Response Flexibility: The Role of the Lateral Habenula

Hones, Victoria I; Mizumori, Sheri J. Y.

doi:10.3389/fnbeh.2022.852235

Cited by 11 publications

(8 citation statements)

References 273 publications

(346 reference statements)

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“…For C1, earlier work has identified a fear phenotype without comorbid anhedonia (Ritov et al, 2016 ; Ritov and Richter-Levin, 2017 ). Anhedonia, associated with avoidance, as observed in the C2 subgroup, suggests that the habenula may have been targeted, since this brain area is involved in flexible (Hones and Mizumori, 2022 ) and motivated (Hikosaka et al, 2008 ; Hikosaka, 2010 ) behaviors, fear (Durieux et al, 2020 ), context/ emotion interactions during memory tasks (Baker et al, 2022 ), and avoidance behavior (Stamatakis et al, 2013 ). In these cases, it is possible that there is an alteration in the dopaminergic drive involving the habenula (Friedman et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Steady electrocorticogram characteristics predict specific stress-induced behavioral phenotypes

Desnouveaux¹,

Poly²,

Edmond³

et al. 2023

Front. Neurosci.

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IntroductionDepending on the individual, exposure to an intense stressor may, or may not, lead to a stress-induced pathology. Predicting the physiopathological evolution in an individual is therefore an important challenge, at least for prevention. In this context, we developed an ethological model of simulated predator exposure in rats: we call this the multisensorial stress model (MSS). We hypothesized that: (i) MSS exposure can induce stress-induced phenotypes, and (ii) an electrocorticogram (ECoG) recorded before stress exposure can predict phenotypes observed after stress.MethodsForty-five Sprague Dawley rats were equipped with ECoG telemetry and divided into two groups. The Stress group (n = 23) was exposed to an MSS that combined synthetic fox feces odor deposited on filter paper, synthetic blood odor, and 22 kHz rodent distress calls; the Sham group (n = 22) was not exposed to any sensorial stimulus. Fifteen days after initial exposure, the two groups were re-exposed to a context that included a filter paper soaked with water as a traumatic object (TO) reminder. During this re-exposure, freezing behavior and avoidance of the filter paper were measured.ResultsThree behaviors were observed in the Stress group: 39% developed a fear memory phenotype (freezing, avoidance, and hyperreactivity); 26% developed avoidance and anhedonia; and 35% made a full recovery. We also identified pre-stress ECoG biomarkers that accurately predicted cluster membership. Decreased chronic 24 h frontal Low θ relative power was associated with resilience; increased frontal Low θ relative power was associated with fear memory; and decreased parietal β2 frequency was associated with the avoidant-anhedonic phenotype.DiscussionThese predictive biomarkers open the way to preventive medicine for stress-induced diseases.

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Section: Discussionmentioning

confidence: 99%

Steady electrocorticogram characteristics predict specific stress-induced behavioral phenotypes

Desnouveaux¹,

Poly²,

Edmond³

et al. 2023

Front. Neurosci.

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“…In contrast, the LHB is further downstream and receives stress-related signals from the medial prefrontal cortex [73]. Likewise, 57 genes were highly connected in the LHB but lost that connectivity in the medial prefrontal cortex.…”

Section: Plos Computational Biologymentioning

confidence: 99%

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Seal

Basner

et al. 2023

PLoS Comput Biol

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Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, with computational complexity of O(p2K log K), the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a faster alternative of CFGL named rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in both simulation study and real data analysis. We used RCFGL to jointly estimate the gene co-expression networks in different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL.

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“…From PAG, through hypothalamus, to amygdala, neural control is well conserved relative to basal vertebrates. The PAG also receives descending input from the habenula, which is essentially unchanged from the lamprey through vertebrates (Loonen & Ivanova, 2015, despite involvement in many higher order processes (Hones & Mizumori, 2022;Loonen & Ivanova, 2019;Rolls, 2017). Further, "the habenula : : : plays an essential role in regulating the intensity of reward-seeking and adversity-avoiding behavior : : : by regulating the activity of ascending midbrain monoaminergic tracts" (Loonen & Ivanova, 2019, p. 233), which are also highly conserved with their diffuse collateral projections retained as the telencephalon expands.…”

Section: Conservation Of Brain Systemsmentioning

confidence: 99%

Non-human contributions to personality neuroscience – from fish through primates. An introduction to the special issue

Lages

McNaughton²

2022

Personality Neuroscience

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The most fundamental emotional systems that show trait control are evolutionarily old and extensively conserved. Psychology in general has benefited from non-human neuroscience and from the analytical simplicity of behaviour in those with simpler nervous systems. It has been argued that integration between personality, psychopathology, and neuroscience is particularly promising if we are to understand the neurobiology of human experience. Here, we provide some general arguments for a non-human approach being at least as productive in relation to personality, psychopathology, and their interface. Some early personality theories were directly linked to psychopathology (e.g., Eysenck, Panksepp, and Cloninger). They shared a common interest in brain systems that naturally led to the use of non-human data; behavioural, neural, and pharmacological. In Eysenck’s case, this also led to the selective breeding, at the Maudsley Institute, of emotionally reactive and non-reactive strains of rat as models of trait neuroticism or trait emotionality. Dimensional personality research and categorical approaches to clinical disorder then drifted apart from each other, from neuropsychology, and from non-human data. Recently, the conceptualizations of both healthy personality and psychopathology have moved towards a common hierarchical trait perspective. Indeed, the proposed two sets of trait dimensions appear similar and may even be eventually the same. We provide, here, an introduction to this special issue of Personality Neuroscience, where the authors provide overviews of detailed areas where non-human data inform human personality and its psychopathology or provide explicit models for translation to human neuroscience. Once all the papers in the issue have appeared, we will also provide a concluding summary of them.

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Response Flexibility: The Role of the Lateral Habenula

Cited by 11 publications

References 273 publications

Steady electrocorticogram characteristics predict specific stress-induced behavioral phenotypes

Steady electrocorticogram characteristics predict specific stress-induced behavioral phenotypes

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Non-human contributions to personality neuroscience – from fish through primates. An introduction to the special issue

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