Dysfunctional decision-making related to white matter alterations in bipolar I disorder

Scholz, Vanessa; Houenou, Josselin; Kollmann, Bianca; Duclap, Delphine; Poupon, Cyril; Wessa, Michèle

doi:10.1016/j.jad.2015.12.019

Cited by 19 publications

(17 citation statements)

References 58 publications

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“…Two studies (Favre et al, 2016; Souza-Queiroz et al, 2016) investigated whether or not mean gFA in the uncinate fasciculus was different between healthy controls and individuals with either BD1 or BD2. Interestingly, neither of these studies found a group difference in uncinate fasciculus gFA between healthy and patient groups, in accordance with studies by Sarrazin et al (2014) and Scholz et al (2016), but in contrast with the majority of DTI studies that examined FA in the uncinate fasciculus. Both Souza-Queiroz et al (2016) and Favre et al (2016) applied a single shell acquisition with b=1,400 (60 directions in Souza-Queiroz et al, 2016; 30 directions in Favre et al, 2016).…”

Section: A Survey Of Advanced Microstructural Diffusion Mri Studies Isupporting

confidence: 81%

“…In a study of frontal white matter tracts that included the corpus callosum, the cingulum, the uncinate fasciculus, and the inferior fronto-occipital fasciculus, Scholz et al (2016) used a single shell acquisition to extract gFA from a spherical harmonics decomposition. They replicated findings in euthymic BD1 patients (n=24) of reduced gFA in the cingulum bundle, although in the right hemisphere, rather than the left as was found in Sarrazin et al (2014).…”

Section: A Survey Of Advanced Microstructural Diffusion Mri Studies Imentioning

confidence: 99%

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Advances in microstructural diffusion neuroimaging for psychiatric disorders

et al. 2018

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Understanding the neuropathological underpinnings of mental disorders such as schizophrenia, major depression, and bipolar disorder is an essential step towards the development of targeted treatments. Diffusion MRI studies utilizing the diffusion tensor imaging (DTI) model have been extremely successful to date in identifying microstructural brain abnormalities in individuals suffering from mental illness, especially in regions of white matter, although identified abnormalities have been biologically non-specific. Building on DTI's success, in recent years more advanced diffusion MRI methods have been developed and applied to the study of psychiatric populations, with the aim of offering increased sensitivity to subtle neurological abnormalities, as well as improved specificity to candidate pathologies such as demyelination and neuroinflammation. These advanced methods, however, usually come at the cost of prolonged imaging sequences or reduced signal to noise, and they are more difficult to evaluate compared with the more simplified approach taken by the now common DTI model. To date, a limited number of advanced diffusion MRI methods have been employed to study schizophrenia, major depression and bipolar disorder populations. In this review we survey these studies, compare findings across diverse methods, discuss the main benefits and limitations of the different methods, and assess the extent to which the application of more advanced diffusion imaging approaches has led to novel and transformative information with regards to our ability to better understand the etiology and pathology of mental disorders.

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Section: A Survey Of Advanced Microstructural Diffusion Mri Studies Isupporting

confidence: 81%

Section: A Survey Of Advanced Microstructural Diffusion Mri Studies Imentioning

confidence: 99%

Advances in microstructural diffusion neuroimaging for psychiatric disorders

et al. 2018

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“…The finding of significant differential expression in the basal ganglia is particularly interesting given evidence of increased functional connectivity in the nucleus accumbens and increased striatal activity on task-based fMRI in risk-taking adolescents [ 82 , 83 ]. More broadly, these regions mirror neuroimaging and electro-encephalogram studies which implicate the frontal cortex, the anterior cingulate cortex and the striatum in risk-taking in healthy volunteers [ 84 , 85 ], SCZ [ 26 , 30 ] and BIP [ 24 ]. Interestingly, with the addition of the amygdala and the hippocampus, these structures make up the frontal-striatal reward system circuitry [ 86 ].…”

Section: Discussionmentioning

confidence: 74%

“…Various experimental studies have attempted to delineate the neurobiology underlying risk-taking, BIP and SCZ. Impulsivity and dysfunctional reward processing in BIP and SCZ have been associated with deficits in the prefrontal cortex on both functional and structural measures [ 23 – 26 ], loss of grey matter in the anterior cingulate cortex (ACC) [ 27 , 28 ] and reduced white matter integrity in the cingulum and frontal lobes [ 29 , 30 ]. Moreover, dopaminergic neurotransmission in the mesolimbic reward system has been strongly implicated in risk-taking and risky behaviours [ 31 ], demonstrated by the association between impulse control disorders and dopamine agonists [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking

et al. 2021

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Increased risk-taking is a central component of bipolar disorder (BIP) and is implicated in schizophrenia (SCZ). Risky behaviours, including smoking and alcohol use, are overrepresented in both disorders and associated with poor health outcomes. Positive genetic correlations are reported but an improved understanding of the shared genetic architecture between risk phenotypes and psychiatric disorders may provide insights into underlying neurobiological mechanisms. We aimed to characterise the genetic overlap between risk phenotypes and SCZ, and BIP by estimating the total number of shared variants using the bivariate causal mixture model and identifying shared genomic loci using the conjunctional false discovery rate method. Summary statistics from genome wide association studies of SCZ, BIP, risk-taking and risky behaviours were acquired (n = 82,315–466,751). Genomic loci were functionally annotated using FUMA. Of 8.6–8.7 K variants predicted to influence BIP, 6.6 K and 7.4 K were predicted to influence risk-taking and risky behaviours, respectively. Similarly, of 10.2–10.3 K variants influencing SCZ, 9.6 and 8.8 K were predicted to influence risk-taking and risky behaviours, respectively. We identified 192 loci jointly associated with SCZ and risk phenotypes and 206 associated with BIP and risk phenotypes, of which 68 were common to both risk-taking and risky behaviours and 124 were novel to SCZ or BIP. Functional annotation implicated differential expression in multiple cortical and sub-cortical regions. In conclusion, we report extensive polygenic overlap between risk phenotypes and BIP and SCZ, identify specific loci contributing to this shared risk and highlight biologically plausible mechanisms that may underlie risk-taking in severe psychiatric disorders.

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“…Behavioral findings using this approach have been inconsistent in BD and difficult to compare across studies (Bauer et al, 2017;Hidiroǧlu et al, 2013;Holmes et al, 2009;Linke et al, 2013;Ramírez-Martín et al, 2020;Reddy et al, 2014;Scholz et al, 2016). One reason is the heterogeneity of mood state between investigations (Ramírez-Martín et al, 2020).…”

Section: Inconsistent Behavioral Findings Due To Sample Heterogeneitymentioning

confidence: 99%

Mathematical modeling of risk-taking in bipolar disorder: Evidence of reduced behavioral consistency, with altered loss aversion specific to those with history of substance use disorder

Lasagna¹,

Pleskac²,

Burton³

et al. 2021

Preprint

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Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups—18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD-), and 33 healthy comparisons (HC)—completed the BART. We modeled behavior using 4 competing hierarchal Bayesian models, and model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD- and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART (adjusted pumps, total points, total pops) detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD.

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Dysfunctional decision-making related to white matter alterations in bipolar I disorder

Cited by 19 publications

References 58 publications

Advances in microstructural diffusion neuroimaging for psychiatric disorders

Advances in microstructural diffusion neuroimaging for psychiatric disorders

Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking

Mathematical modeling of risk-taking in bipolar disorder: Evidence of reduced behavioral consistency, with altered loss aversion specific to those with history of substance use disorder

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