Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.09.26.559505

Nanoconnectivity: a quantitative approach to examine the structural complexity of the brain

Turegano-Lopez, M.·de las Pozas, F.·Santuy, A.·Rodriguez, J.-R.·Defelipe, J.·Merchan-Perez, A.
View original source ↗Export metadata ↓

Abstract

Unraveling the highly complex connectivity at the synaptic level is critical for the understanding of brain function. However, investigating the structure of the brain is time consuming and technically demanding. We have developed and applied a tracing method that allows us to obtain a simplified or skeletonized version of all nerve fibers present within a given brain sample, without the need for full 3D reconstructions. Every tracing or "skeleton" is linked to its corresponding synaptic contacts, so the result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. The tissue sample is obtained by volume electron microscopy (FIB-SEM) and tracing is performed by dedicated software. In addition, the software provides the length of each skeleton as well as the number and positions of its corresponding synapses. With this approach, we have identified clear as well as subtle quantitative differences between three brain regions (the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex). For example, we have found that axons and dendrites are densely packed in the neuropil of these three regions, reaching close to 8 kilometers of nerve fibers per cubic mm in layer 1. In all cases, the predominant fibers were excitatory axons, followed by spiny dendrites, inhibitory axons and smooth dendrites. Other examples include the fact that the excitatory axons in the stratum radiatum establish many more synapses per micron than the axons of the other regions, or the lack of correlation between the number of synapses established on the spines of a given dendrite and the number of synapses established on the dendritic shaft. Using this methodology, we obtain a set of quantitative parameters including the number and proportions of excitatory and inhibitory synapses; the numbers of dendrites and axons; the proportion of excitatory and inhibitory axons; the linear densities of synapses established on dendritic spines and dendritic shafts; the linear densities of synapses established by excitatory and inhibitory axons; and the lengths of the different types of dendrites and axons. Taken together, these data characterize each region, allowing us to compare them on a quantitative basis and to identify subtle traits and differences that would have escaped a qualitative analysis. With this strategy, we can achieve a better understanding of the structure and dynamics of the brain, and we are better equipped to explore how they change during both normal and pathological conditions.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Turegano-Lopez, M., de las Pozas, F., Santuy, A., Rodriguez, J.-R., Defelipe, J., Merchan-Perez, A.. 2023-09-27. Nanoconnectivity: a quantitative approach to examine the structural complexity of the brain. https://doi.org/10.1101/2023.09.26.559505

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗
bioRxiv2026

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗
bioRxiv2026

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗