Biology subjects

Correia, M. M.

Publications and source records attributed to Correia, M. M..

9 recordsLinked to original sources

bioRxiv2026

Test-retest reliability of resting-state fMRI functional connectivity: impact of scan length and number of participants

Resting-state functional MRI has been widely used to study brain connectivity, yet the test-retest reliability of commonly used metrics remains a concern. To improve reliability, extended scan lengths and larger subject cohorts are often recommended. However, these solutions can be impractical and pose challenges, particularly in studies of clinical populations. Here, we systematically assess the reliability of two main types of functional connectivity measures: node-based connectome metrics (edge-level intraclass correlation coefficient [ICC], connectome-level ICC, fingerprinting, and discriminability); and voxel-based resting-state networks (RSNs) (spatial similarity of independent component analysis [ICA] derived RSN maps quantified using the Dice coefficient). Using data from the Human Connectome Project, we evaluated the effects of scan length (3.6, 7.2, 10.8, and 14.4 minutes) and number of participants (10, 20, 50, and 100), on both within-session and between-session reliability. We found that multivariate connectome metrics demonstrated greater reliability than edge-level measures, and that scan length had stronger influence on test-retest reliability than the number of participants. For connectome metrics, 14 minutes of scanning and a cohort of approximately 10 participants were sufficient to achieve reliable estimates. In contrast, RSN measures benefited from larger cohort sizes. Our findings provide practical guidelines for designing resting-state fMRI studies in terms of scan length and number of participants, balancing reliability and feasibility. Ultimately, protocol choices should be guided by the specific study objectives and the functional connectivity metric of interest. KEY POINTSO_LIThis work systematically evaluates the within- and between-session test-retest reliability of node-based and voxel-based resting-state fMRI functional connectivity metrics as a function of scan length and number of participants. C_LIO_LILonger scan length ([~]11-14 minutes) improved the reliability of connectivity metrics, with specific requirements depending on the metric. C_LIO_LIThe number of participants had minimal effect on node-based metrics, but voxel-based metrics benefited from larger samples. C_LI

bioengineering↗

bioRxiv2025

Optimising 7T-fMRI for imaging regions of magnetic susceptibility

The temporal signal-to-noise ratio (tSNR) of functional magnetic resonance imaging (fMRI) is particularly poor in ventral anterior temporal and orbitofrontal regions because of B0 and B1+ magnetic field inhomogeneity, a problem that is exacerbated at higher field strengths. In this 7T-fMRI study we compared three methods of improving sensitivity in these areas: parallel transmit, which uses multiple transmit elements, controlled independently, to homogenise the flip angle experienced by the tissue; multi-echo, which entails collection of multiple volumes at different echo times following a single radiofrequency pulse; and multiband, in which multiple slices are acquired simultaneously. We found that parallel transmit and multi-echo increased the magnitude of the BOLD signal change, but only multi-echo increased BOLD magnitude in areas prone to susceptibility artefacts. Multiband and denoising of multi-echo data with independent components analysis (ICA) both improved precision of GLM fit. Exploratory results suggested that multi-echo and ICA denoising can both benefit multivariate analyses. In conclusion, a multi-echo, multiband sequence improved fMRI quality in areas prone to susceptibility artefacts while maintaining sensitivity across the whole brain. We recommend this approach for studies investigating the functional roles of ventral temporal and orbitofrontal regions with 7T fMRI.

neuroscience↗

bioRxiv2025

Complementary MR measures of white matter and their relation to cardiovascular health and cognition

Magnetic Resonance Imaging (MRI) offers many ways to non-invasively estimate the properties of white matter (WM) in the brain. In addition to the various metrics derived from diffusion-weighted MRI, one can estimate total WM volume from T1-weighted MRI, WM hyper-intensities from T2-weighted MRI, myelination from the T1:T2 ratio, or from the magnetisation-transfer ratio (MTR). Here we utilise the presence of all of these MR contrasts in a population based life-span cohort of 650 healthy adults [CamCAN cohort] to identify the latent factors underlying the covariance of 11 commonly-used WM metrics. Four factors were needed to explain 89% of the variance, which we interpreted in terms of 1) fibre density / myelination, 2) free-water / tissue damage, 3) fibre-crossing complexity and 4) microstructural complexity. These factors showed distinct effects of age and sex. To test the validity of these factors, we related them to measures of cardiovascular health and cognitive performance. Specifically, we ran path analyses 1) linking cardio-vascular measures to the WM factors, given the idea that WM health is related to cardiovascular health, and 2) linking the WM factors to cognitive measure, given the idea that WM health is important for cognition. Even after adjusting for age, we found that a vascular factor related to pulse pressure predicted the WM factor capturing free-water / tissue damage, and that several WM factors made unique predictions for fluid intelligence and processing speed. Our results show that there is both complementary and redundant information across common MR measures of WM, and their underlying latent factors may be useful for pinpointing the differential causes and contributions of white matter health in healthy aging.

neuroscience↗

bioRxiv2024

Comparing the effect of multi gradient echo and multi band fMRI during a semantic task

The Blood Oxygenation Level Dependent (BOLD) signal, as measured using functional magnetic resonance imaging (fMRI), is known to vary in sensitivity across the brain due to magnetic susceptibility artefacts. In particular, the ventral anterior temporal lobes (vATL) have been implicated with semantic cognition using convergent methods (i.e., neuropsychology, PET, MEG, brain stimulation) but less so with fMRI using conventional gradient-echo protocols. There are methods to alleviate signal loss but multi-echo fMRI has gained popularity. Here, additional volumes are collected that span across a range of T2* values, however, this results sub-optimum parameters (i.e., repetition times, resolution, acceleration). "Multi-band" imaging has been used with multi-echo to speed up data acquisition; however, it is unclear how these modifications contribute to fMRI sensitivity across the brain and for univariate/multivariate analyses. In the current study, we used a factorial design where we manipulated the echo and/or band to assess how well the semantic network can be detected. When comparing the precision with which activations were detected (i.e, average T-statistics), we found that multi-band protocols were beneficial, with no evidence of signal leakage artefacts. When comparing the magnitude of activations, multi-echo protocols increased activations in regions prone to susceptibility artefacts (specifically the anterior temporal lobes, ATLs). Both multi-banding and independent component analysis (ICA)-denoising of multi-echo data tended to improve multi-voxel decoding of conditions. However, multi-echo protocols reduced activation magnitude in more central regions, such as the medial temporal lobes, possibly due to higher in-plane acceleration required to collect multiple-echoes. Nonetheless, the multi-echo multi-band protocol is a promising default option for fMRI on most regions, particularly those that suffer from susceptibility artefacts, as well as offering the potential to apply advanced post-processing methods to take advantage of the increased temporal (or spatial) resolution of multi-band protocols and more principled ICA-denoising based on TE-dependence of BOLD signals.

neuroscience↗

bioRxiv2021

Now you see it, now you don't: optimal parameters for interslice stimulation in concurrent TMS-fMRI

The powerful combination of transcranial magnetic stimulation (TMS) concurrent with functional magnetic resonance imaging (fMRI) provides rare insights into the causal relationships between brain activity and behaviour. Despite a recent resurgence in popularity, TMS-fMRI remains technically challenging. Here we examined the feasibility of applying TMS during short gaps between fMRI slices to avoid incurring artefacts in the fMRI data. We quantified signal dropout and changes in temporal signal-to-noise ratio (tSNR) for TMS pulses presented at timepoints from 100ms before to 100ms after slice onset. Up to 3 pulses were delivered per volume using MagVentures MR-compatible TMS coil. We used a spherical phantom, two 7-channel TMS-dedicated surface coils, and a multiband (MB) sequence (factor=2) with interslice gaps of 100ms and 40ms, on a Siemens 3T Prisma-fit scanner. For comparison we repeated a subset of parameters with a more standard single-channel TxRx (birdcage) coil, and with a human participant and surface coil set up. We found that, even at 100% stimulator output, pulses applied at least - 40ms/+50ms from the onset of slice readout avoid incurring artifacts. This was the case for all three setups. Thus, an interslice protocol can be achieved with a frequency of up to ~10 Hz, using a standard EPI sequence (slice acquisition time: 62.5ms, interslice gap: 40ms). Faster stimulation frequencies would require shorter slice acquisition times, for example using in-plane acceleration. Interslice TMS-fMRI protocols provide a promising avenue for retaining flexible timing of stimulus delivery without incurring TMS artifacts.

neuroscience↗

bioRxiv2021

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project - a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and grey matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference mplementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and cognitive neuroscience research. It will ease the translation of DKI advantages into clinical applications.

neuroscience↗

bioRxiv2020

Correcting for Superficial Bias in 7T Gradient Echo fMRI

1The arrival of submillimetre ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the Blood Oxygenation Level Dependent (BOLD) signal measured by Gradient-Echo fMRI is biased towards superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations and example human 7T fMRI data from Regions-of-Interest (ROIs) during a visual attention paradigm. The simulations show that two methods - the ratio of ROI means across conditions and a novel application of Deming regression - offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the example dataset, these methods suggest that attentional modulation of activation is similar across cortical layers within the ventral visual stream, despite a naive activation-based analysis producing stronger modulation in superficial layers. Our study demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of Gradient-Echo fMRI data.

neuroscience↗

bioRxiv2019

Improved motion correction of submillimetre 7T fMRI time series with boundary-based registration (BBR)

Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

neuroscience↗

bioRxiv2018

Prospective Motion correction improves the sensitivity of fMRI pattern decoding

We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in-bore optical camera to track an external marker attached to the participant via a custom-moulded mouthpiece. The study was conducted at two resolutions (1.5mm vs 3mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi-voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist-moulded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.

neuroscience↗