CCNP Meetings

Laura Kaltwasser
Berlin School of Mind and Brain
Humboldt-Universitaet zu Berlin

Look up – what do you see? Probably a ceiling, or the pure sky, most likely a void that you are unable to directly manipulate. Now look down – what do you see? Possibly your feet, your hands, your desk with a keyboard or maybe even your mug. What do they have in common? They provide affordances and thereby in most cases the possibility to assert immediate control over them, i.e. by manipulating them. The prediction and control of one’s own actions and, through them, the course of events in the environment, is trivial to most of us, yet of utmost importance to the experience of a ‘self’. As such the ‘sense of agency’ forms a central aspect of action regulation, which can be disturbed in mental disorders where self-efficacy is impaired. For example, patients suffering from the schizophrenia spectrum report that their actions are not their own but may be imposed on them by some other agent. Schizophrenia as a disorder of the self has been linked to deficits in internal monitoring processes, and, more broadly speaking, to a failure to predict one’s own actions and their consequences in the environment. In my talk I will outline theories of the self before I dive into recent neuroscientific and neurophenomenological research on altered states of the self in mental illness as well as altered states of consciousness. I would like to close by discussing the potential of an interdisciplinary research plan studying the spatio-temporal hierarchy of self-processing in the brain for informing psychopathology and treatment of self disorders.

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Morgan Barense
Department of Psychology
University of Toronto

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. I will present how this approach improves episodic recall, differentiates hippocampal memory representations, and affects mood. Finally, I will also present research investigating which features of distinct real-world memories should be emphasized to maximize their perceived similarity.

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Zhiping Pang
Robert Wood Johnson Medical School
Rutgers University

Neuropsychiatric disorders like schizophrenia and alcohol use disorder are deeply complex and often linked to genetic risk factors. Despite rapid advances in genetics, understanding how specific genes contribute to these conditions remains challenging. In my talk, I’ll discuss how we use human neurons derived from induced pluripotent stem cells (iPSCs) to bridge this gap. For example, by studying neurons with mutations in the SETD1A gene—strongly associated with schizophrenia—we’ve uncovered how these genetic changes disrupt neural structure, communication, and gene regulation, potentially explaining aspects of the disorder. Similarly, exploring microglia (immune cells in the brain) from individuals with a high genetic risk for alcohol use disorder reveals how alcohol exposure alters immune responses and synaptic connections, shedding light on how genetics and the environment interact in the brain. Finally, I’ll share how large-scale efforts advance our understanding of hundreds of neuropsychiatric disorder risk genes using cutting-edge techniques like single-cell sequencing, high-throughput imaging, and functional assays. Together, these studies aim to unravel the intricate biology of neuropsychiatric disorders, paving the way for better treatments and improved patient outcomes.

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Augustin C. Hennings
Department of Psychology
Princeton Neuroscience Institute

The ability to suppress unwanted memories is an adaptive function that not only helps prevents the retrieval of distressing content, but can also lead to lasting forgetting (i.e., suppression-induced forgetting; SIF). However, the precise factors that lead to successful SIF are not fully understood. In this talk, I will discuss data from a novel eye tracking paradigm designed to examine how participants interact with memory cues in order to support their retrieval goals. In a preregistered sample (N=34), we show that instructions to retrieve were associated with gaze reinstatement (eye gaze towards the location where the target object was studied), while instructions to suppress were associated with gaze repulsion (eye gaze away from the studied location). We also show that successful SIF is associated with an initial period of gaze reinstatement during the suppression attempt. These results provide novel evidence for how humans intentionally forget, revealing both the top-down strategies that people employ to suppress retrieval and also the retrieval dynamics that lead to lasting forgetting. I will also describe ongoing efforts to develop a real-time fMRI neurofeedback intervention designed to strengthen the neural circuits supporting top-down inhibitory control of memory retrieval. The long-term goal of this work is to rescue observed behavioral and neural deficits in retrieval suppression in individuals with anxiety- and trauma-related disorders.

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Laura Alethia de la Fuente
Department of Physics
University of Buenos Aires

How can we access the inner experience of another being? This question lies at the heart of neuroscience’s challenge to understand subjectivity. In this talk, I trace how computational and physiological approaches allow us to approach this enigma from different angles: by linking human reports of cannabis experiences with chemical strain profiles, by analyzing the reversibility of neural dynamic time in monkeys under altered states of consciousness, and by designing future experiments where structured stimuli (like music and stories) may evoke measurable but unspoken experiences. Together, these lines of work invite a new science of subjectivity—grounded in data but open to nuance.  

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Austin Avery Coley 
David Geffen School of Medicine 
University of California, Los Angeles

A critical issue within the mental health field is the lack of granularity in diagnostic practices. We speculate the key to developing effective antidepressants that work for everyone is to first identify a strategy to differentiate between heterogeneous conditions. Anhedonia, a condition identified in multiple neuropsychiatric disorders, is described as the inability to experience pleasure and is linked to anomalous medial prefrontal cortex (mPFC) activity. The mPFC is responsible for higher order functions, such as valence processing; however, it remains unknown how mPFC valence-specific neuronal population activity is affected during anhedonic conditions. To test this, we implemented the unpredictable chronic mild stress (CMS) protocol in rodents and examined hedonic behaviors following stress and ketamine treatment. We used unsupervised clustering to delineate individual variability in hedonic values in response to stress. We then performed in vivo 2-photon calcium imaging to longitudinally track mPFC valence-specific neuronal population activity and dynamics during Pavlovian conditioning tasks. We observed reductions in mPFC valence processing neurons within anhedonic mice. Using dimensionality reduction analysis, we identified a critical time point that shows an imbalance in mPFC dynamics that predicts resiliency to chronic mild stress, and rebounds following ketamine treatment. Also, we utilized behavioral pose-estimation tracking systems to detect a reduction in specific facial features during Pavlovian conditioning tasks during anhedonia. Lastly, we applied a linear classifier to decode resiliency prior to stress based on mPFC valence-encoding properties. Altogether, these experiments point to the need for identifying factors that decode the induction conditions of stress-induced anhedonia.

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Kirsten Ziman
Princeton Neuroscience Institute 
Princeton University

Theory of mind is the ability to intuitively reconstruct the mind states of other people. One of the most important aspects of theory of mind is reconstructing the attention of others. My research tests the hypothesis that we innately model others’ attention (where their attention is now, where it might shift next), facilitating our understanding of their thoughts, actions, and intentions. Using a novel class of stimuli derived from eye tracking data, I found that people accurately distinguish between natural patterns of human attention and artificially manipulated patterns of attention through a combination of visual cues. This supports the hypothesis that people possess a rich predictive model of how others’ attention will move and can tell when that model is violated. Building on this behavioral finding, I further investigated neural correlates, computational implementations, and clinical applications of attention modeling, using the same experiment paradigm. Neurally, fMRI investigation revealed that discrimination between natural and manipulated attention patterns is driven by activity in brain areas classically associated with social cognition and attention. Computationally, neural networks incorporating attention modeling components excelled at recognizing the attention of other networks and working collaboratively to accomplish tasks. Clinically, individuals with atypical social processing (participants with Autism) exhibited subtle performance differences in attention discrimination. These findings underscore that attention modelling is an inherently socially oriented task: it involves social cognition mechanisms in the brain, it increases socially oriented behavior in computational systems, and, when social processing differs in humans (e.g. those with Autism), attention modeling also differs. These findings lay the groundwork for naturalistic, multi-disciplinary research at the intersection of attention and social cognition.

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Aaron Bornstein 
Department of Cognitive Sciences
University of California, Irvine

Humans and animals are often maligned as being bad (“suboptimal”) at making decisions, especially decisions under uncertainty. But is this allegation justified? In this talk, I first examine commonly used measures of individual differences in decision-making behavior, drawn from behavioral economics. I present results suggesting that these measures are fundamentally affected by irrelevant contextual features. I then examine a different set of measurements from the domain of patch foraging. Foraging requires individuals to compare a local option to the distribution of alternatives across the environment. Foragers, across a range of species, have been observed to systematically deviate from exogenous notions of optimality by “overharvesting”—staying too long in a patch. I introduce a computational model that explains the appearance of overharvesting as a by-product of two mechanisms: 1) statistically rational learning about the distribution of alternatives and 2) decisions that adapt to the uncertainty of these distributions – looking ahead farther when more sure about the options available. I test this model using a variant of a serial stay-leave task and find that human foragers’ behavior is consistent with both mechanisms. Our findings suggest that overharvesting, rather than reflecting a deviation from optimal decision-making, is instead a consequence of optimal learning and adaptation. I then present new work following up on these findings to understand how individuals adapt to their environments, experiences, and changing neural circuitry across the lifespan.

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Salman Qasim
Robert Wood Johnson Medical School 
Rutgers School of Graduate Studies

Brain implants in neurosurgical patients enable unparalleled spatial and temporal resolution for examining human brain activity – an opportunity for identifying precise mechanisms underlying human cognition. In this talk, I will describe my research using this approach to study human memory, in particular. I will then describe the need for intracranial research in humans to move beyond simplistic behavioral paradigms and leverage expertise in computational cognitive modeling to untangle complex features of cognition, with the long-term goal of identifying biomarkers associated with various psychiatric disorders. The purpose of this talk is to foster specific experimental collaborations between my new lab at Rutgers in the Department of Neurosurgery and labs in the CCNP.

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AZA Stephen Allsop
Department of Psychiatry
Yale University

At the AZA Lab, we merge systems and computational neuroscience approaches with tools like music, mindfulness, and psychedelics to investigate the neural mechanisms underlying socioemotional function, treat mental health symptoms, and enhance social connection. Our unique approach leverages human and animal social behavioral models combined with supervised and unsupervised computational approaches to uncover new insights for improving collective mental health. We do this while staying attuned to the diverse cultural nuances of our communities. By integrating an intersectional framework, we view mental health from a rich social perspective, enabling the creation of innovative, community-based treatments. With a focus on both pharmacological (psychedelics) and non-pharmacological (music, mindfulness) interventions, we lay foundations for advanced integrative therapies. Our ultimate vision is to build a transformative approach to mental health, ensuring accessibility and holistic community-based treatments.

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