Chun Siong Soon

Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance

Sleep deprivation (SD) can alter extrinsic, task-related fMRI signal involved in attention, memory and executive function. However, its effects on intrinsic low-frequency connectivity within the Default Mode Network (DMN) and its related anti-correlated network (ACN) have not been well characterized. We investigated the effect of SD on functional connectivity within the DMN, and on DMN-ACN anti-correlation, both during the resting state and during performance of a visual attention task (VAT). 26 healthy participants underwent fMRI twice: once after a normal night of sleep in rested wakefulness (RW) and once following approximately 24h of total SD. A seed-based approach was used to examine pairwise correlations of low-frequency fMRI signal across different nodes in each state. SD was associated with significant selective reductions in DMN functional connectivity and DMN-ACN anti-correlation. This was congruent across resting state and VAT analyses, suggesting that SD induces a robust alteration in the intrinsic connectivity within and between these networks.

Predicting free choices for abstract intentions

Unconscious neural activity has been repeatedly shown to precede and potentially even influence subsequent free decisions. However, to date, such findings have been mostly restricted to simple motor choices, and despite considerable debate, there is no evidence that the outcome of more complex free decisions can be predicted from prior brain signals. Here, we show that the outcome of a free decision to either add or subtract numbers can already be decoded from neural activity in medial prefrontal and parietal cortex 4 s before the participant reports they are consciously making their choice. These choice-predictive signals co-occurred with the so-called default mode brain activity pattern that was still dominant at the time when the choice-predictive signals occurred. Our results suggest that unconscious preparation of free choices is not restricted to motor preparation. Instead, decisions at multiple scales of abstraction evolve from the dynamics of preceding brain activity.

Cortical surface-based searchlight decoding.

Local voxel patterns of fMRI signals contain specific information about cognitive processes ranging from basic sensory processing to high level decision making. These patterns can be detected using multivariate pattern classification, and localization of these patterns can be achieved with searchlight methods in which the information content of spherical sub-volumes of the fMRI signal is assessed. The only assumption made by this approach is that the patterns are spatially local. We present a cortical surface-based searchlight approach to pattern localization. Voxels are grouped according to distance along the cortical surface-the intrinsic metric of cortical anatomy-rather than Euclidean distance as in volumetric searchlights. Using a paradigm in which the category of visually presented objects is decoded, we compare the surface-based method to a standard volumetric searchlight technique. Group analyses of accuracy maps produced by both methods show similar distributions of informative regions. The surface-based method achieves a finer spatial specificity with comparable peak values of significance, while the volumetric method appears to be more sensitive to small informative regions and might also capture information not located directly within the gray matter. Furthermore, our findings show that a surface centered in the middle of the gray matter contains more information than to the white-gray boundary or the pial surface.

The neural encoding of guesses in the human brain.

Human perception depends heavily on the quality of sensory information. When objects are hard to see we often believe ourselves to be purely guessing. Here we investigated whether such guesses use brain networks involved in perceptual decision making or independent networks. We used a combination of fMRI and pattern classification to test how visibility affects the signals, which determine choices. We found that decisions regarding clearly visible objects are predicted by signals in sensory brain regions, whereas different regions in parietal cortex became predictive when subjects were shown invisible objects and believed themselves to be purely guessing. This parietal network was highly overlapping with regions, which have previously been shown to encode free decisions. Thus, the brain might use a dedicated network for determining choices when insufficient sensory information is available.

Functional imaging correlates of impaired distractor suppression following sleep deprivation

Sleep deprivation (SD) has been shown to affect selective attention but it is not known how two of its component processes: target enhancement and distractor suppression, are affected. To investigate, young volunteers either attended to houses or were obliged to ignore them (when attending to faces) while viewing superimposed face-house pictures. MR signal enhancement and suppression in the parahippocampal place area (PPA) were determined relative to a passive viewing control condition. Sleep deprivation was associated with lower PPA activation across conditions. Critically SD specifically impaired distractor suppression in selective attention, leaving target enhancement relatively preserved. These findings parallel some observations in cognitive aging. Additionally, following SD, attended houses were not significantly better recognized than ignored houses in a post-experiment test of recognition memory contrasting with the finding of superior recognition of attended houses in the well-rested state. These results provide evidence for co-encoding of distracting information with targets into memory when one is sleep deprived.

Reduced visual processing capacity in sleep deprived persons.

Multiple experiments have found sleep deprivation to lower task-related parietal and extrastriate visual activation, suggesting a reduction of visual processing capacity in this state. The perceptual load theory of attention predicts that our capacity to process unattended distractors will be reduced by increasing perceptual difficulty of task-relevant stimuli. Here, we evaluated the effects of sleep deprivation and perceptual load on visual processing capacity by measuring neural repetition-suppression to unattended scenes while healthy volunteers attended to faces embedded in face-scene pictures. Perceptual load did not affect repetition suppression after a normal night of sleep. Sleep deprivation reduced repetition suppression in the parahippocampal place area (PPA) in the high but not low perceptual load condition. Additionally, the extent to which task-related fusiform face area (FFA) activation was reduced after sleep deprivation correlated with behavioral performance and lowered repetition suppression in the PPA. The findings concerning correct responses indicate that a portion of stimulus related activation following a normal night of sleep contributes to potentially useful visual processing capacity that is attenuated following sleep deprivation. Finally, when unattended stimuli are not highly intrusive, sleep deprivation does not appear to increase distractibility.

Preparatory patterns of neural activity predict visual category search speed

Rapidly detecting target object categories when objects are embedded in naturalistic scenes is facilitated by preparatory baseline signal changes. However, it is unclear as to what information most strongly predicts perceptual speed in terms of the minimal exposure duration required for accurate detection. Using novel surface-based spatiotemporal pattern classification, we found that while category-specific biases resulting from merely providing a category name can be detected in multiple cortical areas, only biases in lateral occipital complex predicted perceptual speed. These biases likely carry visual semantic information regarding multiple object categories placed in familiar scene contexts. Discriminatory voxels during the preparatory period showed congruent category-selectivity during visual stimulation.

Unconscious cues bias first saccades in a free-saccade task

Visual-spatial attention can be biased towards salient visual information without visual awareness. It is unclear, however, whether such bias can further influence free-choices such as saccades in a free viewing task. In our experiment, we presented visual cues below awareness threshold immediately before people made free saccades. Our results showed that masked cues could influence the direction and latency of the first free saccade, suggesting that salient visual information can unconsciously influence free actions.

Multivariate dekodierung von fMRT-daten: Auf dem weg zu einer inhaltsbasierten kognitiven neurowissenschaft

Zusammenfassung Seit dem Aufkommen der funktionellen Magnetresonanztomografie (fMRT) vor 20 Jahren steht eine neue Methode zur nicht invasiven Messung von Gehirnfunktionen zur Verfügung, welche in den kognitiven Neurowissenschaften inzwischen weit verbreitet ist. Traditionell wurden fMRT-Daten vor allem verwendet, um globale Änderungen der Aktivität in bestimmten Gehirnregionen zu messen, wie sie etwa während einer kognitiven Verarbeitung auftreten. Die Entwicklung neuer Methoden ermöglicht nun einen verfeinerten, inhaltsbasierten Ansatz. Das „multivariate Decoding“ erlaubt es, die kognitive Information zu untersuchen, die in feinkörnigen fMRT-Aktivitätsmustern enthalten ist. Damit lässt sich die Kodierung spezifischer kognitiver Inhalte und Repräsentationen im Gehirn näher bestimmen. Hier wird ein Überblick über verschiedene Entwicklungen des multivariaten Decoding gegeben von der Anwendung in den kognitiven Neurowissenschaften (Wahrnehmung, Aufmerksamkeit, Belohnung, Entscheidungsfindung, emotionale Kommunikation) über neuere methodische Entwicklungen (Informationsfluss, oberflächenbasiertes Searchlight-Decoding) bis hin zur medizinischen Diagnostik gegeben.