In human perception studies, visual backward masking has been used to understand the temporal dynamics of subliminal vs. conscious perception. When a brief target stimulus is followed by a masking stimulus after a short interval of <100 ms, performance on the target is impaired when the target and mask are in close spatial proximity. While the psychophysical properties of backward masking have been studied extensively, there is still debate on the underlying cortical dynamics. One prevailing theory suggests that the impairment of target performance due to the mask is the result of lateral inhibition between the target and mask in feedforward processing. Another prevailing theory suggests that this impairment is due to the interruption of feedback processing of the target by the mask. This computational study demonstrates that both aspects of these theories may be correct. Using a biophysical model of V1 and V2, visual processing was modeled as interacting neocortical attractors, which must propagate up the visual stream. If an activating target attractor in V1 is quiesced enough with lateral inhibition from a mask, or not reinforced by recurrent feedback, it is more likely to burn out before becoming fully active and progressing through V2 and beyond. Results are presented which simulate metacontrast backward masking with an increasing stimulus interval and with the presence and absence of feedback activity. This showed that recurrent feedback diminishes backward masking effects and can make conscious perception more likely. One model configuration presented a metacontrast noise mask in the same hypercolumns as the target, and produced type-A masking. A second model configuration presented a target line with two parallel adjacent masking lines, and produced type-B masking. Future work should examine how the model extends to more complex spatial mask configurations.

A hypothesis is proposed for five visual fear signaling pathways in humans, based on an analysis of anatomical connectivity from primate studies and human functional connectvity and tractography from brain imaging studies. Earlier work has identified possible subcortical and cortical fear pathways known as the “low road” and “high road,” which arrive at the amygdala independently. In addition to a subcortical pathway, we propose four cortical signaling pathways in humans along the visual ventral stream. All four of these traverse through the LGN to the visual cortex (VC) and branching off at the inferior temporal area, with one projection directly to the amygdala; another traversing the orbitofrontal cortex; and two others passing through the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. These pathways have progressively longer propagation latencies and may have progressively evolved with brain development to take advantage of higher-level processing. Using the anatomical path lengths and latency estimates for each of these five pathways, predictions are made for the relative processing times at selective ROIs and arrival at the amygdala, based on the presentation of a fear-relevant visual stimulus. Partial verification of the temporal dynamics of this hypothesis might be accomplished using experimental MEG analysis. Possible experimental protocols are suggested.

Neurological function in patients with slowly growing brain tumors can be preserved even after extensive tumor resection. However, the global process of cortical reshaping and cerebral redistribution cannot be understood without taking into account the white matter tracts. The aim of this study was to predict the functional consequences of tumor-induced white matter damage by computer simulation. A computational model was proposed, incorporating two cortical patches and the white matter connections of the uncinate fasciculus. Tumor-induced structural changes were modeled such that different aspects of the connectivity were altered, mimicking the biological heterogeneity of gliomas. The network performance was quantified by comparing memory pattern recall and the plastic compensatory capacity of the network was analyzed. The model predicts an optimal level of synaptic conductance boost that compensates for tumor-induced connectivity loss. Tumor density appears to change the optimal plasticity regime, but tumor size does not. Compensatory conductance values that are too high lead to performance loss in the network and eventually to epileptic activity. Tumors of different configurations show differences in memory recall performance with slightly lower plasticity values for dense tumors compared to more diffuse tumors. Simulation results also suggest an optimal noise level that is capable of increasing the recall performance in tumor-induced white matter damage. In conclusion, the model presented here is able to capture the influence of different tumor-related parameters on memory pattern recall decline and provides a new way to study the functional consequences of white matter invasion by slowly growing brain tumors.