A large-scale distributed network for covert spatial attention: further anatomical delineation based on stringent behavioural and cognitive controls.

Spatial attention is mediated through a large-scale network that includes occipital, temporal, parietal and frontal cortical regions. While the network architecture has been well characterized using neuroimaging methods, less is known about the temporal dynamics in local regions and across the network. Here, we explored the modulatory effects of spatial attention throughout this cortical network by analyzing intracranial field potentials recorded from 538 ECoG electrodes implanted in 6 epilepsy patients. We examined the effects of attention using a variant of the Eriksen flanker task, where subjects were cued by an exogenous stimulus to the spatial location of an upcoming target stimulus which, after a variable delay period, was presented embedded in a circular array of shapes. Subjects had to differentiate between barrel or bowtie target stimuli flanked either by congruent or incongruent shapes. Using our probabilistic atlas of the human visual system, we localized the recorded ECoG signals to topographically organized brain areas and linked them to functional brain imaging data from normal subjects. We found spatially constrained visual response fields in 175 electrodes, as indicated by differential cue-related activity, obtained from both induced broadband power and evoked potentials. We examined response modulation from those electrodes during the delay period of the task when subjects either attended to or away from the response field. Further, we related these results to changes in the BOLD signal measured in normal subjects performing the same task. We found that covert spatial attention affected evoked event-related potentials and induced low frequency (4-20Hz) and broadband high gamma (50-200Hz) power differentially along the dorsal pathway through nodes of the fronto-parietal attention network, from V1 through intraparietal sulcus areas IPS0-IPS5 and the frontal eye fields. Meeting abstract presented at VSS 2015

Between-subject variability in cognitive performance has been related to inter-individual differences in functional brain networks. Targeting the dorsal attention network (DAN) we questioned (i) whether resting-state functional connectivity (FC) within the DAN can predict individual performance in spatial attention tasks and (ii) whether there is short-term adaptation of DAN-FC in response to task engagement. Twenty-seven participants first underwent resting-state fMRI (PRE run), they subsequently performed different tasks of spatial attention [including visual search (VS)] and immediately afterwards received another rs-fMRI (POST run). Intra- and inter-hemispheric FC between core hubs of the DAN, bilateral intraparietal sulcus (IPS) and frontal eye field (FEF), was analyzed and compared between PRE and POST. Furthermore, we investigated rs-fMRI-behavior correlations between the DAN-FC in PRE/POST and task performance parameters. The absolute DAN-FC did not change from PRE to POST. However, different significant rs-fMRI-behavior correlations were revealed for intra-/inter-hemispheric connections in the PRE and POST run. The stronger the FC between left FEF and IPS before task engagement, the better was the learning effect (improvement of reaction times) in VS (r = 0.521, p = 0.024). And the faster the VS (mean RT), the stronger was the FC between right FEF and IPS after task engagement (r = −0.502, p = 0.032). To conclude, DAN-FC relates to the individual performance in spatial attention tasks supporting the view of functional brain networks as priors for cognitive ability. Despite a high inter- and intra-individual stability of DAN-FC, the change of FC-behavior correlations after task performance possibly indicates task-related adaptation of the DAN, underlining that behavioral experiences may shape intrinsic brain activity. However, spontaneous state fluctuations of the DAN-FC over time cannot be fully ruled out as an alternative explanation.

Unilateral spatial neglect is a common neurological syndrome following predominantly right hemisphere injuries and is characterized by both spatial and non-spatial deficits. Core spatial deficits involve mechanisms for saliency coding, spatial attention, and short-term memory and occur in conjunction with nonspatial deficits that involve reorienting, target detection, and arousal/vigilance. We argue that neglect is better explained by the dysfunction of distributed cortical networks for the control of attention than by structural damage of specific brain regions. Ventral lesions in right parietal, temporal, and frontal cortex that cause neglect directly impair nonspatial functions partly mediated by a ventral frontoparietal attention network. Structural damage in ventral cortex also induces physiological abnormalities of task-evoked activity and functional connectivity in a dorsal frontoparietal network that controls spatial attention. The anatomy and right hemisphere dominance of neglect follow from the anatomy and laterality of the ventral regions that interact with the dorsal attention network.

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ObjectivesHealthy people have a slight leftward bias of spatial attention as measured on the Landmark task. Former studies indicated that lateralisation of brain activation contributes to this attentional bias. In this study we hypothesised that if the spatial bias was consistent over several measurements there would be structural background of it.MethodsReproducibility of the spatial bias of visuo-spatial attention was measured in twenty healthy subject in a Landmark task over three consecutive days. In order to evaluate the correlation between the spatial attentional bias and the white matter microstructure high angular resolution diffusion MRI was acquired for each subjects. The Track Based Spatial Statistics method was used to measure the hemispheric differences of the white matter microstructure. Probabilistic tractography was used to reveal the connection of the identified regions.ResultsThe analysis showed correlation between the behavioural scores and the lateralisation of the white matter microstructure in the parietal white matter (p<0.05, corrected for multiple correlations). Higher FA values on the left are associated to rightward bias. The parietal cluster showed connectivity along the superior longitudinal fascicle on one end to posterior parietal cortex and anteriorly to the putative frontal eye field. From the frontal eye field some of the fibres run towards the nodes of the dorsal attention network to the intraparietal suclus, while some of the fibres travelled toward to ventral attention network to the temporo-parietal junction.ConclusionsThese results indicate that the structural integrity dorsal fronto-parietal network and the connection between the dorsal and ventral attention networks are responsible for the attentional bias in normal healthy controls.

The three projects documented in this thesis investigated the role of three forms of attention in visual processing. Spatial, feature-based and temporal attention. The spatial and feature-based attentional characteristics were studied by conducting extracellular recordings from neurons of area MT and MSTd of macaque visual cortex. While the role of temporal attention was studied in a human psychophysics project. In the physiology projects we first defined the tuning characteristics of neurons from area MT and MSTd for two stimulus dimensions, namely spiral motion stimuli (SMS) and linear motion stimuli (LMS) on the basis of directionality index, the relative frequency of tuned versus untuned neurons, and the individual cells responses to the two forms of stimuli. We found that MT and MSTd showed differential tuning to LMS and SMS. Area MSTd demonstrated better selectivity for SMS as compared to LMS. Conversely, area MT was better tuned to LMS than SMS. Further, we compared spatial and feature-based attentional modulation in area MT and MSTd across SMS and LMS. We found that spatial attention modulates the responses of neurons in both the areas irrespective of the feature dimension defining the attended stimuli. On the other hand feature-based attention was restricted to the stimulus dimension for which an area showed better selectivity, that is SMS for MSTd and LMS for MT. We also observed that the correlation of firing rates with reaction times was restricted to an area’s preferred stimulus dimension. The presence of a significant negative correlation of firing rate with reaction time in area MSTd when attention was directed to its preferred dimension (SMS), indicated that these neurons were involved in perceptual processing of SMS. Similarly, the presence of a significant negative correlation of firing rates with the reaction times in area MT when attention was directed to LMS, provides evidence that MT neurons were involved in perceptual processing of LMS. Taken together, these results show that neurons across the visual cortex are tuned to a range of stimulus dimensions. This multiple stimulus selectivity is thought to be utilized to develop a saliency map (by interaction of bottom-up and top-down processes), which marks the behaviorally relevant aspects of visual space. These behaviorally relevant aspects of the visual space are then preferentially processed throughout the visual hierarchy, and our results suggests that a given area contributes to visual perception by flexibly processing only the stimulus dimension for which it shows best selectivity as a function of stimulus relevance. Finally in human subjects we investigated the temporal characteristics of visual motion processing, using the ‘attentional blink’ paradigm. Attentional blink is a widely studied phenomenon, but almost all studies to the date have been are restricted to stationary stimuli. We here extended this paradigm to motion stimuli. We found the presence of an attentional blink in the motion domain, in that the detection of second of the two target (motion) stimuli was severely degraded when it was presented within 450ms of the first target (motion) stimulus. However, our results were slightly different from the experiments with the stationary stimuli. First, there was an absence of the lag-1 sparing effect typically found with stationary stimuli and second, the time interval over which the attentional blink was observed was about 100ms shorter than typically reported for stationary stimuli. Post-hoc analysis showed that these effects in the motion domain were dependent upon the direction of motion of the first target. Our data showed that when the first target moved in the opposite direction to the second target, there was no attentional blink, but when the first target moved in an orthogonal direction to the second target, there was an attentional blink. These results are in agreement to previous studies showing that the processing of motion stimuli is facilitated when it is preceded by a stimulus moving in opposite directions, while processing of motion stimuli is suppressed when it is preceded by a stimulus moving in an orthogonal direction. Taken together the results of this thesis provides an important contribution to our understanding of the role of attention in visual motion processing. It will be interesting to extend this study in other visual cortical areas and measure spatial and feature-based attention from same set of neurons across different stimulus dimension.

Self-motion or object motion can elicit optokinetic nystagmus (OKN), which is an integral part of dynamic spatial orientation. We used functional MR imaging during horizontal OKN to study cerebral activation patterns in sensory and ocular motor areas in 10 subjects. We found activation bilaterally in the primary visual cortex, the motion-sensitive areas in the occipitotemporal cortex (the middle temporal and medial superior temporal areas), and in areas known to control several types of saccades such as the precentral and posterior median frontal gyrus, the posterior parietal cortex, and the medial part of the superior frontal gyrus (frontal, parietal, and supplementary eye fields). Additionally, we observed cortical activation in the anterior and posterior parts of the insula and in the prefrontal cortex. Bilateral activation of subcortical structures such as the putamen, globus pallidus, caudate nucleus, and the thalamus traced the efferent pathways of OKN down to the brainstem. Functional MRI during OKN revealed a complex cerebral network of sensorimotor cortical and subcortical activation.

Spatial attention is associated with modulations in prestimulus, anticipatory blood oxygen level-dependent (BOLD) activity across the brain. It is unclear, however, if these anticipatory modulations depend on the computational demands of the upcoming task. Here, we show that anticipation of low-contrast stimuli, relative to high-contrast stimuli, is associated with increased prestimulus BOLD activity in the frontal eye field (FEF) and the posterior inferior frontal sulcus (IFS) but not in the intraparietal sulcus (IPS). In visual cortex, anticipation of low-contrast stimuli is associated with increased suppression of activity corresponding to unattended (but not attended) locations, and this suppression predicts whether subjects will accurately perceive low-contrast stimuli. These results suggest that when a stimulus will be difficult to distinguish from the background, top-down signals from FEF and IFS can facilitate perception by marking its location through the suppression of unattended locations in visual cortex.

Extracting key information from documents with complex layout poses a pivotal yet tough challenge in document image processing. Current solutions tend to rely on multi-stage processing pipelines. However, these pipeline methods are prone to error propagation. This challenge is particularly pronounced when extracting different data types from complex layouts. To address this issue, we propose an efficient model termed Cross-head Spatial Coherence Attention Network (ChSCAN). It is designed to directly identify the locations of key information within documents. ChSCAN is tailored to extract three distinct document elements, including printed text, handwritten text and seals. ChSCAN employs a segmentation-first mechanism that utilizes a ResNet-based feature pyramid network for multi-scale feature extraction. A key aspect of our approach is the cross-head spatial coherent attention mechanism, which improves detection capabilities by enabling efficient information exchange among different head branches. Experimental results demonstrate the importance of integrating spatial attention in a multi-stage framework.

The afferent connections of the squirrel monkey's lateral premotor cortex were investigated with the horseradish peroxidase (HRP) technique. Seven monkeys received small, unilateral injections of HRP spaced between the most dorsal and the most lateral region of the premotor cortex. Results are based on six brains, all of which showed a considerable number of labeled neurons both cortically and subcortically. At the cortical level, labeled cells were found both ipsi- and contralateral to the injection site. Heavy labeling occurred in the fields surrounding the prefrontal, central, and precentral cortex, while the temporal regions usually innervated the premotor cortex with a moderate number of cells, and the parietal fields with only a minor number. Surprisingly, in all brains, but especially in those with the more lateral injections, we also found a considerable number of afferents originating from occipital fields, including a few neurons from the striate cortex. Subcortically, a few labeled cells were seen in basal forebrain regions, the amygdala, and the claustrum; a considerable number of thalamic nuclei and also some hypothalamic nuclei contained labeled cells; and within the brainstem, labeling was found most consistently in the locus ceruleus and in the tegmental fields. It is concluded that the (lateral) premotor cortex is involved in sensorimotor integration to a higher degree than has been recognized up to now.

The corticocortical projections of the physiologically defined eye field in the rat medial frontal cortex

Frontal eye field (FEF) projections to posterior cortical areas were mapped by autoradiography of tritiated amino acids (Leu, Pro) in six macaque monkeys. In three monkeys, the large saccade part of the FEF (IFEF) was identified by microstimulation and injected with tracers. In a fourth monkey, the small saccade part of the FEF (sFEF) was identified by microstimulation and injected with tracer. Tracer injections were placed into the sFEF region of two other monkeys using anatomical landmarks. The IFEF and sFEF generally had distinct and largely segregated projections to posterior cortical areas, and the overall pattern of labeling in visual areas with established topology indicates that IFEF neurons preferentially project to areas having large and eccentric receptive fields, whereas sFEF neurons project to areas having smaller, more centrally located fields. The terminal fields from the sFEF were more widespread than those from IFEF. Projections from sFEF terminated in the lateral intraparietal area (LIP), the ventral intraparietal area (VIP), and the parietal part of visual area V3A, in the fundus of the superior temporal visual area (FST), the middle temporal area (MT), the medial superior temporal area (MST), the temporal part of visual area V4, the inferior temporal area (IT), and the temporal-occipital area (TEO) and in occipital visual areas V2, V3, and V4. Projections from IFEF terminated in parietal areas 7a, LIP, and VIP and the medial part of parietal area PE; in temporal areas MST and the superior temporal polysensory area (STP); and in occipital area V2 and posterior cingulate area 23b. Projections from IFEF and sFEF appeared to terminate in different parts of common target areas in MST, LIP, and V2. The topography of IFEF and sFEF projections to LIP suggests that this posterior eye field may also be organized by saccade amplitude. Most terminal labeling from FEF injections was bilaminar to layers I and V/VI, but labeling in area LIP, area MT, the medial part of area PE, and area 23b was columnar-form to all layers.

Saccades, smooth pursuit, and optokinetic nystagmus (OKN) are three basic eye movements in our ocular motor repertoire that enable us to explore the visual field. These eye movements are cortically controlled in different cortical eye fields, including the frontal eye fields (FEF) and parietal eye fields (PEF), as well as the motion-sensitive visual area MT+/V5. It is not known if this cortical control is organized in parallel cortico-cortical networks or in adjacent subregions of one system. Nor do we know where the specific eye fields are exactly located. Functional magnetic resonance imaging (fMRI) was used to investigate these open questions about the FEF, PEF, and MT+/V5. Activations of the cortical network of eye-movement control were found in the frontal, parietal, and occipital cortex. While the activation pattern for OKN was not a combination of the patterns for saccades and smooth pursuit, the results suggest that cortical control of OKN occurs in a network parallel to that of saccades and smooth pursuit. Furthermore, a division of the FEF and the PEF into two parts was confirmed for the three ocular motor tasks, as well as a division within each of the three paradigms. MT+/V5 showed two partitions only for saccades, but not for smooth pursuit or OKN.

The onset latencies of single-unit responses evoked by flashing visual stimuli were measured in the parvocellular (P) and magnocellular (M) layers of the dorsal lateral geniculate nucleus (LGNd) and in cortical visual areas V1, V2, V3, V4, middle temporal area (MT), medial superior temporal area (MST), and in the frontal eye field (FEF) in individual anesthetized monkeys. Identical procedures were carried out to assess latencies in each area, often in the same monkey, thereby permitting direct comparisons of timing across areas. This study presents the visual flash-evoked latencies for cells in areas where such data are common (V1 and V2), and are therefore a good standard, and also in areas where such data are sparse (LGNd M and P layers, MT, V4) or entirely lacking (V3, MST, and FEF in anesthetized preparation). Visual-evoked onset latencies were, on average, 17 ms shorter in the LGNd M layers than in the LGNd P layers. Visual responses occurred in V1 before any other cortical area. The next wave of activation occurred concurrently in areas V3, MT, MST, and FEF. Visual response latencies in areas V2 and V4 were progressively later and more broadly distributed. These differences in the time course of activation across the dorsal and ventral streams provide important temporal constraints on theories of visual processing.

Studies have shown that spatial attention remarkably affects the trial-to-trial response variability shared between neurons. Difficulty in the attentional task adjusts how much concentration we maintain on what is currently important and what is filtered as irrelevant sensory information. However, how task difficulty mediates the interactions between neurons with separated receptive fields (RFs) that are attended to or attended away is still not clear. We examined spike count correlations between single-unit activities recorded simultaneously in the primary visual cortex (V1) while monkeys performed a spatial attention task with two levels of difficulty. Moreover, the RFs of the two neurons recorded were non-overlapping to allow us to study fluctuations in the correlated responses between competing visual inputs when the focus of attention was allocated to the RF of one neuron. While increasing difficulty in the spatial attention task, spike count correlations were either decreased to become negative between neuronal pairs, implying competition among them, with one neuron (or none) exhibiting attentional enhancement of firing rate, or increased to become positive, suggesting inter-neuronal cooperation, with one of the pair showing attentional suppression of spiking responses. Besides, the modulation of spike count correlations by task difficulty was independent of the attended locations. These findings provide evidence that task difficulty affects the functional interactions between different neuronal pools in V1 when selective attention resolves the spatial competition.