@phdthesis{Akhrif2023,
  author    = {Akhrif, Atae},
  title     = {The BOLD Signal is more than a Brain Activation Index},
  doi       = {10.25972/OPUS-32287},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322879},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {In the recent years, translational studies comparing imaging data of animals and humans have gained increasing scientific interests with crucial findings stemming from both, human and animal work. In order to harmonize statistical analyses of data from different species and to optimize the transfer of knowledge between them, shared data acquisition protocols and combined statistical approaches have to be identified. Following this idea, methods of data analysis, which have until now mainly been used to model neural responses of electrophysiological recordings from rodent data, were applied on human hemodynamic responses (i.e. Blood-Oxygen-Level- Dependent BOLD signal) as measured via functional magnetic resonance imaging (fMRI). At the example of two attention and impulsivity networks, timing dynamics and amplitude of the fMRI signal were determined (study 1). Study 2 described the same parameters frequency-specifically, and in study 3, the complexity of neural processing was quantified in terms of fractality. Determined parameters were compared with regard to the subjects' task performance / impulsivity to validate findings with regard to reports of the current scientific debate. In a general discussion, overlapping as well as additional information of methodological approaches were discussed with regard to its potential for biomarkers in the context of neuropsychiatric disorders.},
  subject      = {funktionelle Kernspintomographie},
  language  = {en}
}
@phdthesis{Akhrif2020,
  author    = {Akhrif, Atae},
  title     = {The BOLD Signal is more than a Brain Activation Index},
  doi       = {10.25972/OPUS-20729},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207299},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {In the recent years, translational studies comparing imaging data of animals and humans have gained increasing scientific interests with crucial findings stemming from both, human and animal work. In order to harmonize statistical analyses of data from different species and to optimize the transfer of knowledge between them, shared data acquisition protocols and combined statistical approaches have to be identified. Following this idea, methods of data analysis, which have until now mainly been used to model neural responses of electrophysiological recordings from rodent data, were applied on human hemodynamic responses (i.e. Blood-Oxygen-Level-Dependent BOLD signal) as measured via functional magnetic resonance imaging (fMRI). At the example of two attention and impulsivity networks, timing dynamics and amplitude of the fMRI signal were determined (study 1). Study 2 described the same parameters frequency-specifically, and in study 3, the complexity of neural processing was quantified in terms of fractality. Determined parameters were compared with regard to the subjects' task performance / impulsivity to validate findings with regard to reports of the current scientific debate. In a general discussion, overlapping as well as additional information of methodological approaches were discussed with regard to its potential for biomarkers in the context of neuropsychiatric disorders.},
  subject      = {funktionelle Kernspintomographie},
  language  = {en}
}
@article{AkhrifRomanosDomschkeetal.2018,
  author    = {Akhrif, Atae and Romanos, Marcel and Domschke, Katharina and Schmitt-Boehrer, Angelika and Neufang, Susanne},
  title     = {Fractal Analysis of BOLD Time Series in a Network Associated With Waiting Impulsivity},
  series = {Frontiers in Physiology},
  volume    = {9},
  journal   = {Frontiers in Physiology},
  issn      = {1664-042X},
  doi       = {10.3389/fphys.2018.01378},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189191},
  pages     = {1378},
  year      = {2018},
  abstract  = {Fractal phenomena can be found in numerous scientific areas including neuroscience. Fractals are structures, in which the whole has the same shape as its parts. A specific structure known as pink noise (also called fractal or 1/f noise) is one key fractal manifestation, exhibits both stability and adaptability, and can be addressed via the Hurst exponent (H). FMRI studies using H on regional fMRI time courses used fractality as an important characteristic to unravel neural networks from artificial noise. In this fMRI-study, we examined 103 healthy male students at rest and while performing the 5-choice serial reaction time task. We addressed fractality in a network associated with waiting impulsivity using the adaptive fractal analysis (AFA) approach to determine H. We revealed the fractal nature of the impulsivity network. Furthermore, fractality was influenced by individual impulsivity in terms of decreasing fractality with higher impulsivity in regions of top-down control (left middle frontal gyrus) as well as reward processing (nucleus accumbens and anterior cingulate cortex). We conclude that fractality as determined via H is a promising marker to quantify deviations in network functions at an early stage and, thus, to be able to inform preventive interventions before the manifestation of a disorder.},
  language  = {en}
}