Prediction of Speech Sounds Is Facilitated by a Functional Fronto-Temporal Network

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Investigating cortico-subcortical circuits during auditory sensory attenuation: A combined magnetoencephalographic and dynamic causal modeling study

Sensory attenuation refers to the decreased intensity of a sensory percept when a sensation is self‐generated compared with when it is externally triggered. However, the underlying brain regions and network interactions that give rise to this phenomenon remain to be determined. To address this issue, we recorded magnetoencephalographic (MEG) data from 35 healthy controls during an auditory task in which pure tones were either elicited through a button press or passively presented. We analyzed the auditory M100 at sensor‐ and source‐level and identified movement‐related magnetic fields (MRMFs). Regression analyses were used to further identify brain regions that contributed significantly to sensory attenuation, followed by a dynamic causal modeling (DCM) approach to explore network interactions between generators. Attenuation of the M100 was pronounced in right Heschl's gyrus (HES), superior temporal cortex (ST), thalamus, rolandic operculum (ROL), precuneus and inferior parietal cortex (IPL). Regression analyses showed that right postcentral gyrus (PoCG) and left precentral gyrus (PreCG) predicted M100 sensory attenuation. In addition, DCM results indicated that auditory sensory attenuation involved bi‐directional information flow between thalamus, IPL, and auditory cortex. In summary, our data show that sensory attenuation is mediated by bottom‐up and top‐down information flow in a thalamocortical network, providing support for the role of predictive processing in sensory‐motor system.

Auditory white matter pathways are associated with effective connectivity of auditory prediction errors within a fronto-temporal network

Auditory prediction errors, i.e. the mismatch between predicted, forthcoming auditory sensations and actual sensory input, trigger the detection of surprising auditory events in the environment. Auditory mismatches engage a hierarchical functional network of cortical sources, which are also interconnected by auditory white matter pathways. Hence it is plausible that these structural and functional networks are quantitatively related. The present study set out to investigate whether structural connectivity of auditory white matter pathways enables the effective connectivity underpinning auditory mismatch responses. Participants (N = 89) underwent diffusion weighted magnetic resonance imaging (MRI) and electroencephalographic (EEG) recordings. Anatomically-constrained tractography was used to extract auditory white matter pathways, namely the bilateral arcuate fasciculi, inferior fronto-occipital fasciculi (IFOF), and the auditory interhemispheric pathway, from which Apparent Fibre Density (AFD) was calculated. EEG data were recorded in the same participants during a stochastic oddball paradigm, which was used to elicit auditory prediction error responses. Dynamic causal modelling was used to investigate the effective connectivity underlying auditory mismatch responses generated in brain regions interconnected by the above mentioned auditory white matter pathways. Our results showed that brain areas interconnected by all auditory white matter pathways best explained the dynamics of auditory mismatch responses. Furthermore, AFD in the right arcuate fasciculus was significantly associated with the effective connectivity between the cortical regions that lie within it. Taken together, these findings indicate that auditory prediction errors recruit a fronto-temporal network of brain regions that are effectively and structurally connected by auditory white matter pathways.