TY - JOUR

T1 - Independent component analysis of high-resolution imaging data identifies distinct functional domains

A1 - Reidl,Juergen

A1 - Starke,Jens

A1 - Omer,David

A1 - Grinvald,Amiram

A1 - Spors,Hartwig

AU - Reidl,Juergen

AU - Starke,Jens

AU - Omer,David

AU - Grinvald,Amiram

AU - Spors,Hartwig

PY - 2007

Y1 - 2007

N2 - In the vertebrate brain external stimuli are often represented in distinct functional domains distributed across the cortical surface. Fast imaging techniques used to measure patterns of population activity record movies with many pixels and many frames, i.e. data sets with high dimensionality. Here we demonstrate that principal component analysis (PCA) followed by spatial independent component analysis (sICA), can be exploited to reduce the dimensionality of data sets recorded in the olfactory bulb and the somatosensory cortex of mice as well as the visual cortex of monkeys, without loosing the stimulus specific responses. Different neuronal populations are separated based on their stimulus specific time courses of activation. Both, spatial and temporal response characteristics can be objectively obtained, simultaneously. In the olfactory bulb, groups of glomeruli with different response latencies can be identified. This is shown for recordings of olfactory receptor neuron input measured with a calcium sensitive axon tracer and for network dynamics measured with the voltage sensitive dye RH 1838. In the somatosensory cortex, barrels responding to the stimulation of single whiskers can be automatically detected. In the visual cortex orientation columns can be extracted. In all cases artifacts due to movement, heartbeat or respiration were separated from the functional signal by sICA and could be removed from the data set. sICA is therefore a powerful technique for data compression, unbiased analysis and dissection of imaging data of population activity, collected with high spatial and temporal resolution.

AB - In the vertebrate brain external stimuli are often represented in distinct functional domains distributed across the cortical surface. Fast imaging techniques used to measure patterns of population activity record movies with many pixels and many frames, i.e. data sets with high dimensionality. Here we demonstrate that principal component analysis (PCA) followed by spatial independent component analysis (sICA), can be exploited to reduce the dimensionality of data sets recorded in the olfactory bulb and the somatosensory cortex of mice as well as the visual cortex of monkeys, without loosing the stimulus specific responses. Different neuronal populations are separated based on their stimulus specific time courses of activation. Both, spatial and temporal response characteristics can be objectively obtained, simultaneously. In the olfactory bulb, groups of glomeruli with different response latencies can be identified. This is shown for recordings of olfactory receptor neuron input measured with a calcium sensitive axon tracer and for network dynamics measured with the voltage sensitive dye RH 1838. In the somatosensory cortex, barrels responding to the stimulation of single whiskers can be automatically detected. In the visual cortex orientation columns can be extracted. In all cases artifacts due to movement, heartbeat or respiration were separated from the functional signal by sICA and could be removed from the data set. sICA is therefore a powerful technique for data compression, unbiased analysis and dissection of imaging data of population activity, collected with high spatial and temporal resolution.

U2 - 10.1016/j.neuroimage.2006.08.031

DO - 10.1016/j.neuroimage.2006.08.031

JO - NeuroImage

JF - NeuroImage

VL - 34

SP - 94

EP - 108

ER -