When math operations have visuospatial meanings versus purely symbolic definitions: Which solving stages and brain regions are affected?

Visual mental imagery: Evidence for a heterarchical neural architecture

Theories of Visual Mental Imagery (VMI) emphasize the processes of retrieval, modification, and recombination of sensory information from long-term memory. Yet, only few studies have focused on the behavioral mechanisms and neural correlates supporting VMI of stimuli from different semantic domains. Therefore, we currently have a limited understanding of how the brain generates and maintains mental representations of colors, faces, shapes - to name a few. Such an undetermined scenario renders unclear the organizational structure of neural circuits supporting VMI, including the role of the early visual cortex. We aimed to fill this gap by reviewing the scientific literature of five semantic domains: visuospatial, face, colors, shapes, and letters imagery. Linking theory to evidence from over 60 different experimental designs, this review highlights three main points. First, there is no consistent activity in the early visual cortex across all VMI domains, contrary to the prediction of the dominant model. Second, there is consistent activity of the frontoparietal networks and the left hemisphere's fusiform gyrus during voluntary VMI irrespective of the semantic domain investigated. We propose that these structures are part of a domain-general VMI sub-network. Third, domain-specific information engages specific regions of the ventral and dorsal cortical visual pathways. These regions partly overlap with those found in visual perception studies (e.g., fusiform face area for faces imagery; lingual gyrus for color imagery). Altogether, the reviewed evidence suggests the existence of domain-general and domain-specific mechanisms of VMI selectively engaged by stimulus-specific properties (e.g., colors or faces). These mechanisms would be supported by an organizational structure mixing vertical and horizontal connections (heterarchy) between sub-networks for specific stimulus domains. Such a heterarchical organization of VMI makes different predictions from current models of VMI as reversed perception. Our conclusions set the stage for future research, which should aim to characterize the spatiotemporal dynamics and interactions among key regions of this architecture giving rise to visual mental images.

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.

Visual mental imagery: Inside the mind's eyes

What are mental images needed for? A variety of everyday situations calls for us to plan ahead; one of the clever ways our mind prepares and strategizes our next move is through mental simulation. A powerful tool in running these simulations is visual mental imagery, which can be conceived as a way to activate and maintain an internal representation of the to-be-imagined object, giving rise to predictions. Therefore, under normal conditions imagination is primarily an endogenous process, and only more rarely can mental images be activated exogenously, for example, by means of intracerebral stimulation. A large debate is still ongoing regarding the neural substrates supporting mental imagery, with the neuropsychological and neuroimaging literature agreeing in some cases, but not others. This chapter reviews the neuroscientific literature on mental imagery, and attempts to reappraise the neuropsychological and neuroimaging evidence by drawing a model of mental imagery informed by both structural and functional brain data. Overall, the role of regions in the ventral temporal cortex, especially of the left hemisphere, stands out unequivocally as a key substrate in mental imagery.

Hemispheric asymmetries in visual mental imagery

Visual mental imagery is the faculty whereby we can "visualize" objects that are not in our line of sight. Longstanding evidence dating back over thirty years has shown that unilateral brain lesions, especially in the left temporal lobe, can impair aspects of this ability. Yet, there is currently no attempt to identify analogies between these neuropsychological findings of hemispheric asymmetry and those from other neuroscientific approaches. Here, we present a critical review of the available literature on the hemispheric laterality of visual mental imagery, by looking at cross-method patterns of evidence in the domains of lesion neuropsychology, neuroimaging, and direct cortical stimulation. Results can be summarized under three main axes. First, frontoparietal networks in both hemispheres appear to be associated with visual mental imagery. Second, lateralization patterns emerge in the temporal lobes, with the left inferior temporal lobe being the most common finding in the literature for endogenously generated images, especially, but not exclusively, when orthographic material is used to ignite imagery. Third, an opposite pattern of hemispheric laterality emerges when visual mental images are induced by exogenous stimulation; direct cortical electrical stimulation tends to produce visual imagery experiences predominantly when applied to the right temporal lobe. These patterns of hemispheric asymmetry are difficult to reconcile with the dominant model of visual mental imagery, which emphasizes the implication of early sensory cortices. They suggest instead that visual mental imagery relies on large-scale brain networks, with a crucial participation of high-level visual regions in the temporal lobes.

Visual mental imagery engages the left fusiform gyrus, but not the early visual cortex: A meta-analysis of neuroimaging evidence

The dominant neural model of visual mental imagery (VMI) stipulates that memories from the medial temporal lobe acquire sensory features in early visual areas. However, neurological patients with damage restricted to the occipital cortex typically show perfectly vivid VMI, while more anterior damages extending into the temporal lobe, especially in the left hemisphere, often cause VMI impairments. Here we present two major results reconciling neuroimaging findings in neurotypical subjects with the performance of brain-damaged patients: (1) A large-scale meta-analysis of 46 fMRI studies, of which 27 investigated specifically visual mental imagery, revealed that VMI engages fronto-parietal networks and a well-delimited region in the left fusiform gyrus. (2) A Bayesian analysis showed no evidence for imagery-related activity in early visual cortices. We propose a revised neural model of VMI that draws inspiration from recent cytoarchitectonic and lesion studies, whereby fronto-parietal networks initiate, modulate, and maintain activity in a core temporal network centered on the fusiform imagery node, a high-level visual region in the left fusiform gyrus.

Space counts! Brain correlates of spatial and numerical representations in synaesthesia

Over-learned semantic representations, such as numbers, are strongly associated with space in normal cognition, and in the phenomenon called number-space synaesthesia. In number-space synaesthesia, numbers are linked to spatial locations in an idiosyncratic way. Synaesthetes report numbers as belonging to a specific location, or feelings that a specific location is the right location for that number. What does really differentiate synaesthetes from non-synaesthetes with respect to their number-space representation? Here we present a number-space synaesthete, MkM, whose number-space representation dramatically differs from that of controls. We examined the impact of spatial distance with respect to MkM's mental number line (MNL), and numerical distance with respect to the conceptualized horizontal representation of numbers. In a behavioural experiment, MkM and controls performed number comparison tasks in which they reported either the larger numerical value (number task) or the larger stimulus (physical task) (Experiment 1). A spatial distance effect was found only for MkM. In a brain imaging experiment, MkM and controls compared a single presented digit with an internal reference (Experiment 2). Consistent with the behavioural results, spatial distance elicited significant brain activations only for MkM in different cortical sites including the left supramarginal gyrus. Numerical distance elicited significant brain activations only for controls in the left somatosensory cortex and in the right operculum. We propose that two types of representation are accessed in synaesthesia: one derived by the semantic coding of numbers across space (described by the MNL), and an explicit spatial representation derived from the position of number within the synaesthetic association. The level of overlap between these two forms of representation depends on the shape of the synaesthetic number-space association.

Data-Driven Subtyping of Executive Function-Related Behavioral Problems in Children

OBJECTIVE

Executive functions (EF) are cognitive skills that are important for regulating behavior and for achieving goals. Executive function deficits are common in children who struggle in school and are associated with multiple neurodevelopmental disorders. However, there is also considerable heterogeneity across children, even within diagnostic categories. This study took a data-driven approach to identify distinct clusters of children with common profiles of EF-related difficulties, and then identified patterns of brain organization that distinguish these data-driven groups.

METHOD

The sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning, and/or memory. We applied community clustering, a data-driven clustering algorithm, to group children by similarities on a commonly used rating scale of EF-associated behavioral difficulties, the Conners 3 questionnaire. We then investigated whether the groups identified by the algorithm could be distinguished on white matter connectivity using a structural connectomics approach combined with partial least squares analysis.

RESULTS

The data-driven clustering yielded 3 distinct groups of children with symptoms of one of the following: (1) elevated inattention and hyperactivity/impulsivity, and poor EF; (2) learning problems; or (3) aggressive behavior and problems with peer relationships. These groups were associated with significant interindividual variation in white matter connectivity of the prefrontal and anterior cingulate cortices.

CONCLUSION

In sum, data-driven classification of EF-related behavioral difficulties identified stable groups of children, provided a good account of interindividual differences, and aligned closely with underlying neurobiological substrates.