Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Electrical method of monitoring percolation and abrasion of conducting spheres due to shear flow of a dense suspension in a narrow gap

This letter describes a method for studying the behavior of rigid particles in a dense suspension when they are forced into contact during flow within a narrow gap. The particles form transient percolating networks spanning the boundary walls, and will be crushed together. The method involves measuring the dc electrical resistance across the gap. The suspension e.g., solder paste consists of electrically conducting particles suspended in an insulating fluid. The electrical resistance drops when the particles are in contact with each other and the walls, and the insulating films on the surface of the conductors have been broken through. The results show a dramatic change in behavior as the ratio of gap to particle diameter is varied

Spatio-temporal dependencies in functional connectivity in rodent cortical cultures

Models of functional connectivity in cortical cultures on multi-electrodes arrays may aid in understanding how cognitive pathways form and improve techniques that aim to interface with neuronal systems. To enable research on such models, this study uses both data- and model-driven approaches to determine what dependencies are present in and between functional connectivity networks derived from bursts of extracellularly recorded activity. Properties of excitation in bursts were analysed using correlative techniques to assess the degree of linear dependence and then two parallel techniques were used to assess functional connectivity. Three models presenting increasing levels of spatio-temporal dependency were used to capture the dynamics of individual functional connections and their consistencies were verified using surrogate data. By comparing network-wide properties between model generated networks and functional networks from data, complex interdependencies were revealed. This indicates the persistent co-activation of neuronal pathways in spontaneous bursts, as can be found in whole brain structures

The reporting quality of natural language processing studies: systematic review of studies of radiology reports.

BACKGROUND: Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. METHODS: We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. RESULTS: Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. CONCLUSIONS: There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies

Grey matter changes can improve the prediction of schizophrenia in subjects at high risk

BACKGROUND: We hypothesised that subjects at familial high risk of developing schizophrenia would have a reduction over time in grey matter, particularly in the temporal lobes, and that this reduction may predict schizophrenia better than clinical measurements. METHODS: We analysed magnetic resonance images of 65 high-risk subjects from the Edinburgh High Risk Study sample who had two scans a mean of 1.52 years apart. Eight of these 65 subjects went on to develop schizophrenia an average of 2.3 years after their first scan. RESULTS: Changes over time in the inferior temporal gyrus gave a 60% positive predictive value (likelihood ratio >10) of developing schizophrenia compared to the overall 13% risk in the cohort as a whole. CONCLUSION: Changes in grey matter could be used as part of a predictive test for schizophrenia in people at enhanced risk for familial reasons, particularly for positive predictive power, in combination with other clinical and cognitive predictive measures, several of which are strong negative predictors. However, because of the limited number of subjects, this test requires independent replication to confirm its validity

Electrical method of monitoring percolation and abrasion of conducting spheres due to shear flow of a dense suspension in a narrow gap

The following article appeared in (MANNAN, S.H., HUTT, D.A.and WHALLEY, D.C., 1999. Electrical method of monitoring percolation and abrasion of conducting spheres due to shear flow of a dense suspension in a narrow gap. Applied Physics Letters, 75(6), pp. 871-2) and may be found at http://link.aip.org/link/?APPLAB/75/871/1This letter describes a method for studying the behavior of rigid particles in a dense suspension when they are forced into contact during flow within a narrow gap. The particles form transient percolating networks spanning the boundary walls, and will be crushed together. The method involves measuring the dc electrical resistance across the gap. The suspension e.g., solder paste consists of electrically conducting particles suspended in an insulating fluid. The electrical resistance drops when the particles are in contact with each other and the walls, and the insulating films on the surface of the conductors have been broken through. The results show a dramatic change in behavior as the ratio of gap to particle diameter is varied

Early Life Socioeconomic Circumstance and Late Life Brain Hyperintensities : A Population Based Cohort Study

Funding: Image acquisition and image analysis for this study was funded by the Alzheimer's Research Trust (now Alzheimer's Research UK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments The authors would like to thank the participants of the Aberdeen 1936 Birth Cohort (ABC36), without whom this research would not have been possible.Peer reviewedPublisher PD

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications