Tensor decomposition of TMS-induced EEG oscillations reveals data-driven profiles of antiepileptic drug effects

AbstractTranscranial magnetic stimulation combined with electroencephalography is a powerful tool to probe human cortical excitability. The EEG response to TMS stimulation is altered by drugs active in the brain, with characteristic “fingerprints” obtained for drugs of known mechanisms of action. However, the extraction of specific features related to drug effects is not always straightforward as the complex TMS-EEG induced response profile is multi-dimensional. Analytical approaches can rely on a-priori assumptions within each dimension or on the implementation of cluster-based permutations which do not require preselection of specific limits but may be problematic when several experimental conditions are tested. We here propose an alternative data-driven approach based on PARAFAC tensor decomposition, which provides a parsimonious description of the main profiles underlying the multidimensional data. We validated reliability of PARAFAC on TMS-induced oscillations before extracting the features of two common anti-epileptic drugs (levetiracetam and lamotrigine) in an integrated manner. PARAFAC revealed an effect of both drugs, significantly suppressing oscillations in the alpha range in the occipital region. Further, this effect was stronger under the intake of levetiracetam. This study demonstrates, for the first time, that PARAFAC can easily disentangle the effects of subject, drug condition, frequency, time and space in TMS-induced oscillations.

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Defining “good” and “poor” outcomes in patients with schizophrenia or schizoaffective disorder: A multidimensional data-driven approach

Psychiatry Research ◽

10.1016/j.psychres.2008.09.004 ◽

2009 ◽

Vol 170 (2-3) ◽

pp. 161-167 ◽

Cited By ~ 19

Author(s):

Ilya A. Lipkovich ◽

Walter Deberdt ◽

John G. Csernansky ◽

Peter Buckley ◽

Joseph Peuskens ◽

...

Keyword(s):

Schizoaffective Disorder ◽

Data Driven ◽

Multidimensional Data ◽

Data Driven Approach ◽

Poor Outcomes

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A Data-Driven Approach to A Priori SNR Estimation

IEEE Transactions on Audio Speech and Language Processing ◽

10.1109/tasl.2010.2045799 ◽

2011 ◽

Vol 19 (1) ◽

pp. 186-195 ◽

Cited By ~ 37

Author(s):

Suhadi Suhadi ◽

Carsten Last ◽

Tim Fingscheidt

Keyword(s):

A Priori ◽

Data Driven ◽

Snr Estimation ◽

Data Driven Approach ◽

A Priori Snr Estimation

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A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues

10.21203/rs.3.rs-1006490/v1 ◽

2021 ◽

Author(s):

Peter Andrew McAtee ◽

Simona Nardozza ◽

Annette Richardson ◽

Mark Wohlers ◽

Robert Schaffer

Keyword(s):

Method Development ◽

A Priori ◽

Distance Matrix ◽

Data Driven ◽

Efficient Manner ◽

Biological Images ◽

Single Colour ◽

Data Driven Approach ◽

Applied Fields ◽

Complex Images

Abstract BackgroundThe ability to quantify the colour of fruit is extremely important for a number of applied fields including plant breeding, postharvest assessment, and consumer quality assessment. Fruit and other plant organs display highly complex colour patterning. This complexity makes it challenging to compare and contrast colours in an accurate and time efficient manner. Multiple methodologies exist that attempt to digitally quantify colour in complex images but these either require a priori knowledge to assign colours to a particular bin, or average the colours present within an assayed region into a single colour value. As such, to date there are no published methodologies that assess colour patterning using a data driven approach. Results In this study we present a methodology to acquire and process digital images of biological samples that contain complex colour gradients. The CIE (Commission internationale de l'éclairage / International Commission on Illumination) ΔE2000 formula was used to determine the perceptually unique colours (PUC) within images of fruit containing complex colour gradients. This process, on average, resulted in a 98% reduction in colour values from the number of unique colours (UC) in the original image. This data driven procedure summarised the colour data values while maintaining a linear relationship with the normalised colour complexity contained in the total image. A weighted ΔE2000 distance metric was used to generate a distance matrix and facilitated clustering of summarised colour data.ConclusionsClustering showed that our data driven methodology has the ability to group these complex images into their respective binomial families while maintaining the ability to detect subtle colour differences. This methodology was also able to differentiate closely related images. We provide a high quality set of complex biological images that span the visual spectrum that can be used in future colorimetric research to benchmark method development.

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Modeling Motivation for Alcohol in Humans using Traditional and Machine Learning Approaches

10.31234/osf.io/ahwsg ◽

2020 ◽

Author(s):

Erica Grodin ◽

Amanda Kay Montoya ◽

Spencer Bujarski ◽

Lara A. Ray

Keyword(s):

Machine Learning ◽

A Priori ◽

Progressive Ratio ◽

Data Driven ◽

Learning Approaches ◽

Self Administration ◽

Breath Alcohol ◽

Data Driven Approach ◽

Preclinical And Clinical Studies ◽

Alcohol Seeking

Given the significant cost of alcohol use disorder, identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self-administration using a novel paradigm. Heavy drinkers (n=67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60mg%) and a progressive-ratio alcohol self-administration paradigm (maximum breath alcohol 120mg%; ratio requirements range = 20-3,139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self-administration. K-means clustering was used to identify motivated (n=41) and unmotivated (n=26) self-administration trajectories. The data was analyzed using two approaches: a theory-driven test of a-priori predictors and a data-driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data-driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self-administration. Additional application of this model to AUD translational science and treatment development appear warranted.

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Whats and Hows? The Practice-Based Typology of Narrative Analyses

Przegląd Socjologii Jakościowej ◽

10.18778/1733-8069.16.3.08 ◽

2020 ◽

Vol 16 (3) ◽

pp. 120-142

Author(s):

Grzegorz Bryda

Keyword(s):

Qualitative Research ◽

Text Mining ◽

Lived Experience ◽

Narrative Analysis ◽

Corpus Linguistics ◽

English Language ◽

Data Driven ◽

Research Practices ◽

Data Driven Approach ◽

Analytical Approaches

The nature of qualitative research practices is multiparadigmaticity which creates coexistence of different research and analytical approaches to the study of human experience in the living world. This diversity is particularly observed in the contemporary field of narrative research and data analysis. The purpose of this article is a methodological reflection on the process of developing typology and a proposition of new data-driven and practice-based typology of narrative analyses used by qualitative researchers in the lived experience research. I merge the CAQDAS, Corpus Linguistics, and Text Mining procedures to examine the analytical strategies inherited in a vivid language of English-language research articles, published in five influential qualitative methodological journals between 2002-2016. Using the dictionary-based content analysis in the coding process, hierarchical clustering, and topic modeling – a text-mining tool for discovering hidden semantic structures in a textual body – I confront Catherine Kohler Riessman’s heuristic typology with the data-driven approach in order to contribute the more coherent image of narrative analysis in the contemporary field of qualitative research. Finally, I propose a new model of thinking about the typology of narrative analyses based upon research practices.

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