Tonotopic organization in the depth of human inferior colliculus

Experiments in animal models indicate that inferior colliculus (IC), the primary auditory midbrain structure, represents sound frequency in a particular spatial organization, a tonotopy, that proceeds from dorsal and superficial to ventral and deeper tissue. Experiments are presented that use high-resolution, sparse-sampling functional magnetic resonance imaging (fMRI) at 3T to determine if tonotopic gradients can be reliably measured in human IC using high-resolution fMRI. Stimuli were sequences of band-pass filtered noise with different center frequencies, presented sequentially while fMRI data were collected. Four subjects performed an adaptive frequency-discrimination task throughout the experiment. Results show statistically significant tonotopic gradients within both ICs of all subjects. Frequency gradients as a function of depth were measured using surface-based analysis methods that make virtual penetrations into the IC tissue. This organization was evident over substantial portions of the IC, at locations that are consistent with the expected location of the central nucleus of IC. The results confirm a laminar tonotopy in the human IC at 3T, but with a heterogeneous, patchy character. The success of these surface-based analysis methods will enable more detailed non-invasive explorations of the functional architecture of other subcortical human auditory structures that have complex, laminar organizatio

Accurately decoding visual information from fMRI data obtained in a realistic virtual environment

Three-dimensional interactive virtual environments are a powerful tool for brain-imaging based cognitive neuroscience that are presently under-utilized. This paper presents machine-learning based methods for identifying brain states induced by realistic virtual environments with improved accuracy as well as the capability for mapping their spatial topography on the neocortex. Virtual environments provide the ability to study the brain under conditions closer to the environment in which humans evolved, and thus to probe deeper into the complexities of human cognition. As a test case, we designed a stimulus to reflect a military combat situation in the Middle East, motivated by the potential of using real-time functional magnetic resonance imaging (fMRI) in the treatment of post-traumatic stress disorder. Each subject experienced moving through the virtual town where they encountered 1—6 animated combatants at different locations, while fMRI data was collected. To analyze the data from what is, compared to most studies, more complex and less controlled stimuli, we employed statistical machine learning in the form of Multi-Voxel Pattern Analysis (MVPA) with special attention given to artificial Neural Networks (NN). Extensions to NN that exploit the block structure of the stimulus were developed to improve the accuracy of the classification, achieving performances from 58%—93% (chance was 16.7%) with 6 subjects. This demonstrates that MVPA can decode a complex cognitive state, viewing a number of characters, in a dynamic virtual environment. To better understand the source of this information in the brain, a novel form of sensitivity analysis was developed to use NN to quantify the degree to which each voxel contributed to classification. Compared with maps produced by general linear models and the searchlight approach, these sensitivity maps revealed a more diverse pattern of information relevant to the classification of cognitive state

Groundwater mixing and geochemical assessment of low-enthalpy resources in the geothermal field of southwestern Tunisia

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