Publications

Abstract: Human orbitofrontal cortex (OFC) has long been implicated in value-based decision making. In recent years, convergent evidence from human and model organisms has further elucidated its role in representing reward-related computations underlying decision making. However, a detailed description of these processes remains elusive due in part to (1) limitations in our ability to observe human OFC neural dynamics at the timescale of decision processes and (2) methodological and interspecies differences that make it challenging to connect human and animal findings or to resolve discrepancies when they arise. Here, we sought to address these challenges by conducting multi-electrode electrocorticography (ECoG) recordings in neurosurgical patients during economic decision making to elucidate the electrophysiological signature, sub-second temporal profile, and anatomical distribution of reward-related computations within human OFC. We found that high-frequency activity (HFA) (70–200 Hz) reflected multiple valuation components grouped in two classes of valuation signals that were dissociable in temporal profile and information content: (1) fast, transient responses reflecting signals associated with choice and outcome processing, including anticipated risk and outcome regret, and (2) sustained responses explicitly encoding what happened in the immediately preceding trial. Anatomically, these responses were widely distributed in partially overlapping networks, including regions in the central OFC (Brodmann areas 11 and 13), which have been consistently implicated in reward processing in animal single-unit studies. Together, these results integrate insights drawn from human and animal studies and provide evidence for a role of human OFC in representing multiple reward computations.

Abstract:
Cognitive impairment is a common non-motor symptom of Parkinson’s disease (PD) that leads to reduced quality of life, and may be the predominant source of disability at long term follow-up [1]. Deep brain stimulation (DBS) in the dorsal motor region of the subthalamic nucleus (STN) often leads to cognitive worsening, particularly in verbal fluency and executive control domains [2]. Oscillatory activity at specific frequencies across brain circuits integrates information important for cognition [3]. Thus, the frequency of stimulation may have an important impact on cognitive outcome following DBS, and could be used to modulate mood and cognition independent of motor symptoms.

In this randomized cross-over pilot study, our primary aim was to determine the effect of theta (5 Hz), and gamma (130 Hz) frequency stimulation in the ventral STN on cognitive control in patients with PD. This study provides a first look at frequency dependent modulation of cognitive function and provides important insights into the utility of DBS for non-motor symptoms of PD and other neuropsychiatric disorders.

Abstract: Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

Abstract: The precise role of serotonin in human brain function remains elusive due at least in part to a lack of rapid measurement technologies available for this neurotransmitter. In order to provide a coherent account of serotonin function, we used a machine learning approach to extract serotonergic signals from fast scan cyclic voltammetry recordings acquired from fourteen human brains. Here we report these first sub- second measurements of serotonin fluctuations in human striatum and show how they correlate with outcomes and decisions in a sequential investment game. We find that serotonergic concentrations transiently increase following negative reward prediction errors. Importantly, these fluctuations reverse when counterfactual losses predominate, suggesting that serotonin tracks the negative outcomes associated with foregone gains. Crucially, these data provide evidence that the serotonergic system acts as an opponent to dopamine signaling, as anticipated by theoretical models. Strikingly, serotonin transients also encoded next actions in a manner that correlated with decreased exposure to both forms of loss. Taken together, these novel findings suggest that serotonin encodes a protective action strategy that mitigates risk, shaping action selection contingent upon negative environmental events.

Abstract:
Traumatic brain injury (TBI) is the leading cause of death and disability in individuals below age 45, and five million Americans live with chronic disability as a result. Mild TBI (mTBI), defined as TBI in the absence of major imaging or histopathological defects, is responsible for a majority of cases. Despite the lack of overt morphological defects, victims of mTBI frequently suffer lasting cognitive deficits, memory difficulties, and behavioral disturbances. There is increasing evidence that cognitive and memory dysfunction is related to subtle physiological changes that occur in the hippocampus, and these impact both the phenotype of deficits observed and subsequent recovery. Therapeutic modulation of physiological activity by means of medications commonly used for other indications or brain stimulation may represent novel treatment approaches. This review summarizes the present body of knowledge regarding neurophysiologic changes that occur in the hippocampus after mTBI, as well as potential targets for therapeutic modulation of neurologic activity.

Abstract: In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.