Spontaneous celiac artery aneurysms in 13-year-old and 10-year-old brothers with PLOD1-related kyphoscoliotic Ehlers-Danlos syndrome.

PLOD1-related kyphoscoliotic Ehlers-Danlos syndrome is a rare, autosomal recessive connective tissue disorder characterized by congenital hypotonia, early-onset, progressive kyphoscoliosis, and generalized joint hypermobility. PLOD1-kyphoscoliotic Ehlers-Danlos syndrome is also associated with heightened vascular fragility, resulting in an elevated susceptibility to recurrent vascular complications such as arterial aneurysms, dissection, and spontaneous arterial rupture. We report the cases of two affected brothers: a 13-year-old boy presenting with spontaneous rupture of a celiac artery aneurysm and a 10-year-old boy presenting with a rapidly enlarging celiac artery aneurysm requiring urgent repair.

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex.

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context dependent categories. We show that lPFC encodes task relevant information with task tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response relevant categories abstractly, while category specific local geometries are high dimensional. In the hierarchy task, a global axis abstractly encodes the higher level context, while low dimensional, context specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

Evaluating the influence of altered cerebral hemodynamics on cognitive performance in asymptomatic carotid artery stenosis: A systematic review.

OBJECTIVE: Substantial controversy exists regarding asymptomatic carotid stenosis (ACS) and its potential role in the pathophysiology of cognitive impairment. If proven, this hypothesis may suggest an additional definition for symptomatic carotid disease that would alter current management. This study aimed to synthesize the literature evaluating the relationship between impaired cerebral hemodynamics and cognition in patients with ACS. METHODS: A literature search was performed using MEDLINE, Embase, and EBM Reviews through May 2022. We included prospective case-control studies that used validated, objective measure(s) of either global cognition or one or more domains of cognitive function and assessed cerebrovascular reserve (CVR). RESULTS: Five studies were included, comprising a total of 782 patients with moderate (50%-69%) to severe (70%-99%) ACS. Patients with ACS and impaired ipsilateral CVR demonstrated significant cognitive impairment compared with controls. Patients with unilateral or bilateral ACS and normal CVR had cognitive scores similar to controls. Those with bilateral CVR impairment demonstrated the lowest cognitive scores. CONCLUSIONS: This review lends support to the claim that cognitive impairment, likely the result of impaired cerebral hemodynamics, is an under-recognized morbidity in patients with ACS. CVR may serve as an additional tool to determine whether patients are in fact symptomatic from their carotid stenosis and warrant consideration for intervention.

Abstract cognitive maps of social network structure aid adaptive inference.

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to diverse output actions depending on goals and contexts. How the brain encodes information to enable this capacity remains one of the longstanding and fundamental problems in cognitive neuroscience. From a neural state-space perspective, solving this problem requires a control representation that can disambiguate similar input neural states, making task-critical dimensions separable depending on the context. Moreover, for action selection to be robust and time-invariant, control representations must be stable in time, thereby enabling efficient readout by downstream processing units. Thus, an ideal control representation should leverage geometry and dynamics that maximize the separability and stability of neural trajectories for task computations. Here, using novel EEG decoding methods, we investigated how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Specifically, we tested the hypothesis that encoding a temporally stable conjunctive subspace that integrates stimulus, response, and context (i.e., rule) information in a high-dimensional geometry achieves the separability and stability needed for context-dependent action selection. Human participants performed a task that requires context-dependent action selection based on pre-instructed rules. Participants were cued to respond immediately at varying intervals following stimulus presentation, which forced responses at different states in neural trajectories. We discovered that in the moments before successful responses, there was a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, we found that the dynamics stabilized in the same time window, and that the timing of entry into this stable and high-dimensional state predicted the quality of response selection on individual trials. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

Uncertainty aversion predicts the neural expansion of semantic representations.

Correctly identifying the meaning of a stimulus requires activating the appropriate semantic representation among many alternatives. One way to reduce this uncertainty is to differentiate semantic representations from each other, thereby expanding the semantic space. Here, in four experiments, we test this semantic-expansion hypothesis, finding that uncertainty-averse individuals exhibit increasingly differentiated and separated semantic representations. This effect is mirrored at the neural level, where uncertainty aversion predicts greater distances between activity patterns in the left inferior frontal gyrus when reading words, and enhanced sensitivity to the semantic ambiguity of these words in the ventromedial prefrontal cortex. Two direct tests of the behavioural consequences of semantic expansion further reveal that uncertainty-averse individuals exhibit reduced semantic interference and poorer generalization. Together, these findings show that the internal structure of our semantic representations acts as an organizing principle to make the world more identifiable.

Cognitive maps of social features enable flexible inference in social networks.

In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people's ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like link prediction in machine learning algorithms.

Noise Correlations for Faster and More Robust Learning.

Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented by stimulus-independent noise correlations that constrain learning to task-relevant dimensions. We test this idea in a set of neural networks that learn to perform a perceptual discrimination task. Correlations among similarly tuned units were manipulated independently of an overall population signal-to-noise ratio to test how the format of stored information affects learning. Higher noise correlations among similarly tuned units led to faster and more robust learning, favoring homogenous weights assigned to neurons within a functionally similar pool, and could emerge through Hebbian learning. When multiple discriminations were learned simultaneously, noise correlations across relevant feature dimensions sped learning, whereas those across irrelevant feature dimensions slowed it. Our results complement the existing theory on noise correlations by demonstrating that when such correlations are produced without significant degradation of the signal-to-noise ratio, they can improve the speed of readout learning by constraining it to appropriate dimensions.SIGNIFICANCE STATEMENT Positive noise correlations between similarly tuned neurons theoretically reduce the representational capacity of the brain, yet they are commonly observed, emerge dynamically in complex tasks, and persist even in well-trained animals. Here we show that such correlations, when embedded in a neural population with a fixed signal-to-noise ratio, can improve the speed and robustness with which an appropriate readout is learned. In a simple discrimination task such correlations can emerge naturally through Hebbian learning. In more complex tasks that require multiple discriminations, correlations between neurons that similarly encode the task-relevant feature improve learning by constraining it to the appropriate task dimension.

Associations of visual paired associative learning task with global cognition and its potential usefulness as a screening tool for Alzheimer's Dementia.

OBJECTIVE: Appropriate screening is integral to the early diagnosis and management of Alzheimer's Dementia (AD). The Paired Associates Learning (PAL) task is a digital cognitive task that is free of cultural, language, and educational biases. This study examined the association between the PAL task performance and global cognition and the usefulness of the PAL task as a screening tool for AD. DESIGN: Cross-sectional. SETTING: Academic hospital. METHODS: Twenty-five participants with AD and 22 healthy comparators (HC) were included. The Cambridge Neuropsychological Test Automated Battery PAL task and the Montreal Cognitive Assessment (MoCA) were used to assess cognition. We assessed the relationship between the PAL task and MoCA performance using Pearson correlation and linear regression. We also examined the PAL task's ability to distinguish between AD and HC participants using Receiver Operating Characteristic curve (ROC) analysis. MEASUREMENTS: MoCA Total Score had a strong positive correlation with PAL Stages Completed score (r = 0.8, p < 0.001), and a strong negative correlation with PAL Total Errors (adjusted) score (r = -0.9, p < 0.001). Further, PAL Total Errors (adjusted) score predicted the MoCA Total Score (F (4, 46) = 37.2, p < 0.001). On ROC analysis, PAL Total Errors (adjusted) score cut-off of 54 errors had 92% sensitivity and 86% specificity to detect AD. CONCLUSIONS: Performance on the PAL task is highly associated with global cognition. Further, the PAL task can differentiate patients with AD from HCs with high sensitivity and specificity. Thus, the PAL task may hold potential usage as an easy-to-administer screening tool for AD.

The dimensionality of neural representations for control.

Cognitive control allows us to think and behave flexibly based on our context and goals. At the heart of theories of cognitive control is a control representation that enables the same input to produce different outputs contingent on contextual factors. In this review, we focus on an important property of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. We will discuss the implications of this trade-off for cognitive control. We will then briefly review current neuroscience findings regarding the dimensionality of control representations in the brain, particularly the prefrontal cortex. We conclude by highlighting open questions and crucial directions for future research.

Carotid Endarterectomy Versus Carotid Artery Stenting: Survey of the Quality, Readability, and Treatment Preference of Carotid Artery Disease Websites.

BACKGROUND: The internet is becoming increasingly more important in the new era of patient self-education. Carotid endarterectomy (CEA) and carotid artery stenting (CAS) are recognized interventions to treat patients with carotid artery stenosis. Using the Google search platform, patients encounter many websites with conflicting information, which are sometimes difficult to understand. This lack of accessibility creates uncertainty or bias toward interventions for carotid artery disease. The quality, readability, and treatment preference of carotid artery disease (CAD) websites have not yet been evaluated. OBJECTIVE: This study aimed to explore the quality, readability, and treatment preference of CAD websites. METHODS: We searched Google Canada for 10 CAD-related keywords. Returned links were assessed for publication date, medical specialty and industry affiliation, presence of randomized controlled trial data, differentiation by symptomatic status, and favored treatment. Website quality and readability were rated by the DISCERN instrument and Gunning Fog Index. RESULTS: We identified 54 unique sites: 18 (33.3%) by medical societies or individual physicians, 11 (20.4%) by government organizations, 9 (16.7%) by laypersons, and 1 (1.9%) that was industry-sponsored. Of these sites, 26 (48.1%) distinguished symptomatic from asymptomatic CAD. A majority of sites overall (57.4%) and vascular-affiliated (72.7%) favored CEA. In contrast, radiology- and cardiology-affiliated sites demonstrated the highest proportion of sites favoring CAS, though they were equally likely to favor CEA. A large proportion (21/54, 38.9%) of sites received poor quality ratings (total DISCERN score <48), and the majority (41/54, 75.9%) required a reading level greater than a high school senior. CONCLUSIONS: CAD websites are often produced by government organizations, medical societies, or physicians, especially vascular surgeons. Sites ranged in quality, readability, and differentiation by symptomatic status. Google searches of CAD-related terms are more likely to yield sites favoring CEA. Future research should determine the extent of website influence on CAD patients' treatment decisions.

Crowdsourcing punishment: Individuals reference group preferences to inform their own punitive decisions.

Justice systems delegate punishment decisions to groups in the belief that the aggregation of individuals' preferences facilitates judiciousness. However, group dynamics may also lead individuals to relinquish moral responsibility by conforming to the majority's preference for punishment. Across five experiments (N = 399), we find Victims and Jurors tasked with restoring justice become increasingly punitive (by as much as 40%) as groups express a desire to punish, with every additional punisher augmenting an individual's punishment rates. This influence is so potent that knowing about a past group's preference continues swaying decisions even when they cannot affect present outcomes. Using computational models of decision-making, we test long-standing theories of how groups influence choice. We find groups induce conformity by making individuals less cautious and more impulsive, and by amplifying the value of punishment. However, compared to Victims, Jurors are more sensitive to moral violation severity and less readily swayed by the group. Conformity to a group's punitive preference also extends to weightier moral violations such as assault and theft. Our results demonstrate that groups can powerfully shift an individual's punitive preference across a variety of contexts, while additionally revealing the cognitive mechanisms by which social influence alters moral values.

An inverse relationship between cortical plasticity and cognitive inhibition in late-life depression.

Executive dysfunction is a common and disabling component of late-life depression (LLD), yet its neural mechanisms remain unclear. In particular, it is not yet known how executive functioning in LLD relates to measures of cortical physiology that may change with age and illness, namely cortical inhibition/excitation and plasticity. Here, we used transcranial magnetic stimulation (TMS) to measure cortical inhibition/excitation (n = 51), and the potentiation of cortical activity following paired associative stimulation, which is thought to reflect long-term potentiation (LTP)-like cortical plasticity (n = 32). We assessed the correlation between these measures of cortical physiology and two measures of executive functioning: cognitive inhibition, assessed using the Delis-Kaplan Executive Function System Color-Word Interference ["Stroop"] Test, and cognitive flexibility, assessed using the Trail Making Test. Correlations with recall memory and processing speed were also performed to assess the specificity of any associations to executive functioning. A significant correlation was found between greater LTP-like cortical plasticity and poorer cognitive inhibition, a core executive function (rp = -0.56, p < 0.001). We did not observe significant associations between cortical inhibition/excitation and executive functioning, or between any neurophysiological measure and cognitive flexibility, memory, or processing speed. Our finding that elevated cortical plasticity is associated with diminished cognitive inhibition emphasizes the importance of balanced synaptic strengthening to healthy cognition. More specifically, our findings suggest that hyper-excitability of cortical circuits following repeated cortical activation may promote inappropriate prepotent responses in LLD. LTP-like cortical plasticity might therefore represent a neural mechanism underlying an inhibitory control cognitive endophenotype of LLD.

Assessment of neuroplasticity in late-life depression with transcranial magnetic stimulation.

BACKGROUND: Studies using Transcranial Magnetic Stimulation (TMS), a non-invasive method of brain stimulation, have implicated impaired neuroplasticity in the pathophysiology of depression in younger adults. The role of neuroplasticity in late-life depression (LLD) has not yet been explored using TMS. OBJECTIVE: This study aimed at evaluating motor cortical neuroplasticity using paired associative stimulation (PAS). Single-pulse TMS was used to induce motor-evoked potentials (MEP) in the contralateral hand muscle before and after PAS. The potentiation of MEP amplitudes after PAS was used as an indirect index of associative plasticity and long-term potentiation (LTP) (i.e. PAS-LTP). RESULTS: 48 older adults with depression and 34 age-matched healthy controls (HC) were compared. PAS- LTP was successfully induced in 68.8% of older adults with depression and 47.1% of HC. At the group level, older adults with depression failed to show statistically significant induction of neuroplasticity, which was observed in HC. However, no significant differences were observed between the two groups for PAS-LTP. CONCLUSION: Our results suggest that associative plasticity does not differ substantially between older adults with depression and age-matched HC. Continued research is needed to more comprehensively understand the role of neuroplasticity in the pathophysiology of LLD.

Reduced GABAergic cortical inhibition in aging and depression.

The neurobiology underlying depression in older adults is less extensively evaluated than in younger adults, despite the putative influence of aging on depression neuropathology. Studies using transcranial magnetic stimulation (TMS), a neurophysiological tool capable of probing inhibitory and excitatory cortical neurotransmission, have identified dysfunctional GABAergic inhibitory activity in younger adults with depression. However, GABAergic and glutamatergic cortical neurotransmission have not yet been studied in late-life depression (LLD). Here, we used single- and paired-pulse TMS to measure cortical inhibition and excitation in 92 LLD patients and 41 age-matched healthy controls. To differentiate the influence of age and depression, we also compared these TMS indices to those of 30 younger depressed adults and 30 age- and sex-matched younger healthy adults. LLD patients, older healthy adults, and younger depressed adults demonstrated significantly lower GABAA receptor-mediated cortical inhibition than younger healthy controls. By contrast, no significant differences in cortical inhibition were observed between older adults with and without depression. No significant differences in GABAB receptor-mediated inhibition or cortical excitation were found between the groups. Altogether, these findings suggest that reduced cortical inhibition may be associated with both advancing age and depression, which (i) supports the model of depression as a disease of accelerated aging, and (ii) prompts future investigation into diminished GABAergic neurotransmission in late-life as a biological predisposing factor to the development of depression. Given that cortical neurophysiology was similar in depressed and healthy older adults, future prospective studies need to establish the relative influence of age and depression on cortical inhibition deficits.

Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.

Learning and transfer of working memory gating policies.

knowledge about the tasks we encounter enables us to rapidly and flexibly adapt to novel task contexts. Previous research has focused primarily on abstract rules that leverage shared structure in stimulus-response (S-R) mappings as the basis of such task knowledge. Here we provide evidence that working memory (WM) gating policies - a type of control policy required for internal control of WM during a task - constitute a form of abstract task knowledge that can be transferred across contexts. In two experiments, we report specific evidence for the transfer of selective WM gating policies across changes of task context. We show that this transfer is not tied to shared structure in S-R mappings, but instead in the dynamic structure of the task. Collectively, our results highlight the importance of WM gating policies in particular, and control policies in general, as a key component of the task knowledge that supports flexible behavior and task generalization.

Complexity and compositionality in fluid intelligence.

Compositionality, or the ability to build complex cognitive structures from simple parts, is fundamental to the power of the human mind. Here we relate this principle to the psychometric concept of fluid intelligence, traditionally measured with tests of complex reasoning. Following the principle of compositionality, we propose that the critical function in fluid intelligence is splitting a complex whole into simple, separately attended parts. To test this proposal, we modify traditional matrix reasoning problems to minimize requirements on information integration, working memory, and processing speed, creating problems that are trivial once effectively divided into parts. Performance remains poor in participants with low fluid intelligence, but is radically improved by problem layout that aids cognitive segmentation. In line with the principle of compositionality, we suggest that effective cognitive segmentation is important in all organized behavior, explaining the broad role of fluid intelligence in successful cognition.

A Nimble Working Memory.

In this issue of Neuron, Sprague et al. (2016) report fMRI evidence that a degraded working memory representation can be restored by a later cue. The findings raise new questions about the neural mechanisms that underlie such dynamic representational shifts.