Cognitive

CAREER: Circuit discovery in the Drosophila connectome

The fruit fly uses its senses, its memory, and its internal states to make decisions about where to lay an egg, what to eat, and whether to pursue or accept a mate. This project seeks to discover the neural pathways and circuits in the fruit fly’s brain that enable it to flexibly make decisions. These parts of the brain are difficult to study with traditional methods. The recent availability of connectome data – full reconstructions of entire brain volumes – now provide researchers with access to the parts of the fly’s brain that ostensibly combine sensory information with memories and internal states to effect an action. This project will analyze the fruit fly connectome using methods that have traditionally been used for studying social networks. Network science tools can identify groups of neurons that interact with regions of known function, thus enabling the discovery of neural circuits that support cognitive-level function in the fruit fly. This project will advance our understanding of how flexible, context-dependent interactions happen in the brain. Current technology cannot autonomously emulate these flexible and context-dependent behaviors at such a small scale. The project’s education plan will broaden participation in scientific research through innovative workshops and courses for middle school, high school and college students that engage the next generation of researchers in learning basic coding and network analyses, and in exploring the connectome. The public availability of connectome data also makes it possible to engage young people in computational neuroscience research at scale. In addition to strengthening the U.S. domestic workforce through student training, this project will increase national competitiveness in science and engineering – specifically in pursuit of elucidating the parts of the fly’s brain that endow it with flexible, context-dependent behavior. The neural circuits that perform context-dependent computations are largely unknown and difficult to study with commonly used methods such as genetic manipulations which require a known target with an associated driver line. Researchers posit that the diffuse neuropils of the superior protocerebrum contain circuits for higher-order processing. The Drosophila connectome is a volumetric reconstruction of an entire brain, including annotated cell types with no currently known function in the diffuse regions of the brain. This project will leverage the connectome to discover the neural circuits that produce cognitive-level computations related to decision-making in oviposition, and other ethologically-relevant behaviors. Community detection methods, together with other statistical network analyses and computational modeling, will be employed to: (1) characterize the circuit structure of the understudied regions of the brain believed to support context-dependent computations; (2) investigate the inputs to the oviposition pre-motor circuit to connect populations of unknown cell types to behaviorally-relevant function; and, (3) determine the utility of community detection methods for detecting microscale circuits for dendritic processing. The project will lead to experimentally testable predictions for the neurons, cell types, and circuits that drive context-dependent behaviors in a model organism that has a wealth of genetic tools available for testing the resulting predictions. Furthermore, these studies will lead to computational principles of Drosophila brain organization and an enhanced understanding of how to use network science tools to reveal and investigate neuronal circuits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CAREER: Inferring Specifications for AI by Modeling Humans

Artificial intelligence (AI) systems increasingly influence both high stakes and everyday decisions across many sectors of the economy. These systems, however, are not developed in isolation. Instead, they depend on people to provide instructions that describe what the system should do and what outcomes it should avoid. These instructions can take many forms. They may be written explicitly by domain experts or learned from data such as human preferences over outcomes. However, providing clear and reliable instructions for intelligent systems is difficult even for relatively narrow applications. Instructions can be too rigid, too vague, or simply incorrect, and any of these problems can cause systems to behave in unintended ways. These failures occur because instructions are created by people, and human reasoning is shaped by limited information, context, and common cognitive mistakes. As AI becomes more widespread, improving how systems interpret human intent will be essential for safety and reliability. This project addresses that challenge by studying how people communicate goals to machines and by designing AI systems that can interpret imperfect instructions by reasoning about the intent behind them. The expected outcomes include safer decision-making technologies and new tools that help organizations deploy AI more effectively. This project develops computational foundations for learning AI specifications from imperfect human input. The research integrates reinforcement learning, Bayesian inference, and computational cognitive modeling with empirical studies of human decision making to better characterize how people communicate goals and where specification errors arise. The work is organized around three research thrusts. The first thrust, Modeling and Inferring AI Specifications, develops probabilistic models of human reasoning that capture systematic specification errors and uses these models to enable AI systems to infer more accurate goals from flawed instructions. The second thrust, Richer Inputs and Representations, expands how AI systems learn from people by incorporating different forms of input such as preferences, demonstrations, explanations, gestures, and structured debate. New algorithms and elicitation interfaces will integrate these signals and resolve inconsistencies across modalities. The third thrust, Personalization and Governance, develops methods for learning multiple reward models that reflect differences in human preferences, enabling scalable personalization and avoiding one-size-fits-all objectives. In parallel, the project will develop educational programs that prepare students to design and govern AI systems. These activities include revising an undergraduate AI course to emphasize human decision-making in the design of AI systems, creating a graduate course on AI policy and governance, and expanding the AI Policy Summer School to help build a national workforce that is fluent in both AI technology and public policy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Neuroinflammation in Cerebral Small Vessel Disease

Project Summary/Abstract Cerebral small vessel disease (cSVD) is a leading cause of vascular contributions to cognitive impairment and dementia (VCID), which is the 2nd leading cause of dementia and a significant contributor to Alzheimer’s disease (AD). Thus far, the underlying pathogenesis of cSVD is poorly understood. Several lines of evidence, including animal models, postmortem human brain pathology, and systemic inflammatory markers, demonstrated the damaging role of chronic neuroinflammation in cSVD. Direct evidence of neuroinflammation at the tissue level in patients with cSVD is still critically needed. The sphingosine-1-phosphate receptor 1 (S1PR1) regulates neuroinflammation through microglial and astrocyte activation and trafficking and has emerged as a promising target for neuroinflammation. In postmortem brains of patients with cSVD, we observed elevated S1PR1 expression and colocalization of S1PR1 with astrocytes and microglia. A novel 11C-CS1P1 PET radiotracer with high affinity and specificity targeting S1PR1 has been recently developed and validated in animal models and post-mortem human specimens. Under an FDA-approved eIND (IND 146548), we have successfully completed the safety and dosimetry study in healthy participants and performed preliminary studies in patients with cSVD. We found that 11C-CS1P1 PET uptake is significantly associated with WMH lesion burden in patients with cSVD after controlling for age, sex, race, vascular risk factors, and amyloid deposition. We hypothesize that 11C-CS1P1 PET uptake is a tissue-level biomarker of neuroinflammation to provide insight into cSVD severity, progression, and prognosis. We will 1) evaluate the relationship between 11C-CS1P1 PET uptake and cSVD neuroimaging abnormalities and cognitive impairment, 2) evaluate the test-retest repeatability and longitudinal evolution, and 3) determine whether 11C-CS1P1 PET uptake at baseline predict cSVD progression. The successful completion of this study will establish 11C-CS1P1 PET as an neuroinflammation imaging biomarker and investigate the role of neuroinflammation in cSVD pathogenesis and progression. It will lay a foundation for developing future therapies in modulating neuroinflammation.

BKCa Channel Contributions to Cerebellar Regulated TSC-Associated Neuropsychiatric Disorders

Project Summary TSC is associated with neurodevelopmental disability including cognitive disability and autism spectrum disorders (ASD) that make up part of TSC associated neuropsychiatric disorders (TAND). The mechanisms for TAND remain poorly understood but studies have increasingly implicated cerebellar dysfunction in the pathogenesis of cognitive and behavioral deficits in both TSC and other neurodevelopmental disorders. A shared feature is cerebellar Purkinje cell (PC) dysfunction. Changes in intrinsic properties of PCs results in both motor and cognitive/ behavioral changes in disease models and in individuals afflicted by these disorders. Mechanistic underpinnings of these altered properties remain unknown, but a significant emerging body of data implicate ion channel dysfunction as the primary etiology of these deficits. The current proposal seeks to delineate the ion channel contribution to PC dysfunction and to TAND-relevant behaviors. In doing so, these studies will produce significant both short- and long-term impact. Short-term: These proposed studies will provide a mechanistic understanding of the contribution of ion channels to the neuronal dysfunction in the cerebellum that has been demonstrated to be causally linked to abnormal TAND-relevant behaviors. In addition, we will target specific ion channels both genetically and pharmacologically to evaluate the benefits of ion channel restoration on both electrophysiological abnormalities but also the TAND-relevant behaviors observed in the model. Long-term: These studies, thus, provide a framework for subsequent clinically-relevant therapeutic development for TAND. First, these studies will uncover the ability for TAND-relevant behaviors to be improved upon targeting ion channel alterations in TSC. These studies will also define molecular targets on which therapeutic development can be targeted, thereby potentially providing a molecular-informed pipeline for therapeutic development. In addition, these studies will utilize clinically-available, FDA-approved pharmacological agents to target ion channel function and investigate the potential therapeutic benefits for these agents for TAND-relevant behaviors. Thus, these studies will address a core gap in knowledge to achieve a better mechanistic understanding of TAND and to develop therapeutic opportunities to address TAND. These studies will not only reveal previously understudied and novel mechanistic underpinnings for these behaviors but will provide pre-clinical insights into the therapeutic utility of clinically-utilized agents for the treatment of TAND-related behaviors, thus potentially providing both immediate and long-term opportunities for the treatment of TAND. Moreover, although these studies focus on TSC, these mechanisms may prove generalizable beyond TSC and provide a shared basis and therapeutic opportunity for other neuropsychiatric/developmental conditions.

Examining the foundations of reading comprehension: a longitudinal study of brain and behavior starting in infancy

SUMMARY Reading comprehension (RC) is one of the most complex skills that we utilize daily and is crucial for functioning in modern society, but despite its significance for academic achievement, employment prospects, and mental health, many children and adults do not exhibit proficient RC abilities. New theoretical models aiming to explain variability in RC suggest a dynamic interplay and co-development among ‘precursor’ foundational and cognitive- linguistic skills, interacting with environmental and socio-ecological factors across the developmental timeline of learning to read. Behavioral and neuroimaging studies in school-age children have demonstrated critical mechanistic support for these multifactorial RC models by identifying the developmental trajectories of precursor skills and further showing that brain areas, tracts, and networks typically underlying language and cognitive skills are also involved in RC. Nevertheless, the precursor skills that support RC start developing in infancy and the brain correlates underlying these precursors begin to develop in utero, which suggests that typical and atypical RC developmental trajectories could diverge long before school age. As such, examining RC development using a multifactorial, longitudinal approach that includes brain and behavior starting in infancy is critical for developing theoretical frameworks that can inform early preventative and intervention strategies. Here, we propose a comprehensive longitudinal study of RC development in which we examine direct and indirect effects on RC from brain, behavioral, familial risk, and environmental data from infancy to adolescence. To achieve this goal, we will combine two existing longitudinal cohorts, one ranging from infancy to late childhood (n = 174) and the other from preschool to early adolescence (n = 137). By applying state-of-the-art pediatric neuroimaging analyses, multiple indicator growth model structural equation models, and an innovative behavior- brain co-development measurement index to this unique, combined dataset, we will be able to identify brain and behavioral measures in infancy that directly and indirectly support subsequent RC development (Aim1). We will further characterize how longitudinal trajectories of behavioral measures as well as brain structure, function, and white matter organization contribute to RC development and how familial risk and environmental factors shape these trajectories (Aim 2). Finally, we will examine how the co-development of brain and behavior, as measured with an innovative co-development index, relates to subsequent RC (Aim 3). If successful, we will contribute the first multifactorial longitudinal model of RC development comprising direct and indirect effects from brain, behavior, brain-behavior co-development, familial risk, and environmental measures beginning in infancy. Understanding RC development using a multifactorial longitudinal lens will be crucial for building theoretical models and developing experimental designs focused on early preventative and intervention approaches long before the start of formal schooling.

Clinical Trial Readiness of MEG Biomarkers in Children Across the Autism Spectrum

PROJECT SUMMARY Biological and phenotypic heterogeneity of autism spectrum disorder (ASD) poses a major challenge for clinically focused research and interventions. Brain electrophysiological phenotyping holds promise for parsing this heterogeneity. Using magnetoencephalography (MEG), findings of diminished and delayed auditory evoked responses (e.g. the ~50ms component, M50 and, specifically, its latency: M50L) have reproducibly been shown in ASD, with correlation to behavior. Additionally, abnormal resting state activity and network functional connectivity has been identified as an electrophysiological hallmark. Such passively-acquired signatures may serve as objective biomarkers in subtyping autistic individuals, including stratifying patients for inclusion in clinical trials according to biology, rather than behavior alone. However, despite their abundant promise, these measures are not yet permeating clinical trial design, nor being utilized in clinical practice, in part because of their lack of standardized implementation and analysis. This proposal seeks to remedy this by using rigorous and standardized, scalable and sharable methods with two leading MEG measures to determine their measurement- reliability as well as their sensitivity to inter-individual differences in clinically-relevant aspects of autism features, general cognitive ability and language and communication. Specifically adopting a 12-week repeated scanning design, mimicking the duration of a typical pharmaceutical trial or behavioral intervention, we will acquire each of these two MEG metrics at baseline and 12-week follow-up to assess interval change. Additionally, we will evaluate test-retest variability with an intermediate measurement point 4-weeks after baseline. As such we will characterize both intra-subject variability (measurement precision) and inter-subject variability which will be correlated with dimension axes of autism features, general cognitive ability and language skills, as well as major co-occurring condition confounds. These studies will recruit a broad range of 240 autistic children, paralleling the CDC’s prevalence data on intellectual ability and encompassing the group considered as having “profound autism”. This is enabled by our adoption of MEG-PLAN, a strategy developed over the last decade in our group and demonstrated to enhance inclusive participation in MEG scanning studies, even in non-verbal participants. Data will be compared to a control group of age-matched typically-developing peers. The two MEG measures will also be assessed for their ability to identify clusters of less heterogeneous neurophysiological phenotype as a novel basis for stratification or subtyping of the heterogeneous autism population. In culmination, this study addresses key “clinical readiness” aspects of utilization of MEG biomarkers for ASD including profound autism, for both stratification (inclusion/trial selection) and monitoring of response to intervention, and will, ultimately, pave the way for the adoption of such biomarkers as adjunctive tests in increasingly-routine clinical practice.

Mechanisms and consequences of cerebrovascular dysfunction in preeclampsia

PROJECT SUMMARY/ABSTRACT Preeclampsia (PE) is a common hypertensive disorder of pregnancy that causes significant maternal and fetal morbidity and mortality worldwide. PE women are at a high risk of stroke, including intracerebral hemorrhage, during the peripartum period, suggesting the sequelae of PE adversely impacts the cerebral circulation to promote hemorrhage. In addition, women with severe early-onset PE are at an 85-fold increased risk of death from intracerebral hemorrhage, importantly suggesting severity of disease promotes greater vulnerability of the cerebral circulation to degradation and rupture. However, the consequences of PE extend far beyond pregnancy and are associated with excessive cardiovascular and cerebrovascular disease risk later in life. Women with previous pregnancy complicated by PE can develop cognitive impairment as early as in their 30’s and 40’s, suggesting PE predisposes the brain to early-onset cognitive impairment. Studies have shown that formerly PE women have changes in gray matter volume and increased white matter lesion burden that occurs as a function of time from pregnancy, suggesting that PE continues to progressively damage the brain long after the affected pregnancy. Thus, our overall goal is to elucidate mechanisms by which women with PE are at risk of intracerebral hemorrhage in pregnancy and cognitive decline later in life. Our preliminary studies found greater vascular degradation, hematoma and cerebral edema in a model of severe PE that was associated with vascular inflammation and microglia activation (neuroinflammation). In addition, we found endothelial dysfunction and diminished neurovascular coupling in PE rats that persisted 5 months postpartum. Impaired neurovascular coupling is well-recognized as an underlying contributor to cognitive decline. These effects in postpartum animals with previous exposure to PE were associated with memory impairment that was not present in the pregnant state, suggesting neurovascular dysfunction precedes cognitive decline. Our central hypothesis is that the sequela of PE accelerates hypertension-induced cerebrovascular dysfunction that predisposes to intracerebral hemorrhage during pregnancy and its persistence postpartum results in early-onset cognitive decline. We will therefore elucidate mechanisms by which PE accelerates vascular degradation and worsens outcome from hemorrhagic stroke, probing pathways involved in oxidative degradative processes using multi-omics and multivariate analysis (Aim 1). We will also determine underlying molecular mechanisms that cause persistent cerebral microvascular dysfunction and cognitive decline postpartum, including oxidative stress-induced BBB leakage and persistent neuroinflammation that drives potassium channel dysfunction, reduced neurovascular coupling and neurovascular uncoupling (Aim 2). We will also use machine learning approaches together with multi-omics and outcome measures to identify factors and cellular pathways that are most impactful for prediction of intracerebral hemorrhage and cognitive impairment. The ability to predict and prevent devasting neurovascular disorders associated with PE has the potential to have long-lasting impacts on the lives of women with PE.

REU Site: Research Experiences for Undergraduates in Neuroscience

This REU Site award to the University of Missouri, Columbia, MO will support the training of 10 students for 10 weeks during the summers of 2027-2029. It is anticipated that a total of 30 students from institutions across the US, including those with limited access to research will be trained in the program. The program focuses on the rapidly growing area of interdisciplinary neuroscience highlighting computational and Artificial Intelligence (AI) approaches. Projects span across all levels in neuroscience from molecular studies to understanding behavior. Program activities include professional workshops tailored to prepare students for the next steps in their academic and STEM careers. Students will learn the research process; many will present the results of their work at scientific conferences. Program assessment will utilize the SALG URSSA tool. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with the NSF priorities in AI and Biotechnology. This REU Site will train undergraduates in interdisciplinary neuroscience, with a focus on computational and AI methods, starting with a one-week boot camp on computational/AI tools in biosciences and on technical writing. An interdisciplinary team of 20 neuroscience faculty members with different expertise will train undergraduates in research. The projects range across the neuroscience spectrum and include analysis at the molecular, cellular, network, system, cognitive, and behavioral levels. Examples include: neural network plasticity; biophysical single-cell and network modeling of brain states; mechanisms of associative learning and memory; the computational logic and architecture of neural systems; memory in human cognition; neural processing of behavior; and social and cognitive information processing. The selection process will involve the faculty mentors and site directors. Professional development will include research workshops, responsible conduct of research, exploring diverse careers in industry, policy, and entrepreneurship, and educational enrichment activities, such as navigating graduate school applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

CDS&E: Spectral Analysis of High-Dimensional Point Processes from Neuronal Spike Train Data

This project develops new statistical machine learning tools and theory for inferring brain functional connectivity networks from neuronal spike train data, as well as changes in these networks. The methods and software proposed in this project are motivated by new technological advances that facilitate live recording of neuronal activities while subjects are presented with stimuli and or perform cognitive tasks. The networks estimated using the proposed methods will shed light into how neurons interact with each other to process information presented in new stimuli. The key difference between the methods proposed in this project and existing approaches is that analyses are conducted in the "spectral domain" rather than the usual time domain. The spectral domain is more informative for neuroscience applications as it naturally captures neuronal syncing and rhythms. However, inferring functional connectivity networks in the spectral domain analysis gives rise to new theoretical and methodological challenges. To address these challenges, the project develops a new theoretical framework for analyzing spike train data in the spectral domain and leverages this framework to develop regularized estimation procedures that facilitate estimation of brain connectivity networks in high-dimensional settings, when the number of observed neurons is large compared to the number of observations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Development of an at-home weight-shifting balance game with musical biofeedback for older adults

Reducing fall risk is a dire societal need that requires interventions that over-prepare individuals to perform maneuvers important to daily mobility. Falling is often caused by improper weight shifting, and interventions that focus on developing weight-shifting abilities have shown improvements in clinical balance outcomes, including reduced fall incidence. Interventions that combine challenges to the cognitive and motor systems may be necessary to reduce fall-risk. Our central hypothesis is that leveraging gamification and “musical biofeedback” will improve balance abilities through practicing weight-shifting skills with increased cognitive and physical demands. Musical biofeedback conveys biological sensor data from the participant through specific musical sound parameters in real-time. Of particular interest in the proposal is the applicability to use musical biofeedback to train weight-shifting skills in a musical game. The goal is to develop a wearable sensor system that can be used at-home to practice and develop balance skills, while supporting cognitive engagement and motivation to adhere to exercise goals. To start, we are focusing on older adult end-users who typically have home exercise programs focused on weight-shifting. However, in the future, many other populations can benefit from this technology. In this Trailblazer award, the PI is leveraging her background in studying complex human maneuvers, developing musical biofeedback for older adults, and in algorithm development for mHealth sensors. The transdisciplinary team includes expertise in engineering, gamified rehabilitation technologies, home exercise programs, psychology of aging, and music. In the proposed research, our goals are to evaluate responses to the musical biofeedback game (Aim 1), validate the mHealth sensor system (Aim 2), and phenotype the gameplay behavior of fallers vs. non-fallers (Aim 3), relative to their baseline characteristics (Sub-Aim 3). Our long-term goal is for a variety of people to improve their balance control patterns while supporting and building their self-efficacy. We envision users, including older adults, training with musical biofeedback to safely (and enjoyably) prepare themselves to ambulate in their community – improving and preserving their mobility. The proposed research will pioneer using an emerging clinical technology – musical biofeedback – to train balance during weight-shifting tasks. The proposed research innovates how musical biofeedback, gamification, and focusing on weight-shifting and turns in balance training can be leveraged to challenge cognitive and physical body systems in fall-risk populations. By developing new therapy options and better understanding responses relative to baseline characteristics, this research improves clinical practices to reduce fall risk and deepens our understanding of dynamic balance control. Finally, the results of the proposed research will have translational impacts to help other fall-risk groups.

Impact of environmental toxicants on frontal cortical circuits

Abstract: Human mercury (Hg) exposure has been known for many decades to produce cognitive impairment and mood disorder symptoms. Hg is a global pollutant that poses widespread potential for neurotoxic exposure, earning it a position on the WHO’s list of the top 10 chemicals of major public health concern. However, little is known about the neural mechanisms that lead to neuropsychiatric symptoms from Hg exposure. The objective of this application is to identify specific mechanisms, within the neocortical circuits that control emotion and cognition, that are disrupted by the neurotoxicant, methylmercury (MeHg). The neocortex exhibits especially strong bioaccumulation of Hg, magnifying the risk to these circuits. Therefore, we hypothesize that chronic MeHg exposure leads to persistent circuit dysfunction in prefrontal and insular cortices (mPFC and aIC) – two brain regions critical in control of emotion and cognition. Our recent work showed that mPFC neurons in brain slices are negatively affected by acute MeHg exposure, resulting in hyperexcitability and altered synaptic transmission. Currently, it unknown how these acute effects on synaptic transmission translate to altered neuronal function in vivo. This proposal applies an integrative approach to determine the in vivo effects of MeHg on mPFC and aIC circuits, at the systems neurophysiology, synaptic and molecular levels. We will compare the effects of MeHg exposure on in vivo spiking activity patterns in brain regions of the mPFC-aIC circuit, using multiunit electrophysiological recordings in awake animals. Action potentials will be recorded simultaneously from multiple neurons, distributed across cortical layers, to evaluate effects on spike frequency, temporal patterning and correlation. Using acute brain slices derived from animals chronically treated with MeHg in vivo, electrophysiologically recorded synaptic estimates will be made to compare the effects of MeHg exposure on synaptic transmission and EI-balance within brain regions of the mPFC-aIC circuit. Based on previous evidence, we hypothesize that TDP-43 hyper-phosphorylation and aggregation link MeHg exposure to mPFC and aIC dysfunction. Therefore, immunohistochemistry will be used to measure TDP-43 hyper-phosphorylation and nuclear redistribution from animals treated in vivo +/- MeHg. In addition, tissue will be co-labeled with antibodies for nPAS4, a well-stablished molecular marker of activity, to determine whether TDP-43 hallmarks correlate with MeHg-induced hyper-excitability. The results of our study will substantively improve our mechanistic understanding of how Hg disrupts frontal cortical function and contribute to our understanding of the biological basis of emotional and cognitive sympoms. Identifying specific actions of MeHg at the functional microcircuitry level and cellular/molecular level will help significantly in finding novel targets for therapeutic interventions. If our hypothesis is correct, this will also raise the question of the extent to which chronic low-level environmental mercury exposure contributes to the etiology of fronto-cortical disorders with symptoms that overlap mercury exposure but do not have definitive genetic origins. This is particularly important because fronto-cortical disorders are predominantly sporadic in nature.

Role of Two Medial Prefrontal Long-Range Recurrent Networks in Behavior Initiation and Inhibition

Abstract The medial prefrontal cortex (mPFC) is critical for executive function, yet how its dorsal (dmPFC) and ventral (vmPFC) motor-projecting (MP) neurons coordinate behavioral initiation, inhibition, and cognitive flexibility remains poorly understood. This R21 leverages four translational behavioral paradigms (head-fixed Persistent Licking/Shock-Escape; freely moving FED3-based Reversal Learning/Stop-Signal), high-density neural recordings, circuit manipulations, and Brian2 spiking neural network modeling to test our central hypothesis: dmPFC MP neurons drive action initiation and adaptive switching, while vmPFC MP neurons suppress impulsivity and perseveration. In Aim 1a, we quantify behavior using kinematic analyses (jerk, velocity, z-scored) aligned with human executive dysfunction metrics (Action Latency [AL], Reversal Accuracy [RA], Perseveration Errors [PE], Stop-Signal Reaction Time [SSRT]), combined with optogenetic (stGtACR2/ChR2) and chemogenetic (PSAM/varenicline) perturbations. Aim 1b employs optotagging and population analyses (PCA, SVM, Total Spiking Probability Edges) to decode dmPFC/vmPFC MP dynamics across tasks, resolving specialized versus mixed functional roles. Aim 1c integrates these datasets into Brian2 spiking network models to predict neural-behavioral correlations, validated through cross-validation. Exploratory analyses will link murine kinematic signatures to human stop-signal/reversal learning metrics. By elucidating strain-specific (C57BL/6 vs. CD1) circuit mechanisms and delivering translatable biomarkers (AL, RA, PE, SSRT, kinematics), this work addresses a critical gap in understanding neuropsychiatric disorders like ADHD (impulsivity) and schizophrenia (perseveration). The study’s innovative combination of recurrent neural network theory, FED3-based assays, and New Approach Methodology (NAM)-compliant computational modeling pioneers high-risk, high-reward tools for circuit dissection, fully aligning with NIH’s 2025 priorities.

Addressing C-F bonds and amyloid-formation in biological systems

The ingestion, pulmonary inhalation, and dermal infiltration of C-F bond-containing compounds, most commonly found in the form of per- and polyfluoroalkyl organic acids, causes oxidative stress, inflammation, DNA damage, and developmental defects in infants and adults. These chemicals accumulate in the brain, disrupt neurological function and compromise cognitive and locomotory behavior. Yet, we lack a high-resolution road-map of the interactions between C-F bonds and biomolecular assemblies driving the trajectory towards neurodegenerative outcomes. This gap constitutes a significant barrier to advancing measures designed to mitigate C-F chemistry-associated neurotoxicity. Emerging experimental and computational data from our laboratory reveals that perfluorooctanoic acid, perfluorodecanoic acid and perfluorosulfonic acid corrupt biomolecular structures through C-F:side-chain interactions in tested soluble, globular proteins found in milk and tissues (matrices where C-F chemistries have been detected). Furthermore, they impaired the physiological function in these proteins through displacement of physiological ligands or by compromising the binding of co-factors. The neuroblastoma-derived SHSY-5Y cell line insulted with the said C-F moieties displayed altered gene expression corresponding to reactive oxygen species (ROS), protein ubiquitination, inflammation along with compromised cytoskeletal integrity. C-F bond ingestion ablated dopaminergic (DA) neurons in the nematode C. elegans and induced locomotory deficits in a manner mimicking paraquat. Based on these findings, we propose to gather data towards our hypothesis that C-F bond exposure perturbs biomolecular, cellular and organismal assemblies to onset neurodegeneration-linked trajectories. In Aim 1, we will determine whether organic fluoroacids alter mRNA levels in differentiated SHSY-5Y cells and in neuroprotective gut bacteria (Lactobacillus rhamnosus, Bifidobacterium lactis and Lactobacillus acidophilus). We will examine whether the neuroblastoma cell line exposed to C-F chemistry displays readouts designed to inform the onset of neurodegeneration-associated trajectories (including α-synuclein aggregation). In Aim 2, we will further address in a preclinical model whether C-F burden induces protein aggregation (α-synuclein, amyloid β, mHTT), interferes with dopaminergic neuronal assembles and induces locomotory deficits. Completion of the proposed work will complement ongoing experimental biophysical, structural (crystallographic, NMR) and computational (docking, molecular dynamics simulations) mapping of the interactions between these anthropogenic “forever” chemicals and amyloid-forming proteins potentially resulting in a soluble-to-toxic transformation. It will prepare the stage for vertebrate testing. The findings from this relatively understudied area likely exposes interventional targets for C-F chemistry associated neurotoxicity, spurs therapeutic efforts and can also guide the development of more biocompatible alternatives.

Stability in disrupted maternal representations over the perinatal period: Contributors and consequences

Abstract High-quality mother-infant relationships promote social, emotional, and cognitive development while protecting against poor child behavioral, health, and psychological adaptation that create risk for long- term negative outcomes. As mothers transition to parenthood, their own experiences of being cared for influence their emerging views of parenting and representations of their developing child. Evidence suggests that ‘disrupted’ maternal representations of the child, i.e., representations characterized by mixed communication, role merging, extreme withdrawal, and other unusual psychological processes, are tied to both poor child socioemotional adjustment and both insecure and disorganized attachment. However, it is unclear whether disrupted representations that emerge during pregnancy remain stable across the first several years of the child’s life. In addition, to date, research has not examined how change/stability in these representations may affect maternal caregiving and subsequent child adaptation. Using data from a longitudinal, multi-method study, this proposed project will examine the stability of maternal representations of the child for 99 women living in high risk contexts using the Working Model of the Child Interview during the third trimester and again when the child is two years of age. Mothers’ demographic characteristics (i.e. SES and relationship status), interpersonal violence experiences (i.e. child maltreatment or intimate violence exposure), psychological health (i.e. depressive, anxious, and PTSD symptoms), and parenting stress (i.e. perceptions of the child as difficult and parent-child interactions as dysfunctional) are measured as well to examine influences on representation stability. Finally, the observed quality of maternal caregiving and child adaptation are measured and examined in relation to stability in maternal representations of the child. Findings from this study have the potential to identify which mother-child dyads are at greatest risk for poor adaptation across the perinatal period and to delineate the contributors and consequences of maternal representational stability. These findings will serve as an important step towards informing the development or modification of existing prevention/intervention approaches that are targeted specifically towards mother-child dyads who are most at need.

FENS Forum 2026

Europe’s leading neuroscience conference, bringing together researchers, clinicians, and innovators across molecular, cellular, systems, cognitive, and clinical neuroscience.

Organization of thalamic networks and mechanisms of dysfunction in schizophrenia and autism

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

Endocannabinoid System Dysregulations in Binge Eating Disorder and Obesity

Neural Representations of Abstract Cognitive Maps in Prefrontal Cortex and Medial Temporal Lobe

OpenNeuro FitLins GLM: An Accessible, Semi-Automated Pipeline for OpenNeuro Task fMRI Analysis

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Is it Time for MS Patients to Receive Cognitive Rehabilitation?

Functional Plasticity in the Language Network – evidence from Neuroimaging and Neurostimulation

Efficient cognition requires flexible interactions between distributed neural networks in the human brain. These networks adapt to challenges by flexibly recruiting different regions and connections. In this talk, I will discuss how we study functional network plasticity and reorganization with combined neurostimulation and neuroimaging across the adult life span. I will argue that short-term plasticity enables flexible adaptation to challenges, via functional reorganization. My key hypothesis is that disruption of higher-level cognitive functions such as language can be compensated for by the recruitment of domain-general networks in our brain. Examples from healthy young brains illustrate how neurostimulation can be used to temporarily interfere with efficient processing, probing short-term network plasticity at the systems level. Examples from people with dyslexia help to better understand network disorders in the language domain and outline the potential of facilitatory neurostimulation for treatment. I will also discuss examples from aging brains where plasticity helps to compensate for loss of function. Finally, examples from lesioned brains after stroke provide insight into the brain’s potential for long-term reorganization and recovery of function. Collectively, these results challenge the view of a modular organization of the human brain and argue for a flexible redistribution of function via systems plasticity.

Single-neuron correlates of perception and memory in the human medial temporal lobe

The human medial temporal lobe contains neurons that respond selectively to the semantic contents of a presented stimulus. These "concept cells" may respond to very different pictures of a given person and even to their written or spoken name. Their response latency is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from single unit recordings in the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala during paradigms involving object recognition and conscious perception as well as encoding of episodic memories in order to characterize the role of concept cells in these cognitive functions.

Using Fast Periodic Visual Stimulation to measure cognitive function in dementia

Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detect subtle deficits in recognition memory, visual processing and attention in dementia patients using EEG in the lab, at home and in clinic. The method is quick, cost-effective, and scalable, utilizing widely available EEG technology. FPVS holds significant potential as a functional biomarker for early diagnosis and monitoring of dementia, paving the way for timely interventions and improved patient outcomes.

Cognitive maps, navigational strategies, and the human brain

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

What it’s like is all there is: The value of Consciousness

Over the past thirty years or so, cognitive neuroscience has made spectacular progress understanding the biological mechanisms of consciousness. Consciousness science, as this field is now sometimes called, was not only inexistent thirty years ago, but its very name seemed like an oxymoron: how can there be a science of consciousness? And yet, despite this scepticism, we are now equipped with a rich set of sophisticated behavioural paradigms, with an impressive array of techniques making it possible to see the brain in action, and with an ever-growing collection of theories and speculations about the putative biological mechanisms through which information processing becomes conscious. This is all good and fine, even promising, but we also seem to have thrown the baby out with the bathwater, or at least to have forgotten it in the crib: consciousness is not just mechanisms, it’s what it feels like. In other words, while we know thousands of informative studies about access-consciousness, we have little in the way of phenomenal consciousness. But that — what it feels like — is truly what “consciousness” is about. Understanding why it feels like something to be me and nothing (panpsychists notwithstanding) for a stone to be a stone is what the field has always been after. However, while it is relatively easy to study access-consciousness through the contrastive approach applied to reports, it is much less clear how to study phenomenology, its structure and its function. Here, I first overview work on what consciousness does (the "how"). Next, I ask what difference feeling things makes and what function phenomenology might play. I argue that subjective experience has intrinsic value and plays a functional role in everything that we do.

Oligodendrocyte dyfunction drives human cognitive decline

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Digital Minds: Brain Development in the Age of Technology

Digital Minds: Brain Development in the Age of Technology examines how our increasingly connected world shapes mental and cognitive health. From screen time and social media to virtual interactions, this seminar delves into the latest research on how technology influences brain development, relationships, and emotional well-being. Join us to explore strategies for harnessing technology's benefits while mitigating its potential challenges, empowering you to thrive in a digital age.

Dimensionality reduction beyond neural subspaces

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new tensor decomposition that captures and demixes these different types of covariability to reveal task-relevant structure in neural activity. Finally, I will close with some thoughts regarding the circuit mechanisms that could generate mixed covariability. Together this work points to a need to consider new possibilities for how neural populations encode sensory, cognitive, and behavioral variables beyond neural subspaces.

Enhancing Real-World Event Memory

Memory is essential for shaping how we interpret the world, plan for the future, and understand ourselves, yet effective cognitive interventions for real-world episodic memory loss remain scarce. This talk introduces HippoCamera, a smartphone-based intervention inspired by how the brain supports memory, designed to enhance real-world episodic recollection by replaying high-fidelity autobiographical cues. It will showcase how our approach improves memory, mood, and hippocampal activity while uncovering links between memory distinctiveness, well-being, and the perception of time.

The Cognitive Roots of the Problem of Free Will

On the principle of accentuation in perceptual organization: Visual, cognitive and biological implications

Gene regulatory mechanisms of neocortex development and evolution

The neocortex is considered to be the seat of higher cognitive functions in humans. During its evolution, most notably in humans, the neocortex has undergone considerable expansion, which is reflected by an increase in the number of neurons. Neocortical neurons are generated during development by neural stem and progenitor cells. Epigenetic mechanisms play a pivotal role in orchestrating the behaviour of stem cells during development. We are interested in the mechanisms that regulate gene expression in neural stem cells, which have implications for our understanding of neocortex development and evolution, neural stem cell regulation and neurodevelopmental disorders.

Screen Savers : Protecting adolescent mental health in a digital world

In our rapidly evolving digital world, there is increasing concern about the impact of digital technologies and social media on the mental health of young people. Policymakers and the public are nervous. Psychologists are facing mounting pressures to deliver evidence that can inform policies and practices to safeguard both young people and society at large. However, research progress is slow while technological change is accelerating.My talk will reflect on this, both as a question of psychological science and metascience. Digital companies have designed highly popular environments that differ in important ways from traditional offline spaces. By revisiting the foundations of psychology (e.g. development and cognition) and considering digital changes' impact on theories and findings, we gain deeper insights into questions such as the following. (1) How do digital environments exacerbate developmental vulnerabilities that predispose young people to mental health conditions? (2) How do digital designs interact with cognitive and learning processes, formalised through computational approaches such as reinforcement learning or Bayesian modelling?However, we also need to face deeper questions about what it means to do science about new technologies and the challenge of keeping pace with technological advancements. Therefore, I discuss the concept of ‘fast science’, where, during crises, scientists might lower their standards of evidence to come to conclusions quicker. Might psychologists want to take this approach in the face of technological change and looming concerns? The talk concludes with a discussion of such strategies for 21st-century psychology research in the era of digitalization.

Sensory cognition

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.

Introducing the 'Cognitive Neuroscience & Neurotechnolog' group: From real-time fMRI to layer-fMRI & back

Unmotivated bias

In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.

Principles of Cognitive Control over Task Focus and Task

2024 BACN Mid-Career Prize Lecture Adaptive behavior requires the ability to focus on a current task and protect it from distraction (cognitive stability), and to rapidly switch tasks when circumstances change (cognitive flexibility). How people control task focus and switch-readiness has therefore been the target of burgeoning research literatures. Here, I review and integrate these literatures to derive a cognitive architecture and functional rules underlying the regulation of stability and flexibility. I propose that task focus and switch-readiness are supported by independent mechanisms whose strategic regulation is nevertheless governed by shared principles: both stability and flexibility are matched to anticipated challenges via an incremental, online learner that nudges control up or down based on the recent history of task demands (a recency heuristic), as well as via episodic reinstatement when the current context matches a past experience (a recognition heuristic).

Sophie Scott - The Science of Laughter from Evolution to Neuroscience

Keynote Address to British Association of Cognitive Neuroscience, London, 10th September 2024

Personalized medicine and predictive health and wellness: Adding the chemical component

Wearable sensors that detect and quantify biomarkers in retrievable biofluids (e.g., interstitial fluid, sweat, tears) provide information on human dynamic physiological and psychological states. This information can transform health and wellness by providing actionable feedback. Due to outdated and insufficiently sensitive technologies, current on-body sensing systems have capabilities limited to pH, and a few high-concentration electrolytes, metabolites, and nutrients. As such, wearable sensing systems cannot detect key low-concentration biomarkers indicative of stress, inflammation, metabolic, and reproductive status.  We are revolutionizing sensing. Our electronic biosensors detect virtually any signaling molecule or metabolite at ultra-low levels. We have monitored serotonin, dopamine, cortisol, phenylalanine, estradiol, progesterone, and glucose in blood, sweat, interstitial fluid, and tears. The sensors are based on modern nanoscale semiconductor transistors that are straightforwardly scalable for manufacturing. We are developing sensors for >40 biomarkers for personalized continuous monitoring (e.g., smartwatch, wearable patch) that will provide feedback for treating chronic health conditions (e.g., perimenopause, stress disorders, phenylketonuria). Moreover, our sensors will enable female fertility monitoring and the adoption of more healthy lifestyles to prevent disease and improve physical and cognitive performance.

Frequency tagging: a powerful method to investigate neurocognitive development with EEG

Exploring the cerebral mechanisms of acoustically-challenging speech comprehension - successes, failures and hope

Comprehending speech under acoustically challenging conditions is an everyday task that we can often execute with ease. However, accomplishing this requires the engagement of cognitive resources, such as auditory attention and working memory. The mechanisms that contribute to the robustness of speech comprehension are of substantial interest in the context of hearing mild to moderate hearing impairment, in which affected individuals typically report specific difficulties in understanding speech in background noise. Although hearing aids can help to mitigate this, they do not represent a universal solution, thus, finding alternative interventions is necessary. Given that age-related hearing loss (“presbycusis”) is inevitable, developing new approaches is all the more important in the context of aging populations. Moreover, untreated hearing loss in middle age has been identified as the most significant potentially modifiable predictor of dementia in later life. I will present research that has used a multi-methodological approach (fMRI, EEG, MEG and non-invasive brain stimulation) to try to elucidate the mechanisms that comprise the cognitive “last mile” in speech acousticallychallenging speech comprehension and to find ways to enhance them.

Applied cognitive neuroscience to improve learning and therapeutics

Advancements in cognitive neuroscience have provided profound insights into the workings of the human brain and the methods used offer opportunities to enhance performance, cognition, and mental health. Drawing upon interdisciplinary collaborations in the University of California San Diego, Human Performance Optimization Lab, this talk explores the application of cognitive neuroscience principles in three domains to improve human performance and alleviate mental health challenges. The first section will discuss studies addressing the role of vision and oculomotor function in athletic performance and the potential to train these foundational abilities to improve performance and sports outcomes. The second domain considers the use of electrophysiological measurements of the brain and heart to detect, and possibly predict, errors in manual performance, as shown in a series of studies with surgeons as they perform robot-assisted surgery. Lastly, findings from clinical trials testing personalized interventional treatments for mood disorders will be discussed in which the temporal and spatial parameters of transcranial magnetic stimulation (TMS) are individualized to test if personalization improves treatment response and can be used as predictive biomarkers to guide treatment selection. Together, these translational studies use the measurement tools and constructs of cognitive neuroscience to improve human performance and well-being.

The Role of Cognitive Appraisal in the Relationship between Personality and Emotional Reactivity

Emotion is defined as a rapid psychological process involving experiential, expressive and physiological responses. These emerge following an appraisal process that involves cognitive evaluations of the environment assessing its relevance, implication, coping potential, and normative significance. It has been suggested that changes in appraisal processes lead to changes in the resulting emotional nature. Simultaneously, it was demonstrated that personality can be seen as a predisposition to feel more frequently certain emotions, but the personality-appraisal-emotional response chain is rarely fully investigated. The present project thus sought to investigate the extent to which personality traits influence certain appraisals, which in turn influence the subsequent emotional reactions via a systematic analysis of the link between personality traits of different current models, specific appraisals, and emotional response patterns at the experiential, expressive, and physiological levels. Major results include the coherence of emotion components clustering, and the centrality of the pleasantness, coping potential and consequences appraisals, in context; and the differentiated mediating role of cognitive appraisal in the relation between personality and the intensity and duration of an emotional state, and autonomic arousal, such as Extraversion-pleasantness-experience, and Neuroticism-powerlessness-arousal. Elucidating these relationships deepens our understanding of individual differences in emotional reactivity and spot routes of action on appraisal processes to modify upcoming adverse emotional responses, with a broader societal impact on clinical and non-clinical populations.

Characterizing the causal role of large-scale network interactions in supporting complex cognition

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

Executive functions in the brain of deaf individuals – sensory and language effects

Executive functions are cognitive processes that allow us to plan, monitor and execute our goals. Using fMRI, we investigated how early deafness influences crossmodal plasticity and the organisation of executive functions in the adult human brain. Results from a range of visual executive function tasks (working memory, task switching, planning, inhibition) show that deaf individuals specifically recruit superior temporal “auditory” regions during task switching. Neural activity in auditory regions predicts behavioural performance during task switching in deaf individuals, highlighting the functional relevance of the observed cortical reorganisation. Furthermore, language grammatical skills were correlated with the level of activation and functional connectivity of fronto-parietal networks. Together, these findings show the interplay between sensory and language experience in the organisation of executive processing in the brain.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Where Cognitive Neuroscience Meets Industry: Navigating the Intersections of Academia and Industry

In this talk, Mirta will share her journey from her education a mathematically-focused high school to her currently unconventional career in London, emphasizing the evolution from a local education in Croatia to international experiences in the US and UK. We will explore the concept of interdisciplinary careers in the modern world, viewing them through the framework of increasing demand, flexibility, and dynamism in the current workplace. We will underscore the significance of interdisciplinary research for launching careers outside of academia, and bolstering those within. I will challenge the conventional norm of working either in academia or industry, and encourage discussion about the opportunities for combining the two in a myriad of career opportunities. I’ll use examples from my own and others’ research to highlight opportunities for early career researchers to extend their work into practical applications. Such an approach leverages the strengths of both sectors, fostering innovation and practical applications of research findings. I hope these insights can offer valuable perspectives for those looking to navigate the evolving demands of the global job market, illustrating the advantages of a versatile skill set that spans multiple disciplines and allows extensions into exciting career options.

The Role of Spatial and Contextual Relations of real world objects in Interval Timing

In the real world, object arrangement follows a number of rules. Some of the rules pertain to the spatial relations between objects and scenes (i.e., syntactic rules) and others about the contextual relations (i.e., semantic rules). Research has shown that violation of semantic rules influences interval timing with the duration of scenes containing such violations to be overestimated as compared to scenes with no violations. However, no study has yet investigated whether both semantic and syntactic violations can affect timing in the same way. Furthermore, it is unclear whether the effect of scene violations on timing is due to attentional or other cognitive accounts. Using an oddball paradigm and real-world scenes with or without semantic and syntactic violations, we conducted two experiments on whether time dilation will be obtained in the presence of any type of scene violation and the role of attention in any such effect. Our results from Experiment 1 showed that time dilation indeed occurred in the presence of syntactic violations, while time compression was observed for semantic violations. In Experiment 2, we further investigated whether these estimations were driven by attentional accounts, by utilizing a contrast manipulation of the target objects. The results showed that an increased contrast led to duration overestimation for both semantic and syntactic oddballs. Together, our results indicate that scene violations differentially affect timing due to violation processing differences and, moreover, their effect on timing seems to be sensitive to attentional manipulations such as target contrast.

Visual mechanisms for flexible behavior

Perhaps the most impressive aspect of the way the brain enables us to act on the sensory world is its flexibility. We can make a general inference about many sensory features (rating the ripeness of mangoes or avocados) and map a single stimulus onto many choices (slicing or blending mangoes). These can be thought of as flexibly mapping many (features) to one (inference) and one (feature) to many (choices) sensory inputs to actions. Both theoretical and experimental investigations of this sort of flexible sensorimotor mapping tend to treat sensory areas as relatively static. Models typically instantiate flexibility through changing interactions (or weights) between units that encode sensory features and those that plan actions. Experimental investigations often focus on association areas involved in decision-making that show pronounced modulations by cognitive processes. I will present evidence that the flexible formatting of visual information in visual cortex can support both generalized inference and choice mapping. Our results suggest that visual cortex mediates many forms of cognitive flexibility that have traditionally been ascribed to other areas or mechanisms. Further, we find that a primary difference between visual and putative decision areas is not what information they encode, but how that information is formatted in the responses of neural populations, which is related to difference in the impact of causally manipulating different areas on behavior. This scenario allows for flexibility in the mapping between stimuli and behavior while maintaining stability in the information encoded in each area and in the mappings between groups of neurons.

Imaging the subcortex; Microstructural and connectivity correlates of outcome variability in functional neurosurgery for movement disorders

We are very much looking forward to host Francisca Ferreira and Birte Forstmann on December 14th, 2023, at noon ET / 6PM CET. Francisca Ferreira is a PhD student and Neurosurgery trainee at the University College of London Queen Square Institute of Neurology and a Royal College of Surgeons “Emerging Leaders” program laureate. Her presentation title will be: “Microstructural and connectivity correlates of outcome variability in functional neurosurgery for movement disorders”. Birte Forstmann, PhD, is the Director of the Amsterdam Brain and Cognition Center, a Professor of Cognitive Neuroscience at the University of Amsterdam, and a Professor by Special Appointment of Neuroscientific Testing of Psychological Models at the University of Leiden. Besides her scientific presentation (“Imaging the human subcortex”), she will give us a glimpse at the “Person behind the science”. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

Soft Discrimination of Healthy Controls and Patients with Mild Cognitive Impairment Based on EEG Data

Deep language models as a cognitive model for natural language processing in the human brain

Connectome-based models of neurodegenerative disease

Neurodegenerative diseases involve accumulation of aberrant proteins in the brain, leading to brain damage and progressive cognitive and behavioral dysfunction. Many gaps exist in our understanding of how these diseases initiate and how they progress through the brain. However, evidence has accumulated supporting the hypothesis that aberrant proteins can be transported using the brain’s intrinsic network architecture — in other words, using the brain’s natural communication pathways. This theory forms the basis of connectome-based computational models, which combine real human data and theoretical disease mechanisms to simulate the progression of neurodegenerative diseases through the brain. In this talk, I will first review work leading to the development of connectome-based models, and work from my lab and others that have used these models to test hypothetical modes of disease progression. Second, I will discuss the future and potential of connectome-based models to achieve clinically useful individual-level predictions, as well as to generate novel biological insights into disease progression. Along the way, I will highlight recent work by my lab and others that is already moving the needle toward these lofty goals.

Consciousness in the cradle: on the emergence of infant experience

Although each of us was once a baby, infant consciousness remains mysterious and there is no received view about when, and in what form, consciousness first emerges. Some theorists defend a ‘late-onset’ view, suggesting that consciousness requires cognitive capacities which are unlikely to be in place before the child’s first birthday at the very earliest. Other theorists defend an ‘early-onset’ account, suggesting that consciousness is likely to be in place at birth (or shortly after) and may even arise during the third trimester. Progress in this field has been difficult, not just because of the challenges associated with procuring the relevant behavioral and neural data, but also because of uncertainty about how best to study consciousness in the absence of the capacity for verbal report or intentional behavior. This review examines both the empirical and methodological progress in this field, arguing that recent research points in favor of early-onset accounts of the emergence of consciousness.

Great ape interaction: Ladyginian but not Gricean

Non-human great apes inform one another in ways that can seem very humanlike. Especially in the gestural domain, their behavior exhibits many similarities with human communication, meeting widely used empirical criteria for intentionality. At the same time, there remain some manifest differences. How to account for these similarities and differences in a unified way remains a major challenge. This presentation will summarise the arguments developed in a recent paper with Christophe Heintz. We make a key distinction between the expression of intentions (Ladyginian) and the expression of specifically informative intentions (Gricean), and we situate this distinction within a ‘special case of’ framework for classifying different modes of attention manipulation. The paper also argues that the attested tendencies of great ape interaction—for instance, to be dyadic rather than triadic, to be about the here-and-now rather than ‘displaced’—are products of its Ladyginian but not Gricean character. I will reinterpret video footage of great ape gesture as Ladyginian but not Gricean, and distinguish several varieties of meaning that are continuous with one another. We conclude that the evolutionary origins of linguistic meaning lie in gradual changes in not communication systems as such, but rather in social cognition, and specifically in what modes of attention manipulation are enabled by a species’ cognitive phenotype: first Ladyginian and in turn Gricean. The second of these shifts rendered humans, and only humans, ‘language ready’.

Trends in NeuroAI - SwiFT: Swin 4D fMRI Transformer

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

Prefrontal mechanisms involved in learning distractor-resistant working memory in a dual task

Working memory (WM) is a cognitive function that allows the short-term maintenance and manipulation of information when no longer accessible to the senses. It relies on temporarily storing stimulus features in the activity of neuronal populations. To preserve these dynamics from distraction it has been proposed that pre and post-distraction population activity decomposes into orthogonal subspaces. If orthogonalization is necessary to avoid WM distraction, it should emerge as performance in the task improves. We sought evidence of WM orthogonalization learning and the underlying mechanisms by analyzing calcium imaging data from the prelimbic (PrL) and anterior cingulate (ACC) cortices of mice as they learned to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of protecting the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. To gain mechanistic insight into how these low-dimensional WM representations evolve with learning we built a recurrent spiking network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which induced early-training neural dynamics in well-trained animals. In the model, learning drives the network from a double-well attractor toward a more continuous ring attractor regime. We tested signatures for this dynamical evolution in the experimental data by estimating the energy landscape of the dynamics on a one-dimensional ring. In sum, our study defines network dynamics underlying the process of learning to shield WM representations from distracting tasks.

Movements and engagement during decision-making

When experts are immersed in a task, a natural assumption is that their brains prioritize task-related activity. Accordingly, most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. Surprisingly, we observed that during decision-making, the cortex-wide activity of multiple cell types is dominated by movements, especially “uninstructed movements”, that are spontaneously expressed. These observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity. To understand the relationship between these movements and decision-making, we examined the movements more closely. We tested whether the magnitude or the timing of the movements was correlated with decision-making performance. To do this, we partitioned movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This hinted that the timing of spontaneous movements could indicate periods of disengagement. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity. Surprisingly, we found that the same movement impacts neural activity more strongly when animals are disengaged. An intriguing possibility is that these larger movement signals disrupt cognitive computations, leading to poor decision-making performance. Taken together, these observations argue that movements and cognitionare closely intertwined, even during expert decision-making.

Identifying mechanisms of cognitive computations from spikes

Higher cortical areas carry a wide range of sensory, cognitive, and motor signals supporting complex goal-directed behavior. These signals mix in heterogeneous responses of single neurons, making it difficult to untangle underlying mechanisms. I will present two approaches for revealing interpretable circuit mechanisms from heterogeneous neural responses during cognitive tasks. First, I will show a flexible nonparametric framework for simultaneously inferring population dynamics on single trials and tuning functions of individual neurons to the latent population state. When applied to recordings from the premotor cortex during decision-making, our approach revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Second, I will show an approach for inferring an interpretable network model of a cognitive task—the latent circuit—from neural response data. We developed a theory to causally validate latent circuit mechanisms via patterned perturbations of activity and connectivity in the high-dimensional network. This work opens new possibilities for deriving testable mechanistic hypotheses from complex neural response data.

Neuroinflammation in Epilepsy: what have we learned from human brain tissue specimens ?

Epileptogenesis is a gradual and dynamic process leading to difficult-to-treat seizures. Several cellular, molecular, and pathophysiologic mechanisms, including the activation of inflammatory processes.  The use of human brain tissue represents a crucial strategy to advance our understanding of the underlying neuropathology and the molecular and cellular basis of epilepsy and related cognitive and behavioral comorbidities,  The mounting evidence obtained during the past decade has emphasized the critical role of inflammation  in the pathophysiological processes implicated in a large spectrum of genetic and acquired forms of  focal epilepsies. Dissecting the cellular and molecular mediators of  the pathological immune responses and their convergent and divergent mechanisms, is a major requisite for delineating their role in the establishment of epileptogenic networks. The role of small regulatory molecules involved in the regulation of  specific pro- and anti-inflammatory pathways  and the crosstalk between neuroinflammation and oxidative stress will be addressed.    The observations supporting the activation of both innate and adaptive immune responses in human focal epilepsy will be discussed and elaborated, highlighting specific inflammatory pathways as potential targets for antiepileptic, disease-modifying therapeutic strategies.

Vocal emotion perception at millisecond speed

The human voice is possibly the most important sound category in the social landscape. Compared to other non-verbal emotion signals, the voice is particularly effective in communicating emotions: it can carry information over large distances and independent of sight. However, the study of vocal emotion expression and perception is surprisingly far less developed than the study of emotion in faces. Thereby, its neural and functional correlates remain elusive. As the voice represents a dynamically changing auditory stimulus, temporally sensitive techniques such as the EEG are particularly informative. In this talk, the dynamic neurocognitive operations that take place when we listen to vocal emotions will be specified, with a focus on the effects of stimulus type, task demands, and speaker and listener characteristics (e.g., age). These studies suggest that emotional voice perception is not only a matter of how one speaks but also of who speaks and who listens. Implications of these findings for the understanding of psychiatric disorders such as schizophrenia will be discussed.

Use of brain imaging data to improve prescriptions of psychotropic drugs - Examples of ketamine in depression and antipsychotics in schizophrenia

The use of molecular imaging, particularly PET and SPECT, has significantly transformed the treatment of schizophrenia with antipsychotic drugs since the late 1980s. It has offered insights into the links between drug target engagement, clinical effects, and side effects. A therapeutic window for receptor occupancy is established for antipsychotics, yet there is a divergence of opinions regarding the importance of blood levels, with many downplaying their significance. As a result, the role of therapeutic drug monitoring (TDM) as a personalized therapy tool is often underrated. Since molecular imaging of antipsychotics has focused almost entirely on D2-like dopamine receptors and their potential to control positive symptoms, negative symptoms and cognitive deficits are hardly or not at all investigated. Alternative methods have been introduced, i.e. to investigate the correlation between approximated receptor occupancies from blood levels and cognitive measures. Within the domain of antidepressants, and specifically regarding ketamine's efficacy in depression treatment, there is limited comprehension of the association between plasma concentrations and target engagement. The measurement of AMPA receptors in the human brain has added a new level of comprehension regarding ketamine's antidepressant effects. To ensure precise prescription of psychotropic drugs, it is vital to have a nuanced understanding of how molecular and clinical effects interact. Clinician scientists are assigned with the task of integrating these indispensable pharmacological insights into practice, thereby ensuring a rational and effective approach to the treatment of mental health disorders, signaling a new era of personalized drug therapy mechanisms that promote neuronal plasticity not only under pathological conditions, but also in the healthy aging brain.

Brain Connectivity Workshop

Founded in 2002, the Brain Connectivity Workshop (BCW) is an annual international meeting for in-depth discussions of all aspects of brain connectivity research. By bringing together experts in computational neuroscience, neuroscience methodology and experimental neuroscience, it aims to improve the understanding of the relationship between anatomical connectivity, brain dynamics and cognitive function. These workshops have a unique format, featuring only short presentations followed by intense discussion. This year’s workshop is co-organised by Wellcome, putting the spotlight on brain connectivity in mental health disorders. We look forward to having you join us for this exciting, thought-provoking and inclusive event.

Self as Processes (BACN Mid-career Prize Lecture 2023)

An understanding of the self helps explain not only human thoughts, feelings, attitudes but also many aspects of everyday behaviour. This talk focuses on a viewpoint - self as processes. This viewpoint emphasizes the dynamics of the self that best connects with the development of the self over time and its realist orientation. We are combining psychological experiments and data mining to comprehend the stability and adaptability of the self across various populations. In this talk, I draw on evidence from experimental psychology, cognitive neuroscience, and machine learning approaches to demonstrate why and how self-association affects cognition and how it is modulated by various social experiences and situational factors

NII Methods (journal club): NeuroQuery, comprehensive meta-analysis of human brain mapping

We will discuss this paper on Neuroquery, a relatively new web-based meta-analysis tool: https://elifesciences.org/articles/53385.pdf. This is different from Neurosynth in that it generates meta-analysis maps using predictive modeling from the string of text provided at the prompt, instead of performing inferential statistics to calculate the overlap of activation from different studies. This allows the user to generate predictive maps for more nuanced cognitive processes - especially for clinical populations which may be underrepresented in the literature compared to controls - and can be useful in generating predictions about where the activity will be for one's own study, and for creating ROIs.

Sleep deprivation and the human brain: from brain physiology to cognition”

Sleep strongly affects synaptic strength, making it critical for cognition, especially learning and memory formation. Whether and how sleep deprivation modulates human brain physiology and cognition is poorly understood. Here we examined how overnight sleep deprivation vs overnight sufficient sleep affects (a) cortical excitability, measured by transcranial magnetic stimulation, (b) inducibility of long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity via transcranial direct current stimulation (tDCS), and (c) learning, memory, and attention. We found that sleep deprivation increases cortical excitability due to enhanced glutamate-related cortical facilitation and decreases and/or reverses GABAergic cortical inhibition. Furthermore, tDCS-induced LTP-like plasticity (anodal) abolishes while the inhibitory LTD-like plasticity (cathodal) converts to excitatory LTP-like plasticity under sleep deprivation. This is associated with increased EEG theta oscillations due to sleep pressure. Motor learning, behavioral counterparts of plasticity, and working memory and attention, which rely on cortical excitability, are also impaired during sleep deprivation. Our study indicates that upscaled brain excitability and altered plasticity, due to sleep deprivation, are associated with impaired cognitive performance. Besides showing how brain physiology and cognition undergo changes (from neurophysiology to higher-order cognition) under sleep pressure, the findings have implications for variability and optimal application of noninvasive brain stimulation.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Cognitive Computational Neuroscience 2023

CCN is an annual conference that serves as a forum for cognitive science, neuroscience, and artificial intelligence researchers dedicated to understanding the computations that underlie complex behavior.

Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing

The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.

Doubting the neurofeedback double-blind do participants have residual awareness of experimental purposes in neurofeedback studies?

Neurofeedback provides a feedback display which is linked with on-going brain activity and thus allows self-regulation of neural activity in specific brain regions associated with certain cognitive functions and is considered a promising tool for clinical interventions. Recent reviews of neurofeedback have stressed the importance of applying the “double-blind” experimental design where critically the patient is unaware of the neurofeedback treatment condition. An important question then becomes; is double-blind even possible? Or are subjects aware of the purposes of the neurofeedback experiment? – this question is related to the issue of how we assess awareness or the absence of awareness to certain information in human subjects. Fortunately, methods have been developed which employ neurofeedback implicitly, where the subject is claimed to have no awareness of experimental purposes when performing the neurofeedback. Implicit neurofeedback is intriguing and controversial because it runs counter to the first neurofeedback study, which showed a link between awareness of being in a certain brain state and control of the neurofeedback-derived brain activity. Claiming that humans are unaware of a specific type of mental content is a notoriously difficult endeavor. For instance, what was long held as wholly unconscious phenomena, such as dreams or subliminal perception, have been overturned by more sensitive measures which show that degrees of awareness can be detected. In this talk, I will discuss whether we will critically examine the claim that we can know for certain that a neurofeedback experiment was performed in an unconscious manner. I will present evidence that in certain neurofeedback experiments such as manipulations of attention, participants display residual degrees of awareness of experimental contingencies to alter their cognition.

Attending to the ups and downs of Lewy body dementia: An exploration of cognitive fluctuations

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) share similarities in pathology and clinical presentation and come under the umbrella term of Lewy body dementias (LBD). Fluctuating cognition is a key symptom in LBD and manifests as altered levels of alertness and attention, with a marked difference between best and worst performance. Cognition and alertness can change over seconds or minutes to hours and days of obtundation. Cognitive fluctuations can have significant impacts on the quality of life of people with LBD as well as potentially contribute to the exacerbation of other transient symptoms including, for example, hallucinations and psychosis as well as making it difficult to measure cognitive effect size benefits in clinical trials of LBD. However, this significant symptom in LBD is poorly understood. In my presentation I will discuss the phenomenology of cognitive fluctuations, how we can measure it clinically and limitations of these approaches. I will then outline the work of our group and others which has been focussed on unpicking the aetiological basis of cognitive fluctuations in LBD using a variety of imaging approaches (e.g. SPECT, sMRI, fMRI and EEG). I will then briefly explore future research directions.

Freeze or flee ? New insights from rodent models of autism

Individuals afflicted with certain types of autism spectrum disorder often exhibit impaired cognitive function alongside enhanced emotional symptoms and mood lability. However, current understanding of the pathogenesis of autism and intellectual disabilities is based primarily on studies in the hippocampus and cortex, brain areas involved in cognitive function. But, these disorders are also associated with strong emotional symptoms, which are likely to involve changes in the amygdala and other brain areas. In this talk I will highlight these issues by presenting analyses in rat models of ASD/ID lacking Nlgn3 and Frm1 (causing Fragile X Syndrome). In addition to identifying new circuit and cellular alterations underlying divergent patterns of fear expression, these findings also suggest novel therapeutic strategies.

How curiosity affects learning and information seeking via the dopaminergic circuit

Over the last decade, research on curiosity – the desire to seek new information – has been rapidly growing. Several studies have shown that curiosity elicits activity within the dopaminergic circuit and thereby enhances hippocampus-dependent learning. However, given this new field of research, we do not have a good understanding yet of (i) how curiosity-based learning changes across the lifespan, (ii) why some people show better learning improvements due to curiosity than others, and (iii) whether lab-based research on curiosity translates to how curiosity affects information seeking in real life. In this talk, I will present a series of behavioural and neuroimaging studies that address these three questions about curiosity. First, I will present findings on how curiosity and interest affect learning differently in childhood and adolescence. Second, I will show data on how inter-individual differences in the magnitude of curiosity-based learning depend on the strength of resting-state functional connectivity within the cortico-mesolimbic dopaminergic circuit. Third, I will present findings on how the level of resting-state functional connectivity within this circuit is also associated with the frequency of real-life information seeking (i.e., about Covid-19-related news). Together, our findings help to refine our recently proposed framework – the Prediction, Appraisal, Curiosity, and Exploration (PACE) framework – that attempts to integrate theoretical ideas on the neurocognitive mechanisms of how curiosity is elicited, and how curiosity enhances learning and information seeking. Furthermore, our findings highlight the importance of curiosity research to better understand how curiosity can be harnessed to improve learning and information seeking in real life.

DUAL TARGETING OF G9A AND HISTAMINE H3 RECEPTORS IMPROVES COGNITIVE FUNCTION AND REDUCES OXIDATIVE STRESS IN BTBR MICE

FENS Forum 2026

TOPOGRAPHICAL EEG OSCILLATION PATTERNS DURING COGNITIVE BIAS MODIFICATION TRAINING

FENS Forum 2026

IMPACT OF COGNITIVE BIAS MODIFICATION TRAINING ON P100 IN DEPRESSION AND HEALTHY CONTROLS

FENS Forum 2026

Defining the Limits: Upper Bound of Non-Neurobiological Treatment Efficacy through Cognitive-Neural Network Alignment

Bernstein Conference 2024

Abstract cognitive encoding in the primate superior colliculus

COSYNE 2022

The geometry of map-like representations under dynamic cognitive control

COSYNE 2022

Neuromodulated online cognitive maps for reinforcement learning

Bernstein Conference 2024

Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization

COSYNE 2022

Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization

COSYNE 2022

The neurocognitive role of working memory load when motivation affects instrumental learning

COSYNE 2022

The neurocognitive role of working memory load when motivation affects instrumental learning

COSYNE 2022

Rethinking Tolman's latent learning with metacognitive exploration

COSYNE 2022

Rethinking Tolman's latent learning with metacognitive exploration

COSYNE 2022

Thalamic role in human cognitive flexibility and routing of abstract information.

COSYNE 2022

An accessible hippocampal dataset for benchmarking models of cognitive mapping

COSYNE 2023

Ablation of Necroptosis Protects Diabetes Associated Cognitive Deficits & Lipotoxicity Induced Neuro-Glia Changes

Intracranial electrophysiological evidence for a novel neuro-computational mechanism of cognitive flexibility in humans

COSYNE 2023

Machine Learning Approaches Reveal Prominent Behavioral Alterations and Cognitive Dysfunction in a Humanized Alzheimer Model

COSYNE 2023

A predictive learning model for cognitive maps that generate replay

COSYNE 2023

Automated discovery of interpretable cognitive programs underlying reward-guided behavior

COSYNE 2025

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

COSYNE 2025

Geometric model manifold of space, time, and belief in hippocampal cognitive maps

COSYNE 2025

Hidden state inference guides formation of hippocampal cognitive maps during learning

COSYNE 2025

Hippocampal theta sweeps are a data-efficient algorithm for cognitive map formation

COSYNE 2025

Multiplicative thalamocortical couplings facilitate rapid computation and cognitive flexibility

COSYNE 2025

Planar, Spiral, and Concentric Traveling Waves Distinguish Cognitive States in Human Memory

COSYNE 2025

Representations of alternative possibilities are flexibly generated to meet cognitive demands

COSYNE 2025

Reshaping Human Cognitive Processes through Model Parameter Loss Minimization

COSYNE 2025

Signal propagation dynamics across the Drosophila hemi-brain connectome reveal parallel-hierarchical sensory-cognitive-motor architecture.

COSYNE 2025

Unveiling the cognitive computation using multi-area RNN with biological constraints

COSYNE 2025

Ablation of carotid body activity prevents cognitive dysfunction and decreases alpha-synuclein levels in the brain of an animal model of dysmetabolism

Thalamic role in human cognitive flexibility and routing of abstract information.

COSYNE 2022

Accelerated cognitive decline in obese mouse model of Alzheimer’s disease is linked to sialic acid-driven immune deregulation

Aged male rats show impaired cognitive flexibility but no change in motivation

Acute endurance exercise modulates cerebrospinal fluid and plasma metabolome in relation to cognitive functions in healthy young individuals

Acute exposure by an intracisternal injection to the Amylin receptor antagonist AC253 improved cognitive deficits in Alzheimer’s disease mouse models

Analysis of soluble TREM2 levels in CSF and plasma of mild cognitive impairment and Alzheimer’s disease subjects

Anti-NKCC1 gene therapy rescues cognitive deficits in a mouse model of Down syndrome

Anti-PD1 immunotherapy exacerbates cognitive deficits induced by immunogenic cancer in mice

The geometry of map-like representations under dynamic cognitive control

COSYNE 2022