Richard Nakamura

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.

Synchronized activity in prefrontal cortex during anticipation of visuomotor processing

It is commonly presumed, though not well established, that the prefrontal cortex exerts top-down control of sensory processing. One aspect of this control is thought to be a facilitation of sensory pathways in anticipation of such processing. To investigate the possible involvement of prefrontal cortex in anticipatory top-down control, we studied the statistical relations between prefrontal activity, recorded while a macaque monkey waited for presentation of a visual stimulus, and subsequent sensory and motor events. Local field potentials were simultaneously recorded from prefrontal, motor, occipital and temporal cortical sites in the left cerebral hemisphere. Spectral power and coherence analysis revealed that during stimulus anticipation three of five prefrontal sites participated in a coherent oscillatory network synchronized in the &bgr;-frequency range. Pre-stimulus network power and coherence were highly correlated with the amplitude and latency of early visual evoked potential components in visual cortical areas, and with response time. The results suggest that synchronized oscillatory networks in prefrontal cortex are involved in top-down anticipatory mechanisms that facilitate subsequent sensory processing in visual cortex. They further imply that stronger top-down control leads to larger and faster sensory responses, and a subsequently faster motor response.

Causal influences in primate cerebral cortex during visual pattern discrimination

Anatomical studies of the visual cortex demonstrate the existence of feedforward, feedback and lateral pathways among multiple cortical areas. Yet relatively little evidence has previously been available to show the causal influences of these areas on one another during visual information processing. We simultaneously recorded event-related local field potentials (LFPs) from surface-to-depth bipolar electrodes at six sites in the ventral region of the right hemisphere visual cortex in a highly trained macaque monkey during performance of a visual pattern discrimination task. Applying a new statistical measure, the short-time directed transfer function (STDTF), to the LFP data set, we charted the changing strength and direction of causal influence between these cortical sites on a fraction-of-a-second time scale. We present results showing, for the first time, the dynamics of distinct feedforward, feedback and lateral influences in the ventral portion of the primate visual cortex during visual pattern processing.

Examining the Predictive Validity of NIH Peer Review Scores.

The predictive validity of peer review at the National Institutes of Health (NIH) has not yet been demonstrated empirically. It might be assumed that the most efficient and expedient test of the predictive validity of NIH peer review would be an examination of the correlation between percentile scores from peer review and bibliometric indices of the publications produced from funded projects. The present study used a large dataset to examine the rationale for such a study, to determine if it would satisfy the requirements for a test of predictive validity. The results show significant restriction of range in the applications selected for funding. Furthermore, those few applications that are funded with slightly worse peer review scores are not selected at random or representative of other applications in the same range. The funding institutes also negotiate with applicants to address issues identified during peer review. Therefore, the peer review scores assigned to the submitted applications, especially for those few funded applications with slightly worse peer review scores, do not reflect the changed and improved projects that are eventually funded. In addition, citation metrics by themselves are not valid or appropriate measures of scientific impact. The use of bibliometric indices on their own to measure scientific impact would likely increase the inefficiencies and problems with replicability already largely attributed to the current over-emphasis on bibliometric indices. Therefore, retrospective analyses of the correlation between percentile scores from peer review and bibliometric indices of the publications resulting from funded grant applications are not valid tests of the predictive validity of peer review at the NIH.

Inter-area Synchronization in Macaque Neocortex During a Visual Pattern Discrimination Task

Synchronization among cortical sites was investigated in a macaque monkey performing a visual pattern discrimination task. Single-site power and between-site coherence were computed from transcortical field potentials. At the time of the behavioral response, power and coherence increased for a select set of cortical sites widely distributed in one hemisphere. The increase was not specific to any narrow frequency band, but occurred over the entire observable range between 0 and 100 Hz.

1/f‐like spectra in cortical and subcortical brain structures: A possible marker of behavioral state‐dependent self‐organization

Recently, power‐law scaling of power spectra with scaling exponents close to −1 (1/f‐like spectra) have been observed in cortical and subcortical brain structures in association with specific behavioral states. Further, 1/f processes at different levels of organization have been reported in the nervous systems of vertebrates and invertebrates. This study describes the 1/f‐like appearance of cross‐spectra between cortical sites in a monkey performing a GO/NO‐GO behavioral task. We found broadband 1/f‐like coherence spectra (average slope =−0.84) during the ‘‘behaviorally flexible’’ state of tonic arousal shortly after the monkey had initiated the trial, suggesting that this brain state is characterized by long‐range cortical correlations. One of the implications of these findings is that the 1/f‐like cortical coherence spectra may provide a signature of brain self‐organization during specific behavioral states. A more general implication is that broadband 1/f‐like processes across many levels of organizati...

Spatio-temporal cortical patterns evoked in monkeys by a discrimination task

The primary experimental objective of this work was to demonstrate localization and temporal sequencing of the functional steps carried out by nonhuman primate subjects during performance of a sensory discrimination task, i.e., to identify the locale and sequence of activation of regions that participate in sensory discrimination, stimulus classification, and response preparation. Multivariate statistical procedures were applied to evoked transcortical recordings to identify the location and order of occurrence of signals that are effective in discriminating task conditions and parameters. (1) Sensory discrimination, (2) stimulus classification, and (3) response preparation occurred in the expected sequence. Information that enabled discrimination using these procedures was distributed widely across the cortex; however, the maximum information content was localized to striate and prestriate cortex, anterior inferior parietal cortex, and temporal and premotor cortex, respectively. This work provides a perspective on brain mechanisms responsible for cognition and demonstrates a set of powerful multivariate analytic tools for functional mapping, i.e., identifying the location and sequencing of cognitive functions.