Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations

A measure of the blink reflex to parametric variation of mechanical stimulation of the trigeminal nerve

The primary goal of this study was to develop a parametric model that relates variation in stimulation of the trigeminal nerve to properties of the blink response. We measured blink responses in 17 healthy, adult participants to air puffs directed at the lateral canthus of the eye at five different, log-spaced intensities (3.5-60 PSI). Lid position over time was decomposed into amplitude and velocity components. We found that blink amplitude was systematically related to log stimulus intensity, with the relationship well described by a sigmoidal function. The parameters of the model fit correspond to the slope of the function and the stimulus intensity required to produce half of a maximal blink response (the half-response threshold). There was a reliable increase in the half-response threshold for the contralateral as compared to the ipsilateral blink response. This increase was consistent across participants despite substantial individual differences in the half-response threshold and slope parameters of the overall sensitivity function, suggesting that the laterality effect arises in the neural circuit subsequent to individual differences in sensitivity. Overall, we find that graded mechanical stimulation of the somatosensory trigeminal afferents elicits a graded response that is well described by a simple parametric model. We discuss the application of parametric measurements of the blink response to the detection of group differences in trigeminal sensitivity.

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

Medial Prefrontal Cortex Dysfunction Mediates Working Memory Deficits in Patients With Schizophrenia

Background

Schizophrenia (SCZ) is marked by working memory (WM) deficits, which predict poor functional outcome. While most functional magnetic resonance imaging studies of WM in SCZ have focused on the dorsolateral prefrontal cortex (PFC), some recent work suggests that the medial PFC (mPFC) may play a role. We investigated whether task-evoked mPFC deactivation is associated with WM performance and whether it mediates deficits in SCZ. In addition, we investigated associations between mPFC deactivation and cortical dopamine release.

Methods

Patients with SCZ (n = 41) and healthy control participants (HCs) (n = 40) performed a visual object n-back task during functional magnetic resonance imaging. Dopamine release capacity in mPFC was quantified with [11C]FLB457 in a subset of participants (9 SCZ, 14 HCs) using an amphetamine challenge. Correlations between task-evoked deactivation and performance were assessed in mPFC and dorsolateral PFC masks and were further examined for relationships with diagnosis and dopamine release.

Results

mPFC deactivation was associated with WM task performance, but dorsolateral PFC activation was not. Deactivation in the mPFC was reduced in patients with SCZ relative to HCs and mediated the relationship between diagnosis and WM performance. In addition, mPFC deactivation was significantly and inversely associated with dopamine release capacity across groups and in HCs alone, but not in patients.

Conclusions

Reduced WM task-evoked mPFC deactivation is a mediator of, and potential substrate for, WM impairment in SCZ, although our study design does not rule out the possibility that these findings could relate to cognition in general rather than WM specifically. We further present preliminary evidence of an inverse association between deactivation during WM tasks and dopamine release capacity in the mPFC.

Fate of the second task in dual-task interference is associated with sensory system interactions with default-mode network

Psychological refractory period (PRP) effect refers to the delay in responding to the second of two tasks occurring in rapid succession. While all the major models of PRP highlight the importance of the frontoparietal control network (FPCN) in prioritizing the neural processing of the first task, the fate of the second task remains poorly understood. Here, we provide novel neural evidence on how the functional connectivity between sensory systems and the default-mode network (DMN) suspends the neural processing of the second task to ensure the efficient completion of the first task in dual-task situation. In a cross-modal PRP paradigm, a visual task could either precede or follow an auditory task. The DMN was generally deactivated during task performance and selectively coupled with the sensory system underlying the second task subjected to the PRP effect. Specifically, the DMN showed neural coupling with the auditory system when the auditory task came after the visual task, and with the visual system vice versa. More critically, the strength of the DMN-Sensory coupling correlated negatively with the size of the PRP effect: the stronger the coupling, the shorter the PRP. Therefore, rather than being detrimental to the dual-task performance, temporary suspension of the second task, via the DMN-Sensory coupling, surprisingly guaranteed the efficient completion of the first task by reducing the interference from the second task. Accordingly, the entry and processing of the second stimuli in the central executive system were speeded up as well.

Categorical encoding of moving colors during location tracking

Categorical perception (CP) describes our tendency to perceive the visual world in a categorical manner, suggesting that high-level cognition may affect perception. While most studies are conducted in static visual scenes, Sun and colleagues found CP effects of color in multiple object tracking (MOT). This study used functional magnetic resonance imaging to investigate the neural mechanism behind the categorical effects of color in MOT. Categorical effects were associated with activities in a broad range of brain regions, including both the ventral (V4, middle temporal gyrus) and dorsal pathways (MT + /V5, inferior parietal lobule) of feature processing, as well as frontal regions (middle frontal gyrus, medial superior frontal gyrus). We proposed that these regions are hierarchically organized and responsible for distinct functions. The color-selective V4 encodes color categories, making cross-category colors more discriminable than within-category colors. Meanwhile, the language and/or semantic regions encode the verbal information of the colors. Both visual and nonvisual codes of color categories then modulate the activities of motion-sensitive MT + areas and frontal areas responsible for attentional processes.

Functional differences in cerebral activation between slow wave-coupled and uncoupled sleep spindles

Spindles are often temporally coupled to slow waves (SW). These SW-spindle complexes have been implicated in memory consolidation that involves transfer of information from the hippocampus to the neocortex. However, spindles and SW, which are characteristic of NREM sleep, can occur as part of this complex, or in isolation. It is not clear whether dissociable parts of the brain are recruited when coupled to SW vs. when spindles or SW occur in isolation. Here, we tested differences in cerebral activation time-locked to uncoupled spindles, uncoupled SW and coupled SW-spindle complexes using simultaneous EEG-fMRI. Consistent with the "active system model," we hypothesized that brain activations time-locked to coupled SW-spindles would preferentially occur in brain areas known to be critical for sleep-dependent memory consolidation. Our results show that coupled spindles and uncoupled spindles recruit distinct parts of the brain. Specifically, we found that hippocampal activation during sleep is not uniquely related to spindles. Rather, this process is primarily driven by SWs and SW-spindle coupling. In addition, we show that SW-spindle coupling is critical in the activation of the putamen. Importantly, SW-spindle coupling specifically recruited frontal areas in comparison to uncoupled spindles, which may be critical for the hippocampal-neocortical dialogue that preferentially occurs during sleep.

Modeling the dynamics of cerebrovascular reactivity to carbon dioxide in fMRI under task and resting-state conditions

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO₂). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO₂ response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO₂ changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCA_opt) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCA_opt and IL methods. Of the three, the CCA_opt method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

Reconstruction of time-shifted hemodynamic response

Regression of voxel time course onto expected response is a standard procedure in functional magnetic resonance imaging that relies on exact onset time and shape of superimposed hemodynamic response functions. Elegant capture of time deviation by time derivative regressors appears complicated by shape distortion and limited to ±1 s, and is usually not exploited for reconstructing the true time-shifted response function together with its magnitude. This analysis of the time-derivative approach provides closed-form functional relations between time shift and regression coefficients that allow for hemodynamic shifts of ±5 s and can explain shape distortion and reconstruction behavior. Reliable absolute latencies were no smaller than 0.6 s in a best-case experiment. Confusions of latency are a previously undiscussed shortcoming where current limitation strategy may eliminate correct latencies and protect incorrect ones.

The effect of self-generated versus externally generated actions on timing, duration, and amplitude of blood oxygen level dependent response for visual feedback processing

It has been widely assumed that internal forward models use efference copies to create predictions about the sensory consequences of our own actions. While these predictions have frequently been associated with a reduced blood oxygen level dependent (BOLD) response in sensory cortices, the timing and duration of the hemodynamic response for the processing of video feedback of self‐generated (active) versus externally generated (passive) movements is poorly understood. In the present study, we tested the hypothesis that predictive mechanisms for self‐generated actions lead to early and shorter neural processing compared with externally generated movements. We investigated active and passive movements using a custom‐made fMRI‐compatible movement device. Visual video feedback of the active and passive movements was presented in real time or with variable delays. Participants had to judge whether the feedback was delayed. Timing and duration of BOLD impulse response was calculated using a first (temporal derivative [TD]) and second‐order (dispersion derivative [DD]) Taylor approximation. Our reanalysis confirmed our previous finding of reduced BOLD response for active compared to passive movements. Moreover, we found positive effects of the TD and DD in the supplementary motor area, cerebellum, visual cortices, and subcortical structures, indicating earlier and shorter hemodynamic responses for active compared to passive movements. Furthermore, earlier activation in the putamen for active compared to passive conditions was associated with reduced delay detection performance. These findings indicate that efference copy‐based predictive mechanisms enable earlier processing of action feedback, which might have reduced the ability to detect short delays between action and feedback.

Functional near-infrared spectroscopy in schizophrenia patients with auditory verbal hallucinations: Preliminary observations

Auditory Signal Detection (ASD) theory postulates that auditory verbal hallucinations (AVH) result from an aberrant association of meaningful connection to abstract noises. In this study, schizophrenia (SZ) patients with persistent AVH (N = 17) and matched controls (N = 25) performed an ASD task with concurrent functional near-infrared spectroscopy recording targetting the left dorsolateral prefrontal cortex (L-DLPFC) and left temporoparietal junction (L-TPJ). During the task, discriminability index had a significant negative correlation, and early deoxyhemoglobin (HbR) latency at L-TPJ positively correlated with AVH scores. Also, patients had significantly lower discriminability, early HbR latency at L-TPJ, and delayed latency at L-DLPFC. This finding suggests the presence of ASD abnormalities and impaired auditory processing in SZ patients with AVH supporting ASD-based pathogenesis.

Auditory Target Detection Enhances Visual Processing and Hippocampal Functional Connectivity

Though dividing one's attention between two input streams typically impairs performance, detecting a behaviorally relevant stimulus can sometimes enhance the encoding of unrelated information presented at the same time. Previous research has shown that selection of this kind boosts visual cortical activity and memory for concurrent items. An important unanswered question is whether such effects are reflected in processing quality and functional connectivity in visual regions and in the hippocampus. In this fMRI study, participants were asked to memorize a stream of naturalistic images and press a button only when they heard a predefined target tone (400 or 1,200 Hz, counterbalanced). Images could be presented with a target tone, with a distractor tone, or without a tone. Auditory target detection increased activity throughout the ventral visual cortex but lowered it in the hippocampus. Enhancements in functional connectivity between the ventral visual cortex and the hippocampus were also observed following auditory targets. Multi-voxel pattern classification of image category was more accurate on target tone trials than on distractor and no tone trials in the fusiform gyrus and parahippocampal gyrus. This effect was stronger in visual cortical clusters whose activity was more correlated with the hippocampus on target tone than on distractor tone trials. In agreement with accounts suggesting that subcortical noradrenergic influences play a role in the attentional boost effect, auditory target detection also caused an increase in locus coeruleus activity and phasic pupil responses. These findings outline a network of cortical and subcortical regions that are involved in the selection and processing of information presented at behaviorally relevant moments.

Entropy Could Quantify Brain Activation Induced by Mechanical Impedance-Restrained Active Arm Motion: A Functional NIRS Study

Brain activation has been used to understand brain-level events associated with cognitive tasks or physical tasks. As a quantitative measure for brain activation, we propose entropy in place of signal amplitude and beta value, which are widely used, but sometimes criticized for their limitations and shortcomings as such measures. To investigate the relevance of our proposition, we provided 22 subjects with physical stimuli through elbow extension-flexion motions by using our exoskeleton robot, measured brain activation in terms of entropy, signal amplitude, and beta value; and compared entropy with the other two. The results show that entropy is superior, in that its change appeared in limited, well established, motor areas, while signal amplitude and beta value changes appeared in a widespread fashion, contradicting the modularity theory. Entropy can predict increase in brain activation with task duration, while the other two cannot. When stimuli shifted from the rest state to the task state, entropy exhibited a similar increase as the other two did. Although entropy showed only a part of the phenomenon induced by task strength, it showed superiority by showing a decrease in brain activation that the other two did not show. Moreover, entropy was capable of identifying the physiologically important location.

A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS

OBJECTIVE

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN).

APPROACH

The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts.

MAIN RESULTS

Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contains variants of shapes and amplitudes of HRF.

SIGNIFICANCE

The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.

Statistical Learning of Frequent Distractor Locations in Visual Search Involves Regional Signal Suppression in Early Visual Cortex

Observers can learn locations where salient distractors appear frequently to reduce potential interference-an effect attributed to better suppression of distractors at frequent locations. But how distractor suppression is implemented in the visual cortex and within the frontoparietal attention networks remains unclear. We used fMRI and a regional distractor-location learning paradigm with two types of distractors defined in either the same (orientation) or a different (color) dimension to the target to investigate this issue. fMRI results showed that BOLD signals in early visual cortex were significantly reduced for distractors (as well as targets) occurring at the frequent versus rare locations, mirroring behavioral patterns. This reduction was more robust with same-dimension distractors. Crucially, behavioral interference was correlated with distractor-evoked visual activity only for same- (but not different-) dimension distractors. Moreover, with different- (but not same-) dimension distractors, a color-processing area within the fusiform gyrus was activated more when a distractor was present in the rare region versus being absent and more with a distractor in the rare versus frequent locations. These results support statistical learning of frequent distractor locations involving regional suppression in early visual cortex and point to differential neural mechanisms of distractor handling with different- versus same-dimension distractors.

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.

The role of right temporo-parietal junction in stimulus evaluation

A predominant model of the temporo-parietal junction (TPJ) claims that this region is critical for attentional orienting/reorienting toward an unexpected, but behaviorally significant stimulus. However, recent studies have suggested that the TPJ is also involved in the process of evaluating stimulus, especially matching between external sensory inputs and internal representations. While some studies provide evidence for the involvement of the TPJ in stimulus evaluation, the nature of the evaluative process mediated by the TPJ remains unclear. To address this issue, we tested whether the TPJ activation amplitude and its peak latency is proportional to the demand of the evaluative process. We found that when the amount of sensory evidence for the matching process was abundant, the TPJ was transiently activated. Importantly, the TPJ activation showed a greater and more sustained pattern while the sensory evidence was accumulating for a longer period of time. These findings suggest that the TPJ function is associated with the evaluative process of matching sensory inputs with internal representations, as well as attentional reorienting.

Neural correlates of cognitive control deficits in children with reading disorder

Reading disorder (RD) is characterized by deficient phonological processing, but children with RD also have cognitive control deficits, the neural correlates of which are not fully understood. We used fMRI to assess neural activity during the resolution of cognitive conflict on the Simon Spatial Incompatibility task and patterns of resting-state functional connectivity (RSFC) from task control (TC) regions in 7-12-year-old children with RD compared to their typically developing (TD) peers. Relative to TD children (n = 17), those with RD (n = 16) over-engaged a right superior/medial frontal cluster during the resolution of conflict (p = .05). Relative to TD children (n = 18), those with RD (n = 17) also showed reduced RSFC (voxel-wise p < .001; cluster-size p < .05, FDR corrected) from cingulo-opercular seeds to left hemisphere fronto-parietal and temporo-parietal reading-related regions, perhaps reflecting reduced organization of TC circuits and reduced integration with reading-related regions. Children with RD additionally showed reduced RSFC between fronto-parietal and default mode network regions. Follow-up analyses in a subset of children with both useable task and resting state data (RD = 13; TD = 17) revealed that greater conflict-related activation of the right frontal Simon task ROI associated with better word-reading, perhaps suggesting a compensatory role for this over-engagement. Connectivity from fronto-parietal seeds significantly associated with Simon task performance and word-reading accuracy in RD children. These findings suggest that altered functioning and connectivity of control circuits may contribute to cognitive control deficits in children with RD. Future studies should assess the utility of adding cognitive control training to reading remediation programs.

Post-traumatic olfactory loss and brain response beyond olfactory cortex

Olfactory impairment after a traumatic impact to the head is associated with changes in olfactory cortex, including decreased gray matter density and decreased BOLD response to odors. Much less is known about the role of other cortical areas in olfactory impairment. We used fMRI in a sample of 63 participants, consisting of 25 with post-traumatic functional anosmia, 16 with post-traumatic hyposmia, and 22 healthy controls with normosmia to investigate whole brain response to odors. Similar neural responses were observed across the groups to odor versus odorless stimuli in the primary olfactory areas in piriform cortex, whereas response in the frontal operculum and anterior insula (fO/aI) increased with olfactory function (normosmia > hyposmia > functional anosmia). Unexpectedly, a negative association was observed between response and olfactory perceptual function in the mediodorsal thalamus (mdT), ventromedial prefrontal cortex (vmPFC) and posterior cingulate cortex (pCC). Finally, connectivity within a network consisting of vmPFC, fO, and pCC could be used to successfully classify participants as having functional anosmia or normosmia. We conclude that, at the neural level, olfactory impairment due to head trauma is best characterized by heightened responses and differential connectivity in higher-order areas beyond olfactory cortex.

Prefrontal cortex activation during working memory task in schizophrenia: A fNIRS study

Neurocognitive cognitive deficits including working memory (WM) impairment is a key component of schizophrenia (SCZ). Though a prefrontal cortex (PFC) abnormality is recognised to contribute to WM impairment, the exact nature of its neurobiological basis in SCZ is not well established. Functional near infra-red spectroscopy (fNIRS) is an emerging low-cost neuroimaging tool to study neuro-hemodynamics. In this background, we examined the hemodynamic activity during a WM task in schizophrenia using fNIRS. fNIRS was acquired during computerised N-back (zero-, one- & two-back) task in 15 SCZ patients and compared with 22 healthy controls. Performance in N-back test were calculated using signal detection theory alongside the mean reaction times. Concentration and latencies of oxy-, deoxy-, and totalhaemoglobin, and oxygen saturation were computed from 8*8 optodes positioned over bilateral PFC. SCZ performed poorly as measured by most of the WM parameters (p < 0.05). Lesser deoxyhemoglobin concentration (two > zero, at right BA10, p = 0.006) was noted in the right frontopolar cortex in SCZ surviving multiple-comparison correction. In addition, olanzapine equivalent doses correlated negatively with right frontopolar cortex activation (two > zero back, BA10, ρ = 0.70, p = 0.004) and better performance in two back (false alarm rate, ρ = 0.61, p = 0.015). A delayed but compensatory hyperactivation of right frontopolar cortex noted in SCZ may underlie the WM deficit in SCZ. Future studies are recommended to replicate the role of right frontopolar cortex in WM using larger samples and systematically explore the effect of antipsychotics on them.

Tailored haemodynamic response function increases detection power of fMRI in awake dogs (Canis familiaris)

Functional magnetic resonance imaging (fMRI) of awake and unrestrained dogs (Canis familiaris) has been established as a novel opportunity for comparative neuroimaging, promising important insights into the evolutionary roots of human brain function and cognition. However, data processing and analysis pipelines are often derivatives of methodological standards developed for human neuroimaging, which may be problematic due to profound neurophysiological and anatomical differences between humans and dogs. Here, we explore whether dog fMRI studies would benefit from a tailored dog haemodynamic response function (HRF). In two independent experiments, dogs were presented with different visual stimuli. BOLD signal changes in the visual cortex during these experiments were used for (a) the identification and estimation of a tailored dog HRF, and (b) the independent validation of the resulting dog HRF estimate. Time course analyses revealed that the BOLD signal in the primary visual cortex peaked significantly earlier in dogs compared to humans, while being comparable in shape. Deriving a tailored dog HRF significantly improved the model fit in both experiments, compared to the canonical HRF used in human fMRI. Using the dog HRF yielded significantly increased activation during visual stimulation, extending from the occipital lobe to the caudal parietal cortex, the bilateral temporal cortex, into bilateral hippocampal and thalamic regions. In sum, our findings provide robust evidence for an earlier onset of the dog HRF in two visual stimulation paradigms, and suggest that using such an HRF will be important to increase fMRI detection power in canine neuroimaging. By providing the parameters of the tailored dog HRF and related code, we encourage and enable other researchers to validate whether our findings generalize to other sensory modalities and experimental paradigms.

Segregated Co-activation Patterns in the Emergence of Decision Confidence During Visual Perception

Visual metacognition-the introspection and evaluation of one's own visual perceptual processes-is measured through both decision confidence and "metacognitive efficiency." Metacognitive efficiency refers to an individual's ability to accurately judge incorrect and correct decisions through confidence ratings given their task performance. Previous imaging studies in humans and nonhuman primates reported widely distributed brain regions being involved in decision confidence and metacognition. However, the neural correlates of metacognition are remarkably inconsistent across studies concerning spatial outline. Therefore, this study investigates the neural correlates of visual metacognition by examining co-activation across regions that scale with visual decision confidence. We hypothesized that interacting processes of perceptual and metacognitive performance contribute to the arising decision confidence in distributed, but segregable co-activating brain regions. To test this hypothesis, we performed task-fMRI in healthy humans during a visual backward masking task with four-scale, post-decision confidence ratings. We measured blood oxygenation covariation patterns, which served as a physiological proxy for co-activation across brain regions. Decision confidence ratings and an individual's metacognitive efficiency served as behavioral measures for metacognition. We found three distinct co-activation clusters involved in decision confidence: the first included right-centered fronto-temporal-parietal regions, the second included left temporal and parietal regions, and the left basal forebrain (BF), and the third included cerebellar regions. The right fronto-temporal-parietal cluster including the supplementary eye field and the right basal forebrain showed stronger co-activation in subjects with higher metacognitive efficiency. Our results provide novel evidence for co-activation of widely distributed fronto-parieto-temporal regions involved in visual confidence. The supplementary eye field was the only region that activated for both decision confidence and metacognitive efficiency, suggesting the supplementary eye field plays a key role in visual metacognition. Our results link findings in electrophysiology studies and human fMRI studies and provide evidence that confidence estimates arise from the integration of multiple information processing pathways.

Reconstruction of respiratory variation signals from fMRI data

Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.

Spatiotemporal dissociation of fMRI activity in the caudate nucleus underlies human de novo motor skill learning

Numerous real-world motor skills require learning arbitrary relationships between actions and their consequences from scratch. However, little is understood about the neural signatures of de novo motor learning and associated individual variability. In a longitudinal fMRI experiment, where participants learned to control a cursor by moving fingers, we found a gradual transition of performance-related activity from the head to tail of the caudate nucleus. This finding reflects the flexible and stable reward representations in the head and tail, respectively. Additionally, intrinsic cortico-caudate connectivity predicted better learners with weaker head–prefrontal and stronger tail–sensorimotor interactions. The present study provides unprecedented insight into de novo motor learning, which may contribute to the understanding of motor-related disorders, and infant learning.

Motor skill learning involves a complex process of generating novel movement patterns guided by evaluative feedback, such as a reward. Previous literature has suggested anteroposteriorly separated circuits in the striatum to be implicated in early goal-directed and later automatic stages of motor skill learning, respectively. However, the involvement of these circuits has not been well elucidated in human de novomotor skill learning, which requires learning arbitrary action–outcome associations and value-based action selection. To investigate this issue, we conducted a human functional MRI (fMRI) experiment in which participants learned to control a computer cursor by manipulating their right fingers. We discovered a double dissociation of fMRI activity in the anterior and posterior caudate nucleus, which was associated with performance in the early and late learning stages. Moreover, cognitive and sensorimotor cortico-caudate interactions predicted individual learning performance. Our results suggest parallel cortico-caudate networks operating in different stages of human de novomotor skill learning.

Identification of an Amygdala-Thalamic Circuit That Acts as a Central Gain Mechanism in Taste Perceptions

Peripheral sources of individual variation in taste intensity perception have been well described. The existence of a central source has been proposed but remains unexplored. Here we used functional magnetic resonance imaging in healthy human participants (20 women, 8 men) to evaluate the hypothesis that the amygdala exerts an inhibitory influence that affects the "gain" of the gustatory system during tasting. Consistent with the existence of a central gain mechanism (CGM), we found that central amygdala response was correlated with mean intensity ratings across multiple tastants. In addition, psychophysiological and dynamic causal modeling analyses revealed that the connection strength between inhibitory outputs from amygdala to medial dorsal and ventral posterior medial thalamus predicted individual differences in responsiveness to taste stimulation. These results imply that inhibitory inputs from the amygdala to the thalamus act as a CGM that influences taste intensity perception.SIGNIFICANCE STATEMENT Whether central circuits contribute to individual variation in taste intensity perception is unknown. Here we used functional magnetic resonance imaging in healthy human participants to identify an amygdala-thalamic circuit where network dynamics and connectivity strengths during tasting predict individual variation in taste intensity ratings. This finding implies that individual differences in taste intensity perception do not arise solely from variation in peripheral gustatory factors.

A Guide to Designing a Memory fMRI Paradigm for Pre-surgical Evaluation in Temporal Lobe Epilepsy

There has been increasing interest in the clinical and experimental use of memory functional Magnetic Resonance Imaging (fMRI). The 2017 American Academy of Neurology practice guidelines on the use of pre-surgical cognitive fMRI suggests that verbal memory fMRI could be used to lateralize memory functions in people with Temporal Lobe Epilepsy (TLE) and should be used to predict post-operative verbal memory outcome. There are however technical and methodological considerations, to optimize both the sensitivity and specificity of this imaging modality. Below we discuss these constraints and suggest recommendations to consider when designing a memory fMRI paradigm.

Content-based Dissociation of Hippocampal Involvement in Prediction

Recent work suggests that a key function of the hippocampus is to predict the future. This is thought to depend on its ability to bind inputs over time and space and to retrieve upcoming or missing inputs based on partial cues. In line with this, previous research has revealed prediction-related signals in the hippocampus for complex visual objects, such as fractals and abstract shapes. Implicit in such accounts is that these computations in the hippocampus reflect domain-general processes that apply across different types and modalities of stimuli. An alternative is that the hippocampus plays a more domain-specific role in predictive processing, with the type of stimuli being predicted determining its involvement. To investigate this, we compared hippocampal responses to auditory cues predicting abstract shapes (Experiment 1) versus oriented gratings (Experiment 2). We measured brain activity in male and female human participants using high-resolution fMRI, in combination with inverted encoding models to reconstruct shape and orientation information. Our results revealed that expectations about shape and orientation evoked distinct representations in the hippocampus. For complex shapes, the hippocampus represented which shape was expected, potentially serving as a source of top-down predictions. In contrast, for simple gratings, the hippocampus represented only unexpected orientations, more reminiscent of a prediction error. We discuss several potential explanations for this content-based dissociation in hippocampal function, concluding that the computational role of the hippocampus in predictive processing may depend on the nature and complexity of stimuli.

Duality of Function: Activation for Meaningless Nonwords and Semantic Codes in the Same Brain Areas

Studies of the neural substrates of semantic (word meaning) processing have typically focused on semantic manipulations, with less consideration for potential differences in difficulty across conditions. While the idea that particular brain regions can support multiple functions is widely accepted, studies of specific cognitive domains rarely test for co-location with other functions. Here we start with standard univariate analyses comparing words to meaningless nonwords, replicating our recent finding that this contrast can activate task-positive regions for words, and default-mode regions in the putative semantic network for nonwords, pointing to difficulty effects. Critically, this was followed up with a multivariate analysis to test whether the same areas activated for meaningless nonwords contained semantic information sufficient to distinguish high- from low-imageability words. Indeed, this classification was performed reliably better than chance at 75% accuracy. This is compatible with two non-exclusive interpretations. Numerous areas in the default-mode network are task-negative in the sense of activating for less demanding conditions, and the same areas contain information supporting semantic cognition. Therefore, while areas of the default mode network have been hypothesized to support semantic cognition, we offer evidence that these areas can respond to both domain-general difficulty effects, and to specific aspects of semantics.

Differential cortical activation during the perception of moving objects along different trajectories

Detection of 3D object-motion trajectories depends on the integration of two distinct visual cues: translational displacement and looming. Electrophysiological studies have identified distinct neuronal populations, whose activity depends on the precise motion cues present in the stimulus. This distinction, however, has been less clear in humans, and it is confounded by differences in the behavioral task being performed. We analyzed whole-brain fMRI, while subjects performed a common time-to-arrival task for objects moving along three trajectories: moving directly towards the observer (collision course), with trajectories parallel to the line of sight (passage course), and with trajectories perpendicular to the line of sight (gap closure). We found that there was substantial overlap in the pattern of activation associated with each of the three tasks, with differences among conditions limited to the human motion area (hMT+), which showed greater activation extent in the gap closure condition than for either collision or passage courses. These results support a common substrate for temporal judgments of an object's time-to-arrival, wherein the special cases of object motion directly toward, or perpendicular to, the observer represent two extremes within the broader continuum of 3D passage trajectories relative to the observer.

Preserved canonicality of the BOLD hemodynamic response reflects healthy cognition: Insights into the healthy brain through the window of Multiple Sclerosis

The hemodynamic response function (HRF), a model of brain blood-flow changes in response to neural activity, reflects communication between neurons and the vasculature that supplies these neurons in part by means of glial cell intermediaries (e.g., astrocytes). Intact neural-vascular communication might play a central role in optimal cognitive performance. This hypothesis can be tested by comparing healthy individuals to those with known white-matter damage and impaired performance, as seen in Multiple Sclerosis (MS). Glial cell intermediaries facilitate the ability of neurons to adequately convey metabolic needs to cerebral vasculature for sufficient oxygen and nutrient perfusion. In this study, we isolated measurements of the HRF that could quantify the extent to which white-matter affects neural-vascular coupling and cognitive performance. HRFs were modeled from multiple brain regions during multiple cognitive tasks using piecewise cubic spline functions, an approach that minimized assumptions regarding HRF shape that may not be valid for diseased populations, and were characterized using two shape metrics (peak amplitude and time-to-peak). Peak amplitude was reduced, and time-to-peak was longer, in MS patients relative to healthy controls. Faster time-to-peak was predicted by faster reaction time, suggesting an important role for vasodilatory speed in the physiology underlying processing speed. These results support the hypothesis that intact neural-glial-vascular communication underlies optimal neural and cognitive functioning.

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

Background:

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

New Methods:

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

Results:

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

Comparison with existing methods:

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

Conclusion:

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

The brain's hemodynamic response function rapidly changes under acute psychosocial stress in association with genetic and endocrine stress response markers

Ample evidence links dysregulation of the stress response to the risk for psychiatric disorders. However, we lack an integrated understanding of mechanisms that are adaptive during the acute stress response but potentially pathogenic when dysregulated. One mechanistic link emerging from rodent studies is the interaction between stress effectors and neurovascular coupling, a process that adjusts cerebral blood flow according to local metabolic demands. Here, using task-related fMRI, we show that acute psychosocial stress rapidly impacts the peak latency of the hemodynamic response function (HRF-PL) in temporal, insular, and prefrontal regions in two independent cohorts of healthy humans. These latency effects occurred in the absence of amplitude effects and were moderated by regulatory genetic variants of KCNJ2, a known mediator of the effect of stress on vascular responsivity. Further, hippocampal HRF-PL correlated with both cortisol response and genetic variants that influence the transcriptional response to stress hormones and are associated with risk for major depression. We conclude that acute stress modulates hemodynamic response properties as part of the physiological stress response and suggest that HRF indices could serve as endophenotype of stress-related disorders.

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging.

Associative Prediction of Visual Shape in the Hippocampus

Perception can be cast as a process of inference, in which bottom-up signals are combined with top-down predictions in sensory systems. In line with this, neural activity in sensory cortex is strongly modulated by prior expectations. Such top-down predictions often arise from cross-modal associations, such as when a sound (e.g., bell or bark) leads to an expectation of the visual appearance of the corresponding object (e.g., bicycle or dog). We hypothesized that the hippocampus, which rapidly learns arbitrary relationships between stimuli over space and time, may be involved in forming such associative predictions. We exposed male and female human participants to auditory cues predicting visual shapes, while measuring high-resolution fMRI signals in visual cortex and the hippocampus. Using multivariate reconstruction methods, we discovered a dissociation between these regions: representations in visual cortex were dominated by whichever shape was presented, whereas representations in the hippocampus reflected only which shape was predicted by the cue. The strength of hippocampal predictions correlated across participants with the amount of expectation-related facilitation in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.SIGNIFICANCE STATEMENT The way we perceive the world is to a great extent determined by our prior knowledge. Despite this intimate link between perception and memory, these two aspects of cognition have mostly been studied in isolation. Here we investigate their interaction by asking how memory systems that encode and retrieve associations can inform perception. We find that upon hearing a familiar auditory cue, the hippocampus represents visual information that had previously co-occurred with the cue, even when this expectation differs from what is currently visible. Furthermore, the strength of this hippocampal expectation correlates with facilitation of perceptual processing in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features.

Using SPM 12's Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users

Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research.

In defense of abstract conceptual representations

An extensive program of research in the past 2 decades has focused on the role of modal sensory, motor, and affective brain systems in storing and retrieving concept knowledge. This focus has led in some circles to an underestimation of the need for more abstract, supramodal conceptual representations in semantic cognition. Evidence for supramodal processing comes from neuroimaging work documenting a large, well-defined cortical network that responds to meaningful stimuli regardless of modal content. The nodes in this network correspond to high-level "convergence zones" that receive broadly crossmodal input and presumably process crossmodal conjunctions. It is proposed that highly conjunctive representations are needed for several critical functions, including capturing conceptual similarity structure, enabling thematic associative relationships independent of conceptual similarity, and providing efficient "chunking" of concept representations for a range of higher order tasks that require concepts to be configured as situations. These hypothesized functions account for a wide range of neuroimaging results showing modulation of the supramodal convergence zone network by associative strength, lexicality, familiarity, imageability, frequency, and semantic compositionality. The evidence supports a hierarchical model of knowledge representation in which modal systems provide a mechanism for concept acquisition and serve to ground individual concepts in external reality, whereas broadly conjunctive, supramodal representations play an equally important role in concept association and situation knowledge.

Encoding model of temporal processing in human visual cortex

How is temporal information processed in human visual cortex? Visual input is relayed to V1 through segregated transient and sustained channels in the retina and lateral geniculate nucleus (LGN). However, there is intense debate as to how sustained and transient temporal channels contribute to visual processing beyond V1. The prevailing view associates transient processing predominately with motion-sensitive regions and sustained processing with ventral stream regions, while the opposing view suggests that both temporal channels contribute to neural processing beyond V1. Using fMRI, we measured cortical responses to time-varying stimuli and then implemented a two temporal channel-encoding model to evaluate the contributions of each channel. Different from the general linear model of fMRI that predicts responses directly from the stimulus, the encoding approach first models neural responses to the stimulus from which fMRI responses are derived. This encoding approach not only predicts cortical responses to time-varying stimuli from milliseconds to seconds but also, reveals differential contributions of temporal channels across visual cortex. Consistent with the prevailing view, motion-sensitive regions and adjacent lateral occipitotemporal regions are dominated by transient responses. However, ventral occipitotemporal regions are driven by both sustained and transient channels, with transient responses exceeding the sustained. These findings propose a rethinking of temporal processing in the ventral stream and suggest that transient processing may contribute to rapid extraction of the content of the visual input. Importantly, our encoding approach has vast implications, because it can be applied with fMRI to decipher neural computations in millisecond resolution in any part of the brain.

Hyper-responsivity to losses in the anterior insula during economic choice scales with depression severity

BACKGROUND

Commonly observed distortions in decision-making among patients with major depressive disorder (MDD) may emerge from impaired reward processing and cognitive biases toward negative events. There is substantial theoretical support for the hypothesis that MDD patients overweight potential losses compared with gains, though the neurobiological underpinnings of this bias are uncertain.

METHODS

Twenty-one unmedicated patients with MDD were compared with 25 healthy controls (HC) using functional magnetic resonance imaging (fMRI) together with an economic decision-making task over mixed lotteries involving probabilistic gains and losses. Region-of-interest analyses evaluated neural signatures of gain and loss coding within a core network of brain areas known to be involved in valuation (anterior insula, caudate nucleus, ventromedial prefrontal cortex).

RESULTS

Usable fMRI data were available for 19 MDD and 23 HC subjects. Anterior insula signal showed negative coding of losses (gain > loss) in HC subjects consistent with previous findings, whereas MDD subjects demonstrated significant reversals in these associations (loss > gain). Moreover, depression severity further enhanced the positive coding of losses in anterior insula, ventromedial prefrontal cortex, and caudate nucleus. The hyper-responsivity to losses displayed by the anterior insula of MDD patients was paralleled by a reduced influence of gain, but not loss, stake size on choice latencies.

CONCLUSIONS

Patients with MDD demonstrate a significant shift from negative to positive coding of losses in the anterior insula, revealing the importance of this structure in value-based decision-making in the context of emotional disturbances.

Long reaction times are associated with delayed brain activity in lewy body dementia

A significant symptom of Lewy body dementia (LBD) is slow cognitive processing or bradyphrenia. In a previous fMRI task‐based study, we found slower responses in LBD, accompanied by greater deactivation in the default mode network. In this study, we investigated the timing and magnitude of the activations and deactivations with respect to reaction time to determine whether the slower responses in LBD were associated with delayed neuronal activity. Using fMRI, we examined the magnitude and latency of activations and deactivations during an event‐related attention task in 32 patients with LBD and 23 healthy controls using predefined regions of interest. Default mode network deactivations did not significantly differ in their timing between groups or task conditions, while the task‐related activations in the parietal, occipital, frontal, and motor cortex were all significantly later in the LBD group. Repeating the analysis with reaction time as a parametric modulator of activation magnitude produced similar findings, with the reaction time modulator being significant in a number of regions including the default mode network, suggesting that the increased deactivation in LBD is partly explained by slower task completion. Our data suggest that the default mode network deactivation is initiated at the start of the task, and remains deactivated until its end, with the increased magnitude of deactivation in LBD reflecting the more prolonged cognitive processing in these patients. These data add substantially to our understanding of the neural origins of bradyphrenia, which will be essential for determining optimum therapeutic strategies for cognitive impairment in LBD. Hum Brain Mapp 39:633–643, 2018. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Simultaneously estimating the task-related and stimulus-evoked components of hemodynamic imaging measurements

Task-related hemodynamic responses contribute prominently to functional magnetic resonance imaging (fMRI) recordings. They reflect behaviorally important brain states, such as arousal and attention, and can dominate stimulus-evoked responses, yet they remain poorly understood. To help characterize these responses, we present a method for parametrically estimating both stimulus-evoked and task-related components of hemodynamic responses from subjects engaged in temporally predictable tasks. The stimulus-evoked component is modeled by convolving a hemodynamic response function (HRF) kernel with spiking. The task-related component is modeled by convolving a Fourier-series task-related function (TRF) kernel with task timing. We fit this model with simultaneous electrode recordings and intrinsic-signal optical imaging from the primary visual cortex of alert, task-engaged monkeys. With high [Formula: see text], the model returns HRFs that are consistent across experiments and recording sites for a given animal and TRFs that entrain to task timing independent of stimulation or local spiking. When the task schedule conflicts with that of stimulation, the TRF remains locked to the task emphasizing its behavioral origins. The current approach is strikingly more robust to fluctuations than earlier ones and gives consistently, if modestly, better fits. This approach could help parse the distinct components of fMRI recordings made in the context of a task.

Verbal-spatial IQ discrepancies impact brain activation associated with the resolution of cognitive conflict in children and adolescents

Verbal-spatial discrepancies are common in healthy individuals and in those with neurodevelopmental disorders associated with cognitive control deficits including: Autism Spectrum Disorder, Non-Verbal Learning Disability, Fragile X, 22q11 deletion, and Turner Syndrome. Previous data from healthy individuals suggest that the magnitude of the difference between verbal IQ (VIQ) and performance IQ (PIQ) scores (the VIQ>PIQ discrepancy) is associated with reduced thickness in frontal and parietal cortices (inferior frontal, anterior cingulate, inferior parietal lobule, and supramarginal gyrus) that support cognitive control. Unknown is whether the VIQ>PIQ discrepancy is associated with functional deficits in these areas in healthy or ill children and adolescents. We assessed the effects of the VIQ>PIQ discrepancy on fMRI BOLD response during the resolution of cognitive conflict in 55 healthy children and adolescents during performance of a Simon Spatial Incompatibility task. As the magnitude of the VIQ>PIQ discrepancy increased, activation of fronto-striatal, limbic, and temporal regions decreased during conflict resolution (p < .05, corrected). In exploratory analyses, the VIQ>PIQ discrepancy was associated with reduced functional connectivity from right inferior frontal gyrus to right thalamus and increased functional connectivity to right supramarginal gyrus (ps < .03, uncorrected). The VIQ>PIQ discrepancy may be an important aspect of an individual's cognitive profile and likely contributes to, or is associated with, deficient cognitive control processes characteristic of many childhood disorders.

Visual Imagery and False Memory for Pictures: A Functional Magnetic Resonance Imaging Study in Healthy Participants

Background

Visual mental imagery might be critical in the ability to discriminate imagined from perceived pictures. Our aim was to investigate the neural bases of this specific type of reality-monitoring process in individuals with high visual imagery abilities.

Methods

A reality-monitoring task was administered to twenty-six healthy participants using functional magnetic resonance imaging. During the encoding phase, 45 words designating common items, and 45 pictures of other common items, were presented in random order. During the recall phase, participants were required to remember whether a picture of the item had been presented, or only a word. Two subgroups of participants with a propensity for high vs. low visual imagery were contrasted.

Results

Activation of the amygdala, left inferior occipital gyrus, insula, and precuneus were observed when high visual imagers encoded words later remembered as pictures. At the recall phase, these same participants activated the middle frontal gyrus and inferior and superior parietal lobes when erroneously remembering pictures.

Conclusions

The formation of visual mental images might activate visual brain areas as well as structures involved in emotional processing. High visual imagers demonstrate increased activation of a fronto-parietal source-monitoring network that enables distinction between imagined and perceived pictures.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods' performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data.

Pros and Cons of Using the Informed Basis Set to Account for Hemodynamic Response Variability with Developmental Data

Conventional analysis of functional magnetic resonance imaging (fMRI) data using the general linear model (GLM) employs a neural model convolved with a canonical hemodynamic response function (HRF) peaking 5 s after stimulation. Incorporation of a further basis function, namely the canonical HRF temporal derivative, accounts for delays in the hemodynamic response to neural activity. A population that may benefit from this flexible approach is children whose hemodynamic response is not yet mature. Here, we examined the effects of using the set based on the canonical HRF plus its temporal derivative on both first- and second-level GLM analyses, through simulations and using developmental data (an fMRI dataset on proprioceptive mapping in children and adults). Simulations of delayed fMRI first-level data emphasized the benefit of carrying forward to the second-level a derivative boost that combines derivative and nonderivative beta estimates. In the experimental data, second-level analysis using a paired t-test showed increased mean amplitude estimate (i.e., increased group contrast mean) in several brain regions related to proprioceptive processing when using the derivative boost compared to using only the nonderivative term. This was true especially in children. However, carrying forward to the second-level the individual derivative boosts had adverse consequences on random-effects analysis that implemented one-sample t-test, yielding increased between-subject variance, thus affecting group-level statistic. Boosted data also presented a lower level of smoothness that had implication for the detection of group average activation. Imposing soft constraints on the derivative boost by limiting the time-to-peak range of the modeled response within a specified range (i.e., 4–6 s) mitigated these issues. These findings support the notion that there are pros and cons to using the informed basis set with developmental data.

Altered Neural Correlate of the Self-Agency Experience in First-Episode Schizophrenia-Spectrum Patients: An fMRI Study

BACKGROUND

The phenomenology of the clinical symptoms indicates that disturbance of the sense of self be a core marker of schizophrenia.

AIMS

To compare neural activity related to the self/other-agency judgment in patients with first-episode schizophrenia-spectrum disorders (FES, n = 35) and healthy controls (HC, n = 35).

METHOD

A functional magnetic resonance imaging (fMRI) using motor task with temporal distortion of the visual feedback was employed. A task-related functional connectivity was analyzed with the use of independent component analysis (ICA).

RESULTS

(1) During self-agency experience, FES showed a deficit in cortical activation in medial frontal gyrus (BA 10) and posterior cingulate gyrus, (BA 31; P < .05, Family-Wise Error [FWE] corrected). (2) Pooled-sample task-related ICA revealed that the self/other-agency judgment was dependent upon anti-correlated default mode and central-executive networks (DMN/CEN) dynamic switching. This antagonistic mechanism was substantially impaired in FES during the task.

DISCUSSION

During self-agency experience, FES demonstrate deficit in engagement of cortical midline structures along with substantial attenuation of anti-correlated DMN/CEN activity underlying normal self/other-agency discriminative processes.