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Bankó ÉM, Weiss B, Hevesi I, Manga A, Vakli P, Havadi-Nagy M, Kelemen R, Somogyi E, Homolya I, Bihari A, Simon Á, Nárai Á, Tóth K, Báthori N, Tomacsek V, Horváth A, Kamondi A, Racsmány M, Dénes Á, Simor P, Kovács T, Hermann P, Vidnyánszky Z. Study protocol of the Hungarian Longitudinal Study of Healthy Brain Aging (HuBA). Ideggyogy Sz 2024; 77:51-59. [PMID: 38321854 DOI: 10.18071/isz.77.0051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Background and purpose Neurocognitive aging and the associated brain diseases impose a major social and economic burden. Therefore, substantial efforts have been put into revealing the lifestyle, the neurobiological and the genetic underpinnings of healthy neurocognitive aging. However, these studies take place almost exclusively in a limited number of highly-developed countries. Thus, it is an important open question to what extent their findings may generalize to neurocognitive aging in other, not yet investigated regions. The purpose of the Hungarian Longitudinal Study of Healthy Brain Aging (HuBA) is to collect multi-modal longitudinal data on healthy neurocognitive aging to address the data gap in this field in Central and Eastern Europe. . Methods We adapted the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging study protocol to local circumstances and collected demographic, lifestyle, mental and physical health, medication and medical history related information as well as recorded a series of magnetic resonance imaging (MRI) data. In addition, participants were also offered to participate in the collection of blood samples to assess circulating inflammatory biomarkers as well as a sleep study aimed at evaluating the general sleep quality based on multi-day collection of subjective sleep questionnaires and whole-night electroencephalographic (EEG) data. . Results Baseline data collection has already been accomplished for more than a hundred participants and data collection in the second session is on the way. The collected data might reveal specific local trends or could also indicate the generalizability of previous findings. Moreover, as the HuBA protocol also offers a sleep study designed for thorough characterization of participants’ sleep quality and related factors, our extended multi-modal dataset might provide a base for incorporating these measures into healthy and clinical aging research. . Conclusion Besides its straightforward national benefits in terms of health expenditure, we hope that this Hungarian initiative could provide results valid for the whole Central and Eastern European region and could also promote aging and Alzheimer’s disease research in these countries. .
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Affiliation(s)
- Éva M Bankó
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - István Hevesi
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Annamária Manga
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Menta Havadi-Nagy
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Rebeka Kelemen
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Eszter Somogyi
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - István Homolya
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Adél Bihari
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Ádám Simon
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
- Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs
| | - Krisztina Tóth
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
- "Momentum" Laboratory of Neuroimmunology, Institute of Experimental Medicine, Budapest
| | - Noémi Báthori
- Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics, Budapest
| | - Vivien Tomacsek
- Doctoral School of Psychology, Eötvös Loránd University, Budapest
- Institute of Psychology, Eötvös Loránd University, Budapest
| | - András Horváth
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest
- Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Budapest
| | - Anita Kamondi
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest
- Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Budapest
- Department of Neurology, Semmelweis University, Budapest
| | - Mihály Racsmány
- Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest
- Institute of Psychology & Centre for Cognitive Medicine, University of Szeged, Szeged
| | - Ádám Dénes
- "Momentum" Laboratory of Neuroimmunology, Institute of Experimental Medicine, Budapest
| | - Péter Simor
- Institute of Psychology, Eötvös Loránd University, Budapest
| | - Tibor Kovács
- Department of Neurology, Semmelweis University, Budapest
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest
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Dawood P, Breuer F, Stebani J, Burd P, Homolya I, Oberberger J, Jakob PM, Blaimer M. Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples. Magn Reson Med 2023; 89:812-827. [PMID: 36226661 DOI: 10.1002/mrm.29482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/12/2022] [Accepted: 09/20/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate an iterative learning approach for enhanced performance of robust artificial-neural-networks for k-space interpolation (RAKI), when only a limited amount of training data (auto-calibration signals [ACS]) are available for accelerated standard 2D imaging. METHODS In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings. RESULTS For limited training data (18 and 22 ACS lines for R = 4 and R = 5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre-scan calibration with varying contrast between training- and undersampled data. CONCLUSION RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.
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Affiliation(s)
- Peter Dawood
- Department of Physics, University of Würzburg, Würzburg, Germany
| | - Felix Breuer
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany
| | - Jannik Stebani
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany
| | - Paul Burd
- Institute for Theoretical Physics and Astrophysics, University of Würzburg, Würzburg, Germany
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Johannes Oberberger
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Peter M Jakob
- Department of Physics, University of Würzburg, Würzburg, Germany
| | - Martin Blaimer
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany
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Nárai Á, Hermann P, Auer T, Kemenczky P, Szalma J, Homolya I, Somogyi E, Vakli P, Weiss B, Vidnyánszky Z. Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans. Sci Data 2022; 9:630. [PMID: 36253426 PMCID: PMC9576686 DOI: 10.1038/s41597-022-01694-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.
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Affiliation(s)
- Ádám Nárai
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Tibor Auer
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.,School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - János Szalma
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Béla Weiss
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
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Darányi V, Hermann P, Homolya I, Vidnyánszky Z, Nagy Z. An empirical investigation of the benefit of increasing the temporal resolution of task-evoked fMRI data with multi-band imaging. MAGMA 2021; 34:667-676. [PMID: 33763764 PMCID: PMC8421273 DOI: 10.1007/s10334-021-00918-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 02/26/2021] [Accepted: 03/03/2021] [Indexed: 11/24/2022]
Abstract
Objective There is a tendency for reducing TR in MRI experiments with multi-band imaging. We empirically investigate its benefit for the group-level statistical outcome in task-evoked fMRI. Methods Three visual fMRI data sets were collected from 17 healthy adult participants. Multi-band acquisition helped vary the TR (2000/1000/410 ms, respectively). Because these data sets capture different temporal aspects of the haemodynamic response (HRF), we tested several HRF models. We computed a composite descriptive statistic, H, from β’s of each first-level model fit and carried it to the group-level analysis. The number of activated voxels and the t value of the group-level analysis as well as a goodness-of-fit measure were used as surrogate markers of data quality for comparison. Results Increasing the temporal sampling rate did not provide a universal improvement in the group-level statistical outcome. Rather, both the voxel-wise and ROI-averaged group-level results varied widely with anatomical location, choice of HRF and the setting of the TR. Correspondingly, the goodness-of-fit of HRFs became worse with increasing the sampling frequency. Conclusion Rather than universally increasing the temporal sampling rate in cognitive fMRI experiments, these results advocate the performance of a pilot study for the specific ROIs of interest to identify the appropriate temporal sampling rate for the acquisition and the correspondingly suitable HRF for the analysis of the data. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-021-00918-z.
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Affiliation(s)
- Virág Darányi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems Research, University of Zürich, Rämistrasse 100, P.O. Box 149, Zürich, Switzerland.
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