bioRxiv Subject Collection: Neuroscience's Journal

Neurophysiological correlates of short-term recognition of sounds: Insights from magnetoencephalography
Understanding the brain's dynamic retrieval and updating of encoded information is a key focus in memory research. This study employed a same versus different auditory paradigm to investigate short-term auditory recognition within a predictive coding (PC) framework, concerning the perceptual interplay between experience-informed predictions and incoming sensory information. Using magnetoencephalography (MEG), we captured the neurophysiological correlates associated with a single-sound, short-term memory task. Twenty-six healthy participants were tasked with recognizing whether presented sounds were same or different compared to strings of standard stimuli. To prompt conscious memory retention, a white noise interlude separated these sounds from the standards. MEG sensor-level results revealed that recognition of same sounds elicited two significantly stronger negative components of the event-related field compared to different sounds. The first one was the N1, peaking 100ms post-sound onset, while the second one corresponded to a slower negative component arising between 300 and 600ms after sound onset. This effect was observed in several significant clusters of MEG sensors, especially temporal and parietal regions of the scalp. Conversely, different sounds produced scattered and smaller clusters of stronger activity than same sounds, peaking later than 600ms after sound onset. Source reconstruction using beamforming algorithms revealed the involvement of auditory cortices, hippocampus, and cingulate gyrus in both conditions. Overall, the results are coherent with PC principles and previous results on the brain mechanisms underlying auditory recognition, highlighting the relevance of early and later negative brain responses for successful prediction of previously listened sounds in the context of conscious short-term memory.

A common cortical basis for variations in visual crowding
Peripheral vision is limited by crowding, the disruptive effect of clutter on object recognition. Crowding varies markedly around the visual field, with e.g. stronger performance decrements in the upper vs. lower visual field. Crowding also changes object appearance - target and flanker objects appear more similar (assimilation) in some instances and dissimilar (repulsion) in others. Here we examined whether these performance and appearance effects co-vary, and in turn whether a common cortical factor could drive all of these effects. Participants judged the orientation of a target Gabor with and without flankers in 3 experiments. The first placed a flanker in either the ipsilateral or (the more cortically distant) contralateral hemifield. Although crowding was observed, flanker location had no effect. We next measured recognition at a range of eccentricities in the upper and lower field, observing that both threshold elevation (performance) and assimilative errors (appearance) were higher in the upper vs. lower field. Similarly, flankers on the radial axis around fixation produced high threshold elevation and assimilation, while tangential flankers gave lower elevation and repulsion errors. This common pattern of variations in performance and appearance is well described by a population-coding model of crowding that varies the weighted combination of target vs. flanker population responses. We further demonstrate that neither the cortical distance between elements nor receptive-field size variations can account for the observed variations. Instead, using a series of models we show that the common factor could be receptive field overlap - the intermixing of the spatial distribution of target/flanker responses. That is, crowding is strong (with high threshold elevation and assimilation) when the degree of overlap in the spatial distribution of population responses is high, and reduced (with low threshold elevation and repulsion) when these responses are separable.

Blood-brain barrier self-repair after ischemic stroke by replenishing E-pericytes directly transdifferentiated from endothelial cell
Ischemic brain repairs itself during the late recovery phase, requiring replenishing vascular pericytes. However, the cell origins for pericyte regeneration after stroke are unclear. By using genetic tracing in a mouse model of middle cerebral artery occlusion, we identified endothelial cells (ECs) undergoing cell-fate changing and become e-pericytes (EC-derived pericyte). This direct transformation requires TGFbeta-signaling. Most E-pericytes integrate into vascular wall to reconstruct BBB integrity. Specifically prevention of E-pericyte generation largely compromises brain volume integrity. This study uncovered a novel mechanism underlying spontaneous brain self-repair, suggesting that ischemic brains may recycle ECs from unfunctional vessels to replenish lost pericytes as an innate self-maintenance program sustaining repair phenomena.

Temporal dynamics and representational consequences of the control of processing conflict between visual working memory and visual perception
Visual working memory (WM) extensively interacts with visual perception. When information between the two processes is in conflict, cognitive control can be recruited to effectively mitigate the resultant interference. The current study investigated the neural bases of the control of conflict between visual WM and visual perception. We recorded the electroencephalogram (EEG) from 25 human subjects (13 male) performing a dual task combining visual WM and tilt discrimination, the latter occurring during the WM delay. The congruity in orientation between the memorandum and the discriminandum was manipulated. Behavioral data were fitted to a reinforcement-learning model of cognitive control to derive trial-wise estimates of demand for proactive and reactive control, which were then used for EEG analyses. The level of proactive control was associated with sustained frontal-midline theta activity preceding trial onset, as well as with the strength of the neural representation of the memorandum. Subsequently, discriminandum onset triggered a control prediction error signal that was reflected in a left frontal positivity. On trials when an incongruent discriminandum was not expected, reactive control that scaled with the prediction error acted to suppress the neural representation of the discriminandum, producing below-baseline decoding of the discriminandum that, in turn, exerted a repulsive serial bias on WM recall on the subsequent trial. These results illustrate the flexible recruitment of two modes of control and how their dynamic interplay acts to mitigate interference between simultaneously processed perceptual and mnemonic representations.

Rapid integration of face detection and task set in visually guided reaching
The superior colliculus (SC) has been increasingly implicated in the rapid processing of evolutionarily relevant visual stimuli like faces, but the behavioural relevance of such processing is not clear. The SC has also been implicated in the generation of upper-limb Express Visuomotor Responses (EVRs) on upper limb muscles, which are very short-latency (within ~80 ms) bursts of muscle activity time-locked to visual target presentation. This reasoning led us to investigate the influence of faces on EVRs. We recorded upper limb muscle activity from young healthy participants as they reached toward left or right targets in the presence of a distractor stimulus presented on the opposite side. Across blocks of trials, we varied the instruction as to which stimulus served as the target or distractor. Doing so allowed us to assess the impact of instruction on muscle recruitment by examining trials when the exact same stimuli required a reach to either the left or right. We found that EVRs were uniquely modulated in tasks involving face selection, promoting reaches toward or away from faces depending on instruction. Follow-up experiments confirmed that this phenomenon required highly salient repeated faces, and was not observed to non-facial salient stimuli nor to faces expressing different affect. We conclude that our results attest to an integration of top-down task set and bottom-up feature detection to promote rapid motor responses to faces at latencies that match or precede the arrival of face information in human cortex.

Trying harder: how cognitive effort sculpts neural representations during working memory
The neural mechanisms by which motivational factors influence cognition remain unknown. Using fMRI, we tested how cognitive effort impacts working memory (WM). Participants were precued whether WM difficulty would be hard or easy. Hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity in prefrontal cortex, especially during hard trials. We found strong decoding of location in visual cortex, where accuracy was higher on hard trials. Connecting these across-region effects, we found that the amplitude of delay period activity in frontal cortex predicted decoded accuracy in visual cortex on a trial-wise basis. We conclude that the gain of persistent activity in frontal cortex may be the source of effort-related feedback signals that improve the quality of WM representations stored in visual cortex.

EEG microstate transition cost correlates with task demands
The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework's effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.

Optimized Mass Spectrometry Detection of Thyroid Hormones and Polar Metabolites in Rodent Cerebrospinal Fluid
BackgroundThyroid hormones (TH) are required for brain development and function. Cerebrospinal fluid (CSF), which bathes the brain and spinal cord, contains TH as free or transthyretin (TTR)-bound. Tight thyroid hormone level regulation in the central nervous system is essential for developmental gene expression that governs neurogenesis, myelination, and synaptogenesis. This integrated function of TH highlights the importance of developing precise and reliable methods for assessing TH levels in CSF. Methods: we report an optimized LC-MS based method to measure thyroid hormones in rodent CSF and serum, applicable to both fresh and frozen samples. Results: We find distinct differences in CSF thyroid hormone in pregnant dams vs. non-pregnant adults and in embryonic vs. adult CSF. Further, targeted LC-MS metabolic profiling uncovers distinct central carbon metabolism in the CSF of these populations. Conclusions: TH detection and metabolite profiling of related metabolic pathways open new avenues of rigorous research into CSF thyroid hormone and will inform future studies on metabolic alterations in CSF during normal development.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits
Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons are capable of producing metastable dynamics, however, it is unclear how their structure may emerge in cortical circuits. Here, we demonstrate the emergence of rich metastable dynamics from a fully local synaptic plasticity rule. The metastable dynamics co-exists with ongoing plasticity; in turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. These results show that it is possible to generate and support metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics and is independent of network size.

Direct stimulation of anterior insula and ventromedial prefrontal cortex disrupts economic choices
Neural activities within the ventromedial prefrontal cortex and anterior insula are associated with economic choices. However, whether these brain regions are causally related to these processes remains unclear. To address this issue, we leveraged rare intracerebral electrical stimulation (iES) data in epileptic patients. We show that opposite effects of iES on choice depend on the location of stimulation on a dorso-ventral axis within each area, thus demonstrating dissociable neural circuits causally involved in accepting versus avoiding challenges.

Structural connectivity predicts individual perceived stress
Many previous studies have investigated the neural mechanisms of perceived stress using either task or resting-state functional connectomes. However, to date, the structural connectivity predictors of individual perceived stress remain unknown. In this study, using connectome-based predictive modeling with a leave-one-out cross-validation framework in a sample of 100 unrelated healthy young adults, we show that individual differences in perceived stress can be reliably predicted from their structural connectivity. The obtained results show that perceived stress could be predicted from the interaction of visual association, and motor and sub-cortical networks. This present work highlights that structural connectivity can be used to investigate the neural mechanism of PS in healthy populations.

Movement-related increases in subthalamic activity optimize locomotion
The subthalamic nucleus (STN) is traditionally thought to restrict movement. Lesion or prolonged STN inhibition increases movement vigor and propensity, while optogenetic excitation has opposing effects. However, most STN neurons typically exhibit movement-related increases in firing. To address this paradox, STN activity was recorded and manipulated in head-fixed mice at rest and during self-initiated and -paced treadmill locomotion. The majority of STN neurons (type 1) exhibited locomotion-dependent increases in activity, with half encoding the locomotor cycle. A minority of neurons exhibited dips in activity or were uncorrelated with movement. Brief optogenetic inhibition of the dorsolateral STN (where type 1 neurons are concentrated) slowed, dysregulated, and prematurely terminated locomotion. In Q175 Huntington's disease mice analogous locomotor deficits were specifically linked to abnormal type 1 hypoactivity. Together, these data argue that movement-related increases in STN activity contribute to optimal locomotor performance.

Early stage of life is characterized by increased excitability of the auditory cortex in both humans and rats
Auditory evoked response (ER) undergoes notable changes during childhood and likely reflects changes in synaptic signaling in the auditory cortex. Establishing the interspecies generalizability of electrophysiological indicators of cortical maturation would offer a means to better understand the neurodevelopment of the human brain. We measured cortical ERs to simple auditory probes in three age groups of human subjects and juvenile rats. These two species exhibited a remarkably similar long-latency (150-350 ms poststimulus) response in the auditory cortex, specifically pronounced in the younger individuals in both species. An age-dependent pattern of activity was evident at the level of single trials, and the late response showed stronger trial-by-trial stability in children than the early, adult-like 100-ms response, especially in humans. This robust development-related pattern of sensory cortex excitability is likely to represent a distinct synaptic event and may be a marker of the maturational stage, especially in GABAergic cortical circuits.

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks
The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.

Dopamine Prediction Error Signaling in a Unique Nigrostriatal Circuit is Critical for Associative Fear Learning
Learning by experience that certain cues in the environment predict danger is crucial for survival. How dopamine (DA) circuits drive this form of associative learning is not fully understood. Here, we demonstrate that DA neurons projecting to a unique subregion of the dorsal striatum, the posterior tail of the striatum (TS), encode an aversive prediction error (PE) signal during associative fear learning. These DA neurons are necessary specifically during acquisition of fear learning, but not once the fear memory is formed, and are not required for forming cue-reward associations. Notably, temporally-precise excitation of DA terminals in TS is sufficient to enhance fear learning. Furthermore, neuronal activity in TS is crucial for acquisition of associative fear learning and learning-induced activity patterns in TS critically depend on DA input. Together, our results reveal that DA PE signaling in a non-canonical nigrostriatal circuit is crucial for driving associative fear learning.

Rhythmic sampling of multiple decision alternatives in the human brain
During decision-making attention plays a pivotal role by guiding information sampling between alternatives. We need to optimally encode different options and compare between them. So far the role of attention in decision-making has only been addressed through saccadic displacements. However, looking is not always attending and attending not always looking. The influence of covert attention on decision-making is poorly understood. Here, we combined a three-alternative perceptual choice task with magnetoencephalography (MEG) recordings to chart covert attention during decision-formation. We found that rhythmic attentional sampling is a neurophysiological mechanism that can temporally disentangle the conflict between focusing and reorienting attention in decision-making. Embedded within an 11Hz oscillation focused processing and reorienting appeared at the peak and trough of the attention oscillation. Reorienting further intrinsically reset the oscillation and covert attention was dissociable from oculomotor activity. We thus propose that (covert) rhythmic sampling is a general cognitive mechanism harnessed to orchestrate flexible information processing in multi-alternative decisions.

The energy metabolic footprint of predictive processing in the human brain
Neural activity is a highly energy-intensive process. In the human brain, signaling consumes up to 75% of the available energy resources with postsynaptic potentials as the largest factor. Visual processing is especially costly, with increases in energy consumption of up to 20% in the visual cortex. In recent years, vision has been cast as a constructive process, harnessing prior knowledge in a constant feedback loop of top-down prediction and bottom-up sensory input. Interestingly, sensory input that is in line with our predictions might be processed at lower energy metabolic cost. However, there is no evidence for this claim yet, possibly due to the scarcity of measures that quantify energy consumption in the human brain. Here, we used a novel MR method measuring the cerebral metabolic rate of oxygen during sensory stimulation of visual sequences that varied in their predictability. Since predictive processing is driven by estimates of uncertainty, we assessed how confident subjects were in their knowledge of the underlying patterns. We found that processing predictable sequences steeply decreased in energetic cost with increasing confidence. Strikingly, these energetic effects were not limited to visual areas, summing up to a cortical difference of 13% between high and low levels of confidence. Furthermore, sequences deviating from expectations were energetically cheaper than predictable ones for low confidence levels, but costlier for high levels. These results speak for a major role of predictive processing in balancing the brain's energy budget and emphasize the impact of interindividual differences when learning predictive patterns.

Category-based attention facilitates memory search
We often need to decide whether the object we look at is also the object we look for. When we look for one specific object, this process can be facilitated by preparatory feature-based attention. However, when we look for multiple objects at the same time (e.g., the products on our shopping list) such a strategy may no longer be possible, as research has shown that we can actively prepare to detect only one object at a time. Therefore, looking for multiple objects may additionally involve search in long-term memory, slowing down decision making. Interestingly, however, previous research has shown that memory search can be very efficient when distractor objects are from a different category than the items in the memory set. Here, using EEG, we show that this efficiency is supported by top-down attention at the category level. In Experiment 1, human participants (both sexes) performed a memory search task on individually presented objects of the same or different category as the objects in the memory set. We observed category-level attentional modulation of distractor processing from ~150 ms after stimulus onset, expressed both as an evoked response modulation and as an increase in decoding accuracy of same-category distractors. In Experiment 2, memory search was performed on two concurrently presented objects. When both objects were distractors, spatial attention (indexed by the N2pc component) was directed to the object that was of the same category as the objects in the memory set. Together, these results demonstrate how attention can facilitate memory search.

Hemispheric divergence of interoceptive processing across psychiatric disorders
Interactions between top-down attention and bottom-up visceral inputs are assumed to produce conscious perceptions of interoceptive states, and while each process has been independently associated with aberrant interoceptive symptomatology in psychiatric disorders, the neural substrates of this interface are unknown. We conducted a preregistered functional neuroimaging study of 46 individuals with anxiety, depression, and/or eating disorders (ADE) and 46 propensity-matched healthy comparisons (HC), comparing their neural activity across two interoceptive tasks differentially recruiting top-down or bottom-up processing within the same scan session. During an interoceptive attention task, top-down attention was voluntarily directed towards cardiorespiratory or visual signals, whereas during an interoceptive perturbation task, intravenous infusions of isoproterenol (a peripherally-acting beta-adrenergic receptor agonist) were administered in a double-blinded and placebo-controlled fashion to drive bottom-up cardiorespiratory sensations. Across both tasks, neural activation converged upon the insular cortex, localizing within the granular and ventral dysgranular subregions bilaterally. However, contrasting hemispheric differences emerged, with the ADE group exhibiting (relative to HCs) an asymmetric pattern of overlap in the left insula, with increased or decreased proportions of co-activated voxels within the left or right dysgranular insula, respectively. The ADE group also showed less agranular anterior insula activation during periods of bodily uncertainty (i.e., when anticipating possible isoproterenol-induced changes that never arrived). Finally, post-task changes in insula functional connectivity were associated with anxiety and depression severity. These findings confirm the dysgranular mid-insula as a key cortical interface where attention and prediction meet real-time bodily inputs, especially during heightened awareness of interoceptive states. Further, the dysgranular mid-insula may indeed be a "locus of disruption" for psychiatric disorders.

Two distinct modes of hemodynamic responses in the human brain
Functional magnetic resonance imaging (fMRI) detects changes in brain activity by measuring fluctuations in blood oxygenation. While fMRI assumes a fixed relationship between changes in cerebral blood flow (CBF) and metabolic activity, its uniform application across the human cortex lacks complete validation. We used quantitative fMRI together with blood oxygenation level-dependent (BOLD) fMRI to assess oxygen metabolism against both positive and negative BOLD signal changes. We found a surprising mismatch between changes in oxygen consumption and the direction of BOLD signal changes ({triangleup}BOLD) in 14-33% of voxels with significant positive, and in 50-66% of voxels with significant negative {triangleup}BOLD. This implies that a substantial number of voxels with negative {triangleup}BOLD actually demonstrate increased metabolic activity, and vice versa. Contrary to the canonical hemodynamic response model, discordant voxels cover their oxygen demand predominantly via changes in the oxygen extraction fraction ({triangleup}OEF) and not via {triangleup}CBF. This coincides with a higher vascular density and a lower OEF during baseline that we identified in discordant voxels. In summary, we identified a second type of hemodynamic response mechanism for regulating oxygen supply in the human brain. Our results suggest that the classical interpretation of positive and negative BOLD signal changes in terms of increased or decreased neural activity may be too simplistic and we recommend incorporating quantitative MRI or additional CBF measurements for a more accurate assessment of neuronal activity.