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Kinney-Lang, E.

Publications and source records attributed to Kinney-Lang, E..

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Novel use of Diffusion Tensor Imaging to Delineate the Rat Basolateral Amygdala

The amygdaloid complex, including the basolateral nucleus (BLA) contributes crucially to emotional and cognitive brain functions, and is thus a major target of research in both humans and rodents. However, delineating structural amygdala plasticity in both normal and disease-related contexts using neuroimaging has been hampered by the difficulty of unequivocally identifying the boundaries of the BLA. This challenge is a result of poor contrast between BLA and the surrounding gray matter, including other amygdala nuclei. Here we describe a novel DTI approach to enhance contrast, enabling optimal identification of BLA in rodent brain from MR images. We employed this methodology together with a slice-shifting approach to measure BLA volume. We then validated the results by direct comparison to both histological and cellular-identity (parvalbumin)-based conventional techniques for defining BLA in the same brains used for MRI. We also confirmed the BLA region using DTI based tractography. The novel approach used here enables accurate and reliable delineation of BLA. Because this nucleus is involved in, and is changed by, developmental, degenerative and adaptive processes, the instruments provided here should be highly useful to a broad range of neuroimaging studies. Finally, the principles used here are readily applicable to numerous brain regions and across species.\n\nSummary StatementUse of MRI directionally encoded diffusion tensor imaging (DTI) can delineate the basolateral amygdala (BLA) and volumes derived from DTI were found to match those obtained using histological methods. Our approach can be used to identify the BLA.

neuroscience

An analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy

Objective: Cognitive impairment (CI) is common in children with epilepsy and can have devastating effects on their quality of life and that of their family. Early identification of CI is a priority to improve outcomes, but the current gold standard of detection with psychometric assessment is resource intensive and not always available. This paper proposes a novel technique of network analysis using routine clinical electroencephalography (EEG) to help identify CI in children with early-onset epilepsy (CWEOE) (0-5 y.o.).\n\nMethods: We analyzed functional networks from routinely acquired EEGs of 51 newly diagnosed CWEOE from a prospective population-based study. Combinations of connectivity metrics (e.g. phase-slope index (PSI)) with sub-network analysis (e.g. cluster-span threshold (CST)) identified significant correlations between network properties and cognition scores via rank correlation analysis with Kendalls{tau} . Predictive properties were investigated using a 5-fold cross-validated K-Nearest Neighbor classification model with normal cognition, mild/moderate CI and severe CI classes.\n\nResults: Phase-dependent connectivity metrics had higher sensitivity to cognition scores, with sub-networks identifying significant functional network changes over a broad range of spectral frequencies. Approximately 70.5% of all children were appropriately classified as normal cognition, mild/moderate CI or severe CI using CST network features. CST classification predicted CI classes 55% better than chance, and reduced misclassification penalties by half.\n\nConclusions: CI in CWEOE can be detected with sensitivity at 85% (with respect to identifying either mild/moderate or severe CI) and specificity of 84%, by EEG network analysis.\n\nSignificance: This study outlines a data-driven methodology for identifying candidate biomarkers of CI in CWEOE from network features. Following additional replication, the proposed method and its use of routinely acquired EEG forms an attractive proposition for supporting clinical assessment of CI.

neuroscience