A Unifying Model for Discordant and Concordant Results in Human Neuroimaging Studies of Facial Viewpoint Selectivity.

Recognizing faces regardless of their viewpoint is critical for social interactions. Traditional theories hold that view-selective early visual representations gradually become tolerant to viewpoint changes along the ventral visual hierarchy. Newer theories, based on single-neuron monkey electrophysiological recordings, suggest a three-stage architecture including an intermediate face-selective patch abruptly achieving invariance to mirror-symmetric face views. Human studies combining neuroimaging and multivariate pattern analysis (MVPA) have provided convergent evidence of view selectivity in early visual areas. However, contradictory conclusions have been reached concerning the existence in humans of a mirror-symmetric representation like that observed in macaques. We believe these contradictions arise from low-level stimulus confounds and data analysis choices. To probe for low-level confounds, we analyzed images from two face databases. Analyses of image luminance and contrast revealed biases across face views described by even polynomials-i.e., mirror-symmetric. To explain major trends across neuroimaging studies, we constructed a network model incorporating three constraints: cortical magnification, convergent feedforward projections, and interhemispheric connections. Given the identified low-level biases, we show that a gradual increase of interhemispheric connections across network-layers is sufficient to replicate view-tuning in early processing stages and mirror-symmetry in later stages. Data analysis decisions-pattern dissimilarity measure and data recentering-accounted for the inconsistent observation of mirror-symmetry across prior studies. Pattern analyses of human fMRI data (of either sex) revealed biases compatible with our model. The model provides a unifying explanation of MVPA studies of viewpoint selectivity and suggests observations of mirror-symmetry originate from ineffectively normalized signal imbalances across different face views.

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.

Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility.

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.

How to Interpret Resting-State fMRI: Ask Your Participants.

Resting-state fMRI (rsfMRI) reveals brain dynamics in a task-unconstrained environment as subjects let their minds wander freely. Consequently, resting subjects navigate a rich space of cognitive and perceptual states (i.e., ongoing experience). How this ongoing experience shapes rsfMRI summary metrics (e.g., functional connectivity) is unknown, yet likely to contribute uniquely to within- and between-subject differences. Here we argue that understanding the role of ongoing experience in rsfMRI requires access to standardized, temporally resolved, scientifically validated first-person descriptions of those experiences. We suggest best practices for obtaining those descriptions via introspective methods appropriately adapted for use in fMRI research. We conclude with a set of guidelines for fusing these two data types to answer pressing questions about the etiology of rsfMRI.

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness.

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

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.

Movie-watching outperforms rest for functional connectivity-based prediction of behavior.

A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing-i.e., movie watching-yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.

Magnetization transfer weighted EPI facilitates cortical depth determination in native fMRI space.

The increased availability of ultra-high field scanners provides an opportunity to perform fMRI at sub-millimeter spatial scales and enables in vivo probing of laminar function in the human brain. In most previous studies, the definition of cortical layers, or depths, is based on an anatomical reference image that is collected by a different acquisition sequence and exhibits different geometric distortion compared to the functional images. Here, we propose to generate the anatomical image with the fMRI acquisition technique by incorporating magnetization transfer (MT) weighted imaging. Small flip angle binomial pulse trains are used as MT preparation, with a flexible duration (several to tens of milliseconds), which can be applied before each EPI segment without constraining the acquisition length (segment or slice number). The method's feasibility was demonstrated at 7T for coverage of either a small slab or the near-whole brain at 0.8 mm isotropic resolution. Tissue contrast was found to be similar to that obtained with a state-of-art anatomical reference based on MP2RAGE. This MT-weighted EPI image allows an automatic reconstruction of the cortical surface to support laminar analysis in native fMRI space, obviating the need for distortion correction and registration.

Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration.

Haptic object perception begins with continuous exploratory contact, and the human brain needs to accumulate sensory information continuously over time. However, it is still unclear how the primary sensorimotor cortex (PSC) interacts with these higher-level regions during haptic exploration over time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimations. For this experiment, we designed sixteen haptic stimuli (4 kinds of curves × 4 varieties of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along the surface twice and to estimate one of the two features-roughness or curvature-depending on the task instruction. We found that the brain activity in several higher-level regions (e.g., the bilateral posterior parietal cortex) linearly increased as the number of curves increased during the haptic exploration phase. Surprisingly, we found that the contralateral PSC was parametrically modulated by the number of curves only during the late exploration phase but not during the early exploration phase. In contrast, we found no similar parametric modulation activity patterns during the haptic roughness estimation task in either the contralateral PSC or in higher-level regions. Thus, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, whereas the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon the features of the object.

LayNii: A software suite for layer-fMRI.

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain 'layerification' and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.

Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data.

"Functional connectivity" techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO2) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas.

BACKGROUND: Spatial normalization plays an essential role in multi-subject MRI and functional MRI (fMRI) experiments by facilitating a common space in which group analyses are performed. Although many prominent adult templates are available, their use for pediatric data is problematic. Generalized templates for pediatric populations are limited or constructed using older methods that result in less ideal normalization. OBJECTIVE: The Haskins pediatric templates and atlases aim to provide superior registration and more precise accuracy in labeling of anatomical and functional regions essential for all fMRI studies involving pediatric populations. MATERIALS AND METHODS: The Haskins pediatric templates and atlases were generated with nonlinear methods using structural MRI from 72 children (age range 7-14 years, median 10 years), allowing for a detailed template with corresponding parcellations of labeled atlas regions. The accuracy of these templates and atlases was assessed using multiple metrics of deformation distance and overlap. RESULTS: When comparing the deformation distances from normalizing pediatric data between this template and both the adult templates and other pediatric templates, we found significantly less deformation distance for the Haskins pediatric template (P<0.0001). Further, the correct atlas classification was higher using the Haskins pediatric template in 74% of regions (P<0.0001). CONCLUSION: The Haskins pediatric template results in more accurate correspondence across subjects because of lower deformation distances. This correspondence also provides better accuracy in atlas locations to benefit structural and functional imaging analyses of pediatric populations.

Integrated VASO and perfusion contrast: A new tool for laminar functional MRI.

Earlier research in cats has shown that both cerebral blood volume (CBV) and cerebral blood flow (CBF) can be used to identify layer-dependent fMRI activation with spatial specificity superior to gradient-echo blood-oxygen-level-dependent (BOLD) contrast (Jin and Kim, 2008a). CBF contrast of perfusion fMRI at ultra-high field has not been widely applied in humans to measure laminar activity due to its low sensitivity, while CBV contrast for fMRI using vascular space occupancy (VASO) has been successfully used. However, VASO can be compromised by interference of blood in-flow effects and a temporally limited acquisition window around the blood-nulling time point. Here, we proposed to use DANTE (Delay Alternating with Nutation for Tailored Excitation) pulse trains combined with 3D-EPI to acquire an integrated VASO and perfusion (VAPER) contrast. The signal origin of the VAPER contrast was theoretically evaluated with respect to its CBV and CBF contributions using a four-compartment simulation model. The feasibility of VAPER to measure layer-dependent activity was empirically investigated in human primary motor cortex at 7 â€‹T. We demonstrated this new tool, with its highly specified functional layer profile, robust reproducibility, and improved sensitivity, to allow investigation of layer-specific cortical functions.

Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging.

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or "idiosynchrony". Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning.

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as handling the multiple testing issue. Although the linear mixed-effects (LME) modeling approach (Chen et al., 2017a) is capable of capturing the relatedness in the data and incorporating explanatory variables, there are a few challenging issues: 1) it is difficult to assign accurate degrees of freedom for each testing statistic, 2) multiple testing correction is potentially over-penalizing due to model inefficiency, and 3) thresholding necessitates arbitrary dichotomous decisions. Here we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all regions of interest into one model. By loosely constraining the regions through a weakly informative prior, BML dissolves multiplicity through conservatively pooling the effect of each region toward the center and improves collective fitting and overall model performance. In addition to potentially achieving a higher inference efficiency, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in results reporting. The associated program will be available as part of the AFNI suite for general use.

Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex.

The human brain coordinates a wide variety of motor activities. On a large scale, the cortical motor system is topographically organized such that neighboring body parts are represented by neighboring brain areas. This homunculus-like somatotopic organization along the central sulcus has been observed using neuroimaging for large body parts such as the face, hands and feet. However, on a finer scale, invasive electrical stimulation studies show deviations from this somatotopic organization that suggest an organizing principle based on motor actions rather than body part moved. It has not been clear how the action-map organization principle of the motor cortex in the mesoscopic (sub-millimeter) regime integrates into a body map organization principle on a macroscopic scale (cm). Here we developed and applied advanced mesoscopic (sub-millimeter) fMRI and analysis methodology to non-invasively investigate the functional organization topography across columnar and laminar structures in humans. Compared to previous methods, in this study, we could capture locally specific blood volume changes across entire brain regions along the cortical curvature. We find that individual fingers have multiple mirrored representations in the primary motor cortex depending on the movements they are involved in. We find that individual digits have cortical representations up to 3 â€‹mm apart from each other arranged in a column-like fashion. These representations are differentially engaged depending on whether the digits' muscles are used for different motor actions such as flexion movements, like grasping a ball or retraction movements like releasing a ball. This research provides a starting point for non-invasive investigation of mesoscale topography across layers and columns of the human cortex and bridges the gap between invasive electrophysiological investigations and large coverage non-invasive neuroimaging.

The Integration of Functional Brain Activity from Adolescence to Adulthood.

Age-related changes in human functional neuroanatomy are poorly understood. This is partly due to the limits of interpretation of standard fMRI. These limits relate to age-related variation in noise levels in data from different subjects, and the common use of standard adult brain parcellations for developmental studies. Here we used an emerging MRI approach called multiecho (ME)-fMRI to characterize functional brain changes with age. ME-fMRI acquires blood oxygenation level-dependent (BOLD) signals while also quantifying susceptibility-weighted transverse relaxation time (T2*) signal decay. This approach newly enables reliable detection of BOLD signal components at the subject level as opposed to solely at the group-average level. In turn, it supports more robust characterization of the variability in functional brain organization across individuals. We hypothesized that BOLD components in the resting state are not stable with age, and would decrease in number from adolescence to adulthood. This runs counter to the current assumptions in neurodevelopmental analyses of brain connectivity that the number of BOLD signal components is a random effect. From resting-state ME-fMRI of 51 healthy subjects of both sexes, between 8.3 and 46.2 years of age, we found a highly significant (r = -0.55, p ≪ 0.001) exponential decrease in the number of BOLD components with age. The number of BOLD components were halved from adolescence to the fifth decade of life, stabilizing in middle adulthood. The regions driving this change were dorsolateral prefrontal cortices, parietal cortex, and cerebellum. The functional network of these regions centered on the cerebellum. We conclude that an age-related decrease in BOLD component number concurs with the hypothesis of neurodevelopmental integration of functional brain activity. We show evidence that the cerebellum may play a key role in this process.SIGNIFICANCE STATEMENT Human brain development is ongoing from childhood to at least 30 years of age. Functional MRI (fMRI) is key for characterizing changes in brain function that accompany development. However, developmental fMRI studies have relied on reference maps of adult brain organization in the analysis of data from younger subjects. This approach may limit the characterization of functional activity patterns that are particular to children and adolescents. Here we used an emerging fMRI approach called multi-echo fMRI that is not susceptible to such biases when analyzing the variation in functional brain organization over development. We hypothesized an integration of the components of brain activity over development, and found that the number of components decreases exponentially, halving from 8 to 35 years of age. The brain regions most affected underlie executive function and coordination. In summary, we show major changes in the organization and integration of functional networks over development into adulthood, with both methodological and neurobiological implications for future lifespan and disease studies on brain connectivity.

Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging.

An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNNp) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI) data from individual subjects. During fMRI data acquisition, 10 healthy participants listened to 80 International Affective Digital Sound stimuli and rated their own emotions generated by each sound stimulus in terms of the arousal, dominance, and valence dimensions. The whole-brain spatial patterns from a general linear model (i.e., beta-valued maps) for each sound stimulus and the emotional response ratings were used as the input and output for the DNNP, respectively. Based on a nested five-fold cross-validation scheme, the paired input and output data were divided into training (three-fold), validation (one-fold), and test (one-fold) data. The DNNP was trained and optimized using the training and validation data and was tested using the test data. The Pearson's correlation coefficients between the rated and predicted emotional responses from our DNNP model with weight sparsity optimization (mean ±â€¯standard error 0.52 ±â€¯0.02 for arousal, 0.51 ±â€¯0.03 for dominance, and 0.51 ±â€¯0.03 for valence, with an input denoising level of 0.3 and a mini-batch size of 1) were significantly greater than those of DNN models with conventional regularization schemes including elastic net regularization (0.15 ±â€¯0.05, 0.15 ±â€¯0.06, and 0.21 ±â€¯0.04 for arousal, dominance, and valence, respectively), those of shallow models including logistic regression (0.11 ±â€¯0.04, 0.10 ±â€¯0.05, and 0.17 ±â€¯0.04 for arousal, dominance, and valence, respectively; average of logistic regression and sparse logistic regression), and those of support vector machine-based predictive models (SVMps; 0.12 ±â€¯0.06, 0.06 ±â€¯0.06, and 0.10 ±â€¯0.06 for arousal, dominance, and valence, respectively; average of linear and non-linear SVMps). This difference was confirmed to be significant with a Bonferroni-corrected p-value of less than 0.001 from a one-way analysis of variance (ANOVA) and subsequent paired t-test. The weights of the trained DNNPs were interpreted and input patterns that maximized or minimized the output of the DNNPs (i.e., the emotional responses) were estimated. Based on a binary classification of each emotion category (e.g., high arousal vs. low arousal), the error rates for the DNNP (31.2% ±â€¯1.3% for arousal, 29.0% ±â€¯1.7% for dominance, and 28.6% ±â€¯3.0% for valence) were significantly lower than those for the linear SVMP (44.7% ±â€¯2.0%, 50.7% ±â€¯1.7%, and 47.4% ±â€¯1.9% for arousal, dominance, and valence, respectively) and the non-linear SVMP (48.8% ±â€¯2.3%, 52.2% ±â€¯1.9%, and 46.4% ±â€¯1.3% for arousal, dominance, and valence, respectively), as confirmed by the Bonferroni-corrected p < 0.001 from the one-way ANOVA. Our study demonstrates that the DNNp model is able to reveal neuronal circuitry associated with human emotional processing - including structures in the limbic and paralimbic areas, which include the amygdala, prefrontal areas, anterior cingulate cortex, insula, and caudate. Our DNNp model was also able to use activation patterns in these structures to predict and classify emotional responses to stimuli.

A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping.

This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (ΔR2⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R2⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ΔR2⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ΔR2⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest.

Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of spontaneous cognitive-task-like processes, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states-commonly used to summarize and interpret resting dFC-can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.