Biology subjects

Nishimoto, S.

Publications and source records attributed to Nishimoto, S..

28 records · Page 2Linked to original sources

bioRxiv2021

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. This is thought to be reflected in the decoupling of brain networks from the external stimuli, but a quantitative investigation of this remains outstanding. Here we present a conceptual and analysis framework that links stimulus responses to connectivity between brain networks. This allows us to quantify the coupling of brain networks to the external stimuli. We tested this framework by presenting subjects with an audiovisual stimulus and instructing them to either attend to the stimulus (external task) or engage in mental imagery, recall or arithmetic (internal tasks) while measuring the evoked brain activity using functional MRI. We found that stimulus responses were generally attenuated for the internal tasks, though they increased in a subset of tasks and brain networks. However, using our new measure of coupling, we showed that brain networks became increasingly decoupled from the stimulus, even in the subset of tasks and brain networks in which stimulus responses increased. These results quantitatively demonstrate that during internal cognition brain networks are decoupled from external stimuli, opening the door for a fundamental and quantitative understanding of internal cognition.

neuroscience↗

bioRxiv2020

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 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 semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings. Author summeryWord vectors, which have been originally developed in the field of engineering (natural language processing), have been extensively leveraged in neuroscience studies to model semantic representations in the human brain. These studies have attempted to model brain semantic representations by associating them with the meanings of thousands of words via a word vector space. However, there has been no study explicitly examining whether the modeled semantic representations actually capture our perception of semantic information. To address this issue, we compared the semantic representational structure of words estimated from word vector-based brain models with that evaluated from behavioral data in psychological experiments. The results revealed a significant correlation between these model- and behavior-derived semantic representational structures of words. This indicates that the brain semantic representations modeled using word vectors actually reflect the human perception of word meanings. Our findings contribute to the establishment of word vector-based brain modeling as a useful tool in studying human semantic processing.

neuroscience↗

bioRxiv2020

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 MRI, while spoken narratives (auditory) and written texts (visual) were presented, either separately or simultaneously. Participants were asked to attend to one stimulus when both were presented. We extracted phonemic and semantic features from these auditory and visual modalities, to train multiple, voxel-wise encoding models. Cross-modal examinations of the trained models revealed that perisylvian regions were associated with modality-invariant semantic representations. Attentional modulation was quantified by examining the modeling performance for attended and unattended conditions. We have determined that perisylvian regions exhibited attention selectivity. Both modality invariance and attention selectivity are both prominent in models that use semantic but not phonemic features. Modality invariance was significantly correlated with attention selectivity in some brain regions; however, we also identified cortical regions associated with only modality invariance or only attention selectivity. Thus, paying selective attention to a specific sensory input modality may regulate the semantic information that is partly processed in brain networks that are shared across modalities.

neuroscience↗

bioRxiv2020

Reduced intra- and inter-individual diversity of semantic representations in the brains of schizophrenia patients

Schizophrenia patients often manifest semantic processing deficits. It has been proposed that these deficits stem from disorganized semantic representations in the brain. However, no study has yet examined the neural correlates of semantic disorganization by directly evaluating semantic representations in the brain. We used voxelwise modeling on functional magnetic resonance imaging signals to evaluate the semantic representations associated with several thousand words in individual patient brains. We then compared the structural properties of semantic representations to those in healthy controls. The variability of semantic representations was smaller both within individual patients and across patients compared to controls. Surrogate data analysis suggests that the observed reduction in representational variability is associated with disorganization of categorical information. To our knowledge, these findings provide the first evidence for sematic disorganization in schizophrenia at the level of brain representations.

neuroscience↗

bioRxiv2020

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 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 are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

neuroscience↗

bioRxiv2019

Topographic preservation of functional connectivity among the association areas in the human cerebral cortex

In the human sensorimotor cortex, some long-range corticocortical connections appear to preserve a fine-scale topology, in which physically close locations in the cortical region are functionally connected to physically close locations in other cortical regions. However, little is known about whether such topography preservation is unique to the sensorimotor areas or is general across other cortical areas. To investigate this question, we measured voxel-level functional connectivity using functional magnetic resonance imaging (fMRI) and visualized the fine-scale spatial organization of the connectivity patterns across the cortical surface. We found topographical preservation across regions, including the default mode network. Our results suggest that the topographical preservation of functional connectivity is not restricted to the sensorimotor cortex but also occurs in the association cortex.

neuroscience↗

bioRxiv2019

Data-driven 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, and action. One of the central issues in cognitive neuroscience is to reveal the complete representations underlying such diverse functions. Recent studies have revealed representations of natural perceptual experiences using encoding models1-5. However, there has been little attempt to build a quantitative model describing the cortical organization of multiple active, cognitive processes. Here, we measured brain activity using functional MRI while subjects performed over 100 cognitive tasks, and examined cortical representations with two voxel-wise encoding models6. A sparse task-type encoding model revealed a hierarchical organization of cognitive tasks, their representation in cognitive space, and their mapping onto the cortex. A cognitive factor encoding model utilizing continuous intermediate features by using metadata-based inferences7 predicted brain activation patterns for more than 80 % of the cerebral cortex and decoded more than 95 % of tasks, even under novel task conditions. This study demonstrates the usability of quantitative models of natural cognitive processes and provides a framework for the comprehensive cortical organization of human cognition.

neuroscience↗

bioRxiv2018

Representation of music genres based on the spectro-temporal modulation responses of the human brain

Music genre is an essential category for understanding human musical preferences and is provided based on the abstract categorization upon complex auditory stimuli. Previous neuroimaging studies have reported the involvement of the superior temporal gyrus (STG) in response to general music-related features. However, it remains largely unclear how abstract categories of music genre are represented in the brain and what acoustic features are more suited for explaining such representations. Here we examined comprehensive cortical representations and functional organization of music genres using 540 music clips. We applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging (fMRI). We observed distinct cortical organizations for different music genres in the bilateral STG, which revealed the representational relationship between various music genres, e.g., classical and hip-hop music showed opposite representations. Representations of music genres were largely explained by spectro-temporal modulation, which was modeled by a biologically plausible spectro-temporal modulation-transfer function (MTF) model. Our results elucidate the quantitative representation of music genres in the human cortex and indicate the possibility of modeling our abstract categorization of complex auditory stimuli based on the brain activity.\n\nSignificance statementMusic genre is an essential category for understanding human preferences of music. However, it is largely unknown how abstract categories of music genre are represented in the brain. Here, we examined comprehensive cortical representations of music genres by building voxel-wise models of fMRI data collected while human subjects listened to 540 music clips. We found distinct cortical organizations for various music genres in the bilateral STG. Such genre-specific cortical organization was explained by the biologically plausible MTF model. The current study elucidates the quantitative representation of music genres in the human cortex for the first time and indicates the possibility of modeling our abstract categorization of complex auditory stimuli based on the brain activity.

neuroscience↗

bioRxiv2018

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. While previous emotion studies focused on only a few predefined, restricted emotional states, a recent psychological study found a rich emotional representation in humans using a large set of diverse human-behavioural data. However, no representation of emotional states in the brain using emotion labels has been established on such a scale. To examine that, we used functional MRI to measure blood-oxygen-level-dependent (BOLD) responses when human subjects watched 3-h emotion-inducing movies labelled with 10,800 ratings regarding each of 80 emotion categories. By quantifying canonical correlations between BOLD responses and emotion ratings for the movie scenes, we found 25 significant dimensions of emotion representation in the brain. Then, we constructed a semantic space of the emotion representation and mapped the emotion categories on the cortical surface. We found that the emotion categories were smoothly represented from unimodal to transmodal regions on the cortical surface. This paper presents a cortical representation of a rich variety of emotion categories, which covers most of the emotional states suggested in traditional theories.

neuroscience↗

bioRxiv2018

Predicting the future direction of cell movement with convolutional neural networks

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

systems biology↗