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Llano, D. A.

Publications and source records attributed to Llano, D. A..

3 recordsLinked to original sources
bioRxiv2018

Identification of a simple and novel cut-point based CSF and MRI signature for predicting Alzheimer disease progression that reinforces the 2018 NIA-AA research framework

The 2018 NIA-AA research framework proposes a classification system with beta-Amyloid deposition, pathologic Tau, and neurodegeneration (ATN) for the diagnosis and staging of Alzheimers Disease (AD). Data from the ADNI (AD neuroimaging initiative) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI) and AD subjects followed over a 3-10 year period were measured via multiple reaction monitoring (MRM) mass spectrometry. CSF A{beta}42, total-Tau (tTau), phosphorylated-Tau (pTau-181) and hippocampal volume were also measured. From these candidate markers, optimal diagnostic signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to A{beta}42 ratio, MRI hippocampal volume and a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting this signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used marker, and is much easier to use in practice than recently published complex signatures. In addition, this signature reinforces the ATN construct from the 2018 NIA-AA research framework.\n\nDisclosuresViswanath Devanarayan is an employee of Charles River Laboratories, and as such owns equity in, receives salary and other compensation from Charles River Laboratories.\n\nData collection and sharing for this project was funded by the Alzheimers Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimers Association; Alzheimers Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.;Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimers Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

neuroscience↗
bioRxiv2018

A novel mutual information estimator to measure spike train correlations in a model thalamocortical network

The impact of thalamic state on information transmission to the cortex remains poorly understood. This limitation exists due to the rich dynamics displayed by thalamocortical networks and because of inadequate tools to characterize those dynamics. Here, we introduce a novel estimator of mutual information and use it to determine the impact of a computational model of thalamic state on information transmission. Using several criteria, this novel estimator, which uses an adaptive partition, is shown to be superior to other mutual information estimators with uniform partitions when used to analyze simulated spike train data with different mean spike rates, as well as electrophysiological data from simultaneously recorded neurons. When applied to a thalamocortical model, the estimator revealed that thalamocortical cell T-type calcium current conductance influences mutual information between the input and output from this network. In particular, a T-type calcium current conductance of about 40 nS appears to produce maximal mutual information between the input to this network (conceptualized as afferent input to the thalamocortical cell) and the output of the network at the level of a layer 4 cortical neuron. Furthermore, at particular combinations of inputs to thalamocortical and thalamic reticular nucleus cells, thalamic cell bursting correlated strongly with recovery of mutual information between thalamic afferents and layer 4 neurons. These studies suggest that the novel mutual information estimator has advantages over previous estimators, and that thalamic reticular nucleus activity can enhance mutual information between thalamic afferents and thalamorecipient cells in the cortex.

neuroscience↗
bioRxiv2018

Thalamocortical and intracortical inputs differentiate layer-specific mouse auditory corticocollicular neurons

Long-range descending projections from the auditory cortex play key roles in shaping response properties in the inferior colliculus. The auditory corticocollicular projection is massive and heterogeneous, with axons emanating from cortical layers 5 and 6, and plays a key role in directing plastic changes in the inferior colliculus. However, little is known about the cortical and thalamic networks within which corticocollicular neurons are embedded. Here, laser scanning photostimulation glutamate uncaging and photoactivation of channelrhodopsin-2 were used to probe the local and long-range network differences between pre-identified mouse layer 5 and layer 6 auditory corticocollicular neurons in vitro. Layer 5 corticocollicular neurons were found to vertically integrate supragranular excitatory and inhibitory input to a substantially greater degree than their layer 6 counterparts. In addition, all layer 5 corticocollicular neurons received direct and large thalamic inputs from channelrhodopsin-2 labeled thalamocortical fibers whereas such inputs were less common in layer 6 corticocollicular neurons. Finally, a new low calcium/synaptic blockade approach to separate direct from indirect inputs using laser photostimulation was validated. These data demonstrate that layer 5 and 6 corticocollicular neurons receive distinct sets of cortical and thalamic inputs, supporting the hypothesis that they have divergent roles in modulating the inferior colliculus. Furthermore, the direct connection between the auditory thalamus and layer 5 corticocollicular neurons reveals a novel and rapid link connecting ascending and descending pathways.

neuroscience↗