Ignacio Saez

Sub-Second Dopamine Detection in Human Striatum

Fast-scan cyclic voltammetry at carbon fiber microelectrodes allows rapid (sub-second) measurements of dopamine release in behaving animals. Herein, we report the modification of existing technology and demonstrate the feasibility of making sub-second measurements of dopamine release in the caudate nucleus of a human subject during brain surgery. First, we describe the modification of our electrodes that allow for measurements to be made in a human brain. Next, we demonstrate in vitro and in vivo, that our modified electrodes can measure stimulated dopamine release in a rat brain equivalently to previously determined rodent electrodes. Finally, we demonstrate acute measurements of dopamine release in the caudate of a human patient during DBS electrode implantation surgery. The data generated are highly amenable for future work investigating the relationship between dopamine levels and important decision variables in human decision-making tasks.

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

Significance There is an abundance of circumstantial evidence (primarily work in nonhuman animal models) suggesting that dopamine transients serve as experience-dependent learning signals. This report establishes, to our knowledge, the first direct demonstration that subsecond fluctuations in dopamine concentration in the human striatum combine two distinct prediction error signals: (i) an experience-dependent reward prediction error term and (ii) a counterfactual prediction error term. These data are surprising because there is no prior evidence that fluctuations in dopamine should superpose actual and counterfactual information in humans. The observed compositional encoding of “actual” and “possible” is consistent with how one should “feel” and may be one example of how the human brain translates computations over experience to embodied states of subjective feeling. 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.

Modulation of inhibitory and excitatory synaptic transmission in rat inferior colliculus after unilateral cochleectomy: An in situ and immunofluorescence study

We investigated whether inhibitory synaptic transmission mediated through glycinergic receptor, GABAA receptors, glutamic acid decarboxylase, the enzyme synthesizing GABA, and excitatory synaptic transmission through alpha-amino-3-hydroxi-5-methylisoxazole-4-propionic acid receptors and N-methyl-D-aspartate receptors are affected in the inferior colliculus by unilateral surgical cochleectomy. In situ hybridization and immunohistofluorescence studies were performed in normal and lesioned adult rats at various times following the lesion (1-150 days). Unilateral auditory deprivation decreased glycine receptor alpha1 and glutamic acid decarboxylase 67 expression in the contralateral central nucleus of the inferior colliculus. This decrease began one day after cochleectomy, and continued until day 8; thereafter expression was consistently low until day 150. The glycine receptor alpha1 subunit decrease did not occur if a second contralateral cochleectomy was performed either on day 8 or 150 after the first cochleectomy. Bilateral cochleectomy caused also a bilateral inferior colliculus diminution of glutamic acid decarboxylase 67 mRNA at post-lesion day 8 but there were no changes in glycine receptor alpha1 compared with controls. In contrast, the abundance of other alpha2-3, and beta glycine receptor, gephyrin, the anchoring protein of glycine receptor, the alpha1, beta2 and gamma2 subunits of GABAA receptors, GluR2, R3 subunits of alpha-amino-3-hydroxi-5-methylisoxazole-4-propionic acid receptors, and NR1 and NR2A transcripts of N-methyl-D-aspartate receptors was unaffected during the first week following the lesion. Thus, unilateral cochlear removal resulted in a selective and long-term decrease in the amount of the glycine receptor alpha1 subunit and of glutamic acid decarboxylase 67 in the contralateral central nucleus of the inferior colliculus. These changes most probably result from the induced asymmetry of excitatory auditory inputs into the central nucleus of the inferior colliculus and may be one of the mechanisms involved in the tinnitus frequently encountered in patients suffering from a sudden hearing loss.

Plasticity between Neuronal Pairs in Layer 4 of Visual Cortex Varies with Synapse State

In neocortex, the induction and expression of long-term potentiation (LTP) and long-term depression (LTD) vary depending on cortical area and laminae of presynaptic and postsynaptic neurons. Layer 4 (L4) is the initial site of sensory afference in barrel cortex and primary visual cortex (V1) in which excitatory inputs from thalamus, L6, and neighboring L4 cells are integrated. However, little is known about plasticity within L4. We studied plasticity at excitatory synaptic connections between pairs and triplets of interconnected L4 neurons in guinea pig V1 using a fixed delay pairing protocol. Plasticity outcomes were heterogeneous, with some connections undergoing LTP (n = 7 of 42), some LTD (n = 19 of 42), and some not changing (n = 16 of 42). Although quantal analysis revealed both presynaptic and postsynaptic plasticity expression components, reduction in quantal size (a postsynaptic property) contributing to LTD was ubiquitous, whereas in some cell pairs, this change was overridden by an increase in the probability of neurotransmitter release (a presynaptic property) resulting in LTP. These changes depended on the initial reliability of the connections: highly reliable connections depressed with contributions from presynaptic and postsynaptic effects, and unreliable connections potentiated as a result of the predominance of presynaptic enhancement. Interestingly, very strong, reliable pairs of connected cells showed little plasticity. Pairs of connected cells with a common presynaptic or postsynaptic L4 cell behaved independently, undergoing plasticity of different or opposite signs. Release probability of a connection with initial 100% failure rate was enhanced after pairing, potentially avoiding silencing of the presynaptic terminal and maintaining L4–L4 synapses in a broader dynamic range.

Dopamine Modulates Egalitarian Behavior in Humans

Egalitarian motives form a powerful force in promoting prosocial behavior and enabling large-scale cooperation in the human species [1]. At the neural level, there is substantial, albeit correlational, evidence suggesting a link between dopamine and such behavior [2, 3]. However, important questions remain about the specific role of dopamine in setting or modulating behavioral sensitivity to prosocial concerns. Here, using a combination of pharmacological tools and economic games, we provide critical evidence for a causal involvement of dopamine in human egalitarian tendencies. Specifically, using the brain penetrant catechol-O-methyl transferase (COMT) inhibitor tolcapone [4, 5], we investigated the causal relationship between dopaminergic mechanisms and two prosocial concerns at the core of a number of widely used economic games: (1) the extent to which individuals directly value the material payoffs of others, i.e., generosity, and (2) the extent to which they are averse to differences between their own payoffs and those of others, i.e., inequity. We found that dopaminergic augmentation via COMT inhibition increased egalitarian tendencies in participants who played an extended version of the dictator game [6]. Strikingly, computational modeling of choice behavior [7] revealed that tolcapone exerted selective effects on inequity aversion, and not on other computational components such as the extent to which individuals directly value the material payoffs of others. Together, these data shed light on the causal relationship between neurochemical systems and human prosocial behavior and have potential implications for our understanding of the complex array of social impairments accompanying neuropsychiatric disorders involving dopaminergic dysregulation.

Dissociable contribution of prefrontal and striatal dopaminergic genes to learning in economic games

Significance Game theory is used throughout the social and biological sciences to study behavior in social interactions. Recent research suggests an important role for the dopamine neurotransmitter system in these types of decisions. This study used a competitive game to study how people varied in their decision-making processes and related these differences in the set of genes that carry out biological functions required for dopaminergic functioning. We found that genes differentially expressed in separate brain regions influenced distinct components of people’s decision-making processes and that a surprising degree of consistency exists with what is known at the brain level about how people make decisions in social interactions. Game theory describes strategic interactions where success of players’ actions depends on those of coplayers. In humans, substantial progress has been made at the neural level in characterizing the dopaminergic and frontostriatal mechanisms mediating such behavior. Here we combined computational modeling of strategic learning with a pathway approach to characterize association of strategic behavior with variations in the dopamine pathway. Specifically, using gene-set analysis, we systematically examined contribution of different dopamine genes to variation in a multistrategy competitive game captured by (i) the degree players anticipate and respond to actions of others (belief learning) and (ii) the speed with which such adaptations take place (learning rate). We found that variation in genes that primarily regulate prefrontal dopamine clearance—catechol-O-methyl transferase (COMT) and two isoforms of monoamine oxidase—modulated degree of belief learning across individuals. In contrast, we did not find significant association for other genes in the dopamine pathway. Furthermore, variation in genes that primarily regulate striatal dopamine function—dopamine transporter and D2 receptors—was significantly associated with the learning rate. We found that this was also the case with COMT, but not for other dopaminergic genes. Together, these findings highlight dissociable roles of frontostriatal systems in strategic learning and support the notion that genetic variation, organized along specific pathways, forms an important source of variation in complex phenotypes such as strategic behavior.

Synaptic Output of Individual Layer 4 Neurons in Guinea Pig Visual Cortex

More than 90% of geniculocortical axons from the dorsal lateral geniculate nucleus of the thalamus innervate layer 4 (L4) of V1 (primary visual cortex). Excitatory neurons, which comprise >80% of the neuronal population in L4, synapse mainly onto adjacent L4 neurons and layer 2/3 (L2/3) neurons. It has been suggested that intralaminar L4–L4 connections contribute to amplifying and refining thalamocortical signals before routing to L2/3. To unambiguously probe the properties of the synaptic outputs from these L4 excitatory neurons, we used multiple simultaneous whole-cell patch-clamp recording and stimulation from two to four neighboring L4 neurons. We recorded uEPSCs (evoked unitary synaptic currents) in response to pairs of action potentials elicited in single presynaptic L4 neurons for 102 L4 cell pairs and found that their properties are more diverse than previously described. Averaged unitary synaptic response peak amplitudes spanned almost three orders of magnitude, from 0.42 to 192.92 pA. Although connections were, on average, reliable (average failure rate, 25%), we recorded a previously unknown subset of unreliable (failure rates from 30 to 100%) and weak (averaged response amplitudes, <5 pA) connections. Reliable connections with high probability of neurotransmitter release responded to paired presynaptic pulses with depression, whereas unreliable connections underwent paired-pulse facilitation. Recordings from interconnected sets of L4 triplets revealed that synaptic response amplitudes and reliability were equally variable between independent cell pairs and those that shared a common presynaptic or postsynaptic cell, suggesting local perisynaptic influences on the development and/or state of synaptic function.

From genes to behavior: placing cognitive models in the context of biological pathways

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior.

Integrated analysis of anatomical and electrophysiological human intracranial data

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.This protocol describes how to computationally process, integrate, visualize, and analyze anatomical and functional data obtained during intracranial electroencephalography (iEEG) of the human brain.

Role of GABAA-Mediated Inhibition and Functional Assortment of Synapses onto Individual Layer 4 Neurons in Regulating Plasticity Expression in Visual Cortex

Layer 4 (L4) of primary visual cortex (V1) is the main recipient of thalamocortical fibers from the dorsal lateral geniculate nucleus (LGNd). Thus, it is considered the main entry point of visual information into the neocortex and the first anatomical opportunity for intracortical visual processing before information leaves L4 and reaches supra- and infragranular cortical layers. The strength of monosynaptic connections from individual L4 excitatory cells onto adjacent L4 cells (unitary connections) is highly malleable, demonstrating that the initial stage of intracortical synaptic transmission of thalamocortical information can be altered by previous activity. However, the inhibitory network within L4 of V1 may act as an internal gate for induction of excitatory synaptic plasticity, thus providing either high fidelity throughput to supragranular layers or transmittal of a modified signal subject to recent activity-dependent plasticity. To evaluate this possibility, we compared the induction of synaptic plasticity using classical extracellular stimulation protocols that recruit a combination of excitatory and inhibitory synapses with stimulation of a single excitatory neuron onto a L4 cell. In order to induce plasticity, we paired pre- and postsynaptic activity (with the onset of postsynaptic spiking leading the presynaptic activation by 10ms) using extracellular stimulation (ECS) in acute slices of primary visual cortex and comparing the outcomes with our previously published results in which an identical protocol was used to induce synaptic plasticity between individual pre- and postsynaptic L4 excitatory neurons. Our results indicate that pairing of ECS with spiking in a L4 neuron fails to induce plasticity in L4-L4 connections if synaptic inhibition is intact. However, application of a similar pairing protocol under GABAARs inhibition by bath application of 2μM bicuculline does induce robust synaptic plasticity, long term potentiation (LTP) or long term depression (LTD), similar to our results with pairing of pre- and postsynaptic activation between individual excitatory L4 neurons in which inhibitory connections are not activated. These results are consistent with the well-established observation that inhibition limits the capacity for induction of plasticity at excitatory synapses and that pre- and postsynaptic activation at a fixed time interval can result in a variable range of plasticity outcomes. However, in the current study by virtue of having two sets of experimental data, we have provided a new insight into these processes. By randomly mixing the assorting of individual L4 neurons according to the frequency distribution of the experimentally determined plasticity outcome distribution based on the calculated convergence of multiple individual L4 neurons onto a single postsynaptic L4 neuron, we were able to compare then actual ECS plasticity outcomes to those predicted by randomly mixing individual pairs of neurons. Interestingly, the observed plasticity profiles with ECS cannot account for the random assortment of plasticity behaviors of synaptic connections between individual cell pairs. These results suggest that connections impinging onto a single postsynaptic cell may be grouped according to plasticity states.