An official website of the United States government
Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.
more »
« less
Computational Insights into Schizophrenia: Linking Hyperactive D2 Receptors with Belief Updating Impairments
Genetics are recognized as a significant risk factor in schizophrenia [1], and computational modeling studies have highlighted deficits in belief updating as a key aspect of the disorder and an underlying cause of delusion [2]. In particular, the patients often show strong priors on envi- ronmental volatility. However, the intricate mechanisms bridging these genetic risk factors and belief updating deficits remain poorly understood. Our challenge here was to build a biologically plausible neural network that provides a link between genetic risk factors and impaired belief updating. In constructing our schizophrenia model, we first focused on the prefrontal cortex (PFC)- mediodorsal thalamus (MD) circuit, given mounting evidence implicating alterations in these regions in schizophrenia pathology [3]. Drawing from experimental findings demonstrating the involvement of MD neurons expressing D2 receptors in cognitive flexibility [4], the known asso- ciation of D2 receptor genes with heightened schizophrenia risk [5], and the predominant mode of action of antipsychotic treatments as dopamine antagonists at D2 receptors [6], we simulated schizophrenia by reducing the excitability of MD neurons to mimic the hyperactive D2 receptors in Schizophrenia. To investigate the belief updating process, we consider a probability reversal task, in which the reward structure switches in blocks for every 200 trials. Our normal thalamocortical model is capable of flexibly switching across blocks and its PFC-MD connections learn the contextual model of the world, a neural signature for continual learning. We further mathematically analyze the model and deduce that under mild assumptions, the model approximates CUSUM algorithm, an algorithm known for its optimality in detecting environmental changes [7]. On the other hand, our schizophrenia model exhibited a stronger bias towards environmental volatility, prompting exploratory behaviors following contextual switches. By mathematical analysis, we deduce that the decreased excitability makes the evidence accumulation dynamics leaky and therefore the model can sporadically switch, consistent with the qualitative results in Schizophrenia patients [2]. Additionally, decreased excitability in MD compromised the ability of PFC-MD connections to accurately learn the environmental model. To address this impairment, we applied current injections to MD to restore activity levels to a range conducive to Hebbian plasticity. Remarkably, the rescue model demonstrated reduced exploratory behavior following switches and exhibited a higher threshold for MD activity switching, indicative of a diminished bias towards environmental volatility. Moreover, the rescue model exhibited improved learning of the environmental model within its PFC-MD connections. These findings suggest a potential mechanism for utilizing deep brain stimulation at a novel site to mitigate schizophrenia symptoms.
more »
« less
- Award ID(s):
- 2139936
- PAR ID:
- 10585733
- Publisher / Repository:
- 10th Workshop on Biological Distributed Algorithms (BDA)
- Date Published:
- Format(s):
- Medium: X
- Location:
- Nantes, France
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Fast-spiking parvalbumin (PV)-positive cells are key players in orchestrating pyramidal neuron activity and thus serve an indispensable role in cognitive function and emotional regulation. Feed-forward excitatory inputs, essential for the function of PV cells, are disrupted in various neurological conditions, including schizophrenia (SCZ). However, it is not clear how diseaseassociated stressors such as immune dysregulation contribute to defects in particular cell types or neuronal circuits. We have developed a novel transgenic mouse line that permits conditional, cell-type specific overexpression (OE) of the immune complement component 4 (C4) gene, which is highly associated with SCZ. Using this genetic approach, we demonstrate that specific global OE of mouse C4 (mC4) in PV cells causes pathological anxiety-like behavior in male, but not female mice. In the male medial prefrontal cortex (mPFC), this sexually dimorphic behavioral alteration was accompanied by a reduction in excitatory inputs to fast-spiking cells and an enhancement of their inhibitory connections. Additionally, in PV cells, elevated levels of mC4 led to contrasting effects on the excitability of cortical cells. In males, PV cells and pyramidal neurons exhibited reduced excitability, whereas in females, PV cells displayed heightened excitability. Contrary to the behavioral changes seen with elevated mC4 levels in PV cells, pan-neuronal overexpression did not increase anxiety-like behaviors. This indicates that mC4 dysfunction, particularly in fast-spiking cells, has a more significant negative impact on anxiety-like behavior than widespread alterations in the neuronal complement. Consequently, by employing a novel mouse model, we have demonstrated a causal relationship between the conditional overexpression of the schizophrenia risk gene C4 in fast-spiking neurons and the susceptibility of cortical circuits in male mice, resulting in changes in behaviors associated with prefrontal cortex function.more » « less
-
Mutations in autism spectrum disorder (ASD) risk genes disrupt neural network dynamics that ultimately lead to abnormal behavior. To understand how ASD-risk genes influence neural circuit computation during behavior, we analyzed the hippocampal network by performing large-scale cellular calcium imaging from hundreds of individual CA1 neurons simultaneously in transgenic mice with total knockout of the X-linked ASD-risk geneNEXMIF(neurite extension and migration factor). AsNEXMIFknockout in mice led to profound learning and memory deficits, we examined the CA1 network during voluntary locomotion, a fundamental component of spatial memory. We found thatNEXMIFknockout does not alter the overall excitability of individual neurons but exaggerates movement-related neuronal responses. To quantify network functional connectivity changes, we applied closeness centrality analysis from graph theory to our large-scale calcium imaging datasets, in addition to using the conventional pairwise correlation analysis. Closeness centrality analysis considers both the number of connections and the connection strength between neurons within a network. We found that in wild-type mice the CA1 network desynchronizes during locomotion, consistent with increased network information coding during active behavior. UponNEXMIFknockout, CA1 network is over-synchronized regardless of behavioral state and fails to desynchronize during locomotion, highlighting how perturbations in ASD-implicated genes create abnormal network synchronization that could contribute to ASD-related behaviors.more » « less
-
We propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.more » « less
-
null (Ed.)Prefrontal cortex (PFC) are broadly linked to various aspects of behavior. During sensory discrimination, PFC neurons can encode a range of task related information, including the identity of sensory stimuli and related behavioral outcome. However, it remains largely unclear how different neuron subtypes and local field potential (LFP) oscillation features in the mouse PFC are modulated during sensory discrimination. To understand how excitatory and inhibitory PFC neurons are selectively engaged during sensory discrimination and how their activity relates to LFP oscillations, we used tetrode recordings to probe well-isolated individual neurons, and LFP oscillations, in mice performing a three-choice auditory discrimination task. We found that a majority of PFC neurons, 78% of the 711 recorded individual neurons, exhibited sensory discrimination related responses that are context and task dependent. Using spike waveforms, we classified these responsive neurons into putative excitatory neurons with broad waveforms or putative inhibitory neurons with narrow waveforms, and found that both neuron subtypes were transiently modulated, with individual neurons’ responses peaking throughout the entire duration of the trial. While the number of responsive excitatory neurons remain largely constant throughout the trial, an increasing fraction of inhibitory neurons were gradually recruited as the trial progressed. Further examination of the coherence between individual neurons and LFPs revealed that inhibitory neurons exhibit higher spike-field coherence with LFP oscillations than excitatory neurons during all aspects of the trial and across multiple frequency bands. Together, our results demonstrate that PFC excitatory neurons are continuously engaged during sensory discrimination, whereas PFC inhibitory neurons are increasingly recruited as the trial progresses and preferentially coordinated with LFP oscillations. These results demonstrate increasing involvement of inhibitory neurons in shaping the overall PFC dynamics toward the completion of the sensory discrimination task.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
