bioRxiv Subject Collection: Neuroscience's Journal
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Wednesday, July 10th, 2024
| Time |
Event |
| 9:17a |
A dendritic substrate for temporal diversity of cortical inhibition
In the mammalian neocortex, GABAergic interneurons (INs) inhibit cortical networks in profoundly different ways. The extent to which this depends on how different INs process excitatory signals along their dendrites is poorly understood. Here, we reveal that the functional specialization of two major populations of cortical INs is determined by the unique association of different dendritic integration modes with distinct synaptic organization motifs. We found that somatostatin (SST)-INs exhibit NMDAR-dependent dendritic integration and uniform synapse density along the dendritic tree. In contrast, dendrites of parvalbumin (PV)-INs exhibit passive synaptic integration coupled with proximally enriched synaptic distributions. Theoretical analysis shows that these two dendritic configurations result in different strategies to optimize synaptic efficacy in thin dendritic structures. Yet, the two configurations lead to distinct temporal engagement of each IN during network activity. We confirmed these predictions with in vivo recordings of IN activity in the visual cortex of awake mice, revealing a rapid and linear recruitment of PV-INs as opposed to a long-lasting integrative activation of SST-INs. Our work reveals the existence of distinct dendritic strategies that confer distinct temporal representations for the two major classes of neocortical INs and thus dynamics of inhibition. | | 9:17a |
Sex-specific GABAergic microcircuits that switch vulnerability into resilience to stress and reverse the effects of chronic stress exposure
Clinical and preclinical studies have identified somatostatin (SST)-positive interneurons as key elements that regulate the vulnerability to stress-related psychiatric disorders. Conversely, disinhibition of SST neurons in mice results in resilience to the behavioral effects of chronic stress. Here we established a low-dose chronic chemogenetic protocol to map these changes in positively and negatively motivated behaviors to specific brain regions. AAV-hM3Dq mediated chronic activation of SST neurons in the prelimbic cortex (PLC) had antidepressant drug-like effects on anxiety- and anhedonia-like motivated behaviors in male but not female mice. Analogous manipulation of the ventral hippocampus (vHPC) had such effects in female but not male mice. Moreover, activation of SST neurons in the PLC of male and the vHPC of female mice resulted in stress resilience. Activation of SST neurons in the PLC reversed prior chronic stress-induced defects in motivated behavior in males but was ineffective in females. Conversely, activation of SST neurons in the vHPC reversed chronic stress-induced behavioral alterations in females but not males. Quantitation of c-Fos+ and FosB+ neurons in chronic stress-exposed mice revealed that chronic activation of SST neurons leads to a paradoxical increase in pyramidal cell activity. Collectively, these data demonstrate that GABAergic microcircuits driven by dendrite targeting interneurons enable sex- and brain-region-specific neural plasticity that promotes stress resilience and reverses stress-induced anxiety- and anhedonia-like motivated behavior. Our studies provide a mechanistic rationale for antidepressant efficacy of dendrite-targeting, low-potency GABAA receptor agonists, independent of sex and despite striking sex differences in the relevant brain substrates. | | 9:17a |
Age-dependent predictors of effective reinforcement motor learning across childhood
Across development, children must learn motor skills such as eating with a spoon and drawing with a crayon. Reinforcement learning, driven by success and failure, is fundamental to such sensorimotor learning. It typically requires a child to explore movement options along a continuum (grip location on a crayon) and learn from probabilistic rewards (whether the crayon draws or breaks). Here, we studied the development of reinforcement motor learning using online motor tasks to engage children aged 3 to 17 and adults (cross-sectional sample, N=385). Participants moved a cartoon penguin across a scene and were rewarded (animated cartoon clip) based on their final movement position. Learning followed a clear developmental trajectory when participants could choose to move anywhere along a continuum and the reward probability depended on final movement position. Learning was incomplete or absent in 3 to 8-year-olds and gradually improved to adult-like levels by adolescence. A reinforcement learning model fit to each participant identified three age-dependent factors underlying improvement: amount of exploration after a failed movement, learning rate, and level of motor noise. We predicted, and confirmed, that switching to discrete targets and deterministic reward would improve 3 to 8-year-olds' learning to adult-like levels by increasing exploration after failed movements. Overall, we show a robust developmental trajectory of reinforcement motor learning abilities under ecologically relevant conditions i.e., continuous movement options mapped to probabilistic reward. This learning appears to be limited by immature spatial processing and probabilistic reasoning abilities in young children and can be rescued by reducing the demands in these domains. | | 5:23p |
Characterization of Dnajc12 knockout mice, a model of hypodopaminergia
Homozygous DNAJC12 c.79-2A>G (p. V27Wfs*14) loss-of-function mutations were first reported as a cause of young-onset Parkinson's disease. However, bi-allelic autosomal recessive pathogenic variants in DNAJC12 may lead to an alternative constellation of neurological features including infantile dystonia, developmental delay, intellectual disability and neuropsychiatric disorders. DNAJC12 is understood to co-chaperone aromatic amino acid hydroxylases to enhance the synthesis of biogenic amines. In vitro, we confirm overexpressed DNAJC12 forms a complex with tyrosine hydroxylase, the rate-limiting enzyme in dopamine (DA) synthesis. Now we describe a conditional knockout mouse (cDKO) in which loxP sites flanking Dnajc12 exon 2 enable its excision by cre-recombinase to create a constitutive Dnajc12 knock out (DKO). At three months of age, DKO animals exhibit reduced locomotion and exploratory behavior in automated open-field testing. DKO mice also manifest increased plasma phenylalanine levels, a cardinal feature of patients with DNAJC12 pathogenic variants. In striatal tissue, total DA and serotonin, and their metabolites, are reduced. Biochemical alterations in synaptic proteins and tyrosine hydroxylase are also apparent, with enhanced phosphorylation of pSer31 and pSer40 sites that may reflect biological compensation. Electrically-evoked striatal DA is reduced. Most immediately, cDKO and DKO mice present models to develop and refined therapeutic approaches for the treatment of DNAJC12 dystonia and parkinsonism. These models may also enable the pleiotropic functions of biogenic amines (including DA) to be individually investigated in the brain and periphery. | | 6:32p |
Deep-diffeomorphic networks for conditional brain templates
Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates have advantages over single population templates by enabling improved registration accuracy and capturing common processes in brain development and degeneration. Conventional methods require large, evenly-spread cohorts to develop conditional templates, limiting their ability to create templates that could reflect richer combinations of clinical and demographic variables. More recent deep-learning methods, which can infer relationships in very high dimensional spaces, open up the possibility of producing conditional templates that are jointly optimised for these richer sets of conditioning parameters. We have built on recent deep-learning template generation approaches using a diffeomorphic framework to create a purely geometric method of conditional template construction that learns diffeomorphisms between: (i) a global or group template and conditional templates, and (ii) conditional templates and individual brain scans. We evaluated our method, as well as other recent deep-learning approaches, on a dataset of cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), using age as the conditioning parameter of interest. We assessed the effectiveness of these networks at capturing age-dependent anatomical differences. Our results demonstrate that while the assessed deep-learning methods have a number of strengths, they require further refinement to capture morphological changes in ageing brains with an acceptable degree of accuracy. The volumetric output of our method, and other recent deep-learning approaches, across four brain structures (grey matter, white matter, the lateral ventricles and the hippocampus), was measured and showed that although each of the methods captured some changes well, each method was unable to accurately track changes in all of the volumes. However, our method was able to produce T1-weighted conditional templates with high spatial fidelity and with consistent topology as age varies, making these conditional templates advantageous for spatial registrations. The use of diffeomorphisms in these deep-learning methods represents an important strength of these approaches, as it produces conditional templates that can be explicitly linked across age as well as to fixed, unconditional templates or brain atlases. The use of deep-learning in conditional template generation provides a framework for creating templates for more complex conditioning parameters, such as pathologies and demographic variables, in order to facilitate a broader application of conditional brain templates in neuroimaging studies. | | 6:32p |
Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control
Nonergodicity and Simpson's paradox pose significant and underappreciated challenges for neuroscience. Using stop signal task data from over 4,000 children and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying inhibitory control at both between-subjects and within-subjects levels. Strikingly, between-subjects associations of inhibitory control activations with stop signal reaction times, probabilities of proactivity, and proactive delays were reversed within subjects, revealing the nonergodic nature of these processes. Nonergodicity was observed throughout the brain but was most pronounced in the salience network. Furthermore, within-subjects analysis revealed dissociated brain representations of reactive and proactive processing, and distinct brain-behavior associations for subjects who adaptively and who maladaptively regulated inhibitory control. This work advances our knowledge of the dynamic neural mechanisms of inhibitory control during a critical developmental period and has implications for personalized interventions in cognitive disorders. Embracing nonergodicity is crucial for understanding brain-behavior relationships and developing effective interventions. | | 7:47p |
Optimization and model averaging of histogram-based place cell firing rate maps using the point process framework
Background: The firing rate of hippocampal place cells depends on the spatial position of the organism in an environment. This position dependence is often quantified by constructing spike-in-location and time-in-location histograms, the ratio of which yields a firing rate map. New Method: The purpose of this study is to present a new method for optimizing the spatial resolution of histogram-based firing rate maps. Results: It is pointed out that histogram-based firing rate maps are conditional intensity functions of inhomogeneous Poisson process models of neural spike trains, and, as such, they can be optimized through model selection within the point process framework. Results: The point process framework is used here for optimizing the size and the aspect ratio of the histogram bins using the Akaike Information Criterion (AIC). It is also used for model averaging using Akaike weights, when maps of various bin sizes provide comparable fits. Application of the method is illustrated on data from real rat hippocampal place cells. Comparison with existing methods: Existing methods do not optimize the number of bins used in each dimension of the firing rate map. Conclusion: The proposed approach allows for the construction of the AIC-best histogram-based firing rate map for each individual place cell. | | 7:47p |
Chemogenetic control of GABAergic activity within the interpeduncular nucleus reveals dissociable behavioral components of the nicotine withdrawal phenotype
Chronic exposure to nicotine results in the development of a dependent state such that a withdrawal syndrome is elicited upon cessation of nicotine. The habenulo-interpeduncular (Hb-IPN) circuit contains a high concentration of nAChRs and has been identified as a main circuit involved in nicotine withdrawal. Here we investigated the contribution of two distinct subpopulations of IPN GABAergic neurons to nicotine withdrawal behaviors. Using a chenogenetic approach to specifically target Amigo1-expressing or Epyc-expressing neurons within the IPN, we found that activity of the Amigo1 and not the Epyc subpopulation of GABAergic neuron is critical for anxiety-like behaviors both in naive mice and in those undergoing nicotine withdrawal. Moreover, data revealed that stimulation of Amigo1 neurons in nicotine-naive mice elicits opposite effects on affective and somatic signs of withdrawal. Taken together, these results suggest that somatic and affective behaviors constitute dissociable components of the nicotine withdrawal phenotype and are likely supported by distinct subpopulations of neurons within the IPN. | | 7:47p |
Main Sequence of Human Luminance-Evoked Pupil Dynamics
Pupil responses are commonly used to provide insight into visual perception, autonomic control, cognition, and various brain disorders. However, making inferences from pupil data can be complicated by nonlinearities in pupil dynamics and variability within and across individuals, which challenge the assumptions of linearity or group level homogeneity required for common analysis methods. In this study, we evaluated luminance evoked pupil dynamics in young healthy adults (N=10, 5:5 M:F, ages 19-25) by identifying nonlinearities, variability, and conserved relationships across individuals to improve the ability to make inferences from pupil data. We found a nonlinear relationship between final pupil diameter and luminance, linearized by considering the logarithm of luminance. Peak diameter change and peak velocity were nonlinear functions of log-luminance for constriction but not dilation responses. Across participants, curve fit parameters characterizing pupil responses as a function of luminance were highly variable, yet there was an across-participant linear correlation between overall pupil size and pupil gain (i.e. diameter change per unit log-luminance change). In terms of within-participant trial-by-trial variability, participants showed greater variability in final pupil size compared to constriction peak diameter change as a function of log-luminance. Despite the variability in stimulus-response metrics within and across participants, we found that all participants showed a highly stereotyped main sequence relationship between peak diameter change and peak velocity (independent of luminance). The main sequence relationship can be used to inform models of the neural control of pupil dynamics and as an empirical analysis tool to evaluate variability and abnormalities in pupil behaviour. |
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