2,566 research outputs found
A role for recurrent processing in object completion: neurophysiological, psychophysical and computational"evidence
Recognition of objects from partial information presents a significant
challenge for theories of vision because it requires spatial integration and
extrapolation from prior knowledge. We combined neurophysiological recordings
in human cortex with psychophysical measurements and computational modeling to
investigate the mechanisms involved in object completion. We recorded
intracranial field potentials from 1,699 electrodes in 18 epilepsy patients to
measure the timing and selectivity of responses along human visual cortex to
whole and partial objects. Responses along the ventral visual stream remained
selective despite showing only 9-25% of the object. However, these visually
selective signals emerged ~100 ms later for partial versus whole objects. The
processing delays were particularly pronounced in higher visual areas within
the ventral stream, suggesting the involvement of additional recurrent
processing. In separate psychophysics experiments, disrupting this recurrent
computation with a backward mask at ~75ms significantly impaired recognition of
partial, but not whole, objects. Additionally, computational modeling shows
that the performance of a purely bottom-up architecture is impaired by heavy
occlusion and that this effect can be partially rescued via the incorporation
of top-down connections. These results provide spatiotemporal constraints on
theories of object recognition that involve recurrent processing to recognize
objects from partial information
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The role of HG in the analysis of temporal iteration and interaural correlation
Increased Modulation by Cognitive Control Region during fMRI Working Memory Task Suggest Inefficiencies in Network Connectivity in Children with ADHD
Attention Deficit / Hyperactivity Disorder is a neurodevelopmental disorder common among children and adolescent populations whose symptoms are believed to be caused by deficits in executive functioning processes such as working memory. Using fMRI analyses, differences in the modulatory influence exhibited by the dorsal anterior cingulate cortex (dACC) on cortico-striatal regions implicated in working memory (2-back) (Owen et al 2005) was assessed between children with ADHD (twenty-three participants; mean age 6 yrs: 6.4-14.9 yrs) and healthy controls (twenty-six participants; mean age 10.1yrs: 6.3-14.1 yrs). Modulatory influence is defined as the degree to which one region exerts control on another region and was investigated using the analysis tool Psychophysiological Interactions (PPI) (Friston et al. 1997). Results of second level analyses show an increased level of dACC modulation on target regions (parietal lobe, middle frontal gyrus, dorsal pre frontal cortex) in children with ADHD and suggest an underlying inefficiency in control network circuitry. Further investigation into network efficiency was conducted using performance (d’) and latency response data. Statistical analyses of performance and latency response times show similar averages between groups and indicate children with ADHD were not compromised in their ability to complete the 2-Back task. This suggests the differential pattern of dACC modulation observed in children with ADHD is not driven by behavioral symptoms of the psychiatric disorder and allude to functional differences in network circuitry driving the apparent inefficiency. Our inefficiency hypothesis is consistent with other fMRI studies investigating working memory in subjects with ADHD. Future analyses using longitudinal studies of subjects may highlight potential developmental implications on the modulatory behavior of the dACC in children with ADHD
Neural Correlates of Auditory Perceptual Awareness and Release from Informational Masking Recorded Directly from Human Cortex: A Case Study.
In complex acoustic environments, even salient supra-threshold sounds sometimes go unperceived, a phenomenon known as informational masking. The neural basis of informational masking (and its release) has not been well-characterized, particularly outside auditory cortex. We combined electrocorticography in a neurosurgical patient undergoing invasive epilepsy monitoring with trial-by-trial perceptual reports of isochronous target-tone streams embedded in random multi-tone maskers. Awareness of such masker-embedded target streams was associated with a focal negativity between 100 and 200 ms and high-gamma activity (HGA) between 50 and 250 ms (both in auditory cortex on the posterolateral superior temporal gyrus) as well as a broad P3b-like potential (between ~300 and 600 ms) with generators in ventrolateral frontal and lateral temporal cortex. Unperceived target tones elicited drastically reduced versions of such responses, if at all. While it remains unclear whether these responses reflect conscious perception, itself, as opposed to pre- or post-perceptual processing, the results suggest that conscious perception of target sounds in complex listening environments may engage diverse neural mechanisms in distributed brain areas
Multiscale adaptive smoothing models for the hemodynamic response function in fMRI
In the event-related functional magnetic resonance imaging (fMRI) data
analysis, there is an extensive interest in accurately and robustly estimating
the hemodynamic response function (HRF) and its associated statistics (e.g.,
the magnitude and duration of the activation). Most methods to date are
developed in the time domain and they have utilized almost exclusively the
temporal information of fMRI data without accounting for the spatial
information. The aim of this paper is to develop a multiscale adaptive
smoothing model (MASM) in the frequency domain by integrating the spatial and
frequency information to adaptively and accurately estimate HRFs pertaining to
each stimulus sequence across all voxels in a three-dimensional (3D) volume. We
use two sets of simulation studies and a real data set to examine the finite
sample performance of MASM in estimating HRFs. Our real and simulated data
analyses confirm that MASM outperforms several other state-of-the-art methods,
such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
State Dependence of Stimulus-Induced Variability Tuning in Macaque MT
Behavioral states marked by varying levels of arousal and attention modulate
some properties of cortical responses (e.g. average firing rates or pairwise
correlations), yet it is not fully understood what drives these response
changes and how they might affect downstream stimulus decoding. Here we show
that changes in state modulate the tuning of response variance-to-mean ratios
(Fano factors) in a fashion that is neither predicted by a Poisson spiking
model nor changes in the mean firing rate, with a substantial effect on
stimulus discriminability. We recorded motion-sensitive neurons in middle
temporal cortex (MT) in two states: alert fixation and light, opioid
anesthesia. Anesthesia tended to lower average spike counts, without decreasing
trial-to-trial variability compared to the alert state. Under anesthesia,
within-trial fluctuations in excitability were correlated over longer time
scales compared to the alert state, creating supra-Poisson Fano factors. In
contrast, alert-state MT neurons have higher mean firing rates and largely
sub-Poisson variability that is stimulus-dependent and cannot be explained by
firing rate differences alone. The absence of such stimulus-induced variability
tuning in the anesthetized state suggests different sources of variability
between states. A simple model explains state-dependent shifts in the
distribution of observed Fano factors via a suppression in the variance of gain
fluctuations in the alert state. A population model with stimulus-induced
variability tuning and behaviorally constrained information-limiting
correlations explores the potential enhancement in stimulus discriminability by
the cortical population in the alert state.Comment: 36 pages, 18 figure
Neurophysiological and BOLD signal uncoupling of giant somatosensory evoked potentials in progressive myoclonic epilepsy: a case-series study
In progressive myoclonic epilepsy (PME), a rare epileptic syndrome caused by a variety of genetic disorders, the combination of peripheral stimulation and functional magnetic resonance imaging (fMRI) can shed light on the mechanisms underlying cortical dysfunction. The aim of the study is to investigate sensorimotor network modifications in PME by assessing the relationship between neurophysiological findings and blood oxygen level dependent (BOLD) activation. Somatosensory-evoked potential (SSEP) obtained briefly before fMRI and BOLD activation during median-nerve electrical stimulation were recorded in four subjects with typical PME phenotype and compared with normative data. Giant scalp SSEPs with enlarger N20-P25 complex compared to normal data (mean amplitude of 26.2\u2009\ub1\u20098.2\u2009\u3bcV after right stimulation and 27.9\u2009\ub1\u20093.7\u2009\u3bcV after left stimulation) were detected. Statistical group analysis showed a reduced BOLD activation in response to median nerve stimulation in PMEs compared to controls over the sensorimotor (SM) areas and an increased response over subcortical regions (p\u2009\u20092.3, corrected). PMEs show dissociation between neurophysiological and BOLD findings of SSEPs (giant SSEP with reduced BOLD activation over SM). A direct pathway connecting a highly restricted area of the somatosensory cortex with the thalamus can be hypothesized to support the higher excitability of these areas
Brain Responses Track Patterns in Sound
This thesis uses specifically structured sound sequences, with electroencephalography (EEG) recording and behavioural tasks, to understand how the brain forms and updates a model of the auditory world. Experimental chapters 3-7 address different effects arising from statistical predictability, stimulus repetition and surprise. Stimuli comprised tone sequences, with frequencies varying in regular or random patterns. In Chapter 3, EEG data demonstrate fast recognition of predictable patterns, shown by an increase in responses to regular relative to random sequences. Behavioural experiments investigate attentional capture by stimulus structure, suggesting that regular sequences are easier to ignore. Responses to repetitive stimulation generally exhibit suppression, thought to form a building block of regularity learning. However, the patterns used in this thesis show the opposite effect, where predictable patterns show a strongly enhanced brain response, compared to frequency-matched random sequences. Chapter 4 presents a study which reconciles auditory sequence predictability and repetition in a single paradigm. Results indicate a system for automatic predictability monitoring which is distinct from, but concurrent with, repetition suppression. The brain’s internal model can be investigated via the response to rule violations. Chapters 5 and 6 present behavioural and EEG experiments where violations are inserted in the sequences. Outlier tones within regular sequences evoked a larger response than matched outliers in random sequences. However, this effect was not present when the violation comprised a silent gap. Chapter 7 concerns the ability of the brain to update an existing model. Regular patterns transitioned to a different rule, keeping the frequency content constant. Responses show a period of adjustment to the rule change, followed by a return to tracking the predictability of the sequence. These findings are consistent with the notion that the brain continually maintains a detailed representation of ongoing sensory input and that this representation shapes the processing of incoming information
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