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  <doc>
    <id>8159</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>8</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>159</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Morphological effects of input data quantity in AI-powered dental crown design</title>
    <abstract language="eng">OBJECTIVES: This retrospective in vitro study evaluated the impact of input data quantity on the morphology of dental crowns generated by AI-based software. The hypothesis suggests that increased input data quantity improves the quality of generated occlusal surfaces.&#13;
METHODS: A dataset comprising n=30 patients (11 males, 19 females; age: 22-31 years) was analyzed. Input data was categorized into full dentition (full), quadrant data (quad), and adjacent teeth (adj). AI-based software (Dentbird Crown, Imageworks Inc.) generated crowns for a single lower first molar (36/46). Metrics were proposed to assess the morphology and occlusal relationships of the crowns, with the original tooth as reference.&#13;
STATISTICS: Friedman Chi-Square tests, Wilcoxon signed rank tests, Kendall correlation and Fligner-Killeen tests (α = 0.05).&#13;
RESULTS: Full and quad groups provided consistent reconstruction quality with no significant differences in morphology and occlusal relationships. The adj group showed significant (p&lt;0.05) morphological deviations and higher reconstruction failure rates compared to the full and quad groups. Correlations (median: 0.19; min-max range: 0.01-0.54) indicate that the proposed metrics capture distinct morphological and functional crown aspects.&#13;
CONCLUSION: The software reliably reconstructed crowns with at least quadrant-level input data. Performance declined with reduced input. Full-jaw scans did not enhance accuracy compared to quadrant data.&#13;
CLINICAL SIGNIFICANCE: Increased input data quantity can improve the accuracy of AI-based restorations. As a result, prosthodontists benefit from predictable, accurate restoration proposals that reduce the need for digital chairside adjustments as well as manual modifications after fabrication. This streamlines clinical workflows and enhances the quality of restorations. Quadrant-level data has proven sufficient to generate high-quality reconstructions. Further input data did not significantly improve the accuracy of the reconstructions. The proposed metrics enable quantitative assessments of morphological and functional restoration quality, supporting reliable AI-driven workflows.</abstract>
    <parentTitle language="eng">Journal of dentistry</parentTitle>
    <identifier type="doi">10.1016/j.jdent.2025.105767</identifier>
    <identifier type="pmid">40345427</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:32:12+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">begutachtet</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alexander Broll</author>
    <author>Markus Goldhacker</author>
    <author>Sebastian Hahnel</author>
    <author>Martin Rosentritt</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>7302</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>7</volume>
    <type>article</type>
    <publisherName>Frontiers Media SA</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning</title>
    <abstract language="eng">Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.</abstract>
    <parentTitle language="eng">Frontiers in Artificial Intelligence</parentTitle>
    <identifier type="issn">2624-8212</identifier>
    <identifier type="doi">10.3389/frai.2024.1339193</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alexander Broll</author>
    <author>Martin Rosentritt</author>
    <author>Thomas Schlegl</author>
    <author>Markus Goldhacker</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inlay restoration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>dental prosthesis design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>StyleGAN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital dentistry</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>4573</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Preoperative Assessment of Language Dominance through Combined Resting-State and Task-Based Functional Magnetic Resonance Imaging</title>
    <abstract language="eng">Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years, the resting-state fMRI (RS-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Twenty patients suffering from brain lesions close to supposed language-relevant cortical areas were included. RS-fMRI and task-based (TB-fMRI) were performed for the purpose of preoperative language assessment. TB-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language-critical and language-supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the TB-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice index. Thereby, the RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. In general, the results suggest that determining language dominance in the human brain is feasible both with TB-fMRI and RS-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.</abstract>
    <parentTitle language="eng">Journal of personalized medicine</parentTitle>
    <identifier type="doi">10.3390/jpm11121342</identifier>
    <enrichment key="opus.import.date">2022-06-30T19:37:23+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Christian Ott</author>
    <author>Katharina Rosengarth</author>
    <author>Christian Doenitz</author>
    <author>Julius Hoehne</author>
    <author>Christina Wendl</author>
    <author>Frank Dodoo-Schittko</author>
    <author>Elmar Wolfgang Lang</author>
    <author>Nils Ole Schmidt</author>
    <author>Markus Goldhacker</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>resting-state fMRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>task-based fMRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>brain mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>language assessment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data-driven analysis</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>7244</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>145</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generative deep learning approaches for the design of dental restorations: A narrative review</title>
    <abstract language="eng">Objectives:&#13;
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.&#13;
&#13;
Data/Sources:&#13;
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.&#13;
&#13;
Study selection:&#13;
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.&#13;
&#13;
Conclusions:&#13;
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.&#13;
&#13;
Clinical significance:&#13;
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.</abstract>
    <parentTitle language="eng">Journal of Dentistry</parentTitle>
    <identifier type="issn">0300-5712</identifier>
    <identifier type="doi">10.1016/j.jdent.2024.104988</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alexander Broll</author>
    <author>Markus Goldhacker</author>
    <author>Sebastian Hahnel</author>
    <author>Martin Rosentritt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tooth reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dental prosthesis design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital dentistry</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>4572</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 2</issue>
    <volume>1</volume>
    <type>conferencepresentation</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Preoperative assessment of language dominance through combined resting-state and task-based functional magnetic resonance imaging</title>
    <abstract language="eng">Background: &#13;
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years the resting-state fMRI (rs-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere.&#13;
&#13;
Methods: &#13;
Twenty patients suffering from brain lesions close to supposed language relevant cortical areas were included. Rs-fMRI and task-based (tb-fMRI) were performed for the purpose of preoperative language assessment. Tb-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language critical and language supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice-index.&#13;
&#13;
Results: &#13;
The RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly.&#13;
&#13;
Conclusion: &#13;
In general, the results suggest that determining language dominance in the human brain is feasible both with tb-fMRI and rs-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.</abstract>
    <parentTitle language="eng">Brain and Spine</parentTitle>
    <identifier type="doi">10.1016/j.bas.2021.100523</identifier>
    <enrichment key="opus.import.date">2022-06-30T19:37:23+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christian Ott</author>
    <author>K. Rosengarth</author>
    <author>Christian Doenitz</author>
    <author>Julius Hoehne</author>
    <author>C. Wendl</author>
    <author>Frank Dodoo-Schittko</author>
    <author>Elmar Wolfgang Lang</author>
    <author>Nils Ole Schmidt</author>
    <author>Markus Goldhacker</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>8501</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of digital crown design software on morphology, occlusal characteristics, fracture force and marginal fit</title>
    <abstract language="eng">Objectives&#13;
The study evaluated the influence of digital design software on crown morphology, occlusal characteristics, fracture force, and marginal fit across varying preparation designs for an identical target tooth.&#13;
&#13;
Methods&#13;
A resin-based tooth (tooth 36) was digitized, manufactured (&#13;
), individually prepared and re-digitized. Five design groups were established using conventional software proposals, technician designs, two AI-based software solutions, and natural tooth-based reference designs. All systems employed consistent parameters. Crown designs were digitally assessed using quantitative morphological and occlusal metrics in reference to the original tooth. Crowns were milled, marginal fit was measured via digital microscopy, and fracture resistance was determined after thermal cycling and mechanical loading.&#13;
&#13;
Results&#13;
Morphological metrics revealed statistically significant deviations across groups, with the technician design achieving the best performance. Occlusal metrics showed high deviations in the positional accuracy of the contact points across all groups. Technician and AI-based designs exhibited comparable functional results. None of the design groups were able to achieve contact with all relevant antagonist teeth, due to high deviations in the mesiolingual cusp. Conventional software designs exhibited the lowest fracture forces. Significant improvements were achieved through technician intervention. Vertical marginal discrepancies remained comparable across groups.&#13;
&#13;
Significance&#13;
Improved functional and morphological design combined with high fracture resistance can reduce the need for clinical adjustments, minimize wear, and enhance crown longevity. Digital design software significantly influences crown morphology, occlusal characteristics and fracture forces. Vertical marginal discrepancies remain similar. AI-driven approaches demonstrate comparability with technician designs in terms of fracture forces, functional performance, and marginal fit.</abstract>
    <parentTitle language="eng">Dental Materials</parentTitle>
    <identifier type="issn">0109-5641</identifier>
    <identifier type="doi">10.1016/j.dental.2025.09.003</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alexander Broll</author>
    <author>Sebastian Hahnel</author>
    <author>Markus Goldhacker</author>
    <author>Jakob Rossel</author>
    <author>Michael Schmidt</author>
    <author>Martin Rosentritt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dental prosthesis design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital Dentistry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Crown design software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Preparation design</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Materialien und Produktion</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2416</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>16</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName>Frontiers Research Foundation</publisherName>
    <publisherPlace>Lausanne</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States</title>
    <abstract language="eng">Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.</abstract>
    <parentTitle language="eng">Frontiers in Human Neuroscience</parentTitle>
    <identifier type="doi">10.3389/fnhum.2018.00253</identifier>
    <identifier type="issn">1662-5161</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Markus Goldhacker</author>
    <author>Ana Maria Tomé</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5101</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>16</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>3</volume>
    <type>article</type>
    <publisherName>Society for Neuroscience</publisherName>
    <publisherPlace>Washington, DC</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Spatial mnemonic encoding: Theta power decreases and medial temporal lobe BOLD increases co-occur during the usage of the method of loci</title>
    <abstract language="eng">The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3–8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL.</abstract>
    <parentTitle language="eng">eNeuro</parentTitle>
    <identifier type="doi">10.1523/ENEURO.0184-16.2016</identifier>
    <identifier type="issn">2373-2822</identifier>
    <identifier type="pmid">28101523</identifier>
    <enrichment key="opus.import.date">2022-08-05T07:16:41+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Marie-Christin Fellner</author>
    <author>Gregor Volberg</author>
    <author>Maria Wimber</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Simon Hanslmayr</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>EEG</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fMRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>memory encoding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>method of loci</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mnemonics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>theta oscillations</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2455</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>91</pageFirst>
    <pageLast>99</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>151</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A multi-variate blind source separation algorithm</title>
    <abstract language="eng">Background and objective&#13;
&#13;
The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation.&#13;
Methods&#13;
&#13;
The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations.&#13;
Results&#13;
&#13;
The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar.&#13;
Conclusion&#13;
&#13;
The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.</abstract>
    <parentTitle language="deu">Computer Methods and Programs in Biomedicine</parentTitle>
    <identifier type="doi">10.1016/j.cmpb.2017.08.019</identifier>
    <identifier type="issn">1872-7565</identifier>
    <identifier type="issn">0169-2607</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>P. Keck</author>
    <author>A. Igel</author>
    <author>Elmar Wolfgang Lang</author>
    <author>Ana Maria Tomé</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Blind source separation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Independent component analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fMRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Resting state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Retinotopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatio temporal</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>4828</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>31</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Hindawi</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning</title>
    <abstract language="eng">This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.</abstract>
    <parentTitle language="eng">Computational intelligence and neuroscience</parentTitle>
    <identifier type="doi">10.1155/2021/5573740</identifier>
    <identifier type="pmid">34135951</identifier>
    <enrichment key="opus.import.date">2022-07-16T21:56:15+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Wein</author>
    <author>Gustavo Deco</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Wilhelm M. Malloni</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain Mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain/diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diffusion Tensor Imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nerve Net/diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural Pathways/diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structure-Activity Relationship</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2670</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1726</pageFirst>
    <pageLast>1735</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Conception of a Reference Architecture for Machine Learning in the Process Industry</title>
    <abstract language="eng">The increasing global competition demands continuous optimization of products and processes from companies in the process industry. Where conventional methods of Lean Management and Six Sigma reach their limits, new opportunities and challenges arise through increasing connectivity in the Industrial Internet of Things and machine learning. The majority of industrial projects do not reach the deployment or are isolated solutions, as the structures for data integration, training, deployment and maintenance of models are not established. This paper presents the conception of a reference architecture for machine learning in the process industry to support companies in implementing their own specific structures. The focus is on the development process and an exemplary implementation in the brewing industry.</abstract>
    <parentTitle language="eng">2020 IEEE International Conference on Big Data (Big Data): 10.12.2020 - 13.12.2020 Atlanta, GA, USA</parentTitle>
    <identifier type="isbn">978-1-7281-6251-5</identifier>
    <identifier type="doi">10.1109/bigdata50022.2020.9378290</identifier>
    <enrichment key="opus.import.date">2022-02-04T08:58:18+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Rene Wostmann</author>
    <author>Philipp Schlunder</author>
    <author>Fabian Temme</author>
    <author>Ralf Klinkenberg</author>
    <author>Josef Kimberger</author>
    <author>Andrea Spichtinger</author>
    <author>Markus Goldhacker</author>
    <author>Jochen Deuse</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Big data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Companies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industrial Internet of Things</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industries</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>process industry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Production</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reference architecture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Six sigma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Training</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5325</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>14</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>5</volume>
    <type>article</type>
    <publisherName>Frontiers Research Foundation</publisherName>
    <publisherPlace>Lausanne</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Perceptual learning in patients with macular degeneration</title>
    <abstract language="eng">Patients with age-related macular degeneration (AMD) or hereditary macular dystrophies (JMD) rely on an efficient use of their peripheral visual field. We trained eight AMD and five JMD patients to perform a texture-discrimination task (TDT) at their preferred retinal locus (PRL) used for fixation. Six training sessions of approximately one hour duration were conducted over a period of approximately 3 weeks. Before, during and after training twelve patients and twelve age-matched controls (the data from two controls had to be discarded later) took part in three functional magnetic resonance imaging (fMRI) sessions to assess training-related changes in the BOLD response in early visual cortex. Patients benefited from the training measurements as indexed by significant decrease (p = 0.001) in the stimulus onset asynchrony (SOA) between the presentation of the texture target on background and the visual mask, and in a significant location specific effect of the PRL with respect to hit rate (p = 0.014). The following trends were observed: (i) improvement in Vernier acuity for an eccentric line-bisection task; (ii) positive correlation between the development of BOLD signals in early visual cortex and initial fixation stability (r = 0.531); (iii) positive correlation between the increase in task performance and initial fixation stability (r = 0.730). The first two trends were non-significant, whereas the third trend was significant at p = 0.014, Bonferroni corrected. Consequently, our exploratory study suggests that training on the TDT can enhance eccentric vision in patients with central vision loss. This enhancement is accompanied by a modest alteration in the BOLD response in early visual cortex.</abstract>
    <parentTitle language="eng">Frontiers in psychology</parentTitle>
    <identifier type="doi">10.3389/fpsyg.2014.01189</identifier>
    <identifier type="issn">1664-1078</identifier>
    <identifier type="pmid">25368597</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tina Plank</author>
    <author>Katharina Rosengarth</author>
    <author>Carolin Schmalhofer</author>
    <author>Markus Goldhacker</author>
    <author>Sabine Brandl-Rühle</author>
    <author>Mark W. Greenlee</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>perceptual learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fMRI BOLD</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cortical plasticity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visual cortex</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>macular degeneration</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5311</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>12</volume>
    <type>conferencepresentation</type>
    <publisherName>ARVO</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Neural correlates of perceptual filling-in: fMRI evidence in the foveal projection zone of patients with central scotoma</title>
    <abstract language="eng">Patients with juvenile retinal dystrophy often report that they are unaware of their central scotoma, suggesting the presence of perceptual filling-in. We used functional Magnetic Resonance Imaging (fMRI) to determine possible neural correlates of perceptual filling-in in patients with retinal distrophy and clinically established central scotoma in both eyes. The data of 5 patients (Stargardt disease, cone-rod dystrophy; mean age 45 yrs; scotoma diameter 10-20°) and of 5 normally sighted controls were analyzed. Fixation behaviour and perimetry were measured with a Nidek microperimeter. Magnetic resonance imaging was performed using a Siemens 3T Allegra scanner. We stimulated the central visual field (30 deg) with a vertically oriented, low spatial frequency (1 c/deg) high-contrast sinewave grating that was either a) continuous, or b) was interrupted by a central grey disk. The disk was either slightly larger than the scotoma (detectable on 75% of trials) or slightly smaller (detectable on 25% of trials). To control for attention, an eccentric fixation task was performed during scanning. Data were analyzed using SPM8 (GLM with ROI analysis to obtain percent signal change for foveal projection zone). Results: for all patients, the BOLD signal in the foveal projection area was significantly higher for the small disk (i.e., condition leading to complete filling-in) than for the large disk (i.e., no filling-in). This effect was absent in the control subjects. Our findings support the existence of an active neural process that leads to filling-in in patients with central visual field scotomata.</abstract>
    <parentTitle language="eng">Journal of Vision / Vision Sciences Society Annual Meeting Abstract</parentTitle>
    <identifier type="doi">10.1167/12.9.1303</identifier>
    <identifier type="issn">1534-7362</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Mark W. Greenlee</author>
    <author>Stuart Anstis</author>
    <author>Katharina Rosengarth</author>
    <author>Markus Goldhacker</author>
    <author>Sabine Brandl-Rühle</author>
    <author>Tina Plank</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5309</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>99</pageFirst>
    <pageLast>110</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>99</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The effect of feedback on performance and brain activation during perceptual learning</title>
    <abstract language="eng">We investigated the role of informative feedback on the neural correlates of perceptual learning in a coherent-motion detection paradigm. Stimulus displays consisted of four patches of moving dots briefly (500 ms) presented simultaneously, one patch in each visual quadrant. The coherence level was varied in the target patch from near threshold to high, while the other three patches contained only noise. The participants judged whether coherent motion was present or absent in the target patch. To guarantee central fixation, a secondary RSVP digit-detection task was performed at fixation. Over six training sessions subjects learned to detect coherent motion in a predefined quadrant (i.e., the learned location). Half of our subjects were randomly assigned to the feedback group, where they received informative feedback after each response during training, whereas the other group received non-informative feedback during training that a response button was pressed. We investigated whether the presence of informative feedback during training had an influence on the learning success and on the resulting BOLD response. Behavioral data of 24 subjects showed improved performance with increasing practice. Informative feedback promoted learning for motion displays with high coherence levels, whereas it had little effect on learning for displays with near-threshold coherence levels. Learning enhanced fMRI responses in early visual cortex and motion-sensitive area MT+ and these changes were most pronounced for high coherence levels. Activation in the insular and cingulate cortex was mainly influenced by coherence level and trained location. We conclude that feedback modulates behavioral performance and, to a lesser extent, brain activation in areas responsible for monitoring perceptual learning.</abstract>
    <parentTitle language="eng">Vision research</parentTitle>
    <identifier type="doi">10.1016/j.visres.2013.11.010</identifier>
    <identifier type="issn">1878-5646</identifier>
    <identifier type="issn">0042-6989</identifier>
    <identifier type="pmid">24325851</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Katharina Rosengarth</author>
    <author>Tina Plank</author>
    <author>Mark W. Greenlee</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Perceptual learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Functional MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feedback</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Coherent motion detection</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
</export-example>
