Non-parametric group-level statistics for source-resolved ERP analysis

In this paper, we demonstrate how machine learning (ML) can be used to beneficially complement the traditional analysis of behavioral and physiological data to provide new insights into the structure of mental states, in this case, executive functions (EFs) with a focus on inhibitory control. We used a modified Flanker task with the aim to distinguish three levels of inhibitory control: no inhibition, readiness for inhibition and the actual execution of inhibitory control. A simultaneously presented n-back task was used to additionally induce demands on a second executive function. This design enabled us to investigate how the overlap of resources influences the distinction between three levels of inhibitory control. A support vector machine (SVM) based classification approach has been used on EEG data to predict the level of inhibitory control on single-subject and single-trial level. The SVM classification is a subject-specific and single-trial based approach which will be compared to standard group-level statistical approaches to reveal that both methodologies access different properties of the data. We show that considering both methods can give new insights into mental states which cannot be discovered when only using group-level statistics alone. Machine learning results indicate that three different levels of inhibitory control can be distinguished, while the group-average analysis does not give rise to this assumption. In addition, we highlight one other important benefit of the ML approach. We are able to define specific properties of the executive function inhibition by investigating the neural activation patterns that were used during the classification process.

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Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals

International Journal of Sports Physiology and Performance ◽

10.1123/ijspp.2021-0126 ◽

2021 ◽

pp. 1-9

Author(s):

Niklas D. Neumann ◽

Nico W. Van Yperen ◽

Jur J. Brauers ◽

Wouter Frencken ◽

Michel S. Brink ◽

...

Keyword(s):

Perceived Exertion ◽

Rating Of Perceived Exertion ◽

Group Level ◽

Total Quality ◽

Individual Level ◽

Quality Of Recovery ◽

Level Statistics ◽

The Individual ◽

Level Analysis

Purpose: The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the “ergodicity issue.” Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field. Methods: The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level. Results: Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery. Conclusions: The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery.

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From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2019.2922553 ◽

2019 ◽

Vol 27 (7) ◽

pp. 1492-1501 ◽

Cited By ~ 4

Author(s):

Rober Boshra ◽

Kiret Dhindsa ◽

Omar Boursalie ◽

Kyle I. Ruiter ◽

Ranil Sonnadara ◽

...

Keyword(s):

Machine Learning ◽

Single Subject ◽

Group Level ◽

Level Statistics

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Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Brain Imaging and Behavior ◽

10.1007/s11682-021-00523-7 ◽

2021 ◽

Author(s):

Mary Clare McKenna ◽

Marlene Tahedl ◽

Jasmin Lope ◽

Rangariroyashe H. Chipika ◽

Stacey Li Hi Shing ◽

...

Keyword(s):

Disease Burden ◽

Clinical Decision Making ◽

Clinical Care ◽

Primary Objective ◽

Cortical Atrophy ◽

Diagnostic Classification ◽

Precentral Gyrus ◽

Group Level ◽

Imaging Data ◽

Level Statistics

AbstractImaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

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Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

10.1101/245530 ◽

2018 ◽

Author(s):

Marijn van Vliet ◽

Mia Liljeström ◽

Susanna Aro ◽

Riitta Salmelin ◽

Jan Kujala

Keyword(s):

Functional Connectivity ◽

Spectral Power ◽

Brain Regions ◽

Dynamic Imaging ◽

Oscillatory Activity ◽

Complete Analysis ◽

Cortical Mapping ◽

Group Level ◽

Connectivity Analysis ◽

Level Statistics

AbstractCommunication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

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Between Group-Level Minimally Important Change and Individual Treatment Responders

10.21203/rs.3.rs-224611/v1 ◽

2021 ◽

Author(s):

Ron D. Hays® ◽

John Devin Peipert

Keyword(s):

Secondary Data ◽

Important Change ◽

Reliable Change Index ◽

Individual Treatment ◽

Individual Change ◽

Group Level ◽

Individual Level ◽

Level Statistics ◽

Need To Evaluate ◽

Minimally Important Change

Abstract Purpose Estimates of the minimally important change (MIC) can be used to evaluate whether group-level differences are large enough to be important. But responders to treatment have been based upon group-level MIC thresholds, resulting in inaccurate classification of change over time. This article reviews options and provides suggestions about individual-level statistics to assess whether individuals have improved, stayed the same, or declined. Methods Review of MIC estimation and an example of misapplication of MIC group-level estimates to assess individual change. Secondary data analyses to show how perceptions about meaningful change can be used along with significance of individual change. Results MIC thresholds yield over-optimistic conclusions (i.e., classify those who have not changed as responders to treatment). Individual change statistics can be used along with individual retrospective ratings of change. Conclusions Future studies need to evaluate the significance of individual change using appropriate individual-level statistics such as the reliable change index or the equivalent coefficient of repeatability.

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