A Spectral Network Model of Pitch Perception

A model of pitch perception, called the Spatial Pitch Network or SPINET model, is developed and analyzed. The model neurally instantiates ideas front the spectral pitch modeling literature and joins them to basic neural network signal processing designs to simulate a broader range of perceptual pitch data than previous spectral models. The components of the model arc interpreted as peripheral mechanical and neural processing stages, which arc capable of being incorporated into a larger network architecture for separating multiple sound sources in the environment. The core of the new model transforms a spectral representation of an acoustic source into a spatial distribution of pitch strengths. The SPINET model uses a weighted "harmonic sieve" whereby the strength of activation of a given pitch depends upon a weighted sum of narrow regions around the harmonics of the nominal pitch value, and higher harmonics contribute less to a pitch than lower ones. Suitably chosen harmonic weighting functions enable computer simulations of pitch perception data involving mistuned components, shifted harmonics, and various types of continuous spectra including rippled noise. It is shown how the weighting functions produce the dominance region, how they lead to octave shifts of pitch in response to ambiguous stimuli, and how they lead to a pitch region in response to the octave-spaced Shepard tone complexes and Deutsch tritones without the use of attentional mechanisms to limit pitch choices. An on-center off-surround network in the model helps to produce noise suppression, partial masking and edge pitch. Finally, it is shown how peripheral filtering and short term energy measurements produce a model pitch estimate that is sensitive to certain component phase relationships.Air Force Office of Scientific Research (F49620-92-J-0225); American Society for Engineering Educatio

Self-administration of edible Δ9-tetrahydrocannabinol and associated behavioral effects in mice

Background With increasing access to legal cannabis across the globe, it is imperative to more closely study its behavioral and physiological effects. Furthermore, with the proliferation of cannabis use, modes of consumption are changing, with edible formulations becoming increasingly popular. Nevertheless, there are relatively few animal models of self-administration of the primary psychoactive component of cannabis, Δ9-tetrahydrocannabinol (THC), and almost all incorporate routes of administration other than those used by humans. The aim of the current study was to develop a model of edible THC self-administration and assess its impact on CB1 receptor-mediated behaviors in female and male mice. Methods Mice were given limited access to a palatable dough which occasionally contained THC in doses ranging from 1 to 10 mg/kg. Following dough consumption, mice were assessed for home cage locomotor activity, body temperature, or analgesia. Locomotor activity was also assessed in conjunction with the CB1 receptor antagonist SR141716A. Results Dough was well-consumed, but consumption decreased at the highest THC concentrations. Edible THC produced dose-dependent decreases in locomotor activity and body temperature in both sexes, and these effects were more pronounced in male mice. Hypolocomotion induced by edible THC was attenuated by SR141716A, indicating mediation by CB1 receptor activation. Conclusions In contrast to other cannabinoid self-administration models, edible THC is relatively low in stress and uses a route of administration analogous to one used by humans. Potential applications include chronic THC self-administration, determining THC reward/reinforcement, and investigating consequences of oral THC use

Efficient Inflation Estimation

This paper investigates the use of trimmed means as high-frequency estimators of" inflation. The known characteristics of price change distributions, specifically the observation" that they generally exhibit high levels of kurtosis, imply that simple averages of price data are" unlikely to produce efficient estimates of inflation. Trimmed means produce superior estimates" of core inflation,' which we define as a long-run centered moving average of CPI and PPI" inflation. We find that trimming 9% from each tail of the CPI price-change distribution from the tails of the PPI price-change distribution, yields an efficient estimator of core inflation" for these two series, although lesser trims also produce substantial efficiency gains. Historically the optimal trimmed estimators are found to be nearly 23% more efficient (in terms of root-mean-square error) than the standard mean CPI Moreover, the efficient estimators are robust to sample period and to the definition of the" presumed underlying long-run trend in inflation.

The Contribution of Blood Serum Biomarkers to the Prediction of Cognitive Decline by fMRI and Apolipoprotein-E in Healthy Older Adults

Biomarkers are a promising approach to the prediction and early intervention of Alzheimer\u27s disease. We demonstrated that cortical functional MRI (fMRI) activation during a semantic memory task and apolipoprotein-E ?4 allele inheritance (APOE?4) effectively predicted cognitive decline after 18-months in healthy, asymptomatic elders. Hippocampal volume added modest prediction, while AD family history and demographics were ineffective. Previous studies have linked plasma homocysteine (tHcy), vitamin B12 and creatinine values to cognitive funcitoning, cortical atrophy, hippocampal atrophy and neuropathology, and vascular integrity. Here we incorporated total plasma homocysteine (tHcy), B12 creatinine values into our previous predictive models. Of 78 healthy elders, 27 (34.6%) exhibited significant cognitive decline after 18-months. tHcy, but not B12 or creatinine, was marginally positively correlated with cortical semantic memory fMRI activation, particularly in stable participants. Logistic regression showed that tHcy, when added to APOE?4 and cortical fMRI, was a significant predictor of outcome and strengthed the already significant model (p = .007; C = .80 and R2 = .37). However, control for B12 and creatinine covariates diminished tHcy as a predictor (p = .084), though the model was still stronger than without this factor (C = .78 and R = 31). tHcy did not significantly interact with APOE?4, as has previously been reported. Neither B12 nor creatinine was similarly effective as a predictor. These results suggest that commonly investigated blood serum biomarkers are at best weakly associated with predicting age- and dementia-related cognitive decline in healthy, asymptomatic elders. fMRI and APOE?4 presently provided the best predictive model

The Contribution of Blood Serum Biomarkers to the Prediction of Cognitive Decline by fMRI and Apolipoprotein-E in Healthy Older Adults

Biomarkers are a promising approach to the prediction and early intervention of Alzheimer\u27s disease. We demonstrated that cortical functional MRI (fMRI) activation during a semantic memory task and apolipoprotein-E ?4 allele inheritance (APOE?4) effectively predicted cognitive decline after 18-months in healthy, asymptomatic elders. Hippocampal volume added modest prediction, while AD family history and demographics were ineffective. Previous studies have linked plasma homocysteine (tHcy), vitamin B12 and creatinine values to cognitive funcitoning, cortical atrophy, hippocampal atrophy and neuropathology, and vascular integrity. Here we incorporated total plasma homocysteine (tHcy), B12 creatinine values into our previous predictive models. Of 78 healthy elders, 27 (34.6%) exhibited significant cognitive decline after 18-months. tHcy, but not B12 or creatinine, was marginally positively correlated with cortical semantic memory fMRI activation, particularly in stable participants. Logistic regression showed that tHcy, when added to APOE?4 and cortical fMRI, was a significant predictor of outcome and strengthed the already significant model (p = .007; C = .80 and R2 = .37). However, control for B12 and creatinine covariates diminished tHcy as a predictor (p = .084), though the model was still stronger than without this factor (C = .78 and R = 31). tHcy did not significantly interact with APOE?4, as has previously been reported. Neither B12 nor creatinine was similarly effective as a predictor. These results suggest that commonly investigated blood serum biomarkers are at best weakly associated with predicting age- and dementia-related cognitive decline in healthy, asymptomatic elders. fMRI and APOE?4 presently provided the best predictive model

Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Performance Variability During a Multitrial List-Learning Task as a Predictor of Future Cognitive Decline in Healthy Elders

Introduction: In clinical settings, neuropsychological test performance is traditionally evaluated with total summary scores (TSS). However, recent studies demonstrated that indices of intraindividual variability (IIV) yielded unique information complementing TSS. This 18-month longitudinal study sought to determine whether IIV indices derived from a multitrial list-learning test (the Rey Auditory Verbal Learning Test) provided incremental utility in predicting cognitive decline in older adults compared to TSS. Method: Ninety-nine cognitively intact older adults (aged 65 to 89 years) underwent neuropsychological testing (including the Rey Auditory Verbal Learning Test) at baseline and 18-month follow-up. Participants were classified as cognitively stable (n = 65) or declining (n = 34) based on changes in their neuropsychological test performance. Logistic regression modeling tested the ability of baseline TSS indices (sum of Trials 1–5, immediate recall, and delayed recall) and IIV indices (lost access and gained access) to discriminate between stable and declining individuals. Results: Higher values of both lost access and gained access at baseline were associated with an increased risk for decline at 18-month follow-up. Further, the IIV indices provided predictive utility above and beyond the TSS indices. Conclusion: These results highlight the value of analyzing IIV in addition to TSS during neuropsychological evaluation in older adults. High levels of IIV may reflect impairment in anterograde memory systems and/or executive dysfunction that may serve as a prognostic indicator of cognitive decline