The cerebellum is involved in processing of predictions and prediction errors in a fear conditioning paradigm

Ernst, Thomas; Brol, Anna Evelina; Gratz, Marcel; Bingel, Ulrike; Schlamann, Marc; Maderwald, Stefan; Quick, Harald H.; Merz, Christian J.; Timmann, Dagmar

doi:10.1101/600494

Cited by 17 publications

(29 citation statements)

References 97 publications

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“…Resting state network modules also predict pain intensity, especially somatomotor, salience and attention networks. Salience networks respond to painful stimuli (Seeley et al 2007;Downar et al 2000;Legrain et al 2011;Mouraux et al 2011 (Kong et al 2006;Ploner et al 2011;Ernst et al 2019;Keltner et al 2006;Rainville et al 1997;Seminowicz, Mikulis, and Davis 2004;Ji et al 2010;Wager et al 2004;Atlas et al 2012;Woo et al 2015) , with specific evidence of a double dissociation of expectation and stimulus intensity effects along the anteroposterior axis of the insula (Geuter et al 2017;Frot, Faillenot, and Mauguière 2014) . Although characterizing the diversity of pain information in the brain exceeds the scope of this study, this was precisely the scope of (Atlas et al 2010) and (Atlas et al 2014) who use Study 2 and Study 5 (respectively) to identify expectation mediators in dorsolateral PFC, amygdala, pons, and dorsal striatum , thermal stimulus mediators in somatomotor areas and cerebellum, and mediators of both components in dorsal ACC, anterior insula and thalamus.…”

Section: Discussionmentioning

confidence: 99%

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Petre

Kragel

Atlas

et al. 2020

Preprint

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ABSTRACTInformation is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature (‘multi-system models’); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains.Significance StatementWe define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.

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Section: Discussionmentioning

confidence: 99%

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Petre

Kragel

Atlas

et al. 2020

Preprint

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“…In humans, the CB is consistently activated during decision making, particularly during risk-or reward-based tasks 6,7 , and during aversive experiences and emotional learning 8,9 . Further support for non-motor CB roles stems from clinical translational studies, which have linked CB dysfunction with neurodevelopmental disorders, posttraumatic stress disorder, generalized anxiety disorder, addiction, and cognitive and emotional disturbances known as cerebellar cognitive affective syndrome 8,[10][11][12][13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Cerebellar Activation Bidirectionally Regulates Nucleus Accumbens Core and Medial Shell

D’Ambra

Jung

Ganesan

et al. 2020

Preprint

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Traditionally viewed as a motor control center, the cerebellum (CB) is now recognized as an integral part of a broad, long-range brain network that serves limbic functions and motivates behavior. This diverse CB functionality has been at least partly attributed to the multiplicity of its outputs. However, relatively little attention has been paid to CB connectivity with subcortical limbic structures, and nothing is known about how the CB connects to the nucleus accumbens (NAc), a complex striatal region with which the CB shares functionality in motivated behaviors. Here, we report findings from in vivo electrophysiological experiments that investigated the functional connectivity between CB and NAc. We found that electrical microstimulation of deep cerebellar nuclei (DCN) modulates NAc spiking activity. This modulation differed in terms of directionality (excitatory vs. inhibitory) and temporal characteristics, in a manner that depends on NAc subregions: in the medial shell of NAc (NAcMed), slow inhibitory responses prevailed over excitatory ones, whereas the proportion of fast excitatory responses was greater in the NAc core (NAcCore) compared to NAcMed. Slow inhibitory modulation of NAcCore was also observed but it required stronger CB inputs compared to NAcMed. Finally, we observed shorter onset latencies for excitatory responses in NAcCore than in NAcMed, which argues for differential connectivity. If different pathways provide signal to each subregion, the divergence likely occurs downstream of the CB because we did not find any response-type clustering within DCN. Because there are no direct monosynaptic connections between CB and NAc, we performed viral tracing experiments to chart disynaptic pathways that could potentially mediate the newly discovered CB-NAc communication. We identified two anatomical pathways that recruit the ventral tegmental area and intralaminar thalamus as nodes. These pathways and the functional connectivity they support could underlie CB’s role in motivated behaviors.

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“…Therefore, event-related designs allowed us to separate cerebellar fMRI signals related to the visual CS from signals related to the subsequent US (an aversive electric shock). We found that cerebellar activation was most pronounced in unpaired CS+ trials, that is, in trials where the US was expected but did not occur (Figure 3; Ernst et al, 2019). This activation disappeared during extinction when US omission became expected.…”

Section: The Cerebellum As a Frequently Ignored Component Of The Extimentioning

confidence: 79%

“…Our most recent 7T fMRI studies show that findings related to extinction of conditioned eyeblink responses equally apply to extinction of learned fear. In healthy human participants, cerebellar cortical activations related to the acquisition of learned fear responses were reversed during extinction (Ernst et al, 2019). Furthermore, we observed activation of the posterolateral cerebellar hemisphere related to the renewal of previously extinguished conditioned fear responses in the acquisition context (Timmann, 2019).…”

Section: The Cerebellum As a Frequently Ignored Component Of The Extimentioning

confidence: 89%

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Beyond the classic extinction network: a wider, comparative view

et al. 2020

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Extinction learning modifies the dynamics of brain circuits such that a previously learned conditioned response is no longer generated. The majority of extinction studies use fear conditioning in rodents and identified the prefrontal cortex, the hippocampus, and the amygdala as core regions of the extinction circuit. We sought to find answers to two questions: First, do we find a similar functional brain circuit in birds, which underwent a 300-million-year separate evolution from mammals? Second, do we have to incorporate the cerebellum as a key component of the central extinction circuit? We indeed show that the avian extinction pathways are not identical but highly similar to those of mammals. In addition, we reveal that the human cerebellum processes prediction errors, a key element driving extinction of learned fear responses, and contributes to context-related effects of extinction.

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The cerebellum is involved in processing of predictions and prediction errors in a fear conditioning paradigm

Cited by 17 publications

References 97 publications

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Cerebellar Activation Bidirectionally Regulates Nucleus Accumbens Core and Medial Shell

Beyond the classic extinction network: a wider, comparative view

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