scholarly journals Tracing the Neural Signature of Lapsing Attentionin the Pre-Error Period Using Simultaneous EEG and fMRI

2010 ◽  
Vol 4 ◽  
Author(s):  
Robertson I H
Keyword(s):  
2021 ◽  
Vol 7 (15) ◽  
pp. eabf7800
Author(s):  
Jeremie Gaveau ◽  
Sidney Grospretre ◽  
Bastien Berret ◽  
Dora E. Angelaki ◽  
Charalambos Papaxanthis

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain shapes motor patterns that use gravity effects to minimize muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here, we specifically investigate gravity-related movement properties by analyzing muscular activation patterns during single-degree-of-freedom arm movements in various directions. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic electromyograms (EMGs) present systematic negative phases. This negativity reveals the optimal motor plan’s neural signature, where the motor system harvests the mechanical effects of gravity to accelerate downward and decelerate upward movements, thereby saving muscle effort. We compare experimental findings in humans to monkeys, generalizing the Effort-optimization strategy across species.


2021 ◽  
Author(s):  
Franziska Magdalena Kausche ◽  
Gundula Zerbes ◽  
Lea Kampermann ◽  
Christian Büchel ◽  
Lars Schwabe

Author(s):  
Markus Heilig ◽  
James MacKillop ◽  
Diana Martinez ◽  
Jürgen Rehm ◽  
Lorenzo Leggio ◽  
...  

AbstractThe view that substance addiction is a brain disease, although widely accepted in the neuroscience community, has become subject to acerbic criticism in recent years. These criticisms state that the brain disease view is deterministic, fails to account for heterogeneity in remission and recovery, places too much emphasis on a compulsive dimension of addiction, and that a specific neural signature of addiction has not been identified. We acknowledge that some of these criticisms have merit, but assert that the foundational premise that addiction has a neurobiological basis is fundamentally sound. We also emphasize that denying that addiction is a brain disease is a harmful standpoint since it contributes to reducing access to healthcare and treatment, the consequences of which are catastrophic. Here, we therefore address these criticisms, and in doing so provide a contemporary update of the brain disease view of addiction. We provide arguments to support this view, discuss why apparently spontaneous remission does not negate it, and how seemingly compulsive behaviors can co-exist with the sensitivity to alternative reinforcement in addiction. Most importantly, we argue that the brain is the biological substrate from which both addiction and the capacity for behavior change arise, arguing for an intensified neuroscientific study of recovery. More broadly, we propose that these disagreements reveal the need for multidisciplinary research that integrates neuroscientific, behavioral, clinical, and sociocultural perspectives.


2016 ◽  
Author(s):  
Miriam C Klein-Flügge ◽  
Steven W Kennerley ◽  
Karl Friston ◽  
Sven Bestmann

AbstractIntegrating costs and benefits is crucial for optimal decision-making. While much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human functional magnetic resonance imaging during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area (SMA) and the caudal portion of dorsal anterior cingulate cortex (dACC) encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modelled effort-discounted subjective values using a novel behavioural model. This revealed that the same network of regions involving dACC and SMA encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in SMA and ventro-medial PFC (vmPFC) correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not vmPFC as typically reported for outcome-based choice. Furthermore, distinct frontal circuits ‘drive’ behaviour towards reward-maximization and effort-minimization.Significance StatementThe neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behaviour, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioural characterization of how humans trade-off reward-maximization with effort-minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits ‘drive’ behaviour towards reward-maximization or effort-minimization.


2018 ◽  
Author(s):  
John J. Sakon ◽  
Wendy A. Suzuki

AbstractThe CA3 and dentate gyrus (DG) regions of the hippocampus are considered key for disambiguating sensory inputs from similar experiences in memory, a process termed pattern separation. The neural mechanisms underlying pattern separation, however, have been difficult to compare across species: rodents offer robust recording methods with less human-centric tasks while humans provide complex behavior with less recording potential. To overcome these limitations, we trained monkeys to perform a visual pattern separation task similar to those used in humans while recording activity from single CA3/DG neurons. We find that when animals discriminate recently seen novel images from similar (lure) images, behavior indicative of pattern separation, CA3/DG neurons respond to lure images more like novel than repeat images. Using a population of these neurons, we are able to classify novel, lure, and repeat images from each other using this pattern of firing rates. Notably, one subpopulation of these neurons is more responsible for distinguishing lures and repeats—the key discrimination indicative of pattern separation.


2017 ◽  
Vol 17 (10) ◽  
pp. 570
Author(s):  
Yalda Mohsenzadeh ◽  
Aude Oliva ◽  
Dimitrios Pantazis

Author(s):  
Alessandro Miola ◽  
Nicolò Trevisan ◽  
Arcangelo Merola ◽  
Francesco Folena Comini ◽  
Daniele Olivo ◽  
...  

AbstractWidespread regional gray matter volume (GMV) alterations have been reported in bipolar disorder (BD). Structural networks, which are thought to better reflect the complex multivariate organization of the brain, and their clinical and psychological function have not been investigated yet in BD. 24 patients with BD type-I (BD-I), and 30 with BD type-II (BD-II), and 45 controls underwent MRI scan. Voxel-based morphometry and source-based morphometry (SBM) were performed to extract structural covariation patterns of GMV. SBM components associated with morphometric differences were compared among diagnoses. Executive function and emotional processing correlated with morphometric characteristics. Compared to controls, BD-I showed reduced GMV in the temporo-insular-parieto-occipital cortex and in the culmen. An SBM component spanning the prefrontal-temporal-occipital network exhibited significantly lower GMV in BD-I compared to controls, but not between the other groups. The structural network covariance in BD-I was associated with the number of previous manic episodes and with worse executive performance. Compared to BD-II, BD-I showed a loss of GMV in the temporal-occipital regions, and this was correlated with impaired emotional processing. Altered prefrontal-temporal-occipital network structure could reflect a neural signature associated with visuospatial processing and problem-solving impairments as well as emotional processing and illness severity in BD-I.


2019 ◽  
Author(s):  
Faisal Mushtaq ◽  
Samuel D. McDougle ◽  
Matt P. Craddock ◽  
Darius E. Parvin ◽  
Jack Brookes ◽  
...  

AbstractLosing a point playing tennis may result from poor shot selection or poor stroke execution. To explore how the brain responds to these different types of errors, we examined EEG signatures of feedback-related processing while participants performed a simple decision-making task. In Experiment 1, we used a task in which unrewarded outcomes were framed as selection errors, similar to how feedback information is treated in most studies. Consistent with previous work, EEG differences between rewarded and unrewarded trials in the medial frontal negativity (MFN) correlated with behavioral adjustment. In Experiment 2, the task was modified such that unrewarded outcomes could arise from either poor execution or selection. For selection errors, the results replicated that observed in Experiment 1. However, unrewarded outcomes attributed to poor execution produced larger amplitude MFN, alongside an attenuation in activity preceding this component and a subsequent enhanced error positivity (Pe) response in posterior sites. In terms of behavioral correlates, only the degree of the early attenuation and amplitude of the Pe correlated with behavioral adjustment following execution errors relative to reward; the amplitude of the MFN did not correlate with behavioral changes related to execution errors. These results indicate the existence of distinct neural correlates of selection and execution error processing and are consistent with the hypothesis that execution errors can modulate action selection evaluation. More generally, they provide insight into how the brain responds to different classes of error that determine future action.Significance StatementTo learn from mistakes, we must resolve whether decisions that fail to produce rewards are due to poorly selected action plans or badly executed movements. EEG data were obtained to identify and compare the physiological correlates of selection and execution errors, and how these are related to behavioral changes. A neural signature associated with reinforcement learning, a medial frontal negative (MFN) ERP deflection, correlated with behavioral adjustment after selection errors relative to reward outcomes, but not motor execution errors. In contrast, activity preceding and following the MFN response correlated with behavioral adjustment after execution errors relative to reward. These results provide novel insight into how the brain responds to different classes of error that determine future action.


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