GLMdenoise: a fast, automated technique for denoising task-based fMRI data

Effect of task difficulty on blood-oxygen-level-dependent signal: A functional magnetic resonance imaging study in a motion discrimination task

There is much evidence that neural activity in the human brain is modulated by task difficulty, particularly in visual, frontal, and parietal cortices. However, some basic psychophysical tasks in visual perception do not give rise to this expected effect, at least not in the visual cortex. In the current study, we used functional magnetic resonance imaging (fMRI) to record brain activity while systematically manipulating task difficulty in a motion discrimination task, by varying the angular difference between the motion direction of random dots and a reference direction. We used both a blocked and an event-related design, and presented stimuli in both central and peripheral vision. The behavioral psychometric function, across angular differences of 3°, 9°, 15°, or 80°, spanned the full response range, as expected. The mean blood oxygen level dependent (BOLD) signals were also correlated within-participants between the blocked and event-related designs, across all brain areas tested. Within the visual cortex, the voxel response patterns correlated more within-conditions (e.g., 3° and 3°) than between-conditions (e.g., 3° and 9°), in both designs, further attesting to the reasonable quality of the BOLD data. Nevertheless, the BOLD-o-metric functions (i.e., BOLD activity as a function of task difficulty) were flat in the whole-brain and region-of-interest (ROI) analyses, including in the visual cortex, the parietal cortex, in both designs, and in foveal and peripheral visual fields alike. Indeed, there was little difference between BOLD activity during the 3° and 80° conditions. Some suggestive evidence of difficulty modulation was revealed only in the superior and inferior frontal gyri for the blocked design. We conclude that, in motion discrimination, there is no systematic BOLD modulation that accompanies the standard psychometric function across different hierarchies of cortical areas, except for the frontal lobe of the brain.

Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation

The parahippocampal place area (PPA) is a widely studied high-level visual region in the human brain involved in place and scene processing. The goal of the present study was to identify the most probable location of place-selective voxels in medial ventral temporal cortex. To achieve this goal, we first used cortex-based alignment (CBA) to create a probabilistic place-selective region of interest (ROI) from one group of 12 participants. We then tested how well this ROI could predict place selectivity in each hemisphere within a new group of 12 participants. Our results reveal that a probabilistic ROI (pROI) generated from one group of 12 participants accurately predicts the location and functional selectivity in individual brains from a new group of 12 participants, despite between subject variability in the exact location of place-selective voxels relative to the folding of parahippocampal cortex. Additionally, the prediction accuracy of our pROI is significantly higher than that achieved by volume-based Talairach alignment. Comparing the location of the pROI of the PPA relative to published data from over 500 participants, including data from the Human Connectome Project, shows a striking convergence of the predicted location of the PPA and the cortical location of voxels exhibiting the highest place selectivity across studies using various methods and stimuli. Specifically, the most predictive anatomical location of voxels exhibiting the highest place selectivity in medial ventral temporal cortex is the junction of the collateral and anterior lingual sulci. Methodologically, we make this pROI freely available (vpnl.stanford.edu/PlaceSelectivity), which provides a means to accurately identify a functional region from anatomical MRI data when fMRI data are not available (for example, in patient populations). Theoretically, we consider different anatomical and functional factors that may contribute to the consistent anatomical location of place selectivity relative to the folding of high-level visual cortex.

Spectral Diversity in Default Mode Network Connectivity Reflects Behavioral State

Default mode network (DMN) functional connectivity is thought to occur primarily in low frequencies (<0.1 Hz), resulting in most studies removing high frequencies during data preprocessing. In contrast, subtractive task analyses include high frequencies, as these are thought to be task relevant. An emerging line of research explores resting fMRI data at higher-frequency bands, examining the possibility that functional connectivity is a multiband phenomenon. Furthermore, recent studies suggest DMN involvement in cognitive processing; however, without a systematic investigation of DMN connectivity during tasks, its functional contribution to cognition cannot be fully understood. We bridged these concurrent lines of research by examining the contribution of high frequencies in the relationship between DMN and dorsal attention network at rest and during task execution. Our findings revealed that the inclusion of high frequencies alters between network connectivity, resulting in reduced anticorrelation and increased positive connectivity between DMN and dorsal attention network. Critically, increased positive connectivity was observed only during tasks, suggesting an important role for high-frequency fluctuations in functional integration. Moreover, within-DMN connectivity during task execution correlated with RT only when high frequencies were included. These results show that DMN does not simply deactivate during task execution and suggest active recruitment while performing cognitively demanding paradigms.

A non-invasive, quantitative study of broadband spectral responses in human visual cortex

Currently, non-invasive methods for studying the human brain do not routinely and reliably measure spike-rate-dependent signals, independent of responses such as hemodynamic coupling (fMRI) and subthreshold neuronal synchrony (oscillations and event-related potentials). In contrast, invasive methods—microelectrode recordings and electrocorticography (ECoG)—have recently measured broadband power elevation in field potentials (~50–200 Hz) as a proxy for locally averaged spike rates. Here, we sought to detect and quantify stimulus-related broadband responses using magnetoencephalography (MEG). Extracranial measurements like MEG and EEG have multiple global noise sources and relatively low signal-to-noise ratios; moreover high frequency artifacts from eye movements can be confounded with stimulus design and mistaken for signals originating from brain activity. For these reasons, we developed an automated denoising technique that helps reveal the broadband signal of interest. Subjects viewed 12-Hz contrast-reversing patterns in the left, right, or bilateral visual field. Sensor time series were separated into evoked (12-Hz amplitude) and broadband components (60–150 Hz). In all subjects, denoised broadband responses were reliably measured in sensors over occipital cortex, even in trials without microsaccades. The broadband pattern was stimulus-dependent, with greater power contralateral to the stimulus. Because we obtain reliable broadband estimates with short experiments (~20 minutes), and with sufficient signal-to-noise to distinguish responses to different stimuli, we conclude that MEG broadband signals, denoised with our method, offer a practical, non-invasive means for characterizing spike-rate-dependent neural activity for addressing scientific questions about human brain function.

Compressive Temporal Summation in Human Visual Cortex

Combining sensory inputs over space and time is fundamental to vision. Population receptive field models have been successful in characterizing spatial encoding throughout the human visual pathways. A parallel question, how visual areas in the human brain process information distributed over time, has received less attention. One challenge is that the most widely used neuroimaging method, fMRI, has coarse temporal resolution compared with the time-scale of neural dynamics. Here, via carefully controlled temporally modulated stimuli, we show that information about temporal processing can be readily derived from fMRI signal amplitudes in male and female subjects. We find that all visual areas exhibit subadditive summation, whereby responses to longer stimuli are less than the linear prediction from briefer stimuli. We also find fMRI evidence that the neural response to two stimuli is reduced for brief interstimulus intervals (indicating adaptation). These effects are more pronounced in visual areas anterior to V1-V3. Finally, we develop a general model that shows how these effects can be captured with two simple operations: temporal summation followed by a compressive nonlinearity. This model operates for arbitrary temporal stimulation patterns and provides a simple and interpretable set of computations that can be used to characterize neural response properties across the visual hierarchy. Importantly, compressive temporal summation directly parallels earlier findings of compressive spatial summation in visual cortex describing responses to stimuli distributed across space. This indicates that, for space and time, cortex uses a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world.SIGNIFICANCE STATEMENT Combining sensory inputs over time is fundamental to seeing. Two important temporal phenomena are summation, the accumulation of sensory inputs over time, and adaptation, a response reduction for repeated or sustained stimuli. We investigated these phenomena in the human visual system using fMRI. We built predictive models that operate on arbitrary temporal patterns of stimulation using two simple computations: temporal summation followed by a compressive nonlinearity. Our new temporal compressive summation model captures (1) subadditive temporal summation, and (2) adaptation. We show that the model accounts for systematic differences in these phenomena across visual areas. Finally, we show that for space and time, the visual system uses a similar strategy to achieve increasingly invariant representations of the visual world.

Encoding of natural timbre dimensions in human auditory cortex

Timbre, or sound quality, is a crucial but poorly understood dimension of auditory perception that is important in describing speech, music, and environmental sounds. The present study investigates the cortical representation of different timbral dimensions. Encoding models have typically incorporated the physical characteristics of sounds as features when attempting to understand their neural representation with functional MRI. Here we test an encoding model that is based on five subjectively derived dimensions of timbre to predict cortical responses to natural orchestral sounds. Results show that this timbre model can outperform other models based on spectral characteristics, and can perform as well as a complex joint spectrotemporal modulation model. In cortical regions at the medial border of Heschl's gyrus, bilaterally, and regions at its posterior adjacency in the right hemisphere, the timbre model outperforms even the complex joint spectrotemporal modulation model. These findings suggest that the responses of cortical neuronal populations in auditory cortex may reflect the encoding of perceptual timbre dimensions.

Haptic fMRI: Reliability and performance of electromagnetic haptic interfaces for motion and force neuroimaging experiments

Haptic interfaces compatible with functional magnetic resonance imaging (Haptic fMRI) promise to enable rich motor neuroscience experiments that study how humans perform complex manipulation tasks. Here, we present a large-scale study (176 scans runs, 33 scan sessions) that characterizes the reliability and performance of one such electromagnetically actuated device, Haptic fMRI Interface 3 (HFI-3). We outline engineering advances that ensured HFI-3 did not interfere with fMRI measurements. Observed fMRI temporal noise levels with HFI-3 operating were at the fMRI baseline (0.8% noise to signal). We also present results from HFI-3 experiments demonstrating that high resolution fMRI can be used to study spatio-temporal patterns of fMRI blood oxygenation dependent (BOLD) activation. These experiments include motor planning, goal-directed reaching, and visually-guided force control. Observed fMRI responses are consistent with existing literature, which supports Haptic fMRI's effectiveness at studying the brain's motor regions.

Modeling correlated noise is necessary to decode uncertainty

Brain decoding algorithms form an important part of the arsenal of analysis tools available to neuroscientists, allowing for a more detailed study of the kind of information represented in patterns of cortical activity. While most current decoding algorithms focus on estimating a single, most likely stimulus from the pattern of noisy fMRI responses, the presence of noise causes this estimate to be uncertain. This uncertainty in stimulus estimates is a potentially highly relevant aspect of cortical stimulus processing, and features prominently in Bayesian or probabilistic models of neural coding. Here, we focus on sensory uncertainty and how best to extract this information with fMRI. We first demonstrate in simulations that decoding algorithms that take into account correlated noise between fMRI voxels better recover the amount of uncertainty (quantified as the width of a probability distribution over possible stimuli) associated with the decoded estimate. Furthermore, we show that not all correlated variability should be treated equally, as modeling tuning-dependent correlations has the greatest impact on decoding performance. Next, we examine actual noise correlations in human visual cortex, and find that shared variability in areas V1-V3 depends on the tuning properties of fMRI voxels. In line with our simulations, accounting for this shared noise between similarly tuned voxels produces important benefits in decoding. Our findings underscore the importance of accurate noise models in fMRI decoding approaches, and suggest a statistically feasible method to incorporate the most relevant forms of shared noise.

Central insulin modulates food valuation via mesolimbic pathways

Central insulin is thought to act at the neural interface between metabolic and hedonic drives to eat. Here, using pharmacological fMRI, we show that intranasal insulin (INI) changes the value of food cues through modulation of mesolimbic pathways. Overnight fasted participants rated the palatability of food pictures and attractiveness of non-food items (control) after receiving INI or placebo. We report that INI reduces ratings of food palatability and value signals in mesolimbic regions in individuals with normal insulin sensitivity. Connectivity analyses reveal insulinergic inhibition of forward projections from the ventral tegmentum to the nucleus accumbens. Importantly, the strength of this modulation predicts decrease of palatability ratings, directly linking neural findings to behaviour. In insulin-resistant participants however, we observe reduced food values and aberrant central insulin action. These data demonstrate how central insulin modulates the cross-talk between homeostatic and non-homeostatic feeding systems, suggesting that dysfunctions of these neural interactions may promote metabolic disorders.

The influence of insulin on food preference and the corresponding underlying neural circuits are unknown in humans. Here, the authors show that increasing insulin changes food preference by modulating mesolimbic neural circuits, and that this pattern is changed in insulin-resistant individuals.

Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential

The most widespread measures of human brain activity are the blood-oxygen-level dependent (BOLD) signal and surface field potential. Prior studies report a variety of relationships between these signals. To develop an understanding of how to interpret these signals and the relationship between them, we developed a model of (a) neuronal population responses and (b) transformations from neuronal responses into the functional magnetic resonance imaging (fMRI) BOLD signal and electrocorticographic (ECoG) field potential. Rather than seeking a transformation between the two measures directly, this approach interprets each measure with respect to the underlying neuronal population responses. This model accounts for the relationship between BOLD and ECoG data from human visual cortex in V1, V2, and V3, with the model predictions and data matching in three ways: across stimuli, the BOLD amplitude and ECoG broadband power were positively correlated, the BOLD amplitude and alpha power (8-13 Hz) were negatively correlated, and the BOLD amplitude and narrowband gamma power (30-80 Hz) were uncorrelated. The two measures provide complementary information about human brain activity, and we infer that features of the field potential that are uncorrelated with BOLD arise largely from changes in synchrony, rather than level, of neuronal activity.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Episodic Tags Enhance Striatal Valuation Signals during Temporal Discounting in pathological Gamblers

Similar to many addiction disorders, pathological gambling is associated with an increased preference for immediate rewards (steep temporal discounting). In healthy participants, episodic future thinking has been shown to reduce impulsivity during intertemporal choice. Here, we examine for the first time a modulation of temporal discounting via episodic future thinking in a group of pathological gamblers. We investigated a sample of 24 pathological gamblers and 24 matched healthy controls with functional magnetic resonance imaging (fMRI). Participants made intertemporal choices in two experimental conditions. In the control condition, delayed monetary rewards were offered with the respective amount and delay. In the episodic condition, rewards were additionally associated with participant-specific personal future events. We replicated previous findings of increased temporal discounting in pathological gambling. On a trend level, episodic future thinking attenuated discounting across all participants. We found that pathological gamblers could successfully recruit a prospection related network during decision-making in the presence of episodic information. The episodic condition modulated the functional connection between ventromedial prefrontal cortex (vmPFC) and ventral striatum, a mechanism that might support the increase in striatal value coding observed in the episodic condition in gamblers. However, in controls, but not in gamblers, valuation signal changes in the hippocampus were associated with less impulsive behavior. We provide first evidence that by episodic cues during intertemporal decision-making striatal valuation signals can be enhanced in pathological gamblers. Further research is needed to explore interventions that reliably reduce impulsive choice behavior in pathological gambling.

Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T₂* weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T₂ weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T₂* weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T₂* weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T₂ weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked.

Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals

In recent years the field of fMRI research has enjoyed expanded technical abilities related to resolution, as well as use across many fields of brain research. At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data. In this review we discuss an emerging fMRI technology, called multi-echo (ME)-fMRI, which focuses on improving the fidelity and interpretability of fMRI. Where the essential problem of standard single-echo fMRI is the indeterminacy of sources of signals, whether BOLD or artifact, this is not the case for ME-fMRI. By acquiring multiple echo images per slice, the ME approach allows T₂* decay to be modeled at every voxel at every time point. Since BOLD signals arise by changes in T₂* over time, an fMRI experiment sampling the T₂* signal decay can be analyzed to distinguish BOLD from artifact signal constituents. While the ME approach has a long history of use in theoretical and validation studies, modern MRI systems enable whole-brain multi-echo fMRI at high resolution. This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data to make fMRI at once more principled, straightforward, and powerful. After a brief overview of history and theory, T₂* modeling and applications will be discussed. These applications include T₂* mapping and combining echoes from ME data to increase BOLD contrast and mitigate dropout artifacts. Next, the modeling of fMRI signal changes to detect signal origins in BOLD-related T₂* versus artifact-related S₀ changes will be reviewed. A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA). After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed. Applications include removing motion artifacts in resting state data at subject and group level. New imaging methods such as multi-band multi-echo fMRI and imaging at 7T are demonstrated throughout the review, and a practical analysis pipeline is described. The review culminates with evidence from recent studies of major boosts in statistical power from using multi-echo fMRI for detecting activation and connectivity in healthy individuals and patients with neuropsychiatric disease. In conclusion, the review shows evidence that the multi-echo approach expands the range of experiments that is practicable using fMRI. These findings suggest a compelling future role of the multi-echo approach in subject-level and clinical fMRI.

Cognitive Control Modulates Effects of Episodic Simulation on Delay Discounting in Aging

Enhancing prospective thinking by tagging the future with specific episodic events has been shown to reduce delay discounting in young age (“tag-effect”). So far, it is unclear whether such beneficial effect extends to old adulthood. Since the general ability of future thinking and cognitive control are crucial modulators of temporal discounting in young age, potential age-related decline in these functions might impact on the effect. We focused on this issue by combining functional magnetic resonance imaging (fMRI) with an established intertemporal choice task including episodic “tags” in healthy older participants. Future thinking ability was assessed using autobiographical interviews for future event simulations and a visual search task was applied to assess participants’ cognitive control ability. In contrast to previous data in young adults, the group of older participants did not benefit from tagging the future with episodic events. Older participants’ cognitive control function was directly associated with discounting rates in the episodic conditions: the less the older adults were able to focus their attention the less they benefited from the inclusion of episodic events. Consistent with this, imaging results revealed that: (a) subjective value (SV) signals in the hippocampus and the anterior cingulate cortex (ACC) as well as; (b) hippocampal-striatal coupling during the episodic condition were positively related to participants’ control capacity. Our findings highlight the critical role of executive functioning for the simultaneous integration of episodic information with future value computation in aging. Boosting delay gratification by including episodic tags might hence be limited in older individuals with pronounced decline in distraction control.

Bottom-up and top-down computations in word- and face-selective cortex

The ability to read a page of text or recognize a person's face depends on category-selective visual regions in ventral temporal cortex (VTC). To understand how these regions mediate word and face recognition, it is necessary to characterize how stimuli are represented and how this representation is used in the execution of a cognitive task. Here, we show that the response of a category-selective region in VTC can be computed as the degree to which the low-level properties of the stimulus match a category template. Moreover, we show that during execution of a task, the bottom-up representation is scaled by the intraparietal sulcus (IPS), and that the level of IPS engagement reflects the cognitive demands of the task. These results provide an account of neural processing in VTC in the form of a model that addresses both bottom-up and top-down effects and quantitatively predicts VTC responses.

Organization of high-level visual cortex in human infants

How much of the structure of the human mind and brain is already specified at birth, and how much arises from experience? In this article, we consider the test case of extrastriate visual cortex, where a highly systematic functional organization is present in virtually every normal adult, including regions preferring behaviourally significant stimulus categories, such as faces, bodies, and scenes. Novel methods were developed to scan awake infants with fMRI, while they viewed multiple categories of visual stimuli. Here we report that the visual cortex of 4–6-month-old infants contains regions that respond preferentially to abstract categories (faces and scenes), with a spatial organization similar to adults. However, precise response profiles and patterns of activity across multiple visual categories differ between infants and adults. These results demonstrate that the large-scale organization of category preferences in visual cortex is adult-like within a few months after birth, but is subsequently refined through development.

Adult visual cortex is organized into regions that respond to categories such as faces and scenes, but it is unclear if this depends on experience. Here, authors measured brain activity in 4–6 month old infants looking at faces and scenes and find that their visual cortex is organized similarly to adults.

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

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

Identifying Core Affect in Individuals from fMRI Responses to Dynamic Naturalistic Audiovisual Stimuli

Recent research has demonstrated that affective states elicited by viewing pictures varying in valence and arousal are identifiable from whole brain activation patterns observed with functional magnetic resonance imaging (fMRI). Identification of affective states from more naturalistic stimuli has clinical relevance, but the feasibility of identifying these states on an individual trial basis from fMRI data elicited by dynamic multimodal stimuli is unclear. The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing. These findings extend previous results on the distributed representation of affect to multimodal dynamic stimuli.

Semantic representations in the temporal pole predict false memories

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.

Pitch-Responsive Cortical Regions in Congenital Amusia

Congenital amusia is a lifelong deficit in music perception thought to reflect an underlying impairment in the perception and memory of pitch. The neural basis of amusic impairments is actively debated. Some prior studies have suggested that amusia stems from impaired connectivity between auditory and frontal cortex. However, it remains possible that impairments in pitch coding within auditory cortex also contribute to the disorder, in part because prior studies have not measured responses from the cortical regions most implicated in pitch perception in normal individuals. We addressed this question by measuring fMRI responses in 11 subjects with amusia and 11 age- and education-matched controls to a stimulus contrast that reliably identifies pitch-responsive regions in normal individuals: harmonic tones versus frequency-matched noise. Our findings demonstrate that amusic individuals with a substantial pitch perception deficit exhibit clusters of pitch-responsive voxels that are comparable in extent, selectivity, and anatomical location to those of control participants. We discuss possible explanations for why amusics might be impaired at perceiving pitch relations despite exhibiting normal fMRI responses to pitch in their auditory cortex: (1) individual neurons within the pitch-responsive region might exhibit abnormal tuning or temporal coding not detectable with fMRI, (2) anatomical tracts that link pitch-responsive regions to other brain areas (e.g., frontal cortex) might be altered, and (3) cortical regions outside of pitch-responsive cortex might be abnormal. The ability to identify pitch-responsive regions in individual amusic subjects will make it possible to ask more precise questions about their role in amusia in future work.

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing

Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. Effect size boosting occurs via reduction of non-BOLD noise at the subject-level and consequent reductions in between-subject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICA-related effect size enhancements enable much higher-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could mitigate some issues regarding statistical power in fMRI studies and enable novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.

Building a Science of Individual Differences from fMRI

To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity, reliability and statistical assessment that arise when the focus shifts to individual subjects and that are applicable also to other imaging modalities. We emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability as well as sources of within-subject variability.

Using an achiasmic human visual system to quantify the relationship between the fMRI BOLD signal and neural response

Achiasma in humans causes gross mis-wiring of the retinal-fugal projection, resulting in overlapped cortical representations of left and right visual hemifields. We show that in areas V1-V3 this overlap is due to two co-located but non-interacting populations of neurons, each with a receptive field serving only one hemifield. Importantly, the two populations share the same local vascular control, resulting in a unique organization useful for quantifying the relationship between neural and fMRI BOLD responses without direct measurement of neural activity. Specifically, we can non-invasively double local neural responses by stimulating both neuronal populations with identical stimuli presented symmetrically across the vertical meridian to both visual hemifields, versus one population by stimulating in one hemifield. Measurements from a series of such doubling experiments show that the amplitude of BOLD response is proportional to approximately 0.5 power of the underlying neural response. Reanalyzing published data shows that this inferred relationship is general.

Processing of frequency and location in human subcortical auditory structures

To date it remains largely unknown how fundamental aspects of natural sounds, such as their spectral content and location in space, are processed in human subcortical structures. Here we exploited the high sensitivity and specificity of high field fMRI (7 Tesla) to examine the human inferior colliculus (IC) and medial geniculate body (MGB). Subcortical responses to natural sounds were well explained by an encoding model of sound processing that represented frequency and location jointly. Frequency tuning was organized in one tonotopic gradient in the IC, whereas two tonotopic maps characterized the MGB reflecting two MGB subdivisions. In contrast, no topographic pattern of preferred location was detected, beyond an overall preference for peripheral (as opposed to central) and contralateral locations. Our findings suggest the functional organization of frequency and location processing in human subcortical auditory structures, and pave the way for studying the subcortical to cortical interaction required to create coherent auditory percepts.

Conjunctive Coding of Complex Object Features

Critical to perceiving an object is the ability to bind its constituent features into a cohesive representation, yet the manner by which the visual system integrates object features to yield a unified percept remains unknown. Here, we present a novel application of multivoxel pattern analysis of neuroimaging data that allows a direct investigation of whether neural representations integrate object features into a whole that is different from the sum of its parts. We found that patterns of activity throughout the ventral visual stream (VVS), extending anteriorly into the perirhinal cortex (PRC), discriminated between the same features combined into different objects. Despite this sensitivity to the unique conjunctions of features comprising objects, activity in regions of the VVS, again extending into the PRC, was invariant to the viewpoints from which the conjunctions were presented. These results suggest that the manner in which our visual system processes complex objects depends on the explicit coding of the conjunctions of features comprising them.

Visual representations are dominated by intrinsic fluctuations correlated between areas

Intrinsic cortical dynamics are thought to underlie trial-to-trial variability of visually evoked responses in animal models. Understanding their function in the context of sensory processing and representation is a major current challenge. Here we report that intrinsic cortical dynamics strongly affect the representational geometry of a brain region, as reflected in response-pattern dissimilarities, and exaggerate the similarity of representations between brain regions. We characterized the representations in several human visual areas by representational dissimilarity matrices (RDMs) constructed from fMRI response-patterns for natural image stimuli. The RDMs of different visual areas were highly similar when the response-patterns were estimated on the basis of the same trials (sharing intrinsic cortical dynamics), and quite distinct when patterns were estimated on the basis of separate trials (sharing only the stimulus-driven component). We show that the greater similarity of the representational geometries can be explained by coherent fluctuations of regional-mean activation within visual cortex, reflecting intrinsic dynamics. Using separate trials to study stimulus-driven representations revealed clearer distinctions between the representational geometries: a Gabor wavelet pyramid model explained representational geometry in visual areas V1–3 and a categorical animate–inanimate model in the object-responsive lateral occipital cortex.

Attention reduces spatial uncertainty in human ventral temporal cortex

Ventral temporal cortex (VTC) is the latest stage of the ventral "what" visual pathway, which is thought to code the identity of a stimulus regardless of its position or size [1, 2]. Surprisingly, recent studies show that position information can be decoded from VTC [3-5]. However, the computational mechanisms by which spatial information is encoded in VTC are unknown. Furthermore, how attention influences spatial representations in human VTC is also unknown because the effect of attention on spatial representations has only been examined in the dorsal "where" visual pathway [6-10]. Here, we fill these significant gaps in knowledge using an approach that combines functional magnetic resonance imaging and sophisticated computational methods. We first develop a population receptive field (pRF) model [11, 12] of spatial responses in human VTC. Consisting of spatial summation followed by a compressive nonlinearity, this model accurately predicts responses of individual voxels to stimuli at any position and size, explains how spatial information is encoded, and reveals a functional hierarchy in VTC. We then manipulate attention and use our model to decipher the effects of attention. We find that attention to the stimulus systematically and selectively modulates responses in VTC, but not early visual areas. Locally, attention increases eccentricity, size, and gain of individual pRFs, thereby increasing position tolerance. However, globally, these effects reduce uncertainty regarding stimulus location and actually increase position sensitivity of distributed responses across VTC. These results demonstrate that attention actively shapes and enhances spatial representations in the ventral visual pathway.

Representation of pitch chroma by multi-peak spectral tuning in human auditory cortex

Musical notes played at octave intervals (i.e., having the same pitch chroma) are perceived as similar. This well-known perceptual phenomenon lays at the foundation of melody recognition and music perception, yet its neural underpinnings remain largely unknown to date. Using fMRI with high sensitivity and spatial resolution, we examined the contribution of multi-peak spectral tuning to the neural representation of pitch chroma in human auditory cortex in two experiments. In experiment 1, our estimation of population spectral tuning curves from the responses to natural sounds confirmed--with new data--our recent results on the existence of cortical ensemble responses finely tuned to multiple frequencies at one octave distance (Moerel et al., 2013). In experiment 2, we fitted a mathematical model consisting of a pitch chroma and height component to explain the measured fMRI responses to piano notes. This analysis revealed that the octave-tuned populations-but not other cortical populations-harbored a neural representation of musical notes according to their pitch chroma. These results indicate that responses of auditory cortical populations selectively tuned to multiple frequencies at one octave distance predict well the perceptual similarity of musical notes with the same chroma, beyond the physical (frequency) distance of notes.

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Haptic fMRI: using classification to quantify task-correlated noise during goal-directed reaching motions

Neuroimaging artifacts in haptic functional magnetic resonance imaging (Haptic fMRI) experiments have the potential to induce spurious fMRI activation where there is none, or to make neural activation measurements appear correlated across brain regions when they are actually not. Here, we demonstrate that performing three-dimensional goal-directed reaching motions while operating Haptic fMRI Interface (HFI) does not create confounding motion artifacts. To test for artifacts, we simultaneously scanned a subject's brain with a customized soft phantom placed a few centimeters away from the subject's left motor cortex. The phantom captured task-related motion and haptic noise, but did not contain associated neural activation measurements. We quantified the task-related information present in fMRI measurements taken from the brain and the phantom by using a linear max-margin classifier to predict whether raw time series data could differentiate between motion planning or reaching. fMRI measurements in the phantom were uninformative (2σ, 45-73%; chance=50%), while those in primary motor, visual, and somatosensory cortex accurately classified task-conditions (2σ, 90-96%). We also localized artifacts due to the haptic interface alone by scanning a stand-alone fBIRN phantom, while an operator performed haptic tasks outside the scanner's bore with the interface at the same location. The stand-alone phantom had lower temporal noise and had similar mean classification but a tighter distribution (bootstrap Gaussian fit) than the brain phantom. Our results suggest that any fMRI measurement artifacts for Haptic fMRI reaching experiments are dominated by actual neural responses.

Haptic fMRI: accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks

Haptics combined with functional magnetic resonance imaging (Haptic fMRI) can non-invasively study how the human brain coordinates movement during complex manipulation tasks, yet avoiding associated fMRI artifacts remains a challenge. Here, we demonstrate confound-free neural activation measurements using Haptic fMRI for an unconstrained three degree-of-freedom motor task that involves planning, reaching, and visually guided trajectory tracking. Our haptic interface tracked subjects' hand motions, velocities, and accelerations (sample-rate, 350Hz), and provided continuous realtime visual feedback. During fMRI acquisition, we achieved uniform response latencies (reaching, 0.7-1.1s; tracking, 0.4-0.65s); minimized hand jitter (<;8mm); and ensured reliable motion trajectories (tracking, <;7mm root-mean-square error). In addition, our protocol decorrelated head motion from both hand speed (r=-0.03) and acceleration (r=-0.025), which reliably produced low head motion levels (<;0.4mm/s between scan volumes) and a low fMRI temporal noise-to-signal ratio (<;1%) across thirty-five scan runs. Our results address the primary outstanding Haptic fMRI confounds: motion induced low spatial-frequency magnetic field changes, which correlate neural activation across cortex; unreliable motions and response latencies, which reduce statistical power; and task-correlated head motion, which causes spurious fMRI activation. Haptic fMRI can thus reliably elicit and localize heterogeneous neural activation for different tasks in motor (movement), pre-motor (planning), and somatosensory (limb displacement) cortex, demonstrating that it is feasible to use the technique to study how the brain achieves three dimensional motor control.

Recent progress and outstanding issues in motion correction in resting state fMRI

The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI. We will attempt to distill these papers to their most essential content. We will point out some aspects of motion artifact that are easily or often overlooked. Throughout the review, we will highlight gaps in current knowledge and avenues for future research.

Unique semantic space in the brain of each beholder predicts perceived similarity

The unique way in which each of us perceives the world must arise from our brain representations. If brain imaging could reveal an individual's unique mental representation, it could help us understand the biological substrate of our individual experiential worlds in mental health and disease. However, imaging studies of object vision have focused on commonalities between individuals rather than individual differences and on category averages rather than representations of particular objects. Here we investigate the individually unique component of brain representations of particular objects with functional MRI (fMRI). Subjects were presented with unfamiliar and personally meaningful object images while we measured their brain activity on two separate days. We characterized the representational geometry by the dissimilarity matrix of activity patterns elicited by particular object images. The representational geometry remained stable across scanning days and was unique in each individual in early visual cortex and human inferior temporal cortex (hIT). The hIT representation predicted perceived similarity as reflected in dissimilarity judgments. Importantly, hIT predicted the individually unique component of the judgments when the objects were personally meaningful. Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are expressed in an individual's perceptual judgments. The unique way each of us perceives the world thus might reflect the individually unique representation in high-level visual areas.

Using fMRI non-local means denoising to uncover activation in sub-cortical structures at 1.5 T for guided HARDI tractography

In recent years, there has been ever-increasing interest in combining functional magnetic resonance imaging (fMRI) and diffusion magnetic resonance imaging (dMRI) for better understanding the link between cortical activity and connectivity, respectively. However, it is challenging to detect and validate fMRI activity in key sub-cortical areas such as the thalamus, given that they are prone to susceptibility artifacts due to the partial volume effects (PVE) of surrounding tissues (GM/WM interface). This is especially true on relatively low-field clinical MR systems (e.g., 1.5 T). We propose to overcome this limitation by using a spatial denoising technique used in structural MRI and more recently in diffusion MRI called non-local means (NLM) denoising, which uses a patch-based approach to suppress the noise locally. To test this, we measured fMRI in 20 healthy subjects performing three block-based tasks : eyes-open closed (EOC) and left/right finger tapping (FTL, FTR). Overall, we found that NLM yielded more thalamic activity compared to traditional denoising methods. In order to validate our pipeline, we also investigated known structural connectivity going through the thalamus using HARDI tractography: the optic radiations, related to the EOC task, and the cortico-spinal tract (CST) for FTL and FTR. To do so, we reconstructed the tracts using functionally based thalamic and cortical ROIs to initiates seeds of tractography in a two-level coarse-to-fine fashion. We applied this method at the single subject level, which allowed us to see the structural connections underlying fMRI thalamic activity. In summary, we propose a new fMRI processing pipeline which uses a recent spatial denoising technique (NLM) to successfully detect sub-cortical activity which was validated using an advanced dMRI seeding strategy in single subjects at 1.5 T.

Mapping stiffness perception in the brain with an fMRI-compatible particle-jamming haptic interface

We demonstrate reliable neural responses to changes in haptic stiffness perception using a functional magnetic resonance imaging (fMRI) compatible particle-jamming haptic interface. Our haptic interface consists of a silicone tactile surface whose stiffness we can control by modulating air-pressure in a sub-surface pouch of coarsely ground particles. The particles jam together as the pressure decreases, which stiffens the surface. During fMRI acquisition, subjects performed a constant probing task, which involved continuous contact between the index fingertip and the interface and rhythmic increases and decreases in fingertip force (1.6 Hz) to probe stiffness. Without notifying subjects, we randomly switched the interface's stiffness (switch time, 300-500 ms) from soft (200 N/m) to hard (1400 N/m). Our experiment design's constant motor activity and cutaneous tactile sensation helped disassociate neural activation for both from stiffness perception, which helped localized it to a narrow region in somatosensory cortex near the supra-marginal gyrus. Testing different models of neural activation, we found that assuming indepedent stiffness-change responses at both soft-hard and hard-soft transitions provides the best explanation for observed fMRI measurements (three subjects; nine four-minute scan runs each). Furthermore, we found that neural activation related to stiffness-change and absolute stiffness can be localized to adjacent but disparate anatomical locations. We also show that classical finger-tapping experiments activate a swath of cortex and are not suitable for localizing stiffness perception. Our results demonstrate that decorrelating motor and sensory neural activation is essential for characterizing somatosensory cortex, and establish particle-jamming haptics as an attractive low-cost method for fMRI experiments.