Background:LMTM is being developed as a treatment for AD based on inhibition of tau aggregation.Objectives:To examine the efficacy of LMTM as monotherapy in non-randomized cohort analyses as modified primary outcomes in an 18-month Phase III trial in mild AD.Methods:Mild AD patients (n = 800) were randomly assigned to 100 mg twice a day or 4 mg twice a day. Prior to unblinding, the Statistical Analysis Plan was revised to compare the 100 mg twice a day as monotherapy subgroup (n = 79) versus 4 mg twice a day as randomized (n = 396), and 4 mg twice a day as monotherapy (n = 76) versus 4 mg twice a day as add-on therapy (n = 297), with strong control of family-wise type I error.Results:The revised analyses were statistically significant at the required threshold of p < 0.025 in both comparisons for change in ADAS-cog, ADCS-ADL, MRI atrophy, and glucose uptake. The brain atrophy rate was initially typical of mild AD in both add-on and monotherapy groups, but after 9 months of treatment, the rate in monotherapy patients declined significantly to that reported for normal elderly controls. Differences in severity or diagnosis at baseline between monotherapy and add-on patients did not account for significant differences in favor of monotherapy.Conclusions:The results are consistent with earlier studies in supporting the hypothesis that LMTM might be effective as monotherapy and that 4 mg twice a day may serve as well as higher doses. A further suitably randomized trial is required to test this hypothesis.
We apply wavelet-based time-localised phase coherence to investigate the relationship between blood flow and skin temperature, and between blood flow and instantaneous heart rate (IHR), during vasoconstriction and vasodilation provoked by local cooling or heating of the skin. A temperature-controlled metal plate (≈ 10 cm 2) placed on the volar side of the left arm was used to provide the heating and cooling. Beneath the plate, the blood flow was measured by laser Doppler flowmetry and the adjacent skin temperature by a thermistor. Two 1-hour data sets were collected from each of 10 subjects. In each case a 30-min basal recording was followed by a step change in plate temperature, to either 24 • C or 42 • C. The IHR was derived from simultaneously recorded ECG. We confirm the changes in the energy and frequency of blood flow oscillations during cooling and heating reported earlier. That is: during cooling, there was a significant decrease in the average frequency of myogenic blood flow oscillations (p < 0.05) and the myogenic spectral peak became more prominent; and during heating, there was a significant (p < 0.05) general increase in spectral energy, associated with vasodilation, except in the myogenic interval. We also show that significant (p < 0.05) phase coherence exists between blood flow and IHR in the respiratory and myogenic frequency intervals. Cooling did not affect this phase coherence in any of the frequency intervals, whereas heating enhanced the phase coherence in the respiratory and myogenic intervals. This can be explained by the reduction in vascular resistance produced by heating, a process where myogenic mechanisms play a key role. Weak phase coherence between temperature and blood flow was observed for unperturbed skin, but it increased in all frequency intervals as a result of heating. It was not significantly affected by cooling. We conclude that the mechanisms of vasodilation and vasoconstriction, in response to temperature change, are oscillatory in nature and are independent of central sources of variability.
The functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signal is regularly used to assign neuronal activity to cognitive function. Recent analyses have shown that the local field potential (LFP) gamma power is a better predictor of the fMRI BOLD signal than spiking activity. However, LFP gamma power and spiking activity are usually correlated, clouding the analysis of the neural basis of the BOLD signal. We show that changes in LFP gamma power and spiking activity in the primary visual cortex (V1) of the awake primate can be dissociated by using grating and plaid pattern stimuli, which differentially engage surround suppression and cross-orientation inhibition/facilitation within and between cortical columns. Grating presentation yielded substantial V1 LFP gamma frequency oscillations and significant multi-unit activity. Plaid pattern presentation significantly reduced the LFP gamma power while increasing population multi-unit activity. The fMRI BOLD activity followed the LFP gamma power changes, not the multi-unit activity. Inference of neuronal activity from the fMRI BOLD signal thus requires detailed a priori knowledge of how different stimuli or tasks activate the cortical network.
Background: Although hydromethylthionine is a potent tau aggregation inhibitor, no difference was found in either of two Phase III trials in mild to moderate Alzheimer's disease (AD) comparing doses in the range 150-250 mg/day with 8 mg/day intended as a control. Objective: To determine how drug exposure is related to treatment response. Methods: A sensitive plasma assay for the drug was used in a population pharmacokinetic analysis of samples from 1,162 of the 1,686 patients who participated in either of the Phase III trials with available samples and efficacy outcome data. Results: There are steep concentration-response relationships for steady state plasma levels in the range 0.3-0.8 ng/ml at the 8 mg/day dose. Using a threshold based on the lower limit of quantitation of the assay on Day 1, there are highly significant differences in cognitive decline and brain atrophy in patients with above threshold plasma levels, both for monotherapy and add-on therapy, but with effect sizes reduced by half as add-on. Plasma concentrations in the range 4-21 ng/ml produced by the high doses are not associated with any additional benefit.
Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer’s disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.
Experimental fMRI studies have shown that spontaneous brain activity i.e. in the absence of any external input, exhibit complex spatial and temporal patterns of co-activity between segregated brain regions. These so-called large-scale resting-state functional connectivity networks represent dynamically organized neural assemblies interacting with each other in a complex way. It has been suggested that looking at the dynamical properties of complex patterns of brain functional co-activity may reveal neural mechanisms underlying the dynamic changes in functional interactions. Here, we examine how global network dynamics is shaped by different network configurations, derived from realistic brain functional interactions. We focus on two main dynamics measures: synchrony and variations in synchrony. Neural activity and the inferred hemodynamic response of the network nodes are simulated using system of 90 FitzHugh-Nagumo neural models subject to system noise and time-delayed interactions. These models are embedded into the topology of the complex brain functional interactions, whose architecture is additionally reduced to its main structural pathways. In the simulated functional networks, patterns of correlated regional activity clearly arise from dynamical properties that maximize synchrony and variations in synchrony. Our results on the fast changes of the level of the network synchrony also show how flexible changes in the large-scale network dynamics could be. Keywords: network dynamics; whole brain simulations; FitzHugh-Nagumo neural modelExperimental studies of the human brain activity at rest i.e. without any overt-directed behavior have revealed patterns of correlated activity, so called resting-state networks. The neural mechanisms contributing to the formation of these networks are largely unknown. We use modeling approach to interpret these experimental findings, looking at the brain as the dynamical system. We characterize brain network dynamical properties by synchrony and variability in synchrony. We demonstrate that functional brain interactions may arise from the network dynamics which allow flexible changes between different network configurations. We show that these changes reflect almost periodic alternations between network synchronized and desynchronized state.
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