Travis E. Baker

Dissociated roles of the anterior cingulate cortex in reward and conflict processing as revealed by the feedback error-related negativity and N200.

The reinforcement learning theory of the error-related negativity (ERN) holds that the impact of reward signals carried by the midbrain dopamine system modulates activity of the anterior cingulate cortex (ACC), alternatively disinhibiting and inhibiting the ACC following unpredicted error and reward events, respectively. According to a recent formulation of the theory, activity that is intrinsic to the ACC produces a component of the event-related brain potential (ERP) called the N200, and following unpredicted rewards, the N200 is suppressed by extrinsically applied positive dopamine reward signals, resulting in an ERP component called the feedback-ERN (fERN). Here we demonstrate that, despite extensive spatial and temporal overlap between the two ERP components, the functional processes indexed by the N200 (conflict) and the fERN (reward) are dissociable. These results point toward avenues for future investigation.

Individual differences in substance dependence: at the intersection of brain, behaviour and cognition.

Recent theories of drug dependence propose that the transition from occasional recreational substance use to harmful use and dependence results from the impact of disrupted midbrain dopamine signals for reinforcement learning on frontal brain areas that implement cognitive control and decision‐making. We investigated this hypothesis in humans using electrophysiological and behavioral measures believed to assay the integrity of midbrain dopamine system and its neural targets. Our investigation revealed two groups of dependent individuals, one characterized by disrupted dopamine‐dependent reward learning and the other by disrupted error learning associated with depression‐proneness. These results highlight important neurobiological and behavioral differences between two classes of dependent users that can inform the development of individually tailored treatment programs.

Constraints on decision making: Implications from genetics, personality, and addiction

An influential neurocomputational theory of the biological mechanisms of decision making, the “basal ganglia go/no-go model,” holds that individual variability in decision making is determined by differences in the makeup of a striatal system for approach and avoidance learning. The model has been tested empirically with the probabilistic selection task (PST), which determines whether individuals learn better from positive or negative feedback. In accordance with the model, in the present study we examined whether an individual’s ability to learn from positive and negative reinforcement can be predicted by genetic factors related to the midbrain dopamine system. We also asked whether psychiatric and personality factors related to substance dependence and dopamine affect PST performance. Although we found characteristics that predicted individual differences in approach versus avoidance learning, these observations were qualified by additional findings that appear inconsistent with the predictions of the go/no-go model. These results highlight a need for future research to validate the PST as a measure of basal ganglia reward learning.

Reward sensitivity of acc as an intermediate phenotype between drd4-521t and substance misuse

The development and expression of the midbrain dopamine system is determined in part by genetic factors that vary across individuals such that dopamine-related genes are partly responsible for addiction vulnerability. However, a complete account of how dopamine-related genes predispose individuals to drug addiction remains to be developed. Adopting an intermediate phenotype approach, we investigated whether reward-related electrophysiological activity of ACC—a cortical region said to utilize dopamine reward signals to learn the value of extended, context-specific sequences of goal-directed behaviors—mediates the influence of multiple dopamine-related functional polymorphisms over substance use. We used structural equation modeling to examine whether two related electrophysiological phenomena associated with the control and reinforcement learning functions of ACC—theta power and the reward positivity—mediated the relationship between the degree of substance misuse and genetic polymorphisms that regulate dopamine processing in frontal cortex. Substance use data were collected from 812 undergraduate students. One hundred ninety-six returned on a subsequent day to participate in an electrophysiological experiment and to provide saliva samples for DNA analysis. We found that these electrophysiological signals mediated a relationship between the DRD4-521T dopamine receptor genotype and substance misuse. Our results provide a theoretical framework that bridges the gap between genes and behavior in drug addiction and illustrate how future interventions might be individually tailored for specific genetic and neurocognitive profiles.

ERPs and EEG oscillations, best friends forever: comment on Cohen et al.

In their recent Opinion article, Cohen and colleagues discuss the relative strengths of the event-related brain potential (ERP) and time-frequency (TF) techniques for investigating cognitive function [1xCortical electrophysiological network dynamics of feedback learning. Cohen, M.X. et al. Trends Cogn. Sci. 2011; 15: 558–566Abstract | Full Text | Full Text PDF | PubMed | Scopus (41)See all References[1]. Their discussion pivots on the example of an ERP component called the feedback-related negativity (FRN), which we have proposed reflects the impact of dopamine-dependent reward prediction error (RPE) signals on anterior cingulate cortex (ACC) for the purpose of reinforcement learning [2xThe neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Holroyd, C.B. and Coles, M.G.H. Psychol. Rev. 2002; 109: 679–709Crossref | PubMedSee all References[2]. We disagree with several of the authors’ arguments in [1xCortical electrophysiological network dynamics of feedback learning. Cohen, M.X. et al. Trends Cogn. Sci. 2011; 15: 558–566Abstract | Full Text | Full Text PDF | PubMed | Scopus (41)See all References[1], as we explain below.First, the authors question the validity of the RPE-FRN theory on the basis of the fact that FRN amplitude is sometimes seen to vary along a binary rather than continuous scale. This argument confuses a graded response to reward probability with a binary response to reward magnitude: the FRN reflects revisions of an ongoing probabilistic estimate of future reward (the RPE) [3xWhen is an error not a prediction error? An electrophysiological investigation. Holroyd, C.B. et al. Cogn. Affect. Behav. Neurosci. 2009; 9: 59–70Crossref | PubMed | Scopus (100)See all References[3], where the reward itself indicates whether or not a goal is achieved (a binary outcome) [4xThe good, the bad and the neutral: Electrophysiological responses to feedback stimuli. Holroyd, C.B. et al. Brain Res. 2006; 1105: 93–101Crossref | PubMed | Scopus (169)See all References[4].Second, the authors suggest that the theory is difficult to falsify or confirm. Nevertheless, the theory is in fact testable. In line with common practice in cognitive neuroscience [5xDirect and indirect integration of event-related potentials, functional magnetic resonance images, and single unit recordings. Luck, S.J. Hum. Brain Mapp. 1999; 8: 115–120Crossref | PubMed | Scopus (24)See all References[5], our argument is supported by a wealth of converging evidence from multiple experimental techniques [6xMotivation of extended behaviors by anterior cingulate cortex. Holroyd, C.B. and Yeung, N. Trends Cogn. Sci. 2012; 16: 122–128Abstract | Full Text | Full Text PDF | PubMed | Scopus (117)See all References[6]. Animal models provide a particularly promising avenue for testing the theory further. For example, a homolog of the FRN has been identified in the monkey ACC [7xPerformance monitoring local field potentials in the medial frontal cortex of primates: anterior cingulate cortex. Emeric, E.E. et al. J. Neurophysiol. 2008; 99: 759–772Crossref | PubMed | Scopus (59)See all References[7], the scalp manifestation of which is sensitive to a dopamine antagonist [8xFrontal feedback-related potentials in nonhuman primates: Modulation during learning and under haloperidol. Vezoli, J. and Procyk, E. J. Neurosci. 2009; 29: 15675–15683Crossref | PubMed | Scopus (15)See all References[8].Third, the authors suggest that the RPE algorithm cannot account for high-level cognitive function because it is overly simplistic. However, our theory holds that the ACC implements a high-level decision-making mechanism that uses the RPE signals to choose between action plans [2xThe neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Holroyd, C.B. and Coles, M.G.H. Psychol. Rev. 2002; 109: 679–709Crossref | PubMedSee all References[2], a position that we have recently developed in terms of a formal theoretical framework called ‘hierarchical reinforcement learning’ [6xMotivation of extended behaviors by anterior cingulate cortex. Holroyd, C.B. and Yeung, N. Trends Cogn. Sci. 2012; 16: 122–128Abstract | Full Text | Full Text PDF | PubMed | Scopus (117)See all References[6]. This contrasts with the authors’ own emphasis on the learning of simple stimulus-response associations [1xCortical electrophysiological network dynamics of feedback learning. Cohen, M.X. et al. Trends Cogn. Sci. 2011; 15: 558–566Abstract | Full Text | Full Text PDF | PubMed | Scopus (41)See all References[1].Fourth, the authors question whether the brain can – even in principle – produce the fast, phasic deflections that characterize the ERP. Yet in the case of the FRN, we have proposed that negative [2xThe neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Holroyd, C.B. and Coles, M.G.H. Psychol. Rev. 2002; 109: 679–709Crossref | PubMedSee all References[2] and positive [9xThe feedback correct-related positivity: Sensitivity of the event-related brain potential to unexpected positive feedback. Holroyd, C.B. et al. Psychophysiology. 2008; 45: 688–697Crossref | PubMed | Scopus (198)See all References[9] phasic dopamine RPE signals respectively disinhibit and inhibit electrophysiological activity at the apical dendrites of ACC motor neurons, and have provided arguments in support of the plausibility of this hypothesis [2xThe neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Holroyd, C.B. and Coles, M.G.H. Psychol. Rev. 2002; 109: 679–709Crossref | PubMedSee all References, 6xMotivation of extended behaviors by anterior cingulate cortex. Holroyd, C.B. and Yeung, N. Trends Cogn. Sci. 2012; 16: 122–128Abstract | Full Text | Full Text PDF | PubMed | Scopus (117)See all References].Finally, the authors propose that frontal-midline theta reflects a neural mechanism for learning from negative feedback. However, we (unpublished observations) as well as Cavanagh, Cohen and colleagues [10xFrontal theta reflects uncertainty and unexpectedness during exploration and exploitation. Cavanagh, J.F. et al. Cereb. Cortex. 2012; Crossref | PubMed | Scopus (37)See all References[10] have found that unexpected task-relevant events – not errors in particular – elicit theta. Thus this theta response cannot index error processing per se.We applaud the authors for highlighting the advantages of the TF technique. Our own ongoing research on frontal-midline theta, parahippocampal theta [11xWhich way do I go? Neural activation in response to feedback and spatial processing in a Virtual T-Maze. Baker, T.E. and Holroyd, C.B. Cereb. Cortex. 2009; 19: 1708–1722Crossref | PubMed | Scopus (29)See all References[11] and reward-related gamma [12xThe role of beta-gamma oscillations in unexpected rewards processing. HajiHosseini, A. et al. Neuroimage. 2012; 60: 1678–1685Crossref | PubMed | Scopus (27)See all References[12] attests to our belief in its utility. But as the authors themselves point out, TF suffers from an array of methodological concerns [1xCortical electrophysiological network dynamics of feedback learning. Cohen, M.X. et al. Trends Cogn. Sci. 2011; 15: 558–566Abstract | Full Text | Full Text PDF | PubMed | Scopus (41)See all References[1]: uncertainties related to source localization and volume conduction, complex and sometimes questionable assumptions underlying the application of specific TF procedures, and dangers inherent in drawing conclusions about long-term neural plasticity, as well as in inferring causality from correlations. In our view, the mysteries of the brain will be elucidated by harmonious application of both TF and ERP approaches; celebrating the successes of any one experimental technique need not come at the expense of equally solid work in other domains.

It's all about timing: An electrophysiological examination of feedback-based learning with immediate and delayed feedback

ABSTRACT Feedback regarding an individuals action can occur immediately or with a temporal delay. Processing of feedback that varies in its delivery time is proposed to engage different brain mechanisms. fMRI data implicate the striatum in the processing of immediate feedback, and the medial temporal lobe (MTL) in the processing of delayed feedback. The present study offers an electrophysiological examination of feedback processing in the context of timing, by studying the effects of feedback timing on the feedback‐related negativity (FRN), a product of the midbrain dopamine system, and elucidating whether the N170 ERP component could capture MTL activation associated with the processing of delayed feedback. Participants completed a word‐object paired association learning task; they received feedback 500 ms (immediate feedback condition) following a button press during the learning of two sets of 14 items, and at a delay of 6500 ms (delayed feedback condition) during the learning of the other two sets. The results indicated that while learning outcomes did not differ under the two timing conditions, Event Related Potential (ERPs) pointed to differential activation of the examined ERP components. FRN amplitude was found to be larger following the immediate feedback condition when compared with the delayed feedback condition, and sensitive to valence and learning only under the immediate feedback condition. Additionally, the amplitude of the N170 was found larger following the delayed feedback condition when compared with the immediate feedback condition. Taken together, the findings of the present study support the contention that the processing of delayed feedback involves a shift away from midbrain dopamine activation to the recruitment of the MTL. HIGHLIGHTSFRN larger following immediate feedback when compared with delayed feedback.FRN sensitive to valence and learning only under the immediate feedback condition.Amplitude of the N170 larger following delayed feedback when compared with immediate feedback.

The topographical N170: Electrophysiological evidence of a neural mechanism for human spatial navigation

We recently demonstrated that the latency of a component of the event-related brain potential, the topographical N170 (NT170), is sensitive to the spatial location of reward-related stimuli in a virtual maze environment, occurring earlier for rewards found following rightward turns compared to leftward turns. We suggested that this NT170 latency effect may result from phase reset of an ongoing theta rhythm by a parahippocampal system for spatial navigation. Here we tested several predictions that follow from this proposal, namely, that the effect is observed only when the rewards are presented in a spatial environment, that it is sensitive to individual differences in spatial ability, that it is localizable to the right parahippocampal region, and that it is consistent with partial phase resetting of an ongoing theta rhythm. These results hold promise for integrating ERP measures of spatial navigation with extensive animal, human, and computational literatures on parahippocampal function.

Network connectivity determines cortical thinning in early Parkinson’s disease progression

Here we test the hypothesis that the neurodegenerative process in Parkinson’s disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson’s Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a “disease reservoir” evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer’s disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment.In Parkinson’s disease (PD), neurodegeneration spreads from the brainstem to the cerebral cortex. Here, in a longitudinal study of PD patients, the authors found that cortical thinning followed neural connectivity from a “disease reservoir”.

Rightward-biased hemodynamic response of the parahippocampal system during virtual navigation

Phase reset of parahippocampal electrophysiological oscillations in the theta frequency range is said to contribute to item encoding and retrieval during spatial navigation. Although well-studied in non-human animals, this mechanism is poorly understood in humans. Previously we found that feedback stimuli presented in a virtual maze environment elicited a burst of theta power over right-posterior areas of the human scalp, and that the power and phase angle of these oscillations were greater following right turns compared to left turns in the maze. Here we investigated the source of this effect with functional magnetic resonance imaging. Consistent with our predictions, we found that 1) feedback encountered in the maze task activated right parahippocampal cortex (PHC), 2) right PHC was more activated by rewards following right turns compared to left turns in the maze, and 3) the rightward-biased activation was more pronounced in individuals who displayed good spatial abilities. These findings support our previous electrophysiological findings and highlight, in humans, a role for PHC theta oscillations in encoding salient information for the purpose of spatial navigation.

Neurobehavioral correlates of obesity are largely heritable

Significance Obesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in body mass index (BMI) is explained by brain and behavioral factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in BMI had genetic correlations 0.25–0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, BMI was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioral factors. In summary, the genetic vulnerability to BMI is expressed in the brain. This may inform intervention strategies. Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the “right brain hypothesis” for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality–BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor’s genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25–0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77–89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.