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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. •Physiological response functions (PRF) vary considerably across subjects/sessions.•Scan-specific PRF curves can be obtained from data records longer than 5 min.•The shape of the cardiac response function is linked to the mean heart rate (HR).•Brain regions affected by HR and breathing patterns exhibit substantial overlap.•HR and breathing patterns affect distinct regions as compared to cardiac pulsatility.

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.

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.

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. [Display omitted] •We observed increased beta desynchronization during dynamic contractions in aging.•Age groups did not differ in beta desynchronization during sustained contractions.•Age-related differences in beta oscillations were not associated with performance.

Before they become invasive, early cancer cells exhibit specific and characteristic changes that are routinely used by a histopathologist for diagnosis. Currently, these early abnormalities are only detectable ex vivo by histopathology or, non-invasively and in vivo, by optical modalities that have not been clinically implemented due to their complexity and their limited penetration in tissues. Optical coherence tomography (OCT) is a noninvasive medical imaging technology with increasing clinical applications in areas such as ophthalmology, cardiology, gastroenterology, etc. In addition to imaging the tissue micro-structure, OCT can also provide additional information, describing the constituents and state of the cellular components of the tissue. Estimates of the nuclear size, sub-cellular morphological variations, dispersion and index of refraction can be extracted from the OCT images and can serve as diagnostically useful biomarkers. Moreover, the development of fully automated algorithms for tissue segmentation and feature extraction and the application of machine learning, can further enhance the clinical potential of OCT. When fully exploited, OCT has the potential to lead to accurate and sensitive, image-derived, biomarkers for disease diagnosis and treatment monitoring of cancer.

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.

Olfactory dysfunction (OD) is common in elders and almost ubiquitous in patients with neurodegenerative diseases. Early accumulation of pathological protein aggregates in the olfactory bulb and related structures may relate to impaired solute clearance by the glymphatic system - brain waste clearance system mainly active during sleep-. We aimed to explore the association between olfactory performance and diffusion tensor imaging along perivascular spaces (DTI-ALPS) as a proxy of glymphatic function in cognitively unimpaired (CU) individuals with family history of dementia. We analyzed data from 1,369 ALFA cohort participants (age range 47-77 yo, mean[SD]:59.6[6.5], 62.5% women) with family history of dementia (88.5% Alzheimer's disease). Olfaction was assessed with the 16 item-Sniffin' Sticks identification (SSI) test. DTI-ALPS index was computed from diffusion-weighted MR images. OD was defined using a standard cut-off (SSI scores <12,). We explored the association between DTI-ALPS and presence of OD using logistic regression, with OD as dependent variable, high/low median split DTI-ALPS groups as predictor, and age, education, sex, and APOE-ɛ4 status as covariates. Due to the ceiling effect shown by SSI scores, a Generalized Linear Model (GLM) with a log-link function was used to explore the associations between continuous SSI scores and DTI-ALPS measures adjusted by covariates. OD was present in 18.8% of participants and associated with older age (OR: 1.06 [1.04-1.09]; p < 0.001), male sex (OR: 1.51 [1.14-2.01]; p =  0.004), and lower DTI-ALPS scores (OR: 1.35 [1.01-1.80]; p =  0.04). In stratified analyses, similar albeit non-significant trends were observed for both sexes (female OR:1.37 [0.94-2.01]; p = 0.1; male OR:1.34 [0.86-2.11]; p = 0.2). DTI-ALPS scores were positively associated with continuous SSI scores (est=1.084; p = 0.007), particularly in individuals without OD (est=1.04; p = 0.02). CU individuals with low performance on a short olfactory identification test exhibit reduced glymphatic function as measured by DTI-ALPS. This association was observed even in the normal olfactory range. Further longitudinal studies are needed to validate OD as a potential clinical marker of glymphatic dysfunction in individuals at risk of cognitive impairment.

Background Olfactory dysfunction (OD) is common in elders and almost ubiquitous in patients with neurodegenerative diseases. Early accumulation of pathological protein aggregates in the olfactory bulb and related structures may relate to impaired solute clearance by the glymphatic system ‐ brain waste clearance system mainly active during sleep‐. We aimed to explore the association between olfactory performance and diffusion tensor imaging along perivascular spaces (DTI‐ALPS) as a proxy of glymphatic function in cognitively unimpaired (CU) individuals with family history of dementia. Method We analyzed data from 1,369 ALFA cohort participants (age range 47‐77 yo, mean[SD]:59.6[6.5], 62.5% women) with family history of dementia (88.5% Alzheimer's disease). Olfaction was assessed with the 16 item‐Sniffin’ Sticks identification (SSI) test. DTI‐ALPS index was computed from diffusion‐weighted MR images. OD was defined using a standard cut‐off (SSI scores <12,). We explored the association between DTI‐ALPS and presence of OD using logistic regression, with OD as dependent variable, high/low median split DTI‐ALPS groups as predictor, and age, education, sex, and APOE‐ɛ4 status as covariates. Due to the ceiling effect shown by SSI scores, a Generalized Linear Model (GLM) with a log‐link function was used to explore the associations between continuous SSI scores and DTI‐ALPS measures adjusted by covariates. Result OD was present in 18.8% of participants and associated with older age (OR: 1.06 [1.04–1.09]; p < 0.001), male sex (OR: 1.51 [1.14–2.01]; p =  0.004), and lower DTI‐ALPS scores (OR: 1.35 [1.01–1.80]; p =  0.04). In stratified analyses, similar albeit non‐significant trends were observed for both sexes (female OR:1.37 [0.94‐2.01]; p = 0.1; male OR:1.34 [0.86‐2.11]; p = 0.2). DTI‐ALPS scores were positively associated with continuous SSI scores (est=1.084; p = 0.007), particularly in individuals without OD (est=1.04; p = 0.02). Conclusion CU individuals with low performance on a short olfactory identification test exhibit reduced glymphatic function as measured by DTI‐ALPS. This association was observed even in the normal olfactory range. Further longitudinal studies are needed to validate OD as a potential clinical marker of glymphatic dysfunction in individuals at risk of cognitive impairment.

Altered resting-state functional connectivity (RSFC) has been reported in early Alzheimer's disease (AD). Graph metrics derived from RSFC networks provide valuable insights into brain organization. However, their potential in characterizing early network dysfunction and their relationship with AD biomarkers and cognitive performance remains understudied. Using RSFC data from 326 cognitively unimpaired (CU) individuals in the ALFA cohort (mean age=60.8, SD=4.74), we analyzed graph metrics in relation to cerebrospinal fluid (CSF) biomarkers. CSF Aβ42 and Aβ40 were assessed with the NeuroToolKit, a panel of exploratory robust prototype assays, while p-tau181 was measured with the Elecsys® Phospho-Tau (181P) CSF immunoassay (both Roche Diagnostics International Ltd). Interactions with age and sex were further inspected. RSFC networks were computed from 218 regions (Brainnetome atlas) using CONN, and thresholded at a density of 35%. The following graph metrics were extracted: average path length (APL), Local Efficiency (LE), Betweenness Centrality (BC), Closeness Centrality (CC), and Strength. Linear regression models assessed associations between CSF biomarkers and graph metrics, adjusting for age, sex, years of education, and APOE-ε4. Interactions between CSF biomarkers and graph metrics were analyzed for their impact on longitudinal cognitive measures (Preclinical Alzheimer Cognitive Composite [PACC]; mean follow-up=3.35 y, SD=0.53). We found a positive main effect of CSF Aβ42/40 (p = 0.011) and p-tau181 (p = 0.012) on BC of the Dorsal Attention Network (DAN) (Figure 1). Significant interactions between Aβ42/40 and sex were observed on the APL, CC and Strength of the Default Mode Network (DMN) (Figure 2). Finally, significant interactions were observed between Aβ42/40 and graph metrics of the DMN- APL (p = 0.005), LE (p = 0.009), and CC (p = 0.008)- on PACC changes (Figure 3). Our data suggest that, in CU individuals, soluble Ab and p-tau exert opposite effects in the DAN information flow. Moreover, our interaction models suggest that a lower integration between DMN and the rest of the brain, as well as a lower centrality of DMN regions might be beneficial to preserve cognitive performance in the presence of AD. This study highlights the role of network topology in early AD and its potential to support cognitive resilience, providing potential targets for intervention.

Background Altered resting‐state functional connectivity (RSFC) has been reported in early Alzheimer's disease (AD). Graph metrics derived from RSFC networks provide valuable insights into brain organization. However, their potential in characterizing early network dysfunction and their relationship with AD biomarkers and cognitive performance remains understudied. Methods Using RSFC data from 326 cognitively unimpaired (CU) individuals in the ALFA cohort (mean age=60.8, SD=4.74), we analyzed graph metrics in relation to cerebrospinal fluid (CSF) biomarkers. CSF Aβ42 and Aβ40 were assessed with the NeuroToolKit, a panel of exploratory robust prototype assays, while p‐tau181 was measured with the Elecsys® Phospho‐Tau (181P) CSF immunoassay (both Roche Diagnostics International Ltd). Interactions with age and sex were further inspected. RSFC networks were computed from 218 regions (Brainnetome atlas) using CONN, and thresholded at a density of 35%. The following graph metrics were extracted: average path length (APL), Local Efficiency (LE), Betweenness Centrality (BC), Closeness Centrality (CC), and Strength. Linear regression models assessed associations between CSF biomarkers and graph metrics, adjusting for age, sex, years of education, and APOE‐ε4. Interactions between CSF biomarkers and graph metrics were analyzed for their impact on longitudinal cognitive measures (Preclinical Alzheimer Cognitive Composite [PACC]; mean follow‐up=3.35 y, SD=0.53). Results We found a positive main effect of CSF Aβ42/40 (p = 0.011) and p‐tau181 (p = 0.012) on BC of the Dorsal Attention Network (DAN) (Figure 1). Significant interactions between Aβ42/40 and sex were observed on the APL, CC and Strength of the Default Mode Network (DMN) (Figure 2). Finally, significant interactions were observed between Aβ42/40 and graph metrics of the DMN— APL (p = 0.005), LE (p = 0.009), and CC (p = 0.008)— on PACC changes (Figure 3). Conclusion Our data suggest that, in CU individuals, soluble Ab and p‐tau exert opposite effects in the DAN information flow. Moreover, our interaction models suggest that a lower integration between DMN and the rest of the brain, as well as a lower centrality of DMN regions might be beneficial to preserve cognitive performance in the presence of AD. This study highlights the role of network topology in early AD and its potential to support cognitive resilience, providing potential targets for intervention.