Investigation of pairwise functional connections.

<p><b>A)</b> For each pair of channels, the distribution of values of across subjects is built for the control case and ASD. <b>B)</b> The comparison of these two distributions allows us to detect significant connectivity changes in ASD relative to control.</p

Changes in functional brain connectivity in ASD relative to control.

<p><b>A)</b> Changes weighted by their z-score and averaged across inputs or outputs (top) and in absolute value (bottom). <b>B)</b> Same as in A) but normalized so that the changes are relative to the connection strength in control.</p

Separability indices for the covariance matrices of the noise, .

<p>The fractions of correctly classified individuals were 8/9 for ASD and 10/10 for Control.</p

A Model of Functional Brain Connectivity and Background Noise as a Biomarker for Cognitive Phenotypes: Application to Autism

<div><p>We present an efficient approach to discriminate between typical and atypical brains from macroscopic neural dynamics recorded as magnetoencephalograms (MEG). Our approach is based on the fact that spontaneous brain activity can be accurately described with stochastic dynamics, as a multivariate Ornstein-Uhlenbeck process (mOUP). By fitting the data to a mOUP we obtain: 1) the functional connectivity matrix, corresponding to the drift operator, and 2) the traces of background stochastic activity (noise) driving the brain. We applied this method to investigate functional connectivity and background noise in juvenile patients (n = 9) with Asperger’s syndrome, a form of autism spectrum disorder (ASD), and compared them to age-matched juvenile control subjects (n = 10). Our analysis reveals significant alterations in both functional brain connectivity and background noise in ASD patients. The dominant connectivity change in ASD relative to control shows enhanced functional excitation from occipital to frontal areas along a parasagittal axis. Background noise in ASD patients is spatially correlated over wide areas, as opposed to control, where areas driven by correlated noise form smaller patches. An analysis of the spatial complexity reveals that it is significantly lower in ASD subjects. Although the detailed physiological mechanisms underlying these alterations cannot be determined from macroscopic brain recordings, we speculate that enhanced occipital-frontal excitation may result from changes in white matter density in ASD, as suggested in previous studies. We also venture that long-range spatial correlations in the background noise may result from less specificity (or more promiscuity) of thalamo-cortical projections. All the calculations involved in our analysis are highly efficient and outperform other algorithms to discriminate typical and atypical brains with a comparable level of accuracy. Altogether our results demonstrate a promising potential of our approach as an efficient biomarker for altered brain dynamics associated with a cognitive phenotype.</p></div

Statistical properties of the background noise driving the brain network.

<p><b>A)</b> The noise extracted from each channel is Gaussian. <b>B)</b> The noise is not temporally correlated and is practically white. <b>C)</b> The covariance of the noise extracted from the data and the covariance of the noise predicted from model (1) match perfectly.</p

Dominant patterns of background noise driving the brain network.

<p>The spatial complexity of the background noise in control is significantly higher than in ASD, as quantified in the inset (Wilcoxon ranksum text, p<<0.01). Numbers on top of the plots show the values of spatial complexity.</p

Sensor distribution and functional connectivity.

<p><b>A)</b> Stereographic projection of the MEG sensors. The sensor grid covers frontal (F), parietal (P), central (C), temporal (T) and occipital (O) areas from both hemispheres. <b>B)</b> Each sensor provides a recording channel. The recorded signals are then used to infer the functional connectivity matrix, describing the coupling between the areas associated with the <i>i</i>-th and <i>j</i>-th sensors.</p

Dominant changes in functional connectivity as an arrow plot.

<p><b>A)</b> Absolute changes weighted by their z-score. <b>B)</b> Relative changes weighted by their z-score. Clearly, the dominant change in ASD relative to control is an increase in functional excitation from parietal to frontal areas.</p

Separability indices for the connectivity matrices, .

<p>The fractions of correctly classified individuals were 8/9 for ASD and 8/10 for Control.</p

Global properties of the functional connectivity.

<p><b>A)</b> Reshaping the connectivity matrices as high-dimensional vectors. <b>B)</b> Projection of the vectorized connectivity matrices onto the first three principal components. The matrices of control and ASD are clearly separable. <b>C)</b> Linear stability analysis of the connectivity matrices reveals no significant differences between control and ASD. The y-axis plot the largest real part of the eigenvalues of each matrix. <b>D)</b> There are no significant differences in the correlation between nodal input and nodal output (I: input; O: ouput; +: excitatory; −: inhibitory).</p