Biology subjects

Fakhar, K.

Publications and source records attributed to Fakhar, K..

10 recordsLinked to original sources

bioRxiv2026

Mapping the Trade-Offs That Shape Mammalian Brain Network Architecture

A brain's wiring must satisfy conflicting pressures, from the outside world (be capable) and from the inside (stay within finite resources). These pressures often have opposing requirements. Prior work studies these trade-offs mostly between 2-3 pressures at a time, and thus cannot say how many pressures shape brain wiring or for which trade-off brains decide. To see all possible trade-offs at once and to uncover the set of most important pressures, we generate 25,000 synthetic brain networks and describe each by 29 network properties. We then place 635 human (ages 6-22) and 224 mammalian (12 taxonomic orders) brain networks in this property space. The space is triangular, and follows three pressures: computational capability, robustness to damage, and metabolic efficiency. No network is optimal at all three, and biological brains cluster in one small niche of this triangle. There, they accept higher metabolic cost to gain both robustness and capability. Development and evolution move brains in the same direction; additionally, human maturation first raises capability, and later optimizes for efficiency. This study underscores the triangular trade-off between metabolic efficiency, computational capacity and robustness, and the narrow region brains occupy in this trade-off triangle, visible only from a multi-property, cross-species perspective.

neuroscience↗

bioRxiv2026

How Similar Are Two Brains? A Comprehensive Benchmark of Brain Network Similarity Measures

A central goal of neuroscience is to establish how similar (or different) brain networks are across individuals, development, psychiatric conditions, and even between biological and artificial brains. Whenever one such comparison is made, it relies on the assumption that we have a measure that produces an accurate and plausible similarity score. Yet, the field lacks one such measure, and the wide variety of existing measures often produces conflicting results. To address this problem, we perform a systematic comparison of 16 established similarity measures. First, we show that different measures consistently disagree on which brain networks are most similar. Second, we rank all measures according to multiple criteria such as biological plausibility, computational efficiency, and sensitivity. We show that DeltaCon is the most accurate measure in terms of parameter recovery, and that it ranks among the fastest and most noise-tolerant. For this reason, we conclude that DeltaCon is the best general-purpose similarity measure. However, no measure ranks best on more than two of the five criteria we assessed, and different measures should be preferred depending on the exact research question. Measure selection can now be made on evidence and should be a crucial decision in any study that compares brain networks.

neuroscience↗

bioRxiv2026

Curiosity shapes brain-like architectures and functions

How does complex cognition emerge from simpler underlying processes? We show that two components are sufficient: infant-like curiosity and brain-like biophysical constraints jointly drive the emergence of complex neuronal architectures and cognitive abilities. We first tested curiosity-driven exploration in 275 8-to 15-month-old infants. We then implemented these mechanisms in artificial recurrent neural networks, letting them actively sample 20 cognitive tasks during training, while forcing the networks to operate under brain-like biophysical constraints. These two components were sufficient for a range of biological and cognitive phenomena to emerge: The resulting networks captured human synaptic development, the adult brains architecture, and displayed compositional generalisation, a hallmark of human cognition. Curiosity, long viewed as a downstream consequence of complex brains, is also a driver of their complexity.

neuroscience↗

bioRxiv2025

Human cortical networks trade communication efficiency for computational reliability.

Brains are often described as cost-efficient communication networks, optimally balancing the cost of long connections with the benefits of fast communication. Here, inspired by the "use it or lose it" principle, we present a novel game-theoretic model of self-organizing neural units and show that the brain is, in fact, sub-optimal in both regards: First, we demonstrate that regional competition for connectivity under propagative communication dynamics naturally gives rise to network configurations similar to those derived from the human cortex while being even more efficient and economical. Next, we use a reservoir computing framework to compare the information processing capacity of these networks against those of the brain. Although comparable in performance, the more optimal trade-off comes with a tax on computational reliability. Through synthetic lesions, we show that these networks are fragile because, to optimize for communication, they funnel information through a spatially clustered "oligarchy" comprising a tight set of transmodal hubs. In contrast, the human brain uses a more distributed "rich club" backbone that better resists breakdown following targeted attacks, even when it means higher wiring costs and less efficient communication. This reveals a previously overlooked principle: cortical networks trade both cost and efficiency for reliable computation. Thus, our findings highlight computational reliability as another, and even more prominent driver of brain connectivity compared to wiring cost and communication efficiency. TeaserThe brain avoids fragile efficiency: brain networks favor reliable computation, revealing resilience as a hidden design rule.

neuroscience↗

bioRxiv2025

Domain-Specific Functional Network Adaptations Supporting Dual-Task Performance in Older Adults

Aging is associated with declines in both motor and cognitive functions, which are well captured by dual-task gait paradigms. However, the functional brain network mechanisms supporting motor and cognitive aspects of dual-task performance in aging remain unclear. We examined 40 older adults (50-80 years) and 20 younger adults (20-40 years) who performed a motor single-task (pedaling), a cognitive single-task (Go/NoGo), and a combined cognitive-motor dual-task during functional magnetic resonance Imaging (fMRI) using a custom-built MRI-compatible pedaling device. Behaviorally, older adults showed significant dual-task costs in motor performance, while cognitive performance was preserved. Neurally, older adults showed selective increases in connectivity within executive and motor-planning regions of cognitive networks, consistent with compensatory recruitment, whereas motor networks underwent broader reorganization, with strengthened frontoparietal control circuits but weakened cerebello-parietal and sensorimotor pathways. Multivariate analyses further revealed age-related differences in latent connectivity- behavior relationships: motor-network patterns in older adults were more dispersed, reflecting heterogeneous reorganization, whereas cognitive-network patterns were more overlapping across groups, suggesting relative preservation. These findings suggest that aging involves a domain-specific balance of resilience and vulnerability across brain networks and highlight motor-network adaption as a promising target for understanding why some older adults maintain function while others decline.

neuroscience↗

bioRxiv2025

A Computational Perspective on the No-Strong-Loops Principle in Brain Networks

Cerebral cortical networks in the mammalian brain exhibit a non-random organization in which reciprocal projections, although widespread, are systematically asymmetric in strength: feedforward connections are consistently stronger than their feedback counterparts, particularly in sensory cortices. This "no-strong-loops" principle is thought to prevent runaway excitation and maintain stability, yet its actual computational impact remains unclear. Here, we use computational analysis and modeling to show that connectivity asymmetry supports high working-memory capacity, whereas increasing reciprocity reduces memory capacity and representational diversity in reservoir-computing models of recurrent neural networks. We systematically examine synthetic architectures inspired by mammalian cortical connectivity and find that sparse, modular, and hierarchical networks achieve superior performance, relative to random, small-world, or core-periphery graphs, but only when reciprocity is constrained. Validated on directed mammalian (macaque, marmoset, rat, and mouse) connectomes, these results indicate that restricting reciprocal motifs yields functional benefits in sparse networks, consistent with an evolutionary strategy for stable, efficient information processing in the brain. These findings suggest a biologically-inspired design principle for artificial neural systems.

neuroscience↗

bioRxiv2024

Controlling Reciprocity in Binary and Weighted Networks: A Novel Density-Conserving Approach

We introduce efficient Network Reciprocity Control (NRC) algorithms for steering the degree of asymmetry and reciprocity in binary and weighted networks while preserving fundamental network properties. Our methods maintain edge density in binary networks and cumulative edge weight in weighted graphs. We test these algorithms on synthetic benchmark networks, including random, small-world, and modular structures, as well as brain connectivity maps (connectomes) from various species. We demonstrate how adjusting the asymmetry-reciprocity balance under edge density and total weight constraints influences key network features, including spectral properties, degree distributions, community structure, clustering, and path lengths. Additionally, we present a case study on the computational implications of graded reciprocity by solving a memory task within the reservoir computing framework. Furthermore, we establish the scalability of the NRC algorithms by applying them to networks of increasing size. These approaches enable systematic investigation of the relationship between directional asymmetry and network topology, with potential applications in computational and network sciences, social network analysis, and other fields studying complex network systems where the directionality of connections is essential.

neuroscience↗

bioRxiv2024

A General Framework for Characterizing Optimal Communication in Brain Networks

Communication in brain networks is the foundation of cognitive function and behavior. A multitude of evolutionary pressures, including the minimization of metabolic costs while maximizing communication efficiency, contribute to shaping the structure and dynamics of these networks. However, how communication efficiency is characterized depends on the assumed model of communication dynamics. Traditional models include shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Yet, a general and model-agnostic framework for characterizing optimal neural communication remains to be established. Our study addresses this challenge by assigning communication efficiency through game theory, based on a combination of structural data from human cortical networks with computational models of brain dynamics. We quantified the exact influence exerted by each brain node over every other node using an exhaustive multi-site virtual lesioning scheme, creating optimal influence maps for various models of brain dynamics. These descriptions show how communication patterns unfold in the given brain network if regions maximize their influence over one another. By comparing these influence maps with a large variety of brain communication models, we found that optimal communication most closely resembles a broadcasting model in which regions leverage multiple parallel channels for information dissemination. Moreover, we show that the most influential regions within the cortex are formed by its rich-club. These regions exploit their topological vantage point by broadcasting across numerous pathways, thereby significantly enhancing their effective reach even when the anatomical connections are weak. Our work provides a rigorous and versatile framework for characterizing optimal communication across brain networks and reveals the most influential brain regions and the topological features underlying their optimal communication.

neuroscience↗

bioRxiv2023

When Neural Activity Fails to Reveal Causal Contributions

Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a units causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neuronal networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.

neuroscience↗

bioRxiv2021

Systematic Perturbation of an Artificial Neural Network: A Step Towards Quantifying Causal Contributions in The Brain

Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. Historically, it has helped to localize specialized functions in the brain after brain damage, and it has gained new prominence through the arrival of modern optogenetic perturbation techniques that allow probing the functional contributions of neural circuit elements at unprecedented levels of detail. While inferences drawn from brain lesions are conceptually powerful, they face methodological difficulties due to the brains complexity. Particularly, they are challenged to disentangle the functional contributions of individual neural elements because many elements may contribute to a particular function, and these elements may be interacting anatomically as well as functionally. Therefore, studies of real-world data, as in clinical lesion studies, are not suitable for establishing the reliability of lesion approaches due to an unknown, potentially complex ground truth. Instead, ground truth studies of well-characterized artificial systems are required. Here, we systematically and exhaustively lesioned a small Artificial Neural Network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from single-element perturbations and perturbing multiple elements simultaneously. Moreover, we computed pairwise causal functional interactions between the network elements, and looked deeper into the systems inner workings, proposing a mechanistic explanation for the effects of lesions. We found that not every perturbation necessarily reveals causation, as lesioning elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be understood for deriving a causal picture of the system. In the context of rapidly evolving multivariate brain-mapping approaches and inference methods, we advocate using in-silico experiments and ground-truth models to verify fundamental assumptions, technical limitations, and the scope of possible interpretations of these methods. Author summaryThe motto "No causation without manipulation" is canonical to scientific endeavors. In particular, neuroscience seeks to find which brain elements are causally involved in cognition and behavior of interest by perturbing them. However, due to complex interactions among those elements, this goal has remained challenging. In this paper, we used an Artificial Neural Network as a ground-truth model to compare the inferential capacities of lesioning the system one element at a time against sampling from the set of all possible combinations of lesions. We argue for employing more exhaustive perturbation regimes since, as we show, lesioning one element at a time provides misleading results. We further advocate using simulated experiments and ground-truth models to verify the assumptions and limitations of brain-mapping methods.

neuroscience↗