Biomedical subjects

Kevin H Knuth

Publications and source records attributed to Kevin H Knuth.

10 recordsLinked to original sources

PubMed2006

Trial-by-trial estimation of amplitude and latency variability in neuronal spike trains.

The rate function underlying single-trial spike trains can vary from trial to trial. We propose to estimate the amplitude and latency variability in single-trial neuronal spike trains on a trial-by-trial basis. The firing rate over a trial is modeled by a family of rate profiles with trial-invariant waveform and trial-dependent amplitude scaling factors and latency shifts. Using a Bayesian inference framework we derive an iterative fixed-point algorithm from which the single-trial amplitude scaling factors and latency shifts are estimated. We test the performance of the algorithm on simulated data and then apply it to actual neuronal recordings from the sensorimotor cortex of the monkey.

Action Potentials↗

PubMed2006

Differentially variable component analysis: Identifying multiple evoked components using trial-to-trial variability.

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque-demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.

Animals↗

PubMed2006

Stochastic modeling of neurobiological time series: power, coherence, Granger causality, and separation of evoked responses from ongoing activity.

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Action Potentials↗

PubMed2005

An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex.

EEG oscillations are hypothesized to reflect cyclical variations in the neuronal excitability, with particular frequency bands reflecting differing spatial scales of brain operation. However, despite decades of clinical and scientific investigation, there is no unifying theory of EEG organization, and the role of ongoing activity in sensory processing remains controversial. This study analyzed laminar profiles of synaptic activity [current source density CSD] and multiunit activity (MUA), both spontaneous and stimulus-driven, in primary auditory cortex of awake macaque monkeys. Our results reveal that the EEG is hierarchically organized; delta (1-4 Hz) phase modulates theta (4-10 Hz) amplitude, and theta phase modulates gamma (30-50 Hz) amplitude. This oscillatory hierarchy controls baseline excitability and thus stimulus-related responses in a neuronal ensemble. We propose that the hierarchical organization of ambient oscillatory activity allows auditory cortex to structure its temporal activity pattern so as to optimize the processing of rhythmic inputs.

Acoustic Stimulation↗

PubMed2004

Neural dynamics and the fundamental mechanisms of event-related brain potentials.

Event-related potentials (ERPs) provide a critical link between the hemodynamic response, as measured by functional magnetic resonance imaging, and the dynamics of the underlying neuronal activity. Single-trial ERP recordings capture the oscillatory activity that are hypothesized to underlie both communication between brain regions and amplified processing of behaviorally relevant stimuli. However, precise interpretations of ERPs are precluded by uncertainty about their neural mechanisms. One influential theory holds that averaged sensory ERPs are generated by partial phase resetting of ongoing electroencephalographic oscillations, while another states that ERPs result from stimulus-evoked neural responses. We formulated critical predictions of each theory and tested these using direct, intracortical analyses of neural activity in monkeys. Our findings support a predominant role for stimulus-evoked activity in sensory ERP generation, and they outline both logic and methodology necessary for differentiating evoked and phase resetting contributions to cognitive and motor ERPs in future studies.

Animals↗

PubMed2004

Hippocampal structural asymmetry in unsuccessful psychopaths.

BACKGROUND: Structural and functional hippocampal abnormalities have been previously reported in institutionalized psychopathic and aggressive populations. This study assessed whether prior findings of a right greater than left (R > L) functional asymmetry in caught violent offenders generalize to the structural domain in unsuccessful, caught psychopaths. METHODS: Left and right hippocampal volumes were assessed using structural magnetic resonance imaging (MRI) in 23 control subjects, 16 unsuccessful psychopaths, and 12 successful (uncaught) community psychopaths and transformed into standardized space. RESULTS: Unsuccessful psychopaths showed an exaggerated structural hippocampal asymmetry (R > L) relative both to successful psychopaths and control subjects (p <.007) that was localized to the anterior region. This effect could not be explained by environmental and diagnostic confounds and constitutes the first brain imaging analysis of successful and unsuccessful psychopaths. CONCLUSIONS: Atypical anterior hippocampal asymmetries in unsuccessful psychopaths may reflect an underlying neurodevelopmental abnormality that disrupts hippocampal-prefrontal circuitry, resulting in affect dysregulation, poor contextual fear conditioning, and insensitivity to cues predicting capture.

Adult↗

PubMed2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Artificial Intelligence↗

PubMed2003

Estimation of single-trial multicomponent ERPs: differentially variable component analysis (dVCA).

A Bayesian inference framework for estimating the parameters of single-trial, multicomponent, event-related potentials is presented. Single-trial recordings are modeled as the linear combination of ongoing activity and multicomponent waveforms that are relatively phase-locked to certain sensory or motor events. Each component is assumed to have a trial-invariant waveform with trial-dependent amplitude scaling factors and latency shifts. A Maximum a Posteriori solution of this model is implemented via an iterative algorithm from which the component's waveform, single-trial amplitude scaling factors and latency shifts are estimated. Multiple components can be derived from a single-channel recording based on their differential variability, an aspect in contrast with other component analysis techniques (e.g., independent component analysis) where the number of components estimated is equal to or smaller than the number of recording channels. Furthermore, we show that, by subtracting out the estimated single-trial components from each of the single-trial recordings, one can estimate the ongoing activity, thus providing additional information concerning task-related brain dynamics. We test this approach, which we name differentially variable component analysis (dVCA), on simulated data and apply it to an experimental dataset consisting of intracortically recorded local field potentials from monkeys performing a visuomotor pattern discrimination task.

Algorithms↗

PubMed2002

Trial-to-trial variability of cortical evoked responses: implications for the analysis of functional connectivity.

OBJECTIVES: The time series of single trial cortical evoked potentials typically have a random appearance, and their trial-to-trial variability is commonly explained by a model in which random ongoing background noise activity is linearly combined with a stereotyped evoked response. In this paper, we demonstrate that more realistic models, incorporating amplitude and latency variability of the evoked response itself, can explain statistical properties of cortical potentials that have often been attributed to stimulus-related changes in functional connectivity or other intrinsic neural parameters. METHODS: Implications of trial-to-trial evoked potential variability for variance, power spectrum, and interdependence measures like cross-correlation and spectral coherence, are first derived analytically. These implications are then illustrated using model simulations and verified experimentally by the analysis of intracortical local field potentials recorded from monkeys performing a visual pattern discrimination task. To further investigate the effects of trial-to-trial variability on the aforementioned statistical measures, a Bayesian inference technique is used to separate single-trial evoked responses from the ongoing background activity. RESULTS: We show that, when the average event-related potential (AERP) is subtracted from single-trial local field potential time series, a stimulus phase-locked component remains in the residual time series, in stark contrast to the assumption of the common model that no such phase-locked component should exist. Two main consequences of this observation are demonstrated for statistical measures that are computed on the residual time series. First, even though the AERP has been subtracted, the power spectral density, computed as a function of time with a short sliding window, can nonetheless show signs of modulation by the AERP waveform. Second, if the residual time series of two channels co-vary, then their cross-correlation and spectral coherence time functions can also be modulated according to the shape of the AERP waveform. Bayesian estimation of single-trial evoked responses provides further proof that these time-dependent statistical changes are due to remnants of the evoked phase-locked component in the residual time series. CONCLUSIONS: Because trial-to-trial variability of the evoked response is commonly ignored as a contributing factor in evoked potential studies, stimulus-related modulations of power spectral density, cross-correlation, and spectral coherence measures is often attributed to dynamic changes of the connectivity within and among neural populations. This work demonstrates that trial-to-trial variability of the evoked response must be considered as a possible explanation of such modulation.

Animals↗

PubMed2002

Neuropsychological correlates of hippocampal volumes in patients experiencing a first episode of schizophrenia.

OBJECTIVE: Despite evidence for hippocampal structural abnormalities in patients with schizophrenia, their functional correlates remain largely unknown. This study investigated the neuropsychological correlates of hippocampal volume in 43 men and 32 women experiencing a first episode of schizophrenia. METHOD: Posterior and anterior hippocampal volumes were computed from contiguous 3.1-mm magnetic resonance images and examined in relationship to six domains of neuropsychological functioning. Significant structure-function associations were investigated by examining the correlations between functioning and individual hippocampal slice volumes across the long axis of the hippocampus after interpolation to 10 equally spaced slice positions. RESULTS: Among men, worse executive and motor functioning correlated significantly with smaller anterior, but not posterior, hippocampal volume. The relationship between executive and motor functioning and hippocampal volume was not linear, however, when examined across the long axis of the hippocampus. Anterior hippocampal volume was more strongly correlated with both executive and motor functioning than with either memory or language functioning in men. None of the correlations between either posterior or anterior hippocampal volumes and the neuropsychological domains was significant among women. Anterior hippocampal volume was more strongly correlated with motor functioning in men than in women. CONCLUSIONS: Anterior hippocampal abnormalities associated with deficits on tests considered sensitive to frontal lobe functions implicate a defect in the integrated system linking frontal and mesiotemporal lobe regions. These findings further suggest that there are sex differences in structure-function relations in schizophrenia such that men may have more pronounced frontolimbic system abnormalities.

Adult↗