool to reveal complex neural mechanisms that underlie human cognitive function.

This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Differing Time of Onset of Concurrent TMS-fMRI during Associative Memory Encoding: A Measure of Dynamic Connectivity

There has been a distinct shift in neuroimaging from localization of function into a more network based approach focused on connectivity. While fMRI has proven very fruitful for this, the hemodynamic signal is inherently slow which limits the temporal resolution of fMRI-only connectivity measures. The brain, however, works on a time scale of milliseconds. This study utilized concurrent transcranial magnetic stimulation (TMS)-fMRI in a novel way to obtain measures of dynamic connectivity by measuring changes in fMRI signal amplitude in regions distal to the site of stimulation following differing TMS onset times. Seventeen healthy subjects completed an associative memory encoding task known to involve the DLPFC, viewing pairs of objects which could be semantically related or unrelated. Three pulses of 10 Hz repetitive TMS were applied over the left DLPFC starting either at 200, 600, or 1000 ms after stimulus onset. Associations for related pairs were better remembered than unrelated pairs in a post-scan cued recall test. Differences in neural activity were assessed across different TMS onsets, separately for related and unrelated pairs. Time specific TMS effects were observed in several regions, including those associated with higher-level processing (lateral frontal, anterior cingulate), visual areas (occipital), and regions involved in semantic processing (e.g., left mid-temporal and medial frontal). Activity in the frontal cortex was decreased at 200 ms post-stimulus for unrelated pairs, and 1000 ms post-stimulus for related pairs. This suggests differences in the timing across conditions in which the DLFPC interacts with other PFC regions, consistent with the notion that the DLPFC is facilitating extended semantic processing for related items. This study demonstrates that time-varying TMS onset inside the MRI can be used to reliably measure fast dynamic connectivity with a temporal resolution in the hundreds of milliseconds.

Invariance of surface color representations across illuminant changes in the human cortex

A central problem in color vision is that the light reaching the eye from a given surface can vary dramatically depending on the illumination. Despite this, our color percept, the brain's estimate of surface reflectance, remains remarkably stable. This phenomenon is called color constancy. Here we investigated which human brain regions represent surface color in a way that is invariant with respect to illuminant changes. We used physically realistic rendering methods to display natural yet abstract 3D scenes that were displayed under three distinct illuminants. The scenes embedded, in different conditions, surfaces that differed in their surface color (i.e. in their reflectance property). We used multivariate fMRI pattern analysis to probe neural coding of surface reflectance and illuminant, respectively. While all visual regions encoded surface color when viewed under the same illuminant, we found that only in V1 and V4α surface color representations were invariant to illumination changes. Along the visual hierarchy there was a gradient from V1 to V4α to increasingly encode surface color rather than illumination. Finally, effects of a stimulus manipulation on individual behavioral color constancy indices correlated with neural encoding of the illuminant in hV4. This provides neural evidence for the Equivalent Illuminant Model. Our results provide a principled characterization of color constancy mechanisms across the visual hierarchy, and demonstrate complementary contributions in early and late processing stages.

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex

Multivariate pattern analysis is a powerful technique; however, a significant theoretical limitation in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. This is exemplified by the continued controversy over the source of orientation decoding from fMRI responses in human V1. Recently Carlson (2014) identified a potential source of decodable information by modeling voxel responses based on the Hubel and Wiesel (1972) ice-cube model of visual cortex. The model revealed that activity associated with the edges of gratings covaries with orientation and could potentially be used to discriminate orientation. Here we empirically evaluate whether "edge-related activity" underlies orientation decoding from patterns of BOLD response in human V1. First, we systematically mapped classifier performance as a function of stimulus location using population receptive field modeling to isolate each voxel's overlap with a large annular grating stimulus. Orientation was decodable across the stimulus; however, peak decoding performance occurred for voxels with receptive fields closer to the fovea and overlapping with the inner edge. Critically, we did not observe the expected second peak in decoding performance at the outer stimulus edge as predicted by the edge account. Second, we evaluated whether voxels that contribute most to classifier performance have receptive fields that cluster in cortical regions corresponding to the retinotopic location of the stimulus edge. Instead, we find the distribution of highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels. Our results demonstrate that edge-related activity is likely not necessary for orientation decoding.

SIGNIFICANCE STATEMENT

A significant theoretical limitation of multivariate pattern analysis in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. For example, orientation can be decoded from BOLD activation patterns in human V1, even though orientation columns are at a finer spatial scale than 3T fMRI. Consequently, the source of decodable information remains controversial. Here we test the proposal that information related to the stimulus edges underlies orientation decoding. We map voxel population receptive fields in V1 and evaluate orientation decoding performance as a function of stimulus location in retinotopic cortex. We find orientation is decodable from voxels whose receptive fields do not overlap with the stimulus edges, suggesting edge-related activity does not substantially drive orientation decoding.

On the domain-specificity of the visual and non-visual face-selective regions

What happens in our brains when we see a face? The neural mechanisms of face processing - namely, the face-selective regions - have been extensively explored. Research has traditionally focused on visual cortex face-regions; more recently, the role of face-regions outside the visual cortex (i.e., non-visual-cortex face-regions) has been acknowledged as well. The major quest today is to reveal the functional role of each this region in face processing. To make progress in this direction, it is essential to understand the extent to which the face-regions, and particularly the non-visual-cortex face-regions, process only faces (i.e., face-specific, domain-specific processing) or rather are involved in a more domain-general cognitive processing. In the current functional MRI study, we systematically examined the activity of the whole face-network during face-unrelated reading task (i.e., written meaningful sentences with content unrelated to faces/people and non-words). We found that the non-visual-cortex (i.e., right lateral prefrontal cortex and posterior superior temporal sulcus), but not the visual cortex face-regions, responded significantly stronger to sentences than to non-words. In general, some degree of sentence selectivity was found in all non-visual-cortex cortex. Present result highlights the possibility that the processing in the non-visual-cortex face-selective regions might not be exclusively face-specific, but rather more or even fully domain-general. In this paper, we illustrate how the knowledge about domain-general processing in face-regions can help to advance our general understanding of face processing mechanisms. Our results therefore suggest that the problem of face processing should be approached in the broader scope of cognition in general.

fMRI adaptation revisited

Adaptation has been widely used in functional magnetic imaging (fMRI) studies to infer neuronal response properties in human cortex. fMRI adaptation has been criticized because of the complex relationship between fMRI adaptation effects and the multiple neuronal effects that could underlie them. Many of the longstanding concerns about fMRI adaptation have received empirical support from neurophysiological studies over the last decade. We review these studies here, and also consider neuroimaging studies that have investigated how fMRI adaptation effects are influenced by high-level perceptual processes. The results of these studies further emphasize the need to interpret fMRI adaptation results with caution, but they also provide helpful guidance for more accurate interpretation and better experimental design. In addition, we argue that rather than being used as a proxy for measurements of neuronal stimulus selectivity, fMRI adaptation may be most useful for studying population-level adaptation effects across cortical processing hierarchies.

Selecting appropriate designs and comparison conditions in repetition paradigms

The studies described by Vogels (this issue) demonstrate the complexity of repetition effects in the visual processing stream. In addition to signal suppression, findings of inherited effects from earlier processing, and discrepancies between the stimulus selectivity of cells before and after repetition, have informed the inferences that can be drawn from measures over larger scales such as functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). This work also demonstrates that integration of evidence across recording methods is vital for understanding repetition effects in the brain. It is however difficult to integrate evidence across different recording methods and repetition paradigms. At the crux of this difficulty is the selection of comparison or unrepeated stimulus conditions within paradigms, which influence the observed strength, selectivity and even direction of repetition effects. This viewpoint highlights prevalent methodological issues with regard to repeated-unrepeated stimulus comparisons: inherited adaptation, stimulus specific expectations, concurrent memory retrieval, stimulus novelty and familiarity, attention, and changes in neuronal selectivity with repetition. The extent to which current conflicting and uncertain findings are due to selection of unrepeated stimulus conditions is unknown, but needs to be addressed to develop models of repetition spanning recording methods and repetition paradigms.

Motion parallax links visual motion areas and scene regions

When we move, the retinal velocities of objects in our surrounding differ according to their relative distances and give rise to a powerful three-dimensional visual cue referred to as motion parallax. Motion parallax allows us to infer our surrounding's 3D structure as well as self-motion based on 2D retinal information. However, the neural substrates mediating the link between visual motion and scene processing are largely unexplored. We used fMRI in human observers to study motion parallax by means of an ecologically relevant yet highly controlled stimulus that mimicked the observer's lateral motion past a depth-layered scene. We found parallax selective responses in parietal regions IPS3 and IPS4, and in a region lateral to scene selective occipital place area (OPA). The traditionally defined scene responsive regions OPA, the para-hippocampal place area (PPA) and the retrosplenial cortex (RSC) did not respond to parallax. During parallax processing, the occipital parallax selective region entertained highly specific functional connectivity with IPS3 and with scene selective PPA. These results establish a network linking dorsal motion and ventral scene processing regions specifically during parallax processing, which may underlie the brain's ability to derive 3D scene information from motion parallax.

Follow-up evaluation of oculomotor performance with fMRI in the subacute phase of concussion

OBJECTIVE

To expand on our previous study by performing a follow-up testing session in the subacute phase of injury for participants recently diagnosed with a concussion.

METHODS

A battery of oculomotor tests were administered to participants 30 days postconcussion while simultaneous fMRI was performed.

RESULTS

Three of the 7 oculomotor tasks (antisaccade, self-paced saccade, and memory-guided saccade) administered showed significant differences between the recently concussed group compared with normal volunteers. However, performance in these 3 tasks did show improvement from the acute phase of injury. The fMRI analysis revealed significant differences in brain activation patterns compared with normal volunteers, with the concussed group still demonstrating increased and larger areas of activation. Similar to the oculomotor performance, the fMRI analysis showed that at 30 days postinjury, the concussed group more closely mirrored that of the normal volunteer group compared with at 7 days following insult.

CONCLUSIONS

Even at 30 days postinjury, and despite being clinically asymptomatic, advanced techniques are able to detect subtle lingering alterations in the concussed brain. Therefore, progressive neuroimaging techniques such as fMRI in conjunction with assessment of oculomotor performance may be beneficial in clinical management of concussion.

Evidence for an anterior-posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: focus on the subiculum

Previous studies, predominantly in experimental animals, have suggested the presence of a differentiation of function across the hippocampal formation. In rodents, ventral regions are thought to be involved in emotional behavior while dorsal regions mediate cognitive or spatial processes. Using a combination of modeling the co-occurrence of significant activations across thousands of neuroimaging experiments and subsequent data-driven clustering of these data we were able to provide evidence of distinct subregions within a region corresponding to the human subiculum, a critical hub within the hippocampal formation. This connectivity-based model consists of a bilateral anterior region, as well as separate posterior and intermediate regions on each hemisphere. Functional connectivity assessed both by meta-analytic and resting fMRI approaches revealed that more anterior regions were more strongly connected to the default mode network, and more posterior regions were more strongly connected to 'task positive' regions. In addition, our analysis revealed that the anterior subregion was functionally connected to the ventral striatum, midbrain and amygdala, a circuit that is central to models of stress and motivated behavior. Analysis of a behavioral taxonomy provided evidence for a role for each subregion in mnemonic processing, as well as implication of the anterior subregion in emotional and visual processing and the right posterior subregion in reward processing. These findings lend support to models which posit anterior-posterior differentiation of function within the human hippocampal formation and complement other early steps toward a comparative (cross-species) model of the region.

Dorsal medial prefrontal cortex (MPFC) circuitry in rodent models of cocaine use: implications for drug addiction therapies

Although the importance of the medial prefrontal cortex (MPFC) in cocaine addiction is well established, its precise contribution to cocaine seeking, taking and relapse remains incompletely understood. In particular, across two different models of cocaine self-administration, pharmacological or optogenetic activation of the dorsal MPFC has been reported to sometimes promote and sometimes inhibit cocaine seeking. We highlight important methodological differences between the two experimental paradigms and propose a framework to potentially reconcile the apparent discrepancy. We also draw parallels between these pre-clinical models of cocaine self-administration and human neuro-imaging studies in cocaine users, and argue that both lines of evidence point to dynamic interactions between cue-reactivity processes and control processes within the dorsal MPFC circuitry. From a translational perspective, these findings underscore the importance of interventions and therapeutics targeting not just a brain region, but a specific computational process within that brain region, and may have implications for the design and implementation of more effective treatments for human cocaine addiction.

Functional network overlap as revealed by fMRI using sICA and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity

Functional magnetic resonance imaging (fMRI) studies traditionally use general linear model-based analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-related modulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively across most or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICA might relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.

Object visibility alters the relative contribution of ventral visual stream and mirror neuron system to goal anticipation during action observation

We used fMRI to study the effect of hiding the target of a grasping action on the cerebral activity of an observer whose task was to anticipate the size of the object being grasped. Activity in the putative mirror neuron system (pMNS) was higher when the target was concealed from the view of the observer and anticipating the size of the object being grasped requested paying attention to the hand kinematics. In contrast, activity in ventral visual areas outside the pMNS increased when the target was fully visible, and the performance improved in this condition. A repetition suppression analysis demonstrated that in full view, the size of the object being grasped by the actor was encoded in the ventral visual stream. Dynamic causal modeling showed that monitoring a grasping action increased the coupling between the parietal and ventral premotor nodes of the pMNS. The modulation of the functional connectivity between these nodes was correlated with the subject's capability to detect the size of hidden objects. In full view, synaptic activity increased within the ventral visual stream, and the connectivity with the pMNS was diminished. The re-enactment of observed actions in the pMNS is crucial when interpreting others' actions requires paying attention to the body kinematics. However, when the context permits, visual-spatial information processing may complement pMNS computations for improved action anticipation accuracy.

EEG functional connectivity is partially predicted by underlying white matter connectivity

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales.

Long-range traveling waves of activity triggered by local dichoptic stimulation in V1 of behaving monkeys

Traveling waves of cortical activity, in which local stimulation triggers lateral spread of activity to distal locations, have been hypothesized to play an important role in cortical function. However, there is conflicting physiological evidence for the existence of spreading traveling waves of neural activity triggered locally. Dichoptic stimulation, in which the two eyes view dissimilar monocular patterns, can lead to dynamic wave-like fluctuations in visual perception and therefore, provides a promising means for identifying and studying cortical traveling waves. Here, we used voltage-sensitive dye imaging to test for the existence of traveling waves of activity in the primary visual cortex of awake, fixating monkeys viewing dichoptic stimuli. We find clear traveling waves that are initiated by brief, localized contrast increments in one of the monocular patterns and then, propagate at speeds of ∼ 30 mm/s. These results demonstrate that under an appropriate visual context, circuitry in visual cortex in alert animals is capable of supporting long-range traveling waves triggered by local stimulation.

Inferior parietal lobule encodes visual temporal resolution processes contributing to the critical flicker frequency threshold in humans

The measurement of the Critical Flicker Frequency threshold is used to study the visual temporal resolution in healthy subjects and in pathological conditions. To better understand the role played by different cortical areas in the Critical Flicker Frequency threshold perception we used continuous Theta Burst Stimulation (cTBS), an inhibitory plasticity-inducing protocol based on repetitive transcranial magnetic stimulation. The Critical Flicker Frequency threshold was measured in twelve healthy subjects before and after cTBS applied over different cortical areas in separate sessions. cTBS over the left inferior parietal lobule altered the Critical Flicker Frequency threshold, whereas cTBS over the left mediotemporal cortex, primary visual cortex and right inferior parietal lobule left the Critical Flicker Frequency threshold unchanged. No statistical difference was found when the red or blue lights were used. Our findings show that left inferior parietal lobule is causally involved in the conscious perception of Critical Flicker Frequency and that Critical Flicker Frequency threshold can be modulated by plasticity-inducing protocols.

General overview on the merits of multimodal neuroimaging data fusion

Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain.

Advancing emotion theory with multivariate pattern classification

Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system.

Prior expectations bias sensory representations in visual cortex

Perception is strongly influenced by expectations. Accordingly, perception has sometimes been cast as a process of inference, whereby sensory inputs are combined with prior knowledge. However, despite a wealth of behavioral literature supporting an account of perception as probabilistic inference, the neural mechanisms underlying this process remain largely unknown. One important question is whether top-down expectation biases stimulus representations in early sensory cortex, i.e., whether the integration of prior knowledge and bottom-up inputs is already observable at the earliest levels of sensory processing. Alternatively, early sensory processing may be unaffected by top-down expectations, and integration of prior knowledge and bottom-up input may take place in downstream association areas that are proposed to be involved in perceptual decision-making. Here, we implicitly manipulated human subjects' prior expectations about visual motion stimuli, and probed the effects on both perception and sensory representations in visual cortex. To this end, we measured neural activity noninvasively using functional magnetic resonance imaging, and applied a forward modeling approach to reconstruct the motion direction of the perceived stimuli from the signal in visual cortex. Our results show that top-down expectations bias representations in visual cortex, demonstrating that the integration of prior information and sensory input is reflected at the earliest stages of sensory processing.

Attention selectively modifies the representation of individual faces in the human brain

Attention modifies neural tuning for low-level features, but it is unclear how attention influences tuning for complex stimuli. We investigated this question in humans using fMRI and face stimuli. Participants were shown six faces (F1-F6) along a morph continuum, and selectivity was quantified by constructing tuning curves for individual voxels. Face-selective voxels exhibited greater responses to their preferred face than to nonpreferred faces, particularly in posterior face areas. Anterior face areas instead displayed tuning for face categories: voxels in these areas preferred either the first (F1-F3) or second (F4-F6) half of the morph continuum. Next, we examined the effects of attention on voxel tuning by having subjects direct attention to one of the superimposed images of F1 and F6. We found that attention selectively enhanced responses in voxels preferring the attended face. Together, our results demonstrate that single voxels carry information about individual faces and that the nature of this information varies across cortical face areas. Additionally, we found that attention selectively enhances these representations. Our findings suggest that attention may act via a unitary principle of selective enhancement of responses to both simple and complex stimuli across multiple stages of the visual hierarchy.

Human sensorimotor cortex represents conflicting visuomotor mappings

Behavioral studies have shown that humans can adapt to conflicting sensorimotor mappings that cause interference after intensive training. While previous research works indicate the involvement of distinct brain regions for different types of motor learning (e.g., kinematics vs dynamics), the neural mechanisms underlying joint adaptation to conflicting mappings within the same type of perturbation (e.g., different angles of visuomotor rotation) remain unclear. To reveal the neural substrates that represent multiple sensorimotor mappings, we examined whether different mappings could be classified with multivoxel activity patterns of functional magnetic resonance imaging data. Participants simultaneously adapted to opposite rotational perturbations (+90° and - 90°) during visuomotor tracking. To dissociate differences in movement kinematics with rotation types, we used two distinct patterns of target motion and tested generalization of the classifier between different combinations of rotation and motion types. Results showed that the rotation types were classified significantly above chance using activities in the primary sensorimotor cortex and the supplementary motor area, despite no significant difference in averaged signal amplitudes within the region. In contrast, low-level sensorimotor components, including tracking error and movement speed, were best classified using activities of the early visual cortex. Our results reveal that the sensorimotor cortex represents different visuomotor mappings, which permits joint learning and switching between conflicting sensorimotor skills.

Neural correlates of induced motion perception in the human brain

A physically stationary stimulus surrounded by a moving stimulus appears to move in the opposite direction. There are similarities between the characteristics of this phenomenon of induced motion and surround suppression of directionally selective neurons in the brain. Here, functional magnetic resonance imaging was used to investigate the link between the subjective perception of induced motion and cortical activity. The visual stimuli consisted of a central drifting sinusoid surrounded by a moving random-dot pattern. The change in cortical activity in response to changes in speed and direction of the central stimulus was measured. The human cortical area hMT+ showed the greatest activation when the central stimulus moved at a fast speed in the direction opposite to that of the surround. More importantly, the activity in this area was the lowest when the central stimulus moved in the same direction as the surround and at a speed such that the central stimulus appeared to be stationary. The results indicate that the activity in hMT+ is related to perceived speed modulated by induced motion rather than to physical speed or a kinetic boundary. Early visual areas (V1, V2, V3, and V3A) showed a similar pattern; however, the relationship to perceived speed was not as clear as that in hMT+. These results suggest that hMT+ may be a neural correlate of induced motion perception and play an important role in contrasting motion signals in relation to their surrounding context and adaptively modulating our motion perception depending on the spatial context.