Neuroinformatics = neuroscience + informatics
Neuroinformatics is a NEW multidisciplinary subject dealing with mining, modeling and analyzing the data gathered from the nervous system. The disciplines relevant to neuroinformatics include neuroscience, statistics, biomedical engineering, physics and computer science.
In [Luscombe et al., 2001, page 347] a definition of bioinformatics was proposed. A parallel definition of neuroinformatics is: neuroinformatics is conceptualizing neuroscientific data and applying "informatics techniques" (derived from disciplines such as applied mathematics, computer science and statistics) to understand and organize the information associated with the data on a large scale [Nielsen, F. A. 2004.].
Spatial models for neural count data
We explore the three-dimensional topography of neurons in the spinal cord activated by reflex activity. The flexion withdrawal reflex automatically retracts the limb in response to noxious stimuli. This withdrawal reflex has been widely studied to better understand the organization of spinal motor systems. Identification of neurons involved in specific behaviors such as the flexion withdrawal reflex is important to the understanding of the spatial organization of reflex systems. Currently, data presentation and analysis are most often for two dimensional qualitative comparisons. Statistical tools able to better identify anatomical compartments containing increased or decreased neural activity would substantially improve these studies. The statistical challenge in this type of study is how to model and analyze spatial count data with overdispersion and spatial correlation.
Modeling multi-channel biosignals
Neuroscientists are concerned about brain activation during muscle fatigue. Increased fatigability occurs in every patient with muscle weakness, regardless of whether the weakness is due to a central or peripheral neurological disorder. The underlying mechanisms are not well understood and there is a need to study fatigability systematically in neurology and rehabilitation. The behavior of the peripheral neuromuscular system during muscle fatigue has been studied extensively, but the role of the central nervous system in muscle fatigue is largely unknown. We investigated changes in brain activity during motor performance from non-fatigued to moderately fatigued to severely fatigued conditions in healthy volunteers. Handgrip force, surface electromyographic (EMG) signals of finger flexor and extensor muscles, and scalp electroencephalograms (EEG) were measured simultaneously. The statistical challenge in this study is how to model multichannel force/EMG/EEG signals with appropriate covariance.
