His main research concerns Neuroscience, Neuroimaging, Brain mapping, Cognitive psychology and Functional magnetic resonance imaging. His Neuroscience study frequently links to related topics such as Premotor cortex. His biological study spans a wide range of topics, including Data science, Resting state fMRI, Set and Amygdala.
The study incorporates disciplines such as Parietal lobe, Neural correlates of consciousness, Insula and Developmental psychology in addition to Brain mapping. His study in Cognitive psychology is interdisciplinary in nature, drawing from both Ventrolateral prefrontal cortex, Semantic memory, Fluency and Motor learning. Simon B. Eickhoff interconnects Broca's region, Anatomy and Macaque in the investigation of issues within Functional magnetic resonance imaging.
His primary areas of investigation include Neuroscience, Neuroimaging, Cognition, Functional magnetic resonance imaging and Cognitive psychology. Neuroscience is closely attributed to Premotor cortex in his study. In his research on the topic of Neuroimaging, Machine learning is strongly related with Artificial intelligence.
He has researched Cognition in several fields, including Developmental psychology, Schizophrenia and Perception. His Brain mapping study combines topics in areas such as Parietal lobe, Voxel and Amygdala. His Resting state fMRI research includes elements of Default mode network and Functional connectivity.
Simon B. Eickhoff mainly focuses on Neuroimaging, Neuroscience, Functional magnetic resonance imaging, Resting state fMRI and Major depressive disorder. His Neuroimaging research is multidisciplinary, incorporating elements of Meta-analysis, Cognitive psychology, Sample and Cognition. His studies deal with areas such as Parkinson's disease and Frontotemporal dementia as well as Neuroscience.
His Functional magnetic resonance imaging study incorporates themes from Nonverbal communication, Stroke, Hemiparesis, Disease status and Grey matter. His work carried out in the field of Resting state fMRI brings together such families of science as Neurotransmitter systems, Tractography, Distribution and Functional connectivity. His work deals with themes such as Mood, Bipolar disorder and Gene regulatory network, which intersect with Major depressive disorder.
His primary scientific interests are in Neuroscience, Neuroimaging, Schizophrenia, Resting state fMRI and Functional magnetic resonance imaging. Simon B. Eickhoff combines subjects such as Magnetic resonance imaging and Parkinson's disease with his study of Neuroscience. His Neuroimaging research integrates issues from Sleep in non-human animals, Sleep medicine, Neural correlates of consciousness and Data science.
His Schizophrenia study integrates concerns from other disciplines, such as SNP, Major depressive disorder, Bipolar disorder and Genetic association. His Resting state fMRI study combines topics in areas such as Somatosensory system, Neurotransmitter, Sensorimotor network, Neurodegeneration and Neurotransmission. Within one scientific family, Simon B. Eickhoff focuses on topics pertaining to Meta-analysis under Functional magnetic resonance imaging, and may sometimes address concerns connected to Chronic pain, Clinical psychology, Hyperconnectivity and Attention deficit hyperactivity disorder.
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A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
Simon B. Eickhoff;Klaas E. Stephan;Hartmut Mohlberg;Christian Grefkes.
Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty
Simon B. Eickhoff;Angela R. Laird;Christian Grefkes;Ling E. Wang.
Human Brain Mapping (2009)
ALE meta-analysis of action observation and imitation in the human brain.
Svenja Caspers;Karl Zilles;Karl Zilles;Angela R. Laird;Simon B. Eickhoff;Simon B. Eickhoff.
A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis.
Florian Kurth;Karl Zilles;Peter T. Fox;Angela R. Laird.
Brain Structure & Function (2010)
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data
Theodore D. Satterthwaite;Mark A. Elliott;Raphael T. Gerraty;Kosha Ruparel.
Activation likelihood estimation meta-analysis revisited.
Simon B. Eickhoff;Danilo Bzdok;Danilo Bzdok;Angela R. Laird;Florian Kurth.
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture
Lingzhong Fan;Hai Li;Junjie Zhuo;Yu Zhang.
Cerebral Cortex (2016)
Identification of a common neurobiological substrate for mental illness.
Madeleine Goodkind;Simon B. Eickhoff;Desmond J. Oathes;Desmond J. Oathes;Ying Jiang;Ying Jiang.
JAMA Psychiatry (2015)
Minimizing Within-Experiment and Within-Group Effects in Activation Likelihood Estimation Meta-Analyses
Peter E. Turkeltaub;Simon B. Eickhoff;Simon B. Eickhoff;Angela R. Laird;Mick Fox.
Human Brain Mapping (2012)
Modelling neural correlates of working memory: a coordinate-based meta-analysis.
Claudia Rottschy;Robert Langner;Robert Langner;Imis Dogan;Kathrin Reetz;Kathrin Reetz.
Profile was last updated on December 6th, 2021.
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