bioRxiv Subject Collection: Neuroscience's Journal

The lateral habenula is required for maternal behavior in the mouse dam
Mammalian parenting is an unusually demanding commitment. How did evolution co-opt the reward system to ensure parental care? Previous work has implicated the lateral habenula (LHb), an epithalamic nucleus, as a potential intersection of parenting behavior and reward. Here, we examine the role of the LHb in the maternal behavior of naturally parturient mouse dams. We show that kainic acid lesions of the LHb induced a severe maternal neglect phenotype in dams towards their biological pups. Next, we demonstrate that through chronic chemogenetic inactivation of the LHb using DREADDs impaired acquisition and performance of various maternal behaviors, such as pup retrieval and nesting. We present a random intercepts model suggesting LHb-inactivation prevents the acquisition of the novel pup retrieval maternal behavior and decreases nest building performance, an already-established behavior, in primiparous mouse dams. Lastly, we examine the spatial histology of kainic-acid treated dams with a random intercepts model, which suggests that the role of LHb in maternal behavior may be preferentially localized at the posterior aspect of this structure. Together, these findings serve to establish the LHb as required for maternal behavior in the mouse dam, thereby complementing previous findings implicating the LHb in parental behavior using pup-sensitized virgin female mice.

iSCORE-PD: an isogenic stem cell collection to research Parkinson Disease
Parkinson's disease (PD) is a neurodegenerative disorder caused by complex genetic and environmental factors. Genome-edited human pluripotent stem cells (hPSCs) offer the uniique potential to advance our understanding of PD etiology by providing disease-relevant cell-types carrying patient mutations along with isogenic control cells. To facilitate this experimental approach, we generated a collection of 55 cell lines genetically engineered to harbor mutations in genes associated with monogenic PD (SNCA A53T, SNCA A30P, PRKN Ex3del, PINK1 Q129X, DJ1/PARK7 Ex1-5del, LRRK2 G2019S, ATP13A2 FS, FBXO7 R498X/FS, DNAJC6 c.801 A>G+FS, SYNJ1 R258Q/FS, VPS13C A444P, VPS13C W395C, GBA1 IVS2+1). All mutations were generated in a fully characterized and sequenced female human embryonic stem cell (hESC) line (WIBR3; NIH approval number NIHhESC-10-0079) using CRISPR/Cas9 or prime editing-based approaches. We implemented rigorous quality controls, including high density genotyping to detect structural variants and confirm the genomic integrity of each cell line. This systematic approach ensures the high quality of our stem cell collection, highlights differences between conventional CRISPR/Cas9 and prime editing and provides a roadmap for how to generate gene-edited hPSCs collections at scale in an academic setting. We expect that our isogenic stem cell collection will become an accessible platform for the study of PD, which can be used by investigators to understand the molecular pathophysiology of PD in a human cellular setting.

The transformation of sensory to perceptual braille letter representations in the visually deprived brain
Experience-based plasticity of the human cortex mediates the influence of individual experience on cognition and behavior. The complete loss of a sensory modality is among the most extreme such experiences. Investigating such a selective, yet extreme change in experience allows for the characterization of experience-based plasticity at its boundaries. Here, we investigated information processing in individuals who lost vision at birth or early in life by probing the processing of braille letter information. We characterized the transformation of braille letter information from sensory representations depending on the reading hand to perceptual representations that are independent of the reading hand. Using a multivariate analysis framework in combination with fMRI, EEG and behavioral assessment, we tracked cortical braille representations in space and time, and probed their behavioral relevance. We located sensory representations in tactile processing areas and perceptual representations in sighted reading areas, with the lateral occipital complex as a connecting hinge region. This elucidates the plasticity of the visually deprived brain in terms of information processing. Regarding information processing in time, we found that sensory representations emerge before perceptual representations. This indicates that even extreme cases of brain plasticity adhere to a common temporal scheme in the progression from sensory to perceptual transformations. Ascertaining behavioral relevance through perceived similarity ratings, we found that perceptual representations in sighted reading areas, but not sensory representations in tactile processing areas are suitably formatted to guide behavior. Together, our results reveal a nuanced picture of both the potentials and limits of experience-dependent plasticity in the visually deprived brain.

Molecular signatures of cortical expansion in the human fetal brain
The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas. Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, Brain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, Brain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones. Using Brain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The Brain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.

Individual differences in the boldness of female zebrafish are associated with alterations in serotonin function.
One of the most prevalent axes of behavioral variation in both humans and animals is risk taking, where individuals that are more willing to take risk are characterized as bold while those that are more reserved as shy. Brain monoamines (i.e., serotonin, dopamine, and norepinephrine) have been found to play a role in a variety of behaviors related to risk taking. Genetic variation related to monoamine function have also been linked to personality in both humans and animals. Using zebrafish, we investigated the relationship between monoamine function and boldness behavior during exploration of a novel tank. We found a sex-specific correlation between serotonin metabolism (5-HIAA:5-HT ratio) and boldness that was limited to female animals; there were no relationships between boldness and dopamine or norepinephrine. To probe differences in serotonergic function, we administered a serotonin reuptake inhibitor, escitalopram, to bold and shy fish, and assessed their exploratory behavior. We found that escitalopram had opposing effects on thigmotaxis in female animals with bold fish spending more time near the center of the tank and shy fish spent more time near the periphery. Taken together, our findings suggest that variation in serotonergic function makes sex-specific contributions to individual differences in risk taking behavior.

How muscle synergies fail to solve the muscle redundancy problem during human reaching
The production of movement involves integrating biomechanical, neural, and environmental factors. The biomechanics is complex enough that neural sensorimotor circuits must embed its dynamics for efficient and robust control. However, a problem of redundancy exists, i.e., the problem of choosing among multiple muscles and combinations of joint angles that are possible for a given desired hand position or motion. This problem may be resolved by reducing the dimensionality of the space of motor commands by the central nervous system, i.e., through muscle synergies or motor primitives. Other studies have obtained muscle synergies using decomposition methods. However, we posit that it is not sufficient to show the existence of a low dimensional space, one needs to demonstrate the utility of the obtained synergies in controlling movement. Here we defined a muscle synergy as a single control signal producing specific force direction. We then tested a hypothesis that such muscle synergies exist using dimensionality reduction method. Our approach takes advantage of the close relationship between the temporal profiles of muscle activity observed with electromyography and the joint moments they produce during reaching derived from motion capture. We recorded electromyography of 12 muscles and the kinematics of both arms in 14 right-handed participants performing reaching movements in multiple directions from different starting positions. We used principal component analysis to evaluate the contribution of individual muscles to supporting the arm against gravity and producing propulsive forces. Results show that the joint torques in specific directions (flexion or extension) required to move toward a target were not produced by consistent muscle groups in most conditions as would be expected from the muscle synergy definition outlined above. We further show that both agonistic and antagonistic muscles coactivate in flexible muscle groups but to a different extent between the dominant and non-dominant arms. Our main findings indicate that the nervous system solves the problem of choosing which muscles to activate and when by taking into account limb dynamics rather than reducing the dimensionality through muscle synergies. Furthermore, our data supports the idea of two neural controllers that target different muscle groups in the arm and hand for gross postural and fine goal-directed control of reaching.

The spatial representation of temperature in the thalamus
Although distinct thalamic nuclei encode sensory information for almost all sensory modalities, the existence of a thalamic representation of temperature is debated and the role of the thalamus in thermal perception remains unclear. To address this, we used high-density electrophysiological recordings across mouse forepaw somatosensory thalamus, and identified an anterior and a posterior representation of temperature that spans three thalamic nuclei. These parallel representations show fundamental differences in the cellular encoding of temperature that reflect their cortical output targets, with the anterior representation encoding cool only and the posterior both cool and warm. Moreover, their inactivation profoundly altered thermal perception. Together our data identifies a novel posterior thalamic representation of temperature and a principal role of the thalamus in thermal perception.

Coexistence of asynchronous and clustered dynamics in noisy inhibitory neural networks
A regime of coexistence of asynchronous and clustered dynamics is analyzed for globally coupled homogeneous and heterogeneous inhibitory networks of quadratic integrate-and-fire (QIF) neurons subject to Gaussian noise. The analysis is based on accurate extensive simulations and complemented by a mean-field description in terms of low-dimensional next generation neural mass models for heterogeneously distributed synaptic couplings. The asynchronous regime is observable at low noise and becomes unstable via a sub-critical Hopf bifurcation at sufficiently large noise. This gives rise to a coexistence region between the asynchronous and the clustered regime. The clustered phase is characterized by population bursts in the {gamma}-range (30-120 Hz), where neurons are split in two equally populated clusters firing in alternation. This clustering behaviour is quite peculiar: despite the global activity being essentially periodic, single neurons display switching between the two clusters due to heterogeneity and/or noise.

Atlas for the Lateralized Visuospatial Attention Networks (ALANs): Insights from fMRI and Network Analyses
Hemispheric specialization is central to human evolution and fundamental to human cognitive abilities. While being a defining feature of functional brain architecture, hemispheric specialization is overlooked to derive brain parcellations. Alongside language, which is typically lateralized in the left hemisphere, visuospatial attention is set to be its counterpart in the opposite hemisphere. However, it remains uncertain to what extent the anatomical and functional underpinnings of lateralized visuospatial attention mirror those supporting language. Building on our previous work, which established a lateralized brain atlas for language, we propose a comprehensive cerebral lateralized atlas delineating the anatomo-functional bases of visuospatial attention, ALANs. Combining task and resting-state functional connectivity analyses, we identified 95 lateralized brain areas comprising five networks supporting visuospatial attention processes. Among them, we can find two large-scale networks: the ParietoFrontal and TemporoFrontal networks. We identify hubs playing a pivotal role in the intra-hemispheric interaction within visuospatial attentional networks. The rightward lateralized ParietoFrontal encompasses one hub, the inferior frontal sulcus, while the TemporoFrontal network encompasses two right hubs: the inferior frontal cortex (pars triangularis and the anterior insula) and the posterior part of the superior temporal sulcus. Compared with our language lateralized atlas, we demonstrated that specific regions within these networks encompass the homotope of the language network from the left hemisphere. This atlas of visuospatial attention provides valuable insights for future investigations into the variability of visuospatial attention and hemispheric specialization research. Additionally, it facilitates more effective comparisons among different studies, thereby enhancing the robustness and reliability of research in the field of attention.

Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014)
One organizing principle of the human brain is hemispheric specialization, or the dominance of a specific function or cognitive process in one hemisphere or the other. Previously, Wang et al. (2014) identified networks putatively associated with language and attention as being specialized to the left and right hemispheres, respectively; and a dual-specialization of the executive control network. However, it remains unknown which networks are specialized when specialization is examined within individuals using a higher resolution parcellation, as well as which connections are contributing the most to a given network's specialization. In the present study, we estimated network specialization across three datasets using the autonomy index and a novel method of deconstructing network specialization. After examining the reliability of these methods as implemented on an individual level, we addressed two hypotheses. First, we hypothesized that the most specialized networks would include those associated with language, visuospatial attention, and executive control. Second, we hypothesized that within-network contributions to specialization would follow a within-between network gradient or a specialization gradient. We found that the majority of networks exhibited greater within-hemisphere connectivity than between-hemisphere connectivity. Among the most specialized networks were networks associated with language, attention, and executive control. Additionally, we found that the greatest network contributions were within-network, followed by those from specialized networks.

Neural codes track prior events in a narrative and predict subsequent memory for details
Throughout our lives, we learn schemas that specify what types of events to expect in certain contexts and the temporal order in which these events typically unfold. Here, our first goal was to investigate how such context-dependent temporal structures are represented in the brain during processing of temporally-extended events. To accomplish this, we ran a 2-day fMRI study in which we exposed participants to many unique animated videos of weddings composed of sequences of rituals; each sequence originated from one of two fictional cultures (North and South), where rituals were shared across cultures, but the transition structure between these rituals differed across cultures. The results, obtained using representational similarity analysis and subsequently confirmed with an unsupervised (K-means) algorithm, revealed that context-dependent temporal structure is represented in multiple ways in parallel, including distinct neural representations for the culture, for particular sequences, and for past and current events within the sequence. Our second goal was to test the hypothesis that neural schema representations scaffold memory for specific details. In keeping with this hypothesis, we found that the strength of the neural representation of the North/South schema for a particular wedding predicted subsequent episodic memory for the details of that wedding.

Task-guided Generative Adversarial Networks for Synthesizing and Augmenting Structural Connectivity Matrices for Connectivity-Based Prediction
Recent machine learning techniques have improved the modeling of complex dependencies between brain connectivity and cognitive/behavioral traits, facilitating connectome-based predictions. However, they typically require large datasets. While large open datasets like the Human Connectome Project have offered significant benefits to connectomics research, collecting such large data remains a challenge due to the financial cost and time. To address this issue, we propose Task-guided GAN II, a novel data augmentation method leveraging generative adversarial networks (GANs) to enhance the sample size from limited datasets for connectome-based prediction tasks. Distinguishing from previous approaches, our method incorporates a task- guided branch within the conventional Wasserstein GAN framework, specifically designed to synthesize structural connectivity matrices. It aims to effectively augment data and improve the prediction accuracy of human cognitive traits by capturing more task-directed features within the data. We evaluated the effectiveness of data augmentation using Task-guided GAN II in predicting fluid intelligence utilizing the NIMH Health Research Volunteer Dataset. Our results demonstrate that data augmentation with Task-guided GAN II not only improves prediction accuracy but also ensures that its latent space effectively captures correlations between structural connectivity and cognitive outcomes. Our method would be beneficial in leveraging small datasets for human connectomics research.

MicroRNA-29a-5p Attenuates Oxygen-glucose Deprivation and Reoxygenation Injury in Astrocytes by Targeting Glycogen Synthase Kinase beta
Background: Cerebral ischemia-reperfusion injury (CIRI) after endovascular reperfusion treatment is associated with adverse prognosis in acute ischemic stroke patients. MicroRNAs contribute to CIRI and become the diagnostic and prognosis biomarkers for acute ischemic stroke. In this study, we investigate the role of microRNA-29a-5p in CIRI in the oxygen-glucose deprivation and reoxygenation (OGD/R) model of neurovascular cells. Methods: The expression of microRNA-29a-5p in rat neurons, astrocytes, brain microvascular endothelial cells, microglia, and pericytes model of OGD/R were assessed. The astrocyte injury, phenotype shifting of reactive astrocytes, and regulation of microRNA-29a-5p target genes were evaluated after microRNA-29a-5p mimics and inhibitors treatment in the OGD/R model. Results: MicroRNA-29a-5p decreased in the astrocyte model 24 hours after OGD/R but did not significantly change in the other neurovascular cells after OGD/R. Twelve predicted target genes for microRNA-29a-5p were significantly differentially expressed in the astrocyte OGD/R model; eleven participated in the Wnt signaling pathway. Increased microRNA-29a-5p alleviated astrocyte injury and cell apoptosis. Overexpression of microRNA-29a-5p suppressed neurotoxic A1 astrocyte markers of complement 3, FK506 binding protein 51, and Serping1 and increased neuroprotective A2 astrocyte markers of S100a10, Pentraxin 3, and Emp1. MicroRNA-29a-5p effectively regulated the direct target gene of Glycogen synthase kinase (GSK)-3beta expression and its downstream beta-catenin in astrocytes after OGD/R. Conclusions: MicroRNA-29a-5p alleviated astrocyte injury, transformed the A1/A2 phenotype of reactive astrocyte, and regulated its direct target gene of GSK-3beta and its downstream mediator of beta-catenin in astrocytes after OGD/R. Astrocytic microRNA-29a-5p may be a protective target for reducing CIRI.

Identification of hippocampal area CA2 in hamster and vole brain
Prairie voles (Microtus ochrogaster) and Syrian, or golden, hamsters (Mesocricetus auratus) are closely related to mice (Mus musculus) and rats (Rattus norvegicus, for example) and are commonly used in studies of social behavior including social interaction, social memory, and aggression. The CA2 region of the hippocampus is known to play a key role in social memory and aggression in mice and responds to social stimuli in rats, likely owing to its high expression of oxytocin and vasopressin 1b receptors. However, CA2 has yet to be identified and characterized in hamsters or voles. In this study, we sought to determine whether CA2 could be identified molecularly in vole and hamster. To do this, we used immunofluorescence with primary antibodies raised against known molecular markers of CA2 in mice and rats to stain hippocampal sections from voles and hamsters in parallel with those from mice. Here, we report that, like in mouse and rat, staining for many CA2 proteins in vole and hamster hippocampus reveals a population of neurons that express regulator of G protein signaling 14 (RGS14), Purkinje cell protein 4 (PCP4) and striatal-enriched protein tyrosine phosphatase (STEP), which together delineate the borders with CA3 and CA1. These cells were located at the distal end of the mossy fiber projections, marked by the presence of Zinc Transporter 3 (ZnT-3) and calbindin in all three species. In addition to staining the mossy fibers, calbindin also labeled a layer of CA1 pyramidal cells in mouse and hamster but not in vole. However, Wolframin ER transmembrane glycoprotein (WFS1) immunofluorescence, which marks all CA1 neurons, was present in all three species and abutted the distal end of CA2, marked by RGS14 immunofluorescence. Staining for two stress hormone receptors, the glucocorticoid (GR) and mineralocorticoid (MR) receptors, was also similar in all three species, with GR staining found primarily in CA1 and MR staining enriched in CA2. Interestingly, although perineuronal nets (PNNs) are known to surround CA2 cells in mouse and rat, we found that staining for PNNs differed across species in that both CA2 and CA3 showed staining in voles and primarily CA3 in hamsters with only some neurons in proximal CA2 showing staining. These results demonstrate that, like in mouse, CA2 in voles and hamsters can be molecularly distinguished from neighboring CA1 and CA3 areas, but PNN staining is less useful for identifying CA2 in the latter two species. These findings reveal commonalities across species in molecular profile of CA2, which will facilitate future studies of CA2 in these species. Yet to be determined is how differences in PNNs might relate to differences in social behavior across species.

NMDAR-dependent supralinear dendritic integration in murine neurogliaform interneurons
Non-linear summation of synaptic inputs to the dendrites of pyramidal neurons has been proposed to increase the computation capacity of neurons through coincidence detection, signal amplification, and additional logic operations such as XOR. Supralinear dendritic integration has been documented extensively in principal neurons, mediated by several voltage-dependent conductances. It has also been reported in parvalbumin-positive hippocampal basket cells, although only in dendrites innervated by feedback excitatory synapses. Whether other interneurons, which exclusively support feed-forward inhibition of principal neurons, also exhibit local non-linear integration of synaptic excitation is not known. Here we use patch-clamp electrophysiology, and 2-photon calcium imaging and glutamate uncaging, to show that supralinear dendritic integration of near-synchronous spatially clustered glutamate-receptor mediated depolarization occurs in NDNF-positive neurogliaform cells in the mouse hippocampus. Supralinear summation was detected via recordings of somatic depolarizations elicited by near- synchronous uncaging of glutamate on dendritic fragments, and concurrent imaging of dendritic calcium transients. Supralinearity was abolished by blocking NMDA receptors (NMDARs) but resisted blockade of voltage-gated sodium channels. Blocking L-type calcium channels abolished supralinear calcium signalling but only had a minor effect on voltage supralinearity. Dendritic boosting of spatially clustered synaptic signals argues for previously unappreciated computational complexity in neurogliaform cells.

Proteostasis as a fundamental principle of Tau immunotherapy
The microtubule-associated protein Tau is a driver of neuronal dysfunction in Alzheimer disease and numerous other tauopathies. In this process, Tau initially undergoes subtle changes to its abundance, subcellular localisation and a vast array of post-translational modifications including phosphorylation, that progressively result in the protein's aggregation and dysregulation of multiple Tau-dependent cellular processes. Given the various loss- and gain-of-functions of Tau in disease and the brain-wide changes in the proteome that characterise tauopathies, we asked whether targeting Tau would restore the alterations in proteostasis observed in disease. To this end, we generated a novel pan-Tau antibody, RNJ1, that preferentially binds human Tau and neutralises proteopathic seeding activity in multiple cell lines and benchmarked it against a clinically tested pan-Tau antibody, HJ8.5 (murine version of tilavonemab). We next evaluated both antibodies, alone and in combination, in the K3 mouse model of tauopathy, showing reduced Tau pathology and improvements in neuronal function following 14 weekly treatments, without obtaining synergistic effects for the combination treatment. To gain insight into molecular mechanisms contributing to improvements in neuronal function, we employed quantitative proteomics and phosphoproteomics to first establish alterations in K3 mice relative to WT controls at the proteome level. This revealed 342 proteins with differential abundance in K3 mice, which are predominantly involved in metabolic and microtubule-associated processes, strengthening previously reported findings of defects in several functional domains in multiple tauopathy models. We next asked whether antibody-mediated Tau target engagement indirectly affects levels of deregulated proteins in the K3 model. Importantly, both immunotherapies, in particular RNJ1, induced abundance shifts in this protein subset towards a restoration to wild-type levels (proteostasis). A total of 257 of 342 (~75.1%) proteins altered in K3 were closer in abundance to WT levels after RNJ1 treatment. The same analysis indicated a similar response in K3 mice treated with HJ8.5, with approximately 72.5% of these altered proteins also showing changes in the same direction as wild-type. Furthermore, analysis of the phosphoproteome showed an even stronger restoration effect with RNJ1, with ~82.1% of altered phosphopeptides in K3 showing a shift to WT levels, and 75.4% with HJ8.5. Gene set over-representation analysis (ORA) further confirmed that proteins undergoing restoration are involved in biological pathways affected in K3 mice. Together, our study suggests that a Tau immunotherapy-induced restoration of proteostasis links target engagement and treatment efficacy.

Aging disrupts blood-brain and blood-spinal cord barrier homeostasis, but does not increase paracellular permeability
Blood-CNS barriers protect the CNS from circulating immune cells and damaging molecules. It is thought barrier integrity becomes disrupted with aging, contributing to impaired CNS function. Using genome-wide and targeted molecular approaches, we found aging affected expression of predominantly immune invasion and pericyte-related genes in most CNS regions investigated, especially after middle age, with spinal cord being most impacted. We did not find significant perturbation of tight junction genes, nor were vascular density or pericyte coverage affected by aging. We evaluated barrier paracellular permeability using small molecular weight tracers, serum protein extravasation, CNS water content, and iron labelling measures. We found no evidence for age-related increased barrier permeability in any of these tests. We conclude that blood-brain (BBB) and blood-spinal cord barrier (BSCB) paracellular permeability does not increase with normal aging in mouse. Whilst expression changes were not associated with increased permeability, they may represent an age-related primed state whereby additional insults cause increased leakiness.

Same Sentences, Different Grammars, Different Brain Responses: An MEG study on Case and Agreement Encoding in Hindi and Nepali Split-Ergative Structures
At first glance, the brain's language network appears to be universal, but languages clearly differ. How does the language network adapt to the specific details of individual grammatical systems? Here, we present an MEG study on case and agreement in Hindi and Nepali. Both languages use split-ergative case systems. However, these systems interact with verb agreement differently - in Hindi, case features conspire to determine which noun phrase (NP) the verb agrees with, but not in Nepali. We found that left inferior frontal and left anterior temporal regions are sensitive to case features in both languages. However, the left temporoparietal junction shows a unique sensitivity to specific combinations of subject and object case morphology. We suggest that this brain response unique to Hindi reflects the need to determine which NP agrees with the verb, a specific property of Hindi grammar. This shows that brain activity reflects psycholinguistic processes that are intimately tied to grammatical features.

Natural Variation in Age-Related Dopamine Neuron Degeneration is Glutathione-Dependent and Linked to Life Span
Aging is the biggest risk factor for Parkinson's disease (PD), suggesting that age-related changes in the brain promote dopamine neuron vulnerability. It is unclear, however, whether aging alone is sufficient to cause significant dopamine neuron loss and if so, how this intersects with PD-related neurodegeneration. Here, through examining a large collection of naturally varying Drosophila strains, we find a strong relationship between life span and age-related dopamine neuron loss. Naturally short-lived strains exhibit a loss of dopamine neurons but not generalized neurodegeneration, while long-lived strains retain dopamine neurons across age. Metabolomic profiling reveals lower glutathione levels in short-lived strains which is associated with elevated levels of reactive oxygen species (ROS), sensitivity to oxidative stress and vulnerability to silencing the familial PD gene parkin. Strikingly, boosting neuronal glutathione levels via glutamate-cysteine ligase (GCL) overexpression is sufficient to normalize ROS levels, extend life span and block dopamine neurons loss in short-lived backgrounds, demonstrating that glutathione deficiencies are central to neurodegenerative phenotypes associated with short longevity. These findings may be relevant to human PD pathogenesis, where glutathione depletion is frequently reported in idiopathic PD patient brain. Building on this evidence, we detect reduced levels of GCL catalytic and modulatory subunits in brain from PD patients harboring the LRRK2 G2019S mutation, implicating possible glutathione deficits in familial LRRK2-linked PD. Our study across Drosophila and human PD systems suggests that glutathione plays an important role in the influence of aging on PD neurodegeneration.

Low-dimensional interference of mid-level sound statistics predicts human speech recognition in natural environmental noise
Recognizing speech in noise, such as in a busy street or restaurant, is an essential listening task where the task difficulty varies across acoustic environments and noise levels. Yet, current cognitive models are unable to account for changing real-world hearing sensitivity. Here, using natural and perturbed background sounds we demonstrate that spectrum and modulations statistics of environmental backgrounds drastically impact human word recognition accuracy and they do so independently of the noise level. These sound statistics can facilitate or hinder recognition - at the same noise level accuracy can range from 0% to 100%, depending on the background. To explain this perceptual variability, we optimized a biologically grounded hierarchical model, consisting of frequency-tuned cochlear filters and subsequent mid-level modulation-tuned filters that account for central auditory tuning. Low-dimensional summary statistics from the mid-level model accurately predict single trial perceptual judgments, accounting for more than 90% of the perceptual variance across backgrounds and noise levels, and substantially outperforming a cochlear model. Furthermore, perceptual transfer functions in the mid-level auditory space identify multi-dimensional natural sound features that impact recognition. Thus speech recognition in natural backgrounds involves interference of multiple summary statistics that are well described by an interpretable, low-dimensional auditory model. Since this framework relates salient natural sound cues to single trial perceptual judgements, it may improve outcomes for auditory prosthetics and clinical measurements of real-world hearing sensitivity.

Cross-species real time detection of trends in pupil size fluctuation
Pupillometry is a popular method because pupil size is an easily measured and sensitive marker of neural activity and associated with behavior, cognition, emotion, and perception. Currently, there is no method for monitoring the phases of pupillary fluctuation in real time. We introduce rtPupilPhase - a software that automatically detects trends in pupil size in real time, enabling novel implementations of real time pupillometry towards achieving numerous research and translational goals.

Time-varying Spatial Propagation of Brain Networks in fMRI data
Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previousstudies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise networkspecific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated to blood oxygenation level-dependent (BOLD) signal for 6-8 seconds, whereas the visual network propagates much quicker. We also show that summaries of network specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between schizophrenia patients and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

Trappc9 deficiency causes obesity and fatty liver disease by constraining dopamine neurotransmission
Loss-of-function mutations of the gene encoding the trafficking protein particle complex subunit 9 (trappc9) cause intellectual disability and obesity by unknown mechanisms. Genome-wide analysis links trappc9 to non-alcoholic fatty liver disease (NAFLD). Trappc9-deficient mice gain body weight normally in early developmental periods but become overweight shortly after being weaned. Here, we report that trappc9 deficiency in mice disrupts systemic glucose homeostasis and triggers the onset of obesity and NAFLD. Chronic treatment combining drugs suppressing dopamine receptor D1 (DRD1) and stimulating DRD2 restores systemic glucose homeostasis and counteracts abnormal body weight gain and lipid accumulation in adipose and liver tissues in trappc9-deficient mice. Additional pharmacological studies imply that the disruption of systemic glucose homeostasis in trappc9-deficient mice originates from deficient stimulation of DRD2 in the brain. Transcriptomic and proteomic analyses reveal signs of impairments in dopamine secretion in trappc9-deficient mice. Brain examinations indicate that trappc9-deficient mice synthesize dopamine normally, but their dopamine-secreting neurons have a lower abundance of structures for releasing dopamine. Our study suggests that trappc9 loss-of-function causes obesity and NAFLD by constraining dopamine neurotransmission.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Transition during Practice
Practice not only improves task performance, but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. We tested the hypothesis that strategy transition in task learning results from a cost-benefit analysis of candidate strategies. Participants learned two task sequences and were then queried the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis. Model-guided fMRI analysis shows frontal and parietal activations scaling with the demand of executing rule and memory strategy, respectively. fMRI activation pattern analysis further reveals widespread strategy-specific neural representations when their corresponding strategy is executed. Lastly, strategy transition is related to neural representation change in the dorsolateral prefrontal cortex and pattern separation in the ventromedial prefrontal cortex and the hippocampus. These findings shed light on how practice optimizes task performance by shifting task representations at the strategy level.

Changes in mitochondrial distribution occur at the axon initial segment in association with neurodegeneration in Drosophila
Changes in mitochondrial distribution are a feature of numerous age-related neurodegenerative diseases. In Drosophila, reducing the activity of Cdk5 causes a neurodegenerative phenotype and is known to affect several mitochondrial properties. Therefore, we investigated whether alterations of mitochondrial distribution are involved in Cdk5-associated neurodegeneration. We find that reducing Cdk5 activity does not alter the balance of mitochondrial localization to the somatodendritic vs. axonal neuronal compartments of the mushroom body, the learning and memory center of the Drosophila brain. We do, however, observe changes in mitochondrial distribution at the axon initial segment (AIS), a neuronal compartment located in the proximal axon involved in neuronal polarization and action potential initiation. Specifically, we observe that mitochondria are partially excluded from the AIS in wild-type neurons, but that this exclusion is lost upon reduction of Cdk5 activity, concomitant with the shrinkage of the AIS domain that is known to occur in this condition. This mitochondrial redistribution into the AIS is not likely due to the shortening of the AIS domain itself but rather due to altered Cdk5 activity. Furthermore, mitochondrial redistribution into the AIS is unlikely to be an early driver of neurodegeneration in the context of reduced Cdk5 activity.

Focused Ultrasound modulates dopamine in a mesolimbic reward circuit
Dopamine is a neurotransmitter that plays a significant role in reward and motivation. Dysfunction in the mesolimbic dopamine pathway has been linked to a variety of psychiatric disorders, including addiction. Low-intensity focused ultrasound (LIFU) has demonstrated effects on brain activity, but how LIFU affects dopamine neurotransmission is not known. Here, we applied three different intensities (6.5, 13, and 26 W/cm2 Isppa) of 2-minute LIFU to the prelimbic region (PLC) and measured dopamine in the nucleus accumbens (NAc) core using fast-scan cyclic voltammetry. Two minutes of LIFU sonication at 13 W/cm2 to the PLC significantly reduced dopamine release by ~ 50% for up to 2 hours. However, double the intensity (26 W/cm2) resulted in less inhibition (~30%), and half the intensity (6.5 W/cm2) did not result in any inhibition of dopamine. Anatomical controls applying LIFU to the primary somatosensory cortex did not change NAc core dopamine, and applying LIFU to the PLC did not affect dopamine release in the caudate or NAc shell. Histological evaluations showed no evidence of cell damage or death. Modeling of temperature rise demonstrates a maximum temperature change of 0.5{degrees}C with 13 W/cm2, suggesting that modulation is not due to thermal mechanisms. These studies show that LIFU at a moderate intensity provides a noninvasive, high spatial resolution means to modulate specific mesolimbic circuits that could be used in future studies to target and repair pathways that are dysfunctional in addiction and other psychiatric diseases.

DORSAL RAPHE NUCLEUS CONTROLS MOTIVATIONAL STATE TRANSITIONS IN MONKEYS
The dorsal raphe nucleus (DRN) is an important source of serotonin in the brain but fundamental aspects of its function remain elusive. Here, we present a combination of minimally invasive recording and disruption studies to show that DRN brings about changes in motivation states. We use recently developed methods for identifying temporal patterns in behaviour to show that monkeys change their motivation depending on the availability of rewards in the environment. Distinctive patterns of DRN activity occur when monkeys transition between a high motivation state occupied when rewards are abundant, to a low motivation state engendered by reward scarcity. Disrupting DRN diminishes sensitivity to the reward environment and perturbs transitions in motivational states.

Individual differences reveal similarities in serial dependencies across perceptual tasks, but no relation to serial dependencies for oculomotor behavior
Serial dependence effects from one trial to the next have been observed across a wide range of perceptual tasks, as well as for oculomotor behavior. This opens up the question of whether the effects observed across all of these studies share underlying mechanisms. Here we measured the same group of observers across four different tasks, two perceptual (color judgments and orientation judgments) and two oculomotor (tracking of moving targets and the pupil light reflex). On the group level, we observed significant attractive serial dependence effects for all tasks, except the pupil response. The rare absence of a serial dependence effect for the reflex like pupil light response suggests that sequential effects require cortical processing or even higher-level cognition. In the following step, we leveraged reliable individual differences between observers in the other tasks to test whether there is a trait-like behavior of some observers showing stronger serial dependence effects across all of these tasks. We observed a significant relationship in the strength of serial dependence for the two perceptual experiments, but no relation between the perceptual tasks and oculomotor behavior. This indicates, differences in processing between perception and oculomotor control and the absence of a general trait-like behavior that affects all tasks similarly. However, the shared variance in the strength of serial dependence effects across different perceptual tasks indicates the importance of a similar positive decision bias present, that is reliably different between observers and consistent across different serial dependence tasks.

The Lab Streaming Layer for Synchronized Multimodal Recording
Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.

Scaling Properties for Artificial Neural Network Models of a Small Nervous System
The nematode worm C. elegans provides a unique opportunity for exploring in silico data-driven models of a whole nervous system, given its transparency and well-characterized nervous system facilitating a wealth of measurement data from wet-lab experiments. This study explores the scaling properties that may govern learning the underlying neural dynamics of this small nervous system by using artificial neural network (ANN) models. We investigate the accuracy of self-supervised next time-step neural activity prediction as a function of data and models. For data scaling, we report a monotonic log-linear reduction in mean-squared error (MSE) as a function of the amount of neural activity data. For model scaling, we find MSE to be a nonlinear function of the size of the ANN models. Furthermore, we observe that the dataset and model size scaling properties are influenced by the particular choice of model architecture but not by the precise experimental source of the C. elegans neural data. Our results fall short of producing long-horizon predictive and generative models of C. elegans whole nervous system dynamics but suggest directions to achieve those. In particular our data scaling properties extrapolate that recording more neural activity data is a fruitful near-term approach to obtaining better predictive ANN models of a small nervous system.

Pheromone representation in the ant antennal lobe changes with age
While the neural basis of age-related decline has been extensively studied (1-3), less is known about changes in neural function during the pre-senescent stages of adulthood. Adult neural plasticity is likely a key factor in social insect age polyethism, where individuals perform different tasks as they age and divide labor in an age-dependent manner (4-9). Primarily, workers transition from nursing to foraging tasks (5, 10), become more aggressive, and more readily display alarm behavior (11-16) as they get older. While it is unknown how these behavioral dynamics are neurally regulated, they could partially be generated by altered salience of behaviorally relevant stimuli (4, 6, 7). Here, we investigated how odor coding in the antennal lobe (AL) changes with age in the context of alarm pheromone communication in the clonal raider ant (Ooceraea biroi) (17). Similar to other social insects (11, 12, 16), older ants responded more rapidly to alarm pheromones, the chemical signals for danger. Using whole-AL calcium imaging (18), we then mapped odor representations for five general odorants and two alarm pheromones in young and old ants. Alarm pheromones were represented sparsely at all ages. However, alarm pheromone responses within individual glomeruli changed with age, either increasing or decreasing. Only two glomeruli became sensitized to alarm pheromones with age, while at the same time becoming desensitized to general odorants. Our results suggest that the heightened response to alarm pheromones in older ants occurs via increased sensitivity in these two core glomeruli, illustrating the importance of sensory modulation in social insect division of labor and age-associated behavioral plasticity.

Dissecting the contribution of vagal subcircuits in sepsis-induced brain dysfunctions.
Sepsis, a life-threatening syndrome caused by a dysregulated host response to infection, induces a range of acute effects on the brain, including sickness behaviour and sepsis-associated encephalopathy. In addition, sepsis can lead to durable changes in neuronal circuits, resulting in long-term impairments such as post-traumatic stress disorder (PTSD). These brain dysfunctions are not directly caused by brain infection but result from peripheral inflammatory signals relayed to the brain via neural and humoral pathways. The vagal complex in the brainstem, composed of the nucleus of the solitary tract (NTS) and the area postrema, plays a crucial role in sensing and relaying these signals. Notably, the activation of the vagal complex triggers neurovegetative, neuroendocrine, and behavioural responses to infection. Chronic electrical vagus nerve stimulation has been used clinically to treat various brain disorders and is being investigated for its potential to alleviate inflammation and immune diseases through the anti-inflammatory reflex. However, a deeper understanding of the involvement of the vagus nerve and downstream brain circuits in sepsis-induced brain activation and dysfunction is needed to optimize therapeutic strategies. To investigate the role of the vagal complex in sepsis-induced brain dysfunction, various techniques were employed to manipulate vagus nerve activity and downstream circuits in a rodent model of sepsis by caecal ligation and puncture. Subdiaphragmatic vagotomy and pharmacogenetic manipulation of NTS and nodose (i.e. vagus sensory neurons) were implemented, revealing that vagotomy effectively reduced acute brain activation, inflammatory responses, and sickness behaviour triggered by sepsis. Additionally, transient activation of NTS neurons had a significant impact on inflammatory responses, sickness behaviour, and long-term PTSD-like consequences. This study underscores the complex interplay among the vagus nerve, brain circuits, and systemic inflammation during sepsis, emphasizing the critical importance of understanding these interactions in the development of targeted therapeutic interventions.

Monkey dorsolateral prefrontal cortex shows anatomically and functionally specific responses to sequential but not temporal or image changes
Sequential information permeates our daily lives, such as when listening to music. These sequences are potentially abstract in that they do not depend on the exact identity of the stimuli (pitch of the notes), but on the rule that they follow (interval between them). Previously, we showed that a subregion of monkey lateral prefrontal cortex (LPFC), area 46, responds to abstract visual sequences in a manner that parallels human responses. However, area 46 has several mapped subregions and abstract sequences require of multiple stimulus features (such as stimulus and time), leaving open questions as to the specificity of responses in the LPFC. To determine the anatomical and functional specificity of abstract visual sequence responses within area 46 subregions, we used awake functional magnetic resonance imaging in three male macaque monkeys during two no-report visual tasks. One task presented images in an abstract visual sequence; the other used the same timing properties and image variation, but no sequential information. We found, using subdivisions from a multimodal parcellation of area 46, that responses to abstract visual sequences were unique to the posterior fundus of area 46, which did not respond to changes in timing or image alone. In contrast, posterior shoulder regions of area 46 showed selectivity to more concrete stimulus changes (i.e., timing and image). These results align with organizational hierarchies observed in monkeys and humans, and suggest that interactions between adjacent LPFC subregions is key scaffolding for complex daily behaviors.

Contributions of sub-community based on short and long-range white matter tracts in personalized age-associated neurocompensatory mechanism
Brain function is shaped by the local and global connections between its dynamical units and biological parameters. As we age, the anatomical topology undergoes significant deterioration (e.g., long-range white matter fiber loss), affecting overall brain function. Despite the structural loss, existing studies have pinpointed that normative patterns of functional integrity, defined as the compensatory mechanism of the aging brain, remain intact across the lifespan. However, the crucial components in guiding the adaptive mechanism by which the brain readjusts its bio-logical parameters to maintain optimal compensatory function with age still needs to be uncovered. Here, we provide a parsimonious mechanism, which, together with the data-driven whole-brain generative model, establishes an individualized structure-function link with aging and uncovers the role of the subcommunity in driving the neurocompensation process. We use two neuroimaging datasets of healthy human cohorts with large sample sizes to systematically investigate which of the brain sub-graphs (connected via short or long-range white matter tracts) drives the compensatory mechanisms and modulates intrinsic global scaling parameters, such as interaction strength and conduction delay, in preserving functional integrity. The functional integrity is evaluated under the hypothesis of preserved metastability, measured from individual fMRI BOLD signals. Our findings uncover that the sub-graph connected via short-range tracts mainly modulates global coupling strength to compensate for structural loss. In contrast, long-range connections contribute to the conduction delay, which may play a complementary role in neurocompensation. For the first time, these findings shed light on the underlying neural mechanisms of age-related compensatory mechanisms and provide a mechanistic explanation for the importance of short-range connections in the face of the loss of long-range connections during aging using BOLD fMRI data. This crucial insight could open an avenue to understanding the role of subgraphs for targeted interventions to address aging-associated neurodegenerative diseases where long-range connections are significantly deteriorated.

Trial-by-trial detection of cognitive events in neural time-series
Measuring the time-course of neural events that make up cognitive processing is crucial to understand the relation between brain and behavior. To this aim, we formulated a method to discover a trial-wise sequence of events in multivariate neural signals such as electro- or magneto-encephalograpic (E/MEG) recordings. This sequence of events is assumed to be represented by multivariate patterns in neural time-series, with inter-event durations following probability distributions. By estimating event-specific multivariate patterns, and between-event duration distributions, the method allows to recover the by-trial onsets of brain responses. We demonstrate the properties and robustness of this hidden multivariate pattern (HMP) method through simulations, including robustness to low signal-to-noise ratio, as typically observed in EEG recordings. The applicability of HMP is illustrated using previously published data from a speed-accuracy trade-off task. We show how HMP provides, for any experiment or condition, an estimate of the number of events, the sensors contributing to each event (e.g. EEG scalp topography), and the durations between each event. Traditional exploration of tasks' cognitive architectures can thus be enhanced by HMP estimates.

Microglia/macrophage-specific deletion of TLR-4 protects against neural effects of diet-induced obesity
Obesity is associated with numerous adverse neural effects, including reduced neurogenesis, cognitive impairment, and increased risks for developing Alzheimers disease (AD) and vascular dementia. Obesity is also characterized by chronic, low-grade inflammation that is implicated in mediating negative consequences body-wide. Toll-like receptor 4 (TLR4) signaling from peripheral macrophages is implicated as an essential regulator of the systemic inflammatory effects of obesity. In the brain, obesity drives chronic neuroinflammation that involves microglial activation, however the contributions of microglia-derived TLR4 signaling to the consequences of obesity are poorly understood. To investigate this issue, we first generated mice that carry an inducible, microglia/macrophage-specific deletion of TLR4 that yields long-term TLR4 knockout only in brain indicating microglial specificity. Next, we analyzed the effects of microglial TLR4 deletion on systemic and neural effects of a 16-week of exposure to control versus obesogenic high-fat diets. In male mice, TLR4 deletion generally yielded limited effects on diet-induced systemic metabolic dysfunction but significantly reduced neuroinflammation and impairments in neurogenesis and cognitive performance. In female mice maintained on obesogenic diet, TLR4 deletion partially protected against weight gain, adiposity, and metabolic impairments. Compared to males, females showed milder diet-induced neural consequences, against which TLR4 deletion was protective. Collectively, these findings demonstrate a central role of microglial TLR4 signaling in mediating the neural effects of obesogenic diet and highlight sexual dimorphic responses to both diet and TLR4.

Characterizing directional dynamics of semantic prediction based on inter-regional temporal generalization
The event-related potential/field component N400(m) has been widely used as a neural index for semantic prediction. It has long been hypothesized that feedback information from inferior frontal areas plays a critical role in generating the N400. However, due to limitations in causal connectivity estimation, direct testing of this hypothesis has remained difficult. Here, magnetoencephalography (MEG) data was obtained during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences. To estimate causality, we implemented a novel approach based on machine learning and temporal generalization to estimate the effect of inferior frontal gyrus (IFG) on temporal areas. In this method, a support vector machine (SVM) classifier is trained on each time point of the neural activity in IFG to classify less predicted (LP) and highly predicted (HP) nouns and then tested on all time points of superior/middle temporal sub- regions activity (and vice versa, to establish spatio-temporal evidence for or against causality). The decoding accuracy was significantly above chance level when the classifier was trained on IFG activity and tested on future activity in superior and middle temporal gyrus (STG/MTG). The results present new evidence for a model predictive speech comprehension where predictive IFG activity is fed back to shape subsequent activity in STG/MTG, implying a feedback mechanism in N400 generation. In combination with the also observed strong feedforward effect from left STG/MTG to IFG, our findings provide evidence of dynamic feedback and feedforward influences between IFG and temporal areas during N400 generation.

Ramping cells in rodent mPFC encode time to past and future events via real Laplace transform
In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from rodent mPFC (Henke et al., 2021) during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.