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De Sousa Ferreira, C.

Publications and source records attributed to De Sousa Ferreira, C..

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Non-invasive real-time access to spatial attention information from 3T fMRI BOLD signals

Access to higher cognitive functions in real-time remains very challenging, because these functions are internally driven and their assessment is based onto indirect measures. In addition, recent finding show that these functions are highly dynamic. Previous studies using intra-cortical recordings in monkeys, succeed to access the (x,y) position of covert spatial attention, in real-time, using classification methods applied to monkey prefrontal multi-unit activity and local field potentials. In contrast, the direct access to attention with non-invasive methods is limited to predicting the attention localisation based on a quadrant classification. Here, we demonstrate the feasibility to track covert spatial attention localization using non-invasive fMRI BOLD signals, with an unprecedented spatial resolution. We further show that the errors produced by the decoder are not randomly distributed but concentrate on the locations neighbouring the cued location and that behavioral errors correlate with weaker decoding performance. Last, we also show that the voxels contributing to the decoder precisely match the visual retinotopic organization of the occipital cortex and that single trial access to attention is limited by the intrinsic dynamics of spatial attention. Taken together, these results open the way to the development of remediation and enhancement neurofeedback protocols targeting the attentional function.

neuroscience↗

Information-based signal selection improves decoding of attention spotlight from multi-units & local field potentials and enhances correlation with behavior.

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 primate (Astrand et al., 2016). 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 (Gaillard et al., 2020). 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. 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, suggesting that LFP signals may contain other sources of task-related variability than spatial attention information. 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. HighlightsWe use machine learning to decode attention spotlight from prefrontal MUA & LFP. We achieve high decoding accuracy of (x,y) spatial attention spotlight. (x,y) attention spotlight position accuracy is maximal from LFP gamma frequency range. MUA and LFP decoded attention position predicts behavioral performances. Selecting high information signals improves decoding and behavioral correlates.

neuroscience↗