Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning

Ziegler, Sigurd; Pedersen, Mads Lund; Mowinckel, Athanasia M.; Biele, Guido

doi:10.1016/j.neubiorev.2016.09.002

Cited by 105 publications

(101 citation statements)

References 214 publications

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“…On a psychological level, the joint consideration of (change of) boundary separation and drift rate can help clarify how the shift from explorative to exploitative choices, fatigue, or boredom influence decision making in instrumental learning. In addition to supporting the exploration of basic RL processes, the RLDD model should also be useful in shedding light on cognitive deficiencies of learning and on decision making in clinical groups (Maia & Frank, 2011; Montague, Dolan, Friston, & Dayan, 2012; Ziegler, Pedersen, Mowinckel, & Biele, 2016), as in the effect of stimulant medication on cognitive processes in ADHD shown here (Fig. 5), but also in other groups with deficient learning and decision making (Mowinckel, Pedersen, Eilertsen, & Biele, 2015), such as in drug addiction (Everitt & Robbins, 2013; Schoenbaum, Roesch, & Stalnaker, 2006), schizophrenia (Doll et al, 2014), and Parkinson’s disease (Frank, Samanta, et al, 2007; Moustafa, Sherman, & Frank, 2008; Yechiam, Busemeyer, Stout, & Bechara, 2005).…”

Section: Discussionmentioning

confidence: 99%

The drift diffusion model as the choice rule in reinforcement learning

2016

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Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

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Section: Discussionmentioning

confidence: 99%

The drift diffusion model as the choice rule in reinforcement learning

2016

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“…Several lines of evidence suggest that the dopaminergic system, which MPH also targets, regulates the SNR of neural information processing; that is, it affects gain modulation principles (Li et al, 2001;Servan-Schreiber et al, 1990;Yousif et al, 2016;Ziegler et al, 2016). …”

Section: Discussionmentioning

confidence: 99%

“…Several lines of evidence suggest that the DA system not only affects top-down attentional control processes (Noudoost & Moore, 2011b;Sarter, Albin, Kucinski, & Lustig, 2014;Sarter, Gehring, & Kozak, 2006) (Colzato, Pratt, & Hommel, 2010;Colzato, Slagter, de Rover, & Hommel, 2011), but also modulates bottom-up perceptual processes (Bao, Chan, & Merzenich, 2001;Noudoost & Moore, 2011a;Ziegler, Pedersen, Mowinckel, & Biele, 2016), possibly by improving the signal-to-noise ratio (SNR) of sensory input (Kroener, Chandler, Phillips, & Seamans, 2009;Yousif et al, 2016). This modulation of the SNR is considered to reflect neuronal gain control mechanisms (Li, Lindenberger, & Sikström, 2001;Servan-Schreiber, Printz, & Cohen, 1990;Yousif et al, 2016;Ziegler et al, 2016). Increasing gain control can be conceptualized as amplifying an information processing system's responsivity to input signals from multiple levels, for example, from the sensory level (Salinas & Thier, 2000).…”

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confidence: 99%

“…Notably, the C-cluster is also crucial to consider regarding MPH effects of perceptual-attentional conflicts: The reason is that stimulus-response translation processes reflected by the C-cluster have been shown to be modulated during conflict resolution processes (Bluschke, Chmielewski, Mückschel, Roessner, & Beste, 2017;Schreiter et al, 2018). Furthermore, the dopamine system (Li et al, 2001;Li & Sikström, 2002;Servan-Schreiber et al, 1990;Yousif et al, 2016;Ziegler et al, 2016) as well as the norepinephrine system Mückschel, Chmielewski et al, 2017) have been shown to affect task context representation, task-related decision, or response selection processes during the resolution of processing conflicts. Moreover, modulations of the C-cluster are associated with the inferior parietal cortex (temporoparietal junction, TPJ, BA40) and the superior parietal cortex (BA7) (Bodmer & Beste, 2017;Wolff et al, 2017).…”

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confidence: 99%

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The system‐neurophysiological basis for how methylphenidate modulates perceptual–attentional conflicts during auditory processing

Adelhöfer

Gohil

Passow

et al. 2018

Human Brain Mapping

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The ability to selectively perceive and flexibly attend to relevant sensory signals in the environment is essential for action control. Whereas neuromodulation of sensory or attentional processing is often investigated, neuromodulation of interactive effects between perception and attention, that is, high attentional control demand when the relevant sensory information is perceptually less salient than the irrelevant one, is not well understood. To fill this gap, this pharmacological-electroencephalogram (EEG) study applied an intensity-modulated, focused-attention dichotic listening paradigm together with temporal EEG signal decomposition and source localization analyses. We used a double-blind MPH/placebo crossover design to delineate the effects of methylphenidate (MPH)-a dopamine/norepinephrine transporter blocker-on the resolution of perceptual-attentional conflicts, when perceptual saliency and attentional focus favor opposing ears, in healthy young adults. We show that MPH increased behavioral performance specifically in the condition with the most pronounced conflict between perceptual saliency and attentional focus. On the neurophysiological level, MPH effects in line with the behavioral data were observed after accounting for intraindividual variability in the signal. More specifically, MPH did not show an effect on stimulus-related processes but modulated the onset latency of processes between stimulus evaluation and responding. These modulations were further shown to be associated with activation differences in the temporoparietal junction (BA40) and the superior parietal cortex (BA7) and may reflect neuronal gain modulation principles. The findings provide mechanistic insights into the role of modulated dopamine/norepinephrine transmitter systems for the interactions between perception and attention.

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“…Functional connectivity is commonly deduced from intervoxel cross-correlations and is often assumed to reflect also the interregional coherence of fluctuations in activity of the underlying neuronal networks within the different brain regions implicated. Currently, Volume [ 29,30,31] while more recent approaches point to the importance of dopamine-glutamate interactions (Miller et al, 2014) [32]. Several lines of evidence have implied that sporadic disturbances in the signaling of glutamate receptors during a critical period of brain development may contribute to the ADHD pathophysiology (Archer and Garcia, 2016) [2].…”

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confidence: 99%

Regional Dysregulation and Aberrant Functional Connectivity in ADHD

Archer¹,

Abedini²

2017

Journal of Neuroscience and Neuropsychology

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The trajectory of neurodevelopmental adversity determining attention-deficit/hyperactivity disorder (ADHD) from childhood to adulthood which is recognized increasingly by the long-term impairment and the burgeoning persistence beyond childhood, through adolescence, to adulthood poses a prolonged history defined by a concatenation of symptom profiles and an underlying dysregulation accompanied by aberrant brain regional networks. Abnormalities within the "default mode network" and disharmonizing fluctuations afflicting brain regional connectivity, even inducing dysconnectivity, appear to be associated both with symptoms profiles and the relative efficacy of putative interventional outcomes. Co-morbidity in ADHD, a persistently debilitating and potentially regressive issue, is viewed from a perspective of eating disorders and substance use disorders although several other combinations proliferate. Among the factors contributing to dysregulation and aberrant connectivity in brain development are included genetic and epigenetic proclivities, neurotoxins and neurodegeneration, predisposing characteristics and environmental pressures. Finally, the likelihood of perinatal stress trauma and/or inflammation as contributory circumstances ought to be considered.

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Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning

Cited by 105 publications

References 214 publications

The drift diffusion model as the choice rule in reinforcement learning

The drift diffusion model as the choice rule in reinforcement learning

The system‐neurophysiological basis for how methylphenidate modulates perceptual–attentional conflicts during auditory processing

Regional Dysregulation and Aberrant Functional Connectivity in ADHD

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