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  <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>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>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>
  <doc>
    <id>2472</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>194</pageFirst>
    <pageLast>197</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">Hybridizing EMD with cICA for fMRI Analysis of Patient Groups</title>
    <abstract language="eng">Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.</abstract>
    <parentTitle language="eng">2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 23-27 July 2019, Berlin, Germany</parentTitle>
    <identifier type="doi">10.1109/EMBC.2019.8856355</identifier>
    <identifier type="pmid">31945876</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>S. Wein</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>biomedical MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>independent component analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>medical image processing</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>5103</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>354</pageFirst>
    <pageLast>366</pageLast>
    <pageNumber/>
    <edition/>
    <issue>June</issue>
    <volume>133</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
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    <title language="eng">Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement</title>
    <abstract language="eng">Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR scanner still remain a major obstacle. This study, employing commonly used artifact correction steps, shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG–BOLD correlations. Specifically, low-frequency EEG (&lt; 20 Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (&lt; 0.2 mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low-frequency effects, and, as motion concurrently influences fMRI data, EEG–BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~ 3–7 Hz) effects and channel-level theta–BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.</abstract>
    <parentTitle language="eng">NeuroImage</parentTitle>
    <identifier type="pmid">27012498</identifier>
    <identifier type="doi">10.1016/j.neuroimage.2016.03.031</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Marie-Christin Fellner</author>
    <author>G. Volberg</author>
    <author>K. J. Mullinger</author>
    <author>Mark W. Greenlee</author>
    <author>Simon Hanslmayr</author>
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    <volume>14</volume>
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    <title language="eng">A Constrained ICA-EMD Model for Group Level fMRI Analysis</title>
    <abstract language="eng">Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.</abstract>
    <parentTitle language="eng">Frontiers in Neuroscience</parentTitle>
    <identifier type="doi">10.3389/fnins.2020.00221</identifier>
    <identifier type="issn">1662-453X</identifier>
    <identifier type="issn">1662-4548</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Wein</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
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    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
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    <publishedYear>2015</publishedYear>
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    <publisherName>Academic Press</publisherName>
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    <title language="eng">Early meta-level: deeper understanding of connectivity-states and consequences for state definition</title>
    <parentTitle language="eng">21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii</parentTitle>
    <identifier type="doi">10.13140/RG.2.1.2561.3929</identifier>
    <enrichment key="opus.import.date">2022-08-24T10:21:08+00:00</enrichment>
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    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</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>
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    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
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