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The cortical processing of pain
Published in Camille Chatelle, Steven Laureys, Assessing Pain and Communication in Disorders of Consciousness, 2015
Several studies have attempted to address these questions using methods such as Dynamic Causal Modeling (Liang et al., 2011) to assess functional connectivity based on fMRI data. However, interpretation of the results obtained using these techniques is undermined by the fact that the low temporal resolution of BOLD signals precludes determining whether functional connectivity between two regions reflects a direct connection or an indirect connection involving multiple intervening regions (Greicius, Supekar, Menon, & Dougherty, 2009). In addition, correlation measurements performed using BOLD signals could at least partly be influenced by fluctuations in cerebral metabolism and blood flow unrelated to the elicited neural activity (Fukunaga et al., 2008). A smaller number of studies have attempted to assess functional connectivity using EEG or MEG data (e.g., Ploner, Schoffelen, Schnitzler, & Gross, 2009). However, this also has significant drawbacks due mainly to the low spatial resolution of the recorded signals.
An update on EEG in migraine
Published in Expert Review of Neurotherapeutics, 2019
In the past few years, functional studies investigating how the brain draws fundamental connections among neuronal networks have been performed. The complexity of brain function is based on dynamic relationships among cortical and subcortical areas, which enable the brain to adapt itself to different physiological and pathological conditions. The high temporal resolution of EEG enables the study of functional connectivity, which could support the effective and structural connectivity data based on magnetic resonance imaging (MRI). Methods such as correlations, spectral coherence, and phase synchronization, demonstrate the extent of the statistical connection of two variables and reveal functional connectivity. Functional connectivity enables the detection of common temporal features of two―even distant―neural populations due to weak reciprocal interactions or shared influence of a third variable [51]. The properties of EEG signals also enable the evaluation of the flow of connections and information across different brain areas. This permits the extension of functional connectivity by explaining the architecture of connections between two correlated time series. Methods, such as Granger causality, or biologically inspired, such as dynamic causal modeling, could shed light on the information flow in the brain intended as a nonlinear system [52–56]. The application of such methods to resting-state EEG and EEG rhythm perturbation induced by multimodal stimuli appears to be particularly adapt in describing the complexity of the migraine brain.
The role of the precuneus and posterior cingulate cortex in the neural routes to action
Published in Computer Assisted Surgery, 2019
Zijian Wang, Liu Fei, Yaoru Sun, Jie Li, Fang Wang, Zheng Lu
Dynamic causal modeling (DCM) [12] on fMRI time series during the action generation from object pictures for the direct evidence of the direct visual route between the frontal area and the posterior area is used. By building DCM models for different connection hypotheses and comparing them, the best hypothesis can be selected as evidence.
Understanding, facilitating and predicting aphasia recovery after rehabilitation
Published in International Journal of Speech-Language Pathology, 2022
Maria Varkanitsa, Swathi Kiran
After identifying the degree to which structural metrics affect language impairment and recovery after rehabilitation, Meier et al. (2019) investigated the effect of connectivity between brain regions on language function. Using fMRI data from the same cohort of patients who received treatment for naming difficulties and healthy controls, using a dynamic causal modelling approach, the authors created biologically-plausible models corresponding to potential neural recovery patterns. The first set of models (family A) simulated left-lateralised connectivity (i.e. no/minimal damage); the second set (family B) modelled bilateral anterior-weighted connectivity (i.e. with posterior damage); the third set (family C) modelled bilateral posterior-weighted connectivity (i.e. when there was anterior damage); and the fourth set (family D) modelled right-lateralised connectivity (i.e. with extensive damage in the left hemisphere). Relative to control subjects, who demonstrated a strong preference for models that explained left-lateralised connectivity, patients exhibited a split preference for families A and C. With regards to brain regions connections, patients exhibited weaker left intrahemispheric task-modulated connections than did controls. Moreover, the interaction amongst damaged brain regions, functional connectivity and language performance was complex: patients with damage to left superior frontal structures exhibited greater right intrahemispheric connectivity, whereas patients with damage to left ventral structures exhibited heightened modulation of left frontal regions. Finally, the authors reported that lesion metrics (i.e. overall lesion volume and location) best predicted accuracy on the fMRI task and aphasia severity, whereas functional metrics and, more specifically left intrahemispheric connectivity, predicted fMRI task reaction times. Taken together, these results indicate that the neural recovery of language in chronic aphasia is a complex process and most probably cannot be attributed entirely to one hemisphere of the brain versus the other. In addition, brain function is not entirely driven by overt structural damage. As suggested by the authors, a more nuanced hierarchical lesion-connectivity model that also incorporates specific language measures may be more appropriate for characterising aphasia recovery.