bioRxiv Subject Collection: Neuroscience's Journal

The medial entorhinal cortex encodes multisensory spatial information
Animals employ spatial information in multisensory modalities to navigate their natural environments. However, it is unclear whether the brain encodes such information in separate cognitive maps or integrate all into a single, universal map. We addressed this question in the microcircuit of the medial entorhinal cortex (MEC), a cognitive map of space. Using cellular-resolution calcium imaging, we examined the MEC of mice navigating virtual reality tracks, where visual and auditory cues provided comparable spatial information. We uncovered two cell types:"unimodality cells" and "multimodality cells". The unimodality cells specifically represent either auditory or visual spatial information. They are anatomically intermingled and maintain sensory preferences across multiple tracks and behavioral states. The multimodality cells respond to both sensory modalities with their responses shaped differentially by auditory and visual information. Thus, the MEC enables accurate spatial encoding during multisensory navigation by computing spatial information in different sensory modalities and generating distinct maps.

The role of locus coeruleus neuroimmune signaling in the response to social stress in female rats
Neuropsychiatric disorders that result from stress exposure, including post-traumatic stress disorder and substance abuse, are highly associated with central inflammation. Our previous work established that females selectively exhibit increased proinflammatory cytokine release within the noradrenergic locus coeruleus (LC) in response to witnessing social stress, which was associated with a hypervigilant phenotype. Thus, neuroimmune activation in the LC may be responsible for the heightened risk of developing mental health disorders following stress in females. Further, ablation of microglia using pharmacological techniques reduces the hypervigilant behavioral response exhibited by females during social stress. Therefore, these studies were designed to further investigate the impact of stress-induced neuroimmune signaling on the long-term behavioral and neuronal consequences of social stress exposure in females using DREADDs. We first characterized the use of an AAV-CD68-Gi-DREADD virus targeted to microglia within the LC. While the use of AAVs in preclinical research has been limited by observations regarding poor transfection efficiency in mice, recent data suggest that species specific differences in microglial genetics may render rats more receptive to chemogenetic targeting of microglia using a CD68 promoter. Therefore, clozapine-n-oxide (CNO) was used to activate the microglial expressed hM4Di to inhibit microglial activity during acute exposure to vicarious social defeat (witness stress, WS) in female rats. Neuroimmune activity within the LC, quantified by microglial morphology and cytokine release, was augmented by WS and prevented by chemogenetic microglial inhibition. Following confirmation of DREADD selectivity and efficacy, we utilized this technique to inhibit microglial activity during repeated WS. Subsequently, rats were tested in a marble burying paradigm and exposed to the WS cues and context to measure hypervigilant behaviors. Chemogenetic-mediated inhibition of microglial activity prior to each WS exposure prevented both acute and long-term hypervigilant responses induced by WS across multiple behavioral paradigms. Further, a history of microglial inactivation during WS prevented the heightened LC activity typically observed in response to stress cues. These studies are among the first to use a chemogenetic approach to inhibit microglia within the female brain in vivo and establish LC inflammation as a key mechanism underlying the behavioral and neuronal responses to social stress in females.

Characterizing CSNK2A1 Mutant-Induced Morphological Phenotypes in Zebrafish (Danio rerio): Insights into Okur-Chung Neurodevelopmental Syndrome (OCNDS)
Okur-Chung Neurodevelopmental Syndrome (OCNDS) is a rare autosomal dominant disorder caused by mutations in the CSNK2A1 gene. The CSNK2A1 gene encodes for an subunit of the protein kinase CK2, which is involved in various biological processes. Aberrant functioning of CK2 is associated with several conditions and diseases. In 2016, Okur and colleagues reported the discovery of germline de novo missense and canonical splice site mutations in the CSNK2A1 gene from five female patients with neurodevelopmental syndrome. The clinical features commonly observed in individuals with OCNDS include developmental delays, intellectual disability, hypotonia, feeding difficulties, dysmorphic facial features, and disrupted circadian rhythm leading to sleep disturbances. Despite advancements in understanding the genetic underpinnings of OCNDS, significant gaps remain in our knowledge of the molecular mechanisms driving this syndrome. The complex phenotypic spectrum of OCNDS underscores the need to develop robust model systems to bridge the gap between genetic discoveries, genotype-phenotype correlations, molecular mechanisms of disease pathogenesis, and therapeutic advancements. We utilized an overexpression strategy to investigate the functional consequences of CSNK2A1 variants that cause diseases in zebrafish embryos. We observed unique and distinct morphological phenotypes resulting from the overexpression of various CSNK2A1 mutants. Each mutant variant of the CSNK2A1 gene showed a unique morphological phenotype, suggesting a direct connection between the genetic alteration and its phenotypic expression. Among the variants studied, the p.R191X mutation was particularly noteworthy for its severe phenotypic impact. This finding highlights the potential of this variant to serve as a critical marker in understanding the pathophysiology of OCNDS. The use of zebrafish models in this study is advantageous as they provide a highly relevant and adaptable system for investigating the functional consequences of CSNK2A1 mutations and exploring new therapeutic approaches. This approach enhances our understanding of OCNDS at a molecular level and opens up new avenues for developing potential treatments.

PTEN inhibition promotes robust growth of bulbospinal respiratory axons and partial recovery of diaphragm function in a chronic model of cervical contusion spinal cord injury
High spinal cord injury (SCI) leads to persistent and debilitating compromise in respiratory function. Cervical SCI not only causes the death of phrenic motor neurons (PhMNs) that innervate the diaphragm, but also damages descending respiratory pathways originating in the rostral ventral respiratory group (rVRG) located in the brainstem, resulting in denervation and consequent silencing of spared PhMNs located caudal to injury. It is imperative to determine whether interventions targeting rVRG axon growth and respiratory neural circuit reconnection are efficacious in chronic cervical contusion SCI, given that the vast majority of individuals are chronically-injured and most cases of SCI involve contusion-type damage to the cervical region. We therefore employed a clinically-relevant rat model of chronic cervical hemicontusion to test therapeutic manipulations aimed at reconstructing damaged rVRG-PhMN-diaphragm circuitry to achieve recovery of respiratory function. At a chronic time point post-injury, we systemically administered: an antagonist peptide directed against phosphatase and tensin homolog (PTEN), a central inhibitor of neuron-intrinsic axon growth potential; an antagonist peptide directed against receptor-type protein tyrosine phosphatase sigma (PTP{sigma}), another important negative regulator of axon growth capacity; or a combination of these two peptides. PTEN antagonist peptide (PAP4) promoted partial recovery of diaphragm motor activity out to nine months post-injury, while PTP{sigma} peptide did not impact diaphragm function after cervical SCI. Furthermore, PAP4 promoted robust growth of descending bulbospinal rVRG axons caudal to the injury within the denervated portion of the PhMN pool, while PTP{sigma} peptide did not affect rVRG axon growth at this location that is critical to control of diaphragmatic respiratory function. In conclusion, we find that, when PTEN inhibition is targeted at a chronic time point following cervical contusion that is most relevant to the SCI clinical population, our non-invasive PAP4 strategy can successfully promote significant regrowth of damaged respiratory neural circuitry and also partial recovery of diaphragm motor function.

Sensory representations in primary visual cortex are not sufficient for subjective imagery
The contemporary definition of mental imagery is characterized by two aspects: a sensory representation resembling, but not resulting from, perception, and an associated subjective experience. Neuroimaging demonstrated imagery-related sensory representations in primary visual cortex (V1) that show striking parallels to perception. However, it remains unclear whether these representations always reflect subjective experience, or they can be dissociated from it. We addressed this question by comparing sensory representations and subjective imagery among visualizers and aphantasics, the latter with an impaired ability to experience imagery. Importantly, to test for the presence of sensory representations independently of the ability to generate imagery on demand we examined both spontaneous and voluntary imagery forms. Using multivariate fMRI, we tested for decodable sensory representations in V1 and subjective visual imagery reports that occurred either spontaneously (during passive listening of evocative sounds) or in response to the instruction to voluntarily generate imagery of the sound content (always while blindfolded inside the scanner). Among aphantasics, V1 decoding of sound content was at chance during voluntary imagery, and lower than in visualizers, but it succeeded during passive listening, despite them reporting no imagery. In contrast, in visualizers, decoding accuracy in V1 was greater in voluntary than spontaneous imagery (while being positively associated with the reported vividness of both imagery types). Finally, for both conditions, decoding in precuneus was successful in visualizers but at chance for aphantasics. Together, our findings show that V1 representations can be dissociated from subjective imagery, while implicating a key role of precuneus in the latter.

Gating of Memory to Behavior by the Locus Coeruleus
An essential function of memory is to guide behavior for survival and adaptation. While considerable knowledge has been accumulated on memory formation, much less is understood about how retrieved memories direct behavior/action. In the auditory Pavolovian threat conditioning paradigm, retrieval of conditioned threat memory activates dorsomedial prefrontal (dmPFC) neurons exhibiting transient responses (T-neurons), which activate both dmPFC neurons exhibiting sustained responses (S-neurons) and locus coeruleus (LC) neurons. Auditory inputs to S-neurons enable the conversion from transient to sustained responses so that the freezing durations match those of the auditory cues. Activation of LC neurons is required for the conversion by enhancing S-neuron responses, which, interestingly, opens a short time window during which non-conditioned cues also lead to freezing. The transition from memory to behavior thus hinges on the integration of retrieved memory, sensory inputs, and emotional/body state cues to generate a selective, adequate, and finely tuned behavior.

Hebbian activity only temporarily stabilizes synaptic transmission at CA3 - CA1 synapses in the developing hippocampus
ABSTRACT Prolonged low frequency (0.05-1 Hz) stimulation of previously non-stimulated (naive) CA3-CA1 synapses in the developing hippocampus results in a profound synaptic depression explained by a postsynaptic AMPA silencing. It has been proposed that Hebbian activity can stabilize the synapses by preventing such depression. Using field recordings, we have examined to which extent strong repeated high frequency tetanization simulating Hebbian activity results in such prevention. The tetanization resulted within minutes in a field EPSP potentiation to 150-170% of the naive field EPSP level which remained unaltered if stimulation was suspended. If test pulse stimulation (0.2 Hz) was allowed to continue after the tetanization the field EPSP continuously decreased and was after 2700 stimuli depressed by 75% from the potentiated level. This depression did not differ in relative terms from that induced in naive synapses (by 82% from the naive level). The long-lasting component of this depression revealed by a subsequent 30 min stimulus interruption (by 59% from the potentiated level) did not differ from that of naive synapses (by 66% from the naive level). This equal relative degree of depression of tetanized and naive synapses was also observed following 2700 stimuli at 1 Hz. On the other hand, when examined at earlier time points during the test pulse stimulation (e.g. after 400-900 stimuli) tetanized synapses were less depressed than naive synapses, and the long-lasting depression after 900 stimuli at 1 Hz was only half that observed in naive synapses. This effect of tetanization was observed independently of whether the 1 Hz stimulation was commenced 15 min or 2 hours after the tetanization. In conclusion, while a strong preceding tetanization results in a partial stabilization of transmission at CA3-CA1 synapses in the developing hippocampus, this effect appears only temporary. This temporary effect is not linked to time after tetanization but to the number of low frequency stimuli given.

Feedforward circuits enable modality-specific control of cortical inhibition during behavioral state transitions.
Active behavior strongly influences sensory information processing in primary sensory cortices. Local interneurons, especially those involved in disinhibitory circuits like somatostatin (SST) and vasoactive intestinal peptide (VIP)-positive interneurons, play a crucial role in regulating cortical function during behavior. While numerous studies have focused on the recruitment of pyramidal neurons, the intricate network mechanisms governing the behavior-state-dependent modulation of inhibitory neurons remain unclear. Using intravital two-photon calcium imaging we have studied the neuronal pathways that regulate SST- and VIP-IN activity in sensory cortices during spontaneous behaviours. We now reveal that active behavioral states associated with increased locomotor activity strongly recruit L2/3 SST-INs in primary somatosensory (S1) but not visual (V1) cortex indicating modality-specific mechanisms control activity of cortical interneurons during active states. Such a difference between V1 and S1 disappears in the presence of visual sensory drive (ambient light) suggesting a critical involvement of feedforward sensory pathways in the positive modulation of SST-INs during spontaneous behaviors. In agreement, we now unveil that inactivating the somatosensory thalamus significantly reduces the recruitment of SST-INs in S1 by locomotion. In contrast, interfering with disinhibitory circuits via chemogenetic inactivation of VIP-INs further increased the sensitivity of SST-INs to variations in behavioural states. Our work thus reveals a previous unknown role for thalamic inputs in driving SST-INs, suggesting that by integrating feedforward activity with neuromodulation, SST-INs play a central role in adapting sensory processing to behavioral states in a sensory modality-specific manner.

A precision medicine approach for HCN1 Developmental and Epileptic Encephalopathy
Pathogenic variants in HCN1 causing cation leak result in a severe developmental and epileptic encephalopathy (DEE). Current treatment options for patients with HCN1-DEE are limited and are insufficient to fully address both the seizures and clinical comorbidities of this disorder. Org 34167 is a brain penetrant broad-spectrum HCN channel inhibitor that has completed phase I clinical trials. We used a range of assays at molecular, cellular, network and behavioural levels to explore the potential of Org 34167 as a precision medicine for HCN1-DEE. Org 34167 restored the voltage sensitivity of the DEE HCN1M305L mutated channel, significantly reducing cation leak. It also restored Ih-mediated sag, hyperpolarised the resting membrane potential and reduced firing of layer V neurons from the Hcn1M294L mouse model of HCN1-DEE, which was engineered based on the HCN1M305L pathogenic variant. Additionally, Org 34167 reduced neuronal epileptiform activity and restored retinal light sensitivity in these mice, suggesting it may improve both seizures and other clinical comorbidities. However, Org 34167-mediated tremors were noted at therapeutic doses. Org 34167 was also effective at reducing cation leak caused by five additional HCN1 pathogenic variants, suggesting broader utility. Overall, these data demonstrate that a small molecule HCN inhibitor can restore channel and consequent physiological functions, positioning it as a promising precision therapeutic approach for HCN1-DEE.

Recurrent connections enable point attractor dynamics and dimensionality reduction in a connectome-constrained model of the insect learning center
The learning center in the insect, the mushroom body (MB) with its predominant population of Kenyon Cells (KCs), is a widely studied model system to investigate neural processing principles, both experimentally and theoretically. While many computational models of the MB have been studied, the computational role of recurrent connectivity between KCs remains inadequately understood. Dynamical point attractors are a candidate theoretical framework where recurrent connections in a neural network can enable a discrete set of stable activation patterns. However, given that detailed, full recurrent connectivity patterns in biological neuron populations are mostly unknown, how theoretical models are substantiated by specific networks found in biology has not been clear. Leveraging the recent release of the full synapse-level connectivity of the MB in the fly, we performed a series of analyses and network model simulations to investigate the computational role of the recurrent KC connections, especially their significance in attractor dynamics. Structurally, the recurrent excitation (RE) connections are highly symmetric and balanced with feedforward input. In simulations, RE facilitates dimensionality reduction and allows a small set of self-sustaining point attractor states to emerge. To further quantify the possible range of network properties mediated by RE, we systematically explored the dynamical regimes enabled by changing recurrent connectivity strength. Finally, we establish connections between our findings and potential functional or behavioral implications. Overall, our work provides quantitative insights into the possible functional role of the recurrent excitatory connections in the MB by quantifying the point attractor network dynamics within a full synapse-level connectome-constrained highly recurrent network model. These findings advance our understanding of how biological neural networks may utilize point attractor dynamics.

Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings
Current electrophysiology experiments often involve massively parallel recordings of neuronal activity using multielectrode arrays. While researchers have been aware of artifacts arising from electric cross-talk between channels in setups for such recordings, systematic and quantitative assessment of the effects of those artifacts on the data quality has never been reported. Here we present, based on examination of electrophysiology recordings from multiple laboratories, that multielectrode recordings of spiking activity commonly contain extremely precise (at the data sampling resolution) spike coincidences far above the chance level. We derive, through modeling of the electric cross-talk, a systematic relation between the amount of such hyper-synchronous events (HSEs) in channel pairs and the correlation between the raw signals of those channels in the multi-unit activity frequency range (250-7500 Hz). Based on that relation, we propose a method to identify and exclude specific channels to remove artifactual HSEs from the data. We further demonstrate that the artifactual HSEs can severely affect various types of analyses on spike train data. Taken together, our results warn researchers to pay considerable attention to the presence of HSEs in spike train data and to make efforts to remove the artifactual ones from the data to avoid false results.

DNA methylation patterns in the frontal lobe white matter of multiple system atrophy, Parkinson's disease, and progressive supranuclear palsy: A cross-comparative investigation
Multiple system atrophy (MSA) is a rare neurodegenerative disease characterized by neuronal loss and gliosis, with oligodendroglial cytoplasmic inclusions (GCI's) containing alpha-synuclein being the primary pathological hallmark. Clinical presentations of MSA overlap with other parkinsonian disorders such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), and progressive supranuclear palsy (PSP), posing challenges in early diagnosis. Numerous studies have reported perturbations in DNA methylation in neurodegenerative diseases, with candidate loci being identified in various parkinsonian disorders including MSA, PD, and PSP. Although MSA and PSP present with substantial white matter pathology, alterations in white matter have also been reported in PD. However, studies comparing the DNA methylation architectures of white matter in these diseases are lacking. We therefore aimed to investigate three parkinsonian diseases, MSA, PD, and PSP, to identify shared and disease-specific DNA methylation alterations in white matter. Genome-wide DNA methylation profiling of frontal lobe white matter of individuals with MSA (n=17), PD (n=17), and PSP (n=16) and controls (n=15), using the Illumina EPIC array, revealed substantial commonalities in DNA methylation perturbations in MSA, PD, and PSP. We further used weighted gene correlation network analysis to identify disease-associated co-methylation signatures and identified dysregulation in processes relating to Wnt signalling, signal transduction, endoplasmic reticulum stress, mitochondrial processes, RNA interference, and endosomal transport. Our results highlight several shared DNA methylation perturbations and pathways indicative of converging molecular mechanisms contributing towards neurodegeneration in the white matter of all three parkinsonian diseases.

Chemogenetic attenuation of PFC pyramidal neurons restores deficits in recognition memory following adolescent NMDA receptor blockade
During adolescence, the prefrontal cortex (PFC) undergoes dramatic developmental changes, including fine-tuning the balance between excitatory glutamate and inhibitory GABA transmission (i.e., the E/I balance). This process is critical for intact cognitive function and social behavior in adulthood, and its disruption is associated with several psychiatric disorders including schizophrenia (SZ). While acute NMDA receptor (NMDAr) blockade leads to excess glutamate transmission in the PFC, the long-term consequences of MK-801 administration during early adolescence on the E/I balance in adulthood have not been extensively studied. In the current study, we show that chronic MK-801 administration during early adolescence leads to abnormalities in recognition memory and social behavior as well as reduced frequency of miniature inhibitory post-synaptic currents (mIPSCs) in mPFC of adult male rats, with no change in excitatory currents or basal activity. We further show that chemogenetic attenuation of prelimbic mPFC pyramidal neurons reversed deficits in recognition memory, but not social behavior. These findings emphasize the critical role played by NMDAr during adolescence on the E/I balance as well as cognition and social function in adulthood. Moreover, these findings implicate the therapeutic outcomes of reduced mPFC pyramidal neuron activity in recognition memory deficits in early-adolescence MK-801-treated rats. Since recognition memory deficits are key components of the cognitive deficits in SZ, these findings suggest that the future development of treatments aimed at alleviating the cognitive deficits in SZ should focus on regulating the prefrontal E/I balance.

Emotional prosody modulates visual mental imagery
Perceptual stimuli's emotional properties are vital for human evolution and adaptation. While visual imagery is predominantly regarded as a weak form of perception, the influence of cross-modal emotional features on imagery is still unknown. The present study aims to investigate how emotional prosody modulates imagery quality (i.e., accuracy and clarity) and neural mechanisms using a combination of behavioral tasks and functional magnetic resonance imaging (fMRI). At the behavioral level, our results showed that frustrated conditions induced significant prosody effects on visual mental imagery quality measures, and the effects were particularly pronounced in individuals with lower imagery use tendency. At the neural level, compared with the neutral condition, the emotional prosody conditions (both happy and frustrated) showed stronger activation in various regions including the middle occipital gyrus, supporting the critical role of primary visual system in imagery. Moreover, compared to the frustrated prosody condition, the happy prosody showed stronger activation in the precuneus and anterior cingulate cortex, which are core components of the default mode network. A machine learning prediction analysis with a random forest model identified a significant brain-behavioral correlation between prosody-linked neural activity and individual imagery use tendency. A subsequent Shapley Additive exPlanations (SHAP) analysis further highlighted the primary visual and default mode regions as top contributors to this prediction. Taken together, these results provide new insights for the understanding of how emotional prosody modulates visual mental imagery, considering individual differences, and provide compelling evidence for incorporating emotion as important shaping factor in more general model for imagery.

Whole-cortex simulation reveals spatiotemporal patterns emerging from the interplay of network connectivity and intracellular dynamics
Recent advances in Graphics Processing Unit (GPU) computing have allowed for computational models of whole-brain activity at unprecedented scales. In this work, we use desktop computers to build and simulate a whole-cortex mouse brain model using Hodgkin-Huxley type models for all the most active neurons in the mouse cortex. We compare the model dynamics over different types of connectivity, ranging from uniform random to realistic connectivity derived from experimental data on cell positions and the Allen Brain Atlas. By changing the external drive and coupling strength of neurons in the network, we can produce a wide range of oscillations in the gamma through delta bands. While the global mean-field behaviors of different connectivities share some similarities, an experimentally determined hierarchical connectivity allows for complex, heterogeneous behaviors typically seen in EEG recordings that are not observed in networks with nearest neighbors or uniform coupling. Moreover, our simulations reveal a wide range of spatiotemporal patterns, such as rotational or planar traveling waves, that are observed in experiments. Different traveling waves are observed with different connectivity and coupling strengths on the same connectivity. Our simulations show that many cortical behaviors emerge at scale with the full complexity of the network structure and ionic dynamics. We also provide a computational framework to explore these cortex-wide behaviors further.

Human Brain Mapping of Homotopic Functional Affinity
Spatially corresponding areas in the left and right hemispheres of the human brain, also known as homotopic brain regions, often exhibit functional similarities, i.e., functional homotopy. To understand the principles and mechanisms of functional homotopy in human psychological behavior, this paper proposes a method for studying functional homotopy in the human brain: homotopic functional affinity. This method quantifies the functional affinity of homotopic brain regions by calculating the cosine distance of whole-brain functional connectivity patterns of homotopic regions. Using the whole-brain functional MRI database from the Human Connectome Project in the United States and China, we first mapped the homotopic functional affinity atlas with "700 milliseconds - 2 millimeters" spatiotemporal precision, assessing its test-retest reliability for individual differences. Subsequently, we located three specific areas in the human temporo-parietal junction through systematic analysis of this atlas, discovering their hemispheric lateralization patterns and revealing their functional associations with attention, language, and social cognition. Lastly, through multimodal brain atlas correlation calculations, we further explored the correlation of human brain homotopic functional affinity with genetics, evolution, structural, and functional organizational distribution. In summary, our proposed method of homotopic functional affinity provides a reliable and valid functional measurement atlas for population neuroscience research.

Diverse GABA signaling in the inner retina enables spatiotemporal coding
GABA ({gamma}-aminobutyric acid) is the primary inhibitory neurotransmitter in the mammalian central nervous system (CNS). There is a wide range of GABAergic neuronal types, each of which plays an important role in neural processing and the etiology of neurological disorders. However, there is no comprehensive understanding of this functional diversity, due to the lack of genetic tools to target and study the multitude of cell types. Here we perform two-photon imaging of GABA release in the inner plexiform layer (IPL) of the mouse retina using the newly developed GABA sensor iGABASnFR2. By applying varied light stimuli to isolated retinae, we reveal over 40 different GABA-releasing neurons, including some not previously described. Individual types show unique distributions of synaptic release sites in the sublayers comprising the IPL, allowing layer-specific visual encoding. Synaptic input and output sites are aligned along specific retinal orientations for multiple neuronal types. Furthermore, computational modeling reveals that the combination of cell type-specific spatial structure and unique release kinetics enables inhibitory neurons to suppress and sculpt excitatory signals in response to a wide range of behaviorally relevant motion structures. Our high-throughput approach provides the first comprehensive physiological characterization of inhibitory signaling in the vertebrate CNS. Future applications of this method will enable interrogation of the function and dysfunction of diverse inhibitory circuits in health and disease.

Predicting brain age across the adult lifespan with spontaneous oscillations and functional coupling in resting brain networks captured with magnetoencephalography
The functional repertoire of the human brain changes dramatically throughout the developmental trajectories of early life and even all the way throughout the adult lifespan into older age. Capturing this arc is important to understand healthy brain ageing, and conversely, how injury and diseased states can lead to accelerated brain ageing. Regression modelling using lifespan imaging data can reliably predict an individual's brain age based on expected arcs of ageing. One feature of brain function that is important in this respect, and understudied to date, is neural oscillations - the rhythmic fluctuations of brain activity that index neural cell assemblies and their functioning, as well as coordinating information flow around networks. Here, we analysed resting-state magnetoencephalography (MEG) recordings from 367 healthy participants aged 18 to 83, using two distinct statistical approaches to link neural oscillations & functional coupling with that of healthy ageing. Spectral power and leakage-corrected amplitude envelope correlations were calculated for each canonical frequency band from delta through gamma ranges. Spatially and spectrally consistent associations between healthy ageing and neurophysiological features were found across the applied methods, showing differential effects on neural oscillations, with decreasing amplitude of low frequencies throughout the adult lifespan, and increasing high frequency amplitude. Functional connectivity within and between resting-state brain networks mediated by alpha coupling generally decreased throughout adulthood and increased in the beta band. Predictive modelling of brain age via regression showed an age dependent prediction bias resulting in overestimating the age of younger people (<40 years old) and underestimating the age of older individuals. These findings evidence strong age-related neurophysiological changes in oscillatory activity and functional networks of the brain as measured by resting-state MEG and that cortical oscillations are moderately reliable markers for predictive modelling. For researchers in the field of predictive brain age modelling with neurophysiological data, we recommend attention is paid to predictive biases for younger and older age ranges and consider using specific models for different age brackets. Nevertheless, these results suggest brain age prediction from MEG data can be used to model arcs of ageing throughout the adult lifespan and predict accelerated ageing in pathological brain states.

Glioma-Induced Alterations in Excitatory Neurons are Reversed by mTOR Inhibition
Gliomas are highly aggressive brain tumors characterized by poor prognosis and composed of diffusely infiltrating tumor cells that intermingle with non-neoplastic cells in the tumor microenvironment, including neurons. Neurons are increasingly appreciated as important reactive components of the glioma microenvironment, due to their role in causing hallmark glioma symptoms, such as cognitive deficits and seizures, as well as their potential ability to drive glioma progression. Separately, mTOR signaling has been shown to have pleiotropic effects in the brain tumor microenvironment, including regulation of neuronal hyperexcitability. However, the local cellular-level effects of mTOR inhibition on glioma-induced neuronal alterations are not well understood. Here we employed neuron-specific profiling of ribosome-bound mRNA via "RiboTag", morphometric analysis of dendritic spines, and in vivo calcium imaging, along with pharmacological mTOR inhibition to investigate the impact of glioma burden and mTOR inhibition on these neuronal alterations. The RiboTag analysis of tumor-associated excitatory neurons showed a downregulation of transcripts encoding excitatory and inhibitory postsynaptic proteins and dendritic spine development, and an upregulation of transcripts encoding cytoskeletal proteins involved in dendritic spine turnover. Light and electron microscopy of tumor-associated excitatory neurons demonstrated marked decreases in dendritic spine density. In vivo two-photon calcium imaging in tumor-associated excitatory neurons revealed progressive alterations in neuronal activity, both at the population and single-neuron level, throughout tumor growth. This in vivo calcium imaging also revealed altered stimulus-evoked somatic calcium events, with changes in event rate, size, and temporal alignment to stimulus, which was most pronounced in neurons with high-tumor burden. A single acute dose of AZD8055, a combined mTORC1/2 inhibitor, reversed the glioma-induced alterations on the excitatory neurons, including the alterations in ribosome-bound transcripts, dendritic spine density, and stimulus evoked responses seen by calcium imaging. These results point to mTOR-driven pathological plasticity in neurons at the infiltrative margin of glioma - manifested by alterations in ribosome-bound mRNA, dendritic spine density, and stimulus-evoked neuronal activity. Collectively, our work identifies the pathological changes that tumor-associated excitatory neurons experience as both hyperlocal and reversible under the influence of mTOR inhibition, providing a foundation for developing therapies targeting neuronal signaling in glioma.

The inevitability and superfluousness of cell types in spatial cognition
Discoveries of functional cell types, exemplified by the cataloging of spatial cells in the hippocampal formation, are heralded as scientific breakthroughs. We question whether the identification of cell types based on human intuitions has scientific merit and suggest that "spatial cells" may arise in non-spatial computations of sufficient complexity. We show that deep neural networks (DNNs) for object recognition, which lack spatial grounding, contain numerous units resembling place, border, and head-direction cells. Strikingly, even untrained DNNs with randomized weights contained such units and support decoding of spatial information. Moreover, when these "spatial" units are excluded, spatial information can be decoded from the remaining DNN units, which highlights the superfluousness of cell types to spatial cognition. Now that large-scale simulations are feasible, the complexity of the brain should be respected and intuitive notions of cell type, which can be misleading and arise in any complex network, should be relegated to history.

Neural Ensembles in the Lateral Prefrontal Cortex Temporally Multiplex Task Features During Virtual Navigation
Neuronal populations can expand their information encoding capacity using mixed selective neurons. This is particularly prominent in association areas such as the Lateral Prefrontal Cortex (LPFC) that integrate information from multiple sensory systems. During naturalistic conditions where subjects have agency, it is unclear how LPFC neuronal ensembles process space and time varying information. Here we show that during a virtual navigation task that requires associative memory and decision making, individual neurons and neuronal ensembles in the LPFC time-multiplex their selectivity for different task features to fluidly encode the temporal contingencies of the task and the animal's choice. Neurons in ventral regions showed more selectivity for non-spatial features, while dorsal regions showed selectivity for space and eye movements. These results demonstrate that during naturalistic tasks with fluid scenery and spatiotemporal dynamics, LPFC neurons and neuronal ensembles time-multiplex the components of their selectivity, expanding the spatiotemporal capacity of their neural codes.

Sleep selectively and durably enhances real-world sequence memory
Sleep is thought to play a critical role in the retention of episodic memories. Yet it remains unclear whether and how sleep actively transforms memory for specific experiences. More generally, little is known about sleep's effects on memory for multidimensional real-world experiences, both overnight and in the days to months that follow. In an exception to the law of forgetting, we showed that sleep actively and selectively improves retrieval of a one-time real-world experience (a controlled but immersive art tour) - specifically boosting memory for the order of tour items (sequential associations), but not perceptual details from the tour (featural associations). This above-baseline increase in sequence memory was not evident after a matched period of wakefulness. Moreover, the sleep-induced advantage of sequence over featural memory grew over time up to one-year post-encoding. Finally, overnight polysomnography showed that sleep-related memory enhancement was associated with the duration and neurophysiological hallmarks of slow-wave sleep previously linked to neural replay, particularly spindle-slow wave coupling. These results suggest that sleep serves a crucial and selective role in enhancing sequential organization in episodic memory at the expense of specific details, linking sleep-related neural mechanisms to the transformation and enhancement of memory for complex real-life experiences.

Dysregulated synaptic gene expression in oligodendrocytes of spinal and bulbar muscular atrophy
Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disease caused by an expanded CAG repeat in the androgen receptor (AR) gene. To elucidate the cell type-specific temporal gene expression in SBMA, we performed single-nucleus RNA sequencing on the spinal cords of AR-97Q mice. Among all cell types, oligodendrocytes (OLs) had the highest number of differentially expressed genes before disease onset. Analysis of OL clusters suggested that pathways associated with cation channels and synaptic function were activated before disease onset, with increased output from OLs to neurons in AR-97Q mice compared to wild-type mice. These changes in the early stages were abrogated in the advanced stages. An OL cell model of SBMA showed phenotypes similar to those of AR-97Q mice at early stages, such as increased transcriptional changes in synapse organization. Our results indicate that the dysregulation of cell-to-cell communication has a major impact on the early pathology of SBMA and is a potential therapeutic target for SBMA.

Neuromark PET: A multivariate method for Estimating and comparing whole brain functional networks and connectomes from fMRI and PET data
Positron emission tomography (PET) and magnetic resonance imaging (MRI) are both widely used neuroimaging techniques to study brain function. Although whole brain resting functional MRI (fMRI) connectomes are widely used, the integration or association of whole brain functional connectomes with PET data are rarely done. This likely stems from the fact that PET data is typically analyzed by using a regions of interest approach, while whole brain spatial networks and their connectivity (covariation) receive much less attention. As a result, to date, there have been no direct comparisons between whole brain PET and fMRI connectomes. In this study, we present a method that uses spatially constrained independent component analysis (scICA) to estimate corresponding PET and fMRI connectomes and examine the relationship between them using mild cognitive impairment (MCI) datasets. Our results demonstrate highly modularized PET connectome patterns that complement those identified from resting fMRI. In particular, fMRI showed strong intra-domain connectivity with inter-domain anticorrelation in sensorimotor and visual domains as well as default mode network. PET amyloid data showed similar strong intra-domain effects, but showed much higher correlations within cognitive control and default mode domains, as well as anticorrelation between cerebellum and other domains. The estimated PET networks have similar, but not identical, network spatial patterns to the resting fMRI networks, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. We also analyzed the differences between individuals with MCI receiving medication versus a placebo. Results show both common and modality specific treatment effects on fMRI and PET connectomes. From our fMRI analysis, we observed higher activation differences in various regions, such as the connection between the thalamus and middle occipital gyrus, as well as the insula and right middle occipital gyrus. Meanwhile, the PET analysis revealed increased activation between the anterior cingulate cortex and the left inferior parietal lobe, along with other regions, in individuals who received medication versus placebo. In sum, our novel approach identifies corresponding whole-brain PET and fMRI networks and connectomes. While we observed common patterns of network connectivity, our analysis of the MCI treatment and placebo groups revealed that each modality identifies a unique set of networks, highlighting differences between the two groups.

Regional Microglial Response in Enthorino-hippocampal Slice Cultures to Schaffer Collateral Lesion and Metalloproteinases Modulation.
Microglia and astrocytes are essential in sustaining physiological networks on the central nervous system, with their ability to remodel the extracellular matrix, being pivotal for synapse plasticity. Recent findings challenge the traditional view of homogenous glial populations in the brain, uncovering morphological, functional and molecular heterogeneity among glial cells. This di-versity has significant implications for both physiological and pathological brain states. In the present study, we mechanically induced a Schaffer collateral lesion (SCL) in mouse enthori-no-hippocampal slice cultures to investigate glial behavior, i.e., microglia and astrocytes, under metalloproteinases (MMPs) modulation in the lesioned area, CA3, and the denervated region, CA1. We observed distinct response patterns in microglia and astrocytes 3 days after the lesion. Notably, GFAP-expressing astrocytes showed no immediate changes post-SCL. Microglia re-sponses varied depending on their anatomical location. The MMPs inhibitor GM6001 did not affect microglial reactions in CA3, while increasing the Iba1 cells numbers in CA1, underscoring the complexity of the hippocampal neuroglial network post-injury. These findings highlight the importance of understanding glial regionalization following neural injury and MMPs modulation, and pave the way for further research into glia-targeted therapeutic strategies for neurodegen-erative disorders.

Low-intensity focused ultrasound to the insula and dorsal anterior cingulate has site-specific and pressure dependent effects on pain during measures of central sensitization
Background: The insula and dorsal anterior cingulate cortex (dACC) are core brain regions involved in pain processing and central sensitization, a shared mechanism across various chronic pain conditions. Methods to modulate these regions may serve to reduce central sensitization, though it is unclear which target may be most efficacious for different measures of central sensitization. Objective/Hypothesis: Investigate the effect of low-intensity focused ultrasound (LIFU) pressure to the anterior insula (AI), posterior insula (PI) or dACC on conditioned pain modulation (CPM) and temporal summation of pain (TSP). Methods: N = 16 volunteers underwent TSP and CPM pain tasks pre/post a 10 minute LIFU intervention to either the AI, PI, dACC or Sham stimulation. Pain ratings were collected pre/post LIFU. Results: LIFU to the PI significantly attenuated pain ratings in both TSP and the CPM protocols. LIFU to the dACC only affected TSP pain ratings. LIFU to AI had no effect on either TSP or CPM pain ratings. LIFU pressure modulated group means but did not affect overall group differences. Conclusions: LIFU to the PI and dACC differentially affected central sensitization. This may, in part, be due to dosing (pressure) of LIFU. Inhibition of the PI with LIFU may be a future potential therapy in chronic pain populations demonstrating central sensitization. The minimal effective dose of LIFU for efficacious neuromodulation will help to translate LIFU for therapeutic options.

Stopping Speed to Auditory and Visual Stop Signals Depends on Go Signal Modality
Rapidly cancelling movements in response to environmental stimuli is a vital component of human motor control. Past research has found that the speed of the action cancellation process is influenced by the sensory modality/modalities of the environmental change that triggers it. However, the effect on selective stopping processes (where participants must cancel only one component of a multi-component movement) remains unknown, despite these complex movements often being required as we navigate our busy modern world. Thirty healthy adults (mean age = 31.1 years, SD = 10.5) completed five response-selective stop signal tasks featuring different combinations of 'go signal' modality (the environmental change baring an imperative to initiate movement; auditory or visual) and 'stop signal' modality (the environmental change indicating that action cancellation is required; auditory, visual, or audiovisual). Electromyographical (EMG) recordings of effector muscles allowed detailed comparison of the characteristics of voluntary action and cancellation between tasks. Behavioural and physiological measures of stopping speed demonstrated that the modality of the go signal influenced how quickly participants cancelled movement in response to the stop signal: stopping was faster in two cross-modal experimental conditions (auditory go - visual stop; visual go - auditory stop), than in two conditions using the same modality for both signals. A separate condition testing for multisensory facilitation revealed that stopping was fastest when the stop signal consisted of a combined audiovisual stimulus, compared to all other go-stop stimulus combinations. These findings provide novel evidence regarding the role of attentional networks in action cancellation and suggest modality specific cognitive resources influence the latency of the stopping process.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison
Despite a rich and interdisciplinary literature on time-series analysis, the brain's complex distributed dynamics are typically quantified using only a limited set of manually selected representations and statistics. This leaves open the possibility that alternative dynamical properties may outperform those reported for a given application. Here we address this limitation by introducing a systematic procedure to compare diverse, interpretable features of functional magnetic resonance imaging (fMRI) data, drawn from comprehensive algorithmic libraries to capture both dynamical properties of individual brain regions (intra-regional activity) and patterns of statistical dependence between regions (inter-regional functional coupling). We apply our data-driven approach to investigate alterations to dynamical structures in resting-state fMRI in participants with schizophrenia (SCZ), bipolar 1 disorder (BP), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Our findings broadly support the use of linear time-series analysis techniques for fMRI-based case--control analysis, while also identifying novel informative dynamical structures that have previously received less attention. While some simple statistical representations of fMRI dynamics perform surprisingly well (e.g., properties within a single brain region), we found performance improvements through combining intra-regional properties with inter-regional coupling---consistent with distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The data-driven approach here enables the systematic identification and interpretation of disorder-specific dynamical signatures, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.