Modification of persistent responses in medial prefrontal cortex during learning in trace eyeblink conditioning

Siegel, Jennifer J.

doi:10.1152/jn.00372.2014

Cited by 14 publications

(13 citation statements)

References 49 publications

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“…We show here that in the absence of CS-evoked startle and freezing responses, most mice required four to eight training sessions (with baseline CR rates of 0–5%) before beginning to express CRs, which gradually increased over an additional approximately five sessions. Once learning began, the progression of learning was similar across mice and independent of the number of sessions needed before learning was observed, similar to the learning curves often reported for rabbits ( Kalmbach et al, 2010b ; Siegel, 2014 ). Furthermore, using the current training procedures mice were able to fully extinguish CRs within two to three training sessions (0–5% CR rates) and reacquired CRs to previous levels of performance within five sessions.…”

Section: Discussionsupporting

confidence: 82%

Trace Eyeblink Conditioning in Mice Is Dependent upon the Dorsal Medial Prefrontal Cortex, Cerebellum, and Amygdala: Behavioral Characterization and Functional Circuitry

Siegel

Taylor

Gray

et al. 2015

eneuro

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Trace eyeblink conditioning is useful for studying the interaction of multiple brain areas in learning and memory. The goal of the current work was to determine whether trace eyeblink conditioning could be established in a mouse model in the absence of elicited startle responses and the brain circuitry that supports this learning. We show here that mice can acquire trace conditioned responses (tCRs) devoid of startle while head-restrained and permitted to freely run on a wheel. Most mice (75%) could learn with a trace interval of 250 ms. Because tCRs were not contaminated with startle-associated components, we were able to document the development and timing of tCRs in mice, as well as their long-term retention (at 7 and 14 d) and flexible expression (extinction and reacquisition). To identify the circuitry involved, we made restricted lesions of the medial prefrontal cortex (mPFC) and found that learning was prevented. Furthermore, inactivation of the cerebellum with muscimol completely abolished tCRs, demonstrating that learned responses were driven by the cerebellum. Finally, inactivation of the mPFC and amygdala in trained animals nearly abolished tCRs. Anatomical data from these critical regions showed that mPFC and amygdala both project to the rostral basilar pons and overlap with eyelid-associated pontocerebellar neurons. The data provide the first report of trace eyeblink conditioning in mice in which tCRs were driven by the cerebellum and required a localized region of mPFC for acquisition. The data further reveal a specific role for the amygdala as providing a conditioned stimulus-associated input to the cerebellum.

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Section: Discussionsupporting

confidence: 82%

Trace Eyeblink Conditioning in Mice Is Dependent upon the Dorsal Medial Prefrontal Cortex, Cerebellum, and Amygdala: Behavioral Characterization and Functional Circuitry

Siegel

Taylor

Gray

et al. 2015

eneuro

Self Cite

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“…The mPFC has also been implicated in other behaviors that rely on the temporal organization of information, including learning stimulus sequences (Barker et al, 2007;Devito and Eichenbaum, 2011;Hannesson et al, 2004a;Mitchell and Laiacona, 1998) and remembering which locations in an environment have been previously visited (Chiba et al, 1997;Hannesson et al, 2004b). At the physiological level, mPFC neurons have been found to sustain firing rates over short temporal intervals (Hattori et al, 2014;Rainer et al, 1999;Siegel, 2014;Takehara-Nishiuchi and McNaughton, 2008). These patterns parallel well-established observations of neuron activity in the primate dorsolateral prefrontal cortex (dlPFC) during working memory tasks (Fuster and Alexander, 1971;Kojima and Goldman-Rakic, 1982;Quintana and Fuster, 1992).…”

Section: Introductionsupporting

confidence: 66%

Enhancing Prefrontal Neuron Activity Enables Associative Learning of Temporally Disparate Events

Volle

Sun

et al. 2016

Cell Reports

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The ability to link events that are separated in time is important for extracting meaning from experiences and guiding behavior in the future. This ability likely requires the brain to continue representing events even after they have passed, a process that may involve the prefrontal cortex and takes the form of sustained, event-specific neuron activity. Here, we show that experimentally increasing the activity of excitatory neurons in the medial prefrontal cortex (mPFC) enables rats to associate two stimuli separated by a 750-ms long temporal gap. Learning is accompanied by ramping increases in prefrontal theta and beta rhythms during the interval between stimuli. This ramping activity predicts memory-related behavioral responses on a trial-by-trial basis but is not correlated with the same muscular activity during non-memory conditions. Thus, the enhancement of prefrontal neuron excitability extends the time course of evoked prefrontal network activation and facilitates the formation of associations of temporally disparate, but correlated, events.

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“…The mPFC has been revealed to connect with the cerebellum via the pontine nuclei in rodents 15 , rabbits 16 17 , non-human primates 18 and the humans 19 . Moreover, multiple-unit recording studies have shown learning-related neuronal activity in the mPFC during TEBC 20 21 22 23 24 . Pharmacological or physical interventions that interfere with neuronal activity in the mPFC not only impair the expression of acquired trace CRs 3 4 25 26 , but also prevent the CR acquisition 12 13 14 .…”

mentioning

confidence: 99%

Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior

Chen

Wang

Yang

et al. 2016

Sci Rep

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Associative learning is thought to require coordinated activities among distributed brain regions. For example, to direct behavior appropriately, the medial prefrontal cortex (mPFC) must encode and maintain sensory information and then interact with the cerebellum during trace eyeblink conditioning (TEBC), a commonly-used associative learning model. However, the mechanisms by which these two distant areas interact remain elusive. By simultaneously recording local field potential (LFP) signals from the mPFC and the cerebellum in guinea pigs undergoing TEBC, we found that theta-frequency (5.0–12.0 Hz) oscillations in the mPFC and the cerebellum became strongly synchronized following presentation of auditory conditioned stimulus. Intriguingly, the conditioned eyeblink response (CR) with adaptive timing occurred preferentially in the trials where mPFC-cerebellum theta coherence was stronger. Moreover, both the mPFC-cerebellum theta coherence and the adaptive CR performance were impaired after the disruption of endogenous orexins in the cerebellum. Finally, association of the mPFC -cerebellum theta coherence with adaptive CR performance was time-limited occurring in the early stage of associative learning. These findings suggest that the mPFC and the cerebellum may act together to contribute to the adaptive performance of associative learning behavior by means of theta synchronization.

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Modification of persistent responses in medial prefrontal cortex during learning in trace eyeblink conditioning

Abstract: Siegel JJ. Modification of persistent responses in medial prefrontal cortex during learning in trace eyeblink conditioning.

Cited by 14 publications

References 49 publications

Trace Eyeblink Conditioning in Mice Is Dependent upon the Dorsal Medial Prefrontal Cortex, Cerebellum, and Amygdala: Behavioral Characterization and Functional Circuitry

Trace Eyeblink Conditioning in Mice Is Dependent upon the Dorsal Medial Prefrontal Cortex, Cerebellum, and Amygdala: Behavioral Characterization and Functional Circuitry

Enhancing Prefrontal Neuron Activity Enables Associative Learning of Temporally Disparate Events

Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior

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