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1.
Sci Rep ; 12(1): 10731, 2022 06 24.
Article in English | MEDLINE | ID: mdl-35750718

ABSTRACT

Active avoidance behavior, in which an animal performs an action to avoid a stressor, is crucial for survival and may provide insight into avoidance behaviors seen in anxiety disorders. Active avoidance requires the dorsomedial prefrontal cortex (dmPFC), which is thought to regulate avoidance via downstream projections to the striatum and amygdala. However, the endogenous activity of dmPFC projections during active avoidance learning has never been recorded. Here we utilized fiber photometry to record from the dmPFC and its axonal projections to the dorsomedial striatum (DMS) and the basolateral amygdala (BLA) during active avoidance learning in both male and female mice. We examined neural activity during conditioned stimulus (CS) presentations and during clinically relevant behaviors such as active avoidance or cued freezing. Both prefrontal projections showed learning-related increases in activity during CS onset throughout active avoidance training. The dmPFC as a whole showed increased and decreased patterns of activity during avoidance and cued freezing, respectively. Finally, dmPFC-DMS and dmPFC-BLA projections show divergent encoding of active avoidance behavior, with the dmPFC-DMS projection showing increased activity and the dmPFC-BLA projection showing decreased activity during active avoidance. Our results demonstrate task-relevant encoding of active avoidance in projection-specific dmPFC subpopulations that play distinct but complementary roles in active avoidance learning.


Subject(s)
Avoidance Learning , Basolateral Nuclear Complex , Amygdala/physiology , Animals , Avoidance Learning/physiology , Basolateral Nuclear Complex/physiology , Conditioning, Operant , Female , Male , Mice , Prefrontal Cortex/physiology
2.
J Neurosci ; 41(25): 5487-5501, 2021 06 23.
Article in English | MEDLINE | ID: mdl-34001628

ABSTRACT

The dorsomedial prefrontal cortex (dmPFC) has been linked to avoidance and decision-making under conflict, key neural computations altered in anxiety disorders. However, the heterogeneity of prefrontal projections has obscured identification of specific top-down projections involved. While the dmPFC-amygdala circuit has long been implicated in controlling reflexive fear responses, recent work suggests that dmPFC-dorsomedial striatum (DMS) projections may be more important for regulating avoidance. Using fiber photometry recordings in both male and female mice during the elevated zero maze task, we show heightened neural activity in frontostriatal but not frontoamygdalar projection neurons during exploration of the anxiogenic open arms. Additionally, using optogenetics, we demonstrate that this frontostriatal projection preferentially excites postsynaptic D1 receptor-expressing neurons in the DMS and causally controls innate avoidance behavior. These results support a model for prefrontal control of defensive behavior in which the dmPFC-amygdala projection controls reflexive fear behavior and the dmPFC-striatum projection controls anxious avoidance behavior.SIGNIFICANCE STATEMENT The medial prefrontal cortex has been extensively linked to several behavioral symptom domains related to anxiety disorders, with much of the work centered around reflexive fear responses. Comparatively little is known at the mechanistic level about anxious avoidance behavior, a core feature across anxiety disorders. Recent work has suggested that the striatum may be an important hub for regulating avoidance behaviors. Our work uses optical circuit dissection techniques to identify a specific corticostriatal circuit involved in encoding and controlling avoidance behavior. Identifying neural circuits for avoidance will enable the development of more targeted symptom-specific treatments for anxiety disorders.


Subject(s)
Avoidance Learning/physiology , Corpus Striatum/physiology , Neural Pathways/physiology , Prefrontal Cortex/physiology , Animals , Behavior, Animal/physiology , Female , Instinct , Male , Mice , Mice, Inbred C57BL
3.
Clocks Sleep ; 2(4): 523-535, 2020 Dec 01.
Article in English | MEDLINE | ID: mdl-33271811

ABSTRACT

This study examined whether theta oscillations were compromised by the type of circadian disruption that impairs hippocampal-dependent memory processes. In prior studies on Siberian hamsters, we developed a one-time light treatment that eliminated circadian timing in the central pacemaker, the suprachiasmatic nucleus (SCN). These arrhythmic animals had impaired hippocampal-dependent memory whereas animals made arrhythmic with SCN lesions did not. The current study examined whether theta oscillations are compromised by the same light treatment that produced memory impairments in these animals. We found that both methods of inducing circadian-arrhythmia shortened theta episodes in the EEG by nearly 50%. SCN-lesioned animals, however, exhibited a 3-fold increase in the number of theta episodes and more than doubled the total time that theta dominated the EEG compared to SCN-intact circadian-arrhythmic animals. Video tracking showed that changes in theta were paralleled by similar changes in exploration behavior. These results suggest that the circadian-arrhythmic SCN interferes with hippocampal memory encoding by fragmenting theta oscillations. SCN-lesioned animals can, however, compensate for the shortened theta episodes by increasing their frequency. Implications for rhythm coherence and theta sequence models of memory formation are discussed.

4.
IEEE Trans Med Imaging ; 39(4): 1127-1137, 2020 04.
Article in English | MEDLINE | ID: mdl-31567074

ABSTRACT

We present software-based methods for automatic phase control and for mosaicing high-speed, Lissajous-scanned images. To achieve imaging speeds fast enough for mosaicing, we first increase the image update rate tenfold from 3 to 30 Hz, then vertically interpolate each sparse image in real-time to eliminate fixed pattern noise. We validate our methods by imaging fluorescent beads and automatically maintaining phase control over the course of one hour. We then image fixed mouse brain tissues at varying update rates and compare the resulting mosaics. Using reconstructed image data as feedback for phase control eliminates the need for phase sensors and feedback controllers, enabling long-term imaging experiments without additional hardware. Mosaicing subsampled images results in video-rate imaging speeds, nearly fully recovered spatial resolution, and millimeter-scale fields of view.


Subject(s)
Image Processing, Computer-Assisted/methods , Microscopy, Confocal/methods , Software , Video Recording/methods , Algorithms , Animals , Brain/diagnostic imaging , Mice
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