Identification of cerebral networks by classification of the shape of BOLD responses

The Dynamics of Speech Motor Control Revealed with Time-Resolved fMRI

Holding a conversation means that speech must be started, maintained, and stopped continuously. The brain networks that underlie these aspects of speech motor control remain poorly understood. Here we collected functional magnetic resonance imaging (fMRI) data while participants produced normal and fast rate speech in response to sequences of visually presented objects. We took a non-conventional approach to fMRI data analysis that allowed us to study speech motor behavior as it unfolded over time. To this end, whole-brain fMRI signals were extracted in stimulus-locked epochs using slice-based fMRI. These data were then subjected to group independent component analysis to discover spatially independent networks that were associated with different temporal activation profiles. The results revealed two basic brain networks with different temporal dynamics: a cortical network that was activated continuously during speech production, and a second cortico-subcortical network that increased in activity during the initiation and suppression of speech production. Additional analyses explored whether key areas involved in motor suppression such as the right inferior frontal gyrus, sub-thalamic nucleus and pre-supplementary motor area provide first-order signals to stop speech. The results reveal for the first time the brain networks associated with the initiation, maintenance, and suppression of speech motor behavior.

A temporal decomposition method for identifying venous effects in task-based fMRI

The spatial resolution of functional magnetic resonance imaging (fMRI) is fundamentally limited by effects from large draining veins. Here we describe an analysis method that provides data-driven estimates of these effects in task-based fMRI. The method involves fitting a one-dimensional manifold that characterizes variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (large veins), respectively. We show the removal of late components substantially reduces the superficial cortical depth bias of fMRI responses and helps eliminate artifacts in cortical activity maps. This method provides insight into the origins of the fMRI signal and can be used to improve the spatial accuracy of fMRI.

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.

Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal

Background:

fMRI provides spatial resolution that is unmatched by non-invasive neuroimaging techniques. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic signal.

New Methods:

We present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed on single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.

Results:

We demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of mean BOLD amplitude differences. Using Monte-Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and single-subject levels, FWER was either at or significantly below 5%. We reached the desired power with 18 subjects and 12 trials for the group level, and with 14 trials in the single-subject scenario.

Comparison with existing methods:

We compare the tMVPA statistical evaluation to that of a linear support vector machine (SVM). SVM outperformed tMVPA with large N and trial numbers. Conversely, tMVPA, leveraging on single trials analyses, outperformed SVM in low N and trials and in a single-subject scenario.

Conclusion:

Recent evidence suggesting that the BOLD signal carries finer-grained temporal information than previously thought, advocates the need for analytical tools, such as tMVPA, tailored to investigate BOLD temporal dynamics. The comparable performance between tMVPA and SVM, a powerful and reliable tool for fMRI, supports the validity of our technique.

Dimensionally Specific Capture of Attention: Implications for Saliency Computation

Observers automatically orient to a sudden change in the environment. This is demonstrated experimentally using exogenous cues, which prioritize the analysis of subsequent targets appearing nearby. This effect has been attributed to the computation of saliency, obtained by combining features specific signals, which then feed back to drive attention to the salient location. An alternative possibility is that cueing directly effects target-evoked sensory responses in a feed-forward manner. We examined the effects of luminance and equiluminant color cues in a dual task paradigm, which required both a motion and a color discrimination. Equiluminant color cues improved color discrimination more than luminance cues, but luminance cues improved motion discrimination more than equiluminant color cues. This suggests that the effects of exogenous cues are dimensionally specific and may not depend entirely on the computation of a dimension general saliency signal.

The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks

Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context.

Color improves speed of processing but not perception in a motion illusion

When two superimposed surfaces of dots move in different directions, the perceived directions are shifted away from each other. This perceptual illusion has been termed direction repulsion and is thought to be due to mutual inhibition between the representations of the two directions. It has further been shown that a speed difference between the two surfaces attenuates direction repulsion. As speed and direction are both necessary components of representing motion, the reduction in direction repulsion can be attributed to the additional motion information strengthening the representations of the two directions and thus reducing the mutual inhibition. We tested whether bottom-up attention and top-down task demands, in the form of color differences between the two surfaces, would also enhance motion processing, reducing direction repulsion. We found that the addition of color differences did not improve direction discrimination and reduce direction repulsion. However, we did find that adding a color difference improved performance on the task. We hypothesized that the performance differences were due to the limited presentation time of the stimuli. We tested this in a follow-up experiment where we varied the time of presentation to determine the duration needed to successfully perform the task with and without the color difference. As we expected, color segmentation reduced the amount of time needed to process and encode both directions of motion. Thus we find a dissociation between the effects of attention on the speed of processing and conscious perception of direction. We propose four potential mechanisms wherein color speeds figure-ground segmentation of an object, attentional switching between objects, direction discrimination and/or the accumulation of motion information for decision-making, without affecting conscious perception of the direction. Potential neural bases are also explored.

Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis

The brain is the body's largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95% of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models.

Estimating neural signal dynamics in the human brain

Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD) response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course). Knowledge of the neural signal is critical if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by non-invasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential applications ranging from general cognitive studies to assessment of neuropathologies.

Independent components in stimulus-related BOLD signals and estimation of the underlying neural responses

We measured blood oxygen level dependent (BOLD) responses to the onset of dynamic noise stimulation in defined regions of the primary retinotopic projection (V1) in visual cortex. The response waveforms showed a remarkable diversity across stimulus types, violating the basic assumption of a unitary general linear model of a uniform BOLD response function convolved with each stimulus sequence. We used independent component analysis (ICA) to analyze the component mechanisms contributing to these responses. The underlying neural responses for the components were estimated by nonlinear optimization through the Friston-Buxton hemodynamic model of the BOLD response. Our analysis suggests that one of the identified components reflected a sustained neural response to the stimulus and that another reflected an extremely slow neural response. A third component exhibited nonlinear change-specific transient responses. The first two components showed stable spatial structure in the V1 region of interest with respect to the eccentricity of the noise stimulus.

Spatiotopic selectivity of BOLD responses to visual motion in human area MT

Many neurons in the monkey visual extrastriate cortex have receptive fields that are affected by gaze direction. In humans, psychophysical studies suggest that motion signals may be encoded in a spatiotopic fashion. Here we use functional magnetic resonance imaging to study spatial selectivity in the human middle temporal cortex (area MT or V5), an area that is clearly implicated in motion perception. The results show that the response of MT is modulated by gaze direction, generating a spatial selectivity based on screen rather than retinal coordinates. This area could be the neurophysiological substrate of the spatiotopic representation of motion signals.

Cortico-cortical networks and cortico-subcortical loops for the higher control of eye movements

There are multiple distinct regions, or eye fields, in the cerebral cortex that contribute directly to the initiation and control of voluntary eye movements. We concentrate on six of these: the frontal eye field, parietal eye field, supplementary eye field, middle superior temporal area, prefrontal eye field, and area 7 m (precuneus in humans). In each of these regions: (1) there is neural activity closely related to eye movements; (2) electrical microstimulation produces or modifies eye movements; (3) surgical lesions or chemical inactivation impairs eye movements; (4) there are direct neural projections to major structures in the brainstem oculomotor system; and (5) increased activity is observed during eye movement tasks in functional magnetic resonance imaging or positron emission tomography experiments in humans. Each of these eye fields is reciprocally connected with the other eye fields, and each receives visual information directly from visual association cortex. Each eye field has distinct subregions that are concerned with either saccadic or pursuit eye movements. The saccadic subregions are preferentially interconnected with other saccade subregions and the pursuit subregions are preferentially interconnected with other pursuit subregions. Current evidence strongly supports the proposal that there are parallel cortico-cortical networks that control purposeful saccadic and pursuit eye movements, and that the activity in those networks is modulated by feedback information, via the thalamus, from the superior colliculus, basal ganglia, and cerebellum.

The chronoarchitecture of the human brain—natural viewing conditions reveal a time-based anatomy of the brain

A dominant tendency in cerebral studies has been the attempt to locate architecturally distinct parts of the cortex and assign special functions to each, through histological, clinical or hypothesis-based imaging experiments. Here we show that the cerebral cortex can also be subdivided into different components temporally, without any a priori hypotheses, based on the principle of functional independence. This states that distinct functional subdivisions have activity time courses (ATCs) that are, if not independent, at least characteristic to each when the brain is exposed to natural conditions. To approach a time-based anatomy experimentally, we recorded whole-brain activity using functional magnetic resonance imaging (fMRI) and analyzed the data with independent component analysis (ICA). Our results show that a multitude of cortical areas can be identified based purely on their characteristic ATCs during natural conditions. We demonstrate that a more "rich" stimulation (free viewing of a movie) leads to more areas being activated in a specific way than conventional stimuli, allowing for a more detailed dissection of the cortex into its subdivisions. We show that stimulus-driven functionally specialized areas can be identified by intersubject correlation even if their function is unknown. Chronoarchitectonic mapping thus opens the prospect of identifying previously unknown cortical subdivisions based on natural viewing conditions by exploiting the characteristic temporal "fingerprint" that is unique to each.