An accumulation of anatomical, behavioral, and electrophysiological evidence allows us to identify the neuronal circuitry that is involved with vibrissa-mediated sensation and the control of rhythmic vibrissa movement. Anatomical evidence points to a multiplicity of closed sensorimotor loops, while electrophysiological data delineate the flow of electrical signals in these pathways. These loops process sensory input from the vibrissae and send projections to direct vibrissa movement, starting at the level of the hindbrain and proceeding toward loops that involve multiple structures in the forebrain. The nature of the vibrissa-related electrical signals in behaving animals has been studied extensively at the level of neocortical loops. Two types of spike signal are observed that serve as a reference of vibrissa motion: a fast signal that correlates with the relative phase of the vibrissae within a whisk cycle and a slow signal that correlates with the amplitude, and possibly the set-point, of the vibrissae during a whisk. Both signals are observed in vibrissa primary sensory (S1) cortex, and in some cases they are sufficiently robust to allow vibrissa position to be accurately estimated from the spike train of a single neuron. Unlike the case for S1 cortex, only the slow signal has been observed in vibrissa primary motor (M1) cortex. The control capabilities of M1 cortex were estimated from experiments with anesthetized animals in which progressive areas along the vibrissa motor branch were microstimulated with rhythm ically applied currents. The motion of the vibrissae followed stimulation of M1 cortex only for rates that were well below the frequency of rhythmic whisking; in contrast, the vibrissae followed stimulation of the facial nucleus, whose cells directly drive the vibrissae, for rates above that of whisking. In toto, the evidence implies that there is fast signaling from the facial nucleus, through the mystacial pad and the vibrissae and up through sensory cortex, but only slow signaling at the level of the motor cortex and down through the superior colliculus to the facial nucleus. The transformation from fast sensory signals to slow motor control is an unresolved issue. On the other hand, there is a candidate scheme to understand how the fast reference of vibrissa motion in the whisk cycle may be used to decode the angle of the vibrissae upon their contact with an object. We discuss a circuit in which servo mechanisms are used to determine the angle of contact relative to the preferred phase of the fast reference signals. Support for this scheme comes from results with anesthetized animals on the frequency and phase entrainment of intrinsic neuronal oscillators in S1 cortex. A prediction based on this scheme is that the output from a decoder circuit is maximal when the angle of contact differs from the preferred phase of a fast regerence signal. In contrast, for correlation-based schemes the output is maximal when the angle of contact equals the preferred phase.

We tested if coherent signaling between the sensory vibrissa areas of cerebellum and neocortex in rats was enhanced as they whisked in air. Whisking was accompanied by 5- to 15-Hz oscillations in the mystatial electromyogram, a measure of vibrissa position, and by 5- to 20-Hz oscillations in the differentially recorded local field potential (nablaLFP) within the vibrissa area of cerebellum and within the nablaLFP of primary sensory cortex. We observed that only 10% of the activity in either cerebellum or sensory neocortex was significantly phase-locked to rhythmic motion of the vibrissae; the extent of this modulation is in agreement with the results from previous single-unit measurements in sensory neocortex. In addition, we found that 40% of the activity in the vibrissa areas of cerebellum and neocortex was significantly coherent during periods of whisking. The relatively high level of coherence between these two brain areas, in comparison with their relatively low coherence with whisking per se, implies that the vibrissa areas of cerebellum and neocortex communicate in a manner that is incommensurate with whisking. To the extent that the vibrissa areas of cerebellum and neocortex communicate over the same frequency band as that used by whisking, these areas must multiplex electrical activity that is internal to the brain with activity that is that phase-locked to vibrissa sensory input.

Compressed sensing (CS) is a powerful new data acquisition paradigm that seeks to accurately reconstruct unknown sparse signals from very few (relative to the target signal dimension) random projections. The specific objective of this study is to save wireless sensor energy by using CS to simultaneously reduce data sampling rates, on-board storage requirements, and communication data payloads. For field-deployed low power wireless sensors that are often operated with limited energy sources, reduced communication translates directly into reduced power consumption and improved operational reliability. In this study, acceleration data from a multi-girder steel-concrete deck composite bridge are processed for the extraction of mode shapes. A wireless sensor node previously designed to perform traditional uniform, Nyquist rate sampling is modified to perform asynchronous, effectively sub-Nyquist rate sampling. The sub-Nyquist data are transmitted off-site to a computational server for reconstruction using the CoSaMP matching pursuit recovery algorithm and further processed for extraction of the structure's mode shapes. The mode shape metric used for reconstruction quality is the modal assurance criterion (MAC), an indicator of the consistency between CS and traditional Nyquist acquired mode shapes. A comprehensive investigation of modal accuracy from a dense set of acceleration response data reveals that MAC values above 0.90 are obtained for the first four modes of a bridge structure when at least 20% of the original signal is sampled using the CS framework. Reduced data collection, storage and communication requirements are found to lead to substantial reductions in the energy requirements of wireless sensor networks at the expense of modal accuracy. Specifically, total energy reductions of 10-60% can be obtained for a sensor network with 10-100 sensor nodes, respectively. The reduced energy requirements of the CS sensor nodes are shown to directly result in improved battery life and communication reliability.

Objectives: (1) To investigate the prevalence and characteristics of agitation in patients with Alzheimer’s disease (AD) and other forms of dementia; (2) to explore the association between agitation and other clinical variables, including disease severity, functional impairment and other neuropsychiatric symptoms, and (3) to determine the predictors of agitation. Methods: Data for 427 men and women with dementia from outpatient clinics of the University of California, Los Angeles Alzheimer’s Disease Center were analyzed. There were 277 patients with AD, 43 with vascular dementia, 47 with mixed dementia, 45 with frontotemporal dementia and 15 with dementia with Lewy bodies. Patients were evaluated with the Mini-Mental State Examination (MMSE), Neuropsychiatric Inventory (NPI), Functional Activities Questionnaire (FAQ), neuropsychological tests and the Caregiver Appraisal instrument. SPSS10 was utilized for statistical analysis. Results: There was no difference in agitation subscale scores among patients with dementia of various etiologies. In patients with AD, there was increased prevalence of agitation with increasing dementia severity. Agitation contributed substantially to caregiver burden and impact. There was a significant correlation between the FAQ and the NPI agitation subscale score after adjusting for MMSE scores. Delusion, disinhibition and irritability subscale scores in AD patients were correlated with agitation across disease severity. Subscale scores of frontally mediated behaviors including irritability, delusions and disinhibition predicted most of the variance in agitation levels. Conclusion: Agitation is common in AD and other dementias and has a marked impact on caregivers. It is related to dementia severity and to specific types of associated psychopathology implicating frontal lobe dysfunction. The present study is the largest and most comprehensive assessment of agitation reported. The data suggest that agitation in AD is a frontal lobe syndrome. Frontal lobe dysfunction may predispose AD patients to agitation by exaggerating behavioral responses to many types of coexisting psychopathology or environmental provocations.

No abstract