| 1. |
- Abé, Christoph, et al.
(författare)
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Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group.
- 2022
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Ingår i: Biological psychiatry. - : Elsevier BV. - 1873-2402 .- 0006-3223. ; 91:6, s. 582-592
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD.Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables.Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18
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| 2. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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| 3. |
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| 4. |
- Al-Khatib, Iyad, et al.
(författare)
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A Multiprocessor System-on-Chip for Real-Time Biomedical Monitoring and Analysis : Architectural Design Space Exploration
- 2006
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Ingår i: DAC '06. - New York, New York, USA : ACM Press. ; , s. 125-130
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Konferensbidrag (refereegranskat)abstract
- In this paper we focus on MPSoC architectures for human heart ECGreal-time monitoring and analysis. This is a very relevant bio-medicalapplication, with a huge potential market, hence it is an ideal targetfor an application-specific SoC implementation. We investigate asymmetric multi-processor architecture based on STMicroelectronicsVLIW DSPs that process in real-time 12-lead ECG signals. Thisarchitecture improves upon state-of-the-art SoC designs for ECGanalysis in its ability to analyze the full 12 leads in real-time, evenwith high sampling frequencies, and ability to detect heartmalfunction. We explore the design space by considering a number ofhardware and software architectural options.
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| 5. |
- Al Khatib, Iyad, et al.
(författare)
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Hardware/Software architecture for real-time ECG monitoring and analysis leveraging MPSoC technology
- 2007
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Ingår i: Transactions on High-Performance Embedded Architectures and Compilers I. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540715276 ; , s. 239-258
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Konferensbidrag (refereegranskat)abstract
- The interest in high performance chip architectures for biomedical applications is gaining a lot of research and market interest. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine. However, ECG analysis still faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and it is centered on the parallelization of the ECG computation kernel. Our Hardware- Software (HW/SW) Multi-Processor System-on-Chip (MPSoQ design improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final HW/SW architecture is the focus of this paper. We explore the design space by considering a number of hardware and software architectural variants, and deploy industrial components to build up the system.
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| 6. |
- Belov, Vladimir, et al.
(författare)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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| 7. |
- Khatib, Iyad Al, et al.
(författare)
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A multiprocessor system-on-chip for real-time biomedical monitoring and analysis : ECG prototype architectural design space exploration
- 2008
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Ingår i: ACM Transactions on Design Automation of Electronic Systems. - : Association for Computing Machinery (ACM). - 1084-4309 .- 1557-7309. ; 13:2, s. 31-
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Tidskriftsartikel (refereegranskat)abstract
- In this article we focus on multiprocessor system-on-chip (MPSoC) architectures for human heart electrocardiogram (ECG) real time analysis as a hardware/software (HW/SW) platform offering an advance relative to state-of-the-art solutions. This is a relevant biomedical application with good potential market, since heart diseases are responsible for the largest number of yearly deaths. Hence, it is a good target for an application-specific system-on-chip (SoC) and HW/SW codesign. We investigate a symmetric multiprocessor architecture based on STMicroelectronics VLIW DSPs that process in real time 12-lead ECG signals. This architecture improves upon state-of-the-art SoC designs for ECG analysis in its ability to analyze the full 12 leads in real time, even with high sampling frequencies, and its ability to detect heart malfunction for the whole ECG signal interval. We explore the design space by considering a number of hardware and software architectural options. Comparing our design with present-day solutions from an SoC and application point-of-view shows that our platform can be used in real time and without failures.
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| 8. |
- Khatib, Iyad Al, et al.
(författare)
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MPSoC ECG biochip : A multiprocessor system-on-chip for real-time human heart monitoring and analysis
- 2006
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Ingår i: Proceedings of the 3rd Conference on Computing Frontiers 2006, CF '06. - New York, NY, USA : ACM. - 9781595933027 ; , s. 21-28
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Konferensbidrag (refereegranskat)abstract
- The interest in high performance chip architectures for biomedical applications is on the rise. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine, which faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and is centered on the parallelization of the ECG computation kernel. It improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final hardware/software architecture, modeling, and simulation is the focus of this paper. Our system model is based on industrial components. The architectural template we employ is scalable and flexible.
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| 9. |
- Tahmasian, Masoud, et al.
(författare)
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ENIGMA-Sleep : Challenges, opportunities, and the road map
- 2021
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Ingår i: Journal of Sleep Research. - : Wiley. - 0962-1105 .- 1365-2869. ; 30:6
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Forskningsöversikt (refereegranskat)abstract
- Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.
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