Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

Abstract

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time-averaged approaches and sophisticated algorithms that can capture the time-varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word 'chronnectome' (integration of the Greek word 'Chronos', which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex

How ‘visual’ is the visual cortex? The interactions between the visual cortex and other sensory, motivational and motor systems as enabling factors for visual perception

“…Not all relevant non-visual areas are indicated here; the strength of connections is less well known than for rodent or macaque brains, although the connectivity patterns are generally supported by diffusion tensor imaging, functional connectivity and dynamic studies (e.g. [157,158]). PFC, prefrontal cortex; PPC, posterior parietal cortex; MTL, medial temporal lobe memory system.…”

“…'Photically driven cortex' is used here instead of 'visual cortex' to emphasize that vision is not intrinsically coded in the occipital lobe, but arises in interaction with other areas. Not all relevant non-visual areas are indicated here; the strength of connections is less well known than for rodent or macaque brains, although the connectivity patterns are generally supported by diffusion tensor imaging, functional connectivity and dynamic studies (e.g [157,158]…”

New Challenges for Anatomists in the Era of Omics

“…Moreover, the function of the network involves mapping the input–output relationships and identifying patterns in the data ( Figure 2 ). This functional mapping reveals how sensory inputs are transformed into motor outputs or cognitive processes [ 152 , 153 , 154 ].…”

Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography

“…The connectivity within the DMN was determined by the Phase Lag Index (PLI) [ 35 ], calculated using the python module dyconnmap version 1.0.2 [ 36 ]. PLI effectively eliminates volume conduction, which is a common problem when estimating functional connectivity based on EEG [ 35 ].…”