Chapter
2.5.3 Capacitance compensation of electrodes
3 Intracellular recording
3.1.1 A brief history of intracellular recording techniques
3.1.2 Experimental setups
3.2 Recording the membrane potential
3.2.1 The ideal current clamp
3.2.2 Measuring spontaneous activity
3.2.2.1 Junction potentials
3.2.2.2 Damage induced by the electrode
3.2.2.3 Electrode filtering
3.2.3 Measuring the response to an injected current
3.3.1 The ideal voltage clamp
3.3.2 Double-electrode voltage clamp
3.3.3 Single-electrode voltage clamp
3.3.3.1 Series resistance compensation
3.3.3.2 Discontinuous voltage clamp
3.3.3.3 Voltage clamp with AEC
3.4 Recording conductances
3.4.1 Models for conductance measurements
3.4.1.1 Current clamp model
3.4.1.2 Voltage clamp model
3.4.1.3 Visibility of dendritic synaptic inputs
3.4.1.4 Sharp electrodes and patch electrodes
3.4.2 Multi-trial conductance measurements
3.4.3 Statistical measurements
3.4.3.1 Estimating synaptic conductance distributions
3.4.3.2 Estimating synaptic time constants from the power spectrum
3.4.3.3 Estimating spike-triggered average conductances
3.4.3.4 Estimating the time course of synaptic conductances
4 Extracellular spikes and CSD
4.2 Biophysical origin of extracellular potentials
4.2.1 Biophysical forward-modeling formula
4.2.2 Numerical forward-modeling scheme
4.2.3 Current source density (CSD)
4.3 Local field potential (LFP) from a single neuron
4.3.1 Characteristic features of the LFP
4.3.2 Low-pass filtering of the LFP
4.4 Extracellular signatures of action potentials
4.4.1 Example forward-modeling result
4.4.2 Dendritic sticks and AC length constant
4.4.3 Low-pass filtering for the ball-and-stick neuron
4.4.4 Parameter dependence of spike amplitude
4.4.5 Active dendritic conductances
4.5 Extracellular potentials from columnar population activity
4.5.1 Columnar population model
4.5.2 Population response
4.5.3 Spatial spread of LFP and MUA signals
4.5.4 MUA as a measure of population firing rate
4.6 Estimation of current source density (CSD) from LFP
4.6.1 Standard CSD method
4.6.2 Inverse CSD methods
4.6.3 Validation of iCSD with population forward modeling
5.2 Modeling LFPs in resistive media
5.2.1 Extracellular potential in homogeneous resistive media
5.2.2 Example of modeling LFPs in a homogeneous resistive medium
5.2.3 Multipolar configurations
5.2.4 Is extracellular space electrically uniform?
5.3 Modeling LFPs in non-resistive media: general theory
5.3.3 Simplified geometry for macroscopic parameters
5.3.4 Different models of non-resistive media
5.4 Modeling LFPs in non-resistive media: the continuum model
5.4.1 Frequency independence in homogeneous media
5.4.2 Conductivity and permittivity of neural tissue
5.4.3 Non-homogeneous extracellular media
5.4.4 Comparison of different conductivity profiles
5.4.5 Biophysical model of the frequency-filtering properties of local field potentials
5.5 Modeling LFPs in non-resistive media: the polarization model
5.5.1 A simple model of cell surface polarization
5.5.2 Frequency dependence of the polarization model
5.5.3 Attenuation as a function of distance
5.5.3.1 Electric potential at the surface of passive membranes at equilibrium
5.5.3.2 Attenuation of electric potential in a system of packed spheres
5.5.4 Polarization of isotropic disorganized media
5.6 Modeling LFPs in non-resistive media: the diffusion model
5.6.1 Is ionic diffusion important for local field potentials?
5.6.2 Frequency scaling of ionic diffusion
5.7 Synthesis of the different models
5.7.1 Non-reactive media with ionic diffusion (model D)
5.7.2 Reactive media with electric fields (model P)
5.7.3 Reactive media with electric field and ionic diffusion (model DP)
5.8 Application of non-resistive LFP models to experimental data
5.8.1 Macroscopic measurements of brain conductivity
5.8.2 Frequency dependence of the power spectral density of local field potentials
6 EEG and MEG: forward modeling
6.2 The current dipole model and the quasi-static approximation
6.2.1 The mathematical physical foundation of the dipole model
6.2.3 EEG and MEG sensors
6.3.1 Models that allow closed form expressions
6.3.1.1 The electric potential in the homogeneous sphere
6.3.1.2 The magnetic induction outside a concentric sphere model
6.3.1.3 The electric potential in an anisotropic infinite medium
6.3.2 Models that can be solved with series expansions
6.3.3 Elementary differences between EEG and MEG
6.3.4 More advanced models that are analytically solvable
6.4 The boundary element method
6.4.1 The double layer BEM
6.4.2 The single layer BEM
6.4.4 Numerical comparison of BEM variants
6.4.5 Non-nested geometries
6.4.6 The fast multipole method for large problems
6.5 The finite element method
6.5.1 The dipole singularity
6.5.2 Numerical comparison of FEM variants
6.5.3 The use of FEM in inverse models
6.6 Other forward methods
6.7 Discussion and conclusion
7 MEG and EEG: source estimation
7.2 Relationship between neural activity and the MEG and EEG source estimates
7.2.1 Source estimates: primary current distribution
7.3 Source estimation methods
7.3.1 Parametric source localization
7.3.2 Distributed source reconstruction
7.4 Interpretation of the source estimates
7.4.1 Effects of measurement noise
7.4.2 Uncertainties in forward modeling
7.4.3 Explicit and implicit consequences of specific a priori assumptions
7.4.4 What is the spatial resolution of MEG and EEG?
7.5 Comparison with other techniques and future developments
8 Intrinsic signal optical imaging
8.2 Background and theory
8.2.1 Principles of cortical functional organization: a brief introduction
8.2.2 Advantages of applying ISOI to the rat barrel cortex
8.2.4 “Spread” versus “preference”; “point” versus “large-scale” stimulation; “global” versus “mapping” signal; “specific” versus “non-specific”signals – a guide to the perplexed
8.3 Relationship between intrinsic signals and underlying neuronal activation
8.4 More on intrinsic signals in the rat barrel cortex
8.4.1 Stimulus-evoked intrinsic signals
8.4.3 Additional considerations
8.4.4 Imaging cortical plasticity
8.5 Current trends and future directions
9 Voltage-sensitive dye imaging
9.2 Voltage-sensitive dye imaging: basics
9.2.2.2 Cortical cartography
9.2.2.3 Dynamics of cortical processing
9.2.2.4 Functional connectivity
9.3 On the origin of the VSD signal
9.3.2 Excitatory versus inhibitory cells
9.3.3 Somas versus axons versus dendrites
9.3.4 Superficial versus deep layers
9.3.5 Thalamic versus horizontal connections
9.4 Models of VSDI signals
9.4.1 The scale of the model
9.4.2 A review of mesoscopic VSDI Models
9.4.2.1 A LISSOM model to account for dynamic maps
9.4.2.2 Mean field models to inspect neural network dynamics
9.4.2.3 Neural field models to reproduce correlates of illusory motion
9.4.2.4 Conductance-based IAF neuronal network model to reproduce correlates of illusory motion
9.4.3 A submesoscopic model to study the VSD signal
9.4.3.1 The submesoscopic sources of the VSD signal
10.1 Fluorescent calcium indicators
10.1.1 Small-molecule indicators
10.1.2 Genetically encoded calcium indicators
10.2 Intracellular calcium dynamics
10.2.5 General formulation of calcium dynamics
10.3 Calcium-dependent fluorescence properties
10.3.1 Fluorescence intensity
10.3.2 Relative fluorescence change ΔF/F
10.3.3 Fluorescence ratio
10.3.4 Fluorescence lifetime
10.3.6 Calibration of calcium indicators
10.4 Simplified models of calcium dynamics
10.4.1 Calcium microdomain model
10.4.2 Buffered calcium diffusion
10.4.3 Cable-equation analog
10.4.4 Single-compartment model
10.4.5 Non-linear calcium dynamics
10.5.1 How to estimate unperturbed calcium dynamics
10.5.2 How to estimate the endogenous calcium binding ratio
10.5.3 How to quantify total calcium fluxes
10.5.4 How to characterize calcium-dependent processes
10.5.5 How to reconstruct neural spike trains
10.6 Comparison with other techniques
11 Functional magnetic resonance imaging
11.2 Physical basis of the fMRI signal
11.3 BOLD contrast mechanism
11.3.1 Properties of the BOLD signal
11.3.2 Spatial resolution and specificity of fMRI
11.3.2.1 Anatomy of the cortical vascular system
11.3.2.2 Regulation of cortical blood flow
11.3.2.3 Specificity of different fMRI methods
11.4 Analysis of fMRI signals
11.4.2 Properties of the data
11.4.4 General linear model (GLM) statistics and design efficiency
11.4.5 fMRI adaptation experiments
11.4.6 Classifiers and high-resolution imaging
11.5 Neural basis of BOLD signals
11.5.1 Single-unit and multi-unit activity
11.5.2 Local field potentials
11.5.3 Spatial extent and propagation of neural signals
11.5.4 Combined measurements of fMRI and electrophysiology
11.5.5 Neural basis of the BOLD response
11.5.6 LFP, spikes, metabolism and blood flow
11.5.7 The cortical circuit and the BOLD response
11.5.8 Perception and attention
12.1 Extracellular recording
12.2 Intracellular recording
12.3 Local field potentials
12.4 EEG and MEG: forward modeling
12.5 EEG and MEG: source estimation
12.6 Intrinsic optical imaging
12.7 Voltage-sensitive dye imaging
12.9 Functional magnetic resonance imaging
Handbook of Neural Activity Measurement
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