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Articles by Patrick Friedrich
Test Retest Reliability of Meta Analytic Networks During Naturalistic Viewing
Abstract Functional connectivity analyses have given considerable insights into human brain function and organization. As research moves towards clinical application, test-retest reliability has become a main focus of the field. So far, the majority of studies have relied on resting-state paradigms to examine brain connectivity, based on its low demand and ease of implementation. However, the reliability of resting-state measures is mostly moderate, potentially due to its unconstrained nature.
Inter- and intra-subject similarity in network functional connectivity across a full narrative movie
Abstract Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown.
Sex classification from functional brain connectivity: Generalization to multiple datasets
1 INTRODUCTION Machine learning (ML) is a powerful tool to relate neuroimaging data to behavior and phenotypes (Genon et al., 2022; Varoquaux & Thirion, 2014) and is therefore increasingly being employed in neuroscience applications (Buch et al., 2018; Jollans et al., 2019; Kohoutova et al., 2020; Varoquaux, 2018).
Sex classification from functional brain connectivity: Generalization to multiple datasets
Abstract Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance.
Laterality 2020: entering the next decade
ABSTRACT In the 2010s, significant progress has been made in several key areas of laterality research, including neuroimaging, genetics and comparative research. In the present article, we discuss which trends are likely to shape laterality research in the 2020s. These include, but are not limited to: (1) Finding laterality-specific solutions to the replication crisis.
Asymmetries in social touch-motor and emotional biases on lateral preferences in embracing, cradling and kissing
In human social interaction, affective touch plays an integral role to communicate intentions and emotions. Three of the most important forms of social touch are embracing, cradling and kissing. These behaviours have been demonstrated to be lateralized, but the underlying mechanisms are still not well understood. Both motor and emotive biases have been suggested to affect laterality of social touch.
Neurite architecture of the planum temporale predicts neurophysiological processing of auditory speech
The left hemispheric advantage in speech perception is reflected in faster neurophysiological processing. On the basis of postmortem data, it has been suggested that asymmetries in the organization of the intrinsic microcircuitry of the posterior temporal lobe may produce this leftward timing advantage. However, whether this hypothetical structure-function relationship exists in vivo has never been empirically validated.
Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence
Two hundred fifty-nine participants (138 males) between 18 and 40 years of age (M = 24.31, SD = 4.41) took part in the study. Since this is the first study to investigate the microstructural correlates of intelligence using NODDI, it was not possible to estimate the necessary sample size a priori based on existing literature. Instead, we collected data from a reasonably large sample and computed the achieved power post hoc using G*Power56.
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