Magnetic resonance imaging (MRI) is one of the most powerful brain imaging techniques. With the emergence of functional MRI (fMRI), not only structures, but also brain activities and network organization are assessable, so that new descriptors for brain disorders can be made. This is particularly interesting in case of cognitive impairments or behavioral and developmental brain disorders, which occurs for individuals with autism spectrum disorder (ASD). In this study we investigate the effectiveness of the functional connectivity of the brain, particularly the connectivity values as a means for the development of pathology such as autism.
Magnetic resonance imaging (MRI) is one of the most powerful brain imaging techniques. It allows, in vivo, visualization at submillimeter scale of brain tissues, and hence represents a powerful tool for diagnosis of brain diseases, disorders or impairments. With the emergence of functional MRI (fMRI), not only structures, but also brain activities are assessable. FMRI images can provide insights on the functional brain and its network organization (functional connectivity), and thus deliver new descriptors for brain disorders when brain structures are not impaired. This is particularly interesting in case of cognitive impairments or behavioral and developmental brain disorders. However, in some pathologies, neither brain morphometry nor its functional connectivity seems to differ when compared with a ‘healthy’ population. This is the case for individuals with autism spectrum disorder (ASD). This motivates why during the past decade, neuroscientists have started to investigate the effectiveness of the functional connectivity between nodes of a given functional network (within the network), or between the networks. More specifically, it was found that the changes in these effective connectivity values can provide new information over the development of pathology such as autism. That is what we describe here as neurodynamics
In order to prove the concept that brain dynamics are insightful in understanding a brain disorder and may be helpful for diagnosing it, we have built a framework to assess and compare brain dynamics between two cohorts, from which interesting and clinically relevant results are derived in case of adolescents with ASD. Figure 1 shows the directed brain dynamics pattern between the ventral stream network and the executive network which has a weaker effective connectivity in autism compared to controls.
The study on autism applies Granger causality for comparing neurodynamics patterns, and therefore assumes stationarity in brain signals, which is not within brain dynamics. Also Granger causality gives only one value to describe the causal effect between two networks over the full scan length, and hence discards the temporal aspects of the effective connectivity: when is the causal effect within or between networks strong or weak? Therefore, a new approach based on wavelet coherence is currently investigated, that describes localized (in time and frequency spaces) correlations between two signals. Wavelet coherence maps and their localized phase information between two functional networks have already produced promising results in case of autism, while the creation of a wavelet coherence-based classifier for autism is ongoing.
This project is part of Neu3-ca, a multidisciplinary and international research program. Neu3-ca stands for neurodegeneration, neuronal networks, and neuromodulation in epilepsy-induced cognitive ageing. This program aims at looking at the accelerated cognitive ageing phenomenon observed in certain types of epilepsy, its network organization, and the possible cognitive improvement by neuromodulation (brain stimulation). We consider brain dynamics parameters as important descriptors of the cognitive decline and the origin of the accelerated ageing process. The neurodynamics field is also insightful for describing or predicting possible outcomes of brain stimulation. Finally, the fundamental research on brain dynamics can help building a cognitive ageing model, not only in case of brain pathology, but also for the ‘healthy’ population.
Researchers: Antoine Bernas, MSc PDeng, Dr. Sveta Zinger, Prof.dr. Bert Aldenkamp, Prof.dr. Peter H.N. de With