Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.

We propose that the most efficient among many estimators provides a more informative picture of the functional connectivity than previous analyses of neural correlations.

As neural recordings become increasingly dense, partial correlations may prove useful as indicators of conditional independence (lack of functional connectivity ) between pairs of neurons.

Functional connectivity as a network of pairwise interactions

Functional connectivity is often represented as a graph of pairwise interactions.

Functional connectivity is a statistical description of observed multineuronal activity patterns not reducible to the response properties of the individual cells.

Functional connectivity reflects local synaptic connections, shared inputs from other regions, and endogenous network activity.

Although functional connectivity is a phenomenological description without a strict mechanistic interpretation, it can be used to generate hypotheses about the anatomical architecture of the neural circuit and to test hypotheses about the processing of information at the population level.

Ising models have been used to infer functional connectivity from neuronal spike trains [56].

One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons.

However, the interpretation of functional connectivity is often approximate, since only a small fraction of pre-synaptic inputs are typically observed.

Here we use in Vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled.

By modeling how spikes from one neuron, statistically, affect the spiking of another neuron, statistical inference methods can reveal “functional” connections between neurons.

We study how well functional connectivity methods are able to reconstruct the simulated inputs, and assess the validity and limitations of functional connectivity inference.

However, it has proven difficult to relate functional connectivity reconstructed from spikes to the known anatomy and physiology of cortical connectivity [26,32—34].

Sparse sampling of neurons and large electrode spacing may contribute somewhat to the difficulty in interpreting the results of functional connectivity analyses of cortical circuits, but it is also unclear what information these inference methods can provide about actual synaptic inputs and what limitations there are to the use of these methods in general.

Here we examine to what extent the functional connections estimated from spike trains correspond to simulated synaptic processes in a highly controlled setting.

Here we examine the relationship between simulated synaptic input and functional connections estimated from spikes using in vitro current injection experiments.

Phase lag indeX (PLI), a measure of phase locking between two signals, was calculated between all combinations of EEG channels, and channel pairs constituting the top 30% of PLI values, a threshold at which the results match well with those of model network, were chosen as the functional connections of the network [50].

The time scale of our study lies in between these two extreme limits, where the functional connectivity can reflect underlying structural connectivity yet the effect of local dynamics on the network structure can be disregarded.

For the functional connection in the network, we use two types of phase coherence measures; (1) mean phase coherence (PC) and (2) phase lag indeX (PLI).

PLI was used to define the functional connectivity in the EEG network [50].

Finally, the results we present here provide a co-activation based view of emotion representation that can inform models of functional connectivity .

However, co-activation is not the same as functional connectivity .

However, these latter studies omitted the brainstem (and note that “the functional connectivity of the brainstem should be investigated in the future”).