Representations within the Intraparietal Sulcus Distinguish Numerical Tasks and Formats.

How does our brain understand the number five when it is written as an Arabic numeral, and when presented as five fingers held up? Four facets have been implicated in adult numerical processing: semantic, visual, manual, and phonological/verbal. Here, we ask how the brain represents each, using a combination of tasks and stimuli. We collected fMRI data from adult participants while they completed our novel "four number code" paradigm. In this paradigm, participants viewed one of two stimulus types to tap into the visual and manual number codes, respectively. Concurrently, they completed one of two tasks to tap into the semantic and phonological/verbal number codes, respectively. Classification analyses revealed that neural codes representing distinctions between the number comparison and phonological tasks were generalizable across format (e.g., Arabic numerals to hands) within intraparietal sulcus (IPS), angular gyrus, and precentral gyrus. Neural codes representing distinctions between formats were generalizable across tasks within visual areas such as fusiform gyrus and calcarine sulcus, as well as within IPS. Our results identify the neural facets of numerical processing within a single paradigm and suggest that IPS is sensitive to distinctions between semantic and phonological/verbal, as well as visual and manual, facets of number representations.

Neural Patterns are More Similar across Individuals during Successful Memory Encoding than during Failed Memory Encoding.

After experiencing the same episode, some people can recall certain details about it, whereas others cannot. We investigate how common (intersubject) neural patterns during memory encoding influence whether an episode will be subsequently remembered, and how divergence from a common organization is associated with encoding failure. Using functional magnetic resonance imaging with intersubject multivariate analyses, we measured brain activity as people viewed episodes within wildlife videos and then assessed their memory for these episodes. During encoding, greater neural similarity was observed between the people who later remembered an episode (compared with those who did not) within the regions of the declarative memory network (hippocampus, posterior medial cortex [PMC], and dorsal Default Mode Network [dDMN]). The intersubject similarity of the PMC and dDMN was episode-specific. Hippocampal encoding patterns were also more similar between subjects for memory success that was defined after one day, compared with immediately after retrieval. The neural encoding patterns were sufficiently robust and generalizable to train machine learning classifiers to predict future recall success in held-out subjects, and a subset of decodable regions formed a network of shared classifier predictions of subsequent memory success. This work suggests that common neural patterns reflect successful, rather than unsuccessful, encoding across individuals.

Image memorability is predicted by discriminability and similarity in different stages of a convolutional neural network.

The features of an image can be represented at multiple levels-from its low-level visual properties to high-level meaning. What drives some images to be memorable while others are forgettable? We address this question across two behavioral experiments. In the first, different layers of a convolutional neural network (CNN), which represent progressively higher levels of features, were used to select the images that would be shown to 100 participants through a form of prospective assignment. Here, the discriminability/similarity of an image with others, according to different CNN layers dictated the images presented to different groups, who made a simple indoor versus outdoor judgment for each scene. We found that participants remember more scene images that were selected based on their low-level discriminability or high-level similarity. A second experiment replicated these results in an independent sample of 50 participants, with a different order of postencoding tasks. Together, these experiments provide evidence that both discriminability and similarity, at different visual levels, predict image memorability.

Influences on memory for naturalistic visual episodes: sleep, familiarity, and traits differentially affect forms of recall.

The memories we form are composed of information that we extract from multifaceted episodes. Static stimuli and paired associations have proven invaluable stimuli for understanding memory, but real-life events feature spatial and temporal dimensions that help form new retrieval paths. We ask how the ability to recall semantic, temporal, and spatial aspects (the "what, when, and where") of naturalistic episodes is affected by three influences-prior familiarity, postencoding sleep, and individual differences-by testing their influence on three forms of recall: cued recall, free recall, and the extent that recalled details are recombined for a novel prompt. Naturalistic videos of events with rare animals were presented to 115 participants, randomly assigned to receive a 12- or 24-h delay with sleep and/or wakefulness. Participants' immediate and delayed recall was tested and coded by its spatial, temporal, and semantic content. We find that prior familiarity with items featured in events improved cued recall, but not free recall, particularly for temporal and spatial details. In contrast, postencoding sleep, relative to wakefulness, improved free recall, but not cued recall, of all forms of content. Finally, individuals with higher trait scores in the Survey of Autobiographical Memory spontaneously incorporated more spatial details during free recall, and more event details (at a trend level) in a novel recombination recall task. These findings show that prior familiarity, postencoding sleep, and memory traits can each enhance a different form of recall. More broadly, this work highlights that recall is heterogeneous in response to different influences on memory.

Neural pattern similarity across concept exemplars predicts memory after a long delay.

The irregularities of the world ensure that each interaction we have with a concept is unique. In order to generalize across these unique encounters to form a high-level representation of a concept, we must draw on similarities between exemplars to form new conceptual knowledge that is maintained over a long time. Two neural similarity measures - pattern robustness and encoding-retrieval similarity - are particularly important for predicting memory outcomes. In this study, we used fMRI to measure activity patterns while people encoded and retrieved novel pairings between unfamiliar (Dutch) words and visually presented animal species. We address two underexplored questions: 1) whether neural similarity measures can predict memory outcomes, despite perceptual variability between presentations of a concept and 2) if pattern similarity measures can predict subsequent memory over a long delay (i.e., one month). Our findings indicate that pattern robustness during encoding in brain regions that include parietal and medial temporal areas is an important predictor of subsequent memory. In addition, we found significant encoding-retrieval similarity in the left ventrolateral prefrontal cortex after a month's delay. These findings demonstrate that pattern similarity is an important predictor of memory for novel word-animal pairings even when the concept includes multiple exemplars. Importantly, we show that established predictive relationships between pattern similarity and subsequent memory do not require visually identical stimuli (i.e., are not simply due to low-level visual overlap between stimulus presentations) and are maintained over a month.

Neural activity in human visual cortex is transformed by learning real world size.

The way that our brain processes visual information is directly affected by our experience. Repeated exposure to a visual stimulus triggers experience-dependent plasticity in the visual cortex of many species. Humans also have the unique ability to acquire visual knowledge through instruction. We introduced human participants to the real-world size of previously unfamiliar species, and to the functional motion of novel tools, during a functional magnetic resonance imaging scan. Using machine learning, we compared activity patterns evoked by images of the new items, before and after participants learned the animals' real-world size or tools' motion. We found that, after acquiring size information, participants' visual activity patterns for the new animals became more confusable with activity patterns evoked by similar-sized known animals in early visual cortex, but not in ventral temporal cortex, reflecting an influence of new size knowledge on posterior, but not anterior, components of the ventral stream. In contrast, learning the functional motion of new tools did not lead to an equivalent change in recorded activity. Finally, the time-points marked by evidence of new size information in early visual cortex were more likely to show size information and greater activation in the right angular gyrus, a key hub of semantic knowledge and spatial cognition. Overall, these findings suggest that learning an item's real-world size by instruction influences subsequent activity in visual cortex and in a region that is central to semantic and spatial brain systems.

Word inversion sensitivity as a marker of visual word form area lateralization: An application of a novel multivariate measure of laterality.

An area within the ventral occipitotemporal cortex (vOTC), the "visual word form area" (VWFA), typically exhibits a strongly left-lateralized response to orthographic stimuli in skilled readers. While individual variation in VWFA lateralization has been observed, the behavioral significance of laterality differences remains unclear. Here, we test the hypothesis that differences in VWFA lateralization reflect differing preferences for holistic orthographic analysis. To examine this hypothesis, we implemented a new multivariate method that uses machine learning to assess functional lateralization, along with a traditional univariate lateralization method. We related these neural metrics to behavioral indices of holistic orthographic analysis (inversion sensitivity). The multivariate measure successfully detected the lateralization of orthographic processing in the VWFA, and as hypothesized, predicted behavioral differences in holistic orthographic analysis. An exploratory whole brain analysis identified further regions with a relationship between inversion sensitivity and lateralization: one near the junction of the inferior frontal and precentral sulci, and another along the superior temporal gyrus. We conclude that proficient native readers of English exhibit differences in cortical lateralization of the VWFA that have significant implications for reading behavior.

Semantic knowledge influences whether novel episodic associations are represented symmetrically or asymmetrically.

We provide new evidence concerning two opposing views of episodic associations: The independent-association hypothesis posits that associations are unidirectional and separately modifiable links (A→B and A←B); in contrast, the associative-symmetry hypothesis proposes that a single, bidirectional association exists between A and B (A↔B). We used a novel method to demonstrate that whether or not episodic associations are symmetric depends on whether there is a preexisting semantic relationship between A and B. In two experiments, participants studied 30 semantically unrelated and 30 semantically related pairs intermixed in a single list and then performed a series of up to eight cued-recall test cycles. All pairs were tested in each cycle, and the testing direction (A-? or B-?) alternated between cycles. Unrelated pairs exhibited associative symmetry-that is, accuracy and response times improved gradually on each test-suggesting that testing in both directions strengthened the same association. In contrast, semantically related pairs exhibited a stair-like pattern, in which performance did not change from odd to even tests when the test direction changed; it only improved between tests in the same direction. We concluded that episodic associations can have either a single bidirectional representation or separate directional representations, depending on the semantic relatedness of their constituent items.

Creatures great and small: Real-world size of animals predicts visual cortex representations beyond taxonomic category.

Human occipitotemporal cortex contains neural representations for a variety of perceptual and conceptual features. We report a study examining neural representations of real-world size along the visual ventral stream, while carefully accounting for taxonomic categories that typically co-vary with size. We recorded brain activity during a functional Magnetic Resonance Imaging (fMRI) scan from eighteen participants as they were presented with images of twelve animal species. The animals were selected to vary on a number of dimensions, including taxonomic group, real-world size and prior familiarity. We apply multivariate analysis methods, including representational similarity analysis (RSA) and machine learning classifiers, to probe the distributed patterns of neural activity evoked by these presentations. We find that the real-world size of visually presented animate items is represented in posterior, but not anterior, regions of the ventral stream. A significant linear relationship is present for real-world size representation along the ventral stream. These representations remain after controlling for factors such as taxonomic category, familiarity and models of visual similarity, and even after restricting examinations to within-taxonomic category comparisons, suggesting that size information is found for within, as well as between, taxonomic categories. These findings are consistent with real-world size having an influence on activity patterns in early regions of the visual system.

Creating Concepts from Converging Features in Human Cortex.

To make sense of the world around us, our brain must remember the overlapping features of millions of objects. Crucially, it must also represent each object's unique feature-convergence. Some theories propose that an integration area (or "convergence zone") binds together separate features. We report an investigation of our knowledge of objects' features and identity, and the link between them. We used functional magnetic resonance imaging to record neural activity, as humans attempted to detect a cued fruit or vegetable in visual noise. Crucially, we analyzed brain activity before a fruit or vegetable was present, allowing us to interrogate top-down activity. We found that pattern-classification algorithms could be used to decode the detection target's identity in the left anterior temporal lobe (ATL), its shape in lateral occipital cortex, and its color in right V4. A novel decoding-dependency analysis revealed that identity information in left ATL was specifically predicted by the temporal convergence of shape and color codes in early visual regions. People with stronger feature-and-identity dependencies had more similar top-down and bottom-up activity patterns. These results fulfill three key requirements for a neural convergence zone: a convergence result (object identity), ingredients (color and shape), and the link between them.

Context reinstatement requires a schema relevant virtual environment to benefit object recall.

How does our environment impact what we will later remember? Early work in real-world environments suggested that having matching encoding/retrieval contexts improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects and examine an influence of memory schema and dynamic environments. Participants (N = 240) remembered more objects when in the same virtual environment (context) as during encoding. This traded-off with falsely "recognizing" more similar lures. Experimentally manipulating the virtual objects and environments revealed that a congruent object/environment schema aids recall (but not recognition), though a dynamic background does not. These findings further our understanding of when and how context affects our memory through a naturalistic approach to studying such effects.

The role of sleep in forming a memory representation of a two-dimensional space.

There is ample evidence from human and animal models that sleep contributes to the consolidation of newly learned information. The precise role of sleep for integrating information into interconnected memory representations is less well understood. Building on prior findings that following sleep (as compared to wakefulness) people are better able to draw inferences across learned associations in a simple hierarchy, we ask how sleep helps consolidate relationships in a more complex representational space. We taught 60 subjects spatial relationships between pairs of buildings, which (unknown to participants) formed a two-dimensional grid. Critically, participants were only taught a subset of the many possible spatial relations, which allowed them to potentially infer the remainder. After a 12 h period that either did or did not include a normal period of sleep, participants returned to the lab. We examined the quality of each participant's map of the two-dimensional space, and their knowledge of relative distances between buildings. After 12 h with sleep, subjects could more accurately map the full space than subjects who experienced only wakefulness. The incorporation of untaught, but inferable, associations was particularly improved. We further found that participants' distance judgment performance related to self-reported navigational style, but only after sleep. These findings demonstrate that consolidation over a night of sleep begins to integrate relations into an interconnected complex representation, in a way that supports spatial relational inference.

Distinguishing multi-voxel patterns and mean activation: why, how, and what does it tell us?

The introduction of multi-voxel pattern analysis (MVPA) to the functional magnetic resonance imaging (fMRI) community has brought a deeper appreciation for the diverse forms of information that can be present within fMRI activity. The conclusions drawn from MVPA investigations are frequently influenced by both the ability to decode information from multi-voxel patterns and mean activation levels. In practice, MVPA studies vary widely in why and how they test for differing overall response levels. In this article, I examine the place of univariate information in MVPA investigations. I first discuss the variety of interpretations given to finding univariate response differences. I go on to discuss some of the analysis approaches used to investigate and compare univariate and multivariate sources of information, which can illuminate their respective contributions. It will be important for the MVPA and general fMRI community to continue to discuss and debate these important issues.

Novel approaches to functional lateralization: Assessing information in activity patterns across hemispheres and more accurately identifying structural homologues.

Functional lateralization is typically measured by comparing activation levels across the right and left hemispheres of the brain. Significant additional information, however, exists within distributed multi-voxel patterns of activity - a format not detectable by traditional activation-based analysis of functional magnetic resonance imaging (fMRI) data. We introduce and test two methods -one anatomical, one functional- that allow hemispheric information asymmetries to be detected. We first introduce and apply a novel tool that draws on brain 'surface fingerprints' to pair every location in one hemisphere with its hemispheric homologue. We use anatomical data to show that this approach is more accurate than the common distance-from-midline method for comparing bilateral regions. Next, we introduce a complementary analysis method that quantifies multivariate laterality in functional data. This new 'multivariate Laterality Index' (mLI) reflects both quantitative and qualitative information-differences across homologous activity patterns. We apply the technique here to functional data collected as participants viewed faces and non-faces. Using the previously generated surface fingerprints to pair-up homologous searchlights in each hemisphere, we use the novel multivariate laterality technique to identify face-information asymmetries across right and left counterparts of the fusiform gyrus, inferior temporal gyrus, superior parietal lobule, and early visual areas. The typical location of the fusiform face area has greater information asymmetry for faces than for shapes. More generally, we argue that the field should consider an information-based approach to lateralization.

The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs.

Functional magnetic resonance imaging (fMRI) studies are broken up into runs (or 'sessions'), frequently selected to be long to minimize across-run signal variations. For investigations that use multi-voxel pattern analysis (MVPA), however, employing many short runs might improve a classifier's ability to generalize across irrelevant pattern variations and detect condition-related activity patterns. We directly tested this hypothesis by scanning participants with both long and short runs and comparing MVPA performance using data from each set of runs. Every run included presentations of faces, places, man-made objects and fruit in a blocked 1-back design. MVPA performance significantly improved from using a large number of short runs, compared to several long runs, in across-run classifications with identical amounts of data. Superior classification was found across variations in the classifier employed, feature selection procedure and region of interest. Performance improvements also extended to an information brain mapping 'searchlight' procedure. These results suggest that investigators looking to maximize the detection of subtle multi-voxel patterns across runs might consider employing short fMRI runs.

A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder.

Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data.

Affective-motivational brain responses to direct gaze in children with autism spectrum disorder.

BACKGROUND: It is unclear why children with autism spectrum disorders (ASD) tend to be inattentive to, or even avoid eye contact. The goal of this study was to investigate affective-motivational brain responses to direct gaze in children with ASD. To this end, we combined two measurements: skin conductance responses (SCR), a robust arousal measure, and asymmetry in frontal electroencephalography (EEG) activity which is associated with motivational approach and avoidance tendencies. We also explored whether degree of eye openness and face familiarity modulated these responses. METHODS: Skin conductance responses and frontal EEG activity were recorded from 14 children with ASD and 15 typically developing children whilst they looked at familiar and unfamiliar faces with eyes shut, normally open or wide-open. Stimuli were presented in such a way that they appeared to be looming towards the children. RESULTS: In typically developing children, there were no significant differences in SCRs between the different eye conditions, whereas in the ASD group the SCRs were attenuated to faces with closed eyes and increased as a function of the degree of eye openness. In both groups, familiar faces elicited marginally greater SCRs than unfamiliar faces. In typically developing children, normally open eyes elicited greater relative left-sided frontal EEG activity (associated with motivational approach) than shut eyes and wide-open eyes. In the ASD group, there were no significant differences between the gaze conditions in frontal EEG activity. CONCLUSIONS: Collectively, the results replicate previous finding in showing atypical modulation of arousal in response to direct gaze in children with ASD but do not support the assumption that this response is associated with an avoidant motivational tendency. Instead, children with ASD may lack normative approach-related motivational response to eye contact.

Numerical estrangement and integration between symbolic and non-symbolic numerical information: Task-dependence and its link to math abilities in adults.

Most adults have access to two different number systems to represent numerical information: an exact number system, which relies on different forms of number symbols to represent exact numerical information, and an approximate number system, which allows for approximate estimates of numerical quantities. Here we investigate the integration between the symbolic and non-symbolic numerical information (i.e., "numerical integration"), and how numerical integration relates to adults' formal math abilities. We administered two tasks to measure numerical integration. For a number comparison task with non-symbolic dot arrays and Arabic numerals, participants indicated the larger of two sequentially presented stimuli that were same-format (dot-dot or numeral-numeral), or mixed-format (dot-numeral or numeral-dot). For a number-letter discrimination task, participants identified Arabic numerals or letter pairs that co-occurred with dot arrays (matching or mismatching the quantity represented by the numeral). In the number comparison task, participants were significantly slower when comparing mixed-format stimuli, especially when Arabic numerals were presented first and dot arrays second, suggesting estrangement between symbolic and non-symbolic numerical information and an asymmetry depending on the order in which the numerical information is presented. In contrast, in the number-letter discrimination task, participants were significantly faster in number-letter discrimination for matching dot arrays and numerals, suggesting integration between symbolic and non-symbolic numerical information. Surprisingly, some measures of numerical estrangement derived from the number comparison task significantly correlated with adults' performance on a standardized math assessment. Thus, we conclude that numerical integration or estrangement is task-dependent, and adults with greater levels of symbolic estrangement tend to have higher math skills.

A five-factor model of perseverative thought.

Like diagnostic status, clinically relevant thought remains overwhelmingly conceptualized in terms of discrete categories (e.g., worry, rumination, obsessions). However, definitions can vary widely. The area of perseverative thought (or clinically relevant thought more broadly) would benefit substantially from a consensus-based, empirically grounded taxonomy similar to the Hierarchical Taxonomy of Psychopathology (Kotov et al., 2017) or the Big Five for personality. This article addresses three major barriers to establishing such a taxonomy: (a) a lack of research explicitly comparing categorical (subtype) versus dimensional models, (b) primary reliance on between-person measures rather than modeling at the level of the thought (within person), and (c) insufficient emphasis on replication and refinement. Participants included an unselected crowdsourced sample (790 observations from 286 participants) and an independent anxious-depressed replication sample (808 observations from 277 participants). Participants made dimensional ratings for three idiographic clinically relevant thoughts on a range of features. Multilevel latent class analysis and multilevel exploratory factor analysis were applied to identify and extract natural patterns of covariation among features at the level of the thought, controlling for person-level tendencies. A consistent five-dimension solution emerged across both samples and reliably outperformed the best-fitting categorical solution in terms of fit, replicability, and explanatory power. Identified dimensions were dyscontrol, self-focus, valence, interpersonal, and uncertainty. Findings support a five-factor latent structure of perseverative thought. Theoretical, empirical, and clinical implications and future directions are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity.

Multi-voxel pattern analysis (MVPA) has been applied successfully to a variety of fMRI research questions in healthy participants. The full potential of applying MVPA to functional data from patient groups has yet to be fully explored. Our goal in this study was to investigate whether MVPA might yield a sensitive predictor of patient symptoms. We also sought to demonstrate that this benefit can be realized from existing datasets, even when they were not designed with MVPA in mind. We analyzed data from an fMRI study of the neural basis for face processing in individuals with an Autism Spectrum Disorder (ASD), who often show fusiform gyrus hypoactivation when presented with unfamiliar faces, compared to controls. We found reliable correlations between MVPA classification performance and standardized measures of symptom severity that exceeded those observed using a univariate measure; a relation that was robust across variations in ROI definition. A searchlight analysis across the ventral temporal lobes identified regions with relationships between classification performance and symptom severity that were not detected using mean activation. These analyses illustrate that MVPA has the potential to act as a sensitive functional biomarker of patient severity.