Intergenerational transmission of brain structure and function in humans: a narrative review of designs, methods, and findings.

Children often show cognitive and affective traits that are similar to their parents. Although this indicates a transmission of phenotypes from parents to children, little is known about the neural underpinnings of that transmission. Here, we provide a general overview of neuroimaging studies that explore the similarity between parents and children in terms of brain structure and function. We notably discuss the aims, designs, and methods of these so-called intergenerational neuroimaging studies, focusing on two main designs: the parent-child design and the multigenerational design. For each design, we also summarize the major findings, identify the sources of variability between studies, and highlight some limitations and future directions. We argue that the lack of consensus in defining the parent-child transmission of brain structure and function leads to measurement heterogeneity, which is a challenge for future studies. Additionally, multigenerational studies often use measures of family resemblance to estimate the proportion of variance attributed to genetic versus environmental factors, though this estimate is likely inflated given the frequent lack of control for shared environment. Nonetheless, intergenerational neuroimaging studies may still have both clinical and theoretical relevance, not because they currently inform about the etiology of neuromarkers, but rather because they may help identify neuromarkers and test hypotheses about neuromarkers coming from more standard neuroimaging designs.

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. We also explored regional differences within the piriform cortex, which are influenced by olfactory input and semantic context. We used 2 × 2 combinations of word labels and odorants that were perceived as congruent and measured piriform activity with a 1-mm isotropic resolution using 7T MRI. We found that identical odorants labeled with different words were perceived differently. This labeling effect was observed in multi-voxel activity patterns in the piriform cortex, as the searchlight decoding analysis distinguished identical odors with different labels for half of the examined stimulus pairs. Significant functional connectivity was observed between parts of the piriform cortex that were modulated by labels and regions associated with semantic and memory processing. While the piriform multi-voxel patterns evoked by different olfactory inputs were also distinguishable, the decoding accuracy was significant for only one stimulus pair, preventing definitive conclusions regarding the locational differences between areas influenced by word labels and olfactory inputs. These results suggest that multi-voxel patterns of piriform activity can be modulated by semantic context, possibly due to communication between the piriform cortex and the semantic and memory regions.

Syntactic theory of mathematical expressions.

Mathematical expressions consist of recursive combinations of numbers, variables, and operators. According to theoretical linguists, the syntactic mechanisms of natural language also provide a basis for mathematics. To date, however, no theoretically rigorous investigation has been conducted to support such arguments. Therefore, this study uses a methodology based on theoretical linguistics to analyze the syntactic properties of mathematical expressions. Through a review of recent behavioral and neuroimaging studies on mathematical syntax, we report several inconsistencies with theoretical linguistics, such as the use of ternary structures. To address these, we propose that a syntactic category called Applicative plays a central role in analyzing mathematical expressions with seemingly ternary structures by combining binary structures. Besides basic arithmetic expressions, we also examine algebraic equations and complex expressions such as integral and differential calculi. This study is the first attempt at building a comprehensive framework for analyzing the syntactic structures of mathematical expressions.

Algorithmic voice transformations reveal the phonological basis of language-familiarity effects in cross-cultural emotion judgments.

People have a well-described advantage in identifying individuals and emotions in their own culture, a phenomenon also known as the other-race and language-familiarity effect. However, it is unclear whether native-language advantages arise from genuinely enhanced capacities to extract relevant cues in familiar speech or, more simply, from cultural differences in emotional expressions. Here, to rule out production differences, we use algorithmic voice transformations to create French and Japanese stimulus pairs that differed by exactly the same acoustical characteristics. In two cross-cultural experiments, participants performed better in their native language when categorizing vocal emotional cues and detecting non-emotional pitch changes. This advantage persisted over three types of stimulus degradation (jabberwocky, shuffled and reversed sentences), which disturbed semantics, syntax, and supra-segmental patterns, respectively. These results provide evidence that production differences are not the sole drivers of the language-familiarity effect in cross-cultural emotion perception. Listeners' unfamiliarity with the phonology of another language, rather than with its syntax or semantics, impairs the detection of pitch prosodic cues and, in turn, the recognition of expressive prosody.

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). However, such a relationship has not yet been examined in mathematics. Here we hypothesise that ANN-based distributed representations can explain brain activity patterns of symbolic mathematical operations. We used the fMRI data of a series of mathematical problems with nine different combinations of operators to construct voxel-wise encoding/decoding models using both sparse operator and latent ANN features. Representational similarity analysis demonstrated shared representations between ANN and BNN, an effect particularly evident in the intraparietal sulcus. Feature-brain similarity (FBS) analysis served to reconstruct a sparse representation of mathematical operations based on distributed ANN features in each cortical voxel. Such reconstruction was more efficient when using features from deeper ANN layers. Moreover, latent ANN features allowed the decoding of novel operators not used during model training from brain activity. The current study provides novel insights into the neural code underlying mathematical thought.

Cortical representations of numbers and nonsymbolic quantities expand and segregate in children from 5 to 8 years of age.

Number symbols, such as Arabic numerals, are cultural inventions that have transformed human mathematical skills. Although their acquisition is at the core of early elementary education in children, it remains unknown how the neural representations of numerals emerge during that period. It is also unclear whether these relate to an ontogenetically earlier sense of approximate quantity. Here, we used multivariate fMRI adaptation coupled with within- and between-format machine learning to probe the cortical representations of Arabic numerals and approximate nonsymbolic quantity in 89 children either at the beginning (age 5) or four years into formal education (age 8). Although the cortical representations of both numerals and nonsymbolic quantities expanded from age 5 to age 8, these representations also segregated with learning and development. Specifically, a format-independent neural representation of quantity was found in the right parietal cortex, but only for 5-year-olds. These results are consistent with the so-called symbolic estrangement hypothesis, which argues that the relation between symbolic and nonsymbolic quantity weakens with exposure to formal mathematics in children.

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. In the present study, subjects underwent 3 h of functional magnetic resonance experiments involving math word and math expression problems, and a read word condition without any calculations was used as a control. To evaluate the brain representations of mathematical problems quantitatively, we constructed voxel-wise encoding models. Both intra- and cross-format encoding modelling significantly predicted brain activity predominantly in the left intraparietal sulcus (IPS), even after subtraction of the control condition. Representational similarity analysis and principal component analysis revealed that mathematical problems with different formats had similar cortical organization in the IPS. These findings support the idea that mathematical problems are represented in the brain in a format-invariant manner.

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. Principal component analysis visualizes distinct organizations of abstract cognitive functions in each part of the cerebellum and subcortex. More than 90% of the cognitive tasks are decodable from the cerebellum and subcortical activities, even for the novel tasks not included in model training. Furthermore, we show that the cerebellum and subcortex have sufficient information to reconstruct activity in the cerebral cortex.

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. Perceptual decoupling is thought to be reflected in brain activity transitioning from a stimulus-processing to internally-processing mode, but a direct investigation of this remains outstanding. Here we present a conceptual and analysis framework that quantifies the extent to which brain networks reflect stimulus processing. 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 framework, we showed that brain networks became less reflective of stimulus processing, even in the subset of tasks and brain networks in which stimulus responses increased. These results quantitatively demonstrate that during internal cognition brain networks become decoupled from the external stimuli, opening the door for a fundamental and quantitative understanding of internal cognition.

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. Test stimuli were repeated four times and can be used to evaluate the signal to noise ratio of brain activity. Using this dataset, both neuroimaging and machine learning researchers can test multiple algorithms regarding the prediction performance of brain activity induced by various music stimuli. Although two previous studies have used this dataset, there remains much room to apply different acoustic models. This dataset can contribute to integration of the fields of auditory neuroscience and machine learning.

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. 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 selectivity 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.

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. However, there is considerable uncertainty over how discrete music categories are represented in the brain and which acoustic features are more suited for explaining such representations. METHODS: We used a total of 540 music clips to examine comprehensive cortical representations and the functional organization of music genre categories. For this purpose, we applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging. In addition, we introduced a novel technique for feature-brain similarity analysis and assessed how discrete music categories are represented based on the cortical response pattern to acoustic features. RESULTS: Our findings indicated distinct cortical organizations for different music genres in the bilateral STG, and they revealed representational relationships between different music genres. On comparing different acoustic feature models, we found that these representations of music genres could be explained largely by a biologically plausible spectro-temporal modulation-transfer function model. CONCLUSION: Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity.

Trait Respect Is Linked to Reduced Gray Matter Volume in the Anterior Temporal Lobe.

Respect is a positive other-oriented social emotion upon the recognition of excellence in others. We previously reported that respect-related brain activity in the left anterior temporal lobe (ATL). Since brain activity and structure are often involved in common cognitive functions, we investigated the morphological properties of the left ATL using voxel-based morphometry analysis. We found an association of trait respect with reduced gray matter volume (GMV) in part of the left ATL. Moreover, since the ATL is involved in general conceptual knowledge, we investigated the relationships between other social emotions with similar properties as respect and the GMV of the left ATL. We observed an association of reduced GMV with empathic concern, which is an other-oriented and affective aspect of trait empathy. Our findings indicated an association of the left ATL with other-oriented and affective aspect of social emotions.

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., movies) to reveal high dimensional representational structures of diverse daily-life emotions. However, relatively little is known about how such diverse emotions are represented in the brain because most of the affective neuroscience studies have used only a small number of controlled stimuli. To reveal that, we measured functional MRI to obtain blood-oxygen-level-dependent (BOLD) responses from human subjects while they watched emotion-inducing audiovisual movies over a period of 3 hours. For each of the one-second movie scenes, we annotated the movies with respect to 80 emotions selected based on a wide range of previous emotion literature. By quantifying canonical correlations between the emotion ratings and the BOLD responses, the results suggest that around 25 distinct dimensions (ranging from 18 to 36 and being subject-dependent) of the emotion ratings contribute to emotion representations in the brain. For demonstrating how the 80 emotion categories were represented in the cortical surface, we visualized a continuous semantic space of the emotion representation and mapped it on the cortical surface. We found that the emotion categories were changed from unimodal to transmodal regions on the cortical surface. This study presents a cortical representation of a rich variety of emotion categories, which covers many of the emotional experiences of daily living.

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. However, few attempts have been made to build a quantitative model describing the cortical organization of multiple active, cognitive processes. Here, we measure brain activity using fMRI, while subjects perform 103 cognitive tasks, and examine cortical representations with two voxel-wise encoding models. A sparse task-type model reveals a hierarchical organization of cognitive tasks, together with their representation in cognitive space and cortical mapping. A cognitive factor model utilizing continuous, metadata-based intermediate features predicts brain activity and decodes tasks, even under novel conditions. Collectively, our results show the usability of quantitative models of cognitive processes, thus providing a framework for the comprehensive cortical organization of human cognition.

Cortical collateralization induced by language and arithmetic in non-right-handers.

The functional overlap of language and arithmetic is debatable. Although some studies have reported independent representations of arithmetic and language in the brain, other studies have reported shared activity of the two cognitive domains in the inferior frontal gyrus. Although most previous studies have evaluated right-handed individuals, variability of hemispheric dominance in non-right-handed individuals should provide important information on the functional collateralization of these two cognitive domains. The present study evaluated the cortical lateralization patterns of the two cognitive domains using functional magnetic resonance imaging in 30 non-right-handed participants who performed language and arithmetic tasks. We found that language and arithmetic tasks demonstrated shared activity in the bilateral inferior frontal gyrus (IFG). Furthermore, the lateralization patterns of language and arithmetic tasks were correlated with each other. Most participants with language dominance in the left hemisphere also exhibited dominance of arithmetic tasks in the left hemisphere; similarly, most participants with language dominance in the right hemisphere exhibited dominance of arithmetic tasks in the right hemisphere. Among all the brain regions, the precentral gyrus, which is located slightly posterior to the IFG, exhibited the highest correlation coefficient between laterality indices of language and arithmetic tasks. These results suggest a shared functional property between language and arithmetic in the brain.

Regulation of action selection based on metacognition in humans via a ventral and dorsal medial prefrontal cortical network.

Metacognition is defined as cognition about one's own cognitive state; it enables us to estimate our own performance during goal-directed actions and to select a suitable strategy based on that estimation. Identifying the neural mechanisms that underlie this process will contribute to our understanding of how we realize adaptive self-control in daily life. Here, we focused on the neural substrates that allow us to voluntarily utilize prospective metacognition to carry out such action selection. Participants were asked to bet on their recall of sound stimuli presented at an earlier time in a delayed match-to-sample task of rapidly changing sound stimuli. During the task, brain activity was measured using functional magnetic resonance imaging. We found that the brain network composed of the ventral and dorsal parts of the medial prefrontal cortex and the medial precuneus regulated the strategic selection of risk/return profiles based on metacognition. In particular, increments in functional connectivity between the ventral and dorsal medial prefrontal cortices during high-risk/return bets correlated with the adaptiveness of the bet (as measured by the correspondence between choosing high risk/return bets and high accuracy of task performance). This index is considered to reflect the degree of voluntary use of metacognition to bet. These findings suggest that the strong connectivity within the network involving the ventral and dorsal medial prefrontal cortices enables us to utilize metacognition to select actions for achieving a goal efficiently.

Vines avoid coiling around neighbouring plants infested by polyphagous mites.

Vines that coil around plants heavily infested with ambulate polyphagous mites can be heavily damaged by the mites. To explore whether vines avoid mite-infested plants, we observed the coiling responses of morning glory (Ipomoea nil var. Heavenly Blue) vines and bush killer (Cayratia japonica (Thunb) Gagnep) tendrils around nearby kidney bean (Phaseolus vulgaris L.) plants that were either uninfested or heavily infested with the two-spotted spider mite (Tetranychus urticae Koch). The proportions of I. nil vines that coiled around spider mite-infested and uninfested bean plants did not differ significantly; however, no C. japonica tendril coiled around spider mite-infested plants. The proportion of such tendrils was thus significantly lower than that around uninfested plants. The ability of C. japonica tendrils to avoid spider mite-infested plants would prevent serious "contact infections" by mites. We further found that tendril avoidance seemed to be attributable to the mite webs that covered infested plants; neither spider mite-induced bean volatiles nor spider mite intrusion onto tendrils seemed to explain the avoidance.

Respect and admiration differentially activate the anterior temporal lobe.

Admiration and respect are positive social emotions often experienced when recognizing excellent behavior in another person. Although both strongly rely on appraisal of behavior, admiration focuses on the admirable behavior of a person, while respect focuses on the person as a whole. The evaluation and interpretation of the social behavior of another person are dependent on semantic memory. Social semantic knowledge is represented in the anterior temporal lobe (ATL), and ATL activity is modulated by conceptual details of semantic knowledge. As respect requires evaluation of not only excellent behavior but also of the person as a whole, we hypothesized that the ATL is differentially activated by admiration and respect. To test our hypothesis, we conducted functional magnetic resonance imaging experiments. We presented participants with vignettes describing admirable behavior of fictitious characters and asked them to imagine and report how they would normally feel when encountering the situation described in the vignettes, i.e., admiration or respect and its intensity. A part of the left ATL was more strongly modulated by the intensity of respect than of admiration. Although admiration and respect are often considered to be closely related, our results indicate that the neural substrates underlying these emotions are different.