A practical application of text mining to literature on cognitive rehabilitation and enhancement through neurostimulation

Abstract

The exponential growth in publications represents a major challenge for researchers. Many scientific domains, including neuroscience, are not yet fully engaged in exploiting large bodies of publications. In this paper, we promote the idea to partially automate the processing of scientific documents, specifically using text mining (TM), to efficiently review big corpora of publications. The “cognitive advantage” given by TM is mainly related to the automatic extraction of relevant trends from corpora of literature, otherwise impossible to analyze in short periods of time. Specifically, the benefits of TM are increased speed, quality and reproducibility of text processing, boosted by rapid updates of the results. First, we selected a set of TM-tools that allow user-friendly approaches of the scientific literature, and which could serve as a guide for researchers willing to incorporate TM in their work. Second, we used these TM-tools to obtain basic insights into the relevant literature on cognitive rehabilitation (CR) and cognitive enhancement (CE) using transcranial magnetic stimulation (TMS). TM readily extracted the diversity of TMS applications in CR and CE from vast corpora of publications, automatically retrieving trends already described in published reviews. TMS emerged as one of the important non-invasive tools that can both improve cognitive and motor functions in numerous neurological diseases and induce modulations/enhancements of many fundamental brain functions. TM also revealed trends in big corpora of publications by extracting occurrence frequency and relationships of particular subtopics. Moreover, we showed that CR and CE share research topics, both aiming to increase the brain's capacity to process information, thus supporting their integration in a larger perspective. Methodologically, despite limitations of a simple user-friendly approach, TM served well the reviewing process.

Automatic target validation based on neuroscientific literature mining for tractography

“…An exponentially growing amount of data is being produced and published in neuroscience, propelled by improvements in existing and new measurement recording technologies (Brown, 2007 ; Schierwagen, 2008 ). This staggering growth represents a major challenge to identify useful information and do not lack valuable information (Balan et al, 2014 ). Much legacy information about neural connections is inaccurate or is misleading because it is vastly oversimplified and must be evaluated critically since brain circuitry has been examined with a succession of increasingly reliable methods Already available BAMS (Bota et al, 2003 ) have been designed and implemented for storing and manipulating structural data about the nervous system in text- and table-based format allowing searching by region name, species and references (author, source, year) (Bota and Arbib, 2004 ).…”

Text Mining in Big Data Analytics

“…Text mining was further employed as a tool in literature review-based studies in public health and medical sciences for various key themes, such as the adolescent substance and depression [216], cognitive rehabilitation and enhancement through neurostimulation [217], the protein factors related to the different cancer types [218], and diseases and syndromes in neurology [219].…”

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

“…Finally, TMS is quite bulky, hence not suitable for mobile applications. Nevertheless, several studies have used TMS for human cognitive enhancement (e.g., Hilgetag et al, 2001; Boggio et al, 2009; Chi et al, 2010; Chi and Snyder, 2012; Manenti et al, 2012) involving a variety of core information processing systems in the brain, such as perception, learning and memory—see the review by Balan et al (2014) using text mining technology.…”