Jessica Bulthé

Intact but less accessible phonetic representations in adults with dyslexia

Good Foundations, Poor Access Dyslexia makes reading and spelling difficult. Boets et al. (p. 1251) analyzed whether for adult readers with dyslexia the internal references for word sounds are poorly constructed or whether accessing those references is abnormally difficult. Brain imaging during phonetic discrimination tasks suggested that the internal dictionary for word sounds was correct, but accessing the dictionary was more difficult than normal. The persistent reading problems observed in dyslexia may derive from inefficient communication within the brain. Dyslexia is a severe and persistent reading and spelling disorder caused by impairment in the ability to manipulate speech sounds. We combined functional magnetic resonance brain imaging with multivoxel pattern analysis and functional and structural connectivity analysis in an effort to disentangle whether dyslexics’ phonological deficits are caused by poor quality of the phonetic representations or by difficulties in accessing intact phonetic representations. We found that phonetic representations are hosted bilaterally in primary and secondary auditory cortices and that their neural quality (in terms of robustness and distinctness) is intact in adults with dyslexia. However, the functional and structural connectivity between the bilateral auditory cortices and the left inferior frontal gyrus (a region involved in higher-level phonological processing) is significantly hampered in dyslexics, suggesting deficient access to otherwise intact phonetic representations.

Format-dependent representations of symbolic and non-symbolic numbers in the human cortex as revealed by multi-voxel pattern analyses.

Neuroimaging studies in the last 20 years have tried to unravel the neural correlates of number processing across formats in humans and non-human primates. Results point to the intraparietal sulcus as the core area for an abstract representation of numerical quantity. On the other hand, there exist a variety of behavioral and neuroimaging data that are difficult to reconcile with the existence of such an abstract representation. In this study, we addressed this issue by applying multi-voxel pattern analysis (MVPA) to functional Magnetic Resonance Imaging (fMRI) data to unravel the neural representations of symbolic (digits) and non-symbolic (dots) numbers and their possible overlap on three different spatial scales (entire lobules, smaller regions of interest and a searchlight analysis with 2-voxel radius). Results showed that numbers in both formats are decodable in occipital, frontal, temporal and parietal regions. However, there were no overlapping representations between dots and digits on any of the spatial scales. These data suggest that the human brain does not contain an abstract representation of numerical magnitude.

Visual number beats abstract numerical magnitude: Format-dependent representation of arabic digits and dot patterns in human parietal cortex

In numerical cognition, there is a well-known but contested hypothesis that proposes an abstract representation of numerical magnitude in human intraparietal sulcus (IPS). On the other hand, researchers of object cognition have suggested another hypothesis for brain activity in IPS during the processing of number, namely that this activity simply correlates with the number of visual objects or units that are perceived. We contrasted these two accounts by analyzing multivoxel activity patterns elicited by dot patterns and Arabic digits of different magnitudes while participants were explicitly processing the represented numerical magnitude. The activity pattern elicited by the digit “8” was more similar to the activity pattern elicited by one dot (with which the digit shares the number of visual units but not the magnitude) compared to the activity pattern elicited by eight dots, with which the digit shares the represented abstract numerical magnitude. A multivoxel pattern classifier trained to differentiate one dot from eight dots classified all Arabic digits in the one-dot pattern category, irrespective of the numerical magnitude symbolized by the digit. These results were consistently obtained for different digits in IPS, its subregions, and many other brain regions. As predicted from object cognition theories, the number of presented visual units forms the link between the parietal activation elicited by symbolic and nonsymbolic numbers. The current study is difficult to reconcile with the hypothesis that parietal activation elicited by numbers would reflect a format-independent representation of number.

Visual and Haptic Shape Processing in the Human Brain: Unisensory Processing, Multisensory Convergence, and Top-Down Influences

Humans are highly adept at multisensory processing of object shape in both vision and touch. Previous studies have mostly focused on where visually perceived object-shape information can be decoded, with haptic shape processing receiving less attention. Here, we investigate visuo-haptic shape processing in the human brain using multivoxel correlation analyses. Importantly, we use tangible, parametrically defined novel objects as stimuli. Two groups of participants first performed either a visual or haptic similarity-judgment task. The resulting perceptual object-shape spaces were highly similar and matched the physical parameter space. In a subsequent fMRI experiment, objects were first compared within the learned modality and then in the other modality in a one-back task. When correlating neural similarity spaces with perceptual spaces, visually perceived shape was decoded well in the occipital lobe along with the ventral pathway, whereas haptically perceived shape information was mainly found in the parietal lobe, including frontal cortex. Interestingly, ventrolateral occipito-temporal cortex decoded shape in both modalities, highlighting this as an area capable of detailed visuo-haptic shape processing. Finally, we found haptic shape representations in early visual cortex (in the absence of visual input), when participants switched from visual to haptic exploration, suggesting top-down involvement of visual imagery on haptic shape processing.

How learning might strengthen existing visual object representations in human object-selective cortex

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties.

A validation of a multi-spatialscale method for multivariate pattern analysis

Most fMRI studies using Multi-Voxel Pattern Analysis (MVPA) restrict these analyses to merely one spatial scale. However, recently [1] used a multi-spatial scale method combining three levels of MVPA analysis on fMRI data from 16 subjects who performed a number comparison task: whole-brain MVPA, Regions Of Interest (ROI) based MVPA, and a small radius searchlight. The results of [1] clearly demonstrated the necessity of incorporating different spatial scales in MVPA analysis to draw conclusions on how the neural representations of the effects are distributed across the brain. We tested the validity of the method used in this empirical study by using three simulated fMRI datasets. Both simulated data and the real data [1] confirmed the relevance of analyzing data with MVPA on different spatial scales.

Dyscalculia and dyslexia: Different behavioral, yet similar brain activity profiles during arithmetic

Brain disorders are often investigated in isolation, but very different conclusions might be reached when studies directly contrast multiple disorders. Here, we illustrate this in the context of specific learning disorders, such as dyscalculia and dyslexia. While children with dyscalculia show deficits in arithmetic, children with dyslexia present with reading difficulties. Furthermore, the comorbidity between dyslexia and dyscalculia is surprisingly high. Different hypotheses have been proposed on the origin of these disorders (number processing deficits in dyscalculia, phonological deficits in dyslexia) but these have never been directly contrasted in one brain imaging study. Therefore, we compared the brain activity of children with dyslexia, children with dyscalculia, children with comorbid dyslexia/dyscalculia and healthy controls during arithmetic in a design that allowed us to disentangle various processes that might be associated with the specific or common neural origins of these learning disorders. Participants were 62 children aged 9 to 12, 39 of whom had been clinically diagnosed with a specific learning disorder (dyscalculia and/or dyslexia). All children underwent fMRI scanning while performing an arithmetic task in different formats (dot arrays, digits and number words). At the behavioral level, children with dyscalculia showed lower accuracy when subtracting dot arrays, and all children with learning disorders were slower in responding compared to typically developing children (especially in symbolic formats). However, at the neural level, analyses pointed towards substantial neural similarity between children with learning disorders: Control children demonstrated higher activation levels in frontal and parietal areas than the three groups of children with learning disorders, regardless of the disorder. A direct comparison between the groups of children with learning disorders revealed similar levels of neural activation throughout the brain across these groups. Multivariate subject generalization analyses were used to statistically test the degree of similarity, and confirmed that the neural activation patterns of children with dyslexia, dyscalculia and dyslexia/dyscalculia were highly similar in how they deviated from neural activation patterns in control children. Collectively, these results suggest that, despite differences at the behavioral level, the brain activity profiles of children with different learning disorders during arithmetic may be more similar than initially thought.

Domain-general and domain-specific neural changes underlying visual expertise

ABSTRACT Visual expertise induces changes in neural processing for many different domains of expertise. However, it is unclear how expertise effects for different domains of expertise are related. In the present fMRI study, we combine large‐scale univariate and multi‐voxel analyses to contrast the expertise‐related neural changes associated with two different domains of expertise, bird expertise (ornithology) and mineral expertise (mineralogy). Results indicated distributed expertise‐related neural changes, with effects for both domains of expertise in high‐level visual cortex and effects for bird expertise even extending to low‐level visual regions and the frontal lobe. Importantly, a multivariate generalization analysis showed that effects in high‐level visual cortex were specific to the domain of expertise. In contrast, the neural changes in the frontal lobe relating to expertise showed significant generalization, signaling the presence of domain‐independent expertise effects. In conclusion, expertise is related to a combination of domain‐specific and domain‐general changes in neural processing.

A Multitude of Neural Representations Behind Multisensory “Social Norm” Processing

Humans show a unique capacity to process complex information from multiple sources. Social perception in natural environment provides a good example of such capacity as it typically requires the integration of information from different sensory systems, and also from different levels of sensory processing. Here, instead of studying one isolate system and level of representation, we focused upon a neuroimaging paradigm which allows to capture multiple brain representations simultaneously, i.e., low and high-level processing in two different sensory systems, as well as abstract cognitive processing of congruency. Subjects performed social decisions based on the congruency between auditory and visual processing. Using multivoxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data, we probed a wide variety of representations. Our results confirmed the expected representations at each level and system according to the literature. Further, beyond the hierarchical organization of the visual, auditory and higher order neural systems, we provide a more nuanced picture of the brain functional architecture. Indeed, brain regions of the same neural system show similarity in their representations, but they also share information with regions from other systems. Further, the strength of neural information varied considerably across domains in a way that was not obviously related to task relevance. For instance, selectivity for task-irrelevant animacy of visual input was very strong. The present approach represents a new way to explore the richness of co-activated brain representations underlying the natural complexity in human cognition.

Neural Representations Behind ‘Social Norm’ Inferences In Humans

Humans are highly skilled in social reasoning, e.g., inferring thoughts of others. This mentalizing ability systematically recruits brain regions such as Temporo-Parietal Junction (TPJ), Precuneus (PC) and medial Prefrontal Cortex (mPFC). Further, posterior mPFC is associated with allocentric mentalizing and conflict monitoring while anterior mPFC is associated with self-reference (egocentric) processing. Here we extend this work to how we reason not just about what one person thinks but about the abstract shared social norm. We apply functional magnetic resonance imaging to investigate neural representations while participants judge the social congruency between emotional auditory utterances in relation to visual scenes according to how ‘most people’ would perceive it. Behaviorally, judging according to a social norm increased the similarity of response patterns among participants. Multivoxel pattern analysis revealed that social congruency information was not represented in visual and auditory areas, but was clear in most parts of the mentalizing network: TPJ, PC and posterior (but not anterior) mPFC. Furthermore, interindividual variability in anterior mPFC representations was inversely related to the behavioral ability to adjust to the social norm. Our results suggest that social norm inferencing is associated with a distributed and partially individually specific representation of social congruency in the mentalizing network.