Utility of Independent Component Analysis for Interpretation of Intracranial EEG

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity

Active Spatial Perception in the Vibrissa Scanning Sensorimotor System

Haptic perception is an active process that provides an awareness of objects that are encountered as an organism scans its environment. In contrast to the sensation of touch produced by contact with an object, the perception of object location arises from the interpretation of tactile signals in the context of the changing configuration of the body. A discrete sensory representation and a low number of degrees of freedom in the motor plant make the ethologically prominent rat vibrissa system an ideal model for the study of the neuronal computations that underlie this perception. We found that rats with only a single vibrissa can combine touch and movement to distinguish the location of objects that vary in angle along the sweep of vibrissa motion. The patterns of this motion and of the corresponding behavioral responses show that rats can scan potential locations and decide which location contains a stimulus within 150 ms. This interval is consistent with just one to two whisk cycles and provides constraints on the underlying perceptual computation. Our data argue against strategies that do not require the integration of sensory and motor modalities. The ability to judge angular position with a single vibrissa thus connects previously described, motion-sensitive neurophysiological signals to perception in the behaving animal

Interpretation of mammalian brain rhythms of sensorimotor processing

A fundamental goal of neuroscience is to relate neural signals with external sensory stimuli and with complex behaviors such as movement. In many systems and brain regions, brain oscillations correlate with movement. The body of work presented here examines the role of oscillations in both sensory representation and motor output, spanning multiple scales of measurement from local field potential recordings to the large-scale electrical activity of the whole human brain. The vibrissa system of rats is an active sensory motor system where the whiskers are actively moved to explore the environment. The work described in Chapter II uses a behavioral paradigm to test coding strategies within the rat vibrissa system. We ask whether rats can discriminate the position of objects in the plane within which the whiskers move and whether discrimination can be accomplished with a single vibrissa. We report that rats can locate the position of objects in space relative to its body position with a single whisker, suggesting a neural code based on timing of the whisking cycle. Chapter III examines a salient, widespread oscillation associated with movements in rats (the theta rhythm), to determine whether this signal might drive whisking behavior. We find that hippocampal theta and the whisking rhythm are not coherent although they are oscillatory signals within the same spectral band. In humans, invasive brain measurements are possible in the cases of focal refractory epilepsy patients who are undergoing neurosurgical evaluation. Chapter IV uses intracranial measurements from epilepsy patients who performed a visually-cued finger movement task, to understand the electrical signals that enable a complex sensory motor action. We analyze signals spectrally, examining oscillations with a linear systems approach, specifically using independent component analysis (ICA) to interpret the signals. We find that ICA can separate pathological brain signals from motor signals and decompose intracranial signals into its underlying sources. Together, these results demonstrate that oscillations of peripheral sensors can encode the representation of spatial information, that neural rhythms in overlapping frequency bands are not necessarily entrained, and that ICA is a useful tool for ummixing motor and other signals in the human brai

Interpretation of mammalian brain rhythms of sensorimotor processing

A fundamental goal of neuroscience is to relate neural signals with external sensory stimuli and with complex behaviors such as movement. In many systems and brain regions, brain oscillations correlate with movement. The body of work presented here examines the role of oscillations in both sensory representation and motor output, spanning multiple scales of measurement from local field potential recordings to the large-scale electrical activity of the whole human brain. The vibrissa system of rats is an active sensory motor system where the whiskers are actively moved to explore the environment. The work described in Chapter II uses a behavioral paradigm to test coding strategies within the rat vibrissa system. We ask whether rats can discriminate the position of objects in the plane within which the whiskers move and whether discrimination can be accomplished with a single vibrissa. We report that rats can locate the position of objects in space relative to its body position with a single whisker, suggesting a neural code based on timing of the whisking cycle. Chapter III examines a salient, widespread oscillation associated with movements in rats (the theta rhythm), to determine whether this signal might drive whisking behavior. We find that hippocampal theta and the whisking rhythm are not coherent although they are oscillatory signals within the same spectral band. In humans, invasive brain measurements are possible in the cases of focal refractory epilepsy patients who are undergoing neurosurgical evaluation. Chapter IV uses intracranial measurements from epilepsy patients who performed a visually-cued finger movement task, to understand the electrical signals that enable a complex sensory motor action. We analyze signals spectrally, examining oscillations with a linear systems approach, specifically using independent component analysis (ICA) to interpret the signals. We find that ICA can separate pathological brain signals from motor signals and decompose intracranial signals into its underlying sources. Together, these results demonstrate that oscillations of peripheral sensors can encode the representation of spatial information, that neural rhythms in overlapping frequency bands are not necessarily entrained, and that ICA is a useful tool for ummixing motor and other signals in the human brai

Utility of independent component analysis for interpretation of intracranial EEG.

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20-80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity