Distractibility and impulsivity neural states are distinct from selective attention and modulate the implementation of spatial attention.

In the context of visual attention, it has been classically assumed that missing the response to a target or erroneously selecting a distractor occurs as a consequence of the (miss)allocation of attention in space. In the present paper, we challenge this view and provide evidence that, in addition to encoding spatial attention, prefrontal neurons also encode a distractibility-to-impulsivity state. Using supervised dimensionality reduction techniques in prefrontal neuronal recordings in monkeys, we identify two partially overlapping neuronal subpopulations associated either with the focus of attention or overt behaviour. The degree of overlap accounts for the behavioral gain associated with the good allocation of attention. We further describe the neural variability accounting for distractibility-to-impulsivity behaviour by a two dimensional state associated with optimality in task and responsiveness. Overall, we thus show that behavioral performance arises from the integration of task-specific neuronal processes and pre-existing neuronal states describing task-independent behavioral states.

Different Contribution of the Monkey Prefrontal and Premotor Dorsal Cortex in Decision Making During a Transitive Inference Task.

Several studies have reported similar neural modulations between brain areas of the frontal cortex, such as the dorsolateral prefrontal (DLPFC) and the premotor dorsal (PMd) cortex, in tasks requiring encoding of the abstract rules for selecting the proper action. Here we compared the neuronal modulation of the DLPFC and PMd of monkeys trained to choose the higher rank from a pair of abstract images (target item), selected from an arbitrarily rank-ordered set (A > B > C > D > E > F) in the context of a transitive inference task. Once acquired by trial-and-error, the ordinal relationship between pairs of adjacent images (i.e., A > B; B > C; C > D; D > E; E > F), monkeys were tested in indicating the ordinal relation between items of the list not paired during learning. During these decisions, we observed that the choice accuracy increased and the reaction time decreased as the rank difference between the compared items enhanced. This result is in line with the hypothesis that after learning, the monkeys built an abstract mental representation of the ranked items, where rank comparisons correspond to the items' position comparison on this representation. In both brain areas, we observed higher neuronal activity when the target item appeared in a specific location on the screen with respect to the opposite position and that this difference was particularly enhanced at lower degrees of difficulty. By comparing the time evolution of the activity of the two areas, we observed that the neural encoding of target item spatial position occurred earlier in the DLPFC than in the PMd.

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials.

The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30-250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.

Blood lead levels in shopkeepers and car traffic pollution in Liguria, Italy.

A study was conducted into the exposure to atmospheric pollution caused by car traffic by measuring blood lead (PbB) levels in a sample of 657 adult individuals (shopkeepers) all living in Liguria. The mean level of blood lead in all examined individuals was 9.39 micrograms dl-1 (0.45 mumol per liter; C.I. 95%: 9.06-9.75 micrograms dl-1; 0.44-0.47 mumol per liter) with a range between 2.0 and 46.03 micrograms dl-1 (0.10-2.22 mumol per liter). The average Pb values in individuals working in streets with high and very high traffic was 8.30 micrograms dl-1 (0.40 mumol per liter; C.I. 95%: 7.41-9.31 micrograms dl-1; 0.36-0.45 mumol per liter) and 9.98 micrograms dl-1 (0.48 mumol per liter; C.I. 95%: 9.62-10.37 micrograms dl-1; 0.46-0.50 mumol per liter), respectively. These average blood lead levels were statistically greater than the average PbB values of those working in low traffic streets (7.06 micrograms dl-1; 0.34 mumol per liter; C.I. 95%: 6.22-7.94 micrograms dl-1; 0.30-0.38 mumol per liter). The percentile distribution (50th, 90th and 98th P) for all subgroups surveyed has always proved to be below the maximum limits specified by EC Directive No. 77/312.