Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry

Bart Krekelberg
Center for Molecular and Behavioral Neuroscience
Rutgers University

Transcranial current stimulation is a promising technique used in clinical trials for the treatment of numerous neurological pathologies, including depression, stroke, and epilepsy. In healthy humans it is claimed to enhance perception and learning. However, little is known about how small currents applied to the scalp could achieve all of this and some skepticism about these claims is certainly warranted. We investigate the underlying neural mechanisms in mice, monkeys, and humans.

By recording intracranially while stimulating transcranially, we have discovered a range of neural consequences of transcranial alternating current stimulation. The most prominent of these is a reduction in neural adaptation. I will review the evidence to support this claim, while also pointing out a number of other neural changes that cannot easily be described by a single underlying mechanism. The rational development of noninvasive neuromodulation requires acknowledging and understanding the multiplicity of effects induced by transcranial current stimulation.

Avram Holmes
Department of Psychology
Yale University

Research in my laboratory seeks to establish reliable links between genetic variation, system-level brain function, and behavior in the general population. A core motivation that drives this work is the search for specific network-level signatures or “fingerprints” that co-vary with heritable behavioral variation in the general population and mark vulnerability for psychiatric illness onset. To date, research on the biological origins of psychopathology has largely focused on discrete illness categories. Although patient groups within this diagnostic system are treated as distinct entities, there are often murky boundaries between health and disease and across the disorders themselves. To establish the etiology of these complex syndromes, we must account for diagnostic heterogeneity, both relatively selective and disorder-spanning symptoms, and the dimensional nature of genetic risk. In this talk, I will present two converging lines of research from my laboratory that aim to identify neurobiological markers of psychiatric illness. First, I will discuss a recent effort to link individual variation in the collective set of functional brain connections with the nature and severity of symptom profiles across unipolar depression, bipolar depression, and schizophrenia. Second, I will discuss an ongoing line of research that examines the molecular mechanisms that support the formation and maintenance of functional networks. The results of these analyses suggest that gene expression patterns recapitulate the topography of distributed brain networks and provide novel insights into potential molecular mechanisms contributing to psychiatric illnesses marked by abnormal cortico-striatal function.

Douglas P Munoz
Centre for Neuroscience Studies
Queen’s University

Understanding how humans perceive and act upon complex natural environments is one of the most pressing challenges in neuroscience. Recent work has demonstrated that pupil size is modulated by many cognitive processes in the brain. How does the brain generate cognitive signals that modulate pupil size? This talk will focus on identification of brain circuits that control pupil size and account for the presence of sensory/perceptual, pre-motor/attention, and other cognitive orienting signals on the pupil. Understanding this circuitry for pupil orienting responses allows pupil measures to be used to study normal human development and aging and various disease processes. The pupil provides a rich source of clinical biomarkers that can be easily mapped onto pupil control circuits in the brain.

Ida Momennejad
Princeton Neuroscience Institute
Princeton University

Memory is organized in relational structures. When we remember something due to perceptual exposure or via replay, activation of the retrieved item itself activates other memories that are directly related to it, as well as memories that are multiple relations away. This cascade of activation renders the relations between memories malleable (known as plasticity). I will present a computational model, which demonstrates how the spread of activation in a memory network together with nonmonotonic plasticity can restructure memory. Using simulations, I will show that selective remembering, i.e. repeated re-activation of some but not other memories, can gradually change the structure of memory networks. This model can account for a range of memory phenomena, from retrieval-induced forgetting to conversational mnemonic convergence and consolidation. I will then discuss how these models, together with my previous work on prioritized offline replay can: (1) explain psychiatric phenomena in PTSD, (2) account for selective replay of negative memories and pruning of positive trajectories in rumination, and (3) provide a computational account of how cognitive therapy can leverage selective activation and forgetting to reorganize memory structures and alleviate symptoms. Finally, I will briefly discuss how this computational account might inform future interactive therapy apps that would interactively help users map and restructure the relational structure of their memories.

Phil Corlett
Department of Psychiatry
Yale University

Psychosis is generally defined in terms experiences that deviate appreciably from consensual reality. I will discuss two of its component symptoms; anomalous perceptions (“hallucinations”) and bizarre and inexplicable beliefs (“delusions”). A major challenge in developing a coherent understanding of psychosis is to characterize the disturbances that may give rise both to profoundly altered experiences and to impairments in belief. I will explore the degree to which understanding the brain as a predictive inference device may help us explain psychotic symptoms. I will argue that hallucinations and delusions may arise and be maintained as a result of aberrant prediction errors which garner strong top-down priors as palliative explanatory responses. By refining our understanding of how these disturbances may occur, we gain valuable insights to how the brain generates our experiences more generally.

Philipp Sterzer
Professor of Psychiatry and Computational Neuroscience
Charité – Universitätsmedizin Berlin

Perceptual inference is the process by which current beliefs are used to infer the probable causes of the incoming sensory signals. When these sensory signals are perceptually ambiguous, inference may result in spontaneous alterations between two or more perceptual states, a phenomenon called multistable perception. The neural mechanisms of the underlying inferential process have remained controversial. Whereas some authors argue that multistable perception is governed by local processes in sensory cortices, others have proposed a role for higher-level frontoparietal brain regions in driving perceptual inference. Here, I will propose an account of multistable perception that can reconcile these apparently contradictory views within the computational framework of predictive coding. I will also present results from computational modeling in a Bayesian framework and model-based fMRI that support the proposed account. Finally, I will outline how altered predictive coding may explain abnormal inference in psychotic states and present empirical behavioral and neuroimaging work that used multistable perception to probe the role of predictive feedback signaling in psychosis.

Jordan Hamm
Department of Biological Sciences
Columbia University

While prefrontal cortex and brain-wide, interregional networks are often studied in major neurological and psychiatric disorders, synaptic and cell-type specific pathophysiology has been identified in nearly all cortical areas in diseases like schizophrenia (SZ), suggesting that neuronal circuit function at the most basic level could play a critical role. Two-photon calcium imaging (2P-Ca++) studies in rodents have shown that local neuron populations in neocortical circuits (<1mm3) display distinct activity patterns made up of coactive “ensembles”, a level of detailed dynamics not captured by gross level (EEG, fMRI) or single neuron recordings. These activity patterns repeat during basic sensory and cognitive processing and also spontaneously at rest, suggesting that they make up the preferred, semi-stable “attractor” states of the cortex. Here I present findings from distinct mouse models of SZ-relevant disease processes (NMDA-receptor block, 22q11.2 microdeletion) that support the notion that a disorganization of local ensembles and the underlying “attractor” landscape could underlie basic perceptual and cognitive deficits in the disease. Interestingly, while acute manipulations of excitation, inhibition, or top-down inputs recreated cortical processing abnormalities at the single neuron level, key deficits in population-level synchrony patterns were only observed after chronic disease-relevant manipulations or in genetic models. Implications and future approaches for understanding and targeting altered synaptic plasticity processes across key neurodevelopmental periods will be discussed.

Travis Baker
Rutgers Center for Molecular and Behavioral Neuroscience
Rutgers University

Neurocognitive alterations to mesocorticolimbic reward function by drugs of abuse are thought to facilitate a progression towards excessive drug use. Despite efforts to identify methods to counteract such drug-induced neural alterations, brain-based treatments for this disorder remain underdeveloped and often ineffective. In this talk, I propose that the anterior cingulate cortex (ACC) putative function—selecting and motivating the execution of extended goal-directed behaviors—can be utilized as a biomarker for the abnormal reward processes underlying substance misuse. I will present support for this proposal utilizing the reward positivity as a quantifiable EEG metric of reward-related ACC function. First, people who abuse addictive substances produce a relatively small reward positivity to monetary incentives, and that this impairment is modulated by a genetic polymorphism that codes for the expression of dopamine D4 receptors. Second, drug rewards (puffs of a cigarette) elicit a relatively larger reward positivity than nondrug rewards (money) in abstinent smokers. Third, applying excitatory or inhibitory stimulation to a subject-specific frontal-cingulate reward pathway using robot-assisted fMRI-guided rTMS can alter the amplitude of the reward positivity elicited by drug and non-drug rewards. In sum, our efforts point to a decisive role of integrating multimodal neuroimaging methods as an early stage in treatment development for substance use disorders, with a highly sensitive EEG-based biomarker of addiction severity and treatment efficacy.