Vision loss in the blind is caused not only by primary tissue damage but also by a breakdown of synchronization in brain networks. Because visual field improvements are associated with resynchronization of alpha band coherence, brain connectivity is a key component in partial blindness and in restoration of vision.

How does cognition emerge from neural dynamics? The dominant hypothesis states that interactions among distributed brain regions through phase synchronization give basis for cognitive processing. Such phase-synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But unlike resting-state networks, the complex organization of transient cognitive networks is typically not characterized within the graph theory framework. Thus, it is not known whether cognitive processing merely changes the strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. To address this question, we recorded high-density EEG while subjects performed a visual discrimination task. We conducted an event-related network analysis (ERNA) where source-space weighted functional networks were characterized with graph measures. ERNA revealed rapid, transient, and frequency-specific reorganization of the network's topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the "network fingerprint" of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a "global workspace" necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.

BackgroundVision loss after optic neuropathy is considered irreversible. Here, repetitive transorbital alternating current stimulation (rtACS) was applied in partially blind patients with the goal of activating their residual vision.MethodsWe conducted a multicenter, prospective, randomized, double-blind, sham-controlled trial in an ambulatory setting with daily application of rtACS (n = 45) or sham-stimulation (n = 37) for 50 min for a duration of 10 week days. A volunteer sample of patients with optic nerve damage (mean age 59.1 yrs) was recruited. The primary outcome measure for efficacy was super-threshold visual fields with 48 hrs after the last treatment day and at 2-months follow-up. Secondary outcome measures were near-threshold visual fields, reaction time, visual acuity, and resting-state EEGs to assess changes in brain physiology.ResultsThe rtACS-treated group had a mean improvement in visual field of 24.0% which was significantly greater than after sham-stimulation (2.5%). This improvement persisted for at least 2 months in terms of both within- and between-group comparisons. Secondary analyses revealed improvements of near-threshold visual fields in the central 5° and increased thresholds in static perimetry after rtACS and improved reaction times, but visual acuity did not change compared to shams. Visual field improvement induced by rtACS was associated with EEG power-spectra and coherence alterations in visual cortical networks which are interpreted as signs of neuromodulation. Current flow simulation indicates current in the frontal cortex, eye, and optic nerve and in the subcortical but not in the cortical regions.ConclusionrtACS treatment is a safe and effective means to partially restore vision after optic nerve damage probably by modulating brain plasticity. This class 1 evidence suggests that visual fields can be improved in a clinically meaningful way.Trial RegistrationClinicalTrials.gov NCT01280877

A key mechanism behind preferential processing of self-related information might be an early and automatic capture of attention. Therefore, the present study tested a hypothesis that one’s own face will attract bottom-up attention even without conscious identification. To test this, we used a dot-probe paradigm with electrophysiological recordings, in which participants ( N = 18) viewed masked and unmasked pairs of faces (other, self) presented laterally. Analysis of the sensitivity measure d ′ indicated that faces were not consciously identified in the masked condition. A clear N2 posterior-contralateral (N2pc) component (a neural marker of attention shifts) was found in both the masked and unmasked conditions, revealing that one’s own face automatically captures attention when processed unconsciously. Therefore, our study (a) demonstrates that self-related information is boosted at an early (preconscious) stage of processing, (b) identifies further features (beyond simple physical ones) that cause automatic attention capture, and (c) provides further evidence for the dissociative nature of attention and consciousness.

Unilateral visual cortex lesions caused by stroke or trauma lead to blindness in contralateral visual field – a condition called homonymous hemianopia. Although the visual field area processed by the uninjured hemisphere is thought to be “intact,” it also exhibits marked perceptual deficits in contrast sensitivity, processing speed, and contour integration. Such patients are “sightblind” – their blindness reaches far beyond the primary scotoma. Studies showing perceptual deficits in patients’ intact fields are reviewed and implications of these findings are discussed. It is concluded that consequences of partial blindness are greater than previously thought, since perceptual deficits in the “intact” field likely contribute to subjective vision loss in patients with visual field defect. This has important implications for vision diagnosis and rehabilitation.

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It is well established that stimuli representing or associated with ourselves, like our own name or an image of our own face, benefit from preferential processing. However, two key questions concerning the self-prioritization mechanism remain to be addressed. First, does it operate in an automatic manner during the early processing, or rather in a more controlled fashion at later processing stages? Second, is it specific to the self-related stimuli, or can it be activated also by other stimuli that are familiar or salient? We conducted a dot-probe experiment to investigate the mechanism behind the attentional prioritization of the self-face image and to tackle both questions. The former, by employing a backwards masking procedure to isolate the early and preconscious processing stages. The latter, by investigating whether a face that becomes visually familiar due to repeated presentations is able to capture attention in a similar manner as the self-face. Analysis of the N2pc ERP component revealed that the self-face image automatically captures attention, both when processed consciously and unconsciously. In contrast, the visually familiar face did not attract attention, neither in the conscious, nor in the unconscious condition. We conclude that the self-prioritization mechanism is early and automatic, and is not triggered by mere visual familiarity. More generally, our results provide further evidence for efficient unconscious processing of faces, and for dissociation between attention and consciousness.

Cognition emerges from interactions within spatially distributed but synchronized brain networks. Such networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But it is not known whether topological features of transient cognitive networks contribute to cognitive processing. Cognition might merely change weights of intrinsic functional networks or, conversely, cognitive processing might require qualitatively new topological arrangements. To address this question, we recorded high-density EEG when subjects performed a visual discrimination task and characterized source-space weighted functional networks with graph measures. We revealed rapid, transient, and frequency-specific reorganization of the network’s topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the “network fingerprint” of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a “global workspace” necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.