[How Can We Reconstruct Mental Imagery from Brain Activities?].

Advances in machine learning and artificial intelligence (AI) have enabled the extraction of information such as perception and motor intention from neural activities, which is called neural decoding. A recent study demonstrated that mental imagery can be reconstructed from brain activity measured using functional magnetic resonance imaging. This article describes the method of mental image reconstruction and the underlying technologies that support it.

Homogenization of word relationships in schizophrenia: Topological analysis of cortical semantic representations.

Aim: Patients with schizophrenia typically exhibit symptoms of disorganized thought and display concreteness and over-inclusion in verbal reports, depending on the level of abstraction. While concreteness and over-inclusion may appear contradictory, the underlying psychopathology that explains these symptoms remains unclear. In the current study, we used functional magnetic resonance imaging with an encoding modeling approach to examine how concepts of various words, represented as brain activity, are anomalously connected at different levels of abstraction in patients with schizophrenia.

Semantic context-dependent neural representations of odors in the human piriform cortex revealed by 7T MRI.

Olfactory perception depends not only on olfactory inputs but also on semantic context. Although multi-voxel activity patterns of the piriform cortex, a part of the primary olfactory cortex, have been shown to represent odor perception, it remains unclear whether semantic contexts modulate odor representation in this region. Here, we investigated whether multi-voxel activity patterns in the piriform cortex change when semantic context modulates odor perception and, if so, whether the modulated areas communicate with brain regions involved in semantic and memory processing beyond the piriform cortex.

Text and image generation from intracranial electroencephalography using an embedding space for text and images.

Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex.

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.

Artificial neural network modelling of the neural population code underlying mathematical operations.

Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent neuroimaging studies have compared distributed representations of the visual, auditory and language domains in ANNs and biological neural networks (BNNs).

Quantitative modelling demonstrates format-invariant representations of mathematical problems in the brain.

Mathematical problems can be described in either symbolic form or natural language. Previous studies have reported that activation overlaps exist for these two types of mathematical problems, but it is unclear whether they are based on similar brain representations. Furthermore, quantitative modelling of mathematical problem solving has yet to be attempted.

Disorganization of Semantic Brain Networks in Schizophrenia Revealed by fMRI.

Objectives: Schizophrenia is a mental illness that presents with thought disorders including delusions and disorganized speech. Thought disorders have been regarded as a consequence of the loosening of associations between semantic concepts since the term "schizophrenia" was first coined by Bleuler. However, a mechanistic account of this cardinal disturbance in terms of functional dysconnection has been lacking.

Representations and decodability of diverse cognitive functions are preserved across the human cortex, cerebellum, and subcortex.

Which part of the brain contributes to our complex cognitive processes? Studies have revealed contributions of the cerebellum and subcortex to higher-order cognitive functions; however, it has been unclear whether such functional representations are preserved across the cortex, cerebellum, and subcortex. In this study, we use functional magnetic resonance imaging data with 103 cognitive tasks and construct three voxel-wise encoding and decoding models independently using cortical, cerebellar, and subcortical voxels. Representational similarity analysis reveals that the structure of task representations is preserved across the three brain parts.

Brain networks are decoupled from external stimuli during internal cognition.

Our cognition can be directed to external stimuli or to internal information. While there are many different forms of internal cognition (mind-wandering, recall, imagery etc.), their essential feature is independence from the immediate sensory input, conceptually referred to as perceptual decoupling.

Voluntary control of semantic neural representations by imagery with conflicting visual stimulation.

Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image.

Processing of visual statistics of naturalistic videos in macaque visual areas V1 and V4.

Natural scenes are characterized by diverse image statistics, including various parameters of the luminance histogram, outputs of Gabor-like filters, and pairwise correlations between the filter outputs of different positions, orientations, and scales (Portilla-Simoncelli statistics). Some of these statistics capture the response properties of visual neurons. However, it remains unclear to what extent such statistics can explain neural responses to natural scenes and how neurons that are tuned to these statistics are distributed across the cortex.

What can we experience and report on a rapidly presented image? Intersubjective measures of specificity of freely reported contents of consciousness.

A majority of previous studies appear to support a view that human observers can only perceive coarse information from a natural scene image when it is presented rapidly (<100ms, masked). In these studies, participants were often forced to choose an answer from options that experimenters preselected. These options can underestimate what participants experience and can report on it.

Music genre neuroimaging dataset.

This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the application of machine learning algorithms.

Multiple states in ongoing neural activity in the rat visual cortex.

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive.

Behavioral correlates of cortical semantic representations modeled by word vectors.

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations.

Convergence of Modality Invariance and Attention Selectivity in the Cortical Semantic Circuit.

The human linguistic system is characterized by modality invariance and attention selectivity. Previous studies have examined these properties independently and reported perisylvian region involvement for both; however, their relationship and the linguistic information they harbor remain unknown. Participants were assessed by functional magnetic resonance imaging, while spoken narratives (auditory) and written texts (visual) were presented, either separately or simultaneously.

Expert Programmers Have Fine-Tuned Cortical Representations of Source Code.

Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many studies have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain of programmers are still unclear.

Correspondence of categorical and feature-based representations of music in the human brain.

Introduction: Humans tend to categorize auditory stimuli into discrete classes, such as animal species, language, musical instrument, and music genre. Of these, music genre is a frequently used dimension of human music preference and is determined based on the categorization of complex auditory stimuli. Neuroimaging studies have reported that the superior temporal gyrus (STG) is involved in response to general music-related features.

Distinct dimensions of emotion in the human brain and their representation on the cortical surface.

We experience a rich variety of emotions in daily life, and a fundamental goal of affective neuroscience is to determine how these emotions are represented in the brain. Recent psychological studies have used naturalistic stimuli (e.g.

Quantitative models reveal the organization of diverse cognitive functions in the brain.

Our daily life is realized by the complex orchestrations of diverse brain functions, including perception, decision-making, and action. The essential goal of cognitive neuroscience is to reveal the complete representations underlying these functions. Recent studies have characterised perceptual experiences using encoding models.

Mapping stimulus feature selectivity in macaque V1 by two-photon Ca imaging: Encoding-model analysis of fluorescence responses to natural movies.

In vivo calcium (Ca) imaging using two-photon microscopy allows activity to be monitored simultaneously from hundreds of individual neurons within a local population. While this allows us to gain important insights into how cortical neurons represent sensory information, factors such as photo-bleaching of the Ca indicator limit imaging duration (and thus the numbers of stimuli that can be tested), which in turn hampers the full characterization of neuronal response properties. Here, we demonstrate that using an encoding model combined with presentation of natural movies results in detailed characterization of receptive field (RF) properties despite the relatively short time for data collection.

Decoding naturalistic experiences from human brain activity via distributed representations of words.

Natural visual scenes induce rich perceptual experiences that are highly diverse from scene to scene and from person to person. Here, we propose a new framework for decoding such experiences using a distributed representation of words. We used functional magnetic resonance imaging (fMRI) to measure brain activity evoked by natural movie scenes.

An Exploratory Study on the Anti-inflammatory Effects of Fucoidan in Relation to Quality of Life in Advanced Cancer Patients.

Background: Conventional anticancer therapies still cause difficulties with selective eradication and accompanying side effects that reduce patients' quality of life (QOL). Fucoidan is extracted from seaweeds and has already exhibited broad bioactivities, including anticancer and anti-inflammatory properties, in basic studies. It is expected to enhance therapeutic efficacy and minimize side effects in cancer patients; however, despite its potential benefits, there are very few clinical trials using fucoidans.