Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. Hover, these analyses have limitations for inter-subject or beten-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an alternative framework based on a Bayesian generative modeling approach, i.e. Bayesian exponential random graph modeling (ERGM), which explains an observed network by the joint contribution of local network structures or features (for which  chose neurobiologically meaningful constructs such as connectedness, local clustering or global efficiency).  aimed to identify how these local network structures (or features) are evolving across the life-span, and how sensitive these features are to random and targeted lesions. To that aim  applied Bayesian exponential random graph modeling on structural networks derived from whole-brain diffusion tensor imaging-based tractography of 382 healthy adult subjects (age range: 20.2–86.2 years), with and without lesion simulations. Networks re successfully generated from four local network structures that resulted in excellent goodness-of-fit, i.e. measures of connectedness, local clustering, global efficiency and intrahemispheric connectivity.  found that local structures (i.e. connectedness, local clustering and global efficiency), which give rise to the global network topology, re stable even after lesion simulations across the lifespan, in contrast to overall descriptive network changes – e.g. lor network density and higher clustering – during aging, and despite clear effects of hub damage on network topologies. Our study demonstrates the potential of Bayesian generative modeling to characterize the underlying network structures that drive the brain's global network topology at different developmental stages and/or under pathological conditions.
['Michel R.T. Sinke', 'Rick M. Dijkhuizen', 'Alberto Caimo', 'Cornelis J. Stam', 'Willem M. Otte']
I am writing a research paper on a idea Bayesian exponential random graph modeling of whole-brain structural networks across lifespan
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian exponential random graph modeling of whole-brain structural networks across lifespan"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. Hover, these analyses have limitations for inter-subject or beten-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an alternative framework based on a Bayesian generative modeling approach, i.e. Bayesian exponential random graph modeling (ERGM), which explains an observed network by the joint contribution of local network structures or features (for which  chose neurobiologically meaningful constructs such as connectedness, local clustering or global efficiency).  aimed to identify how these local network structures (or features) are evolving across the life-span, and how sensitive these features are to random and targeted lesions. To that aim  applied Bayesian exponential random graph modeling on structural networks derived from whole-brain diffusion tensor imaging-based tractography of 382 healthy adult subjects (age range: 20.2\u201386.2 years), with and without lesion simulations. Networks re successfully generated from four local network structures that resulted in excellent goodness-of-fit, i.e. measures of connectedness, local clustering, global efficiency and intrahemispheric connectivity.  found that local structures (i.e. connectedness, local clustering and global efficiency), which give rise to the global network topology, re stable even after lesion simulations across the lifespan, in contrast to overall descriptive network changes \u2013 e.g. lor network density and higher clustering \u2013 during aging, and despite clear effects of hub damage on network topologies. Our study demonstrates the potential of Bayesian generative modeling to characterize the underlying network structures that drive the brain's global network topology at different developmental stages and/or under pathological conditions."}]