Biology subjects

Tetereva, A.

Publications and source records attributed to Tetereva, A..

9 recordsLinked to original sources

bioRxiv2026

Source-space EEG functional connectivity and prediction of cognition in Parkinsons disease: No added benefit of individualized head models over standard templates

IntroductionCognitive decline is a major non-motor feature of Parkinsons disease (PD), but reliable and accessible biomarkers remain limited. Resting-state electroencephalography (EEG) is a promising candidate because it is low-cost, portable, and well suited to repeated assessment. Recent work has increasingly focused on source-space functional connectivity (FC) for the prediction of cognition. However, the influence of source modelling based on an individualized MRI-based head model relative to that based on standard template model is unknown. MethodsTo compare these two source-space EEG FC methods, we analysed EEG data from the New Zealand Parkinsons Progression Programme, including 136 people with PD and 51 age-similar controls. Source space resting-state EEG, parcellated with the HCP-MMP1 atlas, was used to derive amplitude envelope correlation (AEC) and debiased weighted phase lag index (dwPLI) across six canonical frequency bands. The resulting twenty-four FC modalities were evaluated using six machine-learning regression algorithms within a nested cross-validation framework. ResultsTheta-, alpha-, and beta-band FC showed the most consistent prediction of global cognition. The strongest performance was observed for theta- and alpha-band AEC and dwPLI features (max R{superscript 2} = 0.170, 95% CI = 0.067-0.262; max r = 0.439, 95% CI = 0.328-0.537). Standard and individualized head models showed comparable predictive performance across nearly all modalities. The feature-importance neuroanatomical patterns for Cole-Anticevic networks were also similar between the two head-model options. ConclusionsWe found that source-space resting-state EEG FC can predict cognitive performance in PD. The comparability of the two head models suggests that the more user-friendly and less resource-intensive standard template head model is sufficient for this purpose. This supports feasible, scalable, and clinically accessible EEG-based FC biomarkers of cognition in PD.

neuroscience↗

bioRxiv2024

Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed a machine-learning "stacking" approach that draws information from whole-brain magnetic resonance imaging (MRI) across different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults (n=873, 22-35 years old) and Human Connectome Projects-Aging (n=504, 35-100 years old) and the Dunedin Multidisciplinary Health and Development Study (Dunedin Study, n=754, 45 years old). For predictability, stacked models led to out-of-sample r[~].5-.6 when predicting cognitive abilities at the time of scanning, primarily driven by task-fMRI contrasts. Notably, using the Dunedin Study, we were able to predict participants cognitive abilities at ages 7, 9, and 11 using their multimodal MRI at age 45, with an out-of-sample r of 0.52. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities. Significance statementScientists have had limited success in predicting cognitive abilities from brain MRI. We proposed a machine learning method, called stacking, to draw information across different types of brain MRI. Using three large databases (n=2,131, 22-100 years old), we found stacking to make the prediction of cognitive abilities 1) closer to actual cognitive scores when applied to a new individual, not part of the modelling process, 2) reliable over times and 3) applicable to the data collected from different age groups and MRI scanners. Indeed, stacking, especially with fMRI task contrasts, allowed us to use MRI of people aged 45 to predict their childhood cognitive abilities reasonably well. Accordingly, stacking may help MRI realise its potential to predict cognitive abilities.

neuroscience↗

bioRxiv2023

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Cognitive Decline

Fluid cognition usually declines as people grow older. For decades, neuroscientists have been on a quest to search for a biomarker that can help capture fluid cognition. One well-known candidate is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI data. Here we aim to formally evaluate the utility of Brain Age as a biomarker for capturing fluid cognition among older individuals. Using 504 aging participants (36-100 years old) from the Human Connectome Project in Aging, we created 26 age-prediction models for Brain Age based on different combinations of MRI modalities. We first tested how much Brain Age from these age-prediction models added to what we had already known from a persons chronological age in capturing fluid cognition. Based on the commonality analyses, we found a large degree of overlap between Brain Age and chronological age, so much so that, at best, Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition. Next, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition, thus not improving the models utility to capture cognitive abilities. Lastly, we tested how much Brain Age missed the variation in the brain MRI that could explain fluid cognition. To capture this variation in the brain MRI that explained fluid cognition, we computed Brain Cognition, or a predicted value based on prediction models built to directly predict fluid cognition (as opposed to chronological age) from brain MRI data. We found that Brain Cognition captured up to an additional 11% of the total variation in fluid cognition that was missing from the model with only Brain Age and chronological age, leading to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.

neuroscience↗

bioRxiv2021

Integrating Task-Based Functional MRI Across Tasks Markedly Boosts Prediction and Reliability of Brain-Cognition Relationship

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n=873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r=.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=~.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r=.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r=.56) while still providing excellent test-retest reliability (ICC=~.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities. HighlightsO_LINon-task MRI (sMRI, rs-fMRI) are often used for the brain-cognition relationship. C_LIO_LITask-based fMRI has been deemed unreliable for capturing individual differences. C_LIO_LIWe tested if drawing task-based fMRI information across regions/tasks improves prediction and reliability of the brain-cognition relationship. C_LIO_LIOur approach boosts prediction of task-based fMRI over non-task MRI. C_LIO_LIOur approach renders task-based fMRI reliable over time. C_LIO_LIOur approach shows the importance of the fronto-parietal areas in cognition. C_LI

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bioRxiv2020

The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity

The resting brain at wakefulness is active even in the absence of goal-directed behavior or salient stimuli. However, patterns of this resting-state (RS) activity can undergo alterations following exposure to meaningful stimuli. This study aimed to develop an unbiased method to detect such changes in the RS activity after exposure to emotionally meaningful stimuli. For this purpose, we used functional magnetic resonance imaging (fMRI) of RS brain activity before and after the acquisition and extinction of experimental conditioned fear. A group of healthy volunteers participated in three fMRI sessions: a RS before fear conditioning, a fear extinction session, and a RS immediately after fear extinction. The fear-conditioning paradigm consisted of three neutral visual stimuli paired with a partial reinforcement by a mild electric current. We used both linear and non-linear dimensionality reduction approaches to distinguish between the initial RS and the RS after stimuli exposure. The principal component analysis (PCA) as a linear dimensionality reduction method did not differentiate these states. Using the non-linear Laplacian eigenmaps manifold learning method, we were able to show significant differences between the two RSs at the level of individual participants. This detection was further improved by smoothing the BOLD signal with the wavelet multiresolution analysis. The developed method can improve the discrimination of functional states collected in longitudinal fMRI studies.

neuroscience↗

bioRxiv2020

Reproducible correlations of corpus callosum and cingulum generalized fractional anisotropy with anxiety ratings in healthy participants

Diffusion tensor imaging revealed that trait anxiety predicts the microstructural properties of fiber tracts between the anterior cingulate cortex, prefrontal cortex, and amygdala. However, the whole-brain structural connectivity has been rarely reported in the non-clinical populations with marginal deviations in trait anxiety and substantial changes in state anxiety. This work is aimed to assess the correlation of state and trait anxiety inventory (STAI) scores and their one-day deviation with white matter connectivity at the whole-brain scale. 64-direction diffusion-weighted images were collected in 25 participants without prior complaints of excess anxiety and/or clinical history of anxiety disorders. The self-reported ranking of STAI was collected twice with 24-hour interval: one day before and several minutes before the MRI scanning. A correlation analysis between the generalized fractional anisotropy (GFA) and the STAI ratings was performed for all regions of the brain. According to the diffusion connectometry, the most reproducible positive correlation of GFA and anxiety scores was observed for corpus callosum. For both days of psychological assessment, the left cingulum GFA correlated negatively with State anxiety ratings, while the right cingulum GFA was strongly associated with Trait anxiety. Our results suggest that the density of the corpus callosum and bilateral cingulum tracts are associated with the individual level of anxiety.

neuroscience↗

bioRxiv2019

Variance and Scale-Free Properties of Resting-State BOLD Signal Alter after Fear Memory Acquisition and Extinction

Previous studies showed differences in brain dynamics during rest and different tasks. We aimed to find changes of variance and scale-free properties of blood oxygenation level-dependent (BOLD) signal between resting-state sessions before and after fear learning and fear memory extinction in twenty-three healthy right-handed volunteers. During a 1-hour break between MRI-scanning, subjects passed through fear extinction procedure, followed by Pavlovian fear conditioning with weak electrical stimulation. After preprocessing, we extracted the average time course of BOLD signal from 245 regions of interest (ROI) taken from the resting-state functional atlas. The variance of the BOLD signal in and Hurst exponent (H), which reflects the scale-free dynamic, were compared in resting states before after fear learning. Six ROIs showed a significant difference in H after fear extinction, including areas from the fear and memory networks. In consistency with the previous results, H decreased during fear extinction but then increased higher than before, specifically in areas related to fear extinction network, whereas the other ROIs restored H to the initial level. The BOLD signal variance showed distinct behavior: the variance in subcortical regions increased permanently, while cortical areas demonstrated a decreasing variance during fear extinction and the reverse growth in resting state after fear extinction. A limited number of ROIs showed both changes in H and the variance. Our results suggest that the variability and scale-free properties of the BOLD signal are sensitive indicators of the residual brain activity related to the recent experience.

neuroscience↗

bioRxiv2019

Longitudinal Changes of Resting-State Functional Connectivity of Amygdala Following Fear Learning and Extinction

Altered functional connectivity of the amygdala has been observed in a resting state immediately after fear learning, even one day after aversive exposure. The persistence of increased resting-state functional connectivity (rsFC) of the amygdala has been a critical finding in patients with stress and anxiety disorders. However, longitudinal changes in amygdala rsFC have rarely been explored in healthy participants. To address this issue, we studied the rsFC of the amygdala in two groups of healthy volunteers. The control group participated in three fMRI scanning sessions of their resting state at the first visit, one day, and one week later. The experimental group participated in three fMRI sessions on the first day: a resting state before fear conditioning, a fear extinction session, and a resting state immediately after fear extinction. Furthermore, this group experienced scanning after one day and week. The fear-conditioning paradigm consisted of visual stimuli with a distinct rate of partial reinforcement by electric shock. During the extinction, we presented the same stimuli in another sequence without aversive pairing. In the control group, rsFC maps were statistically similar between sessions for the left and right amygdala. However, in the experimental group, the increased rsFC mainly of the left amygdala was observed after extinction, one day, and one week. The between-group comparison also demonstrated an increase in the left amygdala rsFC in the experimental group. Our results indicate that functional connections of the left amygdala influenced by fear learning may persist for several hours and days in the human brain.

neuroscience↗

bioRxiv2018

Lateral Asymmetry in Weekly Reproducibility of Resting State Amygdala Functional Connectivity

Abnormal functional connectivity of the amygdala with several other brain regions has been observed in patients with higher anxiety or post-traumatic stress disorder, both in a resting state and threatening conditions. However, findings on the specific connections of the amygdala might be varied due to temporal and individual fluctuations in the resting state functional connectivity (rsFC) of the amygdala and its lateral asymmetry, as well as possible variability in anxiety among healthy subjects. We studied reproducibility of rsFC data for the right and left amygdala, obtained by functional magnetic resonance imaging twice in a one-week interval in 20 healthy volunteers with low to moderate anxiety. We found resting-state amygdala network, which included not only areas involved in the emotion circuit, but regions of the default mode network (DMN) associated with memory and other brain areas involved in motor inhibition and emotion suppression. The amygdala network was stable in time and within subjects, but between-session reproducibility was asymmetrical for the right and left amygdala rsFC. The right amygdala had more significant connections with DMN regions and the right ventrolateral prefrontal cortex. The rsFC values of the right amygdala were more sustained across the week than the left amygdala rsFC. Our results support a hypothesis of functional lateralization of the amygdala. The left amygdala is more responsible for the conscious processing of threats, which may produce more variable rsFC; the right amygdala rsFC is more stable due to its greater engagement in continuous automatic evaluation of stimuli.\n\nHighlightsO_LIAmygdala resting state network included areas of emotion circuit and motor control\nC_LIO_LIDuring rest amygdala was functionally connected with areas of default mode network\nC_LIO_LIFunctional connectivity of the right amygdala was more sustained across the week\nC_LIO_LIFunctional connections of amygdala network were more stable in the right hemisphere\nC_LI

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