ORCID Profile
0000-0002-4744-8721
Current Organisation
Forschungszentrum Jülich
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Publisher: Cold Spring Harbor Laboratory
Date: 19-02-2023
DOI: 10.1101/2023.02.18.529076
Abstract: Changes in spontaneous brain activity at rest provide rich information about behavior and cognition. The mathematical properties of resting-state functional magnetic resonance imaging (rsfMRI) are a depiction of brain function and are frequently used to predict cognitive phenotypes. In idual characteristics such as age, gender, and total intracranial volume (TIV) play an important role in predictive modeling of rsfMRI (for ex le, as “confounders” in many cases). It is unclear, however, to what extent rsfMRI carries independent information from the in idual characteristics that is able to predict cognitive phenotypes. Here, we used predictive modeling to thoroughly examine the predictability of four cognitive phenotypes in 20,000 healthy UK Biobank subjects. We extracted common rsfMRI features of functional brain connectivity (FC) and temporal complexity (TC). We assessed the ability of these features to predict outcomes in the presence and absence of age, gender, and TIV. Additionally, we assessed the predictiveness of age, gender, and TIV only. We find TC and FC features to perform comparably with regard to predicting cognitive phenotypes. As compared to rsfMRI features, in idual characteristics provide systematically better predictions with smaller s le sizes and, to some extent, in larger cohorts. It is also consistent across different levels of inherent temporal noise in rsfMRI. Our results suggest that when the objective is to perform cognitive predictions as opposed to understanding the relationship between brain and behavior, in idual characteristics are more applicable than rsfMRI features.
Publisher: Society for Neuroscience
Date: 25-05-2016
Publisher: Elsevier BV
Date: 04-2021
Publisher: IEEE
Date: 05-2011
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2008
DOI: 10.1142/S0218348X08003879
Abstract: In this paper, a new irregularity criterion based on fractal dimensions is introduced. In fact, this criterion (which is called ONH criterion) is the state space version of Higuchi fractal dimension, which can discriminate stochastic and deterministic time series from each other. To compute this criterion, we have exploited "the amount of state vectors fluctuations" in embedding space. By varying the reconstruction delay in embedding space, one can obtain a logarithmic diagram of overall changes corresponding to reconstructed state space versus delays. The slope of the linear region of this diagram could be considered as a new irregularity criterion. To discriminate random and deterministic time series, this criterion is adopted as a new statistic for hypothesis testing. Probability density function of this statistic under H 0 hypothesis is constructed and regarding to a certain confidence level, one can determine whether randomness is accepted or rejected.
Publisher: Elsevier BV
Date: 05-2013
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 03-2019
DOI: 10.1016/J.CLINPH.2018.11.024
Abstract: The process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy. We implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other 'similar' EEG events. We compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only. In contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy datasets, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases automatic spike detection revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe. Automatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy. Our study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists.
Publisher: MDPI AG
Date: 13-12-2018
DOI: 10.3390/E20120962
Abstract: Approximate entropy (ApEn) and s le entropy (S En) are widely used for temporal complexity analysis of real-world phenomena. However, their relationship with the Hurst exponent as a measure of self-similarity is not widely studied. Additionally, ApEn and S En are susceptible to signal litude changes. A common practice for addressing this issue is to correct their input signal litude by its standard deviation. In this study, we first show, using simulations, that ApEn and S En are related to the Hurst exponent in their tolerance r and embedding dimension m parameters. We then propose a modification to ApEn and S En called range entropy or RangeEn. We show that RangeEn is more robust to nonstationary signal changes, and it has a more linear relationship with the Hurst exponent, compared to ApEn and S En. RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and it has no need for signal litude correction. Finally, we demonstrate the clinical usefulness of signal entropy measures for characterisation of epileptic EEG data as a real-world ex le.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: Cold Spring Harbor Laboratory
Date: 03-10-2022
DOI: 10.1101/2022.09.30.510304
Abstract: Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high- litude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interin idual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of in idual-level phenotypes.
Publisher: MDPI AG
Date: 18-08-2022
DOI: 10.3390/E24081148
Abstract: Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.
Publisher: Elsevier BV
Date: 11-2018
DOI: 10.1016/J.NEUROIMAGE.2018.06.020
Abstract: Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on temporal, topological and anatomical levels. For this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0.72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were compared to IPSA. We found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. The association between CSWA and IPSA was stronger for a window length of ∼20 s (shorter than filtered fMRI wavelength) than ∼100 s (longer than filtered fMRI wavelength), irrespective of the s ling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0.03-0.07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0.01-0.1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated for both short (∼20 s) and long (∼100 s) windows, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA). IPSA and CSWA were anatomically similar in the default mode network, sensory cortex, insula and cerebellum. Our results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, it does not mandate a (semi-)arbitrary choice of window length and window overlap. A code for calculating IPSA is provided.
Publisher: Elsevier BV
Date: 10-2015
DOI: 10.1016/J.NEUROIMAGE.2015.06.091
Abstract: A robust functional bimodality is found in the long-range spatial correlations of newborn cortical activity, and it likely provides the developmentally crucial functional coordination during the initial growth of brain networks. This study searched for possible acute effects on this large scale cortical coordination after acute structural brain lesion in early preterm infants. EEG recordings were obtained from preterm infants without (n=11) and with (n=6) haemorrhagic brain lesion detected in their routine ultrasound exam. The spatial cortical correlations in band-specific litudes were examined within two litude regimes, high and low litude periods, respectively. Technical validation of our analytical approach showed that bimodality of this kind is a genuine physiological characteristic of each brain network. It was not observed in datasets created from uniform noise, neither is it found between randomly paired signals. Hence, the observed bimodality arises from specific interactions between cortical regions. We found that significant long-range litude correlations are found in most signal pairs in both groups at high litudes, but the correlations are generally weaker in newborns with brain lesions. The group difference is larger during high mode, however the difference did not have any statistically apparent topology. Graph theoretical analysis confirmed a significantly larger weight dispersion in the newborns with brain lesion. Comparison of graph measures to a child's performance at two years showed that lower clustering coefficient and weight dispersion were both correlated to better neurodevelopmental outcomes. Our findings suggest that the common preterm brain haemorrhage causes diffuse changes in the functional long-range cortical correlations. It has been recently recognized that the high mode network activity is crucial for early brain development. The present observations may hence offer a mechanistic link between early lesion and the later emergence of complex neurocognitive sequelae.
Publisher: Wiley
Date: 02-2016
DOI: 10.1002/HBM.23723
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 2017
Publisher: IOP Publishing
Date: 21-09-2022
Abstract: Objective . Automatic human alertness monitoring has recently become an important research topic with important applications in many areas such as the detection of drivers’ fatigue, monitoring of monotonous tasks that require a high level of alertness such as traffic control and nuclear power plant monitoring, and sleep staging. In this study, we propose that balanced dynamics of Electroencephalography (EEG) (so called EEG temporal complexity) is a potentially useful feature for identifying human alertness states. Recently, a new signal entropy measure, called range entropy (RangeEn), was proposed to overcome some limitations of two of the most widely used entropy measures, namely approximate entropy (ApEn) and S le Entropy (S En), and showed its relevance for the study of time domain EEG complexity. In this paper, we investigated whether the RangeEn holds discriminating information associated with human alertness states, namely awake, drowsy, and sleep and compare its performance against those of S En and ApEn. Approach . We used EEG data from 60 healthy subjects of both sexes and different ages acquired during whole night sleeps. Using a 30 s sliding window, we computed the three entropy measures of EEG and performed statistical analyses to evaluate the ability of these entropy measures to discriminate among the different human alertness states. Main results . Although the three entropy measures contained useful information about human alertness, RangeEn showed a higher discriminative capability compared to ApEn and S En especially when using EEG within the beta frequency band. Significance . Our findings highlight the EEG temporal complexity evolution through the human alertness states. This relationship can potentially be exploited for the development of automatic human alertness monitoring systems and diagnostic tools for different neurological and sleep disorders, including insomnia.
Publisher: IEEE
Date: 12-2008
Publisher: Cold Spring Harbor Laboratory
Date: 02-01-2020
DOI: 10.1101/2019.12.28.19016089
Abstract: Mark-up of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG mark-up is a time-consuming, subjective, and highly specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. The objective of this study was to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), which is a characteristic type of generalized IED seen in scalp EEG recordings of patients with Lennox-Gastaut syndrome (LGS), a severe form of drug-resistant generalized epilepsy. We studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient’s interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings. GPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This ‘bi-modal’ spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified likely epileptic events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED mark-up. GPFA events have a characteristic bi-modal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. Validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS. This study provides a novel methodology that paves the way for quick, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making. For ex le, automated quantification of generalized discharges may permit faster assessment of treatment response and estimation of future seizure risk.
Publisher: Oxford University Press (OUP)
Date: 05-05-2013
Abstract: The spontaneous brain activity exhibits long-range spatial correlations detected using functional magnetic resonance imaging (fMRI) signals in newborns when (1) long neuronal pathways are still developing, and (2) the electrical brain activity consists of developmentally unique, intermittent events believed to guide activity-dependent brain wiring. We studied this spontaneous electrical brain activity using multichannel electroencephalography (EEG) of premature and fullterm babies during sleep to assess the development of spatial integration during last months of gestation. Correlations of frequency-specific litudes were found to follow a robust bimodality: During low litudes (low mode), brain activity exhibited very weak spatial correlations. In contrast, the developmentally essential high- litude events (high mode) showed strong spatial correlations. There were no clear spatial patterns in the early preterm, but clear frontal and parieto-occipital modules at term age. A significant fronto-occipital gradient was also seen in the development of the graph measure clustering coefficient. Strikingly, no bimodality was found in the fMRI recordings of the fullterm babies, suggesting that early EEG activity and fMRI signal reflect different mechanisms of spatial coordination. The results are compatible with the idea that early developing human brain exhibits intermittent long-range spatial connections that likely provide the endogenous guidance for early activity-dependent development of brain networks.
Publisher: Research Square Platform LLC
Date: 08-03-2023
DOI: 10.21203/RS.3.RS-2631029/V1
Abstract: Changes in spontaneous brain activity at rest provide rich information about behavior and cognition. The mathematical properties of resting-state functional magnetic resonance imaging (rsfMRI) are a depiction of brain function and are frequently used to predict cognitive phenotypes. In idual characteristics such as age, gender, and total intracranial volume (TIV) play an important role in predictive modeling of rsfMRI (for ex le, as “confounders” in many cases). It is unclear, however, to what extent rsfMRI carries independent information from the in idual characteristics that is able to predict cognitive phenotypes. Here, we used kernel ridge regression modeling to thoroughly examine the predictability of four cognitive phenotypes in 20,000 healthy UK Biobank subjects. We extracted common rsfMRI features of functional brain connectivity (FC) and temporal complexity (TC). We assessed the ability of these features to predict outcomes in the presence and absence of age, gender, and TIV. Additionally, we assessed the predictiveness of age, gender, and TIV only. We find TC and FC features to perform comparably with regard to predicting cognitive phenotypes. As compared to rsfMRI features, in idual characteristics provide systematically better predictions with smaller s le sizes and, to some extent, in larger cohorts. It is also consistent across different levels of inherent temporal noise in rsfMRI. Our results suggest that when the objective is to perform cognitive predictions as opposed to understanding the relationship between brain and behavior, in idual characteristics outperform rsfMRI features.
Publisher: Elsevier BV
Date: 2015
Publisher: Mary Ann Liebert Inc
Date: 04-2016
Abstract: In iduals with focal epilepsy have heterogeneous sites of seizure origin. However, there may be brain regions that are common to most cases of intractable focal epilepsy. In this study, we aim to identify these using multivariate analysis of task-free functional MRI. Fourteen subjects with extratemporal focal epilepsy and 14 healthy controls were included in the study. Task-free functional MRI data were used to calculate voxel-wise regional connectivity with regional homogeneity (ReHo) and weighted degree centrality (DCw), in addition to regional activity using fraction of litude of low-frequency fluctuations (fALFF). Multivariate pattern analysis was applied to each of these metrics to discriminate brain areas that differed between focal epilepsy subjects and healthy controls. ReHo and DCw classified focal epilepsy subjects from healthy controls with high accuracy (89.3% and 75%, respectively). However, fALFF did not significantly classify patients from controls. Increased regional network activity in epilepsy subjects was seen in the ipsilateral piriform cortex, insula, and thalamus, in addition to the dorsal anterior cingulate cortex and lateral frontal cortices. Decreased regional connectivity was observed in the ventromedial prefrontal cortex, as well as lateral temporal cortices. Patients with extratemporal focal epilepsy have common areas of abnormality (ReHo and DCw measures), including the ipsilateral piriform cortex, temporal neocortex, and ventromedial prefrontal cortex. ReHo shows additional increase in the "salience network" that includes anterior insula and anterior cingulate cortex. DCw showed additional effects in the ipsilateral thalamus and striatum. These brain areas may represent key regional network properties underlying focal epilepsy.
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 08-2011
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 10-07-2023
DOI: 10.1038/S42003-023-05073-W
Abstract: Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high- litude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interin idual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of in idual-level phenotypes.
Publisher: Proceedings of the National Academy of Sciences
Date: 13-12-2018
Abstract: Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found ( i ) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data ( ii ) brain connectivity was lower during intervals of network switching ( iii ) brain areas with frequent network switching had greater temporal complexity ( iv ) brain areas with high network switching were located in association cortices and ( v ) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Oxford University Press (OUP)
Date: 20-02-2017
DOI: 10.1093/BRAIN/AWX007
Abstract: Epileptic spikes occur on the sub-second timescale and are known to involve not only epileptic foci but also large-scale distributed brain networks. There is likely to be a sequence of neural activity in multiple brain regions that occurs within the duration of a single spike, but standard electroencephalography-functional magnetic resonance imaging analyses, which use only the timing of the spikes to model the functional magnetic resonance imaging data, cannot determine the sequence of these activations. Our aim in this study is to temporally resolve these spatial activations to observe the spatiotemporal dynamics of the spike-related neural activity at a sub-second timescale. We studied eight focal epilepsy patients (age 11-42 years, six female) and used litude features of the electroencephalogram specific to different spike components (early and late peaks and troughs) to encode temporal information into our functional magnetic resonance imaging models. This enables us to associate each activation with a specific model of each of the spike components to infer the temporal order of these spike-related spatial activations. In seven of eight patients the distributed networks were associated with the late spike component. The focal activations were more variably coupled with time epochs, but tended to precede the distributed network effects. We also found that incorporating electroencephalogram features into the models increased sensitivity and in six patients revealed additional regions unseen in the standard analysis result. This included strong bilateral thalamus activation in two patients. We demonstrate the clinical utility of this approach in a patient who recently underwent a successful surgical resection of the region where we saw enhanced activation using electroencephalogram litude information specific to the early spike component. This focal cluster of activation was larger and more precisely tracked the anatomy compared to what was seen using the standard timing-based analysis. Our novel electroencephalography-functional magnetic resonance imaging data fusion approach, which utilizes information based on the single spike variability across all electroencephalogram channels, has the potential to help us better understand epileptic networks and aid in the interpretation of functional magnetic resonance imaging activation maps during treatment planning.
Publisher: Wiley
Date: 28-03-2016
DOI: 10.1002/HBM.23151
Publisher: Cold Spring Harbor Laboratory
Date: 16-09-2019
DOI: 10.1101/770826
Abstract: It has been hypothesized that resting state networks (RSNs) likely display unique temporal complexity fingerprints, quantified by their multi-scale entropy patterns [1]. This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of [1] was that resting state functional magnetic resonance imaging (rsfMRI) data from only 20 healthy in iduals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 1000 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-minute rsfMRI recordings and parcellated into 379 brain regions. We quantified multi-scale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multi-scale entropy is affected by the choice of its tolerance parameter ( r ) and embedding dimension ( m ), we repeated the analyses at multiple values of r and m including the values used in [1]. Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the sub-cortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T R ) after downs ling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across in iduals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. A strong relationship was observed between temporal complexity of RSNs and fluid intelligence (people’s capacity to reason and think flexibly) through step-wise regression analysis suggesting that complex dynamics of the human brain is an important attribute of high-level brain function. Finally, the results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales, likely due to the regulation of neural synchrony at local and global network levels.
Publisher: Cold Spring Harbor Laboratory
Date: 10-02-2023
DOI: 10.1101/2023.02.09.527898
Abstract: Current major efforts in human neuroimaging research aim to understand in idual differences and identify biomarkers for clinical applications. One particularly promising approach is the prediction of in idual-level behavioural phenotypes (e.g. treatment response, cognition) from brain imaging data. An essential prerequisite to identify replicable brain-behaviour prediction models is sufficient measurement reliability. By attenuating the relationship between two variables, low reliability increases the s le size necessary to identify an effect, making large datasets a necessity rather than an advantage. While previous work has evaluated the reliability of brain-based measures, the impact of the reliability of behavioural phenotypes has been largely neglected, as target selection for prediction is often guided by scientific interest or data availability. Here we demonstrate the impact of low phenotypic reliability on in idual-level prediction performance. Using simulated and empirical data from the Human Connectome Projects, we found that even moderate reliability levels of commonly used behavioural phenotypes can markedly limit the ability to link brain and behaviour when underlying relations are weak. Next, using 5000 subjects from the UK Biobank, we show that highly reliable data in smaller s les outperform large amounts of moderately reliable data. These findings suggest that research programmes focused on identifying generalizable brain–behaviour associations must seriously consider the reliability of outcome measures. Ultimately, a stronger focus on reliability will help reduce the financial and societal costs incurred in acquiring large-scale datasets with unreliable “markers” of behaviour.
Publisher: AIP Publishing
Date: 03-2009
DOI: 10.1063/1.3096413
Abstract: In this paper, we propose a new method for detecting regular behavior of time series: this method is based on the Poincaré section and the Higuchi fractal dimension. The new method aims to distinguish random signals from deterministic signals. In fact, our method provides a pattern for decision making about whether a signal is random or deterministic. We apply this method to different time series, such as chaotic signals, random signals, and periodic signals. We apply this method to ex les from all types of route to chaotic signals. This method has also been applied to data about iris tissues. The results show that the new method can distinguish different types of signals.
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 03-2012
Publisher: Cold Spring Harbor Laboratory
Date: 20-11-2021
DOI: 10.1101/2021.11.20.469367
Abstract: Dynamic models of cortical activity, as measured by functional magnetic resonance imaging (fMRI), have recently brought out important insights into the organization of brain function. In terms of temporal complexity, these hemodynamic signals have been shown to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the properties and spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures (i.e., Hurst exponent versus multiscale entropy) and reported high similarity between them. Second, we investigated the influence of experimental paradigms and found high task-specific complexity. We considered four mental tasks in the HCP database for the analysis: Emotion, Working memory, Social, and Language. Third, we tailored a recently-proposed statistical framework that incorporates the structural connectome, to assess the spatial distribution of complexity measures. These results highlight brain regions including parts of the default mode network and cingulate cortex with significantly stronger complex behaviour than the rest of the brain, irrespective of task. In sum, temporal complexity measures of fMRI are reliable markers of the cognitive status.
Location: Iran (Islamic Republic of)
Location: Switzerland
No related grants have been discovered for Amir Omidvarnia.