Traumatic brain injury (TBI) produces cytoskeletal protein derangements within cortical neurons that can be attenuated by protease inhibition

TBI produces a consistent and extensive loss of neurofilament 68 (NF68) and neurofilament 200 (NF200), key intermediate cytoskeletal proteins found in neurons including axons and dendrites, in cortical samples from injured brain. The presence of low molecular weight NF68 breakdown products (BDPs) strongly suggest that calpain proteolysis at least in part contributes to neurofilament (NF) protein loss following injury. Furthermore, one and two-dimensional gel electrophoresis analyses of NF BDPs obtained from in situ and in vitro tissue also implicated the involvement of calpain 2 mediated proteolysis of neurofilaments following TBI. Immunohistochemical examination of derangements in cytoskeletal proteins following traumatic brain injury in rats indicated that preferential dendritic rather than axonal damage occurs within three hours post-TBI. Although proteolysis of cytoskeletal proteins occurred concurrently with early morphological alterations, evidence of proteolysis preceded the full expression of evolutionary histopathological changes. Furthermore, cytoskeletal immunofluorescence alterations were not restricted to the site of impact. Confocal microscopic investigations of NF68 and NF200 immunofluorescence within injured cortical neurons revealed alterations in neurofilament assembly in the absence of NF derangements detectable at the light microscopic level ($<$15 minutes post-TBI). Collectively immunohistochemistry studies suggest that derangements to neuronal processes are biochemical and evolutionary in nature, and not due solely to mechanical shearing. Importantly, a systemically administered calpain inhibitor (calpain inhibitor 2) significantly reduced NF200, NF68, and spectrin protein loss as well as providing marked preservation of NF proteins in neuronal somata, dendrites, and axons at 24 hours post-TBI. ^

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.