Comparative Analysis of Serum microRNA in Diagnosed Ocular Sarcoidosis versus Idiopathic Uveitis with Ocular Manifestations of Sarcoidosis.

"Idiopathic" is the most common category of uveitis, representing cases in which a specific diagnosis has not been established despite work-up. Sarcoidosis is a systemic granulomatous disorder affecting multiple organs including the lungs, skin, kidneys, and eyes. We used microRNA (miRNA) microarrays to investigate serum miRNA profiles of patients with ocular sarcoidosis as diagnosed by specific criteria (diagnosed ocular sarcoidosis), and patients with idiopathic uveitis characterized by ocular manifestations of sarcoidosis (suspected ocular sarcoidosis). Principal component analysis (PCA) and hierarchical clustering showed that serum miRNA profiles of diagnosed ocular sarcoidosis and suspected ocular sarcoidosis were both clearly distinguishable from healthy controls. Furthermore, comparative analysis of the miRNA profiles showed highly similar patterns between diagnosed ocular sarcoidosis and suspected ocular sarcoidosis. Pathway analysis revealed common pathways were involved in the two groups, including those of WNT signaling and TGF-beta signaling. Our study demonstrated a high overlap of differentially expressed serum miRNAs in patients with diagnosed ocular sarcoidosis and suspected ocular sarcoidosis, suggesting that these groups share a similar underlying pathology and may represent possible variants of the disease. Characterization of serum miRNA profiles may provide an opportunity for earlier diagnosis and treatment, and may inform more accurate clinical prognosis in patients with an ocular sarcoidosis phenotype.

A high-generalizability machine learning framework for predicting the progression of Alzheimer's disease using limited data.

Alzheimer's disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

Graded information extraction by neural-network dynamics with multihysteretic neurons.

A major goal in the study of neural networks is to create novel information-processing algorithms inferred from the real brain. Recent neurophysiological evidence of graded persistent activity suggests that the brain possesses neural mechanisms for retrieval of graded information, which could be described by the neural-network dynamics with attractors that are continuously dependent on the initial state. Theoretical studies have also demonstrated that model neurons with a multihysteretic response property can generate robust continuous attractors. Inspired by these lines of evidence, we proposed an algorithm given by the multihysteretic neuron-network dynamics, devised to retrieve graded information specific to a given topic (i.e., context, represented by the initial state). To demonstrate the validity of the proposed algorithm, we examined keyword extraction from documents, which is best fitted for evaluating the appropriateness of retrieval of graded information. The performance of keyword extraction by using our algorithm was significantly high (measured by the average precision of document retrieval, for which the appropriateness of keyword extraction is crucial) compared with standard document-retrieval methods. Moreover, our algorithm exhibited much higher performance than the neural-network dynamics with bistable neurons, which can also produce robust continuous attractors but only represent dichotomous information at the single-cell level. These findings indicate that the capability to manage graded information at the single-cell level was essential for obtaining a high performing algorithm.

Context-dependent retrieval of information by neural-network dynamics with continuous attractors.

Memory retrieval in neural networks has traditionally been described by dynamic systems with discrete attractors. However, recent neurophysiological findings of graded persistent activity suggest that memory retrieval in the brain is more likely to be described by dynamic systems with continuous attractors. To explore what sort of information processing is achieved by continuous-attractor dynamics, keyword extraction from documents by a network of bistable neurons, which gives robust continuous attractors, is examined. Given an associative network of terms, a continuous attractor led by propagation of neuronal activation in this network appears to represent keywords that express underlying meaning of a document encoded in the initial state of the network-activation pattern. A dominant hypothesis in cognitive psychology is that long-term memory is archived in the network structure, which resembles associative networks of terms. Our results suggest that keyword extraction by the neural-network dynamics with continuous attractors might symbolically represent context-dependent retrieval of short-term memory from long-term memory in the brain.