Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation.

Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams.

Functional Diversity of Dopamine Axons in Prefrontal Cortex During Classical Conditioning.

Midbrain dopamine neurons impact neural processing in the prefrontal cortex (PFC) through mesocortical projections. However, the signals conveyed by dopamine projections to the PFC remain unclear, particularly at the single-axon level. Here, we investigated dopaminergic axonal activity in the medial PFC (mPFC) during reward and aversive processing. By optimizing microprism-mediated two-photon calcium imaging of dopamine axon terminals, we found diverse activity in dopamine axons responsive to both reward and aversive stimuli. Some axons exhibited a preference for reward, while others favored aversive stimuli, and there was a strong bias for the latter at the population level. Long-term longitudinal imaging revealed that the preference was maintained in reward- and aversive-preferring axons throughout classical conditioning in which rewarding and aversive stimuli were paired with preceding auditory cues. However, as mice learned to discriminate reward or aversive cues, a cue activity preference gradually developed only in aversive-preferring axons. We inferred the trial-by-trial cue discrimination based on machine learning using anticipatory licking or facial expressions, and found that successful discrimination was accompanied by sharper selectivity for the aversive cue in aversive-preferring axons. Our findings indicate that a group of mesocortical dopamine axons encodes aversive-related signals, which are modulated by both classical conditioning across days and trial-by-trial discrimination within a day.

Distinct roles of monkey OFC-subcortical pathways in adaptive behavior.

To be the most successful, primates must adapt to changing environments and optimize their behavior by making the most beneficial choices. At the core of adaptive behavior is the orbitofrontal cortex (OFC) of the brain, which updates choice value through direct experience or knowledge-based inference. Here, we identify distinct neural circuitry underlying these two separate abilities. We designed two behavioral tasks in which macaque monkeys updated the values of certain items, either by directly experiencing changes in stimulus-reward associations, or by inferring the value of unexperienced items based on the task's rules. Chemogenetic silencing of bilateral OFC combined with mathematical model-fitting analysis revealed that monkey OFC is involved in updating item value based on both experience and inference. In vivo imaging of chemogenetic receptors by positron emission tomography allowed us to map projections from the OFC to the rostromedial caudate nucleus (rmCD) and the medial part of the mediodorsal thalamus (MDm). Chemogenetic silencing of the OFC-rmCD pathway impaired experience-based value updating, while silencing the OFC-MDm pathway impaired inference-based value updating. Our results thus demonstrate a dissociable contribution of distinct OFC projections to different behavioral strategies, and provide new insights into the neural basis of value-based adaptive decision-making in primates.

Reconstructing visual illusory experiences from human brain activity.

Visual illusions provide valuable insights into the brain's interpretation of the world given sensory inputs. However, the precise manner in which brain activity translates into illusory experiences remains largely unknown. Here, we leverage a brain decoding technique combined with deep neural network (DNN) representations to reconstruct illusory percepts as images from brain activity. The reconstruction model was trained on natural images to establish a link between brain activity and perceptual features and then tested on two types of illusions: illusory lines and neon color spreading. Reconstructions revealed lines and colors consistent with illusory experiences, which varied across the source visual cortical areas. This framework offers a way to materialize subjective experiences, shedding light on the brain's internal representations of the world.

Inter-individual deep image reconstruction via hierarchical neural code conversion.

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual content. In this study, we trained a method of functional alignment called neural code converter that predicts a target subject's brain activity pattern from a source subject given the same stimulus, and analyzed the converted patterns by decoding hierarchical visual features and reconstructing perceived images. The converters were trained on fMRI responses to identical sets of natural images presented to pairs of individuals, using the voxels on the visual cortex that covers from V1 through the ventral object areas without explicit labels of the visual areas. We decoded the converted brain activity patterns into the hierarchical visual features of a deep neural network using decoders pre-trained on the target subject and then reconstructed images via the decoded features. Without explicit information about the visual cortical hierarchy, the converters automatically learned the correspondence between visual areas of the same levels. Deep neural network feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The visual images were reconstructed with recognizable silhouettes of objects even with relatively small numbers of data for converter training. The decoders trained on pooled data from multiple individuals through conversions led to a slight improvement over those trained on a single individual. These results demonstrate that the hierarchical and fine-grained representation can be converted by functional alignment, while preserving sufficient visual information to enable inter-individual visual image reconstruction.

Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex.

Sensory perception and memory recall generate different conscious experiences. Although externally and internally driven neural activities signifying the same perceptual content overlap in the sensory cortex, their distribution in the prefrontal cortex (PFC), an area implicated in both perception and memory, remains elusive. Here, we test whether the local spatial configurations and frequencies of neural oscillations driven by perception and memory recall overlap in the macaque PFC using high-density electrocorticography and multivariate pattern analysis. We find that dynamically changing oscillatory signals distributed across the PFC in the delta-, theta-, alpha-, and beta-band ranges carry significant, but mutually different, information predicting the same feature of memory-recalled internal targets and passively perceived external objects. These findings suggest that the frequency-specific distribution of oscillatory neural signals in the PFC serves cortical signatures responsible for distinguishing between different types of cognition driven by external perception and internal memory.

Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

Achievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on neural decoding and encoding analyses where DNN unit activations and human brain activity are predicted from each other. We find that BH scores for 29 pre-trained DNNs with various architectures are negatively correlated with image recognition performance, thus indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that single-path sequential feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method may provide new ways to design DNNs in light of their representational homology to the brain.

Quantum-inspired canonical correlation analysis for exponentially large dimensional data.

Canonical correlation analysis (CCA) serves to identify statistical dependencies between pairs of multivariate data. However, its application to high-dimensional data is limited due to considerable computational complexity. As an alternative to the conventional CCA approach that requires polynomial computational time, we propose an algorithm that approximates CCA using quantum-inspired computations with computational time proportional to the logarithm of the input dimensionality. The computational efficiency and performance of the proposed quantum-inspired CCA (qiCCA) algorithm are experimentally evaluated on synthetic and real datasets. Furthermore, the fast computation provided by qiCCA allows directly applying CCA even after nonlinearly mapping raw input data into high-dimensional spaces. The conducted experiments demonstrate that, as a result of mapping raw input data into the high-dimensional spaces with the use of second-order monomials, qiCCA extracts more correlations compared with the linear CCA and achieves comparable performance with state-of-the-art nonlinear variants of CCA on several datasets. These results confirm the appropriateness of the proposed qiCCA and the high potential of quantum-inspired computations in analyzing high-dimensional data.

End-to-End Deep Image Reconstruction From Human Brain Activity.

Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reconstruction from functional magnetic resonance imaging (fMRI) activity. However, direct training of a DNN with fMRI data is often avoided because the size of available data is thought to be insufficient for training a complex network with numerous parameters. Instead, a pre-trained DNN usually serves as a proxy for hierarchical visual representations, and fMRI data are used to decode individual DNN features of a stimulus image using a simple linear model, which are then passed to a reconstruction module. Here, we directly trained a DNN model with fMRI data and the corresponding stimulus images to build an end-to-end reconstruction model. We accomplished this by training a generative adversarial network with an additional loss term that was defined in high-level feature space (feature loss) using up to 6,000 training data samples (natural images and fMRI responses). The above model was tested on independent datasets and directly reconstructed image using an fMRI pattern as the input. Reconstructions obtained from our proposed method resembled the test stimuli (natural and artificial images) and reconstruction accuracy increased as a function of training-data size. Ablation analyses indicated that the feature loss that we employed played a critical role in achieving accurate reconstruction. Our results show that the end-to-end model can learn a direct mapping between brain activity and perception.

Deep image reconstruction from human brain activity.

The mental contents of perception and imagery are thought to be encoded in hierarchical representations in the brain, but previous attempts to visualize perceptual contents have failed to capitalize on multiple levels of the hierarchy, leaving it challenging to reconstruct internal imagery. Recent work showed that visual cortical activity measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into the hierarchical features of a pre-trained deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features. Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that our method was able to reliably produce reconstructions that resembled the viewed natural images. A natural image prior introduced by a deep generator neural network effectively rendered semantically meaningful details to the reconstructions. Human judgment of the reconstructions supported the effectiveness of combining multiple DNN layers to enhance the visual quality of generated images. While our model was solely trained with natural images, it successfully generalized to artificial shapes, indicating that our model was not simply matching to exemplars. The same analysis applied to mental imagery demonstrated rudimentary reconstructions of the subjective content. Our results suggest that our method can effectively combine hierarchical neural representations to reconstruct perceptual and subjective images, providing a new window into the internal contents of the brain.

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding.

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings. To date, there is no established method of predicting ordinal variables in brain decoding. In this study, we present a new algorithm, sparse ordinal logistic regression (SOLR), that combines ordinal logistic regression with Bayesian sparse weight estimation. We found that, in both simulation and analyses using real functional magnetic resonance imaging (fMRI) data, SOLR outperformed ordinal logistic regression with non-sparse regularization, indicating that sparseness leads to better decoding performance. SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs. Our results suggest that SOLR provides a principled and effective method of decoding ordinal variables.

Heterogeneous Redistribution of Facial Subcategory Information Within and Outside the Face-Selective Domain in Primate Inferior Temporal Cortex.

The inferior temporal cortex (ITC) contains neurons selective to multiple levels of visual categories. However, the mechanisms by which these neurons collectively construct hierarchical category percepts remain unclear. By comparing decoding accuracy with simultaneously acquired electrocorticogram (ECoG), local field potentials (LFPs), and multi-unit activity in the macaque ITC, we show that low-frequency LFPs/ECoG in the early evoked visual response phase contain sufficient coarse category (e.g., face) information, which is homogeneous and enhanced by spatial summation of up to several millimeters. Late-induced high-frequency LFPs additionally carry spike-coupled finer category (e.g., species, view, and identity of the face) information, which is heterogeneous and reduced by spatial summation. Face-encoding neural activity forms a cluster in similar cortical locations regardless of whether it is defined by early evoked low-frequency signals or late-induced high-gamma signals. By contrast, facial subcategory-encoding activity is distributed, not confined to the face cluster, and dynamically increases its heterogeneity from the early evoked to late-induced phases. These findings support a view that, in contrast to the homogeneous and static coarse category-encoding neural cluster, finer category-encoding clusters are heterogeneously distributed even outside their parent category cluster and dynamically increase heterogeneity along with the local cortical processing in the ITC.

Position Information Encoded by Population Activity in Hierarchical Visual Areas.

Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes [V1-V4, lateral occipital complex (LOC), and fusiform face area (FFA)]. We collected functional magnetic resonance imaging (fMRI) responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the ball's position in a separate session was predicted by maximum likelihood estimation using the RF models of individual voxels. We also tested a model-free multivoxel regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RF centers. The results suggest that much position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes and is potentially available in later processing for recognition and behavior.

Selective Suppression of Local Circuits during Movement Preparation in the Mouse Motor Cortex.

Prepared movements are more efficient than those that are not prepared for. Although changes in cortical activity have been observed prior to a forthcoming action, the circuits involved in motor preparation remain unclear. Here, we use in vivo two-photon calcium imaging to uncover changes in the motor cortex during variable waiting periods prior to a forepaw reaching task in mice. Consistent with previous reports, we observed a subset of neurons with increased activity during the waiting period; however, these neurons did not account for the degree of preparation as defined by reaction time (RT). Instead, the suppression of activity of distinct neurons in the same cortical area better accounts for RT. This suppression of neural activity resulted in a distinct and reproducible pattern when mice were well prepared. Thus, the selective suppression of network activity in the motor cortex may be a key feature of prepared movements.

Associative-memory representations emerge as shared spatial patterns of theta activity spanning the primate temporal cortex.

Highly localized neuronal spikes in primate temporal cortex can encode associative memory; however, whether memory formation involves area-wide reorganization of ensemble activity, which often accompanies rhythmicity, or just local microcircuit-level plasticity, remains elusive. Using high-density electrocorticography, we capture local-field potentials spanning the monkey temporal lobes, and show that the visual pair-association (PA) memory is encoded in spatial patterns of theta activity in areas TE, 36, and, partially, in the parahippocampal cortex, but not in the entorhinal cortex. The theta patterns elicited by learned paired associates are distinct between pairs, but similar within pairs. This pattern similarity, emerging through novel PA learning, allows a machine-learning decoder trained on theta patterns elicited by a particular visual item to correctly predict the identity of those elicited by its paired associate. Our results suggest that the formation and sharing of widespread cortical theta patterns via learning-induced reorganization are involved in the mechanisms of associative memory representation.

BrainLiner: A Neuroinformatics Platform for Sharing Time-Aligned Brain-Behavior Data.

Data-driven neuroscience aims to find statistical relationships between brain activity and task behavior from large-scale datasets. To facilitate high-throughput data processing and modeling, we created BrainLiner as a web platform for sharing time-aligned, brain-behavior data. Using an HDF5-based data format, BrainLiner treats brain activity and data related to behavior with the same salience, aligning both behavioral and brain activity data on a common time axis. This facilitates learning the relationship between behavior and brain activity. Using a common data file format also simplifies data processing and analyses. Properties describing data are unambiguously defined using a schema, allowing machine-readable definition of data. The BrainLiner platform allows users to upload and download data, as well as to explore and search for data from the web platform. A WebGL-based data explorer can visualize highly detailed neurophysiological data from within the web browser, and a data-driven search feature allows users to search for similar time windows of data. This increases transparency, and allows for visual inspection of neural coding. BrainLiner thus provides an essential set of tools for data sharing and data-driven modeling.

Alternating zones selective to faces and written words in the human ventral occipitotemporal cortex.

Recognition of faces and written words is associated with category-specific brain activation in the ventral occipitotemporal cortex (vOT). However, topological and functional relationships between face-selective and word-selective vOT regions remain unclear. In this study, we collected data from patients with intractable epilepsy who underwent high-density recording of surface field potentials in the vOT. "Faces" and "letterstrings" induced outstanding category-selective responses among the 24 visual categories tested, particularly in high-γ band powers. Strikingly, within-hemispheric analysis revealed alternation of face-selective and letterstring-selective zones within the vOT. Two distinct face-selective zones located anterior and posterior portions of the mid-fusiform sulcus whereas letterstring-selective zones alternated between and outside of these 2 face-selective zones. Further, a classification analysis indicated that activity patterns of these zones mostly represent dedicated categories. Functional connectivity analysis using Granger causality indicated asymmetrically directed causal influences from face-selective to letterstring-selective regions. These results challenge the prevailing view that different categories are represented in distinct contiguous regions in the vOT.

The large-scale production of an artificial influenza virus-like particle vaccine in silkworm pupae.

We successfully established a mass production system for an influenza virus-like particle (VLP) vaccine using a synthetic H5 hemagglutinin (HA) gene codon-optimized for the silkworm. A recombinant baculovirus containing the synthetic gene was inoculated into silkworm pupae. Four days after inoculation, the hemagglutination titer in homogenates from infected pupae reached a mean value of 0.8 million hemagglutination units (HAU), approximately 2,000 µg HA protein per pupa, more than 50-fold higher than that produced with an embryonated chicken egg. VLPs ranging from 30 nm to 300 nm in diameter and covered with a large number of spikes were detected in the homogenates. The spikes were approximately 14 nm long, similar to an authentic influenza HA spike. Detailed electron micrographs indicated that the VLP spike density was similar to that of authentic influenza virus particles. The results clearly show that the expression of a single HA gene can efficiently produce VLPs in silkworm pupae. When chickens were immunized with the pupae homogenate, the hemagglutination inhibition titer in their sera reached values of 2,048-8,192 after approximately 1 month. This is the first report demonstrating that a large amount of VLP vaccine could be produced by single synthetic HA gene in silkworm pupae. Our system might be useful for future vaccine development against other viral diseases.

Decoding visual object categories from temporal correlations of ECoG signals.

How visual object categories are represented in the brain is one of the key questions in neuroscience. Studies on low-level visual features have shown that relative timings or phases of neural activity between multiple brain locations encode information. However, whether such temporal patterns of neural activity are used in the representation of visual objects is unknown. Here, we examined whether and how visual object categories could be predicted (or decoded) from temporal patterns of electrocorticographic (ECoG) signals from the temporal cortex in five patients with epilepsy. We used temporal correlations between electrodes as input features, and compared the decoding performance with features defined by spectral power and phase from individual electrodes. While using power or phase alone, the decoding accuracy was significantly better than chance, correlations alone or those combined with power outperformed other features. Decoding performance with correlations was degraded by shuffling the order of trials of the same category in each electrode, indicating that the relative time series between electrodes in each trial is critical. Analysis using a sliding time window revealed that decoding performance with correlations began to rise earlier than that with power. This earlier increase in performance was replicated by a model using phase differences to encode categories. These results suggest that activity patterns arising from interactions between multiple neuronal units carry additional information on visual object categories.

Experimental comparison of classification methods for key kinase identification for neurite elongation.

Kinases in a developing neuron play important roles in elongating a neurite with their complex interactions. To elucidate the effect of each kinase on neurite elongation and regeneration from a small set of experiments, we applied machine learning methods to synthetic datasets based on a biologically feasible model. The result showed the ridged partial least squares (RPLS) algorithm performed better than other standard algorithms such as naive Bayes classifier, support vector machines and random forest classification. This suggests the effectiveness of dimension reduction done in RPLS.