The Power of Functional Connectivity and Graph Theory in Neuroscience

Manousos A. Klados
2 days ago
4 min read

The human brain is an intricate and dynamic network composed of billions of neurons interacting across spatial and temporal scales. While traditional neuroscience has made significant strides by identifying the functional roles of individual brain regions, a growing body of research suggests that it is the interaction between these regions — rather than their isolated activity — that underpins cognitive processes. This shift from a localizationist view toward a network-based perspective has been largely driven by advancements in functional connectivity analysis and the application of graph theory.

Functional connectivity refers to the statistical dependence between spatially remote neurophysiological events. In practice, it is commonly estimated through correlations or coherence in neural activity time series across brain regions, as recorded by neuroimaging modalities such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), and functional near-infrared spectroscopy (fNIRS). Functional connectivity does not imply direct anatomical connections or causal interactions, but rather reveals patterns of synchronous activity, which are thought to reflect the coordination necessary for integrated cognitive function.

One of the most impactful developments in this domain is the adoption of graph theory to model and quantify functional brain networks. In this framework, the brain is represented as a graph comprising nodes (brain regions) and edges (functional connections). This abstraction allows researchers to systematically investigate the brain’s topological properties, including how it organizes into modules, maintains efficiency in information transfer, and responds to perturbations. Classical graph-theoretical metrics such as degree centrality, clustering coefficient, characteristic path length, global and local efficiency, modularity, and betweenness centrality provide a mathematical foundation for understanding brain organization in both healthy and pathological states.

The relevance of this approach is underscored by findings across numerous domains. Developmental studies have shown that functional networks evolve from a more local, segregated structure in childhood to a more integrated configuration in adulthood. In aging and neurodegeneration, these networks tend to lose integration and modular structure, often correlating with cognitive decline. In clinical neuroscience, altered functional connectivity and abnormal graph-theoretical metrics have been observed in disorders such as schizophrenia, depression, Alzheimer’s disease, and autism spectrum disorder. For example, patients with schizophrenia often display reduced small-worldness — a balance between segregation and integration — suggesting inefficiencies in brain network communication. Such network biomarkers are increasingly explored as diagnostic and prognostic tools.

Despite these advancements, challenges remain. Functional connectivity is inherently limited by its reliance on statistical associations; it does not reveal directionality or causality. Moreover, preprocessing steps and methodological choices — such as parcellation schemes, thresholding of connectivity matrices, and selection of graph metrics — can significantly influence results, raising issues of reproducibility and interpretability. Nonetheless, the convergence of higher temporal resolution techniques, machine learning, and dynamic connectivity modeling is beginning to address some of these limitations, ushering in a new era of network neuroscience.

To equip researchers and professionals with the skills to navigate this rapidly evolving field, I am offering an online hands-on workshop on Functional Connectivity and Graph Theory in Neuroscience. Designed for those with a background in neuroscience or related disciplines, this 2-hour session provides an intensive introduction to the principles and practical implementation of network analysis. Participants will learn to process neuroimaging data, construct functional connectivity matrices, and apply graph-theoretical metrics using tools such as MATLAB. Beyond technical instruction, the workshop aims to foster critical thinking about the application and interpretation of these methods in both experimental and clinical contexts.

Each participant will receive a Certificate of Completion and exclusive access to a Google Classroom environment containing all workshop materials — including datasets, scripts, and slides — as well as a forum for continued peer and instructor engagement. This ensures that the learning experience extends beyond the workshop itself.

Brain Graphs-From Zero to Hero: Hands on workshop on Connectivity.

7 June 2025, 11:00–13:00 EEST|Webinar

After an overwhelming response, our early-bird registration has sold out. In line with our commitment to inclusivity and global reach, we are launching a new initiative to make the workshop more accessible to early-career researchers and colleagues facing financial barriers.

As part of this effort, we are offering a limited number of 50% discount vouchers for eligible participants. These vouchers are available upon request to MSc and PhD students, as well as researchers from financially under-resourced or underrepresented regions. We strongly believe that cost should not be a barrier for passionate learners, so we hope this initiative will enable more early-career scholars from around the world to participate. (With the 50% voucher, the standard $100 registration fee is reduced to $50.) Please note that the number of vouchers is limited (first-come, first-served) and each is subject to availability, so eligible participants are encouraged to request one soon.