Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution

Aging modulates large-scale neural network interactions during speech comprehension

Speech comprehension in noisy environments constitutes a critical challenge in everyday life and affects people of all ages. This challenging listening situation can be alleviated using semantic context to predict upcoming words (i.e., predictability gain)-a process associated with the domain-specific semantic network. When no such context can be used, speech comprehension in challenging listening conditions relies on cognitive control functions, underpinned by domain-general networks. Most previous studies focused on regional activity of pre-selected cortical regions or networks in healthy young listeners. Thus, it remains unclear how domain-specific and domain-general networks interact during speech comprehension in noise and how this may change across the lifespan. Here, we used correlational psychophysiological interaction (cPPI) to investigate functional network interactions during sentence comprehension under noisy conditions with varying predictability in healthy young and older listeners. Relative to young listeners, older adults showed increased task-related activity in several domain-general networks but reduced between-network connectivity. Across groups, higher predictability was associated with increased positive coupling between semantic and attention networks and increased negative coupling between semantic and control networks. These results highlight the complex interplay between the semantic network and several domain-general networks underlying the predictability gain. The observed differences in connectivity profiles with age inform the current debate on whether age-related changes in neural activity and functional connectivity reflect compensation or dedifferentiation.

Network segregation during episodic memory shows age-invariant relations with memory performance from 7 to 82 years

Lower episodic memory capability, as seen in development and aging compared with younger adulthood, may partly depend on lower brain network segregation. Here, our objective was twofold: (1) test this hypothesis using within- and between-network functional connectivity (FC) during episodic memory encoding and retrieval, in two independent samples (n = 734, age 7-82 years). (2) Assess associations with age and the ability to predict memory comparing task-general FC and memory-modulated FC. In a multiverse-inspired approach, we performed tests across multiple analytic choices. Results showed that relationships differed based on these analytic choices and were mainly present in the largest dataset,. Significant relationships indicated that (i) memory-modulated FC predicted memory performance and associated with memory in an age-invariant manner. (ii) In line with the so-called neural dedifferentiation view, task-general FC showed lower segregation with higher age in adults which was associated with worse memory performance. In development, although there were only weak signs of a neural differentiation, that is, gradually higher segregation with higher age, we observed similar lower segregation-worse memory relationships. This age-invariant relationships between FC and episodic memory suggest that network segregation is pivotal for memory across the healthy lifespan.

Induced zero-phase synchronization as a potential neural code for optimized visuomotor integration

BACKGROUND

Goal-directed behavior requires the integration of information from the outside world and internal (somatosensory) sources about our own actions. Expectations (or 'internal models') are generated from prior knowledge and constantly updated based on sensory feedback. This optimized information integration ('predictive coding') results in a global behavioral advantage of anticipated action in the presence of uncertainty. Our goal was to probe the effect of phase entrainment of the sensorimotor mu-rhythm on visuomotor integration.

METHODS

Participants received transcranial alternating current stimulation over bilateral motor cortices (M1) while performing a visually-guided force adjustment task during functional magnetic resonance imaging.

RESULTS

Inter-hemispheric zero-phase entrainment resulted in effector-specific modulation of performance precision and effector-generic minimization of force signal complexity paralleled by BOLD activation changes in bilateral caudate and increased functional connectivity between the right M1 and contralateral putamen, inferior parietal, and medial temporal regions. While effector-specific changes in performance precision were associated with contralateral caudate and hippocampal activation decreases, only the global reduction in force signal complexity was associated with increased functional M1 connectivity with bilateral striatal regions.

CONCLUSION

We propose that zero-phase synchronization represents a neural mode of optimized information integration related to internal model updating within the recursive perception-action continuum associated with predictive coding.

Unraveling the neurophysiological correlates of phase-specific enhancement of motor memory consolidation via slow-wave closed-loop targeted memory reactivation

Memory consolidation can be enhanced during sleep using targeted memory reactivation (TMR) and closed-loop (CL) acoustic stimulation on the up-phase of slow oscillations (SOs). Here, we test whether applying TMR at specific phases of the SOs (up vs. down vs. no reactivation) can influence the behavioral and neural correlates of motor memory consolidation in healthy young adults. Results show that up- (as compared to down-) state cueing results in greater performance improvement. Sleep electrophysiological data indicate that up- (as compared to down-) stimulated SOs exhibits higher amplitude and greater peak-nested sigma power. Task-related functional magnetic resonance images reveal that up-state cueing strengthens activity in - and segregation of - striato-motor and hippocampal networks; and that these modulations are related to the beneficial effect of TMR on sleep features and performance. Overall, these findings highlight the potential of CL-TMR to induce phase-specific modulations of motor performance, sleep oscillations and brain responses during motor memory consolidation.

Attention-dependent coupling with forebrain and brainstem neuromodulatory nuclei differs across the lifespan

Attentional states reflect the changing behavioral relevance of stimuli in one's environment, having important consequences for learning and memory. Supporting well-established cortical contributions, attentional states are hypothesized to originate from subcortical neuromodulatory nuclei, such as the basal forebrain (BF) and locus coeruleus (LC), which are among the first to change with aging. Here, we characterized the interplay between BF and LC neuromodulatory nuclei and their relation to two common afferent cortical targets important for attention and memory, the posterior cingulate cortex and hippocampus, across the adult lifespan. Using an auditory target discrimination task during functional MRI, we examined the influence of attentional and behavioral salience on task-dependent functional connectivity in younger (19-45 years) and older adults (66-86 years). In younger adults, BF functional connectivity was largely driven by target processing, while LC connectivity was associated with distractor processing. These patterns are reversed in older adults. This age-dependent connectivity pattern generalized to the nucleus basalis of Meynert and medial septal subnuclei. Preliminary data from middle-aged adults indicates a transitional stage in BF and LC functional connectivity. Overall, these results reveal distinct roles of subcortical neuromodulatory systems in attentional salience related to behavioral relevance and their potential reversed roles with aging, consistent with managing increased salience of behaviorally irrelevant distraction in older adults. Such prominent differences in functional coupling across the lifespan from these subcortical neuromodulatory nuclei suggests they may be drivers of widespread cortical changes in neurocognitive aging, and middle age as an opportune time for intervention.

Social Risk Coding by Amygdala Activity and Connectivity with the Dorsal Anterior Cingulate Cortex

Risk is a fundamental factor affecting individual and social economic decisions, but its neural correlates are largely unexplored in the social domain. The amygdala, together with the dorsal anterior cingulate cortex (dACC), is thought to play a central role in risk-taking. Here, we investigated in human volunteers (n = 20; 11 females) how risk (defined as the variance of reward probability distributions) in a social situation affects decisions and concomitant neural activity as measured with fMRI. We found separate variance-risk signals for social and nonsocial outcomes in the amygdala. Specifically, amygdala activity increased parametrically with social reward variance of presented choice options and on separate trials with nonsocial reward variance. Behaviorally, 75% of participants were averse to social risk as estimated in a Becker-DeGroot-Marschak auction-like procedure. The stronger this aversion, the more negative the coupling between risk-related amygdala regions and dACC. This negative relation was significant for social risk attitude but not for the attitude toward variance-risk in juice outcomes. Our results indicate that the amygdala and its coupling with dACC process objective and subjectively evaluated social risk. Moreover, while social risk can be captured with a framework originally established by finance theory for nonsocial risk, the amygdala appears to process social risk largely separately from nonsocial risk.

Age-related decline in cognitive flexibility and inadequate preparation: evidence from task-state network analysis

Behavioral evidence showed decreased cognitive flexibility in older adults. However, task-based network mechanisms of cognitive flexibility in aging (CFA) remain unclear. Here, we provided the first task-state network evidence that CFA was associated with inadequate preparation for switching trials by revealing age-related changes in functional integration. We examined functional integration in a letter-number switch task that distinguished between the cue and target stages. Both young and older adults showed decreased functional integration from the cue stage to the target stage, indicating that control-related processes were executed as the task progressed. However, compared to young adults, older adults showed less cue-to-target reduction in functional integration, which was primarily driven by higher network integration in the target stage. Moreover, less cue-to-target reductions were correlated with age-related decreases in task performance in the switch task. To sum up, compared to young adults, older adults pre-executed less control-related processes in the cue stage and more control-related processes in the target stage. Therefore, the decline in cognitive flexibility in older adults was associated with inadequate preparation for the impending demands of cognitive switching. This study offered novel insights into network mechanisms underlying CFA. Furthermore, we highlighted that training the function of brain networks, in conjunction with providing more preparation time for older adults, may be beneficial to their cognitive flexibility.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Energy of functional brain states correlates with cognition in adolescent-onset schizophrenia and healthy persons

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEM's integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Targeted memory reactivation to augment treatment in post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a psychiatric disorder with traumatic memories at its core. Post-treatment sleep may offer a unique time window to increase therapeutic efficacy through consolidation of therapeutically modified traumatic memories. Targeted memory reactivation (TMR) enhances memory consolidation by presenting reminder cues (e.g., sounds associated with a memory) during sleep. Here, we applied TMR in PTSD patients to strengthen therapeutic memories during sleep after one treatment session with eye movement desensitization and reprocessing (EMDR). PTSD patients received either slow oscillation (SO) phase-targeted TMR, using modeling-based closed-loop neurostimulation (M-CLNS) with EMDR clicks as a reactivation cue (n = 17), or sham stimulation (n = 16). Effects of TMR on sleep were assessed through high-density polysomnography. Effects on treatment outcome were assessed through subjective, autonomic, and fMRI responses to script-driven imagery (SDI) of the targeted traumatic memory and overall PTSD symptom level. Compared to sham stimulation, TMR led to stimulus-locked increases in SO and spindle dynamics, which correlated positively with PTSD symptom reduction in the TMR group. Given the role of SOs and spindles in memory consolidation, these findings suggest that TMR may have strengthened the consolidation of the EMDR-treatment memory. Clinically, TMR vs. sham stimulation resulted in a larger reduction of avoidance level during SDI. TMR did not disturb sleep or trigger nightmares. Together, these data provide first proof of principle that TMR may be a safe and viable future treatment augmentation strategy for PTSD. The required follow-up studies may implement multi-night TMR or TMR during REM sleep to further establish the clinical effect of TMR for traumatic memories.

Connectivity analyses for task-based fMRI

Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.

Uncovering Hemispheric Asymmetry and Directed Oscillatory Brain-Heart Interplay in Anxiety Processing: An fMRI Study

Brain-heart interactions (BHI) are critical for generating and processing emotions, including anxiety. Understanding specific neural correlates would be instrumental for greater comprehension and potential therapeutic interventions of anxiety disorders. While prior work has implicated the pontine structure as a central processor in cardiac regulation in anxiety, the distributed nature of anxiety processing across the cortex remains elusive. To address this, we performed a whole-brain-heart analysis using the full frequency directed transfer function to study resting-state spectral differences in BHI between high and low anxiety groups undergoing fMRI scans. Our findings revealed a hemispheric asymmetry in low-frequency interplay (0.05 Hz - 0.15 Hz) characterized by ascending BHI to the left insula and descending BHI from the right insula. Furthermore, we provide evidence supporting the "pacemaker hypothesis", highlighting the pons' function in regulating cardiac activity. Higher frequency interplay (0.2 Hz - 0.4Hz) demonstrate a preference for ascending interactions, particularly towards ventral prefrontal cortical activity in high anxiety groups, suggesting the heart's role in triggering a cognitive response to regulate anxiety. These findings highlight the impact of anxiety on BHI, contributing to a better understanding of its effect on the resting-state fMRI signal, with further implications for potential therapeutic interventions in treating anxiety disorders.

Evolutionarily conserved neural responses to affective touch in monkeys transcend consciousness and change with age

Affective touch-a slow, gentle, and pleasant form of touch-activates a different neural network than which is activated during discriminative touch in humans. Affective touch perception is enabled by specialized low-threshold mechanoreceptors in the skin with unmyelinated fibers called C tactile (CT) afferents. These CT afferents are conserved across mammalian species, including macaque monkeys. However, it is unknown whether the neural representation of affective touch is the same across species and whether affective touch's capacity to activate the hubs of the brain that compute socioaffective information requires conscious perception. Here, we used functional MRI to assess the preferential activation of neural hubs by slow (affective) vs. fast (discriminative) touch in anesthetized rhesus monkeys (Macaca mulatta). The insula, anterior cingulate cortex (ACC), amygdala, and secondary somatosensory cortex were all significantly more active during slow touch relative to fast touch, suggesting homologous activation of the interoceptive-allostatic network across primate species during affective touch. Further, we found that neural responses to affective vs. discriminative touch in the insula and ACC (the primary cortical hubs for interoceptive processing) changed significantly with age. Insula and ACC in younger animals differentiated between slow and fast touch, while activity was comparable between conditions for aged monkeys (equivalent to >70 y in humans). These results, together with prior studies establishing conserved peripheral nervous system mechanisms of affective touch transduction, suggest that neural responses to affective touch are evolutionarily conserved in monkeys, significantly impacted in old age, and do not necessitate conscious experience of touch.

Validation of real-time fMRI neurofeedback procedure for cognitive training using counterbalanced active-sham study design

Investigation of neural mechanisms of real-time functional MRI neurofeedback (rtfMRI-nf) training requires an efficient study control approach. A common rtfMRI-nf study design involves an experimental group, receiving active rtfMRI-nf, and a control group, provided with sham rtfMRI-nf. We report the first study in which rtfMRI-nf procedure is controlled through counterbalancing training runs with active and sham rtfMRI-nf for each participant. Healthy volunteers (n = 18) used rtfMRI-nf to upregulate fMRI activity of an individually defined target region in the left dorsolateral prefrontal cortex (DLPFC) while performing tasks that involved mental generation of a random numerical sequence and serial summation of numbers in the sequence. Sham rtfMRI-nf was provided based on fMRI activity of a different brain region, not involved in these tasks. The experimental procedure included two training runs with the active rtfMRI-nf and two runs with the sham rtfMRI-nf, in a randomized order. The participants achieved significantly higher fMRI activation of the left DLPFC target region during the active rtfMRI-nf conditions compared to the sham rtfMRI-nf conditions. fMRI functional connectivity of the left DLPFC target region with the nodes of the central executive network was significantly enhanced during the active rtfMRI-nf conditions relative to the sham conditions. fMRI connectivity of the target region with the nodes of the default mode network was similarly enhanced. fMRI connectivity changes between the active and sham conditions exhibited meaningful associations with individual performance measures on the Working Memory Multimodal Attention Task, the Approach-Avoidance Task, and the Trail Making Test. Our results demonstrate that the counterbalanced active-sham study design can be efficiently used to investigate mechanisms of active rtfMRI-nf in direct comparison to those of sham rtfMRI-nf. Further studies with larger group sizes are needed to confirm the reported findings and evaluate clinical utility of this study control approach.

Human brain activity and functional connectivity associated with verbal long-term memory consolidation across 1 month

Introduction

Declarative memories are initially dependent on the hippocampus and become stabilized through the neural reorganization of connections between the medial temporal lobe and neocortex. The exact time-course of these neural changes is not well established, although time-dependent changes in retrieval-related brain function can be detected across relatively short time periods in humans (e.g., hours to months).

Methods

In a study involving older adults with normal cognition (N = 24), we investigated changes in brain activity and functional connectivity associated with the long-term memory consolidation of verbal material over one month. Participants studied fact-like, three-word sentences at 1-month, 1-week, 1-day, and 1-hour intervals before a recognition memory test inside an MRI scanner. Old/new recognition with confidence ratings and response times were recorded. We examined whole-brain changes in retrieval-related brain activity, as well as functional connectivity of the hippocampus and ventromedial prefrontal cortex (vmPFC), as memories aged from 1 hour to 1 month. Secondary analyses minimized the effect of confounding factors affected by memory age (i.e., changes in confidence and response time or re-encoding of targets).

Results

Memory accuracy, confidence ratings, and response times changed with memory age. A memory age network was identified where retrieval-related brain activity in cortical regions increased or decreased as a function of memory age. Hippocampal brain activity in an anatomical region of interest decreased with memory age. Importantly, these changes in retrieval-related activity were not confounded with changes in activity related to concomitant changes in behavior or encoding. Exploratory analyses of vmPFC functional connectivity as a function of memory age revealed increased connectivity with the posterior parietal cortex, as well as with the vmPFC itself. In contrast, hippocampal functional connectivity with the vmPFC and orbitofrontal cortex decreased with memory age.

Discussion

The observed changes in retrieval-related brain activity and functional connectivity align with the predictions of standard systems consolidation theory. These results suggest that processes consistent with long-term memory consolidation can be identified over short time periods using fMRI, particularly for verbal material.

Representation of event and object concepts in ventral anterior temporal lobe and angular gyrus

Semantic knowledge includes understanding of objects and their features and also understanding of the characteristics of events. The hub-and-spoke theory holds that these conceptual representations rely on multiple information sources that are integrated in a central hub in the ventral anterior temporal lobes. The dual-hub theory expands this framework with the claim that the ventral anterior temporal lobe hub is specialized for object representation, while a second hub in angular gyrus is specialized for event representation. To test these ideas, we used representational similarity analysis, univariate and psychophysiological interaction analyses of fMRI data collected while participants processed object and event concepts (e.g. "an apple," "a wedding") presented as images and written words. Representational similarity analysis showed that angular gyrus encoded event concept similarity more than object similarity, although the left angular gyrus also encoded object similarity. Bilateral ventral anterior temporal lobes encoded both object and event concept structure, and left ventral anterior temporal lobe exhibited stronger coding for events. Psychophysiological interaction analysis revealed greater connectivity between left ventral anterior temporal lobe and right pMTG, and between right angular gyrus and bilateral ITG and middle occipital gyrus, for event concepts compared to object concepts. These findings support the specialization of angular gyrus for event semantics, though with some involvement in object coding, but do not support ventral anterior temporal lobe specialization for object concepts.

Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection

Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation ΔR2∗) to the resulting deconvolved events. Remarkably, we demonstrate that Mv-SPFM achieves comparable performance even when using a single-echo formulation. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Furthermore, we show that by employing stability selection, the performance of the algorithm depends less on the selection of temporal and spatial regularization parameters λ and ρ. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of ΔR2∗, for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.

Neurocomputational mechanisms of biased impression formation in lonely individuals

Social impressions are fundamental in our daily interactions with other people but forming accurate impressions of our social partners can be biased to different extents. Loneliness has previously been suggested to induce biases that hinder the formation of accurate impressions of others for successful social bonding. Here, we demonstrated that despite counterfactual evidence, negative first impressions bias information weighting, leading to less favorable trustworthiness beliefs. Lonely individuals did not only have more negative expectations of others' social behavior, but they also manifested a stronger weighting bias. Reduced orbitofrontal cortex (OFC) activity was associated with a stronger weighting bias in lonelier individuals and mediated the relationship between loneliness and this weighting bias. Importantly, stronger coupling between OFC and temporoparietal junction compensated for such effects, promoting more positive trustworthiness beliefs especially in lonelier individuals. These findings bear potential for future basic and clinical investigations on social cognition and the development of clinical symptoms linked to loneliness.

Disrupted network interactions serve as a neural marker of dyslexia

Dyslexia, a frequent learning disorder, is characterized by severe impairments in reading and writing and hypoactivation in reading regions in the left hemisphere. Despite decades of research, it remains unclear to date if observed behavioural deficits are caused by aberrant network interactions during reading and whether differences in functional activation and connectivity are directly related to reading performance. Here we provide a comprehensive characterization of reading-related brain connectivity in adults with and without dyslexia. We find disrupted functional coupling between hypoactive reading regions, especially between the left temporo-parietal and occipito-temporal cortices, and an extensive functional disruption of the right cerebellum in adults with dyslexia. Network analyses suggest that individuals with dyslexia process written stimuli via a dorsal decoding route and show stronger reading-related interaction with the right cerebellum. Moreover, increased connectivity within networks is linked to worse reading performance in dyslexia. Collectively, our results provide strong evidence for aberrant task-related connectivity as a neural marker for dyslexia that directly impacts behavioural performance. The observed differences in activation and connectivity suggest that one effective way to alleviate reading problems in dyslexia is through modulating interactions within the reading network with neurostimulation methods.

Comparison of hemodynamic response functions obtained from resting-state functional MRI and invasive electrophysiological recordings in rats

Resting-state functional MRI (rs-fMRI) is a popular technology that has enriched our understanding of brain and spinal cord functioning, including how different regions communicate (connectivity). But fMRI is an indirect measure of neural activity capturing blood hemodynamics. The hemodynamic response function (HRF) interfaces between the unmeasured neural activity and measured fMRI time series. The HRF is variable across brain regions and individuals, and is modulated by non-neural factors. Ignoring this HRF variability causes errors in FC estimates. Hence, it is crucial to reliably estimate the HRF from rs-fMRI data. Robust techniques have emerged to estimate the HRF from fMRI time series. Although such techniques have been validated non-invasively using simulated and empirical fMRI data, thorough invasive validation using simultaneous electrophysiological recordings, the gold standard, has been elusive. This report addresses this gap in the literature by comparing HRFs derived from invasive intracranial electroencephalogram recordings with HRFs estimated from simultaneously acquired fMRI data in six epileptic rats. We found that the HRF shape parameters (HRF amplitude, latency and width) were not significantly different (p>0.05) between ground truth and estimated HRFs. In the single pathological region, the HRF width was marginally significantly different (p=0.03). Our study provides preliminary invasive validation for the efficacy of the HRF estimation technique in reliably estimating the HRF non-invasively from rs-fMRI data directly. This has a notable impact on rs-fMRI connectivity studies, and we recommend that HRF deconvolution be performed to minimize HRF variability and improve connectivity estimates.

Effects of NMDA-receptor blockade by ketamine on mentalizing and its neural correlates in humans: a randomized control trial

Schizophrenia is associated with various deficits in social cognition that remain relatively unaltered by antipsychotic treatment. While faulty glutamate signaling has been associated with general cognitive deficits as well as negative symptoms of schizophrenia, no direct link between manipulation of glutamate signaling and deficits in mentalizing has been demonstrated thus far. Here, we experimentally investigated whether ketamine, an uncompetitive N-methyl-D-aspartate receptor antagonist known to induce psychotomimetic effects, influences mentalizing and its neural correlates. In a randomized, placebo-controlled between-subjects experiment, we intravenously administered ketamine or placebo to healthy participants performing a video-based social cognition task during functional magnetic resonance imaging. Psychotomimetic effects of ketamine were assessed using the Positive and Negative Syndrome Scale. Compared to placebo, ketamine led to significantly more psychotic symptoms and reduced mentalizing performance (more "no mentalizing" errors). Ketamine also influenced blood oxygen level dependent (BOLD) response during mentalizing compared to placebo. Specifically, ketamine increased BOLD in right posterior superior temporal sulcus (pSTS) and increased connectivity between pSTS and anterior precuneus. These increases may reflect a dysfunctional shift of attention induced by ketamine that leads to mentalizing deficits. Our findings show that a psychotomimetic dose of ketamine impairs mentalizing and influences its neural correlates, a result compatible with the notion that deficient glutamate signaling may contribute to deficits in mentalizing in schizophrenia. The results also support efforts to seek novel psychopharmacological treatments for psychosis and schizophrenia targeting glutamatergic transmission.

Hippocampal-cortical functional connectivity during memory encoding and retrieval

Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.

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.

Effects of prior knowledge on brain activation and functional connectivity during memory retrieval

Previous studies have shown that the ventral medial prefrontal cortex (vmPFC) plays an important role in schema-related memory. However, there is an intensive debate to what extent the activation of subregions of the hippocampus is involved in retrieving schema-related memory. In addition, it is unclear how the functional connectivity (FC) between the vmPFC and the hippocampus, as well as the connectivity of the vmPFC with other regions, are modulated by prior knowledge (PK) during memory retrieval over time. To address these issues, participants learned paragraphs that described features of each unfamiliar word from familiar and unfamiliar categories (i.e., high and low PK conditions) 20 min, 1 day, and 1 week before the test. They then performed a recognition task to judge whether the sentences were old in the scanner. The results showed that the activation of the anterior-medial hippocampus (amHPC) cluster was stronger when the old sentences with high (vs. low) PK were correctly retrieved. The activation of the posterior hippocampus (pHPC) cluster, as well as the vmPFC, was stronger when the new sentences with high (vs. low) PK were correctly rejected (i.e., CR trials), whereas the cluster of anterior-lateral hippocampus (alHPC) showed the opposite. The FC of the vmPFC with the amHPC and perirhinal cortex/inferior temporal gyrus was stronger in the high (vs. low) PK condition, whereas the FC of the vmPFC with the alHPC, thalamus and frontal regions showed the opposite for the CR trials. This study highlighted that different brain networks, which were associated with the vmPFC, subregions of the hippocampus and cognitive control regions, were responsible for retrieving the information with high and low PK.

The confound of hemodynamic response function variability in human resting-state functional MRI studies

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.

What a difference timing makes: Cortisol effects on neural underpinnings of emotion regulation

The ability of emotion regulation under stress is of crucial importance to psychosocial health. Yet, the dynamic function of stress hormones for the cognitive control of emotions over time via non-genomic and genomic cortisol effects remains to be elucidated. In this randomized, double-blind, placebo-controlled neuroimaging experiment, 105 participants (54 men, 51 women) received 20 mg hydrocortisone (cortisol) or a placebo either 30min (rapid, non-genomic cortisol effects) or 90min (slow, genomic cortisol effects) prior to a cognitive reappraisal task including different regulatory goals (i.e., downregulate vs. upregulate negative emotions). On the behavioral level, cortisol rapidly reduced and slowly enhanced emotional responsivity to negative pictures. However, only slow cortisol effects improved downregulation of negative emotions. On the neural level, cortisol rapidly enhanced, but slowly reduced amygdala and dorsolateral prefrontal activation as well as functional connectivity between both structures in the down- minus upregulate contrast. This interaction speaks for an effortful but ineffective regulation of negative emotions during rapid cortisol effects and improved emotion regulation capacities during slow cortisol effects. Taken together, these results indicate a functional shift of cortisol effects on emotion regulation processes over time which may foster successful adaptation to and recovery from stressful life events.

Treatment with the second-generation antipsychotic quetiapine is associated with increased subgenual ACC activation during reward processing in major depressive disorder

BACKGROUND

The second-generation antipsychotic (SGA) quetiapine is an essential option for antidepressant augmentation therapy in major depressive disorder (MDD), yet neurobiological mechanisms behind its antidepressant properties remain unclear. As SGAs interfere with activity in reward-related brain areas, including the anterior cingulate cortex (ACC) - a key brain region in antidepressant interventions, this study examined whether quetiapine treatment affects ACC activity during reward processing in MDD patients.

METHODS

Using the ACC as region of interest, an independent t-test comparing reward-related BOLD response of 51 quetiapine-taking and 51 antipsychotic-free MDD patients was conducted. Monetary reward outcome feedback was measured in a card-guessing paradigm using pseudorandom blocks. Participants were matched for age, sex, and depression severity and analyses were controlled for confounding variables, including total antidepressant medication load, illness chronicity and acute depression severity. Potential dosage effects were examined in a 3 × 1 ANOVA. Differences in ACC-related functional connectivity were assessed in psycho-physiological interaction (PPI) analyses.

RESULTS

Left subgenual ACC activity was significantly higher in the quetiapine group compared to antipsychotic-free participants and dependent on high-dose quetiapine intake. Results remained significant after controlling for confounding variables. The PPI analysis did not yield significant group differences in ACC-related functional connectivity.

LIMITATIONS

Causal interpretation is limited due to cross-sectional findings.

CONCLUSION

Elevated subgenual ACC activity to rewarding stimuli may represent a neurobiological marker and potential key interface of quetiapine's antidepressant effects in MDD. These results underline ACC activity during reward processing as an investigative avenue for future research and therapeutic interventions to improve MDD treatment outcomes.

Neurobiological substrates of the dread of future losses

When anticipating future losses, people respond by exhibiting 1 of 2 starkly distinct behavioral decision patterns: the dread of future losses (DFL) and the preference of future losses (vs. immediate losses). Yet, how to accurately discriminate between those who exhibit dread vs. preference and uncover the potential neurobiological substrates underlying these 2 groups remain understudied. To address this, we designed a novel experimental task in which the DFL group was defined as selecting immediate-loss options >50% in the trials with approximate subjective value in immediate and delayed options (n = 16), otherwise coding as the preference of future losses (PFL). At the behavioral level, DFL exhibited higher weight for delayed losses than immediate losses via the logistic regression model. At the neural level, DFL manifested hypoactivations on subjective valuations of delayed losses, atypical brain pattern when choosing immediate-loss options, and decreased functional coupling between the valuation and choice-systems when making decisions related to immediate-loss alternatives compared with PFL. Moreover, both these brain activations subserving distinct decision processes and their interactions predicted individual decisions and behavioral preferences. Furthermore, morphological analysis also revealed decreased right precuneus volume in DFL compared with PFL, and brain activations related to valuation and choice process mediated the associations between this region volume and behavioral performances. Taken together, these findings help to clarify potential cognitive and neural mechanisms underlying the DFL and provide a clear discrimination strategy.

Lifespan differences in visual short-term memory load-modulated functional connectivity

Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge center for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N = 101, age 23-86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated functional connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention and visual networks during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Whole-brain analyses for the relation between connectivity and behavior were non-significant. Our results give additional support to the sensory recruitment model of working memory. We also demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their neural resources at the lowest load and therefore less able to further increase connectivity with increasing task demands.

What multiplexing means for the interpretation of functional MRI data

Despite technology advances that have enabled routine acquisition of functional MRI data with sub-millimeter resolution, the inferences that cognitive neuroscientists must make to link fMRI data to behavior are complicated. Thus, a single dataset subjected to different analyses can be interpreted in different ways. This article presents two optical analogies that can be useful for framing fMRI analyses in a way that allows for multiple interpretations of fMRI data to be valid simultaneously without undermining each other. The first is reflection: when an object is reflected in a mirrored surface, it appears as if the reflected object is sharing space with the mirrored object, but of course it is not. This analogy can be a good guide for interpreting the fMRI signal, since even at sub-millimeter resolutions the signal is determined by a mixture of local and long-range neural computations. The second is refraction. If we view an object through a multi-faceted prism or gemstone, our view will change-sometimes dramatically-depending on our viewing angle. In the same way, interpretation of fMRI data (inference of underlying neuronal activity) can and should be different depending on the analysis approach. Rather than representing a weakness of the methodology, or the superiority of one approach over the other (for example, simple regression analysis versus multi-voxel pattern analysis), this is an expected consequence of how information is multiplexed in the neural networks of the brain: multiple streams of information are simultaneously present in each location. The fact that any one analysis typically shows only one view of the data also puts some parentheses around fMRI practitioners' constant search for ground truth against which to compare their data. By holding our interpretations lightly and understanding that many interpretations of the data can all be true at the same time, we do a better job of preparing ourselves to appreciate, and eventually understand, the complexity of the brain and the behavior it produces.

The Functional Brain Network of Subcortical and Cortical Regions Underlying Time Estimation: An Functional MRI Study

Time estimation is fundamental for human survival. There have been increasing studies suggesting that distributed brain regions, such as the basal ganglia, cerebellum and the parietal cortex, may contribute to a dedicated neural mechanism of time estimation. However, evidence on the specific function of the subcortical and cortical brain regions and the interplay of them is scare. In this work, we explored how the subcortical and cortical networks function in time estimation during a time reproduction task using functional MRI (fMRI). Thirty healthy participants performed the time reproduction task in both auditory and visual modalities. Results showed that time estimation in visual and auditory modality recruited a subcortical-cortical brain network including the left caudate, left cerebellum, and right precuneus. Besides, the superior temporal gyrus (STG) was found essential in the difference between time estimation in visual and auditory modality. Using psychophysiological interaction (PPI) analysis, we observed an increase in the connection between left caudate and left precuneus using the left caudate as the seed region in temporal reproduction task than control task. This suggested that the left caudate is the key region connecting and transmitting information to other brain regions in the dedicated brain network of time estimation.

Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes

The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

Effective connectivity analysis reveals the time course of the Stroop effect in manual responding

Investigations of the time course of the Stroop effect have contributed to understanding of the mechanisms of cognitive control. However, previous studies have not reached a consistent conclusion regarding such mechanisms. The current study clarified the controversy by adopting a modified stimulus onset asynchrony (SOA) manual Stroop task combined with effective connectivity analysis based on task-related functional magnetic resonance imaging data. The behavioral results showed a decreasing Stroop effect when the distractor was presented before the target stimulus. In addition, significant effective connectivity related to inhibitory control was observed, which decreased with the time interval between stimuli, including the connection from the right inferior frontal gyrus (rIFG) to the pre-supplementary motor area (pre-SMA) and from the pre-SMA to the primary motor cortex (M1). Diversity of active patterns between different congruency types was also detected. The current results revealed that inhibitory control could be actively performed in response to distractors to reduce their interference and further decrease the Stroop effect, and that inhibition is more efficient the earlier it is started.

Brain connectivity modulation by Bayesian surprise in relation to control demand drives cognitive flexibility via control engagement

Human control is characterized by its flexibility and adaptability in response to the conditional probability in the environment. Previous studies have revealed that efficient conflict control could be attained by predicting and adapting to the changing control demand. However, it is unclear whether cognitive flexibility could also be gained by predicting and adapting to the changing control demand. The present study aimed to explore this issue by combining the model-based analyses of behavioral and neuroimaging data with a probabilistic cued task switching paradigm. We demonstrated that the Bayesian surprise (i.e. unsigned precision-weighted prediction error [PE]) negatively modulated the connections among stimulus processing brain regions and control regions/networks. The effect of Bayesian surprise modulation on these connections guided control engagement as reflected by the control PE effect on behavior, which in turn facilitated cognitive flexibility. These results bridge a gap in the literature by illustrating the neural and behavioral effect of control demand prediction (or PE) on cognitive flexibility and offer novel insights into the source of switch cost and the mechanism of cognitive flexibility.

Olfactory decoding is positively associated with ad libitum food intake in sated humans

The role of olfaction in eating behavior and body weight regulation is controversial. Here we reanalyzed data from a previous functional magnetic resonance imaging study to test whether central olfactory coding is associated with hunger/satiety state, food intake, and change in body weight over one year in healthy human adults. Since odor quality and category are coded across distributed neural patterns that are not discernible with traditional univariate analyses, we used multi-voxel pattern analyses to decode patterns of brain activation to food versus nonfood odors. We found that decoding accuracies in the piriform cortex and amygdala were greater in the sated compared to hungry state. Sated decoding accuracies in these and other regions were also associated with post-scan ad libitum food intake, but not with weight change. These findings demonstrate that the fidelity of olfactory decoding is influenced by meal consumption and is associated with immediate food intake, but not longer-term body weight regulation.

Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task

Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instant when any given region becomes active during the decision-making process. Here, we propose the development and validation of an algorithm to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time, thus providing both spatial and temporal brain activation information. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. Using the results of the dynamic activation for the decision-making task, we were able to identify key brain regions involved in some of the phases of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in the form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way in future fMRI brain studies.

Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding

Systems consolidation of new experiences into lasting episodic memories involves hippocampal-neocortical interactions. Evidence of this process is already observed during early post-encoding rest periods, both as increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific patterns following intensive encoding tasks. We investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. We observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimuli encountered during memory encoding. Post-encoding modulations were manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement exceeds perceptual aspects. Reinstatement of encoding patterns was not observed in resting-state scans collected 12 h later, nor when using other candidate seed regions. The similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans involves a spectrum of cognitive processes engaged during the experience of an event. There were no age effects, suggesting that upregulation of hippocampal-neocortical connectivity represents a general phenomenon seen across the adult lifespan.

Representation of motion concepts in occipitotemporal cortex: fMRI activation, decoding and connectivity analyses

Embodied theories of semantic cognition predict that brain regions involved in motion perception are engaged when people comprehend motion concepts expressed in language. Left lateral occipitotemporal cortex (LOTC) is implicated in both motion perception and motion concept processing but prior studies have produced mixed findings on which parts of this region are engaged by motion language. We scanned participants performing semantic judgements about sentences describing motion events and static events. We performed univariate analyses, multivariate pattern analyses (MVPA) and psychophysiological interaction (PPI) analyses to investigate the effect of motion on activity and connectivity in different parts of LOTC. In multivariate analyses that decoded whether a sentence described motion or not, the middle and posterior parts of LOTC showed above-chance level performance, with performance exceeding that of other brain regions. Univariate ROI analyses found the middle part of LOTC was more active for motion events than static ones. Finally, PPI analyses found that when processing motion events, the middle and posterior parts of LOTC (overlapping with motion perception regions), increased their connectivity with cognitive control regions. Taken together, these results indicate that the more posterior parts of LOTC, including motion perception cortex, respond differently to motion vs. static events. These findings are consistent with embodiment accounts of semantic processing, and suggest that understanding verbal descriptions of motion engages areas of the occipitotemporal cortex involved in perceiving motion.

External drivers of BOLD signal's non-stationarity

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.

Common and unique neural mechanisms of social and nonsocial conflict resolving and adaptation

Humans often need to deal with various forms of information conflicts that arise when they receive inconsistent information. However, it remains unclear how we resolve them and whether the brain may recruit similar or distinct brain mechanisms to process different domains (e.g. social vs. nonsocial) of conflicts. To address this, we used functional magnetic resonance imaging and scanned 50 healthy participants when they were asked to perform 2 Stroop tasks with different forms of conflicts: social (i.e. face-gender incongruency) and nonsocial (i.e. color-word incongruency) conflicts. Neuroimaging results revealed that the ventral lateral prefrontal cortex was generally activated in processing incongruent versus congruent stimuli regardless of the task type, serving as a common mechanism for conflict resolving across domains. Notably, trial-based and model-based results jointly demonstrated that the dorsal and rostral medial prefrontal cortices were uniquely engaged in processing social incongruent stimuli, suggesting distinct neural substrates of social conflict resolving and adaptation. The findings uncover that the common but unique brain mechanisms are recruited when humans resolve and adapt to social conflicts.

Somatosensory targeted memory reactivation enhances motor performance via hippocampal-mediated plasticity

Increasing evidence suggests that reactivation of newly acquired memory traces during postlearning wakefulness plays an important role in memory consolidation. Here, we sought to boost the reactivation of a motor memory trace during postlearning wakefulness (quiet rest) immediately following learning using somatosensory targeted memory reactivation (TMR). Using functional magnetic resonance imaging, we examined the neural correlates of the reactivation process as well as the effect of the TMR intervention on brain responses elicited by task practice on 24 healthy young adults. Behavioral data of the post-TMR retest session showed a faster learning rate for the motor sequence that was reactivated as compared to the not-reactivated sequence. Brain imaging data revealed that motor, parietal, frontal, and cerebellar brain regions, which were recruited during initial motor learning, were specifically reactivated during the TMR episode and that hippocampo-frontal connectivity was modulated by the reactivation process. Importantly, the TMR-induced behavioral advantage was paralleled by dynamical changes in hippocampal activity and hippocampo-motor connectivity during task practice. Altogether, the present results suggest that somatosensory TMR during postlearning quiet rest can enhance motor performance via the modulation of hippocampo-cortical responses.

Age-related change in task-evoked amygdala-prefrontal circuitry: A multiverse approach with an accelerated longitudinal cohort aged 4-22 years

The amygdala and its connections with medial prefrontal cortex (mPFC) play central roles in the development of emotional processes. While several studies have suggested that this circuitry exhibits functional changes across the first two decades of life, findings have been mixed - perhaps resulting from differences in analytic choices across studies. Here we used multiverse analyses to examine the robustness of task-based amygdala-mPFC function findings to analytic choices within the context of an accelerated longitudinal design (4-22 years-old; N = 98; 183 scans; 1-3 scans/participant). Participants recruited from the greater Los Angeles area completed an event-related emotional face (fear, neutral) task. Parallel analyses varying in preprocessing and modeling choices found that age-related change estimates for amygdala reactivity were more robust than task-evoked amygdala-mPFC functional connectivity to varied analytical choices. Specification curves indicated evidence for age-related decreases in amygdala reactivity to faces, though within-participant changes in amygdala reactivity could not be differentiated from between-participant differences. In contrast, amygdala-mPFC functional connectivity results varied across methods much more, and evidence for age-related change in amygdala-mPFC connectivity was not consistent. Generalized psychophysiological interaction (gPPI) measurements of connectivity were especially sensitive to whether a deconvolution step was applied. Our findings demonstrate the importance of assessing the robustness of findings to analysis choices, although the age-related changes in our current work cannot be overinterpreted given low test-retest reliability. Together, these findings highlight both the challenges in estimating developmental change in longitudinal cohorts and the value of multiverse approaches in developmental neuroimaging for assessing robustness of results.

Comparison of Hemodynamic Brain Responses Between Big Wave Surfers and Non-big Wave Surfers During Affective Image Presentation

Background

Big wave surfers are extreme sports athletes who expose themselves to life-threatening risk when training and competing. Little is known about how and why extreme sports athletes choose to participate in their chosen sports. This exploratory study investigated potential neurophysiological and psychometric differences between big and non-big wave surfers.

Methods

Thirteen big wave surfers (BWS) and 10 non-big wave surfers (CON) viewed a series of images from the International Affective Picture System (IAPS) while undergoing brain functional magnetic resonance imaging (fMRI). The Fear Schedule Survey-III, Arnett Inventory of Sensation Seeking, Discrete Emotions Questionnaire, and Positive and Negative Affect Schedule were also completed.

Results

The BWS group demonstrated higher blood-oxygen level-dependent (BOLD) signal change in the insula, visual cortex, and periaqueductal gray, whereas the CON group displayed increased hypothalamus activation in response to high amplitude negative-valence (HAN) image presentation. Psychophysiological interaction (PPI) analyses found CON showed significant interactions between frontal and temporal cortical regions as well as between the hypothalamus and the insula, frontal, and temporal cortices during HAN image presentation that were not seen in BWS. No differences between groups were found in their responses to the questionnaires.

Conclusion

Our findings demonstrate significant differences in brain activation between BWS and CON in response to the presentation of HAN IAPS images, despite no significant differences in scores on psychometric questionnaires.

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

Ecological chemosensory stimuli almost always evoke responses in more than one sensory system. Moreover, any sensory processing takes place along a hierarchy of brain regions. So far, the field of chemosensory neuroimaging is dominated by studies that examine the role of brain regions in isolation. However, to completely understand neural processing of chemosensation, we must also examine interactions between regions. In general, the use of connectivity methods has increased in the neuroimaging field, providing important insights to physical sensory processing, such as vision, audition, and touch. A similar trend has been observed in chemosensory neuroimaging, however, these established techniques have largely not been rigorously applied to imaging studies on the chemical senses, leaving network insights overlooked. In this article, we first highlight some recent work in chemosensory connectomics and we summarize different connectomics techniques. Then, we outline specific challenges for chemosensory connectome neuroimaging studies. Finally, we review best practices from the general connectomics and neuroimaging fields. We recommend future studies to develop or use the following methods we perceive as key to improve chemosensory connectomics: (1) optimized study designs, (2) reporting guidelines, (3) consensus on brain parcellations, (4) consortium research, and (5) data sharing.

Social Interaction With an Anonymous Opponent Requires Increased Involvement of the Theory of Mind Neural System: An fMRI Study

An anonymous interaction might facilitate provoking behavior and modify the engagement of theory of mind (TOM) brain mechanisms. However, the effect of anonymity when processing unfair behavior of an opponent remains largely unknown. The current functional magnetic resonance imaging (fMRI) study applied the Taylor aggression paradigm, introducing an anonymous opponent to this task. Thirty-nine healthy right-handed subjects were included in the statistical analysis (13 males/26 females, mean age 24.5 ± 3.6 years). A player winning the reaction-time game could subtract money from the opponent during the task. Participants behaved similarly to both introduced and anonymous opponents. However, when an anonymous opponent (when compared to the introduced opponent) subtracted money, the right inferior frontal gyrus (IFG) demonstrated an increased BOLD signal and increased functional connectivity with the left IFG. Further, increased functional connectivity between the right IFG, the right temporal parietal junction and precuneus was observed during the perception of high provocation (subtracting a large amount of money) from the anonymous compared to the introduced opponent. We speculate that the neural changes may underlie different inferences about the opponents’ mental states. The idea that this reorganization of the TOM network reflects the attempt to understand the opponent by “completing” socially relevant details requires further investigation.

Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity

The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.