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  1. Book ; Online ; Thesis: Design-based stereologische post mortem Studien zur Bestimmung von kortikalem Volumen, Neuronenzahl und Neuronendichte im Gehirn von Patienten mit Schizophrenie und gesunden Kontrollpersonen

    Gaus, Richard [Verfasser] / Schmitz, Christoph [Akademischer Betreuer]

    2024  

    Author's details Richard Gaus ; Betreuer: Christoph Schmitz
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language German
    Publisher Universitätsbibliothek der Ludwig-Maximilians-Universität
    Publishing place München
    Document type Book ; Online ; Thesis
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  2. Article ; Online: Can we diagnose mental disorders in children? A large-scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study.

    Gaus, Richard / Pölsterl, Sebastian / Greimel, Ellen / Schulte-Körne, Gerd / Wachinger, Christian

    JCPP advances

    2023  Volume 3, Issue 4, Page(s) e12184

    Abstract: Background: Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings ... ...

    Abstract Background: Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings obtained on adults can be transferred to children.
    Methods: Using data from 6916 children aged 9-10 in the multicenter Adolescent Brain Cognitive Development study, we extracted 136 regional volume and thickness measures from structural magnetic resonance images to rigorously evaluate the capabilities of machine learning to predict 10 different psychiatric disorders: major depressive disorder, bipolar disorder (BD), psychotic symptoms, attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, post-traumatic stress disorder, obsessive-compulsive disorder, generalized anxiety disorder, and social anxiety disorder. For each disorder, we performed cross-validation and assessed whether models discovered a true pattern in the data via permutation testing.
    Results: Two of 10 disorders can be detected with statistical significance when using advanced models that (i) allow for non-linear relationships between neuroanatomy and disorder, (ii) model interdependencies between disorders, and (iii) avoid confounding due to sociodemographic factors: ADHD (AUROC = 0.567,
    Conclusion: While the modest absolute classification performance does not warrant application in the clinic, our results provide empirical evidence that embracing and explicitly accounting for the complexities of mental disorders via advanced machine learning models can discover patterns that would remain hidden with traditional models.
    Language English
    Publishing date 2023-06-28
    Publishing country United States
    Document type Journal Article
    ISSN 2692-9384
    ISSN (online) 2692-9384
    DOI 10.1002/jcv2.12184
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  3. Article ; Online: Reduced cortical neuron number and neuron density in schizophrenia with focus on area 24: a post-mortem case-control study.

    Gaus, Richard / Popal, Melanie / Heinsen, Helmut / Schmitt, Andrea / Falkai, Peter / Hof, Patrick R / Schmitz, Christoph / Vollhardt, Alisa

    European archives of psychiatry and clinical neuroscience

    2022  Volume 273, Issue 6, Page(s) 1209–1223

    Abstract: Structural and functional abnormalities of the anterior cingulate cortex (ACC) have frequently been identified in schizophrenia. Alterations of von Economo neurons (VENs), a class of specialized projection neurons, have been found in different ... ...

    Abstract Structural and functional abnormalities of the anterior cingulate cortex (ACC) have frequently been identified in schizophrenia. Alterations of von Economo neurons (VENs), a class of specialized projection neurons, have been found in different neuropsychiatric disorders and are also suspected in schizophrenia. To date, however, no definitive conclusions can be drawn about quantitative histologic changes in the ACC in schizophrenia because of a lack of rigorous, design-based stereologic studies. In the present study, the volume, total neuron number and total number of VENs in layer V of area 24 were determined in both hemispheres of postmortem brains from 12 male patients with schizophrenia and 11 age-matched male controls. To distinguish global from local effects, volume and total neuron number were also determined in the whole area 24 and whole cortical gray matter (CGM). Measurements were adjusted for hemisphere, age, postmortem interval and fixation time using an ANCOVA model. Compared to controls, patients with schizophrenia showed alterations, with lower mean total neuron number in CGM (- 14.9%, P = 0.007) and in layer V of area 24 (- 21.1%, P = 0.002), and lower mean total number of VENs (- 28.3%, P = 0.027). These data provide evidence for ACC involvement in the pathophysiology of schizophrenia, and complement neuroimaging findings of impaired ACC connectivity in schizophrenia. Furthermore, these results support the hypothesis that the clinical presentation of schizophrenia, particularly deficits in social cognition, is associated with pathology of VENs.
    MeSH term(s) Humans ; Male ; Gyrus Cinguli/diagnostic imaging ; Gyrus Cinguli/pathology ; Schizophrenia/diagnostic imaging ; Schizophrenia/pathology ; Case-Control Studies ; Neurons/pathology ; Brain/pathology
    Language English
    Publishing date 2022-11-09
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1045583-8
    ISSN 1433-8491 ; 0175-758X ; 0940-1334
    ISSN (online) 1433-8491
    ISSN 0175-758X ; 0940-1334
    DOI 10.1007/s00406-022-01513-6
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  4. Book ; Online: Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data

    Yeganeh, Yousef / Farshad, Azade / Boschmann, Johann / Gaus, Richard / Frantzen, Maximilian / Navab, Nassir

    2022  

    Abstract: Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, ... ...

    Abstract Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions. Data heterogeneity poses a challenge for FL and the personalization of the local models. In this work, we investigate an adaptive hierarchical clustering method for FL to produce intermediate semi-global models, so clients with similar data distribution have the chance of forming a more specialized model. Our method forms several clusters consisting of clients with the most similar data distributions; then, each cluster continues to train separately. Inside the cluster, we use meta-learning to improve the personalization of the participants' models. We compare the clustering approach with classical FedAvg and centralized training by evaluating our proposed methods on the HAM10k dataset for skin lesion classification with extreme heterogeneous data distribution. Our experiments demonstrate significant performance gain in heterogeneous distribution compared to standard FL methods in classification accuracy. Moreover, we show that the models converge faster if applied in clusters and outperform centralized training while using only a small subset of data.
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-07-07
    Publishing country us
    Document type Book ; Online
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