scholarly journals Focus on the breath: Brain decoding reveals internal states of attention during meditation

2018 ◽  
Author(s):  
H.Y. Weng ◽  
J.A. Lewis-Peacock ◽  
F.M. Hecht ◽  
M.R. Uncapher ◽  
D.A. Ziegler ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Mind Wandering ◽  
Mental States ◽  
Single Subject ◽  
Meditation Practice ◽  
Individual Level ◽  
Referential Processing ◽  
Internal States ◽  
The Individual ◽  
Internal Attention

AbstractMeditation practices are used to cultivate internally-oriented attention to bodily sensations, which may improve health via cognitive and emotion regulation of bodily signals. However, it remains unclear how meditation impacts internal attention states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to (1) learn and recognize internal attentional (IA) states relevant for meditation during a directed IA task, and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N=16), we first used MVPA to develop single-subject brain classifiers for 5 modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states (i.e., meditation-related states: breath attention, mind wandering, and self-referential processing, and control states: attention to feet and sounds). Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all block volumes were tested, N=2160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were then applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, mind wandering, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to mind wandering or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.

scholarly journals Focus on the breath: Brain decoding reveals internal states of attention during meditation

2018 ◽  
Author(s):  
Helen Weng ◽  
Jarrod Lewis-Peacock ◽  
Frederick Hecht ◽  
Melina Uncapher ◽  
David Ziegler ◽  
...  
Keyword(s):  
Emotional Regulation ◽  
Predictive Accuracy ◽  
Mental States ◽  
Single Subject ◽  
Meditation Practice ◽  
Individual Level ◽  
Referential Processing ◽  
Internal States ◽  
The Individual ◽  
Internal Attention

Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health via cognitive and emotional regulation of bodily signals. However, it remains unclear how meditation impacts internal attention (IA) states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to: (1) learn and recognize internal attentional states relevant for meditation during a directed IA task; and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N = 16), we first used MVPA to develop single-subject brain classifiers for five modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states [i.e., meditation-related states: breath attention, mind wandering (MW), and self-referential processing, and control states: attention to feet and sounds]. Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all volumes were tested, N = 2,160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, MW, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to MW or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.

scholarly journals Detecting expert’s eye using a multiple-kernel Relevance Vector Machine

2014 ◽  
Vol 7 (2) ◽  
Author(s):  
Giuseppe Boccignone ◽  
Mario Ferraro ◽  
Sofia Crespi ◽  
Carlo Robino ◽  
Claudio De’Sperati
Keyword(s):  
Ad Hoc ◽  
Feature Space ◽  
Mental States ◽  
Relevance Vector Machine ◽  
Individual Level ◽  
Oculomotor Behavior ◽  
Overt Behavior ◽  
Additional Signal ◽  
Applied Machine Learning ◽  
The Individual

Decoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. Here we applied machine learning to detect expertise from the oculomotor behavior of novice and expert billiard players during free viewing of a filmed billiard match with no specific task, and in a dynamic trajectory prediction task involving ad-hoc, occluded billiard shots. We have adopted a ground framework for feature space fusion and a Bayesian sparse classifier, namely, a Relevance Vector Machine. By testing different combinations of simple oculomotor features (gaze shifts amplitude and direction, and fixation duration), we could classify on an individual basis which group - novice or expert - the observers belonged to with an accuracy of 82% and 87%, respectively for the match and the shots. These results provide evidence that, at least in the particular domain of billiard sport, a signature of expertise is hidden in very basic aspects of oculomotor behavior, and that expertise can be detected at the individual level both with ad-hoc testing conditions and under naturalistic conditions - and suitable data mining. Our procedure paves the way for the development of a test for the “expert’s eye”, and promotes the use of eye movements as an additional signal source in Brain-Computer-Interface (BCI) systems.

scholarly journals Can education be personalised using pupils’ genetic data?

2019 ◽  
Author(s):  
Tim T Morris ◽  
Neil M Davies ◽  
George Davey Smith
Keyword(s):  
Predictive Power ◽  
Predictive Accuracy ◽  
Educational Performance ◽  
Phenotypic Data ◽  
Individual Level ◽  
Polygenic Scores ◽  
Prior Achievement ◽  
Using Data ◽  
The Individual ◽  
Potential Tool

AbstractThe increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How well polygenic scores predict educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study, we investigated how well polygenic scores for education predicted pupils’ realised achievement over and above phenotypic data that are available to schools. Across our sample, prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. There was high overlap between the polygenic score and achievement distributions, leading to weak predictive accuracy at the individual level. Furthermore, conditional on prior achievement polygenic scores were not predictive of later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for predicting individual educational performance or for personalised education.

scholarly journals Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

2013 ◽  
Vol 43 (12) ◽  
pp. 2547-2562 ◽  
Author(s):  
W. Pettersson-Yeo ◽  
S. Benetti ◽  
A. F. Marquand ◽  
F. Dell‘Acqua ◽  
S. C. R. Williams ◽  
...  
Keyword(s):  
High Risk ◽  
First Episode Psychosis ◽  
First Episode ◽  
Support Vector ◽  
Single Subject ◽  
Individual Level ◽  
Episode Psychosis ◽  
Ultra High Risk ◽  
The Individual ◽  
Cognitive Data

BackgroundGroup-level results suggest that relative to healthy controls (HCs), ultra-high-risk (UHR) and first-episode psychosis (FEP) subjects show alterations in neuroanatomy, neurofunction and cognition that may be mediated genetically. It is unclear, however, whether these groups can be differentiated at single-subject level, for instance using the machine learning analysis support vector machine (SVM). Here, we used a multimodal approach to examine the ability of structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor neuroimaging (DTI), genetic and cognitive data to differentiate between UHR, FEP and HC subjects at the single-subject level using SVM.MethodThree age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEPversusHC, UHRversusHC and FEPversusUHR, respectively. Genetic, sMRI, DTI, fMRI and cognitive data were obtained for each participant and the ability of each to discriminate subjects at the individual level in conjunction with SVM was tested.ResultsSuccessful classification accuracies (p < 0.05) comprised FEPversusHC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHRversusHC (sMRI, 68.42%; DTI, 65.79%), and FEPversusUHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%).ConclusionsThe results suggest that FEP subjects are identifiable at the individual level using a range of biological and cognitive measures. Comparatively, only sMRI and DTI allowed discrimination of UHR from HC subjects. For the first time FEP and UHR subjects have been shown to be directly differentiable at the single-subject level using cognitive, sMRI and fMRI data. Preliminarily, the results support clinical development of SVM to help inform identification of FEP and UHR subjects, though future work is needed to provide enhanced levels of accuracy.

Language convergence and bilingual acquisition

2003 ◽  
Vol 3 ◽  
pp. 89-137 ◽  
Author(s):  
Ee San Chen
Keyword(s):  
Preschool Children ◽  
Structural Features ◽  
Single Subject ◽  
Elicited Imitation ◽  
Cross Sectional ◽  
Individual Level ◽  
Simultaneous Acquisition ◽  
Societal Level ◽  
Target Languages ◽  
The Individual

This study concerns the simultaneous acquisition of conditional constructions in Chinese-English bilingual preschool children in Singapore. Cross-sectional data are obtained from subjects ranging from 2;10 to 6;06. The target languages are Singapore Colloquial English (SCE) and Singapore Colloquial Mandarin (SCM), both of which are contact varieties that are representative of the Low varieties in a diglossic situation. The bilingual acquisition of structural features of conditionals is investigated by adopting the Head-marking and Dependent-marking typological framework (Nichols 1986). Two elicited imitation experiments were conducted, with Experiment 1 aimed at examining if the structural convergence between SCE and SCM that is evident at a societal level is also represented in the developing linguistic repertoires of the bilingual preschool children. Experiment 2 is a follow-up study designed to investigate if convergence between the two languages is also evident at an individual level. Results obtained in Experiment 1 suggest that convergence between the two developing linguistic systems is indeed apparent. Also evident is the influence of Chinese on SCE, while the influence of English on SCM is less apparent. At the individual level, however, a general lack of convergence between the two developing grammars within a single subject is observed in Experiment 2: the marking preferences for SCE and SCM conditionals tend not to coincide for a particular subject, suggesting that the two languages are represented as distinct grammatical systems for the bilingual children.

scholarly journals Individual response to exercise training - a statistical perspective

2015 ◽  
Vol 118 (12) ◽  
pp. 1450-1459 ◽  
Author(s):  
Anne Hecksteden ◽  
Jochen Kraushaar ◽  
Friederike Scharhag-Rosenberger ◽  
Daniel Theisen ◽  
Stephen Senn ◽  
...  
Keyword(s):  
Exercise Training ◽  
Predictive Accuracy ◽  
Signal To Noise Ratio ◽  
Interactive Effects ◽  
Control Group ◽  
Individual Response ◽  
Individual Level ◽  
Exercise Training Program ◽  
The Individual ◽  
Response To Exercise

In the era of personalized medicine, interindividual differences in the magnitude of response to an exercise training program (subject-by-training interaction; “individual response”) have received increasing scientific interest. However, standard approaches for quantification and prediction remain to be established, probably due to the specific considerations associated with interactive effects, in particular on the individual level, compared with the prevailing investigation of main effects. Regarding the quantification of subject-by-training interaction in terms of variance components, confounding sources of variability have to be considered. Clearly, measurement error limits the accuracy of response estimates and thereby contributes to variation. This problem is of particular importance for analyses on the individual level, because a low signal-to-noise ratio may not be compensated by increasing sample size (1 case). Moreover, within-subject variation in training efficacy may contribute to gross response variability. This largely unstudied source of variation may not be disclosed by comparison to a control group but calls for repeated interventions. A second critical point concerns the prediction of response. There is little doubt that exercise training response is influenced by a multitude of determinants. Moreover, indications of interaction between influencing factors of training efficacy lead to the hypothesis that optimal predictive accuracy may be attained using an interactive rather than additive approach. Taken together, aiming at conclusive inference and optimal predictive accuracy in the investigation of subject-by-training interaction entails specific requirements that are deducibly based on statistical principles but beset with many practical difficulties. Therefore, pragmatic alternatives are warranted.

scholarly journals Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1416
Author(s):  
Ziyu Zhu ◽  
Du Lei ◽  
Kun Qin ◽  
Xueling Suo ◽  
Wenbin Li ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Deep Learning ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Support Vector ◽  
Single Subject ◽  
Theoretic Approach ◽  
Individual Level ◽  
Graph Theoretic ◽  
The Individual

Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.

scholarly journals ICT Adoption and the Digital Divide in Serbia: Factors and Policy Implications

10.28945/3247 ◽  
2008 ◽  
Author(s):  
Zlatko Kovacic ◽  
Dragan Vukmirovic
Keyword(s):  
Logistic Regression ◽  
Digital Divide ◽  
Predictive Accuracy ◽  
Classification Tree ◽  
Policy Implications ◽  
Individual Level ◽  
Internet Skills ◽  
Ict Adoption ◽  
Parsimonious Model ◽  
The Individual

This paper explores factors such as socio-demographics, income and wealth and e-skills that may influence the adoption of the ICTs at the individual level. We examine to what extent these factors contribute to the digital divide between different social groups in Serbia. We use the panel data from the survey “ICT usage in Republic of Serbia” in 2006 and 2007, covering over 3000 individuals/households, to perform a quantitative analysis of the digital divide and ICT adoption. Based on a classification tree and a logistic regression model, a profile of the typical ICT adopter and non-adopter is identified. The empirical results show the following: (i) the digital divide between age group 55-74 and those younger that 55 slightly increased in 2007 in case of regular Internet use; (ii) at the individual level the ICT adoption (use of PC, Internet and mobile phones) in Serbia is mainly influenced by the income and wealth of an individual, its computer and Internet skills and age; (iii) this result is quite robust across the methodological approaches used; and (iv) the classification tree approach is preferable since it gives the same predictive accuracy as the logistic regression with a more parsimonious model. The policy implications of these results are discussed.

scholarly journals Is it all in our head? When subjective beliefs about receiving an intervention are better predictors of experimental results than the intervention itself

2020 ◽  
Author(s):  
Luisa Fassi ◽  
Roi Cohen Kadosh
Keyword(s):  
Training Programs ◽  
Active Form ◽  
Mind Wandering ◽  
Placebo Control ◽  
Individual Level ◽  
Subjective Beliefs ◽  
Non Invasive ◽  
The Individual ◽  
Experimental Findings ◽  
Actual Intervention

AbstractIn recent years, there has been debate about the effectiveness of interventions from different fields (e.g., non-invasive brain stimulation (NIBS), neurofeedback, cognitive training programs) due to contradictory and nuanced experimental findings. Up to date, studies are focused on comparing the effects of an active form of the intervention to a placebo/control condition. However, a neglected question is how to consider individual differences in response to blinding procedures, and their effect on behavioural outcomes, rather than merely compare the efficacy of blinding using a group-based approach. To address this gap in the literature, we here suggest using subjective intervention—the participants’ subjective beliefs about receiving or not receiving an intervention—as a factor. Specifically, we examined whether subjective intervention and subjective dosage (i.e. participants’ subjective beliefs about the intensity of the intervention they received) affected performance scores independently, or interacting with, the active experimental condition. We carried out data analysis on an open-access dataset that has shown the efficacy of active NIBS in altering mind wandering. We show that subjective intervention and subjective dosage successfully explained alteration in mind wandering scores, over and beyond the objective intervention. These findings highlight the importance of accounting for the participants’ beliefs about receiving interventions at the individual level by demonstrating their effect on human behaviour independently of the actual intervention. Altogether, our approach allows more rigorous and improved experimental design and analysis, which will strengthen the conclusions coming from basic and clinical research, for both NIBS and non-NIBS interventions.

Testimonial Injustice and Mindreading

Hypatia ◽  
2016 ◽  
Vol 31 (4) ◽  
pp. 858-873 ◽  
Author(s):  
Krista Hyde
Keyword(s):  
Human Behavior ◽  
Mental States ◽  
Cognitive Capacity ◽  
Conscious Awareness ◽  
Individual Level ◽  
Transmission Of Knowledge ◽  
Level Of Consciousness ◽  
Testimonial Injustice ◽  
The Individual ◽  
Miranda Fricker

Miranda Fricker maintains that testimonial responsibility is the proper corrective to testimonial injustice. She proposes a perceptual‐like “testimonial sensibility” to explain the transmission of knowledge through testimony. This sensibility is the means by which a hearer perceives an interlocutor's credibility level. When prejudice causes a hearer to inappropriately deflate the credibility attributed to a speaker, the sensibility may have functioned unreliably. Testimonial responsibility, she claims, will make the capacity reliable by reinflating credibility levels to their proper degree. I argue that testimonial sensitivity may be or involve “mindreading,” the cognitive capacity by which we predict human behavior and explain it in terms of mental states. Further, I claim that, if testimonial sensibility is or involves mindreading, and mindreading is a function of brain processes (as claimed by cognitive neuroscientists), testimonial injustice cannot be corrected by testimonial responsibility. This is because 1) it appears to rely on conscious awareness of prejudice, whereas much bias occurs implicitly, and 2) it works at the individual level, whereas testimonial injustice occurs both individually and socially. I argue that the remedy for testimonial injustice is, instead, engaging in social efforts that work below the level of consciousness.

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