Probabilistic functional tractography of the human cortex revisited

Abstract

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

Keywords: Brain atlas; Connectivity mapping; Cortico-cortical evoked potentials; Epilepsy; Intracranial electroencephalogram.

Fig. 1

Description of the features on a z-CCEP: onset and peak latencies, component duration, peak amplitude and component integral. The raw z-CCEP is shown on the figure but the features were computed over its absolute value.

Fig. 2

Spatial log-distribution of the number of recording contacts (A), of contacts showing a significant CCEP (B) and of the number of implantations used for each parcels pair (C) for the MarsAtlas parcellation. The parcels were clustered by lobe and the values merged between the two hemispheres. A logarithmic scale was used for allowing distinguishing the large sampling differences between parcels.

Fig. 3

(A) Connectivity probability matrix for the MarsAtlas parcellation. The black color corresponds to parcels pair with no data., (B) the symmetrical connectivity probability with merged values across hemispheres and (C) the symmetrical p-value matrix (minimum of 0.005).

Fig. 4

Maps of connectivity probability (A) and the associated p-value (B) for the stimulation of the insula (black arrow).

Fig. 5

(A) Connectivity probability matrices for different z thresholds, showing only significant connections (p < 0.005). (B) Distributions of the connectivity probability (all and only significant connections). (C) Correlation coefficients between probability matrices pairs computed for different z thresholds. (D) Number of significant connections for the different thresholds. (E) Number of connections as a function of the distance.

Fig. 6

(A) Fibers distance matrix between all recorded parcels pairs. (B) Mean connectivity probability as a function of distance (black line) and charge levels: colored surfaces correspond to the difference of the mean probability between higher charge levels (2: blue, 3: red, 4: green) and charge level 1, on the same patients and for the same parcels.

Fig. 7

Connectivity probability matrices for stimulations performed in the white (A) and the gray (B) matter. The effect of distance is reflected by higher values in the white matter for parcels pairs close to the diagonal.

Fig. 8

Connectivity probability matrices for different parcellations ordered in a similar way.

Fig. 9

Matrices of the features and their distribution between parcels for the MarsAtlas parcellation: (A) onset latency matrix, (B) onset latency distribution, (C) peak latency matrix, (D) peak latency distribution, (E) velocity matrix, (F) velocity distribution, (G) duration matrix, (H) duration distribution, (I) integral matrix, (J) integral distribution, (K) amplitude matrix, (L) amplitude distribution. For the velocity, the duration, the integral and the amplitude, a log scale was used and outliers (with values above the maximum displayed) have been removed to best display the overall data: they account respectively for 0.9%, 0.3%, 0.3% and 0.1% of the values.

Fig. 10

Mean features values as a function of distance. (A) Dynamical features: onset and peak latencies, and duration. (B) Strength features: amplitude and integral.

Fig. 11

Features computed for the stimulation of the insula (black arrow): the results were merged between the two hemispheres to represent ipsi- and controlateral connectivity. A log-scale was used to best display the values distribution of the velocity and the integral.