Quantifying the distribution of feature values over data represented in arbitrary dimensional spaces.

Identifying the structured distribution (or lack thereof) of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., sensory coding) or in anatomical image segmentation. We introduce the Structure Index (SI) as a directed graph-based metric to quantify the distribution of feature values projected over data in arbitrary D-dimensional spaces (defined from neurons, time stamps, pixels, genes, etc). The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood of the cloud. Using arbitrary data clouds, we show how the SI provides quantification of the degree and directionality of the local versus global organization of feature distribution. SI can be applied to both scalar and vectorial features permitting quantification of the relative contribution of related variables. When applied to experimental studies of head-direction cells, it is able to retrieve consistent feature structure from both the high- and low-dimensional representations, and to disclose the local and global structure of the angle and speed represented in different brain regions. Finally, we provide two general-purpose examples (sound and image categorization), to illustrate the potential application to arbitrary dimensional spaces. Our method provides versatile applications in the neuroscience and data science fields.

Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations.

The reactivation of experience-based neural activity patterns in the hippocampus is crucial for learning and memory. These reactivation patterns and their associated sharp-wave ripples (SWRs) are highly variable. However, this variability is missed by commonly used spectral methods. Here, we use topological and dimensionality reduction techniques to analyze the waveform of ripples recorded at the pyramidal layer of CA1. We show that SWR waveforms distribute along a continuum in a low-dimensional space, which conveys information about the underlying layer-specific synaptic inputs. A decoder trained in this space successfully links individual ripples with their expected sinks and sources, demonstrating how physiological mechanisms shape SWR variability. Furthermore, we found that SWR waveforms segregated differently during wakefulness and sleep before and after a series of cognitive tasks, with striking effects of novelty and learning. Our results thus highlight how the topological analysis of ripple waveforms enables a deeper physiological understanding of SWRs.

From cell types to population dynamics: Making hippocampal manifolds physiologically interpretable.

The study of the hippocampal code is gaining momentum. While the physiological approach targets the contribution of individual cells as determined by genetic, biophysical and circuit factors, the field pushes for a population dynamic approach that considers the representation of behavioural variables by a large number of neurons. In this alternative framework, neuronal activity is projected into low-dimensional manifolds. These manifolds can reveal the structure of population representations, but their physiological interpretation is challenging. Here, we review the recent literature and propose that integrating information regarding behavioral traits, local field potential oscillations and cell-type-specificity into neural manifolds offers strategies to make them interpretable at the physiological level.

Dysfunctional serotonergic neuron-astrocyte signaling in depressive-like states.

Astrocytes play crucial roles in brain homeostasis and are regulatory elements of neuronal and synaptic physiology. Astrocytic alterations have been found in Major Depressive Disorder (MDD) patients; however, the consequences of astrocyte Ca2+ signaling in MDD are poorly understood. Here, we found that corticosterone-treated juvenile mice (Cort-mice) showed altered astrocytic Ca2+ dynamics in mPFC both in resting conditions and during social interactions, in line with altered mice behavior. Additionally, Cort-mice displayed reduced serotonin (5-HT)-mediated Ca2+ signaling in mPFC astrocytes, and aberrant 5-HT-driven synaptic plasticity in layer 2/3 mPFC neurons. Downregulation of astrocyte Ca2+ signaling in naïve animals mimicked the synaptic deficits found in Cort-mice. Remarkably, boosting astrocyte Ca2+ signaling with Gq-DREADDS restored to the control levels mood and cognitive abilities in Cort-mice. This study highlights the important role of astrocyte Ca2+ signaling for homeostatic control of brain circuits and behavior, but also reveals its potential therapeutic value for depressive-like states.

Ca2+-modulated photoactivatable imaging reveals neuron-astrocyte glutamatergic circuitries within the nucleus accumbens.

Astrocytes are key elements of brain circuits that are involved in different aspects of the neuronal physiology relevant to brain functions. Although much effort is being made to understand how the biology of astrocytes affects brain circuits, astrocytic network heterogeneity and plasticity is still poorly defined. Here, we have combined structural and functional imaging of astrocyte activity recorded in mice using the Ca2+-modulated photoactivatable ratiometric integrator and specific optostimulation of glutamatergic pathways to map the functional neuron-astrocyte circuitries in the nucleus accumbens (NAc). We showed pathway-specific astrocytic responses induced by selective optostimulation of main inputs from the prefrontal cortex, basolateral amygdala, and ventral hippocampus. Furthermore, co-stimulation of glutamatergic pathways induced non-linear Ca2+-signaling integration, revealing integrative properties of NAc astrocytes. All these results demonstrate the existence of specific neuron-astrocyte circuits in the NAc, providing an insight to the understanding of how the NAc integrates information.

Insulin-like growth factor I sensitization rejuvenates sleep patterns in old mice.

Sleep disturbances are common during aging. Compared to young animals, old mice show altered sleep structure, with changes in both slow and fast electrocorticographic (ECoG) activity and fewer transitions between sleep and wake stages. Insulin-like growth factor I (IGF-I), which is involved in adaptive changes during aging, was previously shown to increase ECoG activity in young mice and monkeys. Furthermore, IGF-I shapes sleep architecture by modulating the activity of mouse orexin neurons in the lateral hypothalamus (LH). We now report that both ECoG activation and excitation of orexin neurons by systemic IGF-I are abrogated in old mice. Moreover, orthodromical responses of LH neurons are facilitated by either systemic or local IGF-I in young mice, but not in old ones. As orexin neurons of old mice show dysregulated IGF-I receptor (IGF-IR) expression, suggesting disturbed IGF-I sensitivity, we treated old mice with AIK3a305, a novel IGF-IR sensitizer, and observed restored responses to IGF-I and rejuvenation of sleep patterns. Thus, disturbed sleep structure in aging mice may be related to impaired IGF-I signaling onto orexin neurons, reflecting a broader loss of IGF-I activity in the aged mouse brain.

Reduced Insulin-Like Growth Factor-I Effects in the Basal Forebrain of Aging Mouse.

It is known that aging is frequently accompanied by a decline in cognition. Furthermore, aging is associated with lower serum IGF-I levels that may contribute to this deterioration. We studied the effect of IGF-I in neurons of the horizontal diagonal band of Broca (HDB) of young (≤6 months old) and old (≥20-month-old) mice to determine if changes in the response of these neurons to IGF-I occur along with aging. Local injection of IGF-I in the HDB nucleus increased their neuronal activity and induced fast oscillatory activity in the electrocorticogram (ECoG). Furthermore, IGF-I facilitated tactile responses in the primary somatosensory cortex elicited by air-puffs delivered in the whiskers. These excitatory effects decreased in old mice. Immunohistochemistry showed that cholinergic HDB neurons express IGF-I receptors and that IGF-I injection increased the expression of c-fos in young, but not in old animals. IGF-I increased the activity of optogenetically-identified cholinergic neurons in young animals, suggesting that most of the IGF-I-induced excitatory effects were mediated by activation of these neurons. Effects of aging were partially ameliorated by chronic IGF-I treatment in old mice. The present findings suggest that reduced IGF-I activity in old animals participates in age-associated changes in cortical activity.

GABAergic signaling to astrocytes in the prefrontal cortex sustains goal-directed behaviors.

GABA interneurons play a critical role in higher brain functions. Astrocytic glial cells interact with synapses throughout the whole brain and are recognized as regulatory elements of excitatory synaptic transmission. However, it is largely unknown how GABAergic interneurons and astrocytes interact and contribute to stable performance of complex behaviors. Here, we found that genetic ablation of GABAB receptors in medial prefrontal cortex astrocytes altered low-gamma oscillations and firing properties of cortical neurons, which affected goal-directed behaviors. Remarkably, working memory deficits were restored by optogenetic stimulation of astrocytes with melanopsin. Furthermore, melanopsin-activated astrocytes in wild-type mice enhanced the firing rate of cortical neurons and gamma oscillations, as well as improved cognition. Therefore, our work identifies astrocytes as a hub for controlling inhibition in cortical circuits, providing a novel pathway for the behaviorally relevant midrange time-scale regulation of cortical information processing and consistent goal-directed behaviors.