Increased Action Potential Firing Rates of Layer 2/3 Pyramidal Cells in the Prefrontal Cortex are Significantly Related to Cognitive Performance in Aged Monkeys

The neurobiological substrates of significant age-related deficits in higher cognitive abilities mediated by the prefrontal cortex (PFC) are unknown. To address this issue, whole-cell current-clamp recordings were used to compare the intrinsic membrane and action potential (AP) firing properties of layer 2/3 pyramidal cells in PFC slices from young and aged behaviorally characterized rhesus monkeys. Most aged subjects demonstrated impaired performance in Delayed Non-Match to Sample (DNMS) task acquisition, DNMS 2 min delay and the Delayed Recognition Span task. Resting membrane potential and membrane time constant did not differ in aged relative to young cells, but input resistance was significantly greater in aged cells. Single APs did not differ in terms of threshold, duration or rise time, but their amplitude and fall time were significantly decreased in aged cells. Repetitive AP firing rates were significantly increased in aged cells. Within the aged group, there was a U-shaped quadratic relationship between firing rate and performance on each behavioral task. Subjects who displayed either low or very high firing rates exhibited poor performance, while those who displayed intermediate firing rates exhibited relatively good performance. These data indicate that an increase in AP firing rate may be responsible, in part, for age-related PFC dysfunction

Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse models

Abstract In neurodegenerative disorders, such as Alzheimer's disease, neuronal dendrites and dendritic spines undergo significant pathological changes. Because of the determinant role of these highly dynamic structures in signaling by individual neurons and ultimately in the functionality of neuronal networks that mediate cognitive functions, a detailed understanding of these changes is of paramount importance. Mutant murine models, such as the Tg2576 APP mutant mouse and the rTg4510 tau mutant mouse have been developed to provide insight into pathogenesis involving the abnormal production and aggregation of amyloid and tau proteins, because of the key role that these proteins play in neurodegenerative disease. This review showcases the multidimensional approach taken by our collaborative group to increase understanding of pathological mechanisms in neurodegenerative disease using these mouse models. This approach includes analyses of empirical 3D morphological and electrophysiological data acquired from frontal cortical pyramidal neurons using confocal laser scanning microscopy and whole-cell patchclamp recording techniques, combined with computational modeling methodologies. These collaborative studies are designed to shed insight on the repercussions of dystrophic changes in neocortical neurons, define the cellular phenotype of differential neuronal vulnerability in relevant models of neurodegenerative disease, and provide a basis upon which to develop meaningful therapeutic strategies aimed at preventing, reversing, or compensating for neurodegenerative changes in dementia

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective “brain age” metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan

A randomized trial comparing treatments for varicose veins

Supported by a grant from the Health Technology Assessment Programme of the National Institute for Health Research (06/45/02). The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. We thank Janice Cruden for her secretarial support and data management; Gladys McPherson and the programming team at the Centre for Healthcare Randomised Trials; Tracey Davidson, Lynda Constable, Jackie Ellington, Laura Elliott, and Yvonne Fernie for help with scoring the Aberdeen Varicose Vein Questionnaire; Luke Vale and Laura Ternent, our original economists in the group; members of the Project Management Group for their ongoing advice and support of the trial; members of the study team (Graeme MacLennan, Maria Prior, and Denise Bolsover) who contributed to the behavioral recovery component of the trial; the independent members of the trial steering committee (Alun Davies [chair], Ian Loftus, and Jane Nixon) and the data and safety monitoring committee (Gerry Stansby [chair], Winston Banya, and Marcus Flather); and the staff members at recruitment sites (see the Supplementary Appendix) who facilitated recruitment, treatment, and follow-up of trial participants.Peer reviewedPublisher PD

Tau accumulation causes mitochondrial distribution deficits in neurons in a mouse model of tauopathy and in human Alzheimer's disease brain

Neurofibrillary tangles (NFT), intracellular inclusions of abnormal fibrillar forms of microtubule associated protein tau, accumulate in Alzheimer's disease (AD) and other tauopathies and are believed to cause neuronal dysfunction, but the mechanism of tau-mediated toxicity are uncertain. Tau overexpression in cell culture impairs localization and trafficking of organelles. Here we tested the hypothesis that, in the intact brain, changes in mitochondrial distribution occur secondary to pathological changes in tau. Array tomography, a high-resolution imaging technique, was used to examine mitochondria in the reversible transgenic (rTg)4510, a regulatable transgenic, mouse model and AD brain tissue. Mitochondrial distribution is progressively disrupted with age in rTg4510 brain, particularly in somata and neurites containing Alz50-positive tau aggregates. Suppression of soluble tau expression with doxycycline resulted in complete recovery of mitochondrial distribution, despite the continued presence of aggregated tau. The effect on mitochondrial distribution occurs without concomitant alterations in neuropil mitochondrial size, as assessed by both array tomography and electron microscopy. Similar mitochondrial localization alterations were also observed in human AD tissue in Alz50+ neurons, confirming the relevance of tau to mitochondrial trafficking observed in this animal model. Because abnormalities reverted to normal if soluble tau was suppressed in rTg4510 mice, even in the continued presence of fibrillar tau inclusions, we suggest that soluble tau plays an important role in mitochondrial abnormalities, which likely contribute to neuronal dysfunction in AD

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong