bioRxiv Subject Collection: Neuroscience's Journal

Concurrent single-pulse (sp) TMS/fMRI to reveal the causal connectome in healthy and patient populations
Neuroimaging and cognitive neuroscience studies have identified neural circuits linked to anxiety, mood, and trauma-related symptoms and focused on their interaction with the medial prefrontal default mode circuitry. Despite these advances, developing new neuromodulatory treatments based on neurocircuitry remains challenging. It remains unclear which nodes within and controlling these circuits are affected and how their impairment is connected to psychiatric symptoms. Concurrent single-pulse (sp) TMS/fMRI offers a promising approach to probing and mapping the integrity of these circuits. In this study, we present concurrent sp-TMS/fMRI data along with structural MRI scans from 152 participants, including both healthy and depressed individuals. The sp-TMS was administered to 11 different cortical sites, providing a dataset that allows researchers to investigate how brain circuits are modulated by spTMS.

The Neural Underpinnings of Aphantasia: A Case Study of Identical Twins
Aphantasia is a condition characterized by reduced voluntary mental imagery. As this lack of mental imagery disrupts visual memory, understanding the nature of this condition can provide important insight into memory, perception, and imagery. Here, we leveraged the power of case studies to better characterize this condition by running a pair of identical twins, one with aphantasia and one without, through mental imagery tasks in an fMRI scanner. We identified objective, neural measures of aphantasia, finding less visual information in their memories which may be due to lower connectivity between frontoparietal and occipitotemporal lobes of the brain. However, despite this difference, we surprisingly found more visual information in the aphantasic twin's memory than anticipated, suggesting that aphantasia is a spectrum rather than a discrete condition.

Bridging Glucose Metabolism and Intrinsic Functional Organization of the Human Cortex
The human brain requires a continuous supply of energy to function effectively. Here, we investigated how the low-dimensional organization of intrinsic functional connectivity patterns based on resting-state functional magnetic resonance imaging relates to brain energy expenditure measured by fluorodeoxyglucose positron emission tomography. By incrementally adding more dimensions of brain organization (via functional gradients), we were able to show that increasing amounts of variance in the map of brain energy expenditure could be accounted for. In particular, the brain organization dimensions that explained a large amount of the variance in intrinsic brain function also explained a large amount of the regional variance in the energy expenditure maps. This was particularly true for brain organization maps based on the strongest connections, suggesting that "weak" connections may not explain as much energy variance. Notably, our topological model was more effective than random brain organization configurations, suggesting that brain organization may be specifically associated with energy optimization. Finally, using brain asymmetry as a model for metabolic efficiency, we found that optimizing energy expenditure independently in each hemisphere outperformed non-independent optimization. This supports the concept of hemispheric competition rather than lateralization in energy allocation. Our results demonstrate how the spatial organization of functional connections is systematically linked to optimized energy expenditure in the human brain, providing new insights into the metabolic basis of brain function.

Individual-specific strategies inform category learning
Categorization is an essential task for sensory perception. Individuals learn category labels using a variety of strategies to ensure that sensory signals, such as sounds or images, can be assigned to proper categories. Categories are often learned on the basis of extreme examples, and the boundary between categories can differ among individuals. The trajectories for learning also differ among individuals, as different individuals rely on different strategies, such as repeating or alternating choices. However, little is understood about the relationship between individual learning trajectories and learned categorization. To study this relationship, we trained mice to categorize auditory stimuli into two categories using a two-alternative forced choice task. Because the mice took several weeks to learn the task, we were able to quantify the time course of individual strategies and how they relate to how mice categorize stimuli around the categorization boundary. Different mice exhibited different trajectories in learning the task. Mice displayed preferences for a specific category, manifested by a choice bias in their responses, but this bias drifted with learning. We found that this drift in choice bias correlated with variability in the category boundary for sounds with ambiguous category membership. Next, we asked how stimulus-independent, individual-specific strategies informed learning. We found that the tendency to repeat choices, which is a form of perseveration, contributed to long-term learning. These results indicate that long-term trends in individual strategies during category learning affect learned category boundaries.

Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes
Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

Properties of rhythmogenic currents in spinal Shox2 interneurons across postnatal development
Locomotor behaviors are performed by organisms throughout life, despite developmental changes in cellular properties, neural connectivity, and biomechanics. The basic rhythmic activity in the central nervous system that underlies locomotion is thought to be generated via a complex balance between network and intrinsic cellular properties. Within mature mammalian spinal locomotor circuitry, we have yet to determine which properties of spinal interneurons (INs) are critical to rhythmogenesis and how they change during development. Here, we combined whole cell patch clamp recordings, immunohistochemistry, and RNAscope targeting lumbar Shox2 INs in mice, which are known to be involved in locomotor rhythm generation. We focused on the properties of putatively rhythmogenic ionic currents and the expression of corresponding ion channels across postnatal time points in mice. We show that subsets of Shox2 INs display voltage-sensitive conductances, in addition to respective ion channels, which may contribute to or shape rhythmic bursting. Persistent inward currents, M-type potassium currents, slow afterhyperpolarization, and T-type calcium currents are enhanced with age. In contrast, the hyperpolarization-activated and A-type potassium currents were either found with low prevalence in subsets of neonatal, juvenile, and adult Shox2 INs or did not developmentally change. We show that Shox2 INs become more electrophysiologically diverse by juvenile and adult ages, when locomotor behavior is weight-bearing. These results suggest a developmental shift in the magnitude of rhythmogenic ionic currents and the expression of corresponding ion channels that may be important for mature, weight-bearing locomotor behavior.

Brain-derived neurotrophic factor and adenosine A2A receptor interaction modulates oligodendrogenesis derived from postnatal SVZ neural stem cells
Oligodendrocytes (OLs) are vital for myelin formation in the Central Nervous System. OLs can be produced by the maturation of oligodendrocyte precursor cells (OPCs) present throughout the brain parenchyma or from the differentiation of subventricular zone-derived neural stem cells (SVZ-NSCs). Importantly, efficient differentiation from SVZ-NSCs remains a significant area of research due to its potential for remyelination in demyelinating disorders. In this work, we studied the role of brain-derived neurotrophic factor (BDNF) and adenosine A2A receptors (A2ARs), as well as the putative crosstalk between these two modulatory mechanisms, in regulating oligodendrogenesis from SVZ-NSCs. Using a neurosphere culture system, we observed that BDNF significantly increased the mRNA expression levels of OPC cell markers after 2 days in vitro (DIV), an effect blocked by the A2AR antagonist ZM 241385. This early transcriptional regulation by BDNF was followed by changes in the percentage of both OPCs and mature OLs in culture at DIV 7 and 14. Interestingly, blocking A2ARs prevented the potentiating effect of BDNF on the percentage of OLs at DIV 14. Concerning the morphology of mature OLs, BDNF influenced their maturation by reducing branching near the soma at DIV 7, an effect that was not observed at 14 DIV, when all treatments resulted in similar OL morphology. Overall, our results establish BDNF as a regulator of OL formation from SVZ-NSCs, with A2AR-BDNF interaction modulating the differentiation process.

Distinct modulation of Ih by synaptic potentiation in excitatory and inhibitory neurons
Selective modifications in the expression or function of dendritic ion channels regulate the propagation of synaptic inputs and determine the intrinsic excitability of a neuron. Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels open upon membrane hyperpolarization and conduct a depolarizing inward current (Ih). HCN channels are enriched in the dendrites of hippocampal pyramidal neurons where they regulate the integration of synaptic inputs. Synaptic plasticity can bidirectionally modify dendritic HCN channels in excitatory neurons depending on the strength of synaptic potentiation. In inhibitory neurons, however, the dendritic expression and modulation of HCN channels is largely unknown. In this study, we systematically compared the modulation of Ih by synaptic potentiation in hippocampal CA1 pyramidal neurons and stratum Radiatum (sRad) interneurons. Ih properties were similar in inhibitory and excitatory neurons and contributed to resting membrane potential and action potential firing. We found that in sRad interneurons, HCN channels were downregulated after synaptic plasticity, irrespective of the strength of synaptic potentiation. This suggest differential regulation of Ih in excitatory and inhibitory neurons, possibly signifying their distinct role in network activity.

Comparative transcriptomic rhythms in the mouse and human prefrontal cortex
Alterations in multiple subregions of the human prefrontal cortex (PFC) have been heavily implicated in psychiatric diseases. Moreover, emerging evidence suggests that circadian rhythms in gene expression are present across the brain, including in the PFC, and that these rhythms are altered in disease. However, investigation into the potential circadian mechanisms underlying these diseases in animal models must contend with the fact that the human PFC is highly evolved and specialized relative to that of rodents. Here, we use RNA sequencing to lay the groundwork for translational studies of molecular rhythms through a sex-specific, cross species comparison of transcriptomic rhythms between the mouse medial PFC (mPFC) and two subregions of the human PFC, the anterior cingulate cortex (ACC) and the dorsolateral PFC (DLPFC). We find that while circadian rhythm signaling is conserved across species and subregions, there is a phase shift in the expression of core clock genes between the mouse mPFC and human PFC subregions that differs by sex. Furthermore, we find that the identity of rhythmic transcripts is largely unique between the mouse mPFC and human PFC subregions, with the most overlap (20%, 236 transcripts) between the mouse mPFC and the human ACC in females. Nevertheless, we find that basic biological processes are enriched for rhythmic transcripts across species, with key differences between regions and sexes. Together, this work highlights both the evolutionary conservation of transcriptomic rhythms and the advancement of the human PFC, underscoring the importance of considering cross-species differences when using animal models.

Cross-species evidence for a developmental origin of adult hypersomnia with loss of synaptic adhesion molecules beat-Ia/CADM2
Idiopathic hypersomnia (IH) is a poorly-understood sleep disorder characterized by excessive daytime sleepiness despite normal nighttime sleep. Combining human genomics with behavioral and mechanistic studies in fish and flies, we uncover a role for beat-Ia/CADM2, synaptic adhesion molecules of the immunoglobulin superfamily, in excessive sleepiness. Neuronal knockdown of Drosophila beat-Ia results in sleepy flies and loss of the vertebrate ortholog of beat-Ia, CADM2, results in sleepy fish. We delineate a developmental function for beat-Ia in synaptic elaboration of neuropeptide F (NPF) neurites projecting to the suboesophageal zone (SEZ) of the fly brain. Brain connectome and experimental evidence demonstrate these NPF outputs synapse onto a subpopulation of SEZ GABAergic neurons to stabilize arousal. NPF is the Drosophila homolog of vertebrate neuropeptide Y (NPY), and an NPY receptor agonist restores sleep to normal levels in zebrafish lacking CADM2. These findings point towards NPY modulation as a treatment target for human hypersomnia.

Cerebellar control of targeted tongue movements
The cerebellum is critical for coordinating movements related to eating, drinking and swallowing. Cerebellar Purkinje cell activity has been shown to encode ongoing tongue movements, but it is unclear how this activity can alter the trajectory of the tongue. To elucidate the impact of Purkinje cells on goal-directed tongue movements, we recorded their activity in the vermis and hemispheres during spontaneous licking from a stationary or moving water spout. Some Purkinje cells encode rhythmic tongue movements with their complex spikes, others with their simple spikes or a combination of both. Complex spikes predominantly marked the start and end of a licking bout, and thus encoded behavioural state changes, while simple spike firing was more related to individual licks. In addition, complex spikes reported unexpected changes in the position of the water spout and subsequent modulation of simple spike firing caused bending of the tongue, reaching out for the new target position. Using machine learning, we demonstrated that it is possible to predict licking activity based on the spiking patterns of individual Purkinje cells. Using optogenetic stimulation of Purkinje cells, we could experimentally replicate the impact of modulated simple spike firing, suggesting that increased simple spike activity indeed causes ipsilateral bending of the tongue during goal-directed movements. Our data highlight that directional control of movements is paramount in cerebellar function and that complex spike and simple spike modulation complement each other during sensorimotor coordination. These results bring us closer to understanding clinical implications of cerebellar disorders during eating, drinking and swallowing.

Regressing Away Common Neural Choice Signals does not make them Artifacts. Comment on Frömer et al (2024, Nature Human Behaviour)
The recent paper by Fromer et al (2024, Nature Human Behaviour) examines a component of the event-related potential (ERP) known as the centro-parietal positivity (CPP) that has been widely implicated in tracing the sensory evidence accumulation process underpinning perceptual decisions. Based on re-analysis of three pre-existing perceptual choice datasets, the authors claim that application of a deconvolution method designed to account for the overlap of stimulus- and response-aligned components, eliminates key signatures of evidence accumulation from the CPP measured in response-aligned average waveforms. From this the authors conclude that these apparent signatures were an artifact of component overlap and that the CPP may not trace evidence accumulation. Here, we argue that the analysis and interpretation of these perceptual choice data are critically flawed. First, we demonstrate with simple simulations that the deconvolution method used by the authors is categorically not designed to correctly capture evidence accumulation signals and, consequently, cannot reliably determine their presence or absence. Second, even within the parameters of their approach, we highlight that the results presented do not in fact support their claim that deconvolution eliminates evidence accumulation signatures from the CPP. Lastly, we list numerous signatures of evidence accumulation identified in the CPP in previous research, other than the response-aligned average waveform criteria on which the present work narrowly focused.

Protocol for simultaneous evaluation of neuronal activity and neurotransmitter release following chronic amyloid-beta oligomer injections into the hippocampus
In Alzheimer's disease, there is an imbalance in neurotransmitter release and altered neuronal activation. We present a novel approach to analyze neuronal activity by combining local field potential (LFP) recording with microdialysis within the same animal. This method measures glutamate and GABA levels following chronic hippocampal amyloid-beta oligomer (A{beta}o) injections in rats. We outline the design of our electrode and canula, the surgical procedure, and the steps for LFP recording, interstitial fluid collection, and A{beta}o injections simultaneously in living animal.

Intranasal therapies for neonatal hypoxic-ischemic encephalopathy: Systematic review, synthesis, and implications for global accessibility to care
Neonatal hypoxic-ischemic encephalopathy (HIE) is the leading cause of neurodevelopmental morbidity in term infants worldwide. Incidence of HIE is highest in low and middle-income communities with minimal access to neonatal intensive care and an underdeveloped infrastructure for advanced neurologic interventions. Moreover, therapeutic hypothermia, standard of care for HIE in high resourced settings, is shown to be ineffective in low and middle-income communities. With their low cost, ease of administration, and capacity to potently target the central nervous system, intranasal therapies pose a unique opportunity to be a more globally accessible treatment for neonatal HIE. Intranasal experimental therapeutics have been studied in both rodent and piglet models, but no intranasal therapeutics for neonatal HIE have undergone human clinical trials. Additional research must be done to expand the array of treatments available for use as intranasal therapies for neonatal HIE thus improving the neurologic outcomes of infants worldwide.

Spontaneous brain activity and synaptic density in schizophrenia: a combined UCB-J PET and fMRI study
Schizophrenia is associated with altered Amplitude of Low Frequency Fluctuations (ALFF), a functional Magnetic Resonance Imaging (fMRI) measure of spontaneous brain activity at rest. ALFF in healthy controls has been linked with presynaptic density levels measured by [11C]UCB-J positron emission tomography (PET). Given the growing body of evidence for low presynaptic density levels in schizophrenia, we set out to test if altered [11C]UCB-J binding may be associated with changes in ALFF in schizophrenia, and secondly to test whether the relationships between ALFF and [11C]UCB-J binding differ at the group level. Subjects with schizophrenia had higher ALFF in the medial prefrontal cortex and other regions, in line with published meta-analyses. In control subjects, there was a significant positive relationship between [11C]UCB-J distribution volume ratio (DVRcs) and ALFF in the medial prefrontal cortex (r=0.54, p=0.0365, n=16), but not in subjects with schizophrenia (r=-0.14, p=0.5564, n=22); r-coefficients significantly differed between groups (Zobserved=2.07, p=0.019). At the whole brain level, there were significant positive correlations between [11C]UCB-J DVRcs and ALFF in control subjects in the putamen, insular cortex, precentral gyrus and occipital regions, while in the schizophrenia group, there were significant positive correlations in the bilateral dorsolateral prefrontal cortex and negative correlations in the cuneus, parietal lobule and supramarginal gurus. Correlation coefficients were significantly different between groups across all cortical and subcortical regions with both higher and lower correlation coefficients in the control group. Our results suggest a link between spontaneous brain activity and presynaptic density in control subjects and that this relationship may be disrupted in schizophrenia patients, despite higher ALFF in this group, indicating altered neurobiological mechanisms. Widespread significant differences in ALFF-[11C]UCB-J DVRcs correlation coefficients between controls and schizophrenia subjects highlight the complexity of synaptic dysfunction in schizophrenia and underscore the need for further research to explore the underlying biological mechanisms.

A Method of Feature Selection via Deep Convolution Neural Networks For Encoding Nonlinear Functional Network Connectivity and Its Application To The Classification of Mental Disorders
In functional magnetic resonance imaging (fMRI) studies, it is common to evaluate the brain's functional network connectivity (FNC) which captures the temporal coupling between hemodynamic signals. FNC has been linked to various psychological phenomena. However, current FNCs mainly represent linear statistical relationships, which may not capture the fully complexity of the interactions among brain intrinsic connectivity networks (ICNs). Therefore, it is crucial to explore approaches that can better account for possible intricate nonlinear interactions involved in cognitive operations and the changes observed in psychiatric conditions such as schizophrenia. This exploration can lead to a better understanding of brain function and provide crucial insights into the underlying mechanisms of various psychological and psychiatric conditions. In this paper, we present an innovative approach which utilizes a deep convolutional neural network (DCNN) to extract nonlinear heatmaps from functional network connectivity matrices. By analyzing the heatmaps, multi-level nonlinear interactions can be derived from the corresponding input FNC data. Our results show these networks represent a significant improvement over previous approaches and offer a robust framework for understanding the complex inter-actions between brain regions. By incorporating two stages in the training process, our method ensures optimal efficiency and effectiveness. In the initial stage, a deep convolutional neural network is trained to create heatmaps from various convolution layers of the network. In the next stage, by utilizing a t-test-based feature selection method, we can effectively analyze each heatmap from different convolution layers. This approach ensures that we are able to extract linear and nonlinear functional connectivity from the heatmaps that play an important role in distinguishing different groups. We used a large dataset consisting of both schizophrenia patients and healthy controls, which were divided into separate training and validation sets to evaluate this approach. Our system shows the potential to accurately distinguish differences between the schizophrenia and healthy control groups with high accuracy.

The relationship between brain activation and mitochondrial complex I protein levels during cognitive function in healthy humans: an BCPP-EF PET and functional MRI study of task switching
Mitochondrial complex I is the largest enzyme complex in the respiratory chain and can be non-invasively measured using [18F]BCPP-EF positron emission tomography (PET). Neurological conditions associated with mitochondria complex I pathology are also associated with altered blood oxygen level dependent (BOLD) response and impairments in cognition. To evaluate the link between mitochondrial complex I, cognition and associated neural activity, 23 cognitively healthy adults underwent a [18F]BCPP-EF PET scan and a functional magnetic resonance imaging (fMRI) scan during which they performed a task switching exercise. We found significant positive associations between [18F]BCPP-EF volume of distribution (VT), which measures mitochondrial complex I levels and the task switching fMRI response (Partial Least Squares (PLS) Canonical Analysis (CA), first component r=0.51, p=0.03). Exploratory Pearson's correlations showed significant positive associations between mitochondrial complex I levels and the fMRI response in regions including the dorsolateral prefrontal cortex (r=0.61, p=0.0019), insula (r=0.46, p=0.0264) parietal-precuneus (r=0.51, p=0.0139) and anterior cingulate cortex (r=0.45, p=0.0293). Mitochondrial complex I levels across task-relevant regions were also predictive of task switching accuracy (PLS-Regression (PLS-R), R2=0.48, RMSE=0.154, p=0.011) and of switch cost (PLS-R, R^2=0.38, RMSE=0.07, p=0.048). Our findings suggest that higher mitochondrial complex I levels may underlie an individual's ability to exhibit a stronger BOLD response during task switching and are predictive of better task switching performance. This provides the first evidence linking the BOLD response with mitochondrial complex I and suggests a possible biological mechanism for aberrant BOLD response in conditions associated with mitochondrial complex I dysfunction, that should be tested in future studies.

CCR5 antagonists as neuroprotective and stroke recovery enhancing agents: a preclinical systematic review and meta-analysis
Background: C-C chemokine receptor type 5 (CCR5) antagonists may improve both acute stroke outcome and long-term recovery. Despite their evaluation in ongoing clinical trials, gaps remain in the evidence supporting their use.

Methods: With a panel of patients with lived experiences of stroke, we performed a systematic review of animal models of stroke that administered a CCR5 antagonist and assessed infarct size or behavioral outcomes. MEDLINE, Web of Science, and Embase were searched. Article screening and data extraction were completed in duplicate. We pooled outcomes using random effects meta-analyses. We assessed risk of bias using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool and alignment with the Stroke Treatment Academic Industry Roundtable (STAIR) and Stroke Recovery and Rehabilitation Roundtable (SRRR) recommendations.

Results: Five studies representing 10 experiments were included. CCR5 antagonists reduced infarct volume (standard mean difference -1.02; 95% confidence interval -1.58 to -0.46) when compared to stroke-only controls. Varied timing of CCR5 administration (pre- or post-stroke induction) produced similar benefit. CCR5 antagonists significantly improved 11 of 16 behavioral outcomes reported. High risk of bias was present in all studies and critical knowledge gaps in the preclinical evidence were identified using STAIR/SRRR.

Conclusions: CCR5 antagonists demonstrate promise; however, rigorously designed preclinical studies that better align with STAIR/SRRR recommendations and downstream clinical trials are warranted.

Registration: Prospective Register of Systematic Reviews (PROSPERO CRD42023393438)

Causality Mapping Using Resting-state fMRI Reveals Hyperactivity and Hypoconnectivity in Schizophrenia Patients
Schizophrenia (SZ) is a debilitating disorder in which patients exhibit psychotic behavior due to aberrant connectivity between different regions of the brain. Advances in neuroimaging have now enabled the diagnosis and analysis of SZ in order to elucidate the whole brain functional connectivity networks. In the present study, we have used resting-state functional magnetic resonance imaging (rs-fMRI) to elucidate the causal relationships amongst the differentially activated brain regions between SZ patients (n=10) and healthy controls (n=10). Vector auto-regression (VAR) model and Granger causality (GC) were then applied to construct a functional connectivity network and analyze the causal effects in SZ patients. Our results revealed that the average voxel activation in the frontal lobe (FL), basal ganglia (BG), and ventricular system (VS) was significantly higher in patients indicating hyper-activity as compared to controls. Conversely, cerebellum white matter (CBWM) showed higher activation in the controls as compared to patients. A higher Pearson correlation was observed between the controls as compared to patients while VAR and GC showed higher functional connectivity among all the regions of interest (ROIs) along with more causal relations in the controls. Finally, mediation analysis showed that right middle superior frontal gyrus acts as a strong partial mediator between left accumbens area and left middle superior frontal gyrus. Taken together, this study decodes the dysregulated brain activity in schizophrenia showing hyperactivation in patients when compared with the healthy controls which leads to alterations in neural connections resulting in hypoconnectivity.

Repeated low-intensity focused ultrasound led to microglial profile changes at long term in TgF344-AD rats
Alzheimer's disease (AD), the most common cause of dementia, represents one of the main clinical challenges of the century as the number of patients is predicted to triple by 2050. Despite the recent approval of three monoclonal antibodies targeting Amyloid {beta} (A{beta}) aggregates by the Food and Drug Administration (FDA), immunotherapies still face challenges due to the difficulty of antibodies crossing the blood-brain barrier (BBB). This necessitates administering large doses of drugs to achieve their therapeutic effects, which is associated with significant side effects. In this context, low-intensity focused ultrasound (LiFUS) appears as an innovative and non-invasive method which, in association with intravenous injection of microbubbles (MB), leads to a transient BBB opening. This innovative strategy has been extensively studied in different preclinical models and more recently in human clinical trials, particularly in the context of AD. LiFUS+MB seems to increase the inflammatory response at short term, but the time course of this response is not consistent between studies, certainly due to the discrepancy between LiFUS protocols used. Moreover, the impact at longer term is understudied and the mechanisms underlying this effect are still not well understood. In our study, we therefore used the TgF344-AD rat model of AD, to investigate the effect of a single or multiple exposures to LiFUS+MB in the entire brain, on inflammatory response and amyloid load. The ultrasound attenuation through the skull was corrected to apply a peak negative acoustic pressure of 450 kHz in all treated animals. Single LiFUS+MB exposure induces a slight astrocyte and microglial response 24 hours post-treatment whereas repeated LiFUS treatment seems to induce microglial reprogramming, leading to the adaptation of gene expression related to key functions such as inflammatory response, mitochondrial and energetic metabolism. In our rat model and LiFUS+MB protocol conditions, multiple exposures did not modulate soluble/poorly aggregated forms nor the highly aggregated forms of A{beta}40 and A{beta}42. For therapeutic AD management, LiFUS+MB could be combined with drugs such as immunotherapies. In a proof-of-concept experiment, we validated that LiFUS was also efficient to improve the brain entry of the anti-A{beta} antibody, Aducanumab.

Disarming emotional memories using Targeted Memory Reactivation during Rapid Eye Movement sleep
Emotional responses are dampened across sleep, and this is thought to be mediated by neural reactivation during Rapid Eye Movement (REM) Sleep. Such reactivation can be triggered by targeted memory reactivation (TMR), a technique in which a tone previously associated with a memory during wake is re-presented during subsequent sleep. Prior work has shown that TMR in REM reduces arousal responses to negative stimuli. The present study builds on this by measuring autonomic responses and brain activity as well as behaviour. Participants rated the arousal of 48 affective images, paired with semantically matching sounds. Half of these sounds were cued during REM in the subsequent overnight sleep cycle. Participants rated the images in a Magnetic Resonance Imaging (MRI) scanner with pulse oximetry 48 hours after encoding, and again after two weeks. Results showed that TMR during REM was also associated with reduced brain activity in the two primary nodes of the Salience Network (SN): the Anterior Insula and dorsal Anterior Cingulate Cortex (dACC), as well as the orbitofrontal cortex, subgenual cingulate, and left amygdala, all of which are known to be important for emotional processing. TMR markedly reduced the emotional heart rate deceleration (HRD) response, and also reduced subjective arousal ratings for highly arousing images, while increasing ratings for less arousing images. We conclude that REM TMR can facilitate a decrease in physiological and neurological responses to arousal. These findings have potential implications for the use of TMR in treatment of depression and anxiety disorders.

Temporal Information Encoding in Isolated Cortical Networks
Time-dependent features are present in many sensory stimuli. In the sensory cortices, timing features of stimuli are represented by spatial as well as temporal code. A potential mechanism by which cortical neuronal networks perform temporal-to-spatial conversion is reservoir computing. The state of a recurrently-connected network (reservoir) represents not only the current stimulus, or input, but also prior inputs. In this experimental study, we determined whether the state of an isolated cortical network could be used to accurately determine the timing of occurrence of an input pattern or, in other words, to convert temporal input features into spatial state of the network. We used an experimental system based on patterned optogenetic stimulation of dissociated primary rat cortical cultures, and read out activity via fluorescent calcium indicator. We delivered input sequences of patterns such that a pattern of interest occurred at different times. We developed a readout function for network state based on a support vector machine (SVM) with recursive feature elimination and custom error correcting output code. We found that the state of these experimental networks contained information about inputs for at least 900 msec. Timing of input pattern occurrence was determined with 100 msec precision. Accurate classification required many neurons, suggesting that timing information was encoded via population code. Trajectory of network state was largely determined by spatial features of the stimulus, with temporal features having a more subtle effect. Local reservoir computation may be a plausible mechanism for temporal/spatial code conversion that occurs in sensory cortices.

Foundry-fabricated dual-color nanophotonic neural probes for photostimulation and electrophysiological recording
Significance: Compact tools capable of delivering multicolor optogenetic stimulation to deep tissue targets with sufficient span, spatiotemporal resolution, and optical power remain challenging to realize. Here, we demonstrate foundry-fabricated nanophotonic neural probes for blue and red photostimulation and electrophysiological recording, which use a combination of spatial multiplexing and on-shank wavelength-demultiplexing to increase the number of on-shank emitters. Aim: We demonstrate Si photonic neural probes with 26 photonic channels and 26 recording sites, which were fabricated on 200-mm diameter wafers at a commercial Si photonics foundry. Each photonic channel consists of an on-shank demultiplexer and separate grating coupler emitters for blue and red light, for a total of 52 emitters. Approach: We evaluate neural probe functionality through bench measurements and in vivo experiments by photostimulating through 16 of the available 26 emitter pairs. Results: We report neural probe electrode impedances, optical transmission, and beam profiles. We validated a packaged neural probe in optogenetic experiments with mice sensitive to blue or red photostimulation. Conclusions: Our foundry-fabricated nanophotonic neural probe demonstrates dense dual-color emitter integration on a single shank for targeted photostimulation. Given its two emission wavelengths, high emitter density, and long site span, this probe will facilitate experiments involving bidirectional circuit manipulations across both shallow and deep structures simultaneously.

Oscillatory dynamics of sustained attention states
Sustained attention enables individuals to concentrate on a task over an extended period of time. This ability is known to fluctuate, resulting in periods of effective task focus ('in the zone') and periods of increased performance variability and susceptibility to errors ('out of the zone'). Little is known about the oscillatory neural dynamics that underlie each of these states and their transitions during sustained attention tasks. To address this, we had thirty young adults perform the gradual onset continuous performance task (gradCPT), during which their EEG and behavioural responses were recorded. States of sustained attention (in vs. out of the zone) throughout the task were identified based on the variance time course of participants' reaction times. Out of the zone states were associated with increased errors of commission and reduced perceptual sensitivity compared to in the zone states, as expected. Importantly, a significant decline in theta oscillations at mid-prefrontal regions was found in out of the zone (vs. in the zone) states over a ~400 ms period around the transition point between stimuli, and the extent of this decline predicted commission errors, RT variability, and response bias. In addition, individual differences in the variability of frontal theta rhythm were associated with RT variability. Finally, participants exhibiting greater theta variability showed a more pronounced decline in perceptual sensitivity when out of the zone and less stable RTs compared to those with lower variability. Our results suggest that states of diminished sustained attention, even during short lapses, are characterized by a reduction in frontal theta activity, and that fluctuations in this rhythm covary with fluctuations in attentional control.

Multiplatform lipid analysis of the brain of aging mice by mass spectrometry
Lipids are an integral part of brain structure and function and represent about 50% of the dry weight of the brain. Despite their importance, the complexity and variations in the abundance of brain lipids due to aging remain poorly understood. For maximum coverage and multi-platform validation, we applied three complementary mass spectrometry-based analytical approaches: multiple reaction monitoring (MRM) profiling, untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS), and desorption electrospray ionization-MS imaging (DESI-MSI). We used three different age groups of mice, namely adult (3-4 months), middle-aged (10 months) and old (19-21 months). Phospholipids including phosphatidylcholine (PC), phosphatidylethanolamine (PE) and phosphatidylglycerol (PG) showed higher abundance, while phosphatidylinositols (PI) and phosphatidylserines (PS) generally showed lower abundance in the brains of old mice compared to adults or middle-aged mice. Polyunsaturated fatty acids, such as docosahexaenoic acid (DHA) and arachidonic acid (AA), as well as hexosylceramides (HexCer), sulfated hexosylceramides (SHexCer) and sphingomyelins (SM) were among the most abundant lipid species in the brains of old mice. DESI-MSI showed variations in the spatial distribution of many of the lipids confirmed by MRM and LC-MS/MS profiling. Interrogation of lipidomic data with recent proteomics data obtained from the same tissues revealed changes in the abundance and phosphorylation levels of several proteins potentially linked to ceramide (Cer), hexosylceramide (HexCer), fatty acids (FA), phosphatidylinositol (PI), sphingomyelin (SM) and sulfatides (SHexCer) metabolism and correlated well with the multiplatform lipid surveillance. Our findings offer insight into age-dependent changes in brain lipid profiles and their potential contribution to age-related cognitive decline.

Challenges and advances for huntingtin detection in cerebrospinal fluid: in support of relative quantification
Huntington disease (HD) is a progressive and devastating neurodegenerative disease caused by expansion of a glutamine-coding CAG tract in the huntingtin (HTT) gene above a critical threshold of ~35 repeats resulting in expression of mutant HTT (mHTT). A promising treatment approach being tested in clinical trials is HTT lowering, which aims to reduce levels of the mHTT protein. Target engagement of these therapies in the brain are inferred using antibody-based assays to measure mHTT levels in the cerebrospinal fluid (CSF), which is frequently reported as absolute mHTT concentration based on a monomeric protein standard used to generate a standard curve. However, patient biofluids are a complex milieu of different mHTT protein species, suggesting that absolute quantitation is challenging, and a single, recombinant protein standard may not be sufficient to interpret assay signal as molar mHTT concentration. In this study, we used immunoprecipitation and flow cytometry (IP-FCM) to investigate different factors that influence mHTT detection assay signal. Our results show that HTT protein fragmentation, protein-protein interactions, affinity tag positioning, oligomerization and polyglutamine tract length affect assay signal intensity, indicating that absolute HTT quantitation in heterogeneous biological samples is not possible with current technologies using a single standard protein. We also explore the binding specificity of the MW1 anti-polyglutamine antibody, commonly used in these assays as a mHTT-selective reagent and demonstrate that mHTT binding is preferred but not specific. Furthermore, we find that MW1 depletion is not only incomplete, leaving residual mHTT, but also non-specific, resulting in pull down of some wildtype HTT protein. Based on these observations, we recommend that mHTT detection assays report only relative mHTT quantitation using normalized arbitrary units of assay signal intensity, rather than molar concentrations, in the assessment of central nervous system HTT lowering in ongoing clinical and preclinical studies, and that MW1-depletion not be used a method for quantifying wildtype HTT protein.

The representation of visual motion and landmark position aligns with heading direction in the zebrafish interpeduncular nucleus
Sensory information is fundamental for navigation. Visual motion is used by animals to estimate their traveling distance and direction, and visual landmarks allow animals to tether their location and orientation to their environment. How such signals are integrated in the vertebrate brain is poorly understood. Here we investigate the representation of directional whole field visual motion and landmark position in a circuit in the larval zebrafish consisting of the habenula, interpeduncular nucleus (IPN) and anterior hindbrain (aHB). This circuit has been recently implicated in the representation of heading direction. Using calcium imaging we show that these stimuli are represented in the habenula, IPN and aHB. We further show that their representation in the IPN of both these stimuli is topographically arranged in a way that aligns itself with the representation of the heading signal in this region. We use neuronal ablations to show that the landmark responses, but not the whole field motion responses, require intact habenula input to the IPN. Overall our findings suggest the IPN as a site for integration of the heading signal from the aHB with visual information, shedding light on how different types of navigational signals are processed in the vertebrate brain.

Maintenance of synaptic plasticity by negative-feedback of synaptic protein elimination: Dynamic modeling of KIBRA-PKMζ interactions in LTP and memory
Activity-dependent modifications of synaptic efficacies are a cellular substrate of learning and memory. Current theories propose that the long-term maintenance of synaptic efficacies and memory is accomplished via a positive-feedback loop at the level of production of a protein species or a protein state. Here we propose a qualitatively different theoretical framework based on negative-feedback at the level of protein elimination. This theory is motivated by recent experimental findings regarding the binding of PKM{zeta} and KIBRA, two synaptic proteins involved in maintenance of memory, and on how this binding affects the proteins' degradation. We demonstrate this theoretical framework with two different models, a simple abstract model to explore generic features of such a process, and an experimentally motivated phenomenological model. The results of these models are qualitatively consistent with existing data, and generate novel predictions that could be experimentally tested to further validate or reject the negative-feedback theory.

Retinotopic coding organizes the opponent dynamic between internally and externally oriented brain networks
How the human brain integrates internally- (i.e., mnemonic) and externally-oriented (i.e., perceptual) information is a long-standing puzzle in neuroscience. In particular, the internally-oriented networks like the default network (DN) and externally-oriented dorsal attention networks (dATNs) are thought to be globally competitive, which implies DN disengagement during cognitive states that drive the dATNs and vice versa. If these networks are globally opposed, how is internal and external information integrated across these networks? Here, using precision neuroimaging methods, we show that these internal/external networks are not as dissociated as traditionally thought. Using densely sampled high-resolution fMRI data, we defined individualized whole-brain networks from participants at rest, and the retinotopic preferences of individual voxels within these networks during an independent visual mapping task. We show that while the overall network activity between the DN and dATN is opponent at rest, a latent retinotopic code structures this global opponency. Specifically, the anti-correlation (i.e., global opponency) between the DN and dATN at rest is structured at the voxel-level by each voxel's retinotopic preferences, such that the spontaneous activity of voxels preferring similar visual field locations are more anti-correlated than those that prefer different visual field locations. Further, this retinotopic scaffold integrates with the domain-specific preferences of subregions within these networks, enabling efficient, parallel processing of retinotopic and domain-specific information. Thus, DN and dATN dynamics are opponent, but not competitive: voxel-scale anti-correlation between these networks preserves and encodes information in the negative BOLD responses, even in the absence of visual input or task demands. These findings suggest that retinotopic coding may serve as a fundamental organizing principle for brain-wide communication, providing a new framework for understanding how the brain balances and integrates internal cognition with external perception.

Mapping the aggregate g-ratio of white matter tracts using multi-modal MRI
The g-ratio of a myelinated axon is defined as the ratio of the inner-to-outer diameter of the myelin sheath and modulates conduction speed of action potentials along axons. This g-ratio can be mapped in vivo at the macroscopic scale across the entire human brain using multi-modal MRI and sampled along white matter streamlines reconstructed from diffusion-weighted images to derive the g-ratio of a white matter tract. This tractometry approach has shown spatiotemporal variations in myelin g-ratio across white matter tracts and networks. However, tractometry is biased by partial volume effects where voxels contain multiple fiber populations. To address this limitation, we used the Convex Optimization Modeling for Microstructure-Informed Tractography (COMMIT) framework to derive tract-specific axonal and myelin volumes, which are used to compute the tract-specific aggregate g-ratio. We compare our novel COMMIT-based tract-specific g-ratio mapping approach to conventional tractometry in a group of 10 healthy adults. Our findings demonstrate that the tract-specific g-ratio mapping approach preserves the overall spatial distribution observed in tractometry and enhances contrast between tracts. Additionally, our scan-rescan data shows high repeatability for medium to large caliber tracts. We show that short and large caliber tracts have a lower g-ratio, whereas tractometry results show the opposite trends. This technique advances tract-specific analysis by reducing biases introduced by the complex network of crossing white matter fibers.

Maximizing memory capacity in heterogeneous networks
A central problem in neuroscience is identifying the features of neural networks that determine their memory capacity and assessing whether these features are optimized in the brain. In this study, we estimate the capacity of a general class of network models. Our derivation extends previous theoretical results, which assumed homogeneous connections and coding levels (i.e., activation rates of the neurons in memory patterns), to models with arbitrary architectures (varying constraints on the arrangement of connections between cells) and heterogeneous coding levels. Using our analytical results, we estimate the memory capacity of two types of brain-inspired networks: a general class of heterogeneous networks and a two-layer model simulating the CA3-Dentate Gyrus circuit in the hippocampus, known to be crucial for memory encoding. In the first case, we demonstrate that to maximize memory capacity, the number of inward connections and the coding levels of neurons must be correlated, presenting a normative prediction that is amenable to experimental testing. In the second case, we show that memory capacity is maximized when the connectivity and coding levels are consistent with the formation of memory "indices" in the Dentate Gyrus, which bind features in the CA3 layer. This suggests specific neural substrates for the hippocampal index theory of memory encoding and retrieval.