Reversible fronto-occipitotemporal signaling complements task encoding and switching under ambiguous cues

Abstract

Flexible adaptation to changing environments is one of the representative executive control functions, and requires appropriate extraction of environmental information to achieve a behavioral goal. It still remains unclear however, how the behavioral flexibility is guided under situations where the relevant behavior is ambiguous. Using functional brain mapping of machine-learning decoders and directional functional connectivity, we show that brain-wide reversible neural signaling underpins behavioral flexibility in ambiguously changing environments. When relevant behavior is cued ambiguously during behavioral shifting, neural coding of the behavior is attenuated in distributed cortical regions, but top-down signals from the prefrontal cortex complements the coding. On the other hand, when shifting to the alternative behavior is cued more explicitly, modality-specialized occipitotemporal regions implement distinct neural coding about the relevant behavior, and bottom-up signals from the occipitotemporal region to the prefrontal cortex supplements the behavioral shift. These results suggest that our adaptation to an ever-changing world is orchestrated by the alternation of top-down and bottom-up signaling in the fronto-occipitotemporal circuit depending on the availability of environmental evidences.Significance statementHow does the brain work when appropriate behavior is unclear? We found that when proper behavior was cued ambiguously, the prefrontal cortex signaled toward occipitotemporal regions. Functional brain mapping based on deep neural network revealed that neural coding of appropriate task was diminished in the occipitotemporal regions, which was complemented by the prefrontal signal. When the proper behavior was cued unambiguously, the occipitotemporal regions signaled the prefrontal cortex, which increased efficiency of the flexibility. Our results suggest that dynamic reversal of prefrontal-occipitotemporal signaling optimizes the behavioral flexibility depending on the perceptual ambiguity of the external world.

Perceptual uncertainty alternates top-down and bottom-up fronto-temporal network signaling during response inhibition

“…Many studies of visual motion perception have consistently reported the involvement of the MT (Newsome and Paré, 1988;Britten et al, 1992Britten et al, , 1993Zohary et al, 1994;Shadlen et al, 1996;Beauchamp et al, 1997;Britten and Newsome, 1998;Kim and Shadlen, 1999;Braddick et al, 2001;Corbetta and Shulman, 2002;Huk et al, 2002;Mazurek et al, 2003), but some neuroimaging studies have also reported the STS is active during the perception of visual motion stimuli (Braddick et al, 2001;Noguchi et al, 2005;Kayser et al, 2010;Tsumura et al, 2021a;Tsumura et al, 2021b). This STS involvement may partially reflect executive control processing, as is shown in the present study.…”

Perceptual Uncertainty Alternates Top-down and Bottom-up Fronto-Temporal Network Signaling during Response Inhibition

“…In this context, neural mechanisms underlying response inhibition and perceptual decision-making may be coordinately involved in a brain-wide functional network. We hypothesized that there are three possibilities regarding the topological structure of the network: 1) a functionally merged region implements response inhibition under perceptual uncertainty (Shackman et al, 2011;Yarkoni et al, 2011); 2) distinct regions responsible for response inhibition (e.g., rIFC) and perceptual decision-making (e.g., MT) interact to guide response inhibition (Konishi et al, 1996;Egner and Hirsch, 2005;Kayser et al, 2010;Waskom et al, 2014;Tsumura et al, 2021a;Tsumura et al, 2021b);Fig 1B middle); and 3) a hub-like region links the regions involved in response inhibition and perceptual decision-making (Cole et al, 2013;Osada et al, 2015;Nee and D'Esposito, 2016;Jiang et al, 2018). To test these possibilities, functional MRI was performed while human participants performed a go/no-go task based on visually presented motion stimuli in which perceptual uncertainties was manipulated by the coherence of randomly moving dots (Figs.…”

Multimodal deep neural decoding of visual object representation in humans

“…DNNs, which have achieved state-of-the-art results for the large-scale image recognition (Simonyan and Zisserman, 2015), are now being applied to neural data, in what is known as neural decoding (Hong et al, 2016;Horikawa and Kamitani, 2017;Yamins and DiCarlo, 2016). In an fMRI DNN, we managed to use 3D voxel data as inputs, whereas some previous studies used 2D slice data (Sarraf et al, 2017) or 2D flattened cortical data as inputs (Tsumura et al, 2021b) (see also other studies in which 3D voxel data were used as input data (Frey et al, 2021;Vu et al, 2020;Wang et al, 2020;Zhao et al, 2018)). Such image-like input could be learned by DNNs that have already been trained with a large-scale image set (e.g., ImageNet), such as VGG.…”

“…fMRI univariate analysis. The fMRI univariate analysis was conducted as in previous studies (Matsui et al, 2022;Tanaka et al, 2020;Tsumura et al, 2021a;Tsumura et al, 2021b) (Figure 3A-C). In the first-level analysis, a GLM (Worsley and Friston, 1995) approach was used to estimate parameter values for task events.…”