Models and Physiology of Macroscopic Brain Ac5vity. Jose C. Principe University of Florida

Transcription

1 Models and Physiology of Macroscopic Brain Ac5vity Jose C. Principe University of Florida

2 Literature W. Freeman- Mass Ac5on in the Nervous System P. Nunez Electric Fields of the Brain H. Berger- On the Electroencephalogram of Man J. Desmedt- Visual Evoked Poten5als in Man K. Oweiss- Sta5s5cal Signal Processing for NeuroScience and Neurotechnology

3 Terminology When an electrode is placed on the scalp, one measures a faint con5nuous amplitude signal called the Electroencephalogram (EEG). If the electrode is placed over the brain surface it is called the Electrocor5cogram (ECoG). When a microelectrode is placed inside the neural 5ssue a con5nuous amplitude signal is also collected that is called the Local Field Poten5al (LFP). EEG and ECoG are called macroscopic brain ac5vity since they measure broad excitability in the cortex LFPs are called mesoscopic brain ac5vity because they measure electrical ac5vity in a confined region of the cortex. Spike ac5vity is some5mes called microscopic brain ac5vity and it is a 0/1 (impulsive) signal. It is collected with microelectrode arrays inside the brain 5ssue and it is highpass filtered at 300 Hz.

4 How Spikes Create Mesoscopic Brain Ac5vity? This is a complicated, mul5scale nonlinear process. Local field poten5als (LFPs) are sums of dendri5c poten5als created from post synap5c ac5on poten5als that are propagated by the ionic currents in the neural 5ssue. As such they represent synchronized and transient ac5vity of large popula5on of cells (10 4 ). They are not lowpass filtered spikes! (in fact one can not dis5nguish a spike in the LFP). LFPs are a bulk effect on the excitable medium!

5 How Spikes Create Mesoscopic Brain Ac5vity? One can approximately model the physiology of transmission from spikes to LFPs as the following nonlinear dynamical system (K0 model).

6 How Spikes Create Mesoscopic Brain Ac5vity? More complex dynamics are obtained when there are excitatory and inhibitory neural popula5ons (KII model) Spindle ac5vity

7 Current Source Density Viewed from the extracellular space, the recorded field poten5al results from the flow of current in closed loops from sources to sinks. The extracellular poten5al can be recorded in a line along the axis of the pyramidal cells (the z direc5on). The first deriva5ve of poten5al with depth is propor5onal to longitudinal current in that direc5on. The first deriva5ve of longitudinal current is propor5onal to current crossing the membrane. Thus, the second spa5al deriva5ve of poten5al is propor5onal to the current density (Current Source Density).

8 Current Source Density Viewed from the extracellular space, the recorded field poten5al results from the flow of current in closed loops from sources to sinks. The extracellular poten5al can be recorded in a line along the axis of the pyramidal cells (the z direc5on). The first deriva5ve of poten5al with depth is propor5onal to longitudinal current in that direc5on. The first deriva5ve of longitudinal current is propor5onal to current crossing the membrane. Thus, the second spa5al deriva5ve of poten5al is propor5onal to the current density (Current Source Density).

9 Dipoles Making the assump5on that the cerebral 5ssue is homogeneous, isopoten5al lines are drawn perpendicular to lines of current. The zero isopoten5al line is halfway between the center of mass of all sources and that of all sinks. Poten5al changes most rapidly between the two poles of the "dipole".

10 Dipoles We assume that there are large numbers of pyramidal cell generators aligned in parallel in a sheet in the cor5cal 5ssue. We also assume synchronous ac5va5on of the popula5on. The solid angle principle is an approxima5on that allows the poten5al es5ma5on measured in a homogeneous medium at a distance from an ac5ve dipole sheet.

11 Dipole Layers The poten5al measured with respect to a neutral reference is propor5onal to the poten5al across the dipole layer, and the solid angle subtended by the dipole layer at point P. On the posi5ve side of the dipole, the recorded poten5al is posi5ve. On the nega5ve side, the recorded poten5al is nega5ve. Halfway between the poles, on the zero isopoten5al plane, the recorded poten5al is zero. In going from a single dipole generator to a flat tangen5al dipole sheet, the shape of the poten5al field becomes elongated in the tangen5al direc5on.

12 Sulcus and Gyrus The cerebral cortex is a highly convoluted structure with gyriand sulci. For a dipole surface on one wall of a sulcus, the solid angle at a point directly over the sulcus on the scalp is zero. Moving this point in one direc5on yields a nega5ve poten5al, and in the other direc5on a posi5ve poten5al.

13 Brain Anatomy

14 Brain Anatomy

15 Brain Hierarchical Architecture

16 EEG Montage

17 EEG Basics The EEG was discovered by Hans Berger in It is a very faint electrical signal that can be collected by silver silver cloride electrodes placed on the scalp. The amplitude varies from a few microvolts up to 500 µv. The bandwidth is from 0.5 Hz to 100Hz. The signal has a 1/f 2 type spectrum Its 5me structure is very complex (perhaps chao5c) with abrupt changes and wax and waning rhythms. It is the most widely used tool to quan5fy brain ac5vity in the clinic.

18 EEG Basics gamma 60

19 EEG Basics Through experience, some of the rhythms have been related to cogni5ve condi5ons. Alpha rhythm is created when eyes close During sleep, which is a sequence of 5 states (st 1 - drowsy, st 2- light sleep, st3and 4- deep sleep, st 5 or REM), the EEG changes radically. The EEG is very useful to diagnose brain disorders such epilepsy.

20 EEG Ar5facts Due to the low amplitude signal, the EEG is very prone to ar5facts. The most severe ar5fact is 60Hz line noise that can be corrected with notch filters at 60 Hz and odd harmonics The second type is muscle ar5fact that can overpower all the frequencies of the signal (lowpass filter at 100 Hz helps) The third type is eye blinks that contaminate mostly the frontal channels. They can be subtracted if eye channels are included. These issues must be taken seriously! Otherwise no processing works!!!!

21 EEG Signals for BCIs One can use basically three types: ERPs Mu rhythm General EEG

22 Event Related Poten5als ERPs are a signature of cogni5on. They signal a massive communica5on amongst brain areas (kind of the brain s impulse response to an internal s5mulus). This is very good, but the problem is that it is normally much smaller than the ongoing EEG ac5vity (i.e. the SNR is nega5ve).

23 Event Related Poten5als The ERP shape is well known and preiy stable across individuals, and has a known distribu5on across the channels. The P300 is the most used for BMIs because it is task relevant N100- P200 complex is pre- aien5ve response appearing over sensory areas P300 signals a rare tasks relevant event (Cz) N400 signals an unexpected event (Cz)

24 Event Related Poten5als In order to deal with the nega5ve SNR, we use averaging of the s5mulus. If you have a transient that appears in white Gaussian noise, align the transient and average across trielas you obtain an increase of SNR by, where N is the number of trials. This is normally done but has three shortcomings: It is not real 5me It assumes that the shape of the ERP is the same It assumes that the latency is constant

25 Mu Rhythm When a subject imagines movement or sees movement made by others a burst of ac5vity in the 8-12Hz range appears over the sensorimotor areas in the brain The subject can synchronize the rhythm and by moving desynchronize it, hence it ia good signal to be used for motor BMI tasks.