Natural Sensory Stimuli Research Articles

What is a Rhythm for the Brain? The Impact of Contextual Temporal Variability on Auditory Perception.

Temporal predictions can be formed and impact perception when sensory timing is fully predictable: for instance, the discrimination of a target sound is enhanced if it is presented on the beat of an isochronous rhythm. However, natural sensory stimuli, like speech or music, are not entirely predictable, but still possess statistical temporal regularities. We investigated whether temporal expectations can be formed in non-fully predictable contexts, and how the temporal variability of sensory contexts affects auditory perception. Specifically, we asked how "rhythmic" an auditory stimulation needs to be in order to observe temporal predictions effects on auditory discrimination performances. In this behavioral auditory oddball experiment, participants listened to auditory sound sequences where the temporal interval between each sound was drawn from gaussian distributions with distinct standard deviations. Participants were asked to discriminate sounds with a deviant pitch in the sequences. Auditory discrimination performances, as measured with deviant sound discrimination accuracy and response times, progressively declined as the temporal variability of the sound sequence increased. Moreover, both global and local temporal statistics impacted auditory perception, suggesting that temporal statistics are promptly integrated to optimize perception. Altogether, these results suggests that temporal predictions can be set up quickly based on the temporal statistics of past sensory events and are robust to a certain amount of temporal variability. Therefore, temporal predictions can be built on sensory stimulations that are not purely periodic nor temporally deterministic.

Does Amount of Information Support Aesthetic Values?

Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli. Hence, given its importance, the amount of available information may underlie aesthetic values. Such information-based aesthetic values would be significant because they would compete with others to drive decision-making. In this article, we ask, “What is the evidence that amount of information support aesthetic values?” An important concept in the measurement of informational volume is entropy. Research on aesthetic values has thus used Shannon entropy to evaluate the contribution of quantity of information. We review here the concepts of information and aesthetic values, and research on the visual and auditory systems to probe whether the brain uses entropy or other relevant measures, specially, Fisher information, in aesthetic decisions. We conclude that information measures contribute to these decisions in two ways: first, the absolute quantity of information can modulate aesthetic preferences for certain sensory patterns. However, the preference for volume of information is highly individualized, with information-measures competing with organizing principles, such as rhythm and symmetry. In addition, people tend to be resistant to too much entropy, but not necessarily, high amounts of Fisher information. We show that this resistance may stem in part from the distribution of amount of information in natural sensory stimuli. Second, the measurement of entropic-like quantities over time reveal that they can modulate aesthetic decisions by varying degrees of surprise given temporally integrated expectations. We propose that amount of information underpins complex aesthetic values, possibly informing the brain on the allocation of resources or the situational appropriateness of some cognitive models.

Similarities Between Somatosensory Cortical Responses Induced via Natural Touch and Microstimulation in the Ventral Posterior Lateral Thalamus in Macaques.

Lost sensations, such as touch, could be restored by microstimulation (MiSt) along the sensory neural substrate. Such neuroprosthetic sensory information can be used as feedback from an invasive brain-machine interface (BMI) to control a robotic arm/hand, such that tactile and proprioceptive feedback from the sensorized robotic arm/hand is directly given to the BMI user. Microstimulation in the human somatosensory thalamus (Vc) has been shown to produce somatosensory perceptions. However, until recently, systematic methods for using thalamic stimulation to evoke naturalistic touch perceptions were lacking. We have recently presented rigorous methods for determining a mapping between ventral posterior lateral thalamus (VPL) MiSt, and neural responses in the somatosensory cortex (S1), in a rodent model (Choi et al., 2016; Choi and Francis, 2018). Our technique minimizes the difference between S1 neural responses induced by natural sensory stimuli and those generated via VPL MiSt. Our goal is to develop systems that know what neural response a given MiSt will produce and possibly allow the development of natural “sensation.” To date, our optimization has been conducted in the rodent model and simulations. Here, we present data from simple non-optimized thalamic MiSt during peri-operative experiments, where we used MiSt in the VPL of macaques, which have a somatosensory system more like humans, as compared to our previous rat work (Li et al., 2014; Choi et al., 2016). We implanted arrays of microelectrodes across the hand area of the macaque S1 cortex as well as in the VPL. Multi and single-unit recordings were used to compare cortical responses to natural touch and thalamic MiSt in the anesthetized state. Post-stimulus time histograms were highly correlated between the VPL MiSt and natural touch modalities, adding support to the use of VPL MiSt toward producing a somatosensory neuroprosthesis in humans.

The neural basis for violations of Weber’s law in self-motion perception

A prevailing view is that Weber's law constitutes a fundamental principle of perception. This widely accepted psychophysical law states that the minimal change in a given stimulus that can be perceived increases proportionally with amplitude and has been observed across systems and species in hundreds of studies. Importantly, however, Weber's law is actually an oversimplification. Notably, there exist violations of Weber's law that have been consistently observed across sensory modalities. Specifically, perceptual performance is better than that predicted from Weber's law for the higher stimulus amplitudes commonly found in natural sensory stimuli. To date, the neural mechanisms mediating such violations of Weber's law in the form of improved perceptual performance remain unknown. Here, we recorded from vestibular thalamocortical neurons in rhesus monkeys during self-motion stimulation. Strikingly, we found that neural discrimination thresholds initially increased but saturated for higher stimulus amplitudes, thereby causing the improved neural discrimination performance required to explain perception. Theory predicts that stimulus-dependent neural variability and/or response nonlinearities will determine discrimination threshold values. Using computational methods, we thus investigated the mechanisms mediating this improved performance. We found that the structure of neural variability, which initially increased but saturated for higher amplitudes, caused improved discrimination performance rather than response nonlinearities. Taken together, our results reveal the neural basis for violations of Weber's law and further provide insight as to how variability contributes to the adaptive encoding of natural stimuli with continually varying statistics.

EEG-Based Intersubject Correlations Reflect Selective Attention in a Competing Speaker Scenario.

Several solutions have been proposed to study the relationship between ongoing brain activity and natural sensory stimuli, such as running speech. Computing the intersubject correlation (ISC) has been proposed as one possible approach. Previous evidence suggests that ISCs between the participants’ electroencephalogram (EEG) may be modulated by attention. The current study addressed this question in a competing-speaker paradigm, where participants (N = 41) had to attend to one of two concurrently presented speech streams. ISCs between participants’ EEG were higher for participants attending to the same story compared to participants attending to different stories. Furthermore, we found that ISCs between individual and group data predicted whether an individual attended to the left or right speech stream. Interestingly, the magnitude of the shared neural response with others attending to the same story was related to the individual neural representation of the attended and ignored speech envelope. Overall, our findings indicate that ISC differences reflect the magnitude of selective attentional engagement to speech.

Identification of avoidance genes through neural pathway-specific forward optogenetics

Understanding how the nervous system bridges sensation and behavior requires the elucidation of complex neural and molecular networks. Forward genetic approaches, such as screens conducted in C. elegans, have successfully identified genes required to process natural sensory stimuli. However, functional redundancy within the underlying neural circuits, which are often organized with multiple parallel neural pathways, limits our ability to identify ‘neural pathway-specific genes’, i.e. genes that are essential for the function of some, but not all of these redundant neural pathways. To overcome this limitation, we developed a ‘forward optogenetics’ screening strategy in which natural stimuli are initially replaced by the selective optogenetic activation of a specific neural pathway. We used this strategy to address the function of the polymodal FLP nociceptors mediating avoidance of noxious thermal and mechanical stimuli. According to our expectations, we identified both mutations in ‘general’ avoidance genes that broadly impact avoidance responses to a variety of natural noxious stimuli (unc-4, unc-83, and eat-4) and mutations that produce a narrower impact, more restricted to the FLP pathway (syd-2, unc-14 and unc-68). Through a detailed follow-up analysis, we further showed that the Ryanodine receptor UNC-68 acts cell-autonomously in FLP to adjust heat-evoked calcium signals and aversive behaviors. As a whole, our work (i) reveals the importance of properly regulated ER calcium release for FLP function, (ii) provides new entry points for new nociception research and (iii) demonstrates the utility of our forward optogenetic strategy, which can easily be transposed to analyze other neural pathways.

Sound- and current-driven laminar profiles and their application method mimicking acoustic responses in the mouse auditory cortex in vivo

The local application of electrical currents to the cortex is one of the most commonly used techniques to activate neurons, and this intracortical stimulation (ICS) could potentially lead to new types of neuroprosthetic devices that can be directly applied to the cortex. To identify whether ICS-activated circuits are physiological vs. profoundly artificial, it is necessary to record in vivo the responses of the same neuronal population to both natural sensory stimuli and artificial electric stimuli. However, few studies have extensively reported simultaneous electrophysiological recordings combined with ICS. Here, we evaluated the similarity between sound- and ICS-driven cortical response patterns in different cortical layers. In the mouse auditory cortex, we performed laminar recordings using 16-channel silicon electrodes and ICS using sharp glass-pipette electrodes containing biocytin for layer identification. In different cortical depths, short current pulses were delivered in vivo to mice under urethane anesthesia. For the recorded data, we mainly analyzed properties of local field potentials and current source densities (CSDs). We demonstrated that electrical stimulation evoked different excitation patterns according to the stimulated cortical layer; responses to electric stimuli in layer 4 were most likely to mimic acoustic responses. Next, we proposed a CSD-based stimulation method to artificially synthesize sound-driven responses, using an approximation method associated with a linear combination of CSD patterns electrically stimulated in the different cortical layers. The result indicates that synthesized responses were consistent with the canonical model of sound processing. Using these approaches, we provide a new technique in which natural sound-driven responses can be mimicked by well-designed computational stimulation pattern sequences in a layer-dependent manner. These findings may aid in the future development of an electrical stimulation methodology for a cortical prosthesis.

Whisker Vibrations and the Activity of Trigeminal Primary Afferents in Response to Airflow.

Rodents are the most commonly studied model system in neuroscience, but surprisingly few studies investigate the natural sensory stimuli that rodent nervous systems evolved to interpret. Even fewer studies examine neural responses to these natural stimuli. Decades of research have investigated the rat vibrissal (whisker) system in the context of direct touch and tactile stimulation, but recent work has shown that rats also use their whiskers to help detect and localize airflow. The present study investigates the neural basis for this ability as dictated by the mechanical response of whiskers to airflow. Mechanical experiments show that a whisker's vibration magnitude depends on airspeed and the intrinsic shape of the whisker. Surprisingly, the direction of the whisker's vibration changes as a function of airflow speed: vibrations transition from parallel to perpendicular with respect to the airflow as airspeed increases. Recordings from primary sensory trigeminal ganglion neurons show that these neurons exhibit responses consistent with those that would be predicted from direct touch. Trigeminal neuron firing rate increases with airspeed, is modulated by the orientation of the whisker relative to the airflow, and is influenced by the whisker's resonant frequencies. We develop a simple model to describe how a population of neurons could leverage mechanical relationships to decode both airspeed and direction. These results open new avenues for studying vibrissotactile regions of the brain in the context of evolutionarily important airflow-sensing behaviors and olfactory search. Although this study used only female rats, all results are expected to generalize to male rats.SIGNIFICANCE STATEMENT The rodent vibrissal (whisker) system has been studied for decades in the context of direct tactile sensation, but recent work has indicated that rats also use whiskers to help localize airflow. Neural circuits in somatosensory regions of the rodent brain thus likely evolved in part to process airflow information. This study investigates the whiskers' mechanical response to airflow and the associated neural response. Airspeed affects the magnitude of whisker vibration and the response magnitude of whisker-sensitive primary sensory neurons in the trigeminal ganglion. Surprisingly, the direction of vibration and the associated directionally dependent neural response changes with airspeed. These findings suggest a population code for airflow speed and direction and open new avenues for studying vibrissotactile regions of the brain.

Descending pathways generate perception of and neural responses to weak sensory input.

Natural sensory stimuli frequently consist of a fast time-varying waveform whose amplitude or contrast varies more slowly. While changes in contrast carry behaviorally relevant information necessary for sensory perception, their processing by the brain remains poorly understood to this day. Here, we investigated the mechanisms that enable neural responses to and perception of low-contrast stimuli in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. We found that fish reliably detected such stimuli via robust behavioral responses. Recordings from peripheral electrosensory neurons revealed stimulus-induced changes in firing activity (i.e., phase locking) but not in their overall firing rate. However, central electrosensory neurons receiving input from the periphery responded robustly via both phase locking and increases in firing rate. Pharmacological inactivation of feedback input onto central electrosensory neurons eliminated increases in firing rate but did not affect phase locking for central electrosensory neurons in response to low-contrast stimuli. As feedback inactivation eliminated behavioral responses to these stimuli as well, our results show that it is changes in central electrosensory neuron firing rate that are relevant for behavior, rather than phase locking. Finally, recordings from neurons projecting directly via feedback to central electrosensory neurons revealed that they provide the necessary input to cause increases in firing rate. Our results thus provide the first experimental evidence that feedback generates both neural and behavioral responses to low-contrast stimuli that are commonly found in the natural environment.

Fast intensity adaptation enhances the encoding of sound in Drosophila

To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus properties that corrupt pattern recognition. Here we investigate sound intensity adaptation in the Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to wind or gravity, which affect the antenna’s mean position. Song pattern recognition, therefore, requires adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how they interact. A computational model explains our results using a sequence of subtractive and divisive adaptation modules, interleaved by rectification. These results lay the foundation for identifying the molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

Phase-Dependent Modulation of Oscillatory Phase and Synchrony by Long-Lasting Depolarizing Inputs in Central Neurons.

Oscillatory neural activities have been implicated in various types of information processing in the CNS. The procerebral (PC) lobe of the land mollusk Limax valentianus shows an ongoing oscillatory local field potential (LFP). Olfactory input increases both the frequency and spatial synchrony of the LFP oscillation by a nitric oxide (NO)-mediated mechanism, but how NO modulates the activity in a specific manner has been unclear. In the present study, we used electrical stimulation and NO uncaging to systematically analyze the response of the LFP oscillation and found phase-dependent effects on phase shifting and synchrony. The neurons that presumably release NO in the PC lobe preferentially fired at phases in which NO has a synchronizing effect, suggesting that the timing of NO release is regulated to induce a stereotyped response to natural sensory stimuli. The phase-response curve (PRC) describes the timing dependence of responses of an oscillatory system to external input. PRCs are usually constructed by recording the temporal shifts of the neural activity in response to brief electrical pulses. However, NO evokes a long-lasting depolarization persisting for several cycles of oscillation. The phase-response relationship obtained by NO stimulation was approximately the integral of the PRC. A similar relationship was also shown for regular firing of mouse cerebellar Purkinje cells receiving step depolarization, suggesting the generality of the results to oscillatory neural systems with highly distinct properties. These results indicate novel dynamic effects of long-lasting inputs on network oscillation and synchrony, which are based on simple and ubiquitous mechanisms.

Light Sheet Microscopy for Functional Imaging of Brain Activity Evoked by Natural Sensory Stimuli

Light sheet microscopy and the zebrafish emerged recently as exciting tools for studying brain activity with cellular resolution in a vertebrate model system. High temporal and spatial resolution can be achieved in 3D with light sheet microscopy using either single-photon (1p) or 2-photon (2p) excitation. However, effects of the imaging itself on the observed neural activity are rarely addressed, which is of particular concern when the excitation laser power is high, as is needed for fast imaging. Here, we use the brain response evoked by natural sensory stimuli to assess how imaging conditions, such as excitation modality and laser power, affect the observed brain activity in unanesthetized zebrafish larvae. We show that red (630 nm) light flash-induced response is diminished when imaging with 1p-excitation of blue (488 nm) light, a wavelength that is visible to the fish larvae. This effect cannot be explained by saturation of blue-photoreceptors by excitation light, and points to an interference mechanism occurring further downstream of the visual processing. We also assess the effect of the high power of 2p-excitation, required for high speed imaging, to the observed brain activity, and establish a threshold power level above which the neural response starts to be affected, possibly due to the interference between the visual and heat/pain responses. Importantly, the interference effects described above occur at power levels that are not very high (∼ 10 uW and ∼ 100 mW, for 1p and 2p-excitation, respectively), where gross photo-damage effects (e.g. tissue ablation) are not visible. Staying below the perturbation thresholds of laser powers, we proceed to map the brain-wide activity due to evoked responses, providing the necessary groundwork for studies to understand the zebrafish brain function during learning and homeostatic processes.

Modulation of virtual arm trajectories via microstimulation in a spiking model of sensorimotor cortex

Electrical microstimulation can be used to drive neural responses to match meaningful spiking patterns corresponding to natural sensory stimuli or motor behaviors. Optimizing microstimulation sequences requires repeatedly stimulating the neural system to obtain sufficient probing data to construct an inverse model. This is challenging in the real brain where probing time may be limited and plasticity may be induced. Biologically realistic models allow

Differential neural responses to naturally occurring envelopes in the electrosensory system

Natural sensory stimuli frequently consist of a fine structure whose amplitude (i.e. envelope) varies more slowly. Previous studies have demonstrated that envelope signals are found across sensory systems and are necessary for perception. For example, envelope signals in the auditory system carry information pertaining to the perception of pitch fluctuations in communication vocalizations [1], while envelopes in the visual system are responsible for object grouping and the perception of illusory contours [2,3]. However, the neural mechanisms underlying the encoding of these envelope signals are poorly understood in general. Gymnotiform wave-type weakly electric fish constitute an attractive model system for studying envelope processing in the brain because of its well-characterized anatomy and physiology [4]. These fish generate around themselves a weak quasi-sinusoidal electric field through the electric organ discharge (EOD) [5]. Recent studies have shown that the beat amplitude (i.e. the envelope) can vary in time during different behavioral contexts and display differential frequency content: while envelopes caused by movement primarily contain low ( 1 Hz) temporal frequencies [7]. While it is known that electrosensory neurons can respond to envelopes [7], whether these neurons respond differentially to envelopes arising from movement and social interaction is unknown in part because their tuning to envelope frequencies has not been characterized. Here we used well established techniques [8] to record from electrosensory pyramidal neurons across different maps of the body surface within the electrosensory lateral line lobe (ELL) in response to envelope stimuli with different frequency content spanning the behaviorally relevant range (0.05-10 Hz). Using standard measures of linear systems identification techniques as well as more quantitative methods of information theory, we found that pyramidal neurons in different maps displayed differential responses to envelopes arising from different behavioral contexts. Our results suggest that information about envelopes arising from different behavioral contexts begins to be segregated at the level of the ELL and provide new insights as to how these behaviorally relevant stimuli are encoded in early sensory pathways.

Changes in stimulus envelope reveal two classes of peripheral electrosensory neurons

Natural sensory stimuli are characterized by time varying moments such as mean (first-order) and variance (second-order). While psychophysical studies have shown that second order attributes (the envelope) are critical for perception, how they are encoded in the brain remains largely unknown. Here we focused on envelope coding by peripheral electrosensory neurons (P-type afferents) in the weakly electric fish, Apteronotus leptorhynchus, using narrowband noise stimuli that where modulated by sinusoids in the frequency range between 0.05 and 10Hz. Envelopes are an essential feature of natural electrosensory stimuli. When two fish come within close proximity of one another, each animal will experience an amplitude modulation of its own electric signal that oscillates at the difference between the individual EOD frequencies. The envelope varies in time as the distance between two fish changes, and recent studies have shown that it primarily contains low temporal frequencies (<1 Hz). While primary afferents always increase their firing rates in response to an increase in EOD amplitude, we show that they can either increase or decrease their firing rates in response to an increase in the envelope provided that this increase is greater than their threshold. However, the gain and phase of afferent responses are independent of envelope frequency. Therefore, this study gives important insights as to how neural heterogeneities can influence responses to envelopes and might provide an answer as to why primary afferent baseline firing rates vary over such a large range.

Perceptual spaces: mathematical structures to neural mechanisms.

A central goal of neuroscience is to understand how populations of neurons build and manipulate representations of percepts that provide useful information about the environment. This symposium explores the fundamental properties of these representations and the perceptual spaces in which they are organized. Spanning the domains of color, visual texture, environmental sound, music, tactile quality, and odor, we show how the geometric structures of perceptual spaces can be determined experimentally and how these structures provide insights into the principles of neural coding and the neural mechanisms that generate the codes, and into the neural processing of complex sensory stimuli. The diversity of the neural architecture in these different sensory systems provides an opportunity to compare their different solutions to common problems: the need for dimensionality reduction, strategies for topographic or nontopographic mapping, the utility of the higher-order statistical structure inherent in natural sensory stimuli, and the constraints of neural hardware.

Neural correlations code for stimulus variance

Natural sensory stimuli are characterized by time varying moments such as mean (first-order) and variance (second-order). While psychophysical studies have shown that second order attributes are critical for perception, how they are encoded in the brain remains largely unknown. Here we focused on second-order feature coding by correlated activity. We recorded from two example sensory systems that share many similarities: the primate vestibular system and the fish electrosensory system. Peripheral sensory neurons in both systems respond to amplitude modulated noise stimuli (Figure. 1A.1, B.1). We found that the correlation coefficient between spike trains coded second order attributes (i.e. envelope, Figures. 1A.2, B.2) whereas single neuron firing rate coded first order attributes (Figure. 1A.3, B.3). We built a simple phenomenological mathematical model based on the leaky integrate-and-fire formalism that reproduced our experimental data (Figure. 1C.1, C.2) and predicted that optimal coding of second-order stimulus features by correlation is achieved for non-zero values of baseline variability as quantified by the coefficient of variation (CV) (Figure. 1C.3). We tested this prediction by plotting our data as a function of CV and found that our model could explain variations on both vestibular and electrosensory data (Figure. ​(Figure.1D).1D). Our results show that correlated activity codes for stimulus attributes that are distinct from those coded by firing rate and provide a novel role for neural variability. Such codes are predicted to be a general feature of sensory processing. Figure 1

Generation of a Synthetic Memory Trace

We investigated the effect of activating a competing, artificially generated, neural representation on encoding of contextual fear memory in mice. We used a c-fos-based transgenic approach to introduce the hM(3)D(q) DREADD receptor (designer receptor exclusively activated by designer drug) into neurons naturally activated by sensory experience. Neural activity could then be specifically and inducibly increased in the hM(3)D(q)-expressing neurons by an exogenous ligand. When an ensemble of neurons for one context (ctxA) was artificially activated during conditioning in a distinct second context (ctxB), mice formed a hybrid memory representation. Reactivation of the artificially stimulated network within the conditioning context was required for retrieval of the memory, and the memory was specific for the spatial pattern of neurons artificially activated during learning. Similar stimulation impaired recall when not part of the initial conditioning.

Identifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets

Natural sensory stimuli elicit complex brain responses that manifest in fMRI as widely distributed and overlapping clusters of hemodynamic responses. We propose a statistical signal processing method for finding synchronous hemodynamic activity that directly or transiently reflects information about the experimental condition. When applied to fMRI data, the method searches for voxels with activation patterns exhibiting high coherence and simultaneously high variance across brain scans. The crux of the method is functional principal component analysis (fPCA) of activation patterns stored in a two-dimensional data matrix, with rows and columns representing voxels and scans, respectively. Without external information, fPCA is performed directly on this data matrix. Otherwise, the data matrix is first transformed to highlight a specific source of variation, enabling fully or partially supervised fPCA with a single parameter determining the degree of supervision. We evaluated our method on a public benchmark of fMRI scans of subjects viewing natural movies. Our method turns out to be very suitable for flexibly uncovering distributed and overlapping hemodynamic patterns that distinguish well between experimental conditions or cognitive states.