More Like this

1. Fractal geometry predicts dynamic differences in structural and functional connectomes

   https://doi.org/10.1063/5.0283805  

   Rădulescu, Anca; Kaslik, Eva; Fikl, Alexandru; Nakuci, Johan; Muldoon, Sarah; Anderson, Michael (September 2025, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   Understanding the intricate architecture of brain networks and its connection to brain function is essential for deciphering the underlying principles of cognition and disease. While traditional graph-theoretical measures have been widely used to characterize these networks, they often fail to fully capture the emergent properties of large-scale neural dynamics. Here, we introduce an alternative approach to quantify brain networks that is rooted in complex dynamics, fractal geometry, and asymptotic analysis. We apply these concepts to brain connectomes and demonstrate how quadratic iterations and geometric properties of Mandelbrot-like sets can provide novel insights into structural and functional network dynamics. Our findings reveal fundamental distinctions between structural (positive) and functional (signed) connectomes, such as the shift of cusp orientation and the variability in equi-M set geometry. Notably, structural connectomes exhibit more robust, predictable features, while functional connectomes show increased variability for non-trivial tasks. We further demonstrate that traditional graph-theoretical measures, when applied separately to the positive and negative sub-networks of functional connectomes, fail to fully capture their dynamic complexity. Instead, size and shape-based invariants of the equi-M set effectively differentiate between rest and emotional task states, which highlights their potential as superior markers of emergent network dynamics. These results suggest that incorporating fractal-based methods into network neuroscience provides a powerful tool for understanding how information flows in natural systems beyond static connectivity measures, while maintaining simplicity. 

   more » « less
2. Combined topological and spatial constraints are required to capture the structure of neural connectomes

   https://doi.org/10.1162/netn_a_00428  

   Salova, Anastasiya; Kovács, István A (January 2025, Network Neuroscience)

   Abstract Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints—either given by pairwise distances or the contactome—play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. Yet, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent mismatch by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics. 

   more » « less
3. Rewiring Dynamics of Functional Connectomes during Motor-Skill Learning

   https://doi.org/10.1080/26941899.2023.2260431  

   Meamardoost, Saber; Bhattacharya, Mahasweta; Hwang, Eun Jung; Ren, Chi; Wang, Linbing; Mewes, Claudia; Zhang, Ying; Komiyama, Takaki; Gunawan, Rudiyanto (December 2023, Data Science in Science)

   NA (Ed.)

   The brain’s functional connectome continually rewires throughout an organism’s life. In this study, we sought to elucidate the operational principles of such rewiring in mouse primary motor cortex (M1) by analyzing calcium imaging of layer 2/3 (L2/3) and layer 5 (L5) neuronal activity in M1 of awake mice during a lever-press task learning. Our results show that L2/3 and L5 functional connectomes follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with improved motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward/performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning. 

   more » « less
4. Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes

   https://doi.org/10.3389/fnins.2021.549322  

   Ravindra, Vikram; Drineas, Petros; Grama, Ananth (April 2021, Frontiers in Neuroscience)

   null (Ed.)

   Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present new results that identify specific parts of resting state and task-specific connectomes that are responsible for the unique signatures. We show that a very small part of the connectome can be used to derive features for discriminating between individuals. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques. 

   more » « less
5. Physical Network Constraints Define the Lognormal Architecture of the Brain’s Connectome

   https://doi.org/10.1101/2025.02.27.640551  

   Piazza, Ben; Barabási, Dániel L; Ferreira_Castro, André; Menichetti, Giulia; Barabási, Albert-László (February 2025, bioRxiv)

   Abstract The brain has long been conceptualized as a network of neurons connected by synapses. However, attempts to describe the connectome using established network science models have yielded conflicting outcomes, leaving the architecture of neural networks unresolved. Here, by performing a comparative analysis of eight experimentally mapped connectomes, we find that their degree distributions cannot be captured by the well-established random or scale-free models. Instead, the node degrees and strengths are well approximated by lognormal distributions, although these lack a mechanistic explanation in the context of the brain. By acknowledging the physical network nature of the brain, we show that neuron size is governed by a multiplicative process, which allows us to analytically derive the lognormal nature of the neuron length distribution. Our framework not only predicts the degree and strength distributions across each of the eight connectomes, but also yields a series of novel and empirically falsifiable relationships between different neuron characteristics. The resulting multiplicative network represents a novel architecture for network science, whose distinctive quantitative features bridge critical gaps between neural structure and function, with implications for brain dynamics, robustness, and synchronization. 

   more » « less