Functional Magnetic Resonance Imaging of Neuropsychiatric and Neurological Disease

Dear Colleagues,

The use of resting state functional magnetic resonance imaging (rsfMRI) has gained widespread use across several fields of neuroscience, neuropsychiatry, and neurology. The measurement of intrinsic oscillations in the blood oxygenation level dependent (BOLD) signal has grown to become an important approach to determining changes in the functional organization of the brain at rest and under acute and chronic disease conditions. In humans, rsfMRI has identified specialized functional networks, which include the Default Mode, Salience, Attention, Executive and Sensorimotor networks. These functional networks, which reflects the ability of the brain to organize specialized modules, have been experimentally linked with wakefulness, thought, cognitive function and affect, among other behaviours. In addition, rudimentary and evolutionarily conserved variants of these human rsfMRI networks have been identified in various species, including non-human primates and rodents. As rsfMRI develops further, rsfMRI-based networks measured by seed based functional connectivity analysis or model free methods including independent component analysis (ICA), multi-voxel pattern analysis (MVPA) or other machine learning tools, may provide a useful and powerful means to detect and study functional impairments in neuropsychiatric and neurologic conditions not otherwise detected by gross anatomical changes. The present Special Issue of Brain Sciences seeks to catalogue novel avenues of research employing high field fMRI and measurement of rsfMRI networks in assessments of healthy brain and in neuropsychiatric and neurologic disease. Since the translational capacity of MR based neuroimaging techniques is unique among methods in neuroscience, the Special Issue seeks to also invite high quality animal MR imaging studies. The Special Issue encourages submission of manuscripts in which state of the art analysis methods of fMRI data are applied (e.g., MVPA, machine learning, ICA along with multi-echo acquisitions, graph theory analysis of resting state networks). Comparisons between fMRI networks and neuroanatomical networks based on high resolution diffusion tensor imaging or ex vivo tracing studies in brains of animal models are also welcome. The broad areas of studies of interest include drug use disorders, depression, schizophrenia and other neuropsychiatric conditions and neurologic disorders, such as cerebrovascular stroke and Alzheimer’s disease and related dementias.

Prof. Dr. Marcelo Febo
Guest Editor

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• Functional magnetic resonance imaging
• Network neuroscience
• Resting state fMRI
• Neuropsychiatry/Neurology