21 June 2017: Functional MRI of Thalamocortical and Arousal Circuitry Using Multi-Band Multi-Echo EPI

Prantik Kundu
Departments of Radiology and Psychiatry
Icahn School of Medicine at Mount Sinai

The past 25 years of neuroscientific research with functional MRI has shown in detail the functional and connectomic organization of the human cerebral cortex. However, the interactions of the cortex with the subcortex have been more challenging to evaluate in vivo due to poor fMRI signal from subcortical regions. This is a major issue since poor signal fidelity from areas such as striatum, thalamus, and the midbrain limits the progress for understanding neuropsychiatric disease. We addressed this issue by developing the fMRI approach called multi-echo multi-band (MEMB)-fMRI. By using a modified echo planar imaging (EPI) sequence that implements T2* relaxometry alongside BOLD imaging, MEMB-fMRI increases BOLD contrast and mitigates non-BOLD artifact to enhance detection of subcortical-cortical activation and connectivity. After reviewing MEMB-fMRI methodology, we will discuss our recent findings on: frontostriatal connectivity, thalamocortical dysconnectivity in psychosis using 7 T MRI; and activation and connectivity in arousal circuitry including the locus coeruleus based on the NPU threat task.

7 June 2017: Addiction states as dynamic changes in valuation

Anna Konova
Center for Neural Science
New York University

The symptoms of drug addiction imply alterations in decision processes. The integrative field of neuroeconomics, which borrows concepts and methods from economics, psychology, and neuroscience, has identified a “domain general” neural system encompassing the ventromedial prefrontal cortex and the striatum in the computation of subjective value – the basis of idiosyncratic preferences and choice. Indeed, the application of this framework to addiction in both humans and other animals has successfully captured many features of addiction. However, this work has generally considered addiction as a static entity, ignoring addiction’s most elusive (and perhaps most defining) feature – its stereotyped, cyclic nature at the level of the individual, characterized by alternating periods of abstinence and drug use. I will discuss ongoing work in which we aim to better understand two dynamic processes at the transition between abstinence and relapse to drug use: (1) a relatively slower process (in the order of weeks and months) related to risk preferences that tracks drug use vulnerability and which we have modeled with repeated assessments of economic choice behavior through the first months of treatment for opioid addiction; and (2) a faster process (in the order of minutes or hours) related to the motivational state of craving that tracks immediate vulnerability and which we have modeled as a specific, gain-control like shift in the value of the object of craving. Understanding these addiction states as dynamic changes in valuation, we hope, can help identify when additional therapeutic intervention is needed on a timescale that is clinically useful as well as motivate the development of new decision- and valuation-based interventions for breaking the cycle of addiction.

24 May 2017: Taking aim at the cognitive side of motor learning

Jordan Taylor
Department of Psychology and Princeton Neuroscience Institute
Princeton University

The most commonly cited human-specific cognitive abilities are generally language, abstract reasoning, and complex sociality. Our motor talents are often left off that list, as such abilities are generally not considered linked to higher cognition. Indeed, the field of sensorimotor learning often seems stuck in the subcortex, with the lens of research focused on cerebellar-driven implicit sensorimotor recalibration. This trend ignores a key element of human motor learning: rapid and flexible cognitive strategizing. Indeed, recent research from our lab, as well as others, has suggested that explicit cognitive strategies may play a bigger role than previously thought. In fact, we find that implicit learning is surprisingly inflexible — showing only small incremental changes that are insensitive to the task — which calls into question its relative importance in motor learning. Our findings suggest that much of motor learning, at least in the short-term, reflects a more complex, cognitive decision-making process which aids in the rapid, flexible selection of movements to achieve precise goals in distal regions of space. Accordingly, models of the multiple processes at work during motor learning, and hypotheses about the putative neural substrates underlying such processes, need to be broadened to accommodate the important contribution of cognitive strategies. Understanding how multiple neural systems contribute to learning should lead to the development of optimal neurorehabilitation protocols either designed to target impaired systems or bias performance to rely on systems that are relatively intact.

26 April 2017: Neural dynamics of the primate attention network

Sabine Kastner
Princeton Neuroscience Institute
Princeton University

The selection of information from our cluttered sensory environments is one of the most fundamental cognitive operations performed by the primate brain. In the visual domain, the selection process is thought to be mediated by a static spatial mechanism – a ‘spotlight’ that can be flexibly shifted around the visual scene. This spatial search mechanism has been associated with a large-scale network that consists of multiple nodes distributed across all major cortical lobes and includes also subcortical regions. To identify the specific functions of each network node and their functional interactions is a major goal for the field of cognitive neuroscience. In my lecture, I will challenge two common notions of attention research. First, I will show behavioral and neural evidence that the attentional spotlight is neither stationary nor unitary. In the appropriate behavioral context, even when spatial attention is sustained at a given location, additional spatial mechanisms operate flexibly in parallel to monitor the visual environment. Second, spatial attention is assumed to be under ‘top-down’ control of higher order cortex. In contrast, I will provide neural evidence indicating that attentional control is exerted through thalamo-cortical interactions. Together, this evidence indicates the need for major revisions of traditional attention accounts.

5 May 2017: CCNP Mini-Symposium and NIMH Director Joshua Gordon

CCNP will be hosting its inaugural mini-symposium on Friday, May 5 at Rutgers University. CCNP co-directors Yael Niv and Steve Silverstein will be speaking along with CCNP investigators Nathaniel Daw and Molly Erickson. The mini-symposium coincides with a visit from NIMH Director, Joshua Gordon. Dr. Gordon will be giving the 2017 Strongwater Endowed Chair Lecture entitled “On Being a Circuit Psychiatrist”. A brief schedule is listed below. Please see the attached flyers for more information.

10:00-10:30     Steve Silverstein – “Computational modeling of low level visual processing impairments in schizophrenia”

10:30-11:00     Molly Erickson – “Computational modeling as an approach for identifying neural substrates of high-level visual abnormalities in schizophrenia”

11:00-11:30     Nathaniel Daw – “Deliberation, compulsion, and anxiety”

11:30-12:00     Yael Niv – “Tonic dopamine, response vigor and mood”

2:00-3:00        Joshua Gordon
– “On Being a Circuit Psychiatrist”

CCNP Mini-Symposium
Strongwater Lecture – Joshua Gordon

12 April 2017: Neuroimaging in drug addiction: an eye towards intervention purposes

Rita Goldstein
Department of Psychiatry
Icahn School of Medicine at Mount Sinai

Drug addiction is a chronically relapsing disorder characterized by compulsive drug use despite catastrophic personal consequences (e.g., loss of family, job) and even when the substance is no longer perceived as pleasurable. In this talk, Dr. Goldstein will present results of human neuroimaging studies, utilizing a multimodal approach (neuropsychology, functional magnetic resonance imaging, positron emission tomography, event-related potentials recordings), to explore the neurobiology underlying the core psychological impairments in drug addiction (impulsivity, drive/motivation, insight/awareness) as associated with its clinical symptomatology (intoxication, craving, bingeing, withdrawal). The focus of this talk is on understanding the role of the dopaminergic mesocorticolimbic circuit, and especially the prefrontal cortex, in higher-order executive dysfunction (e.g., disadvantageous decision-making such as trading a car for a couple of cocaine hits) in drug addicted individuals. The theoretical model that guides the presented research is called iRISA (Impaired Response Inhibition and Salience Attribution), postulating that abnormalities in the orbitofrontal cortex and anterior cingulate cortex, as related to dopaminergic dysfunction, contribute to the core clinical symptoms in drug addiction. Specifically, Dr. Goldstein’s multi-modality program of research is guided by the underlying working hypothesis that drug addicted individuals disproportionately attribute reward value to their drug of choice at the expense of other potentially but no-longer-rewarding stimuli, with a concomitant decrease in the ability to inhibit maladaptive drug use. In this talk Dr. Goldstein will also explore whether treatment (as usual) and 6-month abstinence enhance recovery in these brain-behavior compromises in treatment seeking cocaine addicted individuals. Promising novel fMRI studies, which combine pharmacological (i.e., oral methylphenidate, or RitalinTM) and salient cognitive tasks or functional connectivity during resting-state, will be discussed as examples for using neuroimaging in the empirical guidance for the development of effective neurorehabilitation strategies in cocaine addiction.

15 March 2017: Addiction states as dynamic changes in valuation

Anna Konova
Center for Neural Science
New York University

The symptoms of drug addiction imply alterations in decision processes. The integrative field of neuroeconomics, which borrows concepts and methods from economics, psychology, and neuroscience, has identified a “domain general” neural system encompassing the ventromedial prefrontal cortex and the striatum in the computation of subjective value – the basis of idiosyncratic preferences and choice. Indeed, the application of this framework to addiction in both humans and other animals has successfully captured many features of addiction. However, this work has generally considered addiction as a static entity, ignoring addiction’s most elusive (and perhaps most defining) feature – its stereotyped, cyclic nature at the level of the individual, characterized by alternating periods of abstinence and drug use. I will discuss ongoing work in which we aim to better understand two dynamic processes at the transition between abstinence and relapse to drug use: (1) a relatively slower process (in the order of weeks and months) related to risk preferences that tracks drug use vulnerability and which we have modeled with repeated assessments of economic choice behavior through the first months of treatment for opioid addiction; and (2) a faster process (in the order of minutes or hours) related to the motivational state of craving that tracks immediate vulnerability and which we have modeled as a specific, gain-control like shift in the value of the object of craving. Understanding these addiction states as dynamic changes in valuation, we hope, can help identify when additional therapeutic intervention is needed on a timescale that is clinically useful as well as motivate the development of new decision- and valuation-based interventions for breaking the cycle of addiction.

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1 March 2017: Show me, don’t tell me: The role of cues, past experience, and beliefs in the generation of anticipatory eye movements

Eileen Kowler
Department of Psychology
Rutgers University

It has been known since the classical work of Dodge (1930) and Westheimer (1954) that smooth pursuit eye movements anticipate the future direction of motion of targets. Anticipation in smooth pursuit is somewhat surprising because pursuit is traditionally viewed as a reactive process that acts to compensate for the motion of the target across the retina. Prior attempts to account for anticipatory pursuit have emphasized either rote learning of the motion path, associative learning, or short-term extrapolation of the motion. I will describe results of several experiments showing that anticipatory smooth eye movements can be evoked by visual cues, or by knowledge of the past history of motion (in the absence of overt cues), or by one’s own intentions to alter the path of target motion. Cues that illustrate the motion path by virtue of their perceptual structure are far more effective than cues that are arbitrarily linked to the motion path, or beliefs derived from simply telling the pursuer about the future motion path. Taken together (and considering the intriguing patterns of anticipatory pursuit movements found in other species), these results suggest that prediction is as fundamental to smooth pursuit as it is to the operation of many other visuo-motor behaviors. The pursuit system appears to be organized so as to take advantage of any source of information that allows future motion to be predicted with a reasonably high level of confidence.

1 February 2017: How do retrieval dynamics drive learning? Insights from fMRI and computational models

Ken Norman
Princeton Neuroscience Institute
Princeton University

About 10 years ago, our lab built a neural network model of how competitive neural dynamics drive learning (Norman, Newman, & Perotte, 2005; Norman et al., 2006; Norman, Newman, & Detre, 2007). This model predicts a U-shaped relationship between neural activation and learning, whereby strong activation causes strengthening of synaptic connections, moderate activation causes weakening of synaptic connections, and lower levels of activation result in no change to synaptic connections. To test this prediction, we have run several studies where we use pattern classifiers (applied to fMRI and EEG data) to track the activation of the competing memories, and then we relate these competitive dynamics to subsequent memory performance. If — according to the classifier — a memory activates to a moderate degree, our theory predicts that it will be weakened and, through this, it will subsequently become harder to retrieve. I will present evidence in support of this prediction from studies using several different paradigms. I will also present more recent data showing how competitive dynamics, coupled with interleaved learning, can result in differentiation of competing memories. I will conclude by discussing possible applications to clinical phenomena.