Normalizing the brain connectome for communication through synchronization

Abstract

Networks in neuroscience determine how brain function unfolds. Perturbations of the network lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual particle-wave-perspective, integrate time delays due to finite transmission speeds and derive a renormalization of the connectome. When applied to the data base of the Human Connectome project, we explain the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N=100) and are a fundamental network property within the wave picture. The renormalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms.One Sentence SummarySpatiotemporal and topological network properties are unified within a novel common framework, the renormalized connectome, that explains the organization of fundamental frequency-specific network cores.

On the topochronic map of the human brain dynamics

“…We propose that, beyond the connection topology imposed by the spatial scaffolding (9), the conjugate property of connectivity is temporal in nature and complementary to the structural topology (38). Together, spatial and temporal constraints reveal the topochronic framework from which oscillatory brain activity emerges.…”

Pattern of frustration formation in the functional brain network

“…It’s an open question that needs more investigation, and computational modeling can be helpful to solve it. Positive and negative links conceptually look the same as in-phase and anti-phase synchronizes (Petkoski & Jirsa, 2019; Petkoski & Jirsa, 2020; Petkoski et al, 2016) whereas Petkoski and his colleagues showed that in-phase synchrony (or perfectly aligned in/anti-phase clustering) makes the lowest energy which is similar to a brain signed network that has no negative links and frustrations located in the lowest balance energy. They also found that when giving the distribution of the time-delays in the brain, it is more probable that the brain minimizes the disorders that are in a way with our previous results (Saberi et al, 2021a), and the self-organizing essence of the brain (Dresp-Langley, 2020).…”

How synchrony and metastable network dynamics are affected in fast and slow timescales with aging: Implication for Cognition

“…Fo synchronization at frequency Ω, equation 1 reads [36], The phase difference between each pair of Kuramoto oscillators is given as, For symmetric coupling C mn = C nm = c (say), τ mn = τ nm = τ (say). In our experiment Δ ω = 0.…”