Dorsolateral septum somatostatin interneurons gate mobility to calibrate context-specific behavioral fear responses

Besnard, Antoine; Gao, Yuan; Kim, Michael TaeWoo; Twarkowski, Hannah; Reed, Alexander K.; Langberg, Tomer; Feng, Wendy; Xu, Xiangmin; Saur, Dieter; Zweifel, Larry S.; Davison, Ian G.; Sahay, Amar

doi:10.1038/s41593-018-0330-y

Cited by 74 publications

(107 citation statements)

References 60 publications

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“…Fos expression was specific to vBNST, insofar as backward and forward rats did not differ in 396 overall levels of Fos in dBNST or ovBNST. Similarly, others have shown elevated Fos expression, 397 in regions in or near vBNST after stress or conditioned fear-related behavior [(Besnard et al, 2019;398 Sterrenburg et al, 2012;Verma et al, 2018); also, see Radley and 399 Sawchenko, 2011)]. 400…”

Section: Backward Css Selectively Increase Fos Expression In Mpfc Affmentioning

confidence: 88%

Bed nucleus of the stria terminalis regulates fear to unpredictable threat signals

Goode

Ressler

Acca

et al. 2018

Preprint

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The bed nucleus of the stria terminalis (BNST) has been implicated in conditioned fear and anxiety, but the specific factors that engage the BNST in defensive behaviors are unclear. Here we examined whether the BNST mediates freezing to conditioned stimuli (CSs) that poorly predict the onset of aversive unconditioned stimuli (USs) in rats. Reversible inactivation of the BNST selectively reduced freezing to CSs that poorly signaled US onset (e.g., a backward CS that followed the US), but did not eliminate freezing to forward CSs even when they predicted USs of variable intensity. Additionally, backward (but not forward) CSs selectively increased Fos in the ventral BNST and in BNST-projecting neurons in the infralimbic region of the medial prefrontal cortex (mPFC), but not in the hippocampus or amygdala. These data reveal that BNST circuits regulate fear to unpredictable threats, which may be critical to the etiology and expression of anxiety.IMPACT STATEMENTThe bed nucleus of the stria terminalis (BNST) is required for the expression of defensive behavior to unpredictable threats, a function that may be central to pathological anxiety.

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Section: Backward Css Selectively Increase Fos Expression In Mpfc Affmentioning

confidence: 88%

Bed nucleus of the stria terminalis regulates fear to unpredictable threat signals

Goode

Ressler

Acca

et al. 2018

Preprint

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“…One mechanism is that information pertaining to certain and ambiguous threats computed in dHC and vHC is relayed to the amygdala, prefrontal cortex, and hypothalamus by vHC outputs. Alternatively, computations underlying certain and ambiguous threats are relayed directly out of the dHC and vHC through distinct pathways and integrated at extrahippocampal sites, such as the DLS (Besnard et al, 2019;Fanselow and Dong, 2010;Luo et al, 2011;Risold and Swanson, 1996) or thalamus (Fanselow and Dong, 2010;Liberzon and Abelson, 2016;Vertes, 2006;Xu and S€ udhof, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The DLS, a brain region comprised of a heterogeneous population of inhibitory neurons, by virtue of receiving direct hippocampal synaptic inputs, is ideally positioned to relay DG-CA3 computations resolving overlapping contextual information to the hypothalamus and the supramammilary nucleus (SUM) (McGlinchey and Aston-Jones, 2018; Pan and McNaughton, 2004;Risold and Swanson, 1996, 1997a, 1997bSheehan et al, 2004;Yang, 2015, 2016), a potent modulator of defensive behavior (Blanchard et al, 1998) (exploration, freezing), arousal, and stress responses (Campeau and Watson, 2000;Pan and McNaughton, 2004). We recently found that within the DLS, somatostatin (SST)-expressing neurons receive monosynaptic inputs from hippocampal CA3, CA1, and subiculum and these neurons gate mobility in aversive contexts (Besnard et al, 2019). Together, these observations suggest that contextual information may be relayed out of CA3 by CA1 or the DLS to cortical and subcortical circuits to calibrate fear responses.…”

Section: Introductionmentioning

confidence: 99%

Distinct Dorsal and Ventral Hippocampal CA3 Outputs Govern Contextual Fear Discrimination

2020

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Considerable work emphasizes a role for hippocampal circuits in governing contextual fear discrimination. However, the intra-and extrahippocampal pathways that route contextual information to cortical and subcortical circuits to guide adaptive behavioral responses are poorly understood. Using terminal-specific optogenetic silencing in a contextual fear discrimination learning paradigm, we identify opposing roles for dorsal CA3-CA1 (dCA3-dCA1) projections and dorsal CA3-dorsolateral septum (dCA3-DLS) projections in calibrating fear responses to certain and ambiguous contextual threats, respectively. Ventral CA3-DLS (vCA3-DLS) projections suppress fear responses in both certain and ambiguous contexts, whereas ventral CA3-CA1 (vCA3-vCA1) projections promote fear responses in both these contexts. Lastly, using retrograde monosynaptic tracing, ex vivo electrophysiological recordings, and optogenetics, we identify a sparse population of DLS parvalbumin (PV) neurons as putative relays of dCA3-DLS projections to diverse subcortical circuits. Taken together, these studies illuminate how distinct dCA3 and vCA3 outputs calibrate contextual fear discrimination.

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“…To initialize our algorithm we use the CNMF-E algorithm on a short initial batch of data of length T b , (e.g., T b = 200). The sufficient statistics are initialized from the components that the offline algorithm finds according to Eqs (11,12).…”

Section: /24mentioning

confidence: 99%

“…This prevalent algorithm (see [6] for an alternative proposal) has been widely used to study neural circuits in cortical and subcortical brain areas, e.g. prefrontal cortex (PFC) and hippocampus [5], as well as previously inaccessible deep brain areas, such as striatum [7], amygdala [8], substantia nigra pars compacta (SNc) [9], nucleus accumbens [10], dorsolateral septum [11], parabrachial nucleus [12], and other brain regions.…”

Section: Introductionmentioning

confidence: 99%

Online analysis of microendoscopic 1-photon calcium imaging data streams

Friedrich

Giovannucci

Pnevmatikakis

2020

Preprint

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In-vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing on GPU hardware. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on two previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. Author summaryCalcium imaging methods enable researchers to measure the activity of genetically-targeted large-scale neuronal subpopulations. Whereas previous methods required the specimen to be stable, e.g. anesthetized or head-fixed, new brain imaging techniques using microendoscopic lenses and miniaturized microscopes have enabled deep brain imaging in freely moving mice.However, the very large background fluctuations, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals complicate the goal of efficiently extracting accurate estimates of neural activity from the observed video data. Further, current activity extraction methods are computationally expensive due to the complex background model and are typically applied to imaging data after the experiment is complete. Moreover, in some scenarios it is necessary to perform experiments in real-time and closed-loop -analyzing data on-the-fly to guide the next experimental steps or to control feedback -, and this calls for new methods for accurate real-time processing. Here we address both issues by adapting a popular extraction method to operate online and extend it to utilize GPU hardware 1/24 that enables real time processing. Our algorithms yield similar high-quality results as the original offline approach, but outperform it with regard to computing time and memory requirements. Our results enable faster and scalable analysis, and open the door to new closed-loop experiments in deep brain areas and on freely-moving preparations.

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Dorsolateral septum somatostatin interneurons gate mobility to calibrate context-specific behavioral fear responses

Cited by 74 publications

References 60 publications

Bed nucleus of the stria terminalis regulates fear to unpredictable threat signals

Bed nucleus of the stria terminalis regulates fear to unpredictable threat signals

Distinct Dorsal and Ventral Hippocampal CA3 Outputs Govern Contextual Fear Discrimination

Online analysis of microendoscopic 1-photon calcium imaging data streams

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