Inhibitory Behavioral Control: A Stochastic Dynamic Causal Modeling Study Using Network Discovery Analysis

Ma, Liangsuo; Steinberg, Joel L.; Cunningham, Kathryn A.; Lane, Scott D.; Kramer, Larry A.; Narayana, Ponnada A.; Kosten, Thomas R.; Bechara, Antoine; Moeller, F. Gerard

doi:10.1089/brain.2014.0275

Cited by 16 publications

(16 citation statements)

References 53 publications

(68 reference statements)

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“…DCM analyses generally focus on a subgraph or limited number of brain regions to reduce the number of extrinsic connections and their conditional dependencies [Daunizeau et al, ]. Studying a few key regions also reduces computational load [Ma et al, ]. Although, we used a relatively small number of regions, the number of potential architectures or models entailed by each DCM is potentially enormous.…”

Section: Methodsmentioning

confidence: 99%

The functional architectures of addition and subtraction: Network discovery using fMRI and DCM

Yang

Friston

Imamura

et al. 2017

Human Brain Mapping

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The neuronal mechanisms underlying arithmetic calculations are not well understood but the differences between mental addition and subtraction could be particularly revealing. Using fMRI and dynamic causal modeling (DCM), this study aimed to identify the distinct neuronal architectures engaged by the cognitive processes of simple addition and subtraction. Our results revealed significantly greater activation during subtraction in regions along the dorsal pathway, including the left inferior frontal gyrus (IFG), middle portion of dorsolateral prefrontal cortex (mDLPFC), and supplementary motor area (SMA), compared with addition. Subsequent analysis of the underlying changes in connectivity - with DCM - revealed a common circuit processing basic (numeric) attributes and the retrieval of arithmetic facts. However, DCM showed that addition was more likely to engage (numeric) retrieval-based circuits in the left hemisphere, while subtraction tended to draw on (magnitude) processing in bilateral parietal cortex, especially the right intraparietal sulcus (IPS). Our findings endorse previous hypotheses about the differences in strategic implementation, dominant hemisphere, and the neuronal circuits underlying addition and subtraction. Moreover, for simple arithmetic, our connectivity results suggest that subtraction calls on more complex processing than addition: auxiliary phonological, visual, and motor processes, for representing numbers, were engaged by subtraction, relative to addition. Hum Brain Mapp 38:3210-3225, 2017. © 2017 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

The functional architectures of addition and subtraction: Network discovery using fMRI and DCM

Yang

Friston

Imamura

et al. 2017

Human Brain Mapping

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“…A rapid-presentation event-related Go/NoGo task ( Lane et al, 2007 ; Ma et al, 2014b ) was used for analyses of response inhibition during fMRI. For all subjects, there were two Go/NoGo fMRI runs.…”

Section: Methodsmentioning

confidence: 99%

“…For all subjects, there were two Go/NoGo fMRI runs. The Go/NoGo task has been described in detail elsewhere ( Lane et al, 2007 ; Ma et al, 2014b ). In brief, during each fMRI run, 208 visual stimuli (including Go, Easy NoGo, or Hard NoGo, please see below) were sequentially presented in random order.…”

Section: Methodsmentioning

confidence: 99%

Inhibitory behavioral control: A stochastic dynamic causal modeling study comparing cocaine dependent subjects and controls

Steinberg

Cunningham

et al. 2015

NeuroImage: Clinical

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Cocaine dependence is associated with increased impulsivity in humans. Both cocaine dependence and impulsive behavior are under the regulatory control of cortico-striatal networks. One behavioral laboratory measure of impulsivity is response inhibition (ability to withhold a prepotent response) in which altered patterns of regional brain activation during executive tasks in service of normal performance are frequently found in cocaine dependent (CD) subjects studied with functional magnetic resonance imaging (fMRI). However, little is known about aberrations in specific directional neuronal connectivity in CD subjects. The present study employed fMRI-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity associated with response inhibition in CD subjects, elicited under performance of a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard). The performance on the Go/NoGo task was not significantly different between CD subjects and controls. The DCM analysis revealed that prefrontal–striatal connectivity was modulated (influenced) during the NoGo conditions for both groups. The effective connectivity from left (L) anterior cingulate cortex (ACC) to L caudate was similarly modulated during the Easy NoGo condition for both groups. During the Hard NoGo condition in controls, the effective connectivity from right (R) dorsolateral prefrontal cortex (DLPFC) to L caudate became more positive, and the effective connectivity from R ventrolateral prefrontal cortex (VLPFC) to L caudate became more negative. In CD subjects, the effective connectivity from L ACC to L caudate became more negative during the Hard NoGo conditions. These results indicate that during Hard NoGo trials in CD subjects, the ACC rather than DLPFC or VLPFC influenced caudate during response inhibition.

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“…We employed stochastic DCM, which allows for modelling of random neuronal noise in 559 the system, to improve network resolution in brainstem areas significantly affected by physiological 560 noise (Brooks et al, 2013). This routine was shown to improve the characterization of network 561 structure and parameter inference over deterministic DCM (Daunizeau et al, 2012;Osório et al, 562 2015) and has been widely used in resting state and task-based fMRI studies since its release (Kahan 563 et al, 2014;Ma et al, 2015Ma et al, , 2014Ray et al, 2016;Zhang et al, 2015). Bayesian Model selection 564 validated the results of the gPPI by excluding, for lack of evidence, a model where no connection was 565 modulated by task.…”

Section: Regions Whose Activity Correlates With Analgesic Effectmentioning

confidence: 99%

Parallel cortical-brainstem pathways to attentional analgesia

Oliva

Gregory

Davies

et al. 2020

Preprint

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13Pain perception is diminished when attention is diverted. Our previous human fMRI study, using a 14 2x2 factorial design with thermal stimuli and concurrent visual attention task, linked the brainstem 15 triad of locus coeruleus (LC), rostroventromedial medulla (RVM) and periaqueductal grey (PAG) to 16 attentional analgesia. This study was repeated with a larger cohort, replicating our earlier findings. 17 Pain intensity was encoded by the RVM, whilst activity in the contralateral LC correlated with the 18 magnitude of attentional analgesia. Psycho-Physiological Interaction analysis informed subsequent 19 Dynamic Causal Modelling and identified two parallel paths between forebrain and the brainstem 20 regions involved in analgesia. These cortico-brainstem connections were modulated by attentional 21 demand: a bidirectional anterior cingulate cortex (ACC)right-LC loop, and a top-down influence 22 of task on ACC-PAG-RVM. Under conditions of competing attentional demands the ACC recruits 23 discrete brainstem circuits to modulate nociceptive input. 24 25 Parallel cortical-brainstem pathways to attentional analgesia activation of the ACC-PAG-RVM and/or the ACC-LC system still needs to be demonstrated in 595humans. It is known that both pathways could involve opioids (Fields, 2004) and so previous studies 596 using naloxone do not discriminate between these possibilities. A connectivity analysis examining 597 the network activity between cortical territories, brainstem nuclei and dorsal horn in toto may help to 598 define the key pathway in attentional analgesia. We further postulate that this network may be of 599 importance in chronic conditions where disruption of attention and cognition (i.e. fibromyalgia) are 600 co-morbid alongside pain.

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Inhibitory Behavioral Control: A Stochastic Dynamic Causal Modeling Study Using Network Discovery Analysis

Cited by 16 publications

References 53 publications

The functional architectures of addition and subtraction: Network discovery using fMRI and DCM

The functional architectures of addition and subtraction: Network discovery using fMRI and DCM

Inhibitory behavioral control: A stochastic dynamic causal modeling study comparing cocaine dependent subjects and controls

Parallel cortical-brainstem pathways to attentional analgesia

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