ARTICLE | doi:10.20944/preprints201710.0073.v1
Subject: Mathematics & Computer Science, Other Keywords: graph alignment; brain network; human connectome
Online: 12 October 2017 (04:07:33 CEST)
A growing area in neurosciences is focused on the modeling and analysis the complex system of connections in neural systems, i.e. the connectome. Here we focus on the representation of connectomes by using graph theory formalisms. The human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. Recently, it has been proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space to define network nodes of individual brain networks. In the network domain, the question of comparison of the structure of networks arises. Such question is tackled by modeling the comparison of brain network as a network alignment (NA) problem. In this paper, we first defined the NA problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.
REVIEW | doi:10.20944/preprints202208.0157.v1
Subject: Life Sciences, Biotechnology Keywords: neural; circuit; synthetic; biology; brain; neuron; psychiatry; neurology; development; connectome; synapse; engineering
Online: 8 August 2022 (11:12:57 CEST)
Recent biotechnological innovations make feasible the new paradigm of creating biological neural circuits de novo. With advances in protein, cell and tissue engineering techniques, as well as cellular reprogramming methods, we are entering an era where the construction of neural circuits can open completely new ways for studying nervous systems and for treating nervous system disorders. I explore here three technologies, namely cellular engraftment, neuronal reprogramming and transsynaptic molecule engineering, and delineate how they are being used in a variety of basic research and translational medicine contexts. In basic neuroscience, neural circuit construction methods are enabling ways to study causality in neural development (e.g. neural precursor differentiation and migration) and circuit function (e.g. excitation/inhibition balance, neural population dynamics). In translational neuroscience, they are providing opportunities for the targeted correction of circuit malfunction in brain disorders, both psychiatric (e.g. schizophrenia) and neurological (e.g. Parkinson’s, Huntington’s and Alzheimer’s disease, as well as epilepsy). I discuss the challenges that these methods currently face, such as targeting specificity and cell survival, and outline future paths and opportunities to realize the full potential of technologies for creating new biological neural circuits.
ARTICLE | doi:10.20944/preprints201905.0031.v1
Subject: Life Sciences, Biophysics Keywords: functional networks; functional magnetic resonance imaging; connectome; connectivity matrices; graphs; reproducibility; granger causality; transfer entropy
Online: 6 May 2019 (07:47:11 CEST)
A growing number of studies focus on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing inter- and intra- subject variability of connectivity matrices as well as graph-theoretical measures in a large (n=1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected as opposed to directed methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra- subject variabilities in both directed and undirected connectomic measures.