Central Executive Network drives delta-9-tetrahydrocannabinol (THC)-induced nonlinear changes in large-scale functional connectivity in adolescent nonhuman primates
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Latent circuit inference from heterogeneous neural responses during cognitive tasks
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.
Simultaneous tACS-fMRI reveals state- and frequency-specific modulation of hippocampal-cortical functional connectivity
Non-invasive indirect hippocampal-targeted stimulation is of broad scientific and clinical interest. Transcranial alternating current stimulation (tACS) is appealing because it allows oscillatory stimulation to study hippocampal theta (3–8 Hz) activity. We found that tACS administered during functional magnetic resonance imaging yielded a frequency-, mental state- and topologically-specific effect of theta stimulation (but not other frequencies) enhancing right (but not left) hippocampal-cortical connectivity during resting blocks but not during task blocks. Control analyses showed that this effect was not due to possible stimulation-induced changes in signal quality or head movement. Our findings are promising for targeted network modulations of deep brain structures for research and clinical intervention.
Universal relations and bounds for fluctuations in quasistatic small heat engines
The efficiency of any heat engine, defined as the ratio of average work output to heat input, is bounded by Carnot’s celebrated result. However, this measure is insufficient to characterize the properties of miniaturized heat engines carrying non-negligible fluctuations, and a study of higher-order statistics of their energy exchanges is required. Here, we generalize Carnot’s result for reversible cycles to arbitrary order moment of the work and heat fluctuations. Our results show that, in the quasistatic limit, higher-order statistics of a small engine’s energetics depend solely on the ratio between the temperatures of the thermal baths. We further prove that our result for the second moment gives universal bounds for the ratio between the variances of work and heat for quasistatic cycles. We test this theory with our previous experimental results of a Brownian Carnot engine and observe the consistency between them, even beyond the quasistatic regime. Our results can be exploited in the design of thermal nanomachines to reduce their fluctuations of work output without marginalizing its average value and efficiency.
Frontostriatal regulation of brain circuits contributes to flexible decision making
Deficits in behavioral or cognitive flexibility that are linked to altered activity in both cortical and subcortical brain regions, are often observed across multiple neuropsychiatric disorders. The medial prefrontal cortex (mPFC)-nucleus accumbens (NAc) pathway in rats plays a critical role in flexible control of behavior. However, the modulation of this pathway on activity and functional connectivity with the rest of the brain remains unclear. In this study, we first confirmed the role of the mPFC-NAc pathway in behavioral flexibility using a set-shifting task in rats and then evaluated the causal effects of mPFC-NAc activation induced by chemogenetic stimulation of the terminal axons of the NAc with DREADD expression on whole-brain activity and functional connectivity measured by functional MRI. mPFC-NAc activation improved performance on the set-shifting task by reducing perseverative errors. Additionally, stimulation of this pathway increased activity in a set of brain regions within the basal ganglia-thalamus-cortical loop network including NAc, thalamus, hypothalamus and various connected cortical regions, while also decreased functional connectivity strength of NAc-mPFC, NAc-secondary motor cortex (M2), and various cortical circuits. Moreover, performance on the set-shifting task was related to the functional connectivity strength of the above frontostriatal and cortical circuits. These findings provide insights into the link between specific frontostriatal circuits on decision making flexibility, which may inform potential future interventions for behavioral flexibility deficits.
Estimation of Jacquard’s genetic identity coefficients with bi-allelic variants by constrained least-squares
The Jacquard genetic identity coefficients are of fundamental importance in relatedness research. We address the estimation of these coefficients as well as other relationship parameters that derive from them such as kinship and inbreeding coefficients using a concise matrix framework. Estimation of the Jacquard coefficients via likelihood methods and the expectation–maximization algorithm is computationally very demanding for large numbers of polymorphisms. We propose a constrained least squares approach to estimate the Jacquard coefficients. A simulation study shows constrained least squares achieves root-mean-squared errors that are comparable with those of the maximum likelihood approach, in particular when founder allele frequencies are unknown, while obtaining enormous computational savings.
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