bioRxiv Subject Collection: Neuroscience's Journal

Skeletal myotubes expressing ALS mutant SOD1 induce pathogenic changes, impair mitochondrial axonal transport, and trigger motoneuron death.
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the loss of motoneurons (MNs), and despite progress, there is no effective treatment. A large body of evidence shows that astrocytes expressing ALS-linked mutant proteins cause non-cell autonomous toxicity of MNs. Although MNs innervate muscle fibers and ALS is characterized by the early disruption of the neuromuscular junction (NMJ) and axon degeneration, there are controversies about whether muscle contributes to non-cell-autonomous toxicity to MNs. In this study, we generated primary skeletal myotubes from myoblasts derived from ALS mice expressing human mutant SOD1G93A (termed hereafter mutSOD1). Characterization revealed that mutSOD1 skeletal myotubes display intrinsic phenotypic and functional differences compared to control myotubes generated from non-transgenic (NTg) littermates. Next, we analyzed whether ALS myotubes exert non-cell-autonomous toxicity to MNs. We report that conditioned media from mutSOD1 myotubes (mutSOD1-MCM), but not from control myotubes (NTg-MCM), induced robust death of primary MNs in mixed spinal cord cultures and compartmentalized microfluidic chambers. Our study further revealed that applying mutSOD1-MCM to the MN axonal side in microfluidic devices rapidly reduces mitochondrial axonal transport while increasing Ca2+ transients and reactive oxygen species (i.e., H2O2). These results indicate that soluble factor(s) released by mutSOD1 myotubes cause MN axonopathy that leads to lethal pathogenic changes.

APP-KI mice do not display the hallmark age-dependent cognitive decline of amyloid diseases
APP knock-in (KI) mice serve as an exciting new model system to understand amyloid beta (A{beta}) pathology, overcoming many of the limitations of previous overexpression-based model systems. The APPSAA mouse model (containing the humanized APP with three familial Alzheimers disease mutations) and the APPWT control are the first commercially available APP KI mice within the United States. While APPSAA mice have been shown to develop progressive A{beta} pathology and neuroinflammation, behavioral changes, particularly in cognitive functions, have yet to be described. Therefore, we performed an in-depth longitudinal study over 12 months, assessing cognition in these two strains, as well as assessments of motor and GI function. We surprisingly note no overt, progressive cognitive impairment or motor deficits. However, we do observe a significant increase in fecal output in APPSAA mice compared to APPWT at 12 months old. These data provide a baseline for these models behavioral attributes.

Perception of task duration influences metabolic cost during split-belt adaptation and biomechanics during both adaptation and post-adaptation
Humans continuously adapt locomotor patterns. In laboratory settings, split-belt treadmills have been used to study locomotor adaptation. Whether metabolic cost reduction is the primary objective or a by-product of the observed biomechanical changes during adaptation is not known. The main goal of our study is to determine if perception of task duration affects the adaptation of locomotor patterns to reduce energetic cost. We tested the hypothesis that individuals who believe they will sustain a split-belt adaptation task for a prolonged time will adapt toward a walking pattern associated with lower cost. N=14 participants adapted for 10 minutes with knowledge of time remaining (group K), while N=15 participants adapted under the assumption that they would walk for 30 minutes with no knowledge of time elapsed or time remaining (group U). Both groups adapted for 10 minutes. We observed a significant main effect of Time (p<0.001, observed power 1.0) and the interaction of Time*Group (p=0.004, observed power 0.84) on metabolic cost. The K group did not reduce metabolic cost during adaptation. The U group reduced metabolic cost during adaptation to a cost 12% lower than the K group. We observed a significant effect of Time*Group (p<0.050) on step lengths and work by the right/slow leg during adaptation and post-adaptation. Our results indicate that metabolic cost reduction has a primary role in tasks that need to be sustained for a prolonged time, and this reduction occurs through a combination of biomechanical changes small in magnitude and a marked influence of non-biomechanical factors.

A tale of two n-backs: Diverging associations of dorsolateral prefrontal cortex activation with n-back task performance
Background In studying the neural correlates of working memory (WM) ability via functional magnetic resonance imaging (fMRI) in health and disease, it is relatively uncommon for investigators to report associations between brain activation and measures of task performance. Additionally, how the choice of WM task impacts observed activation-performance relationships is poorly understood. We sought to illustrate the impact of WM task on brain-behavior correlations using two large, publicly available datasets. Methods We conducted between-participants analyses of task-based fMRI data from two publicly available datasets: the Human Connectome Project (HCP; n = 866) and the Queensland Twin Imaging (QTIM) Study (n = 459). Participants performed two distinct variations of the n-back WM task with different stimuli, timings, and response paradigms. Associations between brain activation ([2-back - 0-back] contrast) and task performance (2-back % correct) were investigated separately in each dataset, as well as across datasets, within the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, and whole cortex. Results Global patterns of activation to task were similar in both datasets. However, opposite associations between activation and task performance were observed in bilateral pre-supplementary motor area and left middle frontal gyrus. Within the dlPFC, HCP participants exhibited a significantly greater activation-performance relationship in bilateral middle frontal gyrus relative to QTIM Study participants. Conclusions The observation of diverging activation-performance relationships between two large datasets performing variations of the n-back task serves as a critical reminder for investigators to exercise caution when selecting WM tasks and interpreting neural activation in response to a WM task.

A fully synthetic three-dimensional human cerebrovascular model based on histological characteristics to investigate the hemodynamic fingerprint of the layer BOLD fMRI signal formation
Recent advances in functional magnetic resonance imaging (fMRI) at ultra-high field (greater than or equal to 7 tesla), novel hardware, and data analysis methods have enabled detailed research on neurovascular function, such as cortical layer-specific activity, in both human and nonhuman species. A widely used fMRI technique relies on the blood oxygen level-dependent (BOLD) signal. BOLD fMRI offers insights into brain function by measuring local changes in cerebral blood volume, cerebral blood flow, and oxygen metabolism induced by increased neuronal activity. Despite its potential, interpreting BOLD fMRI data is challenging as it is only an indirect measurement of neuronal activity. Computational modeling can help interpret BOLD data by simulating the BOLD signal formation. Current developments have focused on realistic 3D vascular models based on rodent data to understand the spatial and temporal BOLD characteristics. While such rodent-based vascular models highlight the impact of the angioarchitecture on the BOLD signal amplitude, anatomical differences between the rodent and human vasculature necessitate the development of human-specific models. Therefore, a computational framework integrating human cortical vasculature, hemodynamic changes, and biophysical properties is essential. Here, we present a novel computational approach: a three-dimensional VAscular MOdel based on Statistics (3D VAMOS), enabling the investigation of the hemodynamic fingerprint of the BOLD signal within a model encompassing a fully synthetic human 3D cortical vasculature and hemodynamics. Our algorithm generates microvascular and macrovascular architectures based on morphological and topological features from the literature on human cortical vasculature. By simulating specific oxygen saturation states and biophysical interactions, our framework characterizes the intravascular and extravascular signal contributions across cortical depth and voxel-wise levels for gradient-echo and spin-echo readouts. Thereby, the 3D VAMOS computational framework demonstrates that using human characteristics significantly affects the BOLD fingerprint, making it an essential step in understanding the fundamental underpinnings of layer-specific fMRI experiments.

Classification of psychedelic drugs based on brain-wide imaging of cellular c-Fos expression
Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 66% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for screening psychoactive drugs with psychedelic properties.

A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations
Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which non-random trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in SONATA format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in a detailed model of rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces
Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present new computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework predicted the outcome of co-adaptive interface interactions and revealed how interface properties can shape user behavior. These findings contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces.

Deep learning and eye-tracking for accurate EOG rejection
Electroencephalography (EEG) is a neuroimaging technique used to record the electrical activity generated by the brain. EEG recordings are often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Independent component analysis (ICA) is commonly applied to isolate EOG artifacts and subtract the corresponding independent components from the EEG signals. However, ICA is an unsupervised technique that contains no knowl- edge of the eye movements during the task or the generative process by which these movements result in EOG artifacts. It is generally difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. To address this limitation, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking. We leveraged the Large Grid task from the open-source EEGEyeNet dataset to develop and validate this approach. In this task, 30 participants looked at a series of dots appearing at 25 predetermined positions on the screen (810 trials/participant). EEG and eye-tracking were collected simultaneously. In this paper, we show that we can train a long short-term memory (LSTM) model to predict the component of EEG signals predictable from eye-tracking data. We further used this eye-tracking-informed evaluation of EOG artifacts to investigate the sensitivity and specificity of ICA, currently the dominant approach for EOG artifact correction. Our analysis indicates that although ICA is very sensitive to EOG, it has a comparatively low specificity.

Complimentary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome
Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments, neurobehavioral phenotypes including autism and attention deficit hyperactivity disorder (ADHD), and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts to GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss of function that will pave the way for future molecular studies into DESSH. These studies begin to uncover some biological underpinnings of DESSH syndrome and elucidate the biology of Wac, with advantages in each model.

Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder
Introduction: There is a lack of research in the existing literature when it comes to analyzing the dynamics of resting-state functional magnetic resonance imaging to understand the underlying mechanisms of isolated rapid eye movement sleep behavior disorder (iRBD). This study aims to contribute to our understanding of abnormalities in brain network dynamics in iRBD and their association with alpha-synucleinopathy. Additionally, I employed graph theoretical metrics to obtain a topological insight into the brain network of iRBD. Methods: Resting-state fMRI data from 55 iRBD patients and 97 healthy controls (HCs) were utilized. A sliding window approach, functional connectivity analysis, and graph theory analysis were applied to the data. I calculated the mean, standard deviation, skewness, and kurtosis of the time series for both dynamic functional connectivity (dFC) and four graph metrics (clustering coefficient, global efficiency, assortativity coefficients, and eigenvector centrality). Subsequently, I compared the those metrices between iRBDs and HCs. Relationships between clinical scales and abnormal dFC were assessed using a general linear model. Results: iRBD patients exhibited abnormal mean dFC, particularly in the default mode network, sensorimotor network, basal ganglia network, and cerebellum. Kurtosis of dFC revealed abnormalities between the middle temporal gyrus and cerebellum. Group differences were also observed in the mean eigenvector centrality of the precentral gyrus and thalamus. Conclusion: The mean of dFC identified impairments putatively in movement functions and various compensatory mechanisms. Moreover, mean eigenvector centrality revealed topological changes in motor-related network in iRBDs. The use of kurtosis as a potential index for extracting dynamic information may provide additional insights into pathophysiology in iRBDs.

Heartbeat perception is causally linked to frontal delta oscillations
The ability to accurately perceive one's own bodily signals, such as the heartbeat, plays a vital role in physical and mental health. However, the neurophysiological mechanisms underlying this ability, termed interoception, are not fully understood. Converging evidence suggests that cardiac rhythms are linked to frontal brain activity, particularly oscillations in the delta (0.5 - 4 Hz) band, but their causal relationship remained elusive. Using amplitude-modulated transcranial alternating current stimulation (AM-tACS), a method to enhance or suppress brain oscillations in a phase-specific manner, we investigated whether frontal delta oscillations are causally linked to heartbeat perception. We found that enhancement of delta phase synchrony suppressed heartbeat detection accuracy, while suppression of delta phase synchrony enhanced heartbeat detection accuracy. These findings suggest that frontal delta oscillations play a critical role in heartbeat perception, paving the way for causal investigations of interoception and potential clinical applications.

lncRNA ADEPTR loss-of-function elicits sex-specific behavioral and spine deficits
Activity dependent changes in neuronal connections are fundamental to learning and long-term memory storage. However, the precise contribution of long noncoding RNAs (lncRNAs) to these modifications remains unclear. In this study, we assessed the role of the lncRNA ADEPTR, a cAMP modulated lncRNA localized in dendrites, which is crucial for synapse morphology. By generating two different mouse models, one with a deletion of ADEPTR (LADEPTR) and one with a deletion of its protein interaction region (SADEPTR) we investigated the sex specific impacts of ADEPTR loss of function on learning, memory, dendritic arborization, and synapse morphology. Our behavioral analyses revealed a reduction in anxiety in adult male mice, while learning and memory remained unaffected in both models. Systematic evaluations of neuronal morphology across various developmental stages (~3 day old postnatal neuronal cultures and postnatal 14 and 42 day old male and female mice) uncovered substantial deficits in neuronal architecture in both S and L ADEPTR male and female neuronal cultures. At postnatal day 42, in contrast to their male counterparts, L ADEPTR female mice exhibited a significant deficiency in thin spines. Additionally, we found that the expression of plasticity related gene BDNF, and immediate early gene cFOS were enhanced in both the cortex and hippocampus of adult male and female S and L ADEPTR mice, suggesting the activation of a compensatory mechanism protecting against learning and memory deficits. Collectively, these observations underscore the sex specific role of lncRNA ADEPTR in shaping neuronal morphology and anxiety behavior.

Deep Learning Detection of Subtle Torsional Eye Movements: Preliminary Results
The control of torsional eye position is a key component of ocular motor function. Ocular torsion can be affected by pathologies that involve ocular motor pathways, spanning from the vestibular labyrinth of the inner ears to various regions of the brainstem and cerebellum. Timely and accurate diagnosis enables efficient interventions and management of each case which are crucial for patients with dizziness, vertical double vision, or imbalance. Such detailed evaluation of eye movements may not be possible in all frontline clinical settings, particularly for detecting torsional abnormalities. These abnormalities are often more challenging to identify at the bedside compared to horizontal or vertical eye movements. To address these challenges, we used a dataset of induced torsional eye movements recorded with video-oculography (VOG) to develop video-based deep learning models for detecting ocular torsion. Our models achieve 0.9308 AUROC and 86.79% accuracy, using ocular features and neurophysiology-supported phenomena.

A cortical locus for modulation of arousal states
Fluctuations in global arousal are key determinants of spontaneous cortical activity and function. Several subcortical structures, including neuromodulatory nuclei like the locus coeruleus (LC), are involved in the regulation of arousal. However, much less is known about the role of cortical circuits that provide top-down inputs to arousal-related subcortical structures. Here, we investigated the role of a major subdivision of the prefrontal cortex, the anterior cingulate cortex (ACC), in arousal modulation. Pupil size, facial movements, heart rate, and locomotion were used as non-invasive measures of arousal and behavioral state. We designed a closed loop optogenetic system based on machine vision and found that real time inhibition of ACC activity during pupil dilations suppresses ongoing arousal events. In contrast, inhibiting activity in a control cortical region had no effect on arousal. Fiber photometry recordings showed that ACC activity scales with the magnitude of spontaneously occurring pupil dilations/face movements independently of locomotion. Moreover, optogenetic ACC activation increases arousal independently of locomotion. In addition to modulating global arousal, ACC responses to salient sensory stimuli scaled with the size of evoked pupil dilations. Consistent with a role in sustaining saliency-linked arousal events, pupil responses to sensory stimuli were suppressed with ACC inactivation. Finally, our results comparing arousal-related ACC and norepinephrinergic LC neuron activity support a role for the LC in initiation of arousal events which are modulated in real time by the ACC. Collectively, our experiments identify the ACC as a key cortical site for sustaining momentary increases in arousal and provide the foundation for understanding cortical-subcortical dynamics underlying the modulation of arousal states.

Lumped parameter simulations of cervical lymphatic vessels reveal dynamics of cerebrospinal fluid efflux from the skull
Background: Growing evidence suggests that for rodents, a substantial fraction of cerebrospinal fluid (CSF) drains by crossing the cribriform plate into the nasopharyngeal lymphatics, eventually reaching the cervical lymphatic vessels (CLVs). Disruption of this drainage pathway is associated with various neurological disorders. Methods: We employ a lumped parameter method to numerically model CSF drainage across the cribriform plate to CLVs. Our model uses intracranial pressure as an inlet pressure and central venous blood pressure as an outlet pressure. The model incorporates initial lymphatic vessels (modeling those in the nasal region) that absorb the CSF and collecting lymphatic vessels (modeling CLVs) to transport the CSF against an adverse pressure gradient. To determine unknown parameters such as wall stiffness and valve properties, we utilize a Monte Carlo approach and validate our simulation against recent in vivo experimental measurements. Results: Our parameter analysis reveals the physical characteristics of CLVs. Our results suggest that the stiffness of the vessel wall and the closing state of the valve are crucial for maintaining the vessel size and volume flow rate observed in vivo. Furthermore, we find that a decreased contraction amplitude and frequency leads to a reduction in volume flow rate. Additionally, we provide evidence that branching of initial lymphatic vessels may deviate from Murray's law to reduce sensitivity to elevated intracranial pressure. Conclusions: This is the first numerical study of CSF drainage through CLVs. Our comprehensive parameter analysis offers guidance for future numerical modeling of CLVs. This study also provides a foundation for understanding physiology of CSF drainage, helping guide future experimental studies aimed at identifying causal mechanisms of reduction in CLV transport and potential therapeutic approaches to enhance flow.

Blast Traumatic Brain Injury Induces Long-Term Alterations in Inflammatory Gene Expression in Chinchilla Brains
Blast traumatic brain injury (bTBI) due to high-intensity impulsive noise exposure from explosions and munitions exposure is highly prevalent among military personnel, which leads to diffuse brain injury resulting in a spectrum of brain dysfunction and cognitive deficits. The resultant prolonged neuroinflammation and consequent failure of inflammation resolution is a key contributor to long-term complications, including post-traumatic stress disorder and early-onset of neurodegenerative disease; however, there is little evidence for the duration and extent of long term neuroinflammation in distinct brain regions. To investigate this, due to human-like audiogram, we use chinchillas as an in-vivo bTBI model to analyze the relative gene expression of inflammatory markers (TNF, TGF{beta}2, Gal1, HSP90, S100B, NRGN, MAPK14, IL8, NFL, and BDNF) in the hippocampus, striatum, and higher centers of the auditory pathway 90 days following varying intensities of blast exposures (144 dB, 155 dB, and 172 dB sound pressure level). Our study revealed aberrant gene expression across all analyzed brain regions and all injury conditions; however, no specific pattern emerged. Many of the inflammatory markers were downregulated, suggesting a possible attempt by the brain to overcome prior inflammation. Conversely, the hippocampus, striatum, inferior colliculus, and medial geniculate body all exhibited upregulation of inflammatory markers, including the TBI prognostic marker, S100B. Thus, chinchilla brains exhibit evidence of prolonged neuroinflammation 90 days following injury, even during mild blast exposure. Ultimately, the observed alterations in the gene expression of inflammatory markers may contribute to the long-term neurological dysfunction and neurodegenerative disease experienced by veterans and other bTBI patients.

Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics
One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed simultaneous recording of up to tens of thousands of neurons, this represents only a tiny fraction of most cortical circuits. Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student, even when the two networks have matching single-unit dynamics. In particular, partial observation of models of low-dimensional cortical dynamics based on functionally feedforward or low-rank connectivity can lead to surrogate models with spurious attractor structure. Our results illustrate the challenges inherent in accurately uncovering neural mechanisms from single-trial data, and suggest the need for new methods of validating data-constrained models for neural dynamics.

CRASH2p: Closed-loop Two Photon Imaging in Freely Moving Animals
Direct measurement of neural activity in freely moving animals is essential for understanding how the brain controls and represents behaviors. Genetically encoded calcium indicators report neural activity as changes in fluorescence intensity, but brain motion confounds quantitative measurement of fluorescence. Translation, rotation, and deformation of the brain and the movements of intervening scattering or auto-fluorescent tissue all alter the amount of fluorescent light captured by a microscope. Compared to single-photon approaches, two photon microscopy is less sensitive to scattering and off-target fluorescence, but more sensitive to motion, and two photon imaging has always required anchoring the microscope to the brain. We developed a closed-loop resonant axial-scanning high-speed two photon (CRASH2p) microscope for real-time 3D motion correction in unrestrained animals, without implantation of reference markers. We complemented CRASH2p with a novel scanning strategy and a multi-stage registration pipeline. We performed volumetric ratiometrically corrected functional imaging in the CNS of freely moving Drosophila larvae and discovered previously unknown neural correlates of behavior.

Different state-dependence of population codes across cortex
During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an optimal state and two sub-optimal states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.

Diffusion model-based image generation from rat brain activity
Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens up new possibilities for creative expression and experimentation.

Testing the application of plasma glucocorticoids and their ratios as biomarkers of acute and chronic stress in rescued wild koala patients: a pilot study
Koalas (Phascolarctos cinereus) are one of the most iconic marsupial species endemic to Australia. However, their population is declining due to threats including habitat loss, disease, dog attacks, and vehicle collisions. These threats also serve as acute or chronic stressors that impact koala welfare and conservation. Cortisol is widely used as a biomarker to study stress in koalas. However, plasma cortisol concentration is less studied due to its limited ability to assess chronic stress and welfare concerns. Dehydroepiandrosterone sulphate (DHEAS) and dihydrotestosterone (DHT) are biomarkers that could potentially detect chronic stress due to their antagonising and inhibitory effects on cortisol. In this study, we used plasma cortisol and the ratio of DHEAS and DHT to cortisol to assess stress in rescued koalas (n = 10) admitted to RSPCA Queensland. Although no significant differences were found between koalas across all biomarkers and the ratios failed to detect chronic stressors, similar trends were found consistently, suggesting the potential use of the biomarkers to assess stress. Across all biomarkers, the highest medians were found in koalas with Chlamydia-related reproductive disease and oxalate nephrosis and the lowest medians were found in koalas with Chlamydia-related conjunctivitis. Higher medians were also found consistently in females (n = 3) and adult koalas. In addition, insignificant negative correlations were found across all biomarkers between age, weight, and body conditioning scores, except for the positive correlation between weight and cortisol and cortisol:DHT. Overall, the consistency of trends and the insignificant differences found across biomarkers in our study suggested that using a single biomarker to assess chronic stress is insufficient, especially for hospital-based studies limited by sample population. Thus, this pilot study provides first step towards developing a koala-specific allostatic load index based on multiple stress biomarkers to understand chronic stress in rescued koalas.

Lay summaryStress in koalas can be challenging for their welfare and conservation. In this study, we tested plasma glucocorticoids and their ratios as biomarkers of acute and chronic stress. Our finding showed ratios of DHEAS and DHT to cortisol are comparable across stress parameters and animal demographic characteristics. This study serves as a foundational framework for developing a stress index based on multiple biomarkers that could be useful tool for koala welfare.

Tonotopy is not preserved in a descending stage of auditory cortex
Previous studies based on layer specificity suggest that ascending signals from the thalamus to sensory neocortex preserve spatially organized information, but it remains unknown whether sensory information descending from sensory neocortex to thalamus also maintains such spatial organization pattern. By focusing on projection specificity, we mapped tone response properties of two groups of cortical neurons in the primary auditory cortex (A1), based on the relationship between their specific connections to other regions and their function in ascending (thalamocortical recipient, TR neurons) or descending (corticothalamic, CT neurons) auditory information. A clear tonotopic gradient was observed among TR, but not CT neurons. Additionally, CT neurons exhibited markedly higher heterogeneity in their frequency tuning and had broader bandwidth than TR neurons. These results reveal that the information flow descending from A1 to thalamus via CT neurons does not arrange tonotopically, suggesting that the descending information flow possibly contributes to higher-order feedback processing of diverse auditory inputs.

Senescent-like microglia limit remyelination through the senescence associated secretory phenotype
The capacity to regenerate myelin in the central nervous system (CNS) diminishes with age. This decline is particularly evident in multiple sclerosis (MS), which has been suggested to exhibit features of accelerated biological aging. Whether cellular senescence, a hallmark of aging, contributes to remyelination impairment remains unknown. Here, we show that senescent cells (SCs) accumulate within demyelinated lesions after injury, and their elimination enhances remyelination in young mice but not in aged mice. In young mice, we observed the upregulation of senescence-associated transcripts primarily in microglia after demyelination, followed by their reduction during remyelination. However, in aged mice, senescence-associated factors persisted within lesions, correlating with inefficient remyelination. We found that SC elimination enhanced remyelination in young mice but was ineffective in aged mice. Proteomic analysis of senescence-associated secretory phenotype (SASP) revealed elevated levels of CCL11/Eotaxin-1 in lesions, which was found to inhibit efficient oligodendrocyte maturation. These results suggest therapeutic targeting of SASP components, such as CCL11, may improve remyelination in aging and MS.

The fruit fly, Drosophila melanogaster, as a micro-robotics platform.
Engineering small autonomous agents capable of operating in the microscale environment remains a key challenge, with current systems still evolving. Our study explores the fruit fly, Drosophila melanogaster, a classic model system in biology and a species adept at microscale interaction, as a biological platform for micro-robotics. Initially, we focus on remotely directing the walking paths of fruit flies in an experimental arena. We accomplish this through two distinct approaches: harnessing the fruit flies opto-motor response and optogenetic modulation of its olfactory system. These techniques facilitate reliable and repeated guidance of flies between arbitrary spatial locations. We guide flies along predetermined trajectories, enabling them to scribe patterns resembling textual characters through their locomotion. We enhance olfactory-guided navigation through additional optogenetic activation of positive valence mushroom body output neurons. We extend this control to collective behaviors in shared spaces and navigation through constrained maze-like environments. We further use our guidance technique to enable flies to carry a load across designated points in space, establishing the upper bound on their weight carrying capabilities. Additionally, we demonstrate that visual guidance can facilitate novel interactions between flies and objects, showing that flies can consistently relocate a small spherical object over significant distances. Beyond expanding tools available for micro-robotics, these novel behavioral contexts can provide insights into the neurological basis of behavior in fruit flies.

Muscle-derived miR-200a-3p through light-intensity exercise may contribute to improve memory dysfunction in type 2 diabetic mice
BackgroundMemory dysfunction associated with type 2 diabetes mellitus (T2DM) poses a considerable threat to overall well-being. Engaging in light-intensity exercise has been shown to exert favorable effects on hippocampal function and molecular profiles, including Mct2 mRNA and miR-200a-3p. Nonetheless, a comprehensive understanding of the mechanisms underlying the positive impact of light-intensity exercise remains elusive. Here, we assessed the influence of exosomal miR-200a-3p secretion from gastrocnemius muscles in T2DM mice undergoing light-intensity exercise intervention, focusing on its potential to ameliorate memory dysfunction.

Basic proceduresWe initially assessed the effects of light-intensity exercise (7.0 m/min for healthy mice, 5.0 m/min for ob/ob mice, 30 min/day, five days/week, over a four-week period) on memory function, hippocampal mRNA associated with memory function, and the secretion of exosomal miR-200a-3p from their gastrocnemius muscle. Subsequently, the impact of a daily intraperitoneal injection of the miR-200a-3p mimic over a four-week duration was investigated, focusing on its influence on hippocampal dysregulation in ob/ob mice.

Main findingsThe light-intensity exercise intervention increased gastrocnemius muscle-derived and plasma exosomal miR-200a-3p levels in ob/ob mice, concomitant with improved memory dysfunction. Intriguingly, the daily intraperitoneal injection of mmu-miR-200a-3p mimic also demonstrated an ameliorative effect on memory function in ob/ob mice. Notably, both the exercise intervention and miR-200a-3p mimic treatment induced downregulation in hippocampal Keap1 mRNA and upregulation in mRNA of Hsp90aa1 and Mct2 in ob/ob mice.

Principal conclusionsThe current results imply that the augmentation of exosomal miR-200a-3p derived from the gastrocnemius muscle contributes to the amelioration of memory dysfunction in T2DM undergoing light-intensity exercise. Additionally, it is proposed that miR-200a-3p emulates the effects of light-intensity exercise, suggesting a potential therapeutic pathway for addressing hippocampal complications in the context of T2DM.

Patterned Electrical Brain Stimulation by a Wireless Network of Implantable Microdevices
Transmitting meaningful information into brain circuits by electronic means is a challenge facing brain-computer interfaces. A key goal is to find an approach to inject spatially structured local current stimuli across swaths of sensory areas of the cortex. Here, we introduce a fully wireless approach to multipoint patterned electrical microstimulation by a spatially distributed epicortically implanted network of silicon microchips to target specific areas of the cortex. Each sub-millimeter-sized microchip harvests energy from an external radio-frequency source and converts this into biphasic current injected focally into tissue by a pair of integrated microwires. The amplitude, period, and repetition rate of injected current from each chip are controlled across the implant network by implementing a pre-scheduled, collision-free bitmap wireless communication protocol featuring sub-millisecond latency. As an in vivo demonstration, a network of 30 wireless stimulators was chronically implanted into motor and sensory areas of the cortex in a freely moving rat for three months. We explored the effects of patterned intracortical electrical stimulation on trained animal behavior at average RF powers well below safety limits.

The UFMylation pathway is impaired in Alzheimer's disease
BackgroundAlzheimers disease (AD) is characterized by the presence of neurofibrillary tangles made of hyperphosphorylated tau and senile plaques composed of beta-amyloid. These pathognomonic deposits have been implicated in the pathogenesis, although the molecular mechanisms and consequences remain undetermined. UFM1 is an important, but understudied ubiquitin-like protein that is covalently attached to substrates. This UFMylation has recently been identified as major modifier of tau aggregation upon seeding in experimental models. However, potential alterations of the UFM1 pathway in human AD brain have not been investigated yet.

MethodsHere we used frontal and temporal cortex samples from individuals with or without AD to measure the protein levels of the UFMylation pathway in human brain. We used multivariable regression analyses followed by Bonferroni correction for multiple testing to analyze associations of the UFMylation pathway with neuropathological characteristics, primary biochemical measurements of tau and additional biochemical markers from the same cases. We further studied associations of the UFMylation cascade with cellular stress pathways using Spearman correlations with bulk RNAseq expression data and functionally validated these interactions using gene-edited neurons that were generated by CRISPR-Cas9.

ResultsCompared to controls, human AD brain had increased protein levels of UFM1. Our data further indicates that this increase mainly reflects conjugated UFM1 indicating hyperUFMylation in AD. UFMylation was strongly correlated with pathological tau in both AD-affected brain regions. In addition, we found that the levels of conjugated UFM1 were negatively correlated with soluble levels of the deUFMylation enzyme UFSP2. Functional analysis of UFM1 and/or UFSP2 knockout neurons revealed that the DNA damage response as well as the unfolded protein response are perturbed by changes in neuronal UFM1 signaling.

ConclusionsThere are marked changes in the UFMylation pathway in human AD brain. These changes are significantly associated with pathological tau, supporting the idea that the UFMylation cascade might indeed act as a modifier of tau pathology in human brain. Our study further nominates UFSP2 as an attractive target to reduce the hyperUFMylation observed in AD brain but also underscores the critical need to identify risks and benefits of manipulating the UFMylation pathway as potential therapeutic avenue for AD.

Circadian clock neurons use activity-regulated gene expression for structural plasticity
Drosophila s-LNv circadian pacemaker neurons show dramatic structural plasticity, with their projections expanded at dawn and then retracted by dusk. This predictable plasticity makes s-LNvs ideal to study molecular mechanisms of plasticity. Although s-LNv plasticity is controlled by their molecular clock, changing s-LNv excitability also regulates plasticity. Here, we tested the idea that s-LNvs use activity-regulated genes to control plasticity. We found that inducing expression of either of the activity-regulated transcription factors Hr38 or Sr (orthologs of mammalian Nr4a1 and Egr1) is sufficient to rapidly expand s-LNv projections. Conversely, transiently knocking down expression of either Hr38 or sr blocks expansion of s-LNv projections at dawn. We show that Hr38 rapidly induces transcription of sif, which encodes a Rac1 GEF required for s-LNv plasticity rhythms. We conclude that the s-LNv molecular clock controls s-LNv excitability, which couples to an activity-regulated gene expression program to control s-LNv plasticity.

Estimating steady-state evoked potentials in the limit of short data duration and low stimulation frequency.
Because of their high signal-to-noise ratio and robustness to artifacts, Steady-State Evoked Potentials (SSEP) - the periodic responses elicited by periodic stimulation designs - are increasingly used in human neuroscience for measuring stimulus-specific brain responses in a short presentation time. While widely applied to measure sensory responses with stimulation frequencies higher than 8 Hz, they are also successful to investigate high-order processes and/or early development characterized by slower time scales, requiring very low stimulation frequencies around 1 Hz. However, applications of these low frequency paradigms on developmental or clinical populations, typically relying on very short data recordings, pose a methodological challenge for SSEP estimation. Here we tackled this challenge by investigating the method of analysis that most efficiently compute SSEP at low stimulation frequencies in the limit of short data, and by estimating the minimum data length necessary to obtain a reliable response. We compared the performance of the three most commonly used measures of SSEP (power spectrum (PS), evoked power spectrum (EPS) and inter-trial coherence (ITC)) at progressively shorter data segments both on simulated data and on EEG responses to on-off checkerboard stimulation at two low frequencies (4 Hz and 0.8 Hz). Results, consistent between simulated and real data, show that while for long data length EPS and ITC outperform PS, for short data length the three measures are equivalent, and the crucial parameter is the length of the sliding window over which each measure is computed: the longer the better for PS and EPS, whereas the opposite occurs for ITC. For the analysed dataset, the shortest data length required to estimate a reliable SSEP is as short as 8 cycles of stimulation, independently from the stimulation frequency. This study provides practical indications for reliable and efficient application of low-frequency SSEP designs on short data recordings.

Characterization of sleep in a mouse model of CLN3 disease revealed sex-specific sleep disturbances
The neuronal ceroid lipofuscinoses (NCLs) are a group of recessively inherited neurodegenerative diseases characterized by lysosomal storage of fluorescent materials. CLN3 disease, or juvenile Batten disease, is the most common NCL that is caused by mutations in the Ceroid Lipofuscinosis, Neuronal 3 (CLN3) gene. Sleep disturbances are among the most common symptoms associated with CLN3 disease, yet this is understudied and has not been delineated in an animal model of the disease. The current study utilized a non-invasive, automated piezoelectric motion sensing system (PiezoSleep) to classify sleep and wakefulness in a Cln3{phi}..ex1-6/{phi}..ex1-6 (Cln3KO) mouse model and age- and sex-matched wild-type (WT) controls. The sleep-wake classification by PiezoSleep was found to be about 90% accurate when validated against simultaneous gold standard polysomnographic recordings including electroencephalography (EEG) and electromyography (EMG) in a small cohort of WT and Cln3KO mice. Our large cohort PiezoSleep study reveals sleep abnormalities during the light period (LP) in male Cln3KO mice compared to WT male, and more subtle differences in Cln3KO female mice throughout the dark period (DP) compared to WT female, recapitulating sleep abnormalities seen in CLN3 disease patients. Our characterization of sleep in a mouse model of CLN3 disease contributes to a better understanding of the sleep disturbances commonly reported for CLN3 disease and other NCLs, which will facilitate the development of new disease treatment and management strategies.

A connectomics-driven analysis reveals novel characterization of border regions in mouse visual cortex
Leveraging retinotopic maps to parcellate the visual cortex into its respective sub-regions has long been a canonical approach to characterizing the functional organization of visual areas in the mouse brain. However, with the advent of extensive connectomics datasets like MICrONS, we can now perform more granular analyses on biological neural networks, enabling us to better characterize the structural and functional profile of the visual cortex. In this work, we propose a statistical framework for analyzing the MICrONS dataset, focusing our efforts on the network encompassed by the retinotopically-induced V1, RL, and AL visual areas. In particular, we bridge the gap between connectomics and retinotopy by identifying several structural and functional differences between these regions. Most notably, by placing our attention on the borders between these regions, we demonstrate how connectomics, in some ways, supersedes retinotopy, providing evidence for two major findings. One, by comparing the V1-RL and RL-AL border regions, we show that not all borders in the visual cortex are the same with respect to structure and function. Two, we propose a novel interpretation for the V1-RL border region in particular, motivating it as a subnetwork that possesses heightened synaptic connectivity and more synchronous neural activity. Going one step further, we analyze structure and function in tandem by measuring information flow along synapses, demonstrating that the V1-RL border serves as a bridge for communication between the V1 and RL visual areas, offering justification as to why it presents itself uniquely with respect to both structure and function.

Angiogenesis in the mature mouse cortex is governed in a region specific and Notch1 dependent manner
Cerebral angiogenesis is well appreciated in development and after injury, but the extent to which it occurs across cortical regions in normal adult mice and underlying mechanisms, is incompletely understood. Using in vivo imaging, we show that angiogenesis in anterior-medial cortical regions (retrosplenial and sensorimotor cortex), was exceptionally rare. By contrast, angiogenesis was significantly elevated in posterior-lateral regions such as visual cortex, primarily within 200{micro}m of the cortical surface. There were no regional differences in vessel pruning or sex effects except for the length and depth of new capillaries. To understand mechanisms, we surveyed gene expression and found Notch related genes were enriched in ultra-stable retrosplenial versus visual cortex. Using endothelial specific knockdown of Notch1, cerebral angiogenesis was significantly increased along with genes implicated in angiogenesis (Apln, Angpt2, Cdkn1a). Our study shows that angiogenesis is regionally dependent and manipulations of Notch1 signaling could unlock the angiogenic potential of the mature vasculature.

Molecular Specializations Underlying Phenotypic Differences in Inner Ear Hair Cells of Zebrafish and Mice
Hair cells (HCs) are the sensory receptors of the auditory and vestibular systems in the inner ears of vertebrates that selectively transduce mechanical stimuli into electrical activity. Although all HCs have the hallmark stereocilia bundle for mechanotransduction, HCs in non-mammals and mammals differ in their molecular specialization in the apical, basolateral and synaptic membranes. HCs of non-mammals, such as zebrafish (zHCs), are electrically tuned to specific frequencies and possess an active process in the stereocilia bundle to amplify sound signals. Mammalian cochlear HCs, in contrast, are not electrically tuned and achieve amplification by somatic motility of outer HCs (OHCs). To understand the genetic mechanisms underlying differences among adult zebrafish and mammalian cochlear HCs, we compared their RNA-seq-characterized transcriptomes, focusing on protein-coding orthologous genes related to HC specialization. There was considerable shared expression of gene orthologs among the HCs, including those genes associated with mechanotransduction, ion transport/channels, and synaptic signaling. For example, both zebrafish and mouse HCs express Tmc1, Lhfpl5, Tmie, Cib2, Cacna1d, Cacnb2, Otof, Pclo and Slc17a8. However, there were some notable differences in expression among zHCs, OHCs, and inner HCs (IHCs), which likely underlie the distinctive physiological properties of each cell type. Tmc2 and Cib3 were not detected in adult mouse HCs but tmc2a and b and cib3 were highly expressed in zHCs. Mouse HCs express Kcna10, Kcnj13, Kcnj16, and Kcnq4, which were not detected in zHCs. Chrna9 and Chrna10 were expressed in mouse HCs. In contrast, chrna10 was not detected in zHCs. OHCs highly express Slc26a5 which encodes the motor protein prestin that contributes to OHC electromotility. However, zHCs have only weak expression of slc26a5, and subsequently showed no voltage dependent electromotility when measured. Notably, the zHCs expressed more paralogous genes including those associated with HC-specific functions and transcriptional activity, though it is unknown whether they have functions similar to their mammalian counterparts. There was overlap in the expressed genes associated with a known hearing phenotype. Our analyses unveil substantial differences in gene expression patterns that may explain phenotypic specialization of zebrafish and mouse HCs. This dataset also includes several protein-coding genes to further the functional characterization of HCs and study of HC evolution from non-mammals to mammals.

Early life intestinal inflammation alters gut microbiome, impairing gut-brain communication and reproductive behavior in mice
Despite recent advances in understanding the connection between the gut microbiota and the brain, there remains a wide knowledge gap in how gut inflammation impacts brain development. Microbiota-derived metabolite signaling from the gut to the brain is required for normal development of microglia, the brains resident immune cells. Disruption of the microbiota-brain communication has been linked to impaired behaviours and Autism Spectrum Disorder. We hypothesized that intestinal inflammation in early life would negatively affect neurodevelopment through dysregulation of microbiota communication to brain microglia. To test this hypothesis, we developed a novel pediatric model of Inflammatory Bowel Disease (IBD). IBD is an incurable condition affecting millions of people worldwide, characterized by chronic intestinal inflammation, and has comorbid symptoms of anxiety, depression and cognitive impairment. Significantly, 25% of IBD patients are diagnosed during childhood, and the effect of chronic inflammation during this critical period of development is largely unknown. We developed a chemical model of pediatric chronic IBD by repeatedly treating juvenile mice with dextran sodium sulfate (DSS) in drinking water. DSS-treated mice displayed increased intestinal inflammation, altered microbiota and changes in circulating metabolites. We also found that alterations in gut microbiota had long-term impacts on female microglia and male sex-specific behaviours and testosterone regulation, consistent with delayed puberty observed in male IBD patients. Our research expands our understanding of microbiota-microglia communication underlying development. The gut-brain axis is an exciting target for personalized medicine as microbiome manipulations could be feasible for early intervention to reverse deficits due to juvenile inflammation.

HighlightsEarly life gut inflammation produces sex-specific i) microbiome, ii) sex hormone and iii) behavioural impacts

Both sexes show disrupted gut bacterial members that regulate sex hormone levels

Male mice demonstrate deficits in mate seeking, which may be mediated by reduced seminal vesicle mass and reduced androgen levels

Female mice lack behavioural deficits, but demonstrate increased amoeboid microglia in the hippocampus

Hippocampal mGluR5 levels are comparable in Alzheimer's and control brains, and divergently influenced by amyloid and tau in control brain
BackgroundMetabotropic glutamate receptor 5 (mGluR5) modulates excitatory glutamatergic synaptic transmission and plays an important role in learning and memory formation and in neurodegeneration and amyloid deposition in Alzheimers disease (AD). Conflicting results on the cerebral mGluR5 levels in AD have been reported based on in vivo and postmortem studies. Here, we aimed to assess alterations in hippocampal mGluR5 expression in AD, and the associations between mGluR5 expression and pathologies.

MethodsImmunofluorescence staining for mGluR5 was performed on postmortem brain tissue from 34 AD patients and 31 nondemented controls (NCs) and from aged 3xTg and arcA{beta} model mice of AD. Autoradiography was performed on brain tissue slices from arcA{beta} mice using mGluR5 tracer [18F]PSS232. Analysis of different cellular source of GRM5 RNA in human and mouse brains was performed. Proteomic profiling and pathway analysis were performed on hippocampal tissue from aged 3xTg mice and wild-type mice.

ResultsNo differences in hippocampal mGluR5 expression or entorhinal cortical GRM5 RNA levels were detected between the AD and NC groups. Hippocampal mGluR5 levels increased with Braak stage and decreased with amyloid level in the NC group. No correlations were detected between the levels of mGluR5 and amyloid, tau, or Iba1/P2X7R in the hippocampus of AD patients and NC cases. Ex vivo autoradiography revealed comparable cerebral levels of [18F]PSS232 in arcA{beta} mice compared to nontransgenic mice. GO and KEGG pathway enrichment analyses revealed that the Shank3, Grm5 and glutamatergic pathways were upregulated in hippocampal tissue from aged 3xTg mice compared to wild-type mice.

ConclusionThis study revealed no difference in hippocampal mGluR5 levels between AD patients and NCs and revealed the divergent influence of amyloid and tau pathologies on hippocampal mGluR5 levels in NCs. Species differences were observed in the GRM5 RNA level as well as at the cellular location.

Graphical abstract

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Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer's spectrum
Digital cognitive assessments, particularly those that can be done at home, present as low burden biomarkers for participants and patients alike, but their effectiveness in diagnosis of Alzheimers or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk for cognitive decline. We analyzed >500 ADNI participants who underwent a brief digital cognitive assessment and A{beta}/tau PET scans, examining their ability to distinguish cognitive status and predict cognitive decline. Performance on the digital cognitive assessment were superior to both cortical A{beta} and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. Digital assessments are effective in identifying at-risk individuals, supporting their utility as low-burden tools for early Alzheimers detection and monitoring.

Multimodal identification of the mouse brain using simultaneous Ca2+ imaging and fMRI
Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.