ORCID Profile
0000-0002-1540-5516
Current Organisation
Nanyang Technological University
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Publisher: Wiley
Date: 02-09-2022
DOI: 10.1111/EJN.15800
Abstract: There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
Publisher: Center for Open Science
Date: 06-2022
Abstract: Complexity quantification, through entropy, information and fractal dimension indices, is gaining a renewed traction in psychophysiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. In this study, we systematically compared 125 indices of complexity by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. We propose that a selection of indices, including ShanEn (D), MSWPEn, CWPEn, FuzzyMSEn, AttEn, NLDFD, Hjorth, MFDFA (Width), MFDFA (Max), MFDFA (Mean), SVDEn, MFDFA (Increment), might offer a complimentary choice in regards to the quantification of the complexity of time series.
Publisher: Center for Open Science
Date: 26-10-2021
Abstract: Heart Rate Variability (HRV) can be estimated using a myriad of mathematical indices, but the lack of systematic comparison between these indices renders the interpretation and evaluation of results tedious. In this study, we assessed the relationship between 57 HRV metrics collected from 302 human recordings using a variety of structure-analysis algorithms. We then applied a meta-clustering approach that combines their results to obtain a robust and reliable view of the observed relationships. We found that HRV metrics can be clustered into 3 groups, representing the core variability features, extreme variability features and frequency/complexity features. From there, we described and discussed their associations, and derived recommendations on which indices to prioritize for parsimonious, yet comprehensive HRV-related data analysis and reporting.
Publisher: Center for Open Science
Date: 13-09-2021
Abstract: While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend towards the use of complexity analysis in quantifying neural activity. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of (1) predictability and (2) regularity. We then synthesize complexity findings across different areas of psychological science, namely in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
Publisher: MDPI AG
Date: 05-05-2021
DOI: 10.20944/PREPRINTS202105.0070.V1
Abstract: The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiogram (ECG) in research and advancement in sensors technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, posing a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas that these indices have shown to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.
Publisher: Elsevier BV
Date: 2016
DOI: 10.1016/J.NEUROIMAGE.2015.09.038
Abstract: The involvement of the human cerebellum in working memory has been well established in the last decade. However, the cerebro-cerebellar network for visual working memory is not as well defined. Our previous fMRI study showed superior and inferior cerebellar activations during a block design visual working memory task, but specific cerebellar contributions to cognitive processes in encoding, maintenance and retrieval have not yet been established. The current study examined cerebellar contributions to each of the components of visual working memory and presence of cerebellar hemispheric laterality was investigated. 40 young adults performed a Sternberg visual working memory task during fMRI scanning using a parametric paradigm. The contrast between high and low memory load during each phase was examined. We found that the most prominent activation was observed in vermal lobule VIIIb and bilateral lobule VI during encoding. Using a quantitative laterality index, we found that left-lateralized activation of lobule VIIIa was present in the encoding phase. In the maintenance phase, there was bilateral lobule VI and right-lateralized lobule VIIb activity. Changes in activation in right lobule VIIIa were present during the retrieval phase. The current results provide evidence that superior and inferior cerebellum contributes to visual working memory, with a tendency for left-lateralized activations in the inferior cerebellum during encoding and right-lateralized lobule VIIb activations during maintenance. The results of the study are in agreement with Baddeley's multi-component working memory model, but also suggest that stored visual representations are additionally supported by maintenance mechanisms that may employ verbal coding.
Publisher: MDPI AG
Date: 27-07-2022
DOI: 10.3390/E24081036
Abstract: Complexity quantification, through entropy, information theory and fractal dimension indices, is gaining a renewed traction in psychophsyiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. Using the NeuroKit2 Python software, we computed a list of 112 (predominantly used) complexity indices on signals varying in their characteristics (noise, length and frequency spectrum). We then systematically compared the indices by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. Based on these considerations, we propose that a selection of 12 indices, together representing 85.97% of the total variance of all indices, might offer a parsimonious and complimentary choice in regards to the quantification of the complexity of time series. Our selection includes CWPEn, Line Length (LL), BubbEn, MSWPEn, MFDFA (Max), Hjorth Complexity, SVDEn, MFDFA (Width), MFDFA (Mean), MFDFA (Peak), MFDFA (Fluctuation), AttEn. Elements of consideration for alternative subsets are discussed, and data, analysis scripts and code for the figures are open-source.
Publisher: Center for Open Science
Date: 10-03-2020
Abstract: The conceptualization of deception as a dispositional trait is under-represented in the literature. Despite scientific evidence supporting the existence of in idual differences in lying, a validated measure of dispositional deception is still lacking. This study aims to explore the structure of dispositional deception by validating a 16-item questionnaire to characterize in iduals' lying patterns. Our findings suggest four distinct latent dimensions: frequency, ability, negativity, and contextuality. We establish the convergent validity of our measure by showing significant relationships with social desirability, malevolent traits, cognitive control deficits, normal and pathological personality traits, as well as demographic variables such as sex, age, and religiosity. Overall, the present study introduces a general framework to understanding deception as a dispositional trait.
Publisher: Springer Science and Business Media LLC
Date: 22-04-2021
Publisher: Elsevier BV
Date: 05-2016
DOI: 10.1016/J.BBR.2016.02.027
Abstract: Previous studies have suggested cerebro-cerebellar circuitry in working memory. The present fMRI study aims to distinguish differential cerebro-cerebellar activation patterns in verbal and visual working memory, and employs a quantitative analysis to deterimine lateralization of the activation patterns observed. Consistent with Chen and Desmond (2005a,b) predictions, verbal working memory activated a cerebro-cerebellar circuitry that comprised left-lateralized language-related brain regions including the inferior frontal and posterior parietal areas, and subcortically, right-lateralized superior (lobule VI) and inferior cerebellar (lobule VIIIA/VIIB) areas. In contrast, a distributed network of bilateral inferior frontal and inferior temporal areas, and bilateral superior (lobule VI) and inferior (lobule VIIB) cerebellar areas, was recruited during visual working memory. Results of the study verified that a distinct cross cerebro-cerebellar circuitry underlies verbal working memory. However, a neural circuitry involving specialized brain areas in bilateral neocortical and bilateral cerebellar hemispheres subserving visual working memory is observed. Findings are discussed in the light of current models of working memory and data from related neuroimaging studies.
Publisher: MDPI AG
Date: 09-06-2021
DOI: 10.3390/S21123998
Abstract: The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.
No related grants have been discovered for SH Annabel Chen.