1
|
Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024:10.1038/s43588-024-00659-8. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
Collapse
Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| |
Collapse
|
2
|
Ballard ED, Greenstein D, Reiss PT, Crainiceanu CM, Cui E, Duncan WC, Hejazi NS, Zarate CA. Functional changes in sleep-related arousal after ketamine administration in individuals with treatment-resistant depression. Transl Psychiatry 2024; 14:238. [PMID: 38834540 PMCID: PMC11150508 DOI: 10.1038/s41398-024-02956-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/06/2024] Open
Abstract
The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in arousal-related sleep metrics between 36 individuals with treatment-resistant major depression (TRD) and 25 healthy volunteers (HVs). It also sought to determine whether ketamine normalizes arousal in individuals with TRD and whether ketamine's effects on arousal mediate its antidepressant and anti-SI effects. This was a secondary analysis of a biomarker-focused, randomized, double-blind, crossover trial of ketamine (0.5 mg/kg) compared to saline placebo. Polysomnography (PSG) studies were conducted one day before and one day after ketamine/placebo infusions. Sleep arousal was measured using spectral power functions over time including alpha (quiet wakefulness), beta (alert wakefulness), and delta (deep sleep) power, as well as macroarchitecture variables, including wakefulness after sleep onset (WASO), total sleep time (TST), rapid eye movement (REM) latency, and Post-Sleep Onset Sleep Efficiency (PSOSE). At baseline, diagnostic differences in sleep macroarchitecture included lower TST (p = 0.006) and shorter REM latency (p = 0.04) in the TRD versus HV group. Ketamine's temporal dynamic effects (relative to placebo) in TRD included increased delta power earlier in the night and increased alpha and delta power later in the night. However, there were no significant diagnostic differences in temporal patterns of alpha, beta, or delta power, no ketamine effects on sleep macroarchitecture arousal metrics, and no mediation effects of sleep variables on ketamine's antidepressant or anti-SI effects. These results highlight the role of sleep-related variables as part of the systemic neurobiological changes initiated after ketamine administration. Clinical Trials Identifier: NCT00088699.
Collapse
Affiliation(s)
- Elizabeth D Ballard
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Deanna Greenstein
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Philip T Reiss
- Department of Statistics, University of Haifa, Haifa, Israel
| | - Ciprian M Crainiceanu
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Erjia Cui
- Division of Biostatistics and Health Data Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Wallace C Duncan
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Nadia S Hejazi
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Zarate
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
3
|
Collazos JAA, Dias R, Medeiros MC. Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil. Stat Med 2023; 42:993-1012. [PMID: 36631172 DOI: 10.1002/sim.9654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/09/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.
Collapse
Affiliation(s)
- Julian A A Collazos
- Department of Mathematics, New Granada Military University, Bogotá, Colombia
| | - Ronaldo Dias
- Department of Statistics, State University of Campinas, Campinas, Brazil
| | - Marcelo C Medeiros
- Department of Economics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| |
Collapse
|
4
|
Traumatic stress load and stressor reactivity score associated with accelerated gray matter maturation in youths indexed by normative models. Mol Psychiatry 2023; 28:1137-1145. [PMID: 36575305 DOI: 10.1038/s41380-022-01908-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022]
Abstract
Understanding how traumatic stress affects typical brain development during adolescence is critical to elucidate underlying mechanisms related to both maladaptive functioning and resilience after traumatic exposures. The current study aimed to map deviations from normative ranges of brain gray matter for youths with traumatic exposures. For each cortical and subcortical gray matter region, normative percentiles of variations were established using structural MRI from typically developing youths without any traumatic exposure (n = 245; age range = 8-23) from the Philadelphia Neurodevelopmental Cohort (PNC). The remaining PNC participants with neuroimaging data (n = 1129) were classified as either within the normative range (5-95%), delayed (>95%) or accelerated (<5%) maturational ranges for each region using the normative model. An averaged quantile regression index was calculated across all regions. Mediation models revealed that high traumatic stress load was positively associated with poorer cognitive functioning and greater psychopathology, and these associations were mediated by accelerated gray matter maturation. Furthermore, higher stressor reactivity scores, which represent a less resilient response under traumatic stress, were positively correlated with greater acceleration of gray matter maturation (r = 0.224, 95% CI = [0.17, 0.28], p < 0.001), suggesting that more accelerated maturation was linked to greater stressor response regardless of traumatic stress load. We conclude that traumatic stress is a source of deviation from normative brain development associated with poorer cognitive functioning and more psychopathology in the long run.
Collapse
|
5
|
He S, Feng Y, Grant PE, Ou Y. Deep Relation Learning for Regression and Its Application to Brain Age Estimation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2304-2317. [PMID: 35320092 PMCID: PMC9782832 DOI: 10.1109/tmi.2022.3161739] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative relation," "relative relation," "maximal relation" and "minimal relation." These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).
Collapse
|
6
|
Hobday H, Cole JH, Stanyard RA, Daws RE, Giampietro V, O'Daly O, Leech R, Váša F. Tissue volume estimation and age prediction using rapid structural brain scans. Sci Rep 2022; 12:12005. [PMID: 35835813 PMCID: PMC9283414 DOI: 10.1038/s41598-022-14904-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
The multicontrast EPImix sequence generates six contrasts, including a T1-weighted scan, in ~1 min. EPImix shows comparable diagnostic performance to conventional scans under qualitative clinical evaluation, and similarities in simple quantitative measures including contrast intensity. However, EPImix scans have not yet been compared to standard MRI scans using established quantitative measures. In this study, we compared conventional and EPImix-derived T1-weighted scans of 64 healthy participants using tissue volume estimates and predicted brain-age. All scans were pre-processed using the SPM12 DARTEL pipeline, generating measures of grey matter, white matter and cerebrospinal fluid volume. Brain-age was predicted using brainageR, a Gaussian Processes Regression model previously trained on a large sample of standard T1-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T1-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T1-weighted scans showed a systematic offset in predictions of chronological age. Supplementary analyses suggest that this is likely caused by the reduced field of view of EPImix scans, and the use of a brain-age model trained using conventional T1-weighted scans. However, this systematic error can be corrected using additional regression of T1-predicted brain-age onto EPImix-predicted brain-age. Finally, retest EPImix scans acquired for 10 participants demonstrated high test-retest reliability in all evaluated quantitative measurements. Quantitative analysis of EPImix scans has potential to reduce scanning time, increasing participant comfort and reducing cost, as well as to support automation of scanning, utilising active learning for faster and individually-tailored (neuro)imaging.
Collapse
Affiliation(s)
- Harriet Hobday
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ryan A Stanyard
- Department of Forensic and Developmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Richard E Daws
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Vincent Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| |
Collapse
|
7
|
Hahn T, Ernsting J, Winter NR, Holstein V, Leenings R, Beisemann M, Fisch L, Sarink K, Emden D, Opel N, Redlich R, Repple J, Grotegerd D, Meinert S, Hirsch JG, Niendorf T, Endemann B, Bamberg F, Kröncke T, Bülow R, Völzke H, von Stackelberg O, Sowade RF, Umutlu L, Schmidt B, Caspers S, Kugel H, Kircher T, Risse B, Gaser C, Cole JH, Dannlowski U, Berger K. An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling. SCIENCE ADVANCES 2022; 8:eabg9471. [PMID: 34985964 PMCID: PMC8730629 DOI: 10.1126/sciadv.abg9471] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 11/11/2021] [Indexed: 06/14/2023]
Abstract
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.
Collapse
Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Nils R. Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vincent Holstein
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Marie Beisemann
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Interdisciplinary Centre for Clinical Research (IZKF) of the Medical Faculty Münster, University of Münster, Münster, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychology, University of Halle, Halle, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | | | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), NAKO imaging site Berlin, Max-Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Beate Endemann
- Berlin Ultrahigh Field Facility (B.U.F.F.), NAKO imaging site Berlin, Max-Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Kröncke
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center, Member of the German Lung Research Center, Heidelberg, Germany
| | - Ramona Felizitas Sowade
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center, Member of the German Lung Research Center, Heidelberg, Germany
| | - Lale Umutlu
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Duisburg, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Duisburg, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany
| | - Harald Kugel
- Institute of Clinical Radiology, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Benjamin Risse
- Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, and Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H. Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department King’s College, London, UK
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| |
Collapse
|
8
|
Bilgili F, Nathaniel SP, Kuşkaya S, Kassouri Y. Environmental pollution and energy research and development: an Environmental Kuznets Curve model through quantile simulation approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:53712-53727. [PMID: 34036502 DOI: 10.1007/s11356-021-14506-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/17/2021] [Indexed: 05/17/2023]
Abstract
Energy research and development (R&D) and environmental sustainability is often referred to as two interrelated trends, especially in the current context of the 4th industrial revolution. As a primary input of energy innovations, R&D in the energy sector constitutes a vital tool in addressing global environmental and energy challenges. In this frame, we observe the effects of disaggregated energy R&D on environmental pollution within the Environmental Kuznets Curve (EKC) framework in thirteen developed countries over the period 2003-2018. By employing the panel quantile regression technique, we find an inverted U-shaped nexus between economic growth and carbon emissions only in higher carbon-emitting countries, thus, confirming the EKC hypothesis. However, the U-shaped nexus is more predominant in lower carbon-emitting countries. As such, we demonstrate that there is not any single dynamic in the relationship between economic growth and pollution as reported in previous studies. Contrary to expectations, we find that energy efficiency research and development is more effective in curbing carbon emissions compared to fossil fuels and renewable energy research and development. The empirical results indicate also that only energy efficiency R&D mitigates significantly the CO2 emissions from the 50th quantile up to 90th quantile, although the magnitude of the negative sign is more pronounced (in absolute term) at the highest quantile (90th). In this light, our findings would guide policymakers in the establishment of sustainable energy research and development schemes that will allow the preservation of equilibrium for the environment while also promoting energy innovations.
Collapse
Affiliation(s)
- Faik Bilgili
- Department of Economics, Faculty of Economics and Administrative Sciences, Erciyes University, 38039, Kayseri, Turkey
| | - Solomon Prince Nathaniel
- Department of Economics, University of Lagos, Akoka, Nigeria.
- Lagos State University, School of Foundation, Badagry, Nigeria.
| | - Sevda Kuşkaya
- Department of Law, Erciyes University, 38280, Kayseri, Turkey
| | - Yacouba Kassouri
- Department of Economics, Erciyes University, 38039, Kayseri, Turkey
| |
Collapse
|
9
|
Bilgili F, Kuşkaya S, Khan M, Awan A, Türker O. The roles of economic growth and health expenditure on CO 2 emissions in selected Asian countries: a quantile regression model approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:44949-44972. [PMID: 33852118 PMCID: PMC8045018 DOI: 10.1007/s11356-021-13639-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/22/2021] [Indexed: 05/05/2023]
Abstract
Continuous economic growth and the rise in energy consumption are linked with environmental pollution. Demand for health care expenditure increased after the COVID-19 pandemic. This study is interesting in modeling the nexus between public and private health expenditure, carbon dioxide emissions, and economic growth. To this end, the present study analyzed the nexus between public and private health care expenditure, economic growth, and environmental pollution for 36 Asian countries for the period 1991-2017. FMOLS, GMM, and quantile regression analysis confirm the EKC hypothesis in Asia. Besides, FMOLS and quantile regressions reached the reducing effects of government and private health expenditures on CO2 emissions. While quantile regression results show that public and private health expenditures can mitigate CO2 emissions; however, these results differ for various levels of CO2. Findings of quantile regression show a significant impact of both public and private health expenditures in reducing CO2 at the 50th and 75th quantiles but results are insignificant for the 25th quantile. Overall, the paper concludes that both government and private health sectors' expenditures caused CO2 emissions to decrease in Asia and that the negative impact of the private health sector on CO2 emissions is greater than that of the government health sector. The concluding remark is that the higher the health spending, the higher the environmental quality will be in Asia. Hence, the health administrators need to increase public and private health expenditures with an effective cost-service and energy-efficient management approach to reach sustainable health services and a sustainable environment in Asia.
Collapse
Affiliation(s)
- Faik Bilgili
- FEAS, Economics, Erciyes University, 38039 Kayseri, Turkey
| | - Sevda Kuşkaya
- Department of Law, Justice Vocational College, Erciyes University, 38280 Kayseri, Turkey
| | - Masreka Khan
- BRAC International, BRAC Centre, 75 Mohakhali, Dhaka, 1212 Bangladesh
| | - Ashar Awan
- The University of Azad Jammu & Kashmir – UAJ&K, University Old Campus, Muzaffarabad, 13100 Pakistan
- Social Sciences Institution, Erciyes University, 38039 Kayseri, Turkey
| | - Oguzhan Türker
- FEAS, Economics, Erciyes University, 38039 Kayseri, Turkey
| |
Collapse
|
10
|
Hahn T, Fisch L, Ernsting J, Winter NR, Leenings R, Sarink K, Emden D, Kircher T, Berger K, Dannlowski U. From 'loose fitting' to high-performance, uncertainty-aware brain-age modelling. Brain 2021; 144:e31. [PMID: 33826702 DOI: 10.1093/brain/awaa454] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Tim Hahn
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Lukas Fisch
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Jan Ernsting
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Nils R Winter
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Kelvin Sarink
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Daniel Emden
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| |
Collapse
|
11
|
Bilgili F, Dundar M, Kuşkaya S, Lorente DB, Ünlü F, Gençoğlu P, Muğaloğlu E. The Age Structure, Stringency Policy, Income, and Spread of Coronavirus Disease 2019: Evidence From 209 Countries. Front Psychol 2021; 11:632192. [PMID: 33643117 PMCID: PMC7907165 DOI: 10.3389/fpsyg.2020.632192] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 12/23/2020] [Indexed: 01/19/2023] Open
Abstract
This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.
Collapse
Affiliation(s)
- Faik Bilgili
- Faculty of Economics and Administrative Sciences, Department of Economics, Erciyes University, Melikgazi, Turkey
| | - Munis Dundar
- Faculty of Medicine, Internal Medical Sciences, Department of Medical Genetics, Erciyes University, Talas, Turkey
| | - Sevda Kuşkaya
- Department of Law, Justice Vocational College, Erciyes University, Talas, Turkey
| | - Daniel Balsalobre Lorente
- Faculty of Social Sciences, Department of Public Finance, University of Castilla La Mancha, Cuenca, Spain
| | - Fatma Ünlü
- Faculty of Economics and Administrative Sciences, Department of Economics, Erciyes University, Talas, Turkey
| | - Pelin Gençoğlu
- Erciyes University Research and Application Center of Kayseri, Melikgazi, Turkey
| | - Erhan Muğaloğlu
- Faculty of Managerial Sciences, Department of Economics, Abdullah Gül University, Kayseri, Turkey
| |
Collapse
|
12
|
Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty. Neuroimage 2020; 223:117316. [PMID: 32890745 DOI: 10.1016/j.neuroimage.2020.117316] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 07/25/2020] [Accepted: 08/24/2020] [Indexed: 12/30/2022] Open
Abstract
MRI-based brain age prediction has been widely used to characterize normal brain development, and deviations from the typical developmental trajectory are indications of brain abnormalities. Age prediction of the fetal brain remains unexplored, although it can be of broad interest to prenatal examination given the limited diagnostic tools available for assessment of the fetal brain. In this work, we built an attention-based deep residual network based on routine clinical T2-weighted MR images of 659 fetal brains, which achieved an overall mean absolute error of 0.767 weeks and R2 of 0.920 in fetal brain age prediction. The predictive uncertainty and estimation confidence were simultaneously quantified from the network as markers for detecting fetal brain anomalies using an ensemble method. The novel markers overcame the limitations in conventional brain age estimation and demonstrated promising diagnostic power in differentiating several types of fetal abnormalities, including small head circumference, malformations and ventriculomegaly with the area under the curve of 0.90, 0.90 and 0.67, respectively. In addition, attention maps were derived from the network, which revealed regional features that contributed to fetal age estimation at each gestational stage. The proposed attention-based deep ensembles demonstrated superior performance in fetal brain age estimation and fetal anomaly detection, which has the potential to be translated to prenatal diagnosis in clinical practice.
Collapse
|
13
|
Li G, Sun X, Wan X, Wang D. Lactoferrin-Loaded PEG/PLA Block Copolymer Targeted With Anti-Transferrin Receptor Antibodies for Alzheimer Disease. Dose Response 2020; 18:1559325820917836. [PMID: 32863801 PMCID: PMC7430085 DOI: 10.1177/1559325820917836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/19/2020] [Accepted: 02/25/2020] [Indexed: 11/15/2022] Open
Abstract
Last few years, struggles have been reported to develop the nanovesicles for drug delivery via the brain-blood barrier (BBB). Novel drugs, for instance, iAβ5, are efficient to inhibit the aggregates connected to the treatment of Alzheimer disease and are being evaluated, but most of the reports reflect some drawbacks of the drugs to reach the brain in preferred concentrations owing to the less BBB penetrability of the surface dimensions. In this report, we designed and developed a new approach to enhance the transport of drug via BBB, constructed with lactoferrin (Lf)-coated polyethylene glycol-polylactide nanoparticles (Lf-PPN) with superficial monoclonal antibody-functionalized antitransferrin receptor and anti-Aβ to deliver the iAβ5 hooked on the brain. The porcine brain capillary endothelial cells were utilized as BBB typically to examine the framework efficacy and toxicity. The cellular uptake of the immuno-nanoparticles with measured conveyance of the iAβ5 peptide was significantly enhanced and associated with Lf-PPN without monoclonal antibody functionalizations.
Collapse
Affiliation(s)
- Guichen Li
- Department of Clinical Psychology, Qingdao Mental Health Center, Qingdao, China
| | - Xianghong Sun
- Second Elderly Ward, Qingdao Mental Health Center, Qingdao, China
| | - Xiaona Wan
- Second Elderly Ward, Qingdao Mental Health Center, Qingdao, China
| | - Dongming Wang
- Second Elderly Ward, Qingdao Mental Health Center, Qingdao, China
| |
Collapse
|