Will you read how I will read? Naturalistic fMRI predictors of emergent reading.

Despite reading being an essential and almost universal skill in the developed world, reading proficiency varies substantially from person to person. To study why, the fMRI field is beginning to turn from single-word or nonword reading tasks to naturalistic stimuli like connected text and listening to stories. To study reading development in children just beginning to read, listening to stories is an appropriate paradigm because speech perception and phonological processing are important for, and are predictors of, reading proficiency. Our study examined the relationship between behavioral reading-related skills and the neural response to listening to stories in the fMRI environment. Functional MRI were gathered in a 3T TIM-Trio scanner. During the fMRI scan, children aged approximately 7 years listened to professionally narrated common short stories and answered comprehension questions following the narration. Analyses of the data used inter-subject correlation (ISC), and representational similarity analysis (RSA). Our primary finding is that ISC reveals areas of increased synchrony in both high- and low-performing emergent readers previously implicated in reading ability/disability. Of particular interest are that several previously identified brain regions (medial temporal gyrus (MTG), inferior frontal gyrus (IFG), inferior temporal gyrus (ITG)) were found to "synchronize" across higher reading ability participants, while lower reading ability participants had idiosyncratic activation patterns in these regions. Additionally, two regions (superior frontal gyrus (SFG) and another portion of ITG) were recruited by all participants, but their specific timecourse of activation depended on reading performance. These analyses support the idea that different brain regions involved in reading follow different developmental trajectories that correlate with reading proficiency on a spectrum rather than the usual dichotomy of poor readers versus strong readers.

Inter-subject correlation during long narratives reveals widespread neural correlates of reading ability.

Recent work using fMRI inter-subject correlation analysis has provided new information about the brain's response to video and audio narratives, particularly in frontal regions not typically activated by single words. This approach is very well suited to the study of reading, where narrative is central to natural experience. But since past reading paradigms have primarily presented single words or phrases, the influence of narrative on semantic processing in the brain - and how that influence might change with reading ability - remains largely unexplored. In this study, we presented coherent stories to adolescents and young adults with a wide range of reading abilities. The stories were presented in alternating visual and auditory blocks. We used a dimensional inter-subject correlation analysis to identify regions in which better and worse readers had varying levels of consistency with other readers. This analysis identified a widespread set of brain regions in which activity timecourses were more similar among better readers than among worse readers. These differences were not detected with standard block activation analyses. Worse readers had higher correlation with better readers than with other worse readers, suggesting that the worse readers had "idiosyncratic" responses rather than using a single compensatory mechanism. Close inspection confirmed that these differences were not explained by differences in IQ or motion. These results suggest an expansion of the current view of where and how reading ability is reflected in the brain, and in doing so, they establish inter-subject correlation as a sensitive tool for future studies of reading disorders.

A highly replicable decline in mood during rest and simple tasks.

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing.

The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory input and generation of error signals. It remains unknown, however, how the layers in the human area 3b contribute to the temporal prediction error processing. To investigate prediction error representation in the area 3b across layers, we acquired layer-specific functional magnetic resonance imaging (fMRI) data at 7T from human area 3b during a task of index finger poking with no-delay, short-delay and long-delay touching sequences. We demonstrate that all three tasks increased activity in both superficial and deep layers of area 3b compared to the random sensory input. The fMRI signal was differentially modulated solely in the deep layers rather than the superficial layers of area 3b by the delay time. Compared with the no-delay stimuli, activity was greater in the deep layers of area 3b during the short-delay stimuli but lower during the long-delay stimuli. This difference activity features in the superficial and deep layers suggest distinct functional contributions of area 3b layers to tactile temporal prediction error processing. The functional segregation in area 3b across layers may reflect that the excitatory and inhibitory interplay in the sensory cortex contributions to flexible communication between cortical layers or between cortical areas.

Rationale and validation of a novel mobile application probing motor inhibition: Proof of concept of CALM-IT.

Identification of behavioral mechanisms underlying psychopathology is essential for the development of novel targeted therapeutics. However, this work relies on rigorous, time-intensive, clinic-based laboratory research, making it difficult to translate research paradigms into tools that can be used by clinicians in the community. The broad adoption of smartphone technology provides a promising opportunity to bridge the gap between the mechanisms identified in the laboratory and the clinical interventions targeting them in the community. The goal of the current study is to develop a developmentally appropriate, engaging, novel mobile application called CALM-IT that probes a narrow biologically informed process, inhibitory control. We aim to leverage the rigorous and robust methods traditionally used in laboratory settings to validate this novel mechanism-driven but easily disseminatable tool that can be used by clinicians to probe inhibitory control in the community. The development of CALM-IT has significant implications for the ability to screen for inhibitory control deficits in the community by both clinicians and researchers. By facilitating assessment of inhibitory control outside of the laboratory setting, researchers could have access to larger and more diverse samples. Additionally, in the clinical setting, CALM-IT represents a novel clinical screening measure that could be used to determine personalized courses of treatment based on the presence of inhibitory control deficits.

The temporal representation of experience in subjective mood.

Humans refer to their mood state regularly in day-to-day as well as clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here, we use a computational approach to quantitatively answer this question and show that early events exert a stronger influence on reported mood (a primacy weighting) compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations across random, consistent, or dynamic reward environments, different age groups, and in both healthy and depressed participants. Moreover, we find evidence for neural encoding of the Primacy, but not the Recency, model in frontal brain regions related to mood regulation. These findings hold implications for the timing of events in experimental or clinical settings and suggest new directions for individualized mood interventions.

Using Transcranial Magnetic Stimulation to Test a Network Model of Perceptual Decision Making in the Human Brain.

Previous research has suggested that the lateral occipital cortex (LOC) is involved with visual decision making, and specifically with the accumulation of information leading to a decision. In humans, this research has been primarily based on imaging and electroencephalography (EEG), and as such only correlational. One line of such research has led to a model of three spatially distributed brain networks that activate in temporal sequence to enable visual decision-making. The model predicted that disturbing neural processing in the LOC at a specific latency would slow object decision-making, increasing reaction time (RT) in a difficult discrimination task. We utilized transcranial magnetic stimulation (TMS) to test this prediction, perturbing LOC beginning at 400 ms post-stimulus onset, a time in the model corresponding to LOC activation at a particular difficulty level, with the expectation of increased RT. Thirteen healthy adults participated in two TMS sessions in which left and right LOC were stimulated separately utilizing neuronavigation and robotic coil guidance. Participants performed a two-alternative forced-choice task selecting whether a car or face was present on each trial amidst visual noise pre-tested to approximate a 75% accuracy level. In an effort to disrupt processing, pairs of TMS pulses separated by 50 ms were presented at one of five stimulus onset asynchronies (SOAs): -200, 200, 400, 450, or 500 ms. Behavioral performance differed systematically across SOAs for RT and accuracy measures. As predicted, TMS at 400 ms resulted in a significant slowing of RT. TMS delivered at -200 ms resulted in faster RT, indicating early stimulation may result in priming and performance enhancement. Use of TMS thus causally demonstrated the involvement of LOC in this task, and more broadly with perceptual decision-making; additionally, it demonstrated the role of TMS in testing well-developed neural models of perceptual processing.

Layer-dependent activity in human prefrontal cortex during working memory.

Working memory involves storing and/or manipulating previously encoded information over a short-term delay period, which is typically followed by a behavioral response based on the remembered information. Although working memory tasks often engage dorsolateral prefrontal cortex, few studies have investigated whether their subprocesses are localized to different cortical depths in this region, and none have done so in humans. Here we use high-resolution functional MRI to interrogate the layer specificity of neural activity during different periods of a delayed-response task in dorsolateral prefrontal cortex. We detect activity time courses that follow the hypothesized patterns: namely, superficial layers are preferentially active during the delay period, specifically in trials requiring manipulation (rather than mere maintenance) of information held in working memory, and deeper layers are preferentially active during the response. Results demonstrate that layer-specific functional MRI can be used in higher-order brain regions to noninvasively map cognitive processing in humans.

Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex.

When humans perceive a sensation, their brains integrate inputs from sensory receptors and process them based on their expectations. The mechanisms of this predictive coding in the human somatosensory system are not fully understood. We fill a basic gap in our understanding of the predictive processing of somatosensation by examining the layer-specific activity in sensory input and predictive feedback in the human primary somatosensory cortex (S1). We acquired submillimeter functional magnetic resonance imaging data at 7T (n = 10) during a task of perceived, predictable, and unpredictable touching sequences. We demonstrate that the sensory input from thalamic projects preferentially activates the middle layer, while the superficial and deep layers in S1 are more engaged for cortico-cortical predictive feedback input. These findings are pivotal to understanding the mechanisms of tactile prediction processing in the human somatosensory cortex.

Ultra-high resolution blood volume fMRI and BOLD fMRI in humans at 9.4 T: Capabilities and challenges.

Functional mapping of cerebral blood volume (CBV) changes has the potential to reveal brain activity with high localization specificity at the level of cortical layers and columns. Non-invasive CBV imaging using Vascular Space Occupancy (VASO) at ultra-high magnetic field strengths promises high spatial specificity but poses unique challenges in human applications. As such, 9.4 T B1+ and B0 inhomogeneities limit efficient blood tagging, while the specific absorption rate (SAR) constraints limit the application of VASO-specific RF pulses. Moreover, short T2* values at 9.4 T require short readout duration, and long T1 values at 9.4 T can cause blood-inflow contaminations. In this study, we investigated the applicability of layer-dependent CBV-fMRI at 9.4 T in humans. We addressed the aforementioned challenges by combining multiple technical advancements: temporally alternating pTx B1+ shimming parameters, advanced adiabatic RF-pulses, 3D-EPI signal readout, optimized GRAPPA acquisition and reconstruction, and stability-optimized RF channel combination. We found that a combination of suitable advanced methodology alleviates the challenges and potential artifacts, and that VASO fMRI provides reliable measures of CBV change across cortical layers in humans at 9.4 T. The localization specificity of CBV-fMRI, combined with the high sensitivity of 9.4 T, makes this method an important tool for future studies investigating cortical micro-circuitry in humans.

A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task.

Sustaining attention to the task at hand is a crucial part of everyday life, from following a lecture at school to maintaining focus while driving. Lapses in sustained attention are frequent and often problematic, with conditions such as attention deficit hyperactivity disorder affecting millions of people worldwide. Recent work has had some success in finding signatures of sustained attention in whole-brain functional connectivity (FC) measures during basic tasks, but since FC can be dynamic and task-dependent, it remains unclear how fully these signatures would generalize to a more complex and naturalistic scenario. To this end, we used a previously defined whole-brain FC network - a marker of attention that was derived from a sustained attention task - to predict the ability of participants to recall material during a free-viewing reading task. Though the predictive network was trained on a different task and set of participants, the strength of FC in the sustained attention network predicted reading recall significantly better than permutation tests where behavior was scrambled to simulate chance performance. To test the generalization of the method used to derive the sustained attention network, we applied the same method to our reading task data to find a new FC network whose strength specifically predicts reading recall. Even though the sustained attention network provided significant prediction of recall, the reading network was more predictive of recall accuracy. The new reading network's spatial distribution indicates that reading recall is highest when temporal pole regions have higher FC with left occipital regions and lower FC with bilateral supramarginal gyrus. Right cerebellar to right frontal connectivity is also indicative of poor reading recall. We examine these and other differences between the two predictive FC networks, providing new insight into the task-dependent nature of FC-based performance metrics.

High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1.

Layer-dependent fMRI allows measurements of information flow in cortical circuits, as afferent and efferent connections terminate in different cortical layers. However, it is unknown to what level human fMRI is specific and sensitive enough to reveal directional functional activity across layers. To answer this question, we developed acquisition and analysis methods for blood-oxygen-level-dependent (BOLD) and cerebral-blood-volume (CBV)-based laminar fMRI and used these to discriminate four different tasks in the human motor cortex (M1). In agreement with anatomical data from animal studies, we found evidence for somatosensory and premotor input in superficial layers of M1 and for cortico-spinal motor output in deep layers. Laminar resting-state fMRI showed directional functional connectivity of M1 with somatosensory and premotor areas. Our findings demonstrate that CBV-fMRI can be used to investigate cortical activity in humans with unprecedented detail, allowing investigations of information flow between brain regions and outperforming conventional BOLD results that are often buried under vascular biases.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

OBJECTIVE: We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs). APPROACH: We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. MAIN RESULTS: We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)-anterior cingulate cortex (ACC) circuit. SIGNIFICANCE: Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI.

Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎) weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data.

NEDE: an open-source scripting suite for developing experiments in 3D virtual environments.

BACKGROUND: As neuroscientists endeavor to understand the brain's response to ecologically valid scenarios, many are leaving behind hyper-controlled paradigms in favor of more realistic ones. This movement has made the use of 3D rendering software an increasingly compelling option. However, mastering such software and scripting rigorous experiments requires a daunting amount of time and effort. NEW METHOD: To reduce these startup costs and make virtual environment studies more accessible to researchers, we demonstrate a naturalistic experimental design environment (NEDE) that allows experimenters to present realistic virtual stimuli while still providing tight control over the subject's experience. NEDE is a suite of open-source scripts built on the widely used Unity3D game development software, giving experimenters access to powerful rendering tools while interfacing with eye tracking and EEG, randomizing stimuli, and providing custom task prompts. RESULTS: Researchers using NEDE can present a dynamic 3D virtual environment in which randomized stimulus objects can be placed, allowing subjects to explore in search of these objects. NEDE interfaces with a research-grade eye tracker in real-time to maintain precise timing records and sync with EEG or other recording modalities. COMPARISON WITH EXISTING METHODS: Python offers an alternative for experienced programmers who feel comfortable mastering and integrating the various toolboxes available. NEDE combines many of these capabilities with an easy-to-use interface and, through Unity's extensive user base, a much more substantial body of assets and tutorials. CONCLUSIONS: Our flexible, open-source experimental design system lowers the barrier to entry for neuroscientists interested in developing experiments in realistic virtual environments.

Neurally and ocularly informed graph-based models for searching 3D environments.

OBJECTIVE: As we move through an environment, we are constantly making assessments, judgments and decisions about the things we encounter. Some are acted upon immediately, but many more become mental notes or fleeting impressions-our implicit 'labeling' of the world. In this paper, we use physiological correlates of this labeling to construct a hybrid brain-computer interface (hBCI) system for efficient navigation of a 3D environment. APPROACH: First, we record electroencephalographic (EEG), saccadic and pupillary data from subjects as they move through a small part of a 3D virtual city under free-viewing conditions. Using machine learning, we integrate the neural and ocular signals evoked by the objects they encounter to infer which ones are of subjective interest to them. These inferred labels are propagated through a large computer vision graph of objects in the city, using semi-supervised learning to identify other, unseen objects that are visually similar to the labeled ones. Finally, the system plots an efficient route to help the subjects visit the 'similar' objects it identifies. MAIN RESULTS: We show that by exploiting the subjects' implicit labeling to find objects of interest instead of exploring naively, the median search precision is increased from 25% to 97%, and the median subject need only travel 40% of the distance to see 84% of the objects of interest. We also find that the neural and ocular signals contribute in a complementary fashion to the classifiers' inference of subjects' implicit labeling. SIGNIFICANCE: In summary, we show that neural and ocular signals reflecting subjective assessment of objects in a 3D environment can be used to inform a graph-based learning model of that environment, resulting in an hBCI system that improves navigation and information delivery specific to the user's interests.

Novelty enhances visual salience independently of reward in the parietal lobe.

Novelty modulates sensory and reward processes, but it remains unknown how these effects interact, i.e., how the visual effects of novelty are related to its motivational effects. A widespread hypothesis, based on findings that novelty activates reward-related structures, is that all the effects of novelty are explained in terms of reward. According to this idea, a novel stimulus is by default assigned high reward value and hence high salience, but this salience rapidly decreases if the stimulus signals a negative outcome. Here we show that, contrary to this idea, novelty affects visual salience in the monkey lateral intraparietal area (LIP) in ways that are independent of expected reward. Monkeys viewed peripheral visual cues that were novel or familiar (received few or many exposures) and predicted whether the trial will have a positive or a negative outcome--i.e., end in a reward or a lack of reward. We used a saccade-based assay to detect whether the cues automatically attracted or repelled attention from their visual field location. We show that salience--measured in saccades and LIP responses--was enhanced by both novelty and positive reward associations, but these factors were dissociable and habituated on different timescales. The monkeys rapidly recognized that a novel stimulus signaled a negative outcome (and withheld anticipatory licking within the first few presentations), but the salience of that stimulus remained high for multiple subsequent presentations. Therefore, novelty can provide an intrinsic bonus for attention that extends beyond the first presentation and is independent of physical rewards.

A hands-on course teaching bioinstrumentation through the design and construction of a benchtop cardiac pacemaker.

We have developed a bioinstrumentation course that emphasizes practical application of engineering and biological concepts by having students focus on the development of a single biomedical device: a cardiac pacemaker. In creating their benchtop pacemaker, students learn about and design sensing circuitry, data acquisition and processing code, control system algorithms, and stimulation electronics. They also gain an understanding of cardiac anatomy and electrophysiology. The separate elements of the pacemaker created throughout the semester will be repeatedly tested, re-designed, and integrated with one another, culminating in an emulated pacemaker whose efficacy will be tested on North American bullfrogs. It is hypothesized that the hands-on learning in this course, coupled with the practical application of concepts in the context of a single biomedical device, will enhance students' skills in bioinstrumentation design.

Closing the loop in cortically-coupled computer vision: a brain-computer interface for searching image databases.

We describe a closed-loop brain-computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of target detection, attentional shifts and self-monitoring processes, which result from the user paying attention to target images interspersed in rapid serial visual presentation (RSVP) sequences. The highest scored images are passed to a semi-supervised computer vision system that reorganizes the image database accordingly, using a graph-based representation that captures visual similarity between images. The system can either query the user for more information, by adaptively resampling the database to create additional RSVP sequences, or it can converge to a 'done' state. The done state includes a final ranking of the image database and also a 'guess' of the user's chosen category of interest. We find that the closed-loop system's re-rankings can substantially expedite database searches for target image categories chosen by the subjects. Furthermore, better reorganizations are achieved than by relying on EEG interest rankings alone, or if the system were simply run in an open loop format without adaptive resampling.

Combining computer and human vision into a BCI: can the whole be greater than the sum of its parts?

Our group has been investigating the development of BCI systems for improving information delivery to a user, specifically systems for triaging image content based on what captures a user's attention. One of the systems we have developed uses single-trial EEG scores as noisy labels for a computer vision image retrieval system. In this paper we investigate how the noisy nature of the EEG-derived labels affects the resulting accuracy of the computer vision system. Specifically, we consider how the precision of the EEG scores affects the resulting precision of images retrieved by a graph-based transductive learning model designed to propagate image class labels based on image feature similarity and sparse labels.