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      Mechanisms Underlying Decision-Making as Revealed by Deep-Brain Stimulation in Patients with Parkinson’s Disease

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          Summary

          To optimally balance opposing demands of speed and accuracy during decision-making, we must flexibly adapt how much evidence we require before making a choice. Such adjustments in decision thresholds have been linked to the subthalamic nucleus (STN), and therapeutic STN deep-brain stimulation (DBS) has been shown to interfere with this function. Here, we performed continuous as well as closed-loop DBS of the STN while Parkinson’s disease patients performed a perceptual decision-making task. Closed-loop STN DBS allowed temporally patterned STN stimulation and simultaneous recordings of STN activity. This revealed that DBS only affected patients’ ability to adjust decision thresholds if applied in a specific temporally confined time window during deliberation. Only stimulation in that window diminished the normal slowing of response times that occurred on difficult trials when DBS was turned off. Furthermore, DBS eliminated a relative, time-specific increase in STN beta oscillations and compromised its functional relationship with trial-by-trial adjustments in decision thresholds. Together, these results provide causal evidence that the STN is involved in adjusting decision thresholds in distinct, time-limited processing windows during deliberation.

          Highlights

          • We performed temporally patterned stimulation of the subthalamic nucleus in humans

          • During stimulation, Parkinson’s patients performed a perceptual decision-making task

          • Stimulation effects on behavior were confined to a short window during deliberation

          • Here, stimulation affected changes in decision thresholds during difficult decisions

          Abstract

          In this article, Herz et al. provide causal evidence that the subthalamic nucleus mediates adjustments of decision thresholds, depending on the difficulty of the decision. This adjustment is processed during a short time window early during deliberation and is related to changes in oscillatory activity of the subthalamic nucleus.

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          Most cited references14

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          Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism.

          Deep brain stimulation (DBS) of the subthalamic nucleus markedly improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. We showed that DBS selectively interferes with the normal ability to slow down when faced with decision conflict. While on DBS, patients actually sped up their decisions under high-conflict conditions. This form of impulsivity was not affected by dopaminergic medication status. Instead, medication impaired patients' ability to learn from negative decision outcomes. These findings implicate independent mechanisms leading to impulsivity in treated Parkinson's patients and were predicted by a single neurocomputational model of the basal ganglia.
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            Robust neuronal dynamics in premotor cortex during motor planning

            Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in mouse premotor cortex to probe robustness of persistent neural representations during motor planning. Preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.
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              The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal.

              Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygen-level-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single- and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.
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                Author and article information

                Contributors
                Journal
                Curr Biol
                Curr. Biol
                Current Biology
                Cell Press
                0960-9822
                1879-0445
                23 April 2018
                23 April 2018
                : 28
                : 8
                : 1169-1178.e6
                Affiliations
                [1 ]MRC Brain Network Dynamics Unit at the University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
                [2 ]Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford OX3 9DU, UK
                [3 ]Sobell Department of Motor Neuroscience and Movement Disorders, University College London Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK
                [4 ]Department of Neurology, University Hospital Marburg, Baldingerstrasse, 35043 Marburg, Germany
                [5 ]Department of Neurology, Bern University Hospital, Freiburgstrasse, 3010 Bern, Switzerland
                Author notes
                []Corresponding author peter.brown@ 123456ndcn.ox.ac.uk
                [6]

                Lead Contact

                Article
                S0960-9822(18)30242-2
                10.1016/j.cub.2018.02.057
                5912902
                29606416
                571704bb-5106-4219-b91b-502615421bc6
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 December 2017
                : 30 January 2018
                : 21 February 2018
                Categories
                Article

                Life sciences
                decision threshold,decision-making,drift diffusion model,subthalamic nucleus,parkinson’s disease,deep-brain stimulation,beta

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