Biology subjects

Bo, K.

Publications and source records attributed to Bo, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatiotemporal Dissociation of Human Amygdala Response to Negative Affect

The amygdala is central to negative affect and a primary target of top-down regulation, yet its temporal dynamics remain poorly characterized. Most fMRI studies model stimulus-evoked amygdala activity with a canonical hemodynamic response function and collapse across subregions, potentially obscuring functional heterogeneity in time and space. We used finite impulse response modeling in two independent datasets (combined N = 358) to characterize blood oxygenation level-dependent responses during negative picture viewing and instructed cognitive reappraisal, complemented by data-driven clustering of voxelwise time courses. Across two probabilistic atlases, six of seven amygdala subregions showed prolonged activation to negative stimuli but differed in temporal profile. The centromedial amygdala showed sustained activation extending into the post-stimulus rating period, whereas laterobasal and superficial subregions peaked earlier during stimulus presentation. Reappraisal did not substantially alter response magnitude or shape, indicating that amygdala downregulation is not a reliable consequence of instructed regulation under these conditions. Data-driven analysis identified four clusters with distinct temporal profiles corresponding to laterobasal, lateral, superficial, and centromedial territories, converging with established anatomical parcellations while crossing some conventional boundaries. FIR modeling thus reveals a temporal architecture of amygdala responses to negative affective stimuli, characterized by early laterobasal and superficial responses followed by sustained centromedial activity.

neuroscience↗

bioRxiv2026

Individualised mapping of living human brain mitochondria by MRI reveals signatures of bioenergetic defects.

Mitochondria support the bioenergetic processes that enable brain function and cognition, but we have lacked a label-free, non-invasive approach to explore how brain mitochondria are linked to ageing, disease, and cognition in humans. A recently introduced MitoBrainMap neuroimaging framework predicts mitochondrial features from magnetic resonance data alone, potentially bridging cellular biology with macroscale brain organization. Here, we tested whether this framework captures meaningful age- and pathology-related mitochondrial variation. Consistent with existing literature, we find that MR-predicted mitochondrial density and tissue respiratory capacity consistently declined with age, whereas mitochondrial respiratory capacity-an index of mitochondrial quality-was relatively preserved across the lifespan. Moreover, the relations among specific mitochondrial features predicted from our algorithm were consistent with their biological organization, supporting preliminary construct validity for MR-predicted mitochondrial features. In patients with rare mitochondrial diseases, predicted maps revealed region-specific alterations in mitochondrial density and respiratory chain components, particularly the expected compensatory upregulation of complex II, but not of other mitochondrial genome-encoded components. Finally, the MR-based mitochondrial features were associated with the energetic stress marker GDF15 measured in blood, as well as with cognitive performance measures, linking the novel predictions of brain mitochondria to systemic stress and behavior. These findings introduce a first-generation, label-free, neuroimaging-based mitochondrial mapping as a non-invasive window into living human brain mitochondria.

neuroscience↗

bioRxiv2026

Graph-based pangenome provides insights into the adaptive evolution of Cucurbita pepo

Understanding how crops respond to environmental variation is crucial for biodiversity conservation and food security. Cucurbita pepo (pumpkin, squash, gourd) is one of the first domesticated crop species and exhibits remarkable phenotypic and ecological diversity, making it a powerful system for investigating the genomic basis of adaptation. Here, we constructed a graph-based C. pepo pangenome using nine chromosome-level assemblies and identified 229,431 high-confidence structural variants (SVs) that were genotyped across 206 wild and cultivated accessions. Our results demonstrate that C. pepo underwent parallel domestication, with two deeply diverged gene pools independently giving rise to the pepo and ovifera cultivated lineages, followed by expansion into diverse environments that produced strong signatures of differentiation largely mediated by young adaptive alleles. Single-nucleotide polymorphisms (SNPs) and SVs contribute complementary dimensions of environmental responsiveness. Biogeographical modeling predicts continued range contraction of wild relatives and elevated genetic offset in Eastern North American populations under projected future climates. Populations with higher genetic load harbor fewer adaptive variants and exhibit greater predicted maladaptation, indicating that deleterious mutations constrain adaptive potential. These findings highlight how genomic variants, adaptive diversity, and genetic load together shape environmental adaptation and inform conservation and crop improvement.

genomics↗

bioRxiv2026

Visual Emotion Perception in a Deep Neural Network Model with Both Bottom-Up and Top-Down Connections

Emotion reshapes perception by modulating sensory processing through top-down feedback--a process referred to as emotional perception. The computational mechanisms by which distinct affective signals influence visual representations however remain poorly understood. Here, we use a deep neural network to simulate this process and test mechanistic hypotheses about how top-down feedback guides emotional peception. Most existing models treat the perception of emotional content as a static, feedforward task, overlooking the dynamic interplay between internal states, external goals, and sensory input that characterizes affective perception in the brain. We introduce EmoFB, a biologically inspired model that integrates an affective system with a visual processing hierarchy through two functionally distinct feedback signals: intrinsic feedback, arising from the models own affective appraisal of perceptual input, and external steering, conveying contextual priors such as task expectations or target categories. We evaluated EmoFB on three tasks varying in perceptual ambiguity (Single Image, Side-by-Side, and Overlay). External steering exerted the strongest influence, not only improving recognition under challenging conditions but also restructuring internal representations by sharpening category-specific clustering in feature space. Crucially, top-down feedback increased brain-model representational similarity, strengthening alignment with human fMRI responses across early visual cortex, ventral visual areas, and the amygdala. EmoFB provides a computational framework for testing neurocognitive theories of emotion appraisal and top-down feedback modulation. It bridges affective neuroscience and artificial intelligence, offering mechanistic insight into how emotional signals shape perception in both brains and machines.

neuroscience↗

bioRxiv2025

Biologically Inspired Deep Neural Network Models for Visual Emotion Processing

The perception of opportunities and threats in complex visual scenes represents one of the main functions of the human visual system. The underlying neurophysiology is often studied by having observers view pictures varying in affective content. While deep neural networks (DNNs) have shown promise in modeling visual recognition of objects, their capacity to model visual affective processing remains to be better understood. In this study, we proposed a biologically inspired deep neural network model, referred to as the Visual Cortex Amygdala (VCA) model, that integrates a vision transformer with an amygdala-mimetic module designed to reflect both the anatomical hierarchy and self-attention-based computational mechanisms of the human brain. We evaluated the model along three dimensions: (1) predictive accuracy for emotional valence and arousal, (2) representational alignment with human amygdala activity, and (3)neural mechanisms of emotion representation. Our results showed that (1) the model can predict with high accuracy human emotion ratings on 1,182 images from the International Affective Picture System (IAPS) dataset (valence: r {approx} 0.9; arousal: r {approx} 0.7), (2) the models internal representations aligned with functional Magnetic Resonance Imaging (fMRI) data from the human amygdala, and (3) at the single neuron level, the amygdala module evolved emotion selectivity, and at the neuronal population level, the representational geometry became progressively more aligned with psychological models of emotion. Additional issues explored included (1) the effect of visual encoding and (2) the effect of structure and computational mechanisms on emotional assessment.

neuroscience↗

bioRxiv2025

Representational Competition of Spatially and Temporally Overlapped Target and Distractor

Representational competition occurs when a task-relevant target stimulus and a distractor overlap in space and time. Given limited neural resources, it is expected that stronger representations of the distractor will result in weaker representations of the target, leading to poorer behavioral performance. We tested this hypothesis by recording fMRI while participants (n = 27) viewed a compound stimulus consisting of randomly moving dots (the target) superimposed on IAPS affective pictures (the distractor). Each trial lasted [~]12 seconds, during which the moving dots and the IAPS pictures were flickered at 4.29 Hz and 6 Hz, respectively. The task was to detect and report brief episodes of coherent motion in the moving dots. Depending on the emotion category of the IAPS pictures, the trials were classified as pleasant, neutral and unpleasant. Focusing on three ROIs: middle temporal cortex (MT), ventral visual cortex (VVC), as well as the primary visual cortex (V1), we performed MVPA analysis to decode the distractor categories in each ROI, and correlated the decoding accuracy, taken to index the strength of distractor representation, with the accuracy in detecting the episodes of coherent motion. The following results were found: (1) the decoding accuracy was above chance level in all ROIs, and (2) in MT and VVC but not in V1, the higher the decoding accuracy, the worse the behavioral performance. These results suggested that distractor information was represented in V1 as well as in the two motion-processing areas, and in the motion-processing areas, stronger representations of the distractor led to poorer ability to process attended information, leading to worse behavioral performance. The hypothesis was thus supported and trade-offs in the fidelity of stimulus representations prompted by neural competition demonstrated.

neuroscience↗

bioRxiv2025

Neural representation of emotional valence in human amygdala

The amygdala is a core structure for encoding the affective value of external stimuli. Animal studies suggest that positive and negative emotions are separately encoded by distinct neuronal populations within the amygdala; however, this hypothesis has rarely been tested in humans. The current study examined this hypothesis by comparing the distributed emotion encoding model, as proposed in animal studies, with the univariate emotion encoding model using functional magnetic resonance (fMRI) imaging. More specifically, we applied univariate regression, using average amygdala activation to represent global activation level, and multivariate regression, using distributed voxel-level pattern within the amygdala, to predict normative valence of affective images from the IAPS library. In the core amygdala, the multivariate models prediction performance was not better than that of the univariate model, with weight map analysis revealing an overwhelming predominance of voxels selectively responsive to negative stimuli. When the region of interest was expanded to include voxels with lower anatomical probability of belonging to the amygdala as well as voxels from adjacent areas, the multivariate model significantly outperformed the univariate model, with the voxels selectively responsive to positive valence primarily located in regions surrounding the core amygdala. These findings suggest that in the human amygdala, the core region encodes emotional valence primarily through a global activation signal, rather than distributed patterns consisting of separate clusters of positive and negative voxels, and a more distributed valence representation emerges when voxels surrounding the amygdala are taken into consideration.

neuroscience↗

bioRxiv2025

Precision Functional Mapping of Imagined and Experienced Pain

Imagination is a principal human capacity, enabling simulation of sensations and situations for planning, learning, and empathy. We examined neural representations of experienced and imagined pain across eight body sites using deep-phenotyping, precision functional MRI with over seven hours of scanning per participant (N = 9). Here we show, using conventional mapping, pattern classification, and representational similarity analyses, that both imagined and experienced pain activated nociceptive regions (e.g., dorsoposterior insula) and non-nociceptive regions (e.g., premotor areas). However, imagined pain did not reliably reactivate body site-selective nociceptive patterns in any region. Instead, imagined and experienced pain shared multivariate representations across widespread cortical and subcortical regions, particularly transmodal cortical areas including dorsomedial and lateral prefrontal cortex, hippocampus, and thalamus. These findings suggest that imagination generates distributed pain-like activity while preserving a neural distinction from experienced pain. This distinction may explain the difficulty of vividly imagining pain and underlie its role in empathy, planning, and pain vulnerability.

neuroscience↗

bioRxiv2025

Mitochondrial Energy Transformation Capacity Influences Brain Activation During Sensory, Affective, and Cognitive Tasks

Mitochondria supply the energy required for brain function, but how energetic capacity constrains human brain activity remains unclear. We compared patients with genetically defined mitochondrial disease to matched controls, testing whether bioenergetic stress selectively limits neural processes assessed with functional magnetic resonance imaging during rest and during tasks probing working memory, sensory perception, and affective responses (cold pain). We report impairment in working memory performance and reduced activation of associated brain networks in a subset of patients with the largest clinical and cognitive impairments. A resilient subgroup showed higher working-memory-related BOLD responses, which were associated with preserved task performance. Neural and behavioral effects scaled with the circulating markers of energetic stress growth differentiation factor 15 (GDF15). Brain activity related to sensory and affective responses and resting-state connectivity were comparable for patients and controls. These findings support an energy-hierarchy model in which complex cognition fails first, while sensory processes remain largely preserved under energetic stress.

neuroscience↗

bioRxiv2025

Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula

Regions that respond to multiple types of information ("convergence zones") are crucial for the brain to generate coherent experiences and behaviors. The insular cortex, known for its functional diversity, has been hypothesized to be a key convergence hub, yet empirical evidence identifying how and where convergence occurs is incomplete. To address this gap, we analyzed functional convergence across four key task domains--somatic pain, non-somatic appetitive processes, aversive processes, and cognitive control--in a large-scale Bayesian mega-analysis of fMRI data (N=540, systematically sampled from 36 studies). Bayes Factor analyses identified both convergent zones responding to multiple domains and selective zones responding to single domains. Results revealed a hierarchical convergence architecture, with a multi-domain convergence zone in bilateral dorsal anterior insula surrounded by zones showing progressively increasing convergence. Functional decoding and coactivation analyses further support the insulas role as a convergence hub, while cytoarchitectonic and neurotransmitter profiling characterizes the potential neuroanatomical underpinnings subserving convergent and domain-selective function. Overall, our results demonstrate a structured functional topography in the insula that bridges specialized and convergent processing, providing a potential neural basis for how diverse information streams combine into unified subjective experiences.

neuroscience↗

bioRxiv2024

Rhythmic Sampling and Competition of Target and Distractor in a Motion Detection Task

It has been suggested that the visual system samples attended information rhythmically. Does rhythmic sampling also apply to distracting information? How do attended information and distracting information compete temporally for neural representations? We recorded electroencephalography (EEG) from participants who detected instances of coherent motion in a random dot kinematogram (RDK; the target), overlayed on different categories (pleasant, neutral, and unpleasant) of affective images from the International Affective System (IAPS) (the distractor). The moving dots were flickered at 4.29 Hz whereas the IAPS pictures were flickered at 6 Hz. The time course of EEG spectral power at 4.29 Hz was taken to index the temporal dynamics of target processing. The spatial pattern of the EEG spectral power at 6 Hz was similarly extracted and subjected to a moving-window MVPA decoding analysis to index the temporal dynamics of processing pleasant, neutral, or unpleasant distractor pictures. We found that (1) both target processing and distractor processing exhibited rhythmicity at [~]1 Hz and (2) the phase difference between the two rhythmic time courses were related to task performance, i.e., relative phase closer to {pi} predicted a higher rate of coherent motion detection whereas relative phase closer to 0 predicted a lower rate of coherent motion detection. These results suggest that (1) in a target-distractor scenario, both attended and distracting information were sampled rhythmically and (2) the more target sampling and distractor sampling were separated in time within a sampling cycle, the less distraction effects were observed, both at the neural and the behavioral level.

neuroscience↗

bioRxiv2024

Stimulus repetition induces a two-stage learning process in primary visual cortex

Repeated stimulus exposure alters the brains response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level ([~]60% to [~]70%) at the beginning of the stage to chance level at the end of the stage ([~]50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the [~]35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

neuroscience↗

bioRxiv2023

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive and easy to apply. The limitation is that it is a linear method and mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs), a class of AI models originally developed for object recognition, are known to have the ability to approximate nonlinear relationships. CNNs are rapidly becoming an alternative to SVMs. The purpose of this study is to compare the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task (the attention dataset) and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content (the emotion dataset). We found that (1) both SVM and CNN are able to achieve above chance level decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain with, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated with each other, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping. These results suggest that (1) there are both linearly separable features and nonlinearly separable features in fMRI data that distinguish cognitive conditions and (2) applying both SVM and CNN to the same data may yield a more comprehensive understanding of neuroimaging data. Key pointsO_LIWe compared the performance and characteristics of SVM and CNN, two major methods in MVPA analysis of neuroimaging data, by applying them to the same two fMRI datasets. C_LIO_LIBoth SVM and CNN achieved decoding accuracies above chance level for both datasets in the chosen ROIs and the CNN decoding accuracies were consistently higher than those of SVM. C_LIO_LIThe heatmaps derived from SVM and CNN, which assess the contribution of voxels or brain regions to MVPA decoding performance, showed no significant overlap, providing evidence that the two methods depend on distinct brain activity patterns for decoding cognitive conditions. C_LI

bioinformatics↗

bioRxiv2023

Deconstructing the brain bases of emotion regulation: A systems-identification approach using Bayes factors

Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems-identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent fMRI studies (n=182 and n=176): (1) An anterior prefrontal system selectively involved in cognitive reappraisal; (2) A fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) A largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus, and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions down-regulated by reappraisal. These systems covaried with individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, implicating cannabinoid and serotonin systems in reappraisal. These findings challenge limbic-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation.

neuroscience↗

bioRxiv2022

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were six regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex(dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions represent signature centers of TN pain and provide target areas for future studies relating to central mechanisms of TN.

neuroscience↗

bioRxiv2022

Decoding the temporal dynamics of affective scene processing

Natural images containing affective scenes are used extensively to investigate the neural mechanisms of visual emotion processing. Functional fMRI studies have shown that these images activate a large-scale distributed brain network that encompasses areas in visual, temporal, and frontal cortices. The underlying spatial and temporal dynamics among these network structures, however, remain to be characterized. We recorded simultaneous EEG-fMRI data while participants passively viewed affective images from the International Affective Picture System (IAPS). Applying multivariate pattern analysis to decode EEG data, and representational similarity analysis to fuse EEG data with simultaneously recorded fMRI data, we found that: (1) ~100 ms after picture onset, perceptual processing of complex visual scenes began in early visual cortex, proceeding to ventral visual cortex at ~160 ms, (2) between ~200 and ~300 ms (pleasant pictures: ~200 ms; unpleasant pictures: ~260 ms), affect-specific neural representations began to form, supported mainly by areas in occipital and temporal cortices, and (3) affect-specific neural representations, lasting up to ~2 s, were stable and exhibited temporally generalizable activity patterns. These results suggest that affective scene representations in the brain are formed in a valence-dependent manner and are sustained by recurrent neural interactions among distributed brain areas.

neuroscience↗

bioRxiv2021

Effects of Frontoparietal Theta tACS on Verbal Working Memory: Behavioral and Neurophysiological Analysis

Left-lateralized frontoparietal theta oscillations are thought to play an important role in verbal working memory. We causally tested this idea by stimulating the frontoparietal theta network at individual theta frequencies (4 to 8 Hz) during verbal working memory and observing the subsequent behavioral and neurophysiological effects. Weak electric currents were delivered via two 4x1 HD electrode arrays centered at F3 and P3. Three stimulation configurations, including in-phase, anti-phase, or sham, were tested on three different days in a cross-over design. On each test day, the subject underwent three experimental sessions: pre-, during- and post-stimulation sessions. In all sessions, the subject performed a Sternberg verbal working memory task with three levels of memory load (load 2, 4 and 6), imposing three levels of cognitive demand. Analyzing behavioral, EEG, and pupillometry data from the post-stimulation sessions, we report three results. First, in-phase stimulation improved task performance only in subjects with higher working memory capacity (WMC) and under higher memory load (load 6). Second, in-phase stimulation enhanced frontoparietal theta synchrony during working memory retention only in subjects with higher WMC under higher memory loads (load 4 and load 6), and the enhanced frontoparietal theta synchronization is mainly driven by enhanced frontal[->]parietal theta Granger causality. Third, the pupil diameter was not different irrespective of whether the preceding stimulation was in-phase, anti-phase, or sham. These findings suggest that theta tACS effects on verbal working memory were load- and subject-dependent, rooted in tACS-induced changes in frontoparietal network interactions, and not driven by changes in arousal levels.

neuroscience↗

bioRxiv2020

Decoding Multivoxel Representations of Affective Scenes in Retinotopic Visual Cortex

The perception of opportunities and threats in complex scenes represents one of the main functions of the human visual system. In the laboratory, its neurophysiological basis is often studied by having observers view pictures varying in affective content. This body of work has consistently shown that viewing emotionally engaging, compared to neutral, pictures (1) heightens blood flow in limbic structures and frontoparietal cortex, as well as in anterior ventral and dorsal visual cortex, and (2) prompts an increase in the late positive event-related potential (LPP), a scalp-recorded and time-sensitive index of engagement within the network of aforementioned neural structures. The role of retinotopic visual cortex in this process has, however, been contentious, with competing theoretical notions predicting the presence versus absence of emotion-specific signals in retinotopic visual areas. The present study used multimodal neuroimaging and machine learning to address this question by examining the large-scale neural representations of affective pictures. Recording EEG and fMRI simultaneously while observers viewed pleasant, unpleasant, and neutral affective pictures, and applying multivariate pattern analysis to single-trial BOLD activities in retinotopic visual cortex, we identified three robust findings: First, unpleasant-versus-neutral decoding accuracy, as well as pleasant-versus-neutral decoding accuracy, were well above chance level in all retinotopic visual areas, including primary visual cortex. Second, the decoding accuracy in ventral visual cortex, but not in early visual cortex or dorsal visual cortex, was significantly correlated with LPP amplitude. Third, effective connectivity from amygdala to ventral visual cortex predicted unpleasant-versus-neutral decoding accuracy, and effective connectivity from ventral frontal cortex to ventral visual cortex predicted pleasant-versus-neutral decoding accuracy. These results suggest that affective pictures evoked valence-specific multivoxel neural representations in retinotopic visual cortex and that these multivoxel representations were influenced by reentry signals from limbic and frontal brain regions.

neuroscience↗