bioRxiv Subject Collection: Neuroscience's Journal

Dissociation of SYNGAP1 Enzymatic and Structural Roles: Intrinsic Excitability and Seizure Susceptibility
SYNGAP1 is a key Ras-GAP protein enriched at excitatory synapses, with mutations causing intel-lectual disability and epilepsy in humans. Recent studies have revealed that in addition to its role as a negative regulator of G-protein signaling through its GAP enzymatic activity, SYNGAP1 plays an important structural role through its interaction with post-synaptic density proteins. Here, we reveal that intrinsic excitability deficits and seizure phenotypes in heterozygous Syngap1 knockout (KO) mice are differentially dependent on Syngap1 GAP activity. Cortical excitatory neurons in heterozy-gous KO mice displayed reduced intrinsic excitability, including lower input resistance, and in-creased rheobase, a phenotype recapitulated in GAP-deficient Syngap1 mutants. However, seizure severity and susceptibility to pentylenetetrazol (PTZ)-induced seizures were significantly elevated in heterozygous KO mice but unaffected in GAP-deficient mutants, implicating the structural rather than enzymatic role of Syngap1 in seizure regulation. These findings highlight the complex interplay between SYNGAP1 structural and catalytic functions in neuronal physiology and disease.

Visuospatial computations vary by category and stream and continue to develop in adolescence
Reading, face recognition, and navigation are supported by visuospatial computations in category-selective regions across ventral, lateral, and dorsal visual streams. However, the nature of visuospatial computations across streams and their development in adolescence remain unknown. Using fMRI and population receptive field (pRF) modeling in adolescents and adults, we estimate pRFs in high-level visual cortex and determine their development. Results reveal that pRF location, size, and visual field coverage vary across category, stream, and hemisphere in both adolescents and adults. While pRF location is mature by adolescence, pRF size and visual field coverage continue to develop - increasing in face-selective and decreasing in place-selective regions - alongside similar development of category selectivity. These findings provide a timeline for differential development of visual functions and suggest that visuospatial computations in high-level visual cortex continue to be optimized to accommodate both category and stream demands through adolescence.

Affiliative behaviours regulate allostasis development and shape biobehavioural trajectories in horse
Social interactions shape both physiological and behavioural development of offspring and poor care/early caregiver loss are known to promote negative outcomes in adulthood in both animals and humans. How affiliative behaviours impact future development of offspring remains unknown. Here, we used Equus caballus (domestic horse) as a model to investigate this question. By coupling magnetic resonance imaging, longitudinal biobehavioural assessment and advanced multivariate statistical modelling we found that maternal presence during childhood promotes maturation of brain territories involved in both social behaviour (anterior cingulate, retrosplenial cortex) and physiological regulation (hypothalamus, amygdala). Additionally, we found that offsprings benefiting from prolonged maternal presence showed higher default mode network (DMN) connectivity, improved social competences, more efficient feeding behaviours, and metabolic profiles. The present study underscores the salient role of social interactions for the development of allostatic regulation in offspring.

The olivocerebellar system differentially encodes the effect sensory events exert on behavior
Inferior olive neurons convey information about sensorimotor events via climbing fibers to the cerebellum, but their functional significance remains unclear. We directly imaged, with two-photon microscopy, climbing fiber axonal terminals in the cerebellum during a task that successively exposed mice to a force perturbation, a movement instruction and reward; each followed by multiple modes of motor activity. Climbing fiber activations by the sensory events were either generic or informative about the consequences the encoded event has on behavior. The number of informative cells and the information strength are regulated by event modality and functional complexity of the cell's activity. We observed an additional, previously unreported activation of climbing fibers: they carried probabilistic information on the behavioral context during idle waiting periods preceding stimulus presentation. Our findings reveal properties of olivary neurons that are key for defining their function in the cerebellum-dependent control of behavior. They suggest that the inferior olive flexibly instructs the cerebellum of any process that may shape an animal's action.

Specialized response of default mode subnetworks and multiple-demand regions to transitions of person, place and time
This study used functional MRI data from the StudyForrest dataset to investigate the role of subnetworks of the default mode network (DMN) during naturalistic stimulus transitions of different types and magnitudes. We found distinct activation profiles within the DMN: the dmPFC subnetwork was specifically associated with character and location transitions, the MTL subnetwork preferred location and temporal transitions, while the Core DMN subnetwork responded to all three transition types. The multiple-demand network instead responded selectively to temporal transitions. These distinct response profiles appeared largely invariant to the semantic distance implied by the transitions. All subnetworks also responded significantly, and in a graded manner, to subjective event boundaries. Results suggest specific roles of the DMN subnetworks in perceiving and segmenting naturalistic events, supporting the view that DMN subnetworks cooperate in interpreting continuous external events and maintaining an updated contextual model of the world.

Robustness of working memory to prefrontal cortex microstimulation
Delay period activity in the dorso-lateral prefrontal cortex (dlPFC) has been linked to the maintenance and control of sensory information in working memory. The stability of working memory related signals found in such delay period activity is believed to support robust memory-guided behavior during sensory perturbations, such as distractors. Here, we directly probed dlPFC's delay period activity with a diverse set of activity perturbations, and measured their consequences on neural activity and behavior. We applied patterned microstimulation to the dlPFC of monkeys implanted with multi-electrode arrays by electrically stimulating different electrodes in the array while the monkeys performed a memory guided saccade task. We found that the microstimulation perturbations affected spatial working memory-related signals in individual dlPFC neurons. However, task performance remained largely unaffected. These apparently contradictory observations could be understood by examining different dimensions of the dlPFC population activity. In dimensions where working memory related signals naturally evolved over time, microstimulation impacted neural activity. In contrast, in dimensions containing working memory related signals that were stable over time, microstimulation minimally impacted neural activity. This dissociation explained how working memory-related information could be stably maintained in dlPFC despite the activity changes induced by microstimulation. Thus, working memory processes are robust to a variety of activity perturbations in the dlPFC.

Spatiotemporal Complexity in the Psychotic Brain
Psychotic disorders, such as schizophrenia and bipolar disorder, pose significant diagnostic challenges with major implications on mental health. The measures of resting-state fMRI spatiotemporal complexity offer a powerful tool for identifying irregularities in brain activity. To capture global brain connectivity, we employed information-theoretic metrics, overcoming the limitations of pairwise correlation analysis approaches. This enables a more comprehensive exploration of higher-order interactions and multiscale intrinsic connectivity networks (ICNs) in the psychotic brain. In this study, we provide converging evidence suggesting that the psychotic brain exhibits states of randomness across both spatial and temporal dimensions. To further investigate these disruptions, we estimated brain network connectivity using redundancy and synergy measures, aiming to assess the integration and segregation of topological information in the psychotic brain. Our findings reveal a disruption in the balance between redundant and synergistic information, a phenomenon we term brainquake in this study, which highlights the instability and disorganization of brain networks in psychosis. Moreover, our exploration of higher-order topological functional connectivity reveals profound disruptions in brain information integration. Aberrant information interactions were observed across both cortical and subcortical ICNs. We specifically identified the most easily affected irregularities in the sensorimotor, visual, temporal, default mode, and fronto-parietal networks, as well as in the hippocampal and amygdalar regions, all of which showed disruptions. These findings underscore the severe impact of psychotic states on multiscale critical brain networks, suggesting a profound alteration in the brain's complexity and organizational states.

Self-organized and self-sustained ensemble activity patterns in simulation of mouse primary motor cortex
The idea of self-organized signal processing in the cerebral cortex has become a focus of research since Beggs and Plentz reported avalanches in local field potential recordings from organotypic cultures and acute slices of rat somatosensory cortex. How the cortex intrinsically organizes signals remains unknown. A current hypothesis was proposed by the condensed matter physicists Bak, Tang, and Wiesenfeld when they conjectured that if neuronal avalanche activity followed inverse power law distributions, then brain activity may be set around phase transitions within self-organized signals. We asked if we would observe self-organized signals in an isolated slice of our data driven detailed simulation of the mouse primary motor cortex? If we did, would we observe avalanches with power-law distributions in size and duration and what would they look like? Our results demonstrate that a brief unstructured stimulus (100ms, 57A current) to a small subset of neurons (about 181 of more than 10,000) in a simulated mouse primary motor cortex slice results in self-organized and self-sustained avalanches with power-law size and duration distributions and values similar to those reported from in vivo and in vitro experiments. We observed 4 cross-layer and cross-neuron population patterns, 3 of which displayed a dominant rhythmic component. Avalanches were each composed of one or more of the 4 population patterns.

One-shot normative modelling of whole-brain functional connectivity
Many brain diseases and disorders lack objective measures of brain function as indicators of pathology, which has recently spurred the use of normative modelling in neuroimaging. Normative models characterize the normal variation of brain measurements given sex and age, thereby allowing identification of abnormalities as deviations from normal. Normative modelling of brain function is typically based on predicting functional connectivity (FC) between each pair of brain regions. But the human brain is an extremely integrated organ, and brain disease often has widespread effects that are not well captured by piecemeal analyses, i.e. connection by connection. We propose Functional Connectivity Integrative Normative Modelling (FUNCOIN), developing a whole-brain normative model of FC from a large resting-state fMRI data set that captures whole-network-level changes associated with sex and age. This model can significantly, and substantially, uncover abnormal FC patterns in Parkinson's disease patients even on scans up to 5.5 years before diagnosis.

Real-time fMRI neurofeedback modulates auditory cortex activity and connectivity in schizophrenia patients with auditory hallucinations: A controlled study
Background and Hypothesis We have reported previously a reduction in superior temporal gyrus (STG) activation and in auditory verbal hallucinations (AHs) after real-time fMRI neurofeedback (NFB) in schizophrenia patients with AHs. Study Design With this randomized, participant-blinded, sham-controlled trial, we expanded our previous results. Specifically, we examined neurofeedback effects from the STG, an area associated with auditory hallucinations. The effects were compared to Sham-NFB from the motor cortex, a region unrelated to hallucinations. Twenty-three adults with schizophrenia or schizoaffective disorder and frequent medication-resistant hallucinations performed mindfulness meditation to ignore pre-recorded stranger's voices while receiving neurofeedback either from the STG (n=10, Real-NFB) or motor cortex (n=13 Sham-NFB). Individuals randomized to Sham-NFB received Real-NFB in a subsequent visit, providing a within-subject 'Real-after-Sham-NFB' comparison. Study Results Both groups showed reduced AHs after NFB, with no group differences. Compared to the Sham-NFB group, the Real-NFB group showed more reduced activation in secondary auditory cortex (AC) and more reduced connectivity between AC and cognitive control regions including dorsolateral prefrontal cortex (DLPFC) and anterior cingulate. The connectivity reduction was also observed in the Real-after-Sham-NFB condition. Secondary AC-DLPFC connectivity reduction correlated with hallucination reduction in the Real-NFB group. Replicating prior results, both groups showed reduced primary auditory cortex activation, suggesting mindfulness meditation may regulate bottom-up processes involved in hallucinations. Conclusions Our findings emphasize delivering NFB from brain regions involved in medication-resistant AHs. They provide insights into auditory cortex and cognitive control network interactions, highlighting complex processing dynamics and top-down modulation of sensory information.

Small or absent Visual Word Form Area is a trait of dyslexia
Understanding the balance between plastic and persistent traits in the dyslexic brain is critical for developing effective interventions. This longitudinal intervention study examines the Visual Word Form Area (VWFA) in dyslexic and typical readers, exploring how this key component of the brain's reading circuitry changes with learning. We found that dyslexic readers show significant differences in VWFA presence, size, and tuning properties compared to typical readers. While reading intervention improved reading skills and increased VWFA size, disparities persisted, suggesting that VWFA abnormalities are an enduring trait of dyslexia. Notably, we found that even with sufficient intervention to close the reading skill gap, dyslexic readers are still expected to have smaller VWFAs. Our results reveal intervention-driven long-term neural and behavioral changes, while also elucidating stable differences in the functional architecture of the dyslexic brain. This provides new insights into the potential and limitations of learning-induced plasticity in the human visual cortex.

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease
Background: A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals. Methods: We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on {beta}-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution. Results: Transcriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience. Conclusions: We identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

An MRI-informed histo-molecular analysis implicates ependymal cells in the pathogenesis of periventricular pathology in multiple sclerosis
It is now widely recognized that the cerebrospinal fluid (CSF)-adjacent brain surfaces - namely the subpial cortical region and the ependyma-adjacent periventricular region - are uniquely susceptible to a distinct, diffuse form of pathology in multiple sclerosis. So-called surface-in gradients of pathology predict future disease relapses independent of classical white matter lesions and are thought to occur as a result of cytotoxic factors in the CSF. Given the underlying mechanisms driving surface-in gradients appear to be distinct, they represent a novel treatment target. However, exactly how cytotoxic factor entry into the brain is regulated at these CSF-facing borders is not understood, particularly at the ventricular interface. Indeed, although studies have indicated that ependymal cells may be damaged in MS, there has yet to be a comprehensive assessment of cell health in the disease. We employed ultra-high-field MRI-guided immunohistochemistry, electron microscopy, and multiomic single nucleus RNA/ATAC sequencing to deeply phenotype human ependymal cells in MS. Our data revealed that ependymal cell pathology is a direct correlate of periventricular surface-in gradients of pathology in MS, and that the immune-responsive, reactive state assumed by ependymal cells is associated with widespread transporter and junctional protein gene dysregulation. We then further defined the gene regulatory networks underpinning the MS ependymal state, predicted ligands known to be enriched in MS CSF that could drive the emergence of this state, and tested one candidate in vivo. We found that IFN{gamma} increased murine ependymal permeability and that conditional knockout of ependymal interferon gamma receptor 1 (Ifngr1) was sufficient to reverse this effect. Our data directly implicate ependymal cell dysregulation in the emergence of periventricular pathology in MS. More widely, we denote the modulatory capacity of CSF ligands on ependymal cell function and how this may influence the inflammatory status of the periventricular region.

Standardized and calibrated light stimuli via head-mounted displays for investigating the non-visual effects of light
Light influences human physiology profoundly, affecting the circadian clock and suppressing the endogenous hormone melatonin. Experimental studies often employ either homogenous full-field stimulation, or overhead illumination, which are hard to standardize across studies and laboratories. Here, we present a novel technique to examine non-visual responses to light using virtual-reality (VR) head-mounted displays (HMDs) for delivering standardized and calibrated light stimuli to observers in a reproducible and controlled fashion. We find that VR HMDs are well-suited for delivering standardized stimuli defined in luminance and across time, with excellent properties up to 20 Hz. We examine melatonin suppression to continuous luminance-defined light stimuli in a sample of healthy participants (n=31, mean+/-SD age: 27.4+/-5.6), and find robust melatonin suppression in 27 out of 31 participants (87% of the sample). Our findings demonstrate that VR HMDs are well-suited for studying the mechanisms underlying human non-visual photoreception in a reproducible and standardized fashion.

Genetic changes linked to two different syndromic forms of autism enhance reinforcement learning in adolescent male but not female mice
Autism Spectrum Disorder (ASD) is characterized by restricted and repetitive behaviors and social differences, both of which may manifest, in part, from underlying differences in corticostriatal circuits and reinforcement learning. Here, we investigated reinforcement learning in mice with mutations in either Tsc2 or Shank3, both high-confidence ASD risk genes associated with major syndromic forms of ASD. Using an odor-based two-alternative forced choice (2AFC) task, we tested adolescent mice of both sexes and found male Tsc2 and Shank3B heterozygote (Het) mice showed enhanced learning performance compared to their wild type (WT) siblings. No gain of function was observed in females. Using a novel reinforcement learning (RL) based computational model to infer learning rate as well as policy-level task engagement and disengagement, we found that the gain of function in males was driven by an enhanced positive learning rate in both Tsc2 and Shank3B Het mice. The gain of function in Het males was absent when mice were trained with a probabilistic reward schedule. These findings in two ASD mouse models reveal a convergent learning phenotype that shows similar sensitivity to sex and environmental uncertainty. These data can inform our understanding of both strengths and challenges associated with autism, while providing further evidence that sex and experience of uncertainty modulate autism-related phenotypes.

Different artificial light modalities during daytime have all positive effects on attention and alertness
Light exposure during the day exerts acute effects on attention, such as how alert and ready a person is for solving problems and goal-oriented behavior. However, to increase the understanding of how different light modalities during daytime affect our attention, there is a need for more studies. The current study tested the acute effects of daytime exposure to four artificial light modalities on attention and alertness with the Psychomotor Vigilance Task (PVT). Healthy, young adults (N = 39; mean age: 21.7, 61.5% females) performed the PVT three times in four light modalities presented on separate days at the same time-of-day (09:00-11:00): short-wavelength light [SWL, 'blue'], long-wavelength light [LWL, 'red'], bright white light [BWL], and dim light [DL] as control. Attention and alertness measures included fluctuations in attention, quantified as the number of lapses and Reaction Time (RT) variability, mean RT, and optimum response capability (10% fastest RTs). Compared to DL, participants had significantly fewer lapses and faster mean RT during all three light conditions and enhanced optimum response capability during SWL and LWL. Light did not appear to have effects on intra-individual RT variability. Results suggest that 2 hours of exposure to artificial light during daytime can induce alerting effects and fewer attentional lapses in young, healthy adults. Surprisingly, neither SWL, LWL, nor BWL were significantly superior to one another.

Distinct modes of dopamine modulation on striatopallidal synaptic transmission
Dopamine (DA) affects voluntary movement by modulating basal ganglia function. In the classical model, DA depletion leads to overactivity of the indirect pathway and excessively inhibits the thalamus, resulting in hypokinesia. The contribution of DA on striatopallidal synapses, an initial hub in the indirect pathway connecting the striatum to the external globus pallidus (GPe), remains poorly understood because of the sparse DA innervation. Here, we combine optogenetic projection targeting, whole cell patch clamp recordings in acute brain slices from mice, and computational modeling to overcome this limitation. We show that DA activates D2R receptors (D2Rs) and D4 receptors (D4Rs) differentially in distinct GPe subregions. In a pinwheel-like fashion, dorsolateral and ventromedial GPe expresses high levels of D2Rs, which exert presynaptic inhibition, while in dorsomedial and ventrolateral GPe D4Rs cause postsynaptic inhibition. DA depletion by 6-OHDA (6-hydroxydopamine) reverses the region-specific effect of DA, shifting it in the opposite direction and contributing to hypokinesia. These findings reveal the mechanism by which the different modality information conveyed spatially through the indirect pathway is differentially modulated by DA at striatopallidal synapses.

Neurite density but not myelination of specific fiber tracts links polygenic scores to general intelligence
White matter is fundamental for efficient and accurate information transfer throughout the human brain and thus crucial for intelligence. Previous studies often demonstrated associations between fractional anisotropy (FA) as a metric of white matter "microstructural integrity" and intelligence, but it is still unclear, whether this relation is due to greater axon density, parallel, homogenous fiber orientation distributions, or greater myelination since all of these measures influence FA. Using neurite orientation dispersion and density imaging (NODDI) and myelin water fraction (MWF) imaging data, we analyzed the microstructural architecture of intelligence in more detail in a sample of 500 healthy young adults. Furthermore, we were interested whether specific white matter microstructural indices play intermediary roles in the pathway that links genetic disposition for intelligence to phenotype. Thus, we conducted for the first time mediation analyses investigating whether neurite density (NDI), orientation dispersion (ODI), and MWF of 64 white matter fiber tracts mediate the effects of polygenic scores for intelligence (PGSGI) on general intelligence. By doing so, we showed that NDI, but not ODI or MWF of white matter fiber tracts was significantly associated with general intelligence and that the NDI of six fiber tracts mediated the relation between genetic variability and g. These findings are a crucial step forward in decoding the neurogenetic underpinnings of general intelligence, as they identify that neurite density of specific fiber tracts relates polygenic variation to g, whereas orientation dispersion and myelination did not.

Exploring the Suitability of Piecewise-Linear Dynamical System Models for Cognitive Neural Dynamics
Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on either linear models or nonlinear models based on artificial neural networks. Piecewise linear approximations of nonlinear dynamics have proven useful in other technical applications, providing a clear advantage over network-based models, when the dynamical system is not only supposed to be observed, but also controlled. Here we explore whether piecewise-linear dynamical system models (recurrent Switching Linear Dynamical System or rSLDS models) could be useful for modeling brain dynamics, in particular in the context of cognitive tasks. We first generate artificial neural data based on a nonlinear computational model of perceptual decision-making and demonstrate that piecewise-linear dynamics can be successfully recovered from these observations. We then demonstrate that the piecewise-linear model outperforms a linear model in terms of predicting future states of the system and associated neural activity. Finally, we apply our approach to a publicly available dataset recorded from monkeys performing perceptual decisions. Much to our surprise, the piecewise-linear model did not provide a significant advantage over a linear model for these particular data, although linear models that were estimated from different trial epochs showed qualitatively different dynamics. In summary, we present a dynamical system modeling approach that could prove useful in situations, where the brain state needs to be controlled in a closed-loop fashion, for example, in new neuromodulation applications for treating cognitive deficits. Future work will have to show under what conditions the brain dynamics are sufficiently nonlinear to warrant the use of a piecewise-linear model over a linear one.

Temporal and protein-specific S-palmitoylation supports synaptic and neural network plasticity
S-palmitoylation, a dynamic post-translational modification, has long been suggested to play a pivotal role in synaptic plasticity, learning, and memory. However, its precise impact on synaptic proteins and function remains unclear. In this study, we show that acute protein depalmitoylation in the hippocampus differentially affects short- and long-term synaptic plasticity, depending on synapse type. Strikingly, depalmitoylation also reprograms neuronal spiking timing following associative network activation. Our research identifies pre- and postsynaptic proteins dynamically regulated by S-palmitoylation during synaptic plasticity and suggests this modification occurs in isolated excitatory synapses. We also demonstrate that S-palmitoylation targets specific proteins within minutes and is not proteome-wide. These findings mark a significant advance in understanding how lipid modifications drive neural adaptability, memory, and learning.

Causal Spike Timing Dependent Plasticity Prevents Assembly Fusion in Recurrent Networks
The organization of neurons into functionally related assemblies is a fundamental feature of cortical networks, yet our understanding of how these assemblies maintain distinct identities while sharing members remains limited. Here we analyze how spike-timing-dependent plasticity (STDP) shapes the formation and stability of overlapping neuronal assemblies in recurrently coupled networks of spiking neuron models. Using numerical simulations and an associated mean-field theory, we demonstrate that the temporal structure of the STDP rule, specifically its degree of causality, critically determines whether assemblies that share neurons maintain segregation or merge together after training is completed. We find that causal STDP rules, where potentiation/depression occurs strictly when presynaptic spikes precede/proceed postsynaptic spikes, allow assemblies to remain distinct even with substantial overlap in membership. This stability arises because causal STDP effectively cancels the symmetric correlations introduced by common inputs from shared neurons. In contrast, acausal STDP rules lead to assembly fusion when overlap exceeds a critical threshold, due to unchecked growth of common input correlations. Our results provide theoretical insight into how spike-timing-dependent learning rules can support distributed representation where individual neurons participate in multiple assemblies while maintaining functional specificity.

Unintended bias in the pursuit of collinearity solutions in fMRI analysis
In task functional magnetic resonance imaging (fMRI), collinearity between task regressors in a design matrix may impact power. Researchers often optimize task designs by assessing collinearity between task regressors to minimize downstream effects. However, some methods intended to reduce collinearity during optimization and data analysis may fail, in some cases introducing unintended bias into the parameter estimates. Although relevant to all task-based fMRI studies, we describe these issues and illustrate them using the Monetary Incentive Delay (MID) task fMRI data from the Adolescent Brain Cognitive Development (ABCD(R)) study. Specifically, we show that omitting regressors for certain task components, using impulse regressors for extended activations, and ignoring response time adjustments can bias common contrast estimates. We present a "Saturated" model that models all stimuli and response times, which minimizes bias in MID task simulations and validly estimates task-relevant whole brain activity, offering greater flexibility in studying contrasts that might otherwise be avoided due to potential biases.