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Introduction: Cerebral blood flow (CBF) is reduced in cognitively impaired (CI) Alzheimer's disease (AD) patients. We checked the sensitivity of time-encoded arterial spin labeling (te-ASL) in measuring CBF alterations in individuals with positive AD biomarkers and associations with relevant biomarkers in cognitively unimpaired (CU) individuals., Methods: We compared te-ASL with single-postlabel delay (PLD) ASL in measuring CBF in 59 adults across the AD continuum, classified as CU amyloid beta (Aβ) negative (-), CU Aβ positive (+), and CI Aβ+. We sought associations of CBF with biomarkers of AD, cerebrovascular disease, synaptic dysfunction, neurodegeneration, and cognition in CU participants., Results: te-ASL was more sensitive at detecting CBF reduction in the CU Aβ+ and CI Aβ+ groups. In CU participants, lower CBF was associated with altered biomarkers of Aβ, tau, synaptic dysfunction, and neurodegeneration., Discussion: CBF reduction occurs early in the AD continuum. te-ASL is more sensitive than single-PLD ASL at detecting CBF changes in AD., Highlights: Lower CBF can be detected in CU subjects in the early AD continuum. te-ASL is more sensitive than single-PLD ASL at detecting CBF alterations in AD. CBF is linked to biomarkers of AD, synaptic dysfunction, and neurodegeneration., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Although the mechanisms of sudden unexpected death in epilepsy (SUDEP) are not yet well understood, generalised- or focal-to-bilateral tonic-clonic seizures (TCS) are a major risk factor. Previous studies highlighted alterations in structures linked to cardio-respiratory regulation; one structure, the amygdala, was enlarged in people at high risk of SUDEP and those who subsequently died. We investigated volume changes and the microstructure of the amygdala in people with epilepsy at varied risk for SUDEP since that structure can play a key role in triggering apnea and mediating blood pressure. The study included 53 healthy subjects and 143 patients with epilepsy, the latter separated into two groups according to whether TCS occur in years before scan. We used amygdala volumetry, derived from structural MRI, and tissue microstructure, derived from diffusion MRI, to identify differences between the groups. The diffusion metrics were obtained by fitting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. The analyses were performed at the whole amygdala level and at the scale of amygdaloid nuclei. Patients with epilepsy showed larger amygdala volumes and lower neurite density indices (NDI) than healthy subjects; the left amygdala volumes were especially enhanced. Microstructural changes, reflected by NDI differences, were more prominent on the left side and localized in the lateral, basal, central, accessory basal and paralaminar amygdala nuclei; basolateral NDI lowering appeared bilaterally. No significant microstructural differences appeared between epilepsy patients with and without current TCS. The central amygdala nuclei, with prominent interactions from surrounding nuclei of that structure, project to cardiovascular regions and respiratory phase switching areas of the parabrachial pons, as well as to the periaqueductal gray. Consequently, they have the potential to modify blood pressure and heart rate, and induce sustained apnea or apneusis. The findings here suggest that lowered NDI, indicative of reduced dendritic density, could reflect an impaired structural organization influencing descending inputs that modulate vital respiratory timing and drive sites and areas critical for blood pressure control., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies., Competing Interests: Declaration of Competing Interest None, (Copyright © 2021. Published by Elsevier Inc.)

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity., Competing Interests: AX, MK, GM No competing interests declared, (© 2021, Xifra-Porxas et al.)

Background: Disruptions in central autonomic processes in people with epilepsy have been studied through evaluation of heart rate variability (HRV). Decreased HRV appears in epilepsy compared to healthy controls, suggesting a shift in autonomic balance toward sympathetic dominance; recent studies have associated HRV changes with seizure severity and outcome of interventions. However, the processes underlying these autonomic changes remain unclear. We examined the nature of these changes by assessing alterations in whole-brain functional connectivity, and relating those alterations to HRV. Methods: We examined regional brain activity and functional organization in 28 drug-resistant epilepsy patients and 16 healthy controls using resting-state functional magnetic resonance imaging (fMRI). We employed an HRV state-dependent functional connectivity (FC) framework with low and high HRV states derived from the following four cardiac-related variables: 1. RR interval, 2. root mean square of successive differences (RMSSD), 4. low-frequency HRV (0.04-0.15 Hz; LF-HRV) and high-frequency HRV (0.15-0.40 Hz; HF-HRV). The effect of group (epilepsy vs. controls), HRV state (low vs. high) and the interactions of group and state were assessed using a mixed analysis of variance (ANOVA). We assessed FC within and between 7 large-scale functional networks consisting of cortical regions and 4 subcortical networks, the amygdala, hippocampus, basal ganglia and thalamus networks. Results: Consistent with previous studies, decreased RR interval (increased heart rate) and decreased HF-HRV appeared in people with epilepsy compared to healthy controls. For both groups, fluctuations in heart rate were positively correlated with BOLD activity in bilateral thalamus and regions of the cerebellum, and negatively correlated with BOLD activity in the insula, putamen, superior temporal gyrus and inferior frontal gyrus. Connectivity strength in patients between right thalamus and ventral attention network (mainly insula) increased in the high LF-HRV state compared to low LF-HRV; the opposite trend appeared in healthy controls. A similar pattern emerged for connectivity between the thalamus and basal ganglia. Conclusion: The findings suggest that resting connectivity patterns between the thalamus and other structures underlying HRV expression are modified in people with drug-resistant epilepsy compared to healthy controls., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kassinopoulos, Harper, Guye, Lemieux and Diehl.)

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses 1 . The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset 2-5 . Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Background: It has been suggested that the use of window functions, other than the rectangular, in the sliding window method, may be beneficial for reducing the effects of motion-related outliers in the time-series, when assessing dynamic functional connectivity (dFC) in resting-state fMRI (rs-fMRI)., Methodology: Ten window functions for a wide range of window lengths (20-150 s) combined with Pearson and Kendall correlation metrics, were investigated. One hundred high quality rs-fMRI datasets from healthy controls, were used to systematically assess the effect of varying the window function and length on dFC assessment. To this end, two approaches were implemented: a) simulated outliers were added to the experimental data and b) the experimental data were divided into low and high motion subgroups., Results: The presence of experimental motion-noise tended to inflate the number of dynamic connections for longer (≥100 s) wide-shaped windows, while shorter (20-30 s) narrow-shaped windows exhibited increased sensitivity in the presence of simulated outliers. Moreover, window sizes from 60 s to 90 s were mildly affected by motion-related effects. In most cases, the number of dynamic connections increased, and gradually lower frequencies were captured, with an increasing window size., Conclusions: Subject motion considerably affects the obtained dFC patterns; thus, it is preferable to perform motion artefact removal in the pre-processing stage rather than using alternative window functions to mitigate their effects. Provided that motion-noise is not excessive, the choice of a rectangular window is adequate. Finally, low frequency oscillations in functional connectivity seem to play an important role in the context of dFC assessment., Competing Interests: Declaration of Competing Interest None., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Muscle contractions are associated with a decrease in beta oscillatory activity, known as movement-related beta desynchronization (MRBD). Older adults exhibit a MRBD of greater amplitude compared to their younger counterparts, even though their beta power remains higher both at rest and during muscle contractions. Further, a modulation in MRBD has been observed during sustained and dynamic pinch contractions, whereby beta activity during periods of steady contraction following a dynamic contraction is elevated. However, how the modulation of MRBD is affected by aging has remained an open question. In the present work, we investigated the effect of aging on the modulation of beta oscillations and their putative link with motor performance. We collected magnetoencephalography (MEG) data from younger and older adults during a resting-state period and motor handgrip paradigms, which included sustained and dynamic contractions, to quantify spontaneous and motor-related beta oscillatory activity. Beta power at rest was found to be significantly increased in the motor cortex of older adults. During dynamic hand contractions, MRBD was more pronounced in older participants in frontal, premotor and motor brain regions. These brain areas also exhibited age-related decreases in cortical thickness; however, the magnitude of MRBD and cortical thickness were not found to be associated after controlling for age. During sustained hand contractions, MRBD exhibited a decrease in magnitude compared to dynamic contraction periods in both groups and did not show age-related differences. This suggests that the amplitude change in MRBD between dynamic and sustained contractions is larger in older compared to younger adults. We further probed for a relationship between beta oscillations and motor behaviour and found that greater MRBD in primary motor cortices was related to degraded motor performance beyond age, but our results suggested that age-related differences in beta oscillations were not predictive of motor performance., (Crown Copyright © 2019. Published by Elsevier Inc. All rights reserved.)

Motor performance decline observed during aging is linked to changes in brain structure and function, however, the precise neural reorganization associated with these changes remains largely unknown. We investigated the neurophysiological correlates of this reorganization by quantifying functional and effective brain network connectivity in elderly individuals (n = 11; mean age = 67.5 years), compared to young adults (n = 12; mean age = 23.7 years), while they performed visually-guided unimanual and bimanual handgrips inside the magnetoencephalography (MEG) scanner. Through a combination of principal component analysis and Granger causality, we observed age-related increases in functional and effective connectivity in whole-brain, task-related motor networks. Specifically, elderly individuals demonstrated (i) greater information flow from contralateral parietal and ipsilateral secondary motor regions to the left primary motor cortex during the unimanual task and (ii) decreased interhemispheric temporo-frontal communication during the bimanual task. Maintenance of motor performance and task accuracy in elderly was achieved by hyperactivation of the task-specific motor networks, reflecting a possible mechanism by which the aging brain recruits additional resources to counteract known myelo- and cytoarchitectural changes. Furthermore, resting-state sessions acquired before and after each motor task revealed that both older and younger adults maintain the capacity to adapt to task demands via network-wide increases in functional connectivity. Collectively, our study consolidates functional connectivity and directionality of information flow in systems-level cortical networks during aging and furthers our understanding of neuronal flexibility in motor processes., (© 2019 Wiley Periodicals, Inc.)

Neural populations coordinate at fast subsecond time-scales during rest and task execution. As a result, functional brain connectivity assessed with different neuroimaging modalities (EEG, MEG, fMRI) may also change over different time scales. In addition to the more commonly used sliding window techniques, the General Linear Kalman Filter (GLFK) approach has been proposed to estimate time-varying brain connectivity. In the present work, we propose a modification of the GLFK approach to model timevarying connectivity. We also propose a systematic method to select the hyper-parameters of the model. We evaluate the performance of the method using MEG and EMG data collected from 12 young subjects performing two motor tasks (unimanual and bimanual hand grips), by quantifying time-varying cortico-cortical and corticomuscular coherence (CCC and CMC). The CMC results revealed patterns in accordance with earlier findings, as well as an improvement in both time and frequency resolution compared to sliding window approaches. These results suggest that the proposed methodology is able to unveil accurate time-varying connectivity patterns with an excellent time resolution.

The self-assembly of short peptides into fibrous nanostructures (such as fibrils and tubes) has recently become the subject of intense theoretical and experimental scrutiny, as such assemblies are promising candidates for nanobiotechnological applications. The sequences of natural fibrous proteins may provide a rich source of inspiration for the design of such short self-assembling peptides. We describe the self-assembly of the aspartate-rich undecapeptide (NH3(+)-LSGSDSDTLTV-NH2), a sequence derived from the shaft of the adenovirus fiber. We demonstrate that the peptide assembles experimentally into amyloid-type fibrils according to widely accepted diagnostic criteria. In addition, we investigate an aqueous solution of undecapeptides by molecular dynamics simulations with an implicit (GB) solvent model. The peptides are frequently arranged in intermolecular β-sheets, in line with their amyloidogenic propensity. On the basis of both experimental and theoretical insights, we suggest possible structural models of the fibrils and their potential use as scaffolds for templating of inorganic materials.

Background: Internet addictive behaviors are associated with a plethora of psychosocial adversities. The study objectives were to assess the determinants and psychosocial correlates associated with Internet addictive behaviors among adolescents., Materials and Methods: A cross-sectional study design was applied among a random sample (n=805) of Cypriot adolescents (mean age: 14.7 years). Self-completed questionnaires, including Internet use characteristics, Young Internet Addiction Test, and Strengths and Difficulties Questionnaire, were utilized., Results: Among the study population, the prevalence rates of borderline addictive Internet use (BIU) and addictive Internet use (AIU) were 18.4% and 2%, respectively. Adolescents with BIU had an increased likelihood of concomitantly presenting with abnormal peer relations (AOR: 5.28; 95% confidence interval, CI: 3.37-23.38), conduct problems (AOR: 4.77; 95% CI: 2.82-8.08), hyperactivity (AOR: 5.58; 95% CI: 2.58-12.10) and emotional symptoms (AOR: 2.85; 95% CI: 1.53-5.32). Adolescent AIU was significantly associated with abnormal conduct (AOR: 22.31; 95% CI: 6.90-72.19), peer problems (AOR: 7.14; 95% CI: 1.36-37.50), emotional symptoms (AOR: 19.06; 95% 6.06-60.61), and hyperactivity (AOR: 9.49, 95% CI: 1.87-48.19). The determinants of BIU and AIU included accessing the Internet for the purposes of retrieving sexual information (AOR: 1.17; 95% CI: 1.17-3.23) and participating in games with monetary awards (AOR: 1.90; 95% CI: 1.15-3.14)., Conclusions: Both BIU and AIU were adversely associated with notable behavioral and social maladjustment among adolescents.

Background and Aims: Adolescent Internet gambling is associated with concomitant addictive behaviors. This study aimed to assess the prevalence of Internet gambling practices, its impact upon psychosocial development and to evaluate the association between gambling practices and Internet addictive behavior among Cypriot adolescents., Methods: A cross-sectional study was conducted in a convenience sample (n = 805) of adolescents attending selected public schools (9th and 10th grades) in Cyprus. Anonymous self-completed questionnaires were used including the Internet Addiction Test and the Strengths and Difficulties Questionnaire., Results: Among the study population (n = 805), approximately one third (n = 28; 34.9%) reported Internet gambling. Internet gamblers were twice as likely to utilize Internet café portals (adjusted odds ratio for gender and age, AOR: 2.13; 95% confidence interval, 95% CI: 1.56-2.91) for interactive game-playing (AOR: 6.84; 95% CI: 4.23-11.07), chat-rooms (AOR: 2.57; 95% CI: 1.31-4.85), and retrieval of sexual information (AOR: 1.99; 95% CI: 1.42-2.81). Among Internet gamblers 26.0% (n = 73) reported borderline addictive Internet use and 4.3% (n = 12) addictive behavior. Internet gamblers more often had comprehensive psychosocial and emotional maladjustment (AOR: 4.00; 95% CI: 1.97-8.13), including Abnormal Conduct Problems (AOR: 3.26; 95% CI: 2.00-5.32), Emotional Symptoms (AOR: 1.78; 95% CI: 1.02-3.11), and Peer Problems (AOR: 2.44; 95% CI: 1.08-5.48) scores. The multivariate regression analyses indicated that the single independent predictor associated with Internet addictive behavior was Internet gambling (AOR: 5.66; 95% CI: 1.45-22.15)., Discussion: Internet gambling is associated with addictive Internet use, as well as emotional maladjustment and behavioral problems, among Cypriot adolescents., Conclusions: Longitudinal studies are needed to elucidate whether Internet gambling constitutes a risk factor for the development of Internet addictive behavior among adolescents.