@article{WehrheimFaskowitzSpornsetal.2023,
  author    = {Wehrheim, Maren H. and Faskowitz, Joshua and Sporns, Olaf and Fiebach, Christian J. and Kaschube, Matthias and Hilger, Kirsten},
  title     = {Few temporally distributed brain connectivity states predict human cognitive abilities},
  series = {NeuroImage},
  volume    = {277},
  journal   = {NeuroImage},
  doi       = {10.1016/j.neuroimage.2023.120246},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349874},
  year      = {2023},
  abstract  = {Highlights • Brain connectivity states identified by cofluctuation strength. • CMEP as new method to robustly predict human traits from brain imaging data. • Network-identifying connectivity 'events' are not predictive of cognitive ability. • Sixteen temporally independent fMRI time frames allow for significant prediction. • Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths. Abstract Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5\% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.},
  language  = {en}
}
@phdthesis{RamirezPasos2019,
  author    = {Ramirez Pasos, Uri Eduardo},
  title     = {Subthalamic Nucleus Neural Synchronization and Connectivity during Limbic Processing of Emotional Pictures: Evidence from Invasive Recordings in Patients with Parkinson's Disease},
  doi       = {10.25972/OPUS-16985},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-169850},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {In addition to bradykinesia and tremor, patients with Parkinson's disease (PD) are known to exhibit non-motor symptoms such as apathy and hypomimia but also impulsivity in response to dopaminergic replacement therapy. Moreover, a plethora of studies observe differences in electrocortical and autonomic responses to both visual and acoustic affective stimuli in PD subjects compared to healthy controls. This suggests that the basal ganglia (BG), as well as the hyperdirect pathway and BG thalamocortical circuits, are involved in affective processing. Recent studies have shown valence and dopamine-dependent changes in synchronization in the subthalamic nucleus (STN) in PD patients during affective tasks. This thesis investigates the role of dopamine, valence, and laterality in STN electrophysiology by analyzing event-related potentials (ERP), synchronization, and inter-hemispheric STN connectivity. STN recordings were obtained from PD patients with chronically implanted electrodes for deep brain stimulation during a passive affective picture presentation task. The STN exhibited valence-dependent ERP latencies and lateralized 'high beta' (28-40 Hz) event-related desynchronization. This thesis also examines the role of dopamine, valence, and laterality on STN functional connectivity with the anterior cingulate cortex (ACC) and the amygdala. The activity of these limbic structures was reconstructed using simultaneously recorded electroencephalographic signals. While the STN was found to establish early coupling with both structures, STN-ACC coupling in the 'alpha' range (7-11 Hz) and uncoupling in the 'low beta' range (14-21 Hz) were lateralized. Lateralization was also observed at the level of synchrony in both reconstructed sources and for ACC ERP amplitude, whereas dopamine modulated ERP latency in the amygdala. These results may deepen our current understanding of the STN as a limbic node within larger emotional-motor networks in the brain.
},
  subject      = {Nucleus subthalamicus},
  language  = {en}
}