The main neurotransmitter in the brain responsible for the inhibition of neuronal activity is γ-aminobutyric acid (GABA). It plays a crucial role in circuit formation during development, both via its primary effects as a neurotransmitter and also as a trophic factor. The GABA_B receptors (GABA_BRs) are G protein-coupled metabotropic receptors; on one hand, they can influence proliferation and migration; and, on the other, they can inhibit cells by modulating the function of K⁺ and Ca²⁺ channels, doing so on a slower time scale and with a longer-lasting effect compared to ionotropic GABA_A receptors. GABA_BRs are expressed pre- and post-synaptically, at both glutamatergic and GABAergic terminals, thus being able to shape neuronal activity, plasticity, and the balance between excitatory and inhibitory synaptic transmission in response to varying levels of extracellular GABA concentration. Furthermore, given their subunit composition and their ability to form complexes with several associated proteins, GABA_BRs display heterogeneity with regard to their function, which makes them a promising target for pharmacological interventions. This review will describe (i) the latest results concerning GABA_BRs/GABA_BR-complex structures, their function, and the developmental time course of their appearance and functional integration in the brain, (ii) their involvement in manifestation of various pathophysiological conditions, and (iii) the current status of preclinical and clinical studies involving GABA_BR-targeting drugs.

Mutations in TSC1 or TSC2 genes are linked to alterations in neuronal function which ultimately lead to the development of a complex neurological phenotype. Here we review current research on the effects that reduction in TSC1 or TSC2 can produce on the developing neural network. A crucial feature of the disease pathophysiology appears to be an early deviation from typical neurodevelopment, in the form of structural abnormalities. Epileptic seizures are one of the primary early manifestation of the disease in the CNS, followed by intellectual deficits and autism spectrum disorders (ASD). Research using mouse models suggests that morphological brain alterations might arise from the interaction of different cellular types, and hyperexcitability in the early postnatal period might be transient. Moreover, the increased excitation-to-inhibition ratio might represent a transient compensatory adjustment to stabilize the developing network rather than a primary factor for the development of ASD symptoms. The inhomogeneous results suggest region-specificity as well as an evolving picture of functional alterations along development. Furthermore, ASD symptoms and epilepsy might originate from different but potentially overlapping mechanisms, which can explain recent observations obtained in patients. Potential treatment is determined not only by the type of medicament, but also by the time point of treatment.

The TSC1 and TSC2 tumor suppressor genes control the activity of mechanistic target of rapamycin (mTOR) pathway. Elevated activity of this pathway in Tsc2^+/- mouse model leads to reduction of postsynaptic GABA_B receptor–mediated inhibition and hyperexcitability in the medial prefrontal cortex (mPFC). In this study, we asked whether presynaptic GABA_B receptors (GABA_BRs) can compensate this shift of hyperexcitability. Experiments were performed in brain slices from adolescent wild-type (WT) and Tsc2^+/- mice. Miniature and spontaneous postsynaptic currents (m/sPSCs) were recorded from layer 2/3 pyramidal neurons in mPFC using patch-clamp technique using a Cs⁺-based intrapipette solution. Presynaptic GABA_BRs were activated by baclofen (10 µM) or blocked by CGP55845 (1 µM). Independent on genotype, GABA_BR modulators bidirectionally change miniature excitatory postsynaptic current (mEPSC) frequency by about 10%, indicating presynaptic GABA_BR-mediated effects on glutamatergic transmission are comparable in both genotypes. In contrast, frequencies of both mIPSCs and sIPCSs were suppressed by baclofen stronger in Tsc2^+/- neurons than in WT ones, whereas CGP55845 significantly increased (m/s)IPSC frequencies only in WT cells. Effects of baclofen and CGP55845 on the amplitudes of evoked (e)IPSCs confirmed these observations. These data indicate (1) that GABAergic synapses are inhibited by ambient GABA in WT but not in Tsc2^+/- slices, and (2) that baclofen shifts the E/I ratio, determined as the ratio of (m/s)EPSC frequency to (m/s)IPSC frequency, towards excitation only in Tsc2^+/- cells. This excitatory presynaptic GABA_BR-mediated action has to be taken into account for a possible medication of mental disorders using baclofen. Graphical abstract

The mature cerebral cortex operates through the segregation and integration of specialized functions to generate complex cognitive states. In the mouse, the anatomical and functional correlates of this organization arise during the perinatal period and are critically shaped by neural activity. Understanding how early activity patterns distribute, interact, and generate large-scale cortical dynamics is essential to elucidate the proper development of the cortex. Here, we investigate spontaneous mesoscale cortical dynamics during the first two postnatal weeks by performing wide-field calcium imaging in GCaMP6s transgenic mice. Our results demonstrate a marked change in the spatiotemporal features of spontaneous cortical activity across fine stages of postnatal development. Already after birth, the cortical hemisphere presents a primordial macroscopic organization, which undergoes a steady refinement based on the parcellation of the cortex. As calcium activity transitions from large, widespread events to swift waves between the first and second postnatal week, significant topographic differences emerge across different cortical regions. Functional connectivity profiles of the cortex gradually segregate into main subnetworks and give rise to a highly modular network topology at the end of the second postnatal week. Overall, spontaneous mesoscale activity reflects the maturation of cortical networks, and reveals critical breakpoints in the development of the functional architecture of the cortex.

In this work, we present a novel methodology for performing the supervised classification of time-ordered noisy data; we call this methodology Entropic Sparse Probabilistic Approximation with Markov regularization (eSPA-Markov). It is an extension of entropic learning methodologies, allowing the simultaneous learning of segmentation patterns, entropy-optimal feature space discretizations, and Bayesian classification rules. We prove the conditions for the existence and uniqueness of the learning problem solution and propose a one-shot numerical learning algorithm that—in the leading order—scales linearly in dimension. We show how this technique can be used for the computationally scalable identification of persistent (metastable) regime affiliations and regime switches from high-dimensional non-stationary and noisy time series, i.e., when the size of the data statistics is small compared to their dimensionality and when the noise variance is larger than the variance in the signal. We demonstrate its performance on a set of toy learning problems, comparing eSPA-Markov to state-of-the-art techniques, including deep learning and random forests. We show how this technique can be used for the analysis of noisy time series from DNA and RNA Nanopore sequencing.

This paper extends previous work (Groom et al., Artif . Intell . Earth Syst ., 2024) in applying the entropy‐optimal Sparse Probabilistic Approximation (eSPA) algorithm to predict ENSO phase, defined by thresholding the Niño3.4 index. Only satellite‐era observational data sets are used for training and validation, while retrospective forecasts from 2012 to 2022 are used to assess out‐of‐sample skill at lead times up to 24 months. Rather than train a single eSPA model per lead, we introduce an ensemble approach in which multiple eSPA models are aggregated via a novel meta‐learning strategy. The features used include the leading principal components from a delay‐embedded EOF analysis of global sea surface temperature, vertical temperature gradient (a thermocline proxy), and tropical Pacific wind stresses. Crucially, the data is processed to prevent any form of information leakage from the future, ensuring realistic real‐time forecasting conditions. Despite the limited number of training instances, eSPA avoids overfitting and produces probabilistic forecasts with skill comparable to the International Research Institute for Climate and Society (IRI) ENSO prediction plume. Beyond the IRI's lead times, eSPA maintains skill out to 20 months for the ranked probability skill score and 24 months for accuracy and area under the ROC curve, all at a fraction of the computational cost of a fully coupled dynamical model. Furthermore, eSPA successfully forecasts the 2015/2016 and 2018/2019 El Niño events at 24 months lead, the 2016/2017, 2017/2018, and 2020/2021 La Niña events at 24 months lead and the 2021/2022 and 2022/2023 La Niña events at 12 and 8 months lead.