Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was under...Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.展开更多
Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases rema...Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.展开更多
Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to ...Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.展开更多
Measurement the viscoelastic properties is important for studying the developmental and pathological behavior of soft biological tissues.Magnetic resonance elastography(MRE)is a non-invasive method for in vivo measure...Measurement the viscoelastic properties is important for studying the developmental and pathological behavior of soft biological tissues.Magnetic resonance elastography(MRE)is a non-invasive method for in vivo measurement of tissue viscoelasticity.As a flexible method capable of testing small samples,indentation has been widely used for characterizing soft tissues.Using 2nd-order Prony series and dimensional analysis,we analyzed and compared the model parameters estimated from both indentation and MRE.Conversions of the model parameters estimated from the two methods were established.We found that the indention test is better at capturing the dynamic response of tissues at a frequency less than 10 Hz,while MRE is better for describing the frequency responses at a relatively higher range.The results provided helpful information for testing soft tissues using indentation and MRE.The models analyzed are also helpful for quantifying the frequency response of viscoelastic tissues.展开更多
Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules,the pote...Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules,the potential precursors to lung cancer, is paramount. In this paper, a computer-aided lung nodule detection system using 3D deep convolutional neural networks(CNNs) is developed. The first multi-scale 11-layer 3D fully convolutional neural network(FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection.展开更多
Deficits in synaptic transmission and plasticity are thought to contribute to the pathophysiology of Alzheimer's disease(AD)and Parkinson's disease(PD).Several brain stimulation techniques are currently availa...Deficits in synaptic transmission and plasticity are thought to contribute to the pathophysiology of Alzheimer's disease(AD)and Parkinson's disease(PD).Several brain stimulation techniques are currently available to assess or modulate human neuroplasticity,which could offer clinically useful interventions as well as quantitative diagnostic and prognostic biomarkers.In this review,we discuss several brain stimulation techniques,with a special emphasis on transcranial magnetic stimulation and deep brain stimulation(DBS),and review the results of clinical studies that applied these techniques to examine or modulate impaired neuroplasticity at the local and network levels in patients with AD or PD.The impaired neuroplasticity can be detected in patients at the earlier and later stages of both neurodegenerative diseases.However,current brain stimulation techniques,with a notable exception of DBS for PD treatment,cannot serve as adequate clinical tools to assist in the diagnosis,treatment,or prognosis of individual patients with AD or PD.Targeting the impaired neuroplasticity with improved brain stimulation techniques could offer a powerful novel approach for the treatment of AD and PD.展开更多
基金supported by the National Natural Science Foundation of China, Nos.82001767(to XJG), 81971577(to MMZ), 82171888(to XJX)the Natural Science Foundation of Zhejiang Province of China, Nos.LQ21H180008(to XJG), LQ20H180012(to MX)+1 种基金the China Postdoctoral Science Foundation, Nos.2021T140599(to XJG), 2019M662082(to XJG)the 13th Five-year Plan for National Key Research and Development Program of China, No.2016YFC1306600(to MMZ)
文摘Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.
基金supported by the Jiangsu Maternal and Child Health Research Project of China,No.F201612(to HXL)Changzhou Science and Technology Support Plan of China,No.CE20165027(to HXL)+1 种基金Changzhou City Planning Commission Major Science and Technology Projects of China,No.ZD201515(to HXL)Changzhou High Level Training Fund for Health Professionals of China,No.2016CZBJ028(to HXL)
文摘Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.
基金supported by the National Natural Science Foundation of China (81571025 and 81702461)the National Key Research and Development Program of China (2018YFC0116400)+6 种基金the International Cooperation Project from Shanghai Science Foundation (18410711300)Shanghai Science and Technology Development Funds (16JC1420100)the Shanghai Sailing Program (17YF1426600)STCSM (19QC1400600, 17411953300)the Shanghai Pujiang Program (19PJ1406800)the Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJlabthe Interdisciplinary Program of Shanghai Jiao Tong University。
文摘Hydrocephalus is often treated with a cerebrospinal fluid shunt(CFS) for excessive amounts of cerebrospinal fluid in the brain.However,it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes,such as brain atrophy after brain damage and surgery.The non-trivial evaluation of the consciousness level,along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made.We studied 32 secondary mild hydrocephalus patients with different consciousness levels,who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage.We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages.Then,we built a regression model to regress the JFK Coma Recovery Scale-Revised(CRS-R) scores to quantify the level of consciousness.The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients.The regression model has high potential for the evaluation of consciousness in clinical practice.
基金This work was supported by the National Natural Science Foundation of China(Grant 31870941)Shanghai Science and Technology Committee(Grant 1944190700).
文摘Measurement the viscoelastic properties is important for studying the developmental and pathological behavior of soft biological tissues.Magnetic resonance elastography(MRE)is a non-invasive method for in vivo measurement of tissue viscoelasticity.As a flexible method capable of testing small samples,indentation has been widely used for characterizing soft tissues.Using 2nd-order Prony series and dimensional analysis,we analyzed and compared the model parameters estimated from both indentation and MRE.Conversions of the model parameters estimated from the two methods were established.We found that the indention test is better at capturing the dynamic response of tissues at a frequency less than 10 Hz,while MRE is better for describing the frequency responses at a relatively higher range.The results provided helpful information for testing soft tissues using indentation and MRE.The models analyzed are also helpful for quantifying the frequency response of viscoelastic tissues.
基金the National Natural Science Foundation of China(No.81371624)the National Key Research and Development Program of China(No.2016YFC0104608)+1 种基金the National Basic Research Program of China(No.2010CB834302)the Shanghai Jiao Tong University Medical Engineering Cross Research Funds(Nos.YG2013MS30 and YG2014ZD05)
文摘Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules,the potential precursors to lung cancer, is paramount. In this paper, a computer-aided lung nodule detection system using 3D deep convolutional neural networks(CNNs) is developed. The first multi-scale 11-layer 3D fully convolutional neural network(FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection.
基金supported by grants from the Science and Technology Commission of Shanghai Municipality(18JC1420302,18JC1420303,18JC1420304)the Shanghai Municipal Science and Technology Major Projea(2018SHZDZX05)+1 种基金SJTU Trans-med Awards Research(2019015)Innovative Research Team of High-Level Local Universities in Shanghai.
文摘Deficits in synaptic transmission and plasticity are thought to contribute to the pathophysiology of Alzheimer's disease(AD)and Parkinson's disease(PD).Several brain stimulation techniques are currently available to assess or modulate human neuroplasticity,which could offer clinically useful interventions as well as quantitative diagnostic and prognostic biomarkers.In this review,we discuss several brain stimulation techniques,with a special emphasis on transcranial magnetic stimulation and deep brain stimulation(DBS),and review the results of clinical studies that applied these techniques to examine or modulate impaired neuroplasticity at the local and network levels in patients with AD or PD.The impaired neuroplasticity can be detected in patients at the earlier and later stages of both neurodegenerative diseases.However,current brain stimulation techniques,with a notable exception of DBS for PD treatment,cannot serve as adequate clinical tools to assist in the diagnosis,treatment,or prognosis of individual patients with AD or PD.Targeting the impaired neuroplasticity with improved brain stimulation techniques could offer a powerful novel approach for the treatment of AD and PD.
