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The inferior frontal gyrus and familial risk for bipolar disorder

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Psychoradiology 2022年第4期2卷 171-179页

作者： Kun Qin john A.sweeney Melissa P.DelBello Department of Psychiatry and Behavioral Neuroscience University of Cincinnati College of MedicineCincinnatiOH 45219USA Huaxi MR Research Center(HMRRC) Department of RadiologyWest China Hospital of Sichuan UniversityChengduSichuan 610041China

Bipolar disorder(BD)is a familial disorder with high *** factors have been linked to the pathogenesis of *** of probands with BD who are at familial risk can exhibit brain abnormalities prior to illness *** its involvement in prefrontal cognitive control and in frontolimbic circuitry that regulates emotional reactivity,the inferior frontal gyrus(IFG)has been a focus of research in studies of BD-related pathology and BD-risk *** this review,we discuss multimodal neuroimaging findings of the IFG based on studies comparing at-risk relatives and low-risk *** of these studies in at-risk cases suggests the presence of both risk and resilience markers related to the ***-risk individuals exhibited larger gray matter volume and increased functional activities in IFG compared with low-risk controls,which might result from an adaptive brain compensation to support emotion regulation as an aspect of psychological *** connectivity between IFG and downstream limbic or striatal areas was typically decreased in at-risk individuals relative to controls,which could contribute to risk-related problems of cognitive and emotional ***-scale and longitudinal investigations on at-risk individuals will further elucidate the role of IFG and other brain regions in relation to familial risk for BD,and together guide identification of at-risk individuals for primary prevention.

关键词： bipolar disorder prefrontal limbic familial risk resilience magnetic resonance imaging genetic risk

Effect of jet lag on brain white matter functional connectivity

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Psychoradiology 2021年第2期1卷 55-65页

作者： Feifei Zhang Zhipeng Yang Kun Qin john A.sweeney Neil Roberts Zhiyun Jia Qiyong Gong Huaxi MR Research Center(HMRRC) Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041China Research Unit of Psychoradiology Chinese Academy of Medical SciencesChengdu 610041China College of Electronic Engineering Chengdu University of Information TechnologyChengdu 610225China Department of Psychiatry and Behavioral Neuroscience University of CincinnatiCincinnatiOHUSA School of Clinical Sciences University of EdinburghEH164TJUK Department of Nuclear Medicine West China HospitalSichuan UniversityChengdu 610041China

Background:A long-haul flight across more than five time zones may produce a circadian rhythm sleep disorder known as jet *** is known about the effect of jet lag on white matter(WM)functional connectivity(FC).Objective:The present study is to investigate changes in WM FC in subjects due to recovery from jet lag after flying across six time ***:Here,resting-state functional magnetic resonance imaging was performed in 23 participants within 24 hours of flying and again 50 days *** matter(GM)and WM networks were identified by k-means *** FC and functional covariance connectivity(FCC)were ***,a sliding window method was used to establish dynamic WM *** static and dynamic FC and FCC were compared between when participants had initially completed their journey and 50 days *** was assessed using the Positive and Negative Affect Schedule and the State Anxiety ***:All participants were confirmed to have jet lag symptoms by the Columbian Jet Lag *** static FC strengthes of cingulate network(WM7)-sensorimotor network and ventral frontal network-visual network were lower after the long-haul flight compared with *** results were obtained for the dynamic FC *** analysis of FCC revealed weakened connections between the WM7 and several other brain networks,especially the precentral/postcentral ***,a negative correlation was found between emotion scores and the FC between the WM7 and sensorimotor related ***:The results of this study provide further evidence for the existence of WM networks and show that jet lag is associated with alterations in static and dynamic WM FC and FCC,especially in sensorimotor *** lag is a complex problem that not only is related to sleep rhythm but also influences emotion.

关键词： circadian rhythm disorder jet lag functional connectivity white matter

On the Validity of the Imbert-Fick Law: Mathematical Modelling of Eye Pressure Measurement

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World Journal of Mechanics 2016年第3期6卷 35-51页

作者： Gabriela González Castro Alastair D. Fitt john sweeney IRC in Polymer Science and Technology University of Bradford Bradford UK Oxford Brookes University Oxford UK

Ophthalmologists rely on a device known as the Goldmann applanation tonometer to make intraocular pressure (IOP) measurements. It measures the force required to press a flat disc against the cornea to produce a flattened circular region of known area. The IOP is deduced from this force using the Imbert-Fick principle. However, there is scant analytical justification for this analysis. We present a mathematical model of tonometry to investigate the relationship between the pressure derived by tonometry and the IOP. An elementary equilibrium analysis suggests that there is no physical basis for traditional tonometric analysis. Tonometry is modelled using a hollow spherical shell of solid material enclosing an elastic liquid core, with the shell in tension and the core under pressure. The shell is pressed against a rigid flat plane. The solution is found using finite element analysis. The shell material is anisotropic. Values for its elastic constants are obtained from literature except where data are unavailable, when reasonable limits are explored. The results show that the force measured by the Goldmann tonometer depends on the elastic constant values. The relationship between the IOP and the tonometer readings is complex, showing potentially high levels of inaccuracy that depend on IOP.

关键词： Tonometry Finite Element Analysis Imbert-Fick Law

Artificial intelligence applications in psychoradiology

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Psychoradiology 2021年第2期1卷 94-107页

作者： Fei Li Huaiqiang Sun Bharat B.Biswal john A.sweeney Qiyong Gong Huaxi MR Research Center(HMRRC) Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041SichuanP.R.China Research Unit of Psychoradiology Chinese Academy of Medical SciencesChengdu 610041SichuanP.R.China Functional and Molecular Imaging Key Laboratory of Sichuan Provience Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041SichuanP.R.China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 610054SichuanP.R.China Department of Psychiatry and Behavioral Neuroscience University of CincinnatiCincinnatiOH 45219USA

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness,pre-diction of prognosis before treatment,and guidance for selection of effective treatments that target patient-relevant pathophysiological *** is the primary aim of the field of *** databases collected from large samples at multiple centers,sophisticated artificial intelligence(AI)algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose,predict,and make treatment *** this review,we selectively summarize psychoradiological research using magnetic reso-nance imaging of the brain to explore the neural mechanism of psychiatric disorders,and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders,as well as limitations in the application of AI that should be considered in future translational research.

关键词： Psychoradiology magnetice resonance imaging brain artificial intelligence machine learning deep learning graph neural network

StarPro 2000型DSP的体系结构和特性

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电子产品世界 2001年第6期 31-35页

作者： Sophie Moulin,john sweeney Agers Systems公司!无线基础设施市场营销经理 Agers Systems公司!无线基础设施应用开发经理

StarPro 2000型DSP的体系结构和特性

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电子产品世界 2001年第5期8卷 31-31,34-35页

作者： Sophie Moulin john sweeney Agers Systems公司

新型通信DSP平台:StarPro 2000体系结构

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电子设计技术 2001年第6期 82-82,84页

作者： Sophie Moulin john sweeney

基于StarCore的DSP解决方案脱颖而出,可以满足第三代无线通信和互联网基础设施的需求。

关键词： DSP 硅芯片下一代话音片上所有单芯片系统体系结构 StarPro 2000 信道容量互连总线核芯

Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement:The role of machine learning

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World Journal of Cardiology 2023年第3期15卷 95-105页

作者： Pradyumna Agasthi Hasan Ashraf Sai Harika Pujari Marlene Girardo Andrew Tseng Farouk Mookadam Nithin Venepally Matthew R Buras Bishoy Abraham Banveet K Khetarpal Mohamed Allam Siva K Mulpuru MD Mackram F Eleid Kevin L Greason Nirat Beohar john sweeney David Fortuin David R Jr Holmes Reza Arsanjani Department of Cardiology Mayo ClinicPhoenixAZ 85054United States Department of Internal Medicine The Brooklyn Hospital CenterBrooklynNY 11201United States Department of Biostatistics Mayo ClinicPhoenixAZ 85054United States Department of Cardiovascular Diseases Mayo ClinicRochesterMN 55905United States Department of Statistics Mayo ClinicPhoenixAZ 85054United States Department of Cardiovascular Surgery Mayo ClinicRochesterMN 55905United States Mount Sinai Medical Center Columbia UniversityMiami BeachFL 33138United States

BACKGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used to predict preprocedural risk for *** To apply machine learning to be used to predict pre-procedural risk for *** A retrospective study of 1200 patients who underwent TAVR(January 2014-December 2017)was performed.964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year *** the exclusion of variables with near-zero variance or≥50%missing data,167 variables were included in the random forest gradient boosting algorithm(GBM)optimized using 5-fold cross-validations repeated 10 *** receiver operator curve(ROC)for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 *** Of 964 patients included in the 30-d analysis without prior PPM,19.6%required PPM *** mean age of patients was 80.9±8.7 years.42.1%were *** 657 patients included in the 1-year analysis,the mean age of the patients was 80.7±*** those,42.6%of patients were female and 26.7%required PPM at 1-year *** area under ROC to predict 30-d and 1-year risk of PPM for the GBM model(0.66 and 0.72)was superior to that of the PPM risk score(0.55 and 0.54)with a P value<*** The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.

关键词： Transcatheter aortic valve replacement Permanent pacemaker implantation Machine learning

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