摘要 :
We propose a method for exploring non-linear directed information transfer in complex systems, which we expect to be useful for analyzing functional MRI (fMRI) ncuroimaging data. In contrast to existing approaches that attempt to ...
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We propose a method for exploring non-linear directed information transfer in complex systems, which we expect to be useful for analyzing functional MRI (fMRI) ncuroimaging data. In contrast to existing approaches that attempt to analyze complex systems by simplification and subsequent analysis of a simplified system, we propose to retain the original system complexity during the analysis. To this end, we introduce large-scale Non-Linear Granger Causality (lsNGC) as a method for effective connectivity network analysis in high-dimensional systems with short time-series. By introducing a dimension reduction step into a non-linear time-series prediction approach. lsNGC aims at directed, non-linear, multivariate time-series causality analysis in large complex networks, such as brain activity in fMRI analysis. We quantitatively evaluate the performance of lsNGC in computer simulations on structural recovery of synthetic networks with known ground truth. We find that our method performs better than a widely used non-linear network analysis method (Convergent Cross Mapping -CCM) with high statistical significance (p<10~(-6)). In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange Ⅱ (ABIDE Ⅱ) data repository. Our results suggest that lsNGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data.
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摘要 :
We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-...
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We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth. As a reference method, we compare our results with cross-correlation, which is typically used as a standard measure of connectivity in the functional MRI (fMRI) literature. Using extensive simulations for a wide range of time-series lengths and two different signal-to-noise ratios of 5 and 15 dB. lsAGC consistently outperforms cross-correlation at accurately detecting network connections, using Receiver Operator Characteristic Curve (ROC) analysis, across all tested time-series lengths and noise levels. In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange Ⅱ (ABIDE Ⅱ) data repository. Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data.
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摘要 :
We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-...
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We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth. As a reference method, we compare our results with cross-correlation, which is typically used as a standard measure of connectivity in the functional MRI (fMRI) literature. Using extensive simulations for a wide range of time-series lengths and two different signal-to-noise ratios of 5 and 15 dB. lsAGC consistently outperforms cross-correlation at accurately detecting network connections, using Receiver Operator Characteristic Curve (ROC) analysis, across all tested time-series lengths and noise levels. In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange Ⅱ (ABIDE Ⅱ) data repository. Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data.
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摘要 :
We propose a method for exploring non-linear directed information transfer in complex systems, which we expect to be useful for analyzing functional MRI (fMRI) ncuroimaging data. In contrast to existing approaches that attempt to ...
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We propose a method for exploring non-linear directed information transfer in complex systems, which we expect to be useful for analyzing functional MRI (fMRI) ncuroimaging data. In contrast to existing approaches that attempt to analyze complex systems by simplification and subsequent analysis of a simplified system, we propose to retain the original system complexity during the analysis. To this end, we introduce large-scale Non-Linear Granger Causality (lsNGC) as a method for effective connectivity network analysis in high-dimensional systems with short time-series. By introducing a dimension reduction step into a non-linear time-series prediction approach. lsNGC aims at directed, non-linear, multivariate time-series causality analysis in large complex networks, such as brain activity in fMRI analysis. We quantitatively evaluate the performance of lsNGC in computer simulations on structural recovery of synthetic networks with known ground truth. We find that our method performs better than a widely used non-linear network analysis method (Convergent Cross Mapping -CCM) with high statistical significance (p<10~(-6)). In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange Ⅱ (ABIDE Ⅱ) data repository. Our results suggest that lsNGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data.
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The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI ...
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The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves as a biomarker for classifying schizophrenia patients from typical controls using a subset of 62 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall's tail rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity. We cross-validate 100 different training/test (90％/10%) data split to obtain mean accuracy and a mean Area Under the receiver operating characteristic Curve (AUC) across all tested numbers of features for lsXGC. Our results demonstrate a mean accuracy range of [0.767, 0.940] and a mean AUC range of [0.861, 0.983] for lsXGC. The result of lsXGC is significantly higher than the results obtained with the cross-correlation, namely mean accuracy of [0.721, 0.751] and mean AUC of [0.744, 0.860]. Our results suggest the applicability of lsXGC as a potential biomarker for schizophrenia.
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摘要 :
The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI ...
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The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves as a biomarker for classifying schizophrenia patients from typical controls using a subset of 62 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall's tail rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity. We cross-validate 100 different training/test (90％/10%) data split to obtain mean accuracy and a mean Area Under the receiver operating characteristic Curve (AUC) across all tested numbers of features for lsXGC. Our results demonstrate a mean accuracy range of [0.767, 0.940] and a mean AUC range of [0.861, 0.983] for lsXGC. The result of lsXGC is significantly higher than the results obtained with the cross-correlation, namely mean accuracy of [0.721, 0.751] and mean AUC of [0.744, 0.860]. Our results suggest the applicability of lsXGC as a potential biomarker for schizophrenia.
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It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using resting-state fM...
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It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 12G adult subjects with a childhood diagnosis of ADHD from the Addiction Connectomc Preproccssed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall's-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714. 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779. 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarkcr for marijuana use.
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摘要 :
It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using resting-state fM...
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It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 12G adult subjects with a childhood diagnosis of ADHD from the Addiction Connectomc Preproccssed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall's-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714. 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779. 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarkcr for marijuana use.
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We introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) based on machine learning applications in medical imaging, for analyzing pandemic-induced effects on healthcare systems. By tracking bo...
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We introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) based on machine learning applications in medical imaging, for analyzing pandemic-induced effects on healthcare systems. By tracking both large-scale utilization and AI results data, the tru-AI approach can establish surrogates for measuring the amount of care provided and estimate the prevalence of certain disease conditions under unusual circumstances, such as pandemic outbreaks. To quantitatively evaluate our approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution (Aidoe, Tel Aviv, Israel). This software is typically used for Al-based prioritization of radiologists' reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism. Imaging data is anonymized. uploaded to a cloud-based inference machine in real time, and Al-bascd ICH detection results are returned. We recorded N = 3,084 emergency-setting non-contrast head CT studies at a major US healthcare system during two observation periods, namely (ⅰ) a pre-pandemic epoch (January 1-31, 2020) and (ⅱ) after the Covid-19 outbreak (March 15 - April 30. 2020). Although daily counts of unique imaged patients were significantly lower during (37.9 ± 7.G) than before (42.0 ± G.2) the Covid-19 outbreak, we found that ICH was more likely to be observed during than before the Covid-19 outbreak (p<0.05). Our results suggest that, by tracking both large-scale utilization and AI results data, the tru-AI approach can contribute clinical value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.
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摘要 :
We introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) based on machine learning applications in medical imaging, for analyzing pandemic-induced effects on healthcare systems. By tracking bo...
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We introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) based on machine learning applications in medical imaging, for analyzing pandemic-induced effects on healthcare systems. By tracking both large-scale utilization and AI results data, the tru-AI approach can establish surrogates for measuring the amount of care provided and estimate the prevalence of certain disease conditions under unusual circumstances, such as pandemic outbreaks. To quantitatively evaluate our approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution (Aidoe, Tel Aviv, Israel). This software is typically used for Al-based prioritization of radiologists' reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism. Imaging data is anonymized. uploaded to a cloud-based inference machine in real time, and Al-bascd ICH detection results are returned. We recorded N = 3,084 emergency-setting non-contrast head CT studies at a major US healthcare system during two observation periods, namely (ⅰ) a pre-pandemic epoch (January 1-31, 2020) and (ⅱ) after the Covid-19 outbreak (March 15 - April 30. 2020). Although daily counts of unique imaged patients were significantly lower during (37.9 ± 7.G) than before (42.0 ± G.2) the Covid-19 outbreak, we found that ICH was more likely to be observed during than before the Covid-19 outbreak (p<0.05). Our results suggest that, by tracking both large-scale utilization and AI results data, the tru-AI approach can contribute clinical value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.
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