PubMed · 15415767
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H W ADES, J M BROOKHART. 1950. The central auditory pathway.. https://doi.org/10.1152/jn.1950.13.3.189
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Over the last three decades, the brainstem auditory evoked response (BAER) has been used to assess functional integrity and development of the auditory system and the brain in conditions that affect the brainstem auditory pathway. As a non-invasive objective test, BAER is particularly suitable in very young or sick infants. It is the major tool to detect hearing impairment in high-risk infants, and a component in universal hearing screening. BAER is also a valuable adjunct to detect neurological impairment in many developmental disorders and functional abnormalities in a range of neurological diseases. The maximum length sequence (MLS) technique has recently been incorporated into neonatal BAER study. Recent results indicate that the MLS has the potential to improve the diagnostic value of BAER in some clinical situations, although the wider utility of this relative new technique remains to be further explored.
Vestibular function is known to be represented in a large-scale network within the brain. Although much is known about the topography of this cortical network, the subcortical anatomo-functional connectivity has received less attention. We present three patients operated on while conscious for cerebral low-grade gliomas, in which we elicited vestibular symptoms during subcortical stimulation. Anatomo-functional correlations between postoperative imaging and intraoperative findings suggest the involvement of the superior longitudinal fasciculus in the spreading of the vestibular symptoms. We argue that this fasciculus plays a major role in the functional connectivity of the areas involved in the complex multimodal network that controls vestibular function.
Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.