CoChat
Sign Up
Cited by 164Neural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on BehaviorFree Will and Agency

The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.

Individual Differences in Premotor Brain Systems Underlie Behavioral Apathy

Open access
Bonnelle, Valerie, Manohar, Sanjay, Behrens, Tim, Husain, Masud·2015·Cerebral Cortex
DOI 10.1093/cercor/bhv247PMC PMC4712805
Open this paper in CoChat — free accountOpen PDF

Abstract

Lack of physical engagement, productivity, and initiative-so-called "behavioral apathy"--is a common problem with significant impact, both personal and economic. Here, we investigate whether there might be a biological basis to such lack of motivation using a new effort and reward-based decision-making paradigm, combined with functional and diffusion-weighted imaging. We hypothesized that behavioral apathy in otherwise healthy people might be associated with differences in brain systems underlying either motivation to act (specifically in effort and reward-based decision-making) or in action processing (transformation of an intention into action). The results demonstrate that behavioral apathy is associated with increased effort sensitivity as well as greater recruitment of neural systems involved in action anticipation: supplementary motor area (SMA) and cingulate motor zones. In addition, decreased structural and functional connectivity between anterior cingulate cortex (ACC) and SMA were associated with increased behavioral apathy. These findings reveal that effort sensitivity and translation of intentions into actions might make a critical contribution to behavioral apathy. We propose a mechanism whereby inefficient communication between ACC and SMA might lead to increased physiological cost--and greater effort sensitivity--for action initiation in more apathetic people.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/cercor/bhv247

Cites

76 of 76 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Effort- and reward-based decision-making task. Each trial starts with an apple tree showing the stake (number of apples) and effort level required to win a fraction of this stake (trunk height). There were 6 possible stakes (1, 3, 6, 9, 12, and 15 apples), and 5 possible effort levels (60%, 70%, 80%, 90%, and 100% of subject's MVC), indicated by the trunk height as well as yellow horizontal lines on tree trunk. After 3–4.5 s, participants decided whether or not they want to engage in an effortful response to gather apples (YES/NO option). Fruit gathering was performed by squeezing a force transducer with right or left hand, which translated on the screen as a red bar gradually filling the trunk. Subjects only won a percentage of the stake if they managed to reach or go beyond the top of the trunk. The expected reward was calculated on the basis of stake, effort level, and maximal force reached within 3 s. To control for the number of effortful responses produced, the selection of the YES option was also sometimes followed by a screen indicating that no response is required.
Figure 1. Effort- and reward-based decision-making task. Each trial starts with an apple tree showing the stake (number of apples) and effort level required to win a fraction of this stake (trunk height). There were 6 possible stakes (1, 3, 6, 9, 12, and 15 apples), and 5 possible effort levels (60%, 70%, 80%, 90%, and 100% of subject's MVC), indicated by the trunk height as well as yellow horizontal lines on tree trunk. After 3–4.5 s, participants decided whether or not they want to engage in an effortful response to gather apples (YES/NO option). Fruit gathering was performed by squeezing a force transducer with right or left hand, which translated on the screen as a red bar gradually filling the trunk. Subjects only won a percentage of the stake if they managed to reach or go beyond the top of the trunk. The expected reward was calculated on the basis of stake, effort level, and maximal force reached within 3 s. To control for the number of effortful responses produced, the selection of the YES option was also sometimes followed by a screen indicating that no response is required.CC BY · PubMed Central
Figure 2. Behavior on task. (a) Percentage of accepted trials (%Yes) and (b) force exerted relative to MVC averaged across participants plotted against effort levels and stakes. Effort levels (1–5) correspond to percentage of MVC (from 60 to 100% MVC).
Figure 2. Behavior on task. (a) Percentage of accepted trials (%Yes) and (b) force exerted relative to MVC averaged across participants plotted against effort levels and stakes. Effort levels (1–5) correspond to percentage of MVC (from 60 to 100% MVC).CC BY · PubMed Central
Figure 3. Choice probability modeling and relation with apathy traits. (a) Mean response bias (β0) and beta weights for stake, effort, expected reward, and probability of success across participants. Positive values indicate a weight toward “Yes”. *One-sample t-test, P < 0.05. (b) Correlation between behavioral apathy scores and effort sensitivity (βEffort).
Figure 3. Choice probability modeling and relation with apathy traits. (a) Mean response bias (β0) and beta weights for stake, effort, expected reward, and probability of success across participants. Positive values indicate a weight toward “Yes”. *One-sample t-test, P < 0.05. (b) Correlation between behavioral apathy scores and effort sensitivity (βEffort).CC BY · PubMed Central
Figure 4. Brain regions associated with behavioral processes during the decision period. Regions showing significant increase in activation with (a) stake increase, (b) effort decrease, (c) expected reward increase, (d) cost-benefit weighing load (WL) with increase in orange and decrease in blue, and (e) increased probability of willing to engage effort, that is, probability of responding YES. Right panel shows BOLD signal increase with probability of accepting an offer for different medial frontal regions (±standard error), parceled out based on connectivity for pSMA), SMA, primary motor cortex (M1), caudal cingulate zone (ccz), posterior, and anterior rostral cingulate zones (rczp; rcza).
Figure 4. Brain regions associated with behavioral processes during the decision period. Regions showing significant increase in activation with (a) stake increase, (b) effort decrease, (c) expected reward increase, (d) cost-benefit weighing load (WL) with increase in orange and decrease in blue, and (e) increased probability of willing to engage effort, that is, probability of responding YES. Right panel shows BOLD signal increase with probability of accepting an offer for different medial frontal regions (±standard error), parceled out based on connectivity for pSMA), SMA, primary motor cortex (M1), caudal cingulate zone (ccz), posterior, and anterior rostral cingulate zones (rczp; rcza).CC BY · PubMed Central
Figure 5. Relationship between brain function and individual differences in apathy traits. (a) Whole-brain correlation between apathy scores and BOLD signal increase with increased probability of accepting an offer controlling (blue–light blue) or not (yellow–red) for variance explained by behavioral model parameters (effort, stake, and reward sensitivity). Top right panel shows correlation between behavioral apathy scores and activation increase with P(yes) in the SMA. (b) Whole-brain correlation between apathy scores and effort-related BOLD signal change (signal increase with decreased effort). Bottom right panel shows relation between behavioral apathy scores and activation increase (as effort level decreased) in the nucleus accumbens (NAc).
Figure 5. Relationship between brain function and individual differences in apathy traits. (a) Whole-brain correlation between apathy scores and BOLD signal increase with increased probability of accepting an offer controlling (blue–light blue) or not (yellow–red) for variance explained by behavioral model parameters (effort, stake, and reward sensitivity). Top right panel shows correlation between behavioral apathy scores and activation increase with P(yes) in the SMA. (b) Whole-brain correlation between apathy scores and effort-related BOLD signal change (signal increase with decreased effort). Bottom right panel shows relation between behavioral apathy scores and activation increase (as effort level decreased) in the nucleus accumbens (NAc).CC BY · PubMed Central
Figure 6. Relationship between cingulum white matter structure and apathy traits. (a) Cingulum bundle mask was parceled into 3 portions: anterior (yellow), middle (green), and posterior (blue). Correlations between behavioral apathy scores and normalized mean FA corrected for age and whole-brain white matter mean FA are plotted for the anterior (b), middle (c), and posterior (d) portions of the cingulum bundle (bilaterally).
Figure 6. Relationship between cingulum white matter structure and apathy traits. (a) Cingulum bundle mask was parceled into 3 portions: anterior (yellow), middle (green), and posterior (blue). Correlations between behavioral apathy scores and normalized mean FA corrected for age and whole-brain white matter mean FA are plotted for the anterior (b), middle (c), and posterior (d) portions of the cingulum bundle (bilaterally).CC BY · PubMed Central
Figure 7. Relation between SMA functional connectivity and apathy traits. (a) In yellow–orange, regions where activity during the decision period on YES trials is more strongly correlated with activity in the SMA (purple) in more motivated individuals. (b) Correlation between behavioral apathy scores and the strength of the correlation (or functional connectivity) between the SMA and the dorsal ACC.
Figure 7. Relation between SMA functional connectivity and apathy traits. (a) In yellow–orange, regions where activity during the decision period on YES trials is more strongly correlated with activity in the SMA (purple) in more motivated individuals. (b) Correlation between behavioral apathy scores and the strength of the correlation (or functional connectivity) between the SMA and the dorsal ACC.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 76 references

Research works printed but not linked: 1

  1. 10.1016/S1053-8119(02)91132-8 · DOI · Paper page

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Bonnelle, V., Manohar, S., Behrens, T., & Husain, M.
Affiliations
University of Oxford (GB)
ORCID
Manohar, Sanjay
Venue
Cerebral Cortex
Publisher
Oxford University Press (OUP)
Published
2015-11-12
Volume
26
Issue
2
Pages
bhv247
ISSN
  • 1047-3211 (ISSN-L)
  • 1460-2199
DOI
10.1093/cercor/bhv247
PMC
PMC4712805
OpenAlex
W2179264811
Cited by
163
References
76
Citations by year
2026: 6 · 2025: 14 · 2024: 14 · 2023: 13 · 2022: 17 · 2021: 25 · 2020: 14 · 2019: 14 · 2018: 17 · 2017: 16 · 2016: 13
FWCI
9.22
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Cerebral Cortex (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
Wellcome Trust
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
  • Social Sciences › Arts and Humanities › Philosophy › Free Will and Agency
Keywords
reward-based decision-making, effort-based decision making, supplementary motor area, anterior cingulate cortex, action prediction, structural connectivity, functional connectivity, motivation
MeSH terms
Adult, Brain Mapping, Cues, Decision Making, Female, Humans, Image Processing, Computer-Assisted, Individuality, Magnetic Resonance Imaging, Male, Motor Cortex, Neural Pathways, Photic Stimulation, Psychophysics, Reward, Young Adult, Feedback, Sensory, Apathy, Self Report, White Matter

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review