Functional integration (neurobiology)
Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain. Imaging techniques A study's choice of imaging modality depends on the desired spatial and temporal resolution. fMRI and PET offer relatively high spatial resolution, with voxel dimensions on the order of a few millimeters, but their relatively low sampling rate hinders the observation of rapid and transient interactions between distant regions of the brain. These temporal limitations are overcome by MEG, but at the cost of only detecting signals from much larger clusters of neurons. fMRI Functional magnetic resonance imaging (fMRI) is a form of MRI that is most frequently used to take advantage of a difference in magnetism between oxy- and deoxyhemoglobin to assess blood flow to different parts of the brain. Typical sampling rates for fMRI images are in the tenths of seconds. MEG Magnetoencephalography (MEG) is an imaging modality that uses very sensitive magnetometers to measure the magnetic fields resulting from ionic currents flowing through neurons in the brain. High-quality MEG machines allow for sub-millisecond sampling rates. PET PET works by introducing a radiolabeled biologically active molecule. The choice of molecule dictates what is visualized: by using a radiolabeled analog of glucose, for example, one can obtain an image whose intensity distribution indicates metabolic activity. PET scanners offer sampling rates in the tenths of seconds. Multimodal imaging Multimodal imaging frequently consists of the coupling of an electrophysiologic measurement technique, such as EEG or MEG, with a hemodynamic one such as fMRI or PET. While the intention is to use the strengths and limitations of each to complement the other, current approaches suffer from experimental limitations. Some previous work has focused on attempting to use the high spatial resolution of fMRI to determine the (spatial) origin of EEG/MEG signals, so that in future work this spatial information could be extracted from a unimodal EEG/MEG signal. While some studies have seen success in correlating signal origins between modalities to within a few millimeters, the results have not been uniformly positive. Another current limitation is the actual experimental setup: taking measurements using both modalities at once yields inferior signals, but the alternative of measuring each modality separately is confounded by trial-to-trial variability. Modes of analysis In functional integration, there