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Kopetzky SJ, Li Y, Kaiser M, Butz-Ostendorf M. Predictability of intelligence and age from structural connectomes. PLoS One 2024; 19:e0301599. [PMID: 38557681 PMCID: PMC10984540 DOI: 10.1371/journal.pone.0301599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.
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Affiliation(s)
- Sebastian J. Kopetzky
- Labvantage—Biomax GmbH, Planegg, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Yong Li
- Labvantage—Biomax GmbH, Planegg, Germany
| | - Marcus Kaiser
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Functional Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Markus Butz-Ostendorf
- Labvantage—Biomax GmbH, Planegg, Germany
- Laboratory for Parallel Programming, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
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Rechberger S, Li Y, Kopetzky SJ, Butz-Ostendorf M. Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's Disease Patients. Front Aging Neurosci 2022; 14:832828. [PMID: 35747446 PMCID: PMC9211026 DOI: 10.3389/fnagi.2022.832828] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 05/06/2022] [Indexed: 11/21/2022] Open
Abstract
Longitudinal MRI studies are of increasing importance to document the time course of neurodegenerative diseases as well as neuroprotective effects of a drug candidate in clinical trials. However, manual longitudinal image assessments are time consuming and conventional assessment routines often deliver unsatisfying study outcomes. Here, we propose a profound analysis pipeline that consists of the following coordinated steps: (1) an automated and highly precise image processing stream including voxel and surface based morphometry using latest highly detailed brain atlases such as the HCP MMP 1.0 atlas with 360 cortical ROIs; (2) a profound statistical assessment using a multiplicative model of annual percent change (APC); and (3) a multiple testing correction adopted from genome-wide association studies that is optimally suited for longitudinal neuroimaging studies. We tested this analysis pipeline with 25 Alzheimer's disease patients against 25 age-matched cognitively normal subjects with a baseline and a 1-year follow-up conventional MRI scan from the ADNI-3 study. Even in this small cohort, we were able to report 22 significant measurements after multiple testing correction from SBM (including cortical volume, area and thickness) complementing only three statistically significant volume changes (left/right hippocampus and left amygdala) found by VBM. A 1-year decrease in brain morphometry coincided with an increasing clinical disability and cognitive decline in patients measured by MMSE, CDR GLOBAL, FAQ TOTAL and NPI TOTAL scores. This work shows that highly precise image assessments, APC computation and an adequate multiple testing correction can produce a significant study outcome even for small study sizes. With this, automated MRI processing is now available and reliable for routine use and clinical trials.
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Affiliation(s)
| | - Yong Li
- Biomax Informatics, Munich, Germany
| | - Sebastian J. Kopetzky
- Biomax Informatics, Munich, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Markus Butz-Ostendorf
- Biomax Informatics, Munich, Germany
- Parallel Programming, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
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Chen X, Wang Y, Kopetzky SJ, Butz‐Ostendorf M, Kaiser M. Cover Image. Hum Brain Mapp 2021. [DOI: 10.1002/hbm.25070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Chen X, Wang Y, Kopetzky SJ, Butz-Ostendorf M, Kaiser M. Connectivity within regions characterizes epilepsy duration and treatment outcome. Hum Brain Mapp 2021; 42:3777-3791. [PMID: 33973688 PMCID: PMC8288103 DOI: 10.1002/hbm.25464] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/13/2021] [Accepted: 04/26/2021] [Indexed: 11/11/2022] Open
Abstract
Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan-Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.
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Affiliation(s)
- Xue Chen
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China.,School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
| | - Sebastian J Kopetzky
- Biomax Informatics AG, Brain Science, Planegg, Germany.,TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | | | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne, UK.,NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Hastreiter M, Jeske T, Hoser J, Kluge M, Ahomaa K, Friedl MS, Kopetzky SJ, Quell JD, Mewes HW, Küffner R. KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis. Bioinformatics 2019; 33:1565-1567. [PMID: 28069593 DOI: 10.1093/bioinformatics/btx003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 01/04/2017] [Indexed: 11/14/2022] Open
Abstract
Summary Analysis of Next Generation Sequencing (NGS) data requires the processing of large datasets by chaining various tools with complex input and output formats. In order to automate data analysis, we propose to standardize NGS tasks into modular workflows. This simplifies reliable handling and processing of NGS data, and corresponding solutions become substantially more reproducible and easier to maintain. Here, we present a documented, linux-based, toolbox of 42 processing modules that are combined to construct workflows facilitating a variety of tasks such as DNAseq and RNAseq analysis. We also describe important technical extensions. The high throughput executor (HTE) helps to increase the reliability and to reduce manual interventions when processing complex datasets. We also provide a dedicated binary manager that assists users in obtaining the modules' executables and keeping them up to date. As basis for this actively developed toolbox we use the workflow management software KNIME. Availability and Implementation See http://ibisngs.github.io/knime4ngs for nodes and user manual (GPLv3 license). Contact robert.kueffner@helmholtz-muenchen.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maximilian Hastreiter
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Tim Jeske
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jonathan Hoser
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Michael Kluge
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Kaarin Ahomaa
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Marie-Sophie Friedl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Sebastian J Kopetzky
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jan-Dominik Quell
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - H-Werner Mewes
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Robert Küffner
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
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Kopetzky SJ, Butz-Ostendorf M. From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome. Front Neuroanat 2018; 12:111. [PMID: 30581382 PMCID: PMC6292998 DOI: 10.3389/fnana.2018.00111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 11/23/2018] [Indexed: 11/18/2022] Open
Abstract
The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks.
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Chursov A, Kopetzky SJ, Bocharov G, Frishman D, Shneider A. RNAtips: Analysis of temperature-induced changes of RNA secondary structure. Nucleic Acids Res 2013; 41:W486-91. [PMID: 23766288 PMCID: PMC3692058 DOI: 10.1093/nar/gkt486] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [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] [Indexed: 11/12/2022] Open
Abstract
Although multiple biological phenomena are related to temperature (e.g. elevation of body temperature due to an illness, adaptation to environmental temperature conditions, biology of coldblooded versus warm-blooded organisms), the molecular mechanisms of these processes remain to be understood. Perturbations of secondary RNA structures may play an important role in an organism’s reaction to temperature change—in all organisms from viruses and bacteria to humans. Here, we present RNAtips (temperature-induced perturbation of structure) web server, which can be used to predict regions of RNA secondary structures that are likely to undergo structural alterations prompted by temperature change. The server can also be used to: (i) detect those regions in two homologous RNA sequences that undergo different structural perturbations due to temperature change and (ii) test whether these differences are specific to the particular nucleotide substitutions distinguishing the sequences. The RNAtips web server is freely accessible without any login requirement at http://rnatips.org.
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Affiliation(s)
- Andrey Chursov
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftzentrum Weihenstephan, Maximus-von-Imhof-Forum 3, D-85354 Freising, Germany
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Chursov A, Kopetzky SJ, Leshchiner I, Kondofersky I, Theis FJ, Frishman D, Shneider A. Specific temperature-induced perturbations of secondary mRNA structures are associated with the cold-adapted temperature-sensitive phenotype of influenza A virus. RNA Biol 2012; 9:1266-74. [PMID: 22995831 PMCID: PMC3583857 DOI: 10.4161/rna.22081] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
For decades, cold-adapted, temperature-sensitive (ca/ts) strains of influenza A virus have been used as live attenuated vaccines. Due to their great public health importance it is crucial to understand the molecular mechanism(s) of cold adaptation and temperature sensitivity that are currently unknown. For instance, secondary RNA structures play important roles in influenza biology. Thus, we hypothesized that a relatively minor change in temperature (32-39°C) can lead to perturbations in influenza RNA structures and, that these structural perturbations may be different for mRNAs of the wild type (wt) and ca/ts strains. To test this hypothesis, we developed a novel in silico method that enables assessing whether two related RNA molecules would undergo (dis)similar structural perturbations upon temperature change. The proposed method allows identifying those areas within an RNA chain where dissimilarities of RNA secondary structures at two different temperatures are particularly pronounced, without knowing particular RNA shapes at either temperature. We identified such areas in the NS2, PA, PB2 and NP mRNAs. However, these areas are not identical for the wt and ca/ts mutants. Differences in temperature-induced structural changes of wt and ca/ts mRNA structures may constitute a yet unappreciated molecular mechanism of the cold adaptation/temperature sensitivity phenomena.
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Affiliation(s)
- Andrey Chursov
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftzentrum Weihenstephan, Freising, Germany
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