bioRxiv Subject Collection: Neuroscience's Journal

Resilience and vulnerability of speech neural tracking to early auditory deprivation
Infants are born with biologically constrained biases that favor language acquisition. One is the auditory system's ability to track the envelope of continuous speech. However, to what extent the synchronization between brain activity and this pivotal speech feature relies on postnatal auditory experience remains unknown. To uncover this, we studied individuals with or without access to functional hearing in the first year of life after they received cochlear implants (CIs) for hearing restoration. We measured the neural synchronization with continuous speech envelope in children with congenital bilateral profound deafness (CD; minimum auditory deprivation 11 months) or who acquired profound deafness later in development (AD; minimum auditory experience after birth 12 months), as well as in hearing controls (HC). Speech envelope tracking was unaffected by the absence of auditory experience in the first year of life. At short timescales, neural tracking had a similar magnitude in CI users and HC. However, in CI users, it was substantially delayed, and its timing depended on the age of hearing restoration. Conversely, we observed alterations at longer timescales, possibly accounting for the comprehension deficits observed in children with CI. These findings highlight (i) the resilience of sensory components of speech envelope tracking to the lack of hearing in the first year of life, supporting its strong biological bias, (ii) the crucial role of when functional hearing restoration takes place in mitigating the impact of atypical auditory development, (iii) the vulnerability of higher hierarchical levels of speech-envelope tracking in CI users. Neural tracking of continuous speech could provide biomarkers along the processing hierarchy between sensory and core linguistic operations, even after cochlear implantation.

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing
Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

Optogenetic fMRI reveals therapeutic circuits of subthalamic nucleus deep brain stimulation
While deep brain stimulation (DBS) is widely employed for managing motor symptoms in Parkinson's disease (PD), its exact circuit mechanisms remain controversial. To identify the neural targets affected by therapeutic DBS in PD, we analyzed DBS-evoked whole brain activity in female hemi-parkinsonian rats using function magnetic resonance imaging (fMRI). We delivered subthalamic nucleus (STN) DBS at various stimulation pulse repetition rates using optogenetics, allowing unbiased examinations of cell-type specific STN feed-forward neural activity. Unilateral STN optogenetic stimulation elicited pulse repetition rate-dependent alterations of blood-oxygenation-level-dependent (BOLD) signals in SNr (substantia nigra pars reticulata), GP (globus pallidus), and CPu (caudate putamen). Notably, these manipulations effectively ameliorated pathological circling behavior in animals expressing the kinetically faster Chronos opsin, but not in animals expressing ChR2. Furthermore, mediation analysis revealed that the pulse repetition rate-dependent behavioral rescue was significantly mediated by optogenetically induced activity changes in GP and CPu, but not in SNr. This suggests that the activation of GP and CPu are critically involved in the therapeutic mechanisms of STN DBS.

A patient-derived amyotrophic lateral sclerosis blood-brain barrier cell model reveals focused ultrasound-mediated anti-TDP-43 antibody delivery.
Background: Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disorder with minimally effective treatment options. An important hurdle in ALS drug development is the non-invasive therapeutic access to the motor cortex currently limited by the presence of the blood-brain barrier (BBB). Focused ultrasound and microbubble (FUS+MB) treatment is an emerging technology that was successfully used in ALS patients to temporarily open the cortical BBB. However, FUS+MB-mediated drug delivery across ALS patients' BBB has not yet been reported. Similarly, the effects of FUS+MB on human ALS BBB cells remain unexplored. Methods: Here we established the first FUS+MB-compatible, fully-human ALS patient-cell-derived BBB model based on induced brain endothelial-like cells (iBECs) to study anti-TDP-43 antibody delivery and FUS+MB bioeffects in vitro. Results: Generated ALS iBECs recapitulated disease-specific hallmarks of BBB pathology, including changes to BBB integrity, permeability and TDP-43 proteinopathy. Our results also identified differences between sporadic ALS and familial (C9orf72 expansion carrying) ALS iBECs reflecting patient heterogeneity associated with disease subgroups. Studies in these models revealed successful ALS iBEC monolayer opening in vitro with a lack of adverse cellular effects of FUS+MB. This was accompanied by the molecular bioeffects of FUS+MB in ALS iBECs including changes in expression of tight and adherens junction markers, and drug transporter and inflammatory mediators, with sporadic and C9orf72 ALS iBECs generating transient specific responses. Additionally, we demonstrated an effective increase in the delivery of anti-TDP-43 antibody with FUS+MB in C9orf72 (2.7-fold) and sporadic (1.9-fold) ALS iBECs providing the first proof-of-concept evidence that FUS+MB can be used to enhance the permeability of large molecule therapeutics across the BBB in a human ALS in vitro model. Conclusions: Together, our study describes the first characterisation of cellular and molecular responses of ALS iBECs to FUS+MB and provides a fully-human platform for FUS+MB-mediated drug delivery screening on an ALS BBB in vitro model.

Brain preparedness: The cortisol awakening response proacts dynamic organization of large-scale brain networks across emotional and executive functions
Emotion and cognition involve an intricate crosstalk of neural and endocrine systems that support allostatic processes for maintenance of dynamic equilibrium and rapid adaptation for upcoming challenges. As a hallmark of human endocrine activity, the cortisol awakening response (CAR) is recognized to play a critical role in modulating emotional and executive functions. Yet, the underlying mechanisms of such effects remain elusive. By leveraging pharmacological neuroimaging technique and Hidden Markov Modeling of brain state dynamics, we show that the CAR proactively modulates rapid reconfigurations (state) of large-scale brain networks across multi-task demands. Behaviorally, suppression of CAR proactively and selectively impaired accuracy for emotional discrimination task but not for working memory (WM). In parallel, suppressed CAR led to a decrease in the occurrence rate of brain state dominant to emotional processing, but an increase in brain state linking to executive control under high WM demand. Further energy-based analyses revealed an increase in transition frequency and sequence complexity along with an increased entropy during emotional tasks when suppressed CAR, suggesting a decreased energy supply. Moreover, an increased transition frequency was observed when shifting from neutral to emotional conditions, but an opposite pattern during WM task, with n decreased transition frequency shifts from low to high-executive demands. Our findings establish a causal link between CAR and dynamic allocation of neural resources for emotional and executive functions, suggesting a cognitive neuroendocrine account for CAR-mediated proactive effects and human allostasis.

Neuronal and behavioral responses to naturalistic texture images in macaque monkeys
The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture percep- tion. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and si- multaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that indi- vidual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and thus unlikely to reflect a critical aspect of the formation of perceptual decisions.

Spontaneous pain dynamics characterized by stochasticity in awake human LFP with chronic pain
Chronic pain involves persistent fluctuations lasting seconds to minutes, yet there are limited studies on spontaneous pain fluctuations utilizing high-temporal-resolution electrophysiological signals in humans. This study addresses the gap, capturing data during awake deep brain stimulation (DBS) surgery in five chronic pain patients. Patients continuously reported pain levels using the visual analog scale (VAS), and local field potentials (LFP) from key pain-processing structures (ventral parietal medial of the thalamus, VPM; subgenual cingulate cortex, SCC; periaqueductal gray, PVG) were recorded. Our novel AMI analysis revealed that regular spike-like events in the theta/alpha band was associated with higher pain; and regular events in the gamma band was associated with opioid effects. We demonstrate a novel methodology that successfully characterizes spontaneous pain dynamics with human electrophysiological signals, holding potential for advancing closed-loop DBS treatments for chronic pain.

Reference curves for harmonizing multi-site regional diffusion MRI metrics across the lifespan
Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan with a unique trajectory in the brain, complementing the process of gray matter development and degeneration. Normative modeling can establish lifespan reference curves for typical WM microstructural aging patterns by pooling data from many independent studies that span different age ranges. Here, we create such reference curves by harmonizing and pooling diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three ComBat harmonization methods to create normative curves for regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a widely used metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with neuroscientific knowledge regarding WM maturation peaks. Harmonized FA metrics were used to create lifespan reference curves, which were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Within-study associations were not affected by normative harmonization, ensuring that large-scale harmonized studies can be conducted across the lifespan, even from distinct age-restricted studies, without compromising individual study findings. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are available through our new Python package, eHarmonize (https://github.com/ahzhu/eharmonize).

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation
Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of features combined. Our results reveal that: i) EEG and residual EMG features provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degree of impairment of the patient; and iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.

Estimating Directed Connectivity with OPM-MEG: A Feasibility Study
We investigated the feasibility of using directed connectivity analysis for magnetoencephalography data recorded via Optically Pumped Magnetometers (OPM-MEG). Ten healthy adult participants were scanned twice in an OPM-MEG system, and Beamformer source localisation was employed to obtain source time series within 62 cortical brain regions. Multivariate transfer entropy (mTE) was used to quantify directed connectivity, with lags ranging from 1 to 10 time points. Our findings showed that out-degree connectivity (node to network) in somatomotor and attention networks was higher than in other networks. In contrast, in-degree connectivity (network to node) was not different between networks. Longer time lags were associated with shorter physical distances between nodes. Transfer entropy networks demonstrated a small-world network topology, reinforcing the biological plausibility of our findings. Brain connections with higher transfer entropy values exhibited a stronger Phase Lag Index and Pearson's correlation coefficient than non-significant transfer entropy connections, suggesting a relationship between directed and non-directed connectivity measures. All results were consistent across the two scans. OPM-MEG research is likely to expand significantly, and based on our preliminary findings, we propose that directed connectivity measures may provide valuable insights into the underlying mechanisms of brain communication.

Directed neural interactions in fMRI: a comparison between Granger Causality and Effective Connectivity
Understanding how neural populations interact is crucial to understand brain function. Most common approaches to infer neural interactions are based on Granger causality (GC) analyses and effective connectivity (EC) models of neural time series. However, an in-depth investigation of the similarity and complementarity of these approaches is currently lacking. GC and EC are classically thought to provide complementary information about the interdependence between neural signals. Whereas GC quantifies the amount of predictability between time series and it is interpreted as a measure of information flow, EC quantifies the amount and sign of the interaction, and it is often interpreted as the causal influence that a neural unit exert over another. Here, we show that, in the context of functional magnetic resonance imaging (fMRI) data analysis and first-order autoregressive models, GC and EC share common assumptions and are mathematically related. More precisely, by defining a 'corrected' version of GC accounting for unequal noise variances affecting the source and target node, we show that the two measures are linked by an approximately quadratic relation, where positive or negative values of EC are associated with identical values of GC. While the relation is obtained in limit of infinite sampling time, we use simulations to show that it can be observed in finite data samples as classically observed in neuroimaging studies, provided sufficiently long sampling, multiple sessions or group averaging. Finally, we compare the GC and EC analyses on fMRI data from the Human Connectome Project, and obtain results consistent with simulation outcomes. While GC and EC analyses do not provide reliable estimates at the single subject or single connection level, they become stable at the group level (more than approximately 20 subjects), where the predicted relation between GC and EC can be clearly observed from the data. To conclude, our study provides a common mathematical framework to make grounded methodological choices in the reconstruction and analysis of directed brain networks from neuroimaging time series.

Subjective confidence modulates individual BOLD patterns of predictive processing
Humans are adept at extracting and learning sequential patterns from sensory input. This ability enables predictions about future states, resulting in anticipation both on a behavioral and neural level. Stimuli deviating from predictions usually evoke higher neural and hemodynamic activity than predicted stimuli. This difference indicates increased surprise, or prediction error signaling in the context of predictive coding. However, interindividual differences in learning performance and uncertainty have rarely been taken into account. Under Bayesian formulations of cortical function, surprise should be strongest if a subject makes incorrect predictions with high confidence. In the present study, we studied the impact of subjective confidence on imaging markers of predictive processing. Participants viewed visual object sequences of varying predictability over multiple days. After each day, we instructed them to complete partially presented sequences and to rate their confidence in the decision. During fMRI scanning, participants saw sequences that either confirmed predictions, deviated from them, or were random. We replicated findings of increased BOLD responses to surprising input in the ventral visual stream. In line with our hypothesis, response magnitude increased with the level of confidence after the training phase. Interestingly, the activity difference between predictable and random input also scaled with confidence: In the anterior cingulate, predictable sequences elicited higher activity for low levels of confidence, but lower activity for high levels of confidence. In summary, we showed that confidence is a crucial moderator of the link between predictive processing and BOLD activity.

Changes in the analgesic mechanism of oxytocin can contribute to hyperalgesia in Parkinsons disease model rats
Pain is a major non-motor symptom of Parkinsons disease (PD). The alterations in the descending pain inhibitory system (DPIS) have been reported to trigger hyperalgesia in PD patients. However, the underlying mechanisms remain unclear. In the current study, dopaminergic nigrostriatal lesions were induced in rats by injecting 6-hydroxydopamine (6-OHDA) into their medial forebrain bundle. The neural mechanisms underlying changes in nociception in the orofacial region of 6-OHDA-lesioned rats was examined by injecting formalin into the vibrissa pad. The 6-OHDA-lesioned rats were seen to exhibit increased frequency of face-rubbing and more c-Fos immunoreactive (c-Fos-IR) cells in the trigeminal spinal subnucleus caudalis (Vc), confirming hyperalgesia. Examination of the number of c-Fos-IR cells in the DPIS nuclei [including the midbrain ventrolateral periaqueductal gray, the locus coeruleus, the nucleus raphe magnus, and paraventricular nucleus (PVN)] showed that 6-OHDA-lesioned rats exhibited a significantly lower number of c-Fos-IR cells in the magnocellular division of the PVN (mPVN) after formalin injection compared to sham-operated rats. Moreover, the 6-OHDA-lesioned rats also exhibited significantly lower plasma oxytocin (OT) concentration and percentage of oxytocin-immunoreactive (OT-IR)neurons expressing c-Fos protein in the mPVN and dorsal parvocellular division of the PVN (dpPVN), which secrete the analgesic hormone OT upon activation by nociceptive stimuli, when compared to the sham-operated rats. The effect of OT on hyperalgesia in 6-OHDA-lesioned rats was examined by injecting formalin into the vibrissa pad after intracisternal administration of OT, and the findings showed a decrease in the frequency of face rubbing and the number of c-Fos-IR cells in the Vc. In conclusion, these findings confirm presence of hyperalgesia in PD patients, potentially due to suppression of the analgesic effects of OT originating from the PVN.

Prediction of neural activity in connectome-constrained recurrent networks
We develop a theory of connectome-constrained neural networks in which a student network is trained to reproduce the activity of a ground-truth teacher, representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here both networks have the same connectivity but they have different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently infer unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different, and provides a framework to determine when such models yield consistent dynamics.

Reliably Measuring Learning-Dependent Distractor Suppression with Eye Tracking
In the field of psychological science, behavioral performance in computer-based cognitive tasks often exhibits poor reliability. The absence of reliable measures of cognitive processes contributes to non-reproducibility in the field and impedes investigation of individual differences. Specifically in visual search paradigms, response time-based measures have shown poor test-retest reliability and internal consistency across attention capture and distractor suppression, but one study has demonstrated the potential for oculomotor measures to exhibit superior reliability. Therefore, in this study, we investigated three datasets to compare the reliability of learning-dependent distractor suppression measured via distractor fixations (oculomotor capture) and latency to fixate the target (fixation times). Our findings reveal superior split-half reliability of oculomotor capture compared to that of fixation times regardless of the critical distractor comparison, with the reliability of oculomotor capture in most cases falling within the range that is acceptable for the investigation of individual differences. We additionally find that older adults have superior oculomotor reliability compared with young adults, potentially addressing a significant limitation in the aging literature of high variability in response time measures due to slower responses. Our findings highlight the utility of measuring eye movements in the pursuit of reliable indicators of distractor processing and the need to further test and develop additional measures in other sensory domains to maximize statistical power, reliability, and reproducibility.

Versatile Functional Interaction between Electrically Silent KV Subunits and KV7 Potassium Channels
Voltage-gated K+ (KV) channels govern K+-ion flux across cell membranes in response to changes in membrane potential. They are formed by the assembly of four subunits, typically from the same family. Electrically silent KV channels (KVS), however, are unable to conduct currents on their own. It has been assumed that these KVS must obligatorily assemble with subunits from the KV2 family into heterotetrameric channels, thereby giving raise to currents distinct from those of homomeric KV2 channels. Herein, we show that KVS subunits indeed also modulate the activity, biophysical properties and surface expression of recombinant KV7 isoforms in a subunit-specific manner. Employing co-immunoprecipitation, and proximity labelling, we unveil the spatial coexistence of KVS and KV7 within a single protein complex. Electrophysiological experiments further indicate functional interaction and probably heterotetramer formation. Finally, single-cell transcriptomic analyses identify native cell types in which this KVS and KV7 interaction may occur. Our finding demonstrate that KV cross-family interaction is much more versatile than previously thought - possibly serving nature to shape potassium conductance to the needs of individual cell types.

Distinct representation of cognitive flexibility and habitual stability in the primate putamen, caudate, and ventral striatum
The putamen is believed to receive value information from the ventral striatum to the caudate and then to the putamen for the generation of habitual actions. However, it is a question what value the putamen neurons process and whether the putamen receives serially processed value through the striatal structures. We found that neurons in the primate putamen, caudate, and ventral striatum selectively encoded flexibly updated values for adaptive choices with similar learning speeds, rather than stably sustained values for habit. In reversal value learning, rostral striatum neurons dynamically adjusted their value discrimination responses for adaptive saccades following reversals. Notably, the value acquisition speeds within trials were similar, proposing a parallel value update in each striatal region. However, in stable value retrieval, most did not encode the values for habitual saccades. Our findings suggest that the rostral striatum including the putamen is selectively involved in the parallel processing of cognitive flexibility.

Working memory representations are spatially transferred in cortex during mental manipulations
Working Memory (WM) enables us to maintain and directly manipulate mental representations, yet we know little about the neural implementation of this privileged online format. We recorded EEG data as human subjects engaged in a task requiring continuous updates to the locations of objects retained in WM. Analysis of contralateral delay activity (CDA) revealed that mental representations moved across cortex in real time as their remembered locations were updated.

Cross-species alignment along the chronological axis reveals evolutionary effect on structural development of human brain
Disentangling evolution mysteries of human brain has always been an imperative endeavor in neuroscience. On the one hand, by spatially aligning the brains between human and nonhuman primates (NHPs), previous efforts in comparative studies revealed both correspondence and difference in brain anatomy, e.g., the morphological and the connectomic patterns. On the other hand, brain anatomical development along the temporal axis is evident for both human and NHPs in early life. However, it remains largely unknown whether we can conjugate the brain development phases between human and NHPs, and, especially, what the role played by the brain anatomy in the conjugation will be. Here, we proposed to embed the brain anatomy of human and macaque in the chronological axis for enabling the cross-species comparison on brain development. Specifically, we separately established the prediction models by using the brain anatomical features in gray matter and white matter tracts to predict the chronological age in the human and macaque samples with brain development. We observed that applying the trained models within-species could well predict the chronological age. Interestingly, by conducting the cross-species application of the trained models, e.g., applying the model trained in humans to the data of macaques, we found a significant cross-species imbalance regarding to the model performance, in which the model trained in macaque showed a higher accuracy in predicting the chronological age of human than the model trained in human in predicting the chronological age of macaque. The cross application of the trained model introduced the brain cross-species age gap (BCAP) as an individual index to quantify the cross-species discrepancy along the temporal axis of brain development for each participant. We further showed that BCAP was associated with the behavioral performance in both visual sensitivity test and picture vocabulary test in the human samples. Taken together, our study situated the cross-species brain development along the chronological axis, which highlighted the disproportionately anatomical development in the human brain to extend our understanding of the potential evolutionary effects.

The transcriptional response of cortical neurons to concussion reveals divergent fates after injury
Traumatic brain injury (TBI) is an important risk factor for neurodegeneration, however little is known about how individual neurons respond to this type of injury. In this study, we follow neuronal populations over several months after a single mild TBI (mTBI) to assess heterogeneous and long ranging consequences of injury. We find that Activating Transcription Factor 3 (ATF3), a stress responsive transcription factor, defines a population of cortical neurons after mTBI. We show that a key feature of all neurons that activate Atf3 is an upregulation of stress-related genes and, importantly, the substantial loss in expression of many genes, including commonly used markers for these cell types. By permanently labeling the Atf3-expressing neurons, we observe that they segregate to two spatially distinct cortical populations that exhibit differential vulnerability to the same insult. A population in layer V undergoes cell death acutely after injury, while another in layer II/III survives long term and retains the ability to fire action potentials. To investigate the mechanism controlling layer V neuron death, we genetically silenced candidate stress response pathways. We found that the axon injury responsive kinase MAP3K12, also known as dual leucine zipper kinase (DLK), is required for the layer V neuron death. This work provides a rationale for targeting the DLK signaling pathway as a therapeutic intervention for traumatic brain injury. Beyond this, our novel approach to track neurons after a mild, subclinical injury can inform our understanding of neuronal susceptibility to repeated impacts.

Active filtering of sequences of neural activity by recurrent circuits of sensory cortex
In daily life, organisms interact with a sensory world that dynamically changes from moment to moment. Recurrent neural networks can generate dynamics, but in sensory cortex any dynamic role for the dense recurrent excitatory-excitatory network has been unclear. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but via filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure responses to natural images and play them back, showing amplification when played back during the correct movie dynamic context and suppression in the incorrect context. The sequence selectivity depends on a network mechanism: inputs to groups of cells produce responses in different local neurons, which interact with later inputs to change responses. We confirm this mechanism by designing sequences of inputs that are amplified or suppressed by the network. Together, these data suggest a novel function, sequence filtering, for recurrent connections in cerebral cortex.

A generative model of the connectome with dynamic axon growth
Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization - axonal growth. Emulating the chemoaffinity guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective.

A half-centre oscillator encodes sleep pressure
Oscillatory neural dynamics are an inseparable part of mammalian sleep. Characteristic rhythms are associated with different sleep stages and variable levels of sleep pressure, but it remains unclear whether these oscillations are passive mirrors or active generators of sleep. Here we report that sleep-control neurons innervating the dorsal fan-shaped body of Drosophila (dFBNs) produce slow-wave activity (SWA) in the delta frequency band (0.2-1 Hz) that is causally linked to sleep. The dFBN ensemble contains one or two rhythmic cells per hemisphere whose membrane voltages oscillate in anti-phase between hyperpolarized DOWN and depolarized UP states releasing bursts of action potentials. The oscillations rely on direct interhemispheric competition of two inhibitory half-centres connected by glutamatergic synapses. Interference with glutamate release from these synapses disrupts SWA and baseline as well as rebound sleep, while the optogenetic replay of SWA (with the help of an intersectional, dFBN-restricted driver) induces sleep. Rhythmic dFBNs generate SWA throughout the sleep-wake cycle--despite a mutually antagonistic 'flip-flop' arrangement with arousing dopaminergic neurons--but adjust its power to sleep need via an interplay of sleep history-dependent increases in dFBN excitability and homeostatic depression of their efferent synapses, as we demonstrate transcriptionally, structurally, functionally, and with a simple computational model. The oscillatory format permits a durable encoding of sleep pressure over long time scales but requires downstream mechanisms that convert the amplitude-modulated periodic signal into binary sleep-wake states.

Neurons underlying aggressive actions that are shared by both males and females in Drosophila
Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless, a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.

Integrative, Segregative, and Degenerate Harmonics of the Structural Connectome
Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the "binding problem." Here, we introduce a novel framework that places integration and segregation within a continuum based on a fundamental property of the brain--its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes--integrative, segregative, and degenerate--that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve "bottom-up" sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve "top-down" networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying development, precision medicine, and treatment response in the brain.

Biomechanical Costs Influence Decisions Made During Ongoing Actions
Accurate interaction with the environment relies on the integration of external information about the spatial layout of potential actions and knowledge of their costs and benefits. Previous studies have shown that when given a choice between voluntary reaching movements, humans tend to prefer actions with lower biomechanical costs. However, these studies primarily focused on decisions made before the onset of movement ("decide-then-act" scenarios), and it is not known to what extent their conclusions generalize to many real-life situations, in which decisions occur during ongoing actions ("decide-while-acting"). For example, one recent study found that biomechanical costs did not influence decisions to switch from a continuous manual tracking movement to a point-to-point movement, suggesting that biomechanical costs may be disregarded in decide-while-acting scenarios. To better understand this surprising result, we designed an experiment in which participants were faced with the decision between continuing to track a target moving along a straight path or changing paths to track a new target that gradually moved along a direction that deviated from the initial one. We manipulated tracking direction, angular deviation rate, and side of deviation, allowing us to compare scenarios where biomechanical costs favored either continuing or changing the path. Crucially, here the choice was always between two continuous tracking actions. Our results show that in this situation, decisions clearly took biomechanical costs into account. Thus, we conclude that biomechanics are not disregarded during decide-while-acting scenarios, but rather, that cost comparisons can only be made between similar types of actions.

Fifty Years After: The N1 Effect Travels Down to the Brainstem
Fifty years ago, it was reported that selective attention affects the N1 wave in auditory event-related potentials. We revisited the original study design but integrated the state of the art knowledge on short auditory stimuli and neural signal processing. In particular, one series of tone bursts has been replaced by chirp stimuli which are optimized to evoke consistent brainstem potentials at low and medium stimulation levels. Auditory selective attention affected the chirp-evoked response in subcortical structures, even at level of the inferior colliculi. A single-trial time-frequency analysis of the full-range (0-250ms) event-related potentials showed that selective attention increases the spectrotemporal consistency across trials in the corticofugal auditory pathway, at least from the N1 wave down to the auditory brainstem response.

Object motion representation in the macaque ventral stream -- a gateway to understanding the brain's intuitive physics engine
Effective interaction with moving objects and the ability to infer and predict their motion (a core component of "intuitive physics") is essential for survival in the dynamic world. How does the primate visual system process such stimuli, enabling predictive capabilities for dynamic stimuli statistics like motion velocity and expected trajectories? In this study, we probed brain areas in the ventral visual pathway of rhesus macaques implicated in object recognition (areas V4 and inferior temporal, IT, cortex) to evaluate how they represent object motion speed and direction. We assessed the relationship between the distributed population activity in the ventral stream and two distinct object motion-based behaviors -- one reliant on information directly available in videos (speed discrimination) and the other predicated on predictive motion estimates from videos (future event predictions). Further, employing microstimulation strategies, we confirm the causal, functional role of the IT cortex in these behaviors. Our results underscore the need to re-examine the traditional functional segregation of the primate visual cortices into "what" and "where" pathways and provide empirical constraints to model their interaction for a better circuit-level understanding of visual motion and intuitive physics.

A generic noninvasive neuromotor interface for human-computer interaction
Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive, and universal. While diverse modalities have been developed, including keyboards, mice, and touchscreens, they require interaction with an intermediary device that can be limiting, especially in mobile scenarios. Gesture-based systems utilize cameras or inertial sensors to avoid an intermediary device, but they tend to perform well only for unobscured or overt movements. Brain computer interfaces (BCIs) have been imagined for decades to solve the interface problem by allowing for input to computers via thought alone. However high-bandwidth communication has only been demonstrated using invasive BCIs with decoders designed for single individuals, and so cannot scale to the general public. In contrast, neuromotor signals found at the muscle offer access to subtle gestures and force information. Here we describe the development of a noninvasive neuromotor interface that allows for computer input using surface electromyography (sEMG). We developed a highly-sensitive and robust hardware platform that is easily donned/doffed to sense myoelectric activity at the wrist and transform intentional neuromotor commands into computer input. We paired this device with an infrastructure optimized to collect training data from thousands of consenting participants, which allowed us to develop generic sEMG neural network decoding models that work across many people without the need for per-person calibration. Test users not included in the training set demonstrate closed-loop median performance of gesture decoding at 0.5 target acquisitions per second in a continuous navigation task, 0.9 gesture detections per second in a discrete gesture task, and handwriting at 17.0 adjusted words per minute. We demonstrate that input bandwidth can be further improved up to 30% by personalizing sEMG decoding models to the individual, anticipating a future in which humans and machines co-adapt to provide seamless translation of human intent. To our knowledge this is the first high-bandwidth neuromotor interface that directly leverages biosignals with performant out-of-the-box generalization across people.

A simulated annealing algorithm for randomizing weighted networks
Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt network null models to weighted network inference, we propose a simulated annealing procedure for generating strength sequence-preserving randomized networks. This model outperforms other commonly used rewiring algorithms in preserving weighted degree (strength). We show that these results generalize to directed networks as well as a wide range of real-world networks, making them generically applicable in neuroscience and in other scientific disciplines. Furthermore, we introduce morphospace representation as a tool for the assessment of null network ensemble variability and feature preservation. Finally, we show how the choice of a network null model can yield fundamentally different inferences about established organizational features of the brain such as the rich-club phenomenon and lay out best practices for the use of rewiring algorithms in brain network inference. Collectively, this work provides a simple but powerful inferential method to meet the challenges of analyzing richly detailed next-generation connectomics datasets.

Wake EEG oscillation dynamics reflect both sleep need and brain maturation across childhood and adolescence
An objective measure of brain maturation is highly insightful for monitoring both typical and atypical development. Slow wave activity, recorded in the sleep electroencephalogram (EEG), reliably indexes changes in brain plasticity with age, as well as deficits related to developmental disorders such as attention-deficit hyperactivity disorder (ADHD). Unfortunately, measuring sleep EEG is resource-intensive and burdensome for participants. We therefore aimed to determine whether wake EEG could likewise index developmental changes in brain plasticity. We analyzed high-density wake EEG collected from 163 participants 3-25 years old, before and after a night of sleep. We compared two measures of oscillatory EEG activity, amplitudes and density, as well as two measures of aperiodic activity, intercepts and slopes. Furthermore, we compared these measures in patients with ADHD (8-17 y.o., N=58) to neurotypical controls. We found that wake oscillation amplitudes behaved the same as sleep slow wave activity: amplitudes decreased with age, decreased after sleep, and this overnight decrease decreased with age. Oscillation densities were also substantially age-dependent, decreasing overnight in children and increasing overnight in adolescents and adults. While both aperiodic intercepts and slopes decreased linearly with age, intercepts decreased overnight, and slopes increased overnight. Overall, our results indicate that wake oscillation amplitudes track both development and sleep need, and overnight changes in oscillation density reflect some yet-unknown shift in neural activity around puberty. No wake measure showed significant effects of ADHD, thus indicating that wake EEG measures, while easier to record, are not as sensitive as those during sleep.

Kir6.2-KATP channels alter glycolytic flux to modulate cortical activity, arousal, and sleep-wake homeostasis
Metabolism plays an important role in the maintenance of vigilance states (e.g. wake, NREM, and REM). Brain lactate fluctuations are a biomarker of sleep. Increased interstitial fluid (ISF) lactate levels are necessary for arousal and wake-associated behaviors, while decreased ISF lactate is required for sleep. ATP-sensitive potassium (KATP) channels couple glucose-lactate metabolism with neuronal excitability. Therefore, we explored how deletion of neuronal KATP channel activity (Kir6.2-/- mice) affected the relationship between glycolytic flux, neuronal activity, and sleep/wake homeostasis. Kir6.2-/- mice shunt glucose towards glycolysis, reduce neurotransmitter synthesis, dampen cortical EEG activity, and decrease arousal. Kir6.2-/- mice spent more time awake at the onset of the light period due to altered ISF lactate dynamics. Together, we show that Kir6.2-KATP channels act as metabolic sensors to gate arousal by maintaining the metabolic stability of each vigilance state and providing the metabolic flexibility to transition between states.

Male and female variability in response to chronic stress and morphine in C57BL/6J, DBA/2J, and their BXD progeny
Drug addiction is a multifactorial syndrome in which genetic predispositions and exposure to environmental stressors constitute major risk factors for the early onset, escalation, and relapse of addictive behaviors. While it is well known that stress plays a key role in drug addiction, the genetic factors that make certain individuals particularly sensitive to stress and thereby more vulnerable to becoming addicted are unknown. In an effort to test a complex set of gene x environment interactions, specifically gene x chronic stress, here we leveraged a systems genetics resource: BXD recombinant inbred mice (BXD5, BXD8, BXD14, BXD22, BXD29, and BXD32) and their parental mouse lines, C57BL/6J and DBA/2J. Utilizing the chronic social defeat stress (CSDS) and chronic variable stress (CVS) paradigms, we first showed sexual dimorphism in the behavioral stress response between the mouse strains. Further, we observed an interaction between genetic background and vulnerability to prolonged exposure to non-social stressors. Finally, we found that DBA/2J and C57BL/6J mice pre-exposed to stress displayed differences in morphine sensitivity. Our results support the hypothesis that genetic variation in predisposition to stress responses influences morphine sensitivity and is likely to modulate the development of drug addiction.