bioRxiv Subject Collection: Neuroscience's Journal

Impaired microglial phagocytosis promotes seizure development
In the central nervous system, triggering receptor expressed on myeloid cells 2 (TREM2) is exclusively expressed by microglia and is critical for microglial proliferation, migration, and phagocytosis. TREM2 plays an important role in neurodegenerative diseases, such as Alzheimers disease and amyotrophic lateral sclerosis. However, little is known about the role TREM2 plays in epileptogenesis. To investigate this, we utilized TREM2 knockout (KO) mice within the murine intra-amygdala kainic acid seizure model. Electroencephalographic analysis, immunocytochemistry, and RNA sequencing revealed that TREM2 deficiency significantly promoted seizure-induced pathology. We found that TREM2 KO increased both acute status epilepticus and spontaneous recurrent seizures characteristic of chronic focal epilepsy. Mechanistically, phagocytic clearance of damaged neurons by microglia was impaired in TREM2 KO mice and the reduced phagocytic capacity correlated with increased spontaneous seizures. Analysis of human tissue from patients who underwent surgical resection for drug resistant temporal lobe epilepsy also showed a negative correlation between microglial phagocytic activity and focal to bilateral tonic-clonic generalized seizure history. These results indicate that microglial TREM2 and phagocytic activity may be important to epileptogenesis and the progression of focal temporal lobe epilepsy.

One Sentence SummaryPhagocytic activity of microglia may impact generalized seizure development within both mice and humans.

Musicians' brains at rest: Multilayer network analysis of MEG data
The ability to proficiently play a musical instrument requires a fine-grained synchronisation between several sensorimotor and cognitive brain regions. Previous studies have demonstrated that the brain undergoes functional changes with intensive musical training, which has also been identified in resting-state data. These studies have investigated changes in brain networks using fMRI or analysing electrophysiological frequency-specific networks in isolation (i.e., neural oscillatory networks). While the analysis of such "mono-layer" networks has proven useful, it fails to capture the complexities of multiple interacting networks. To this end, we applied a multilayer network framework for analysing publicly available data (Open MEG Archive) obtained with magnetoencephalography (MEG). We investigated resting-state differences between participants with musical training (n=31) and those without (n=31). While single-layer Network-Based Statistics analysis did not demonstrate any group differences, our multilayer analysis revealed that musicians show a modular organisation that spans visuomotor and frontotemporal areas, which are known to be involved in the execution of a musical performance. This twofold modular structure was found to be significantly different from non-musicians. The differences between the two groups are primarily seen in the theta (6.5-8Hz), alpha1 (8.5-10Hz) and beta1 (12.5-16Hz) frequency bands. No statistically significant relationships were found between self-reported measures of musical training and network properties, which could be attributed to the heterogeneity of the dataset. However, we demonstrate that the novel analysis method provides additional information that single-layer analysis methods cannot. Overall, the multilayer network method provides a unique opportunity to explore the pan-spectral nature of oscillatory networks, with studies of brain plasticity in musicians as a potential future application of the novel method.

Effects of the overall paradigm context on intensity deviant responses in healthy subjects
Three experiments have been carried out to explore Mismatch Negativity responses to intensity deviants in a roving intensity deviant paradigm in control and tinnitus groups. The first experiment used interspersed blocks of two tinnitus-like frequencies set by each participant with tinnitus, which were usually around 1/3 of an octave apart. On the other hand, two later studies used interspersed blocks tones at tinnitus-like frequencies and at 1 kHz. This was the only difference in the paradigms used, however, there were differences in the patterns displayed by the control group in the first study compared to the other two. Three groups of healthy controls were recruited to measure responses to intensity deviants when different frequencies were used for the alternating blocks. For one group, the whole experiment was set at a single frequency; for the next, blocks were played at 6 kHz and at a frequency 1/3 octave below 6 kHz (small difference); the last group was presented with blocks that had tones at 6 kHz and 1 kHz frequencies (large difference). Overall, the Mismatch Negativity responses in the small difference group were opposite to the large difference and the single frequency group. It would be useful to see whether these results generalise to other experiment designs such as attended and ignored stimulus conditions, different stimulus durations, non-isochronous, or paradigms with frequency deviants.

Long range projections of oxytocin neurons in the marmoset brain
The neurohormone oxytocin (OT) has become a major target for the development of novel therapeutic strategies to treat psychiatric disorders such as autism spectrum disorder because of its integral role in governing many facets of mammalian social behavior. Whereas extensive work in rodents has produced much of our knowledge of OT, we lack basic information about its neurobiology in primates making it difficult to interpret the limited effects that OT manipulations have had in human patients. In fact, previous studies have revealed only limited OT fibers in primate brains. Here, we investigated the OT connectome in marmoset using immunohistochemistry, and mapped OT fibers throughout the brains of adult male and female marmoset monkeys. We found extensive OT projections reaching limbic and cortical areas that are involved in the regulation of social behaviors, such as the amygdala, the medial prefrontal cortex and the basal ganglia. The pattern of OT fibers observed in marmosets is notably similar to the OT connectomes described in rodents. Our findings here contrast with previous results by demonstrating a broad distribution of OT throughout the marmoset brain. Given the prevalence of this neurohormone in the primate brain, methods developed in rodents to manipulate endogenous OT are likely to be applicable in marmosets.

The Neuron as a Direct Data-Driven Controller
In the quest to model neuronal function amidst gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends the current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, act as controllers, steering their environment towards a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. Utilizing the novel Direct Data-Driven Control (DD-DC) framework, we model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states and optimize control. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP) with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a novel and biologically-informed fundamental unit for constructing neural networks.

The asymmetric transfers of visual perceptual learning determined by the stability of geometrical invariants
We could recognize the dynamic world quickly and accurately benefiting from extracting invariance from highly variable scenes, and this process can be continuously optimized through visual perceptual learning. It is widely accepted that more stable invariants are prior to be perceived in the visual system. But how the structural stability of invariants affects the process of perceptual learning remains largely unknown. We designed three geometrical invariants with varying levels of stability for perceptual learning: projective (e.g., collinearity), affine (e.g., parallelism), and Euclidean (e.g., orientation) invariants, following the Kleins Erlangen program. We found that the learning effects of low-stability invariants could transfer to those with higher stability, but not vice versa. To uncover the mechanism of the asymmetric transfers, we used deep neural networks to simulate the learning procedure and further discovered that more stable invariants were learned faster. Additionally, the analysis of the network's weight changes across layers revealed that training on less stable invariants induced more changes in lower layers. These findings suggest that the process of perceptual learning in extracting different invariants is consistent with the Klein hierarchy of geometries and the relative stability of the invariants plays a crucial role in the mode of learning and generalization.

Distinct profiles of tinnitus and hyperacusis in intensity deviant responses and auditory evoked potentials
ERPs in response to intensity deviant stimuli are assessed in four age and hearing matched groups of various combinations of tinnitus and hyperacusis (both conditions, one of the conditions, neither condition). Distinct profiles for tinnitus and hyperacusis are shown, as well as additional more nuanced interactions. This not only moves our understanding of each condition, but also speaks directly to possible mechanistic subtypes of tinnitus (and of hyperacusis) which might be disentangled through the cheap and available technique that is single-channel EEG. The current findings may also explain some discrepant findings in past literature.

Epigenetic Modulation to perturb the SYNGAP1 Intellectual Disability (ID) that ameliorates synaptic and behavioural deficits
Sporadic heterozygous mutations in SYNGAP1 affects social and emotional behaviour that are often observed in intellectual disability (ID) and autism spectrum disorder (ASD). Although neurophysiological deficits have been extensively studied, the epigenetic landscape of SYNGAP1 mutation-mediated intellectual disability is unexplored. Here, we have surprisingly found that the p300/CBP specific acetylation marks of histones are significantly repressed in the adolescent hippocampus of Syngap1+/- mouse. To establish the causal relationship of Syngap1+/- phenotype and the altered histone acetylation signature we have treated 2-4 months old Syngap1+/- mouse with glucose-derived carbon nanosphere (CSP) conjugated potent small molecule activator (TTK21) of p300/CBP lysine acetyltransferase (CSP-TTK21). The enhancement of the p300/CBP specific acetylation marks of histones by CSP-TTK21 restored deficits in spine density, synaptic function, and social preferences of Syngap1+/- mouse that is very closely comparable to wild type littermates. The hippocampal RNA-Seq analysis of the treated mice revealed that the expression of many critical genes related to the ID/ASD reversed due to the treatment of the specific small molecule activator. This study could be the first demonstration of the reversal of autistic behaviour and neural wiring upon the modulation of altered epigenetic modification (s).

Suppression of epileptic seizures by transcranial activation of K+-selective channelrhodopsin
Optogenetics is a valuable tool for studying the mechanisms of neurological diseases and is now being developed for therapeutic applications. In rodents and macaques, improved channelrhodopsins have been applied to achieve transcranial optogenetic stimulation. While transcranial photoexcitation of neurons has been achieved, noninvasive optogenetic inhibition for treating hyperexcitability-induced neurological disorders has remained elusive. There is a critical need for effective inhibitory optogenetic tools that are highly light-sensitive and capable of suppressing neuronal activity in deep brain tissue. In this study, we developed a highly sensitive K+-conductive channelrhodopsin (hsKCR) by molecular engineering of the recently discovered Hyphochytrium catenoides kalium (potassium) channelrhodopsin 1. Transcranial activation of hsKCR significantly prolongs the time to the first seizure, increases survival, and decreases seizure activity in several mouse epileptic models. Our approach for transcranial optogenetic inhibition of neural hyperactivity may be adapted for cell type-specific neuromodulation in both basic and preclinical settings.

Intensity mismatch asymmetry in tinnitus: in which direction should participants pay attention?
The effects attention has on intensity deviant Mismatch Negativity responses is an unknown factor in basic sensory neuroscience. It would be useful to understand how attention would affect responses to intensity deviants compared to each other (upward vs downward), and compared to other sensory dimensions such as frequency. Overall, previous research indicates that attention may modulate neuronal gain in healthy participants and change the amplitudes of evoked responses, and may mainly affect the responses to regularly repeating (standard) stimuli rather than deviants. Gain may respond differently in participants with tinnitus and/or hyperacusis under the same conditions compared to controls. Overall, results of the passive task condition were consistent with previous research. Auditory attention magnified MMN in response to upward deviants, while visual attention attenuated it in both control and tinnitus groups. However, auditory attention selectively enhanced downward deviant MMN in the tinnitus group (compared to passive attention). Using the auditory attention paradigm may be advantageous in MMN studies on tinnitus/hyperacusis because the observed differences would be particularly large.

A combinatory genetic strategy for targeting neurogliaform neurons in the mouse basolateral amygdala
The mouse basolateral amygdala (BLA) contains various GABAergic interneuron subpopulations, which have distinctive roles in the neuronal microcircuit controlling numerous behavioral functions.

In mice, roughly 15% of the BLA GABAergic interneurons express neuropeptide Y (NPY), a reasonably characteristic marker for neurogliaform cells (NGFCs) in cortical-like brain structures. However, genetically labeled putative NPY-expressing interneurons in the BLA yield a mixture of interneuron subtypes besides NGFCs. Thus, selective molecular markers are lacking for genetically accessing NGFCs in the BLA.

Here, we validated the NGFC-specific labeling with a molecular marker, neuron-derived neurotrophic factor (NDNF), in the mouse BLA, as such specificity has been demonstrated in the neocortex and hippocampus. We characterized genetically defined NDNF-expressing (NDNF+) GABAergic interneurons in the mouse BLA by combining the Ndnf-IRES2-dgCre-D transgenic mouse line with viral labeling, immunohistochemical staining, and in vitro electrophysiology.

We found that BLA NDNF+ GABAergic cells mainly expressed NGFC neurochemical markers NPY and reelin (Reln) and exhibited small round soma and dense axonal arborization. Whole-cell patch clamp recordings indicated that most NDNF+ interneurons showed late spiking and moderate firing adaptation. Moreover, [~]81% of BLA NDNF+ cells generated retroaxonal action potential after current injections or optogenetic stimulations, frequently developing into persistent barrage firing. Optogenetic activation of the BLA NDNF+ cell population yielded both GABAA- and GABAB receptor-mediated currents onto BLA pyramidal neurons (PNs). We demonstrate a combinatory strategy combining the NDNF-cre mouse line with viral transfection to specifically target adult mouse BLA NGFCs and further explore their functional and behavioral roles.

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients
Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the timeseries of activity between two brain regions, so-called resting functional connectivity. We show that adding information about the structural disconnections produced by a patients lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects predicts the resting functional connectivity of the patient about as well as fitting the model directly to the patients data. Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients as well as measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that even after fixing those parameters, the model reproduces results from a population very different than the population on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures relationships between the anatomical structure and functional activity of the human brain.

A 'double-edged' role for type-5 metabotropic glutamate receptors in pain disclosed by light-sensitive drugs
Knowing the site of drug action is important to optimize effectiveness and address any side effects. We used light-sensitive drugs to identify the brain region-specific role of mGlu5 metabotropic glutamate receptors in the control of pain. Optical activation of systemic JF-NP-26, a caged, normally inactive, negative allosteric modulator (NAM) of mGlu5 receptors, in cingulate, prelimbic and infralimbic cortices and thalamus inhibited neuropathic pain hypersensitivity. Systemic treatment of alloswitch-1, an intrinsically active mGlu5 receptor NAM, caused analgesia, and the effect was reversed by light-induced drug inactivation in in the prelimbic and infralimbic cortices, and thalamus. This demonstrates that mGlu5 receptor blockade in the medial prefrontal cortex and thalamus is both sufficient and necessary for the analgesic activity of mGlu5 receptor antagonists. Surprisingly, when light was delivered in the basolateral amygdala, local activation of systemic JF-NP-26 reduced pain thresholds, whereas inactivation of alloswitch-1 enhanced analgesia. Electrophysiological analysis showed that alloswitch-1 increased excitatory synaptic responses in prelimbic pyramidal neurons evoked by stimulation of BLA input, and decreased feedforward inhibition of amygdala output neurons by BLA. Both effects were reversed by optical silencing and reinstated by optical reactivation of alloswitch-1. These findings demonstrate for the first time that the action of mGlu5 receptors in the pain neuraxis is not homogenous, and suggest that blockade of mGlu5 receptors in the BLA may limit the overall analgesic activity of mGlu5 receptor antagonists. This could explain the suboptimal effect of mGlu5 NAMs on pain in human studies and validate photopharmacology as an important tool to determine ideal target sites for systemic drugs.

Spatiotemporal models for multisensory integration
Multisensory integration is a process of redundancy exploitation, in which our brains combine information across the senses to obtain more reliable perceptual estimates. While the high-level computational principles of multisensory integration are well understood, little is knowns as to how the low-level properties of the signals ultimately determine the integrated percept. This study demonstrates that a bottom-up approach, based on luminance- and sound-level analyses, is sufficient to jointly explain the spatiotemporal determinants of audiovisual integration and crossmodal attention. When implemented using an architecture analogous to the motion detectors found in the insect brain, such low-level analyses can broadly reproduce human behaviour-as tested in a large-scale simulation of 42 classic experiments on the spatial, temporal and attentional aspects of multisensory integration.

Growth in early infancy drives optimal brain functional connectivity which predicts cognitive flexibility in later childhood
Functional brain network organization, measured by functional connectivity (FC), reflects key neurodevelopmental processes for healthy development. Early exposure to adversity, e.g. undernutrition, affects neurodevelopment, observable via disrupted FC, and leads to poorer outcomes from preschool age onward. We assessed longitudinally the impact of early growth trajectories on developmental FC in a rural Gambian population from age 5 to 24 months. To investigate how these early trajectories relate to later childhood outcomes, we assessed cognitive flexibility at 3-5 years. We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC that in turn predicted cognitive flexibility at pre-school age. In contrast to previously studied developmental populations, this Gambian sample exhibited long-range interhemispheric FC that decreased with age. Our results highlight the measurable effects that poor growth in early infancy has on brain development and the subsequent impact on pre-school age cognitive development, underscoring the need for early life interventions throughout global settings of adversity.

An opponent striatal circuit for distributional reinforcement learning
Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards -- an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons -- D1 and D2 MSNs -- contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.

Threshold-Free Network-Oriented Statistics in Neuroscience
Network neuroscience has emerged as a powerful tool for studying brain structure and function. Currently, the network-based statistic (NBS) method is widely used to control the type I error rate for mass univariate tests on brain network edges. However, the NBS requires the selection of a cluster-forming threshold, which lacks objective guidelines. There is also an urgent need for a powerful and flexible statistical framework for analyzing brain networks in diverse contexts. Here, we introduce a permutation-based framework called "threshold-free network-oriented statistics" (TFNOS). It integrates two "threshold-free" pathways: traversing all cluster-forming thresholds (TT) and using predefined clusters (PC). The TT procedure, building upon the threshold-free network-based statistics, requires the setting of additional parameters. The PC procedures have six variants according to the way of pooling data, the form of the null distribution, and the controlled error rate. Using numerical simulations, we comprehensively evaluated the performance of the TT procedure under 600 parameter combinations, benchmarked TFNOS procedures and baselines across different topologies of effects, sample sizes, and effect sizes, and provided examples of application on real data. We offer recommended parameter values that can make the TT procedure stably maintain leading power while empirically controlling the false discovery rate (FDR) at an appropriate level. Analyses reveal that the recommended parameters available in the field are overly liberal. Furthermore, for the PC procedures, FDR-controlling variants showed improved power compared to FWER-controlling variants, some of which are simple but do not compromise power. The nonparametric PC procedures allow for the selection of any test statistics considered appropriate. Overall, we provide empirical and principled criteria for selecting statistical procedures and the TFNOS is a generalized framework for inference in various contexts on edges or nodes of undirected or directed brain networks.

Dynamical constraints on neural population activity
The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brains computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.

Kernels of Motor Memory Formation: Temporal Generalization in Bimanual Adaptation
In daily life, we coordinate simultaneous and sequential bimanual movements to manipulate objects. Despite the complexity, people adapt rapidly, suggesting neural mechanisms optimized for efficient adaptation. Here we extract the temporal kernel that underlies motor memory formation, by testing the contextual effects of past, simultaneous, and future contralateral arm movements and measuring their temporal generalization in three novel bimanual interference tasks. The actions of one arm can serve as a contextual cue for the other arm for all three timing contexts, facilitating dual adaptation. More importantly, the timing of the learned contextual action plays a pivotal role in the temporal generalization. While motor memories trained with past contextual movements generalize broadly, motor memories trained with future movements exhibit limited generalization, and motor memories trained with simultaneous movements do not generalize to past or future timings. This highlights temporal tuning in sensorimotor plasticity: different training conditions yield substantially different temporal generalization.

Disruption of consciousness depends on insight in OCD and on positive symptoms in schizophrenia
Disruption of conscious access contributes to the advent of psychotic symptoms in schizophrenia but could also explain lack of insight in other psychiatric disorders.

In this study, we explored how insight and psychotic symptoms related to disruption of consciousness. We explored consciousness in patients with schizophrenia, patients with obsessive-compulsive disorder (OCD) with good vs. poor insight and matched controls. Participants underwent clinical assessments and performed a visual masking task allowing us to measure individual consciousness threshold. We used a principal component analysis to reduce symptom dimensionality and explored how consciousness measures related to symptomatology.

We found that clinical dimensions could be well summarized by a restricted set of principal components which also correlated with the extent of consciousness disruption. More specifically, positive symptoms were associated with impaired conscious access in patients with schizophrenia whereas the level of insight delineated two subtypes of OCD patients, those with poor insight who had consciousness impairments similar to patients with schizophrenia, and those with good insight who resemble healthy controls.

Our study provides new insights about consciousness disruption in psychiatric disorders, showing that it relates to positive symptoms in schizophrenia and with insight in OCD. In OCD, it revealed a distinct subgroup sharing neuropathological features with schizophrenia. Our findings refine the mapping between symptoms and cognition, paving the way for a better treatment selection.