Pathology of inherited manganese transporter deficiency.

We followed a patient with manganese transporter deficiency due to homozygous SLC30A10 mutations from age 14 years until his death at age 38 years and present the first postmortem findings of this disorder. The basal ganglia showed neuronal loss, rhodanine-positive deposits, astrocytosis, myelin loss, and spongiosis. SLC30A10 protein was reduced in residual basal ganglia neurons. Depigmentation of the substantia nigra and other brainstem nuclei was present. Manganese content of basal ganglia and liver was increased 16-fold and 9-fold, respectively. Our study provides a pathological foundation for further investigation of central nervous system toxicity secondary to deregulation of manganese metabolism.

Spatial versus graphical representation of distributional semantic knowledge.

Spatial distributional semantic models represent word meanings in a vector space. While able to model many basic semantic tasks, they are limited in many ways, such as their inability to represent multiple kinds of relations in a single semantic space and to directly leverage indirect relations between two lexical representations. To address these limitations, we propose a distributional graphical model that encodes lexical distributional data in a graphical structure and uses spreading activation for determining the plausibility of word sequences. We compare our model to existing spatial and graphical models by systematically varying parameters that contributing to dimensions of theoretical interest in semantic modeling. In order to be certain about what the models should be able to learn, we trained each model on an artificial corpus describing events in an artificial world simulation containing experimentally controlled verb-noun selectional preferences. The task used for model evaluation requires recovering observed selectional preferences and inferring semantically plausible but never observed verb-noun pairs. We show that the distributional graphical model performed better than all other models. Further, we argue that the relative success of this model comes from its improved ability to access the different orders of spatial representations with the spreading activation on the graph, enabling the model to infer the plausibility of noun-verb pairs unobserved in the training data. The model integrates classical ideas of representing semantic knowledge in a graph with spreading activation and more recent trends focused on the extraction of lexical distributional data from large natural language corpora. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech.

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary "deep learning" approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0-3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system.

Loss of fornix white matter volume as a predictor of cognitive impairment in cognitively normal elderly individuals.

IMPORTANCE: Magnetic resonance imaging markers of incipient cognitive decline among healthy elderly individuals have become important for both clarifying the biological underpinnings of dementia and clinically identifying healthy individuals at high risk of cognitive decline. Even though the role of hippocampal atrophy is well known in the later stages of decline, the ability of fornix-hippocampal markers to predict the earliest clinical deterioration is less clear. OBJECTIVES: To examine the involvement of the hippocampus-fornix circuit in the very earliest stages of cognitive impairment and to determine whether the volumes of fornix white matter and hippocampal gray matter would be useful markers for understanding the onset of dementia and for clinical intervention. DESIGN: A longitudinal cohort of cognitively normal elderly participants received clinical evaluations with T1-weighted magnetic resonance imaging and diffusivity scans during repeated visits over an average of 4 years. Regression and Cox proportional hazards models were used to analyze the relationships between fornix and hippocampal measures and their predictive power for incidence and time of conversion from normal to impaired cognition. SETTING: A cohort of community-recruited elderly individuals at the Alzheimer Disease Center of the University of California, Davis. PARTICIPANTS: A total of 102 cognitively normal elderly participants, with an average age of 73 years, recruited through community outreach using methods designed to enhance ethnic diversity. MAIN OUTCOMES AND MEASURES: Our preliminary hypothesis was that fornix white matter volume should be a significant predictor of cognitive decline among normal elderly individuals and that fornix measures would be associated with gray matter changes in the hippocampus. RESULTS: Fornix body volume and axial diffusivity were highly significant predictors (P = .02 and .005, respectively) of cognitive decline from normal cognition. Hippocampal volume was not significant as a predictor of decline but was significantly associated with fornix volume and diffusivity (P = .004). CONCLUSIONS AND RELEVANCE: This could be among the first studies establishing fornix degeneration as a predictor of incipient cognitive decline among healthy elderly individuals. Predictive fornix volume reductions might be explained at least in part by clinically silent hippocampus degeneration. The importance of this finding is that white matter tract measures may become promising candidate biomarkers for identifying incipient cognitive decline in a clinical setting, possibly more so than traditional gray matter measures.