Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence beten brain regions are usually derived from the analysis of covariance beten activation levels in the different areas. Hover, these covariance-based methods are not able to estimate conditional independence beten variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence beten regions by estimating the covariance beten two variables given the rest as constant.  uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters beten subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance beten GM (Gray Matter) densities of different regions reveals different structural covariation patterns beten the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative por for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but  also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.
['Andrés Ortiz', 'Jorge Munilla', 'Ignacio Álvarez-Illán', 'Juan Manuel Górriz', 'Javier Ramírez 0001', "Alzheimer's Disease Neuroimaging Initiative"]
I am writing a research paper on a idea Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.
[{"from": "human", "value": "I am writing a research paper on a idea Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence beten brain regions are usually derived from the analysis of covariance beten activation levels in the different areas. Hover, these covariance-based methods are not able to estimate conditional independence beten variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence beten regions by estimating the covariance beten two variables given the rest as constant.  uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters beten subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance beten GM (Gray Matter) densities of different regions reveals different structural covariation patterns beten the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative por for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but  also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method."}]