Finding Pattern in the Noise: Persistent Implicit Statistical Knowledge Impacts the Processing of Unpredictable Stimuli.

Humans can extract statistical regularities of the environment to predict upcoming events. Previous research recognized that implicitly acquired statistical knowledge remained persistent and continued to influence behavior even when the regularities were no longer present in the environment. Here, in an fMRI experiment, we investigated how the persistence of statistical knowledge is represented in the brain. Participants (n = 32) completed a visual, four-choice, RT task consisting of statistical regularities. Two types of blocks constantly alternated with one another throughout the task: predictable statistical regularities in one block type and unpredictable ones in the other. Participants were unaware of the statistical regularities and their changing distribution across the blocks. Yet, they acquired the statistical regularities and showed significant statistical knowledge at the behavioral level not only in the predictable blocks but also in the unpredictable ones, albeit to a smaller extent. Brain activity in a range of cortical and subcortical areas, including early visual cortex, the insula, the right inferior frontal gyrus, and the right globus pallidus/putamen contributed to the acquisition of statistical regularities. The right insula, inferior frontal gyrus, and hippocampus as well as the bilateral angular gyrus seemed to play a role in maintaining this statistical knowledge. The results altogether suggest that statistical knowledge could be exploited in a relevant, predictable context as well as transmitted to and retrieved in an irrelevant context without a predictable structure.

The effect of head motion on brain age prediction using deep convolutional neural networks.

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Study protocol of the Hungarian Longitudinal Study of Healthy Brain Aging (HuBA).

Background and purpose:

Neuro­cog­nitive aging and the associated brain diseases impose a major social and economic burden. Therefore, substantial efforts have been put into revealing the lifestyle, the neurobiological and the genetic underpinnings of healthy neurocognitive aging. However, these studies take place almost exclusively in a limited number of highly-developed countries. Thus, it is an important open question to what extent their findings may generalize to neurocognitive aging in other, not yet investigated regions. The purpose of the Hungarian Longitudinal Study of Healthy Brain Aging (HuBA) is to collect multi-modal longitudinal data on healthy neurocognitive aging to address the data gap in this field in Central and Eastern Europe.

We adapted the Australian Ima­ging, Biomarkers and Lifestyle (AIBL) study of aging study protocol to local circumstances and collected demographic, lifestyle, men­tal and physical health, medication and medical history related information as well as re­cor­ded a series of magnetic resonance imaging (MRI) data. In addition, participants were al­so offered to participate in the collection of blood samples to assess circulating in­flam­matory biomarkers as well as a sleep study aimed at evaluating the general sleep quality based on multi-day collection of subjective sleep questionnaires and whole-night elec­troencephalographic (EEG) data.

Baseline data collection has al­ready been accomplished for more than a hundred participants and data collection in the se­cond
session is on the way. The collected data might reveal specific local trends or could also indicate the generalizability of previous findings. Moreover, as the HuBA protocol al­so offers a sleep study designed for tho­rough characterization of participants’ sleep quality and related factors, our extended multi-modal dataset might provide a base for incorporating these measures into healthy and clinical aging research. 

Besides its straightforward na­tional benefits in terms of health ex­pen­di­ture, we hope that this Hungarian initiative could provide results valid for the whole Cent­ral and Eastern European region and could also promote aging and Alzheimer’s disease research in these countries.

Neural basis of distractor resistance during visual working memory maintenance.

Visual working memory representations must be protected from the intervening irrelevant visual input. While it is well known that interference resistance is most challenging when distractors match the prioritised mnemonic information, its neural mechanisms remain poorly understood. Here, we identify two top-down attentional control processes that have opposing effects on distractor resistance. We reveal an early selection negativity in the EEG responses to matching as compared to non-matching distractors, the magnitude of which is negatively associated with behavioural distractor resistance. Additionally, matching distractors lead to reduced post-stimulus alpha power as well as increased fMRI responses in the object-selective visual cortical areas and the inferior frontal gyrus. However, the congruency effect found on the post-stimulus periodic alpha power and the inferior frontal gyrus fMRI responses show a positive association with distractor resistance. These findings suggest that distractor interference is enhanced by proactive memory content-guided selection processes and diminished by reactive allocation of top-down attentional resources to protect memorandum representations within visual cortical areas retaining the most selective mnemonic code.

Membrane-Based In Situ Mid-Infrared Spectroscopic Ellipsometry: A Study on the Membrane Affinity of Polylactide-co-glycolide Nanoparticulate Systems.

Mid-infrared (IR) ellipsometry of thin films and molecule layers at solid-liquid interfaces has been a challenge because of the absorption of light in water. It has been usually overcome by using configurations utilizing illumination through the solid substrate. However, the access to the solid-liquid interface in a broad spectral range is also challenging due to the limited transparency of most structural materials in the IR wavelength range. In this work, we propose a concept of a microfabricated analysis cell based on an IR-transparent Si membrane with advantages of a robust design, flexible adaptation to existing equipment, small volume, multiple-angle capabilities, broad wavelength range, and opportunities of multilayer applications for adjusted ranges of high sensitivity. The chamber was prepared by 3D micromachining technology utilizing deep reactive ion etching of a silicon-on-insulator wafer and bonded to a polydimethylsiloxane microfluidic injection system resulting in a cell volume of approximately 50 µL. The mechanical stability of the 2 and 5 µm-thick membranes was tested using different "backbone" reinforcement structures. It was proved that the 5 µm-thick membranes are stable at lateral cell sizes of 5 mm by 20 mm. The cell provides good intensity and adjustment capabilities on the stage of a commercial mid-IR ellipsometer. The membrane configuration also provides optical access to the sensing interfaces at a broad range of incident angles, which is a significant advantage in many potential sensing structure configurations, such as plasmonic, multilayer, 2D, or metamaterial applications.

An empirical investigation of the benefit of increasing the temporal resolution of task-evoked fMRI data with multi-band imaging.

OBJECTIVE: There is a tendency for reducing TR in MRI experiments with multi-band imaging. We empirically investigate its benefit for the group-level statistical outcome in task-evoked fMRI. METHODS: Three visual fMRI data sets were collected from 17 healthy adult participants. Multi-band acquisition helped vary the TR (2000/1000/410 ms, respectively). Because these data sets capture different temporal aspects of the haemodynamic response (HRF), we tested several HRF models. We computed a composite descriptive statistic, H, from ß's of each first-level model fit and carried it to the group-level analysis. The number of activated voxels and the t value of the group-level analysis as well as a goodness-of-fit measure were used as surrogate markers of data quality for comparison. RESULTS: Increasing the temporal sampling rate did not provide a universal improvement in the group-level statistical outcome. Rather, both the voxel-wise and ROI-averaged group-level results varied widely with anatomical location, choice of HRF and the setting of the TR. Correspondingly, the goodness-of-fit of HRFs became worse with increasing the sampling frequency. CONCLUSION: Rather than universally increasing the temporal sampling rate in cognitive fMRI experiments, these results advocate the performance of a pilot study for the specific ROIs of interest to identify the appropriate temporal sampling rate for the acquisition and the correspondingly suitable HRF for the analysis of the data.

Physiological and pharmacological characterization of a molluscan neuronal efflux transporter; evidence for age-related transporter impairment.

Plasma membrane efflux transporters play crucial roles in the removal and release of both harmful and beneficial substances from the interior of cells and tissue types in virtually every extant species. They contribute to the clearance of a broad spectrum of exogenous and endogenous toxicants and harmful metabolites, including the reactive lipid aldehyde byproducts of lipid peroxidation that are a hallmark of cellular ageing. Here, we tested whether declining transporter functionality may contribute to functional decline in a snail model of neuronal ageing. Through measuring the removal of 5(6)-carboxyfluorescein, a known substrate for membrane efflux transporters, we provide, for the first time, physiological evidence for the existence of probenecid-, MK571- and glutathione-sensitive efflux transporters in (gastropod) neurons and demonstrate that their functionality declines with age. Our data support the idea that waning cellular detoxification capacity might be a significant factor in the escalation of (lipo-)toxicity observed in neuronal ageing.

Correction to: Reducing task-based fMRI scanning time using simultaneous multislice echo planar imaging.

The original version of this article contained a mistake. The correct Affiliation 2 is Semmelweis University, János Szentágothai PhD School, MR Research Centre, Balassa Street 6, Budapest 1083, Hungary.

Reducing task-based fMRI scanning time using simultaneous multislice echo planar imaging.

PURPOSE: To maintain alertness and to remain motionless during scanning represent a substantial challenge for patients/subjects involved in both clinical and research functional magnetic resonance imaging (fMRI) examinations. Therefore, availability and application of new data acquisition protocols allowing the shortening of scan time without compromising the data quality and statistical power are of major importance. METHODS: Higher order category-selective visual cortical areas were identified individually, and rapid event-related fMRI design was used to compare three different sampling rates (TR = 2000, 1000, and 410 ms, using state-of-the-art simultaneous multislice imaging) and four different scanning lengths to match the statistical power of the traditional scanning methods to high sampling-rate design. RESULTS: The results revealed that ~ 4 min of the scan time with 1 Hz (TR = 1000 ms) sampling rate and ~ 2 min scanning at ~ 2.5 Hz (TR = 410 ms) sampling rate provide similar localization sensitivity and selectivity to that obtained with 11-min session at conventional, 0.5 Hz (TR = 2000 ms) sampling rate. CONCLUSION: Our findings suggest that task-based fMRI examination of clinical population prone to distress such as presurgical mapping experiments might substantially benefit from the reduced (20-40%) scanning time that can be achieved by the application of simultaneous multislice sequences.

Linking the 'why' and 'how' of ageing: evidence for somatotropic control of long-term memory function in the pond snail Lymnaea stagnalis.

Organisms live on a budget; hence, they cannot maximize all their activities at the same time. Instead, they must prioritize how they spend limiting resources on the many processes they rely on in their lives. Among others, they are thought to economize on the maintenance and repair processes required for survival in favour of maximizing reproduction, with ageing as a consequence. We investigate the biological mechanisms of neuronal ageing. Using Lymnaea stagnalis, we have previously described various aspects of age-associated neuronal decline and appetitive long-term memory failure. In view of postulated trade-offs between somatic maintenance and reproduction, we tested for interactions between resource allocation mechanisms and brain function. We show that removal of the lateral lobes, which are key regulators of energy balance in L. stagnalis, increases body mass and enhances appetitive learning, raising the possibility that the lateral lobes are one of the sites where the 'why' and 'how' of (neuronal) ageing meet.

Neural basis of identity information extraction from noisy face images.

Previous research has made significant progress in identifying the neural basis of the remarkably efficient and seemingly effortless face perception in humans. However, the neural processes that enable the extraction of facial information under challenging conditions when face images are noisy and deteriorated remains poorly understood. Here we investigated the neural processes underlying the extraction of identity information from noisy face images using fMRI. For each participant, we measured (1) face-identity discrimination performance outside the scanner, (2) visual cortical fMRI responses for intact and phase-randomized face stimuli, and (3) intrinsic functional connectivity using resting-state fMRI. Our whole-brain analysis showed that the presence of noise led to reduced and increased fMRI responses in the mid-fusiform gyrus and the lateral occipital cortex, respectively. Furthermore, the noise-induced modulation of the fMRI responses in the right face-selective fusiform face area (FFA) was closely associated with individual differences in the identity discrimination performance of noisy faces: smaller decrease of the fMRI responses was accompanied by better identity discrimination. The results also revealed that the strength of the intrinsic functional connectivity within the visual cortical network composed of bilateral FFA and bilateral object-selective lateral occipital cortex (LOC) predicted the participants' ability to discriminate the identity of noisy face images. These results imply that perception of facial identity in the case of noisy face images is subserved by neural computations within the right FFA as well as a re-entrant processing loop involving bilateral FFA and LOC.

Repetition probability effects for inverted faces.

It has been shown, that the repetition related reduction of the blood-oxygen level dependent (BOLD) signal is modulated by the probability of repetitions (P(rep)) for faces (Summerfield et al., 2008), providing support for the predictive coding (PC) model of visual perception (Rao and Ballard, 1999). However, the stage of face processing where repetition suppression (RS) is modulated by P(rep) is still unclear. Face inversion is known to interrupt higher level configural/holistic face processing steps and if modulation of RS by P(rep) takes place at these stages of face processing, P(rep) effects are expected to be reduced for inverted when compared to upright faces. Therefore, here we aimed at investigating whether P(rep) effects on RS observed for face stimuli originate at the higher-level configural/holistic stages of face processing by comparing these effects for upright and inverted faces. Similarly to previous studies, we manipulated P(rep) for pairs of stimuli in individual blocks of fMRI recordings. This manipulation significantly influenced repetition suppression in the posterior FFA, the OFA and the LO, independently of stimulus orientation. Our results thus reveal that RS in the ventral visual stream is modulated by P(rep) even in the case of face inversion and hence strongly compromised configural/holistic face processing. An additional whole-brain analysis could not identify any areas where the modulatory effect of probability was orientation specific either. These findings imply that P(rep) effects on RS might originate from the earlier stages of face processing.

Oxidative-stress induced increase in circulating fatty acids does not contribute to phospholipase A2-dependent appetitive long-term memory failure in the pond snail Lymnaea stagnalis.

BACKGROUND: Reactive oxygen species (ROS) are essential for normal physiological functioning of the brain. However, uncompensated increase in ROS levels may results in oxidative stress. Phospholipase A2 (PLA2) is one of the key players activated by elevated ROS levels resulting in the hydrolysis of various products from the plasmamembrane such as peroxidized fatty acids. Free fatty acids (FFAs) and fatty acid metabolites are often implicated to the genesis of cognitive impairment. Previously we have shown that age-, and experimentally induced oxidative stress causes PLA2-dependent long-term memory (LTM) failure in an aversive operant conditioning model in Lymnaea stagnalis. In the present study, we investigate the effects of experimentally induced oxidative stress and the role of elevated levels of circulating FFAs on LTM function using a non-aversive appetitive classical conditioning paradigm. RESULTS: We show that intracoelomic injection of exogenous PLA2 or pro-oxidant induced PLA2 activation negatively affects LTM performance in our learning paradigm. In addition, we show that experimental induction of oxidative stress causes significant temporal changes in circulating FFA levels. Importantly, the time of training coincides with the peak of this change in lipid metabolism. However, intracoelomic injection with exogenous arachidonic acid, one of the main FFAs released by PLA2, does not affect LTM function. Moreover, sequestrating circulating FFAs with the aid of bovine serum albumin does not rescue pro-oxidant induced appetitive LTM failure. CONCLUSIONS: Our data substantiates previous evidence linking lipid peroxidation and PLA2 activation to age- and oxidative stress-related cognitive impairment, neuronal dysfunction and disease. In addition however, our data indicate that lipid peroxidation induced increased levels of circulating (per)oxidized FFAs are not a factor in oxidative stress induced LTM impairment.

Amygdala Volume is Associated with ADHD Risk and Severity Beyond Comorbidities in Adolescents: Clinical Testing of Brain Chart Reference Standards.

Understanding atypicalities in ADHD brain correlates is a step towards better understanding ADHD etiology. Efforts to map atypicalities at the level of brain structure have been hindered by the absence of normative reference standards. Recent publication of brain charts allows for assessment of individual variation relative to age- and sex-adjusted reference standards and thus estimation not only of case-control differences but also of intraindividual prediction. METHODS: Aim was to examine, whether brain charts can be applied in a sample of adolescents (N = 140, 38% female) to determine whether atypical brain subcortical and total volumes are associated with ADHD at-risk status and severity of parent-rated symptoms, accounting for self-rated anxiety and depression, and parent-rated oppositional defiant disorder (ODD) as well as motion. RESULTS: Smaller bilateral amygdala volume was associated with ADHD at-risk status, beyond effects of comorbidities and motion, and smaller bilateral amygdala volume was associated with inattention and hyperactivity/impulsivity, beyond effects of comorbidities except for ODD symptoms, and motion. CONCLUSIONS: Individual differences in amygdala volume meaningfully add to estimating ADHD risk and severity. Conceptually, amygdalar involvement is consistent with behavioral and functional imaging data on atypical reinforcement sensitivity as a marker of ADHD-related risk. Methodologically, results show that brain chart reference standards can be applied to address clinically informative, focused and specific questions.

Evidence for inflammation-mediated memory dysfunction in gastropods: putative PLA2 and COX inhibitors abolish long-term memory failure induced by systemic immune challenges.

BACKGROUND: Previous studies associate lipid peroxidation with long-term memory (LTM) failure in a gastropod model (Lymnaea stagnalis) of associative learning and memory. This process involves activation of Phospholipase A2 (PLA2), an enzyme mediating the release of fatty acids such as arachidonic acid that form the precursor for a variety of pro-inflammatory lipid metabolites. This study investigated the effect of biologically realistic challenges of L. stagnalis host defense response system on LTM function and potential involvement of PLA2, COX and LOX therein. RESULTS: Systemic immune challenges by means of ß-glucan laminarin injections induced elevated H2O2 release from L. stagnalis circulatory immune cells within 3 hrs of treatment. This effect dissipated within 24 hrs after treatment. Laminarin exposure has no direct effect on neuronal activity. Laminarin injections disrupted LTM formation if training followed within 1 hr after injection but had no behavioural impact if training started 24 hrs after treatment. Intermediate term memory was not affected by laminarin injection. Chemosensory and motor functions underpinning the feeding response involved in this learning model were not affected by laminarin injection. Laminarin's suppression of LTM induction was reversed by treatment with aristolochic acid, a PLA2 inhibitor, or indomethacin, a putative COX inhibitor, but not by treatment with nordihydro-guaiaretic acid, a putative LOX inhibitor. CONCLUSIONS: A systemic immune challenge administered shortly before behavioural training impairs associative LTM function in our model that can be countered with putative inhibitors of PLA2 and COX, but not LOX. As such, this study establishes a mechanistic link between the state of activity of this gastropod's innate immune system and higher order nervous system function. Our findings underwrite the rapidly expanding view of neuroinflammatory processes as a fundamental, evolutionary conserved cause of cognitive and other nervous system disorders.

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.

Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. For this reason, several machine learning-based methods have been developed for the automatic quality control of structural MRI scans. Deep learning offers a promising solution to this problem, however, given its data-hungry nature and the scarcity of expert-annotated datasets, its advantage over traditional machine learning methods in identifying motion-corrupted brain scans is yet to be determined. In the present study, we investigated the relative advantage of the two methods in structural MRI quality control. To this end, we collected publicly available T1-weighted images and scanned subjects in our own lab under conventional and active head motion conditions. The quality of the images was rated by a team of radiologists from the point of view of clinical diagnostic use. We present a relatively simple, lightweight 3D convolutional neural network trained in an end-to-end manner that achieved a test set (N = 411) balanced accuracy of 94.41% in classifying brain scans into clinically usable or unusable categories. A support vector machine trained on image quality metrics achieved a balanced accuracy of 88.44% on the same test set. Statistical comparison of the two models yielded no significant difference in terms of confusion matrices, error rates, or receiver operating characteristic curves. Our results suggest that these machine learning methods are similarly effective in identifying severe motion artifacts in brain MRI scans, and underline the efficacy of end-to-end deep learning-based systems in brain MRI quality control, allowing the rapid evaluation of diagnostic utility without the need for elaborate image pre-processing.

Failure of delayed nonsynaptic neuronal plasticity underlies age-associated long-term associative memory impairment.

BACKGROUND: Cognitive impairment associated with subtle changes in neuron and neuronal network function rather than widespread neuron death is a feature of the normal aging process in humans and animals. Despite its broad evolutionary conservation, the etiology of this aging process is not well understood. However, recent evidence suggests the existence of a link between oxidative stress in the form of progressive membrane lipid peroxidation, declining neuronal electrical excitability and functional decline of the normal aging brain. The current study applies a combination of behavioural and electrophysiological techniques and pharmacological interventions to explore this hypothesis in a gastropod model (Lymnaea stagnalis feeding system) that allows pinpointing the molecular and neurobiological foundations of age-associated long-term memory (LTM) failure at the level of individual identified neurons and synapses. RESULTS: Classical appetitive reward-conditioning induced robust LTM in mature animals in the first quartile of their lifespan but failed to do so in animals in the last quartile of their lifespan. LTM failure correlated with reduced electrical excitability of two identified serotonergic modulatory interneurons (CGCs) critical in chemosensory integration by the neural network controlling feeding behaviour. Moreover, while behavioural conditioning induced delayed-onset persistent depolarization of the CGCs known to underlie appetitive LTM formation in this model in the younger animals, it failed to do so in LTM-deficient senescent animals. Dietary supplementation of the lipophilic anti-oxidant α-tocopherol reversed the effect of age on CGCs electrophysiological characteristics but failed to restore appetitive LTM function. Treatment with the SSRI fluoxetine reversed both the neurophysiological and behavioural effects of age in senior animals. CONCLUSIONS: The results identify the CGCs as cellular loci of age-associated appetitive learning and memory impairment in Lymnaea and buttress the hypothesis that lipid peroxidation-dependent depression of intrinsic excitability is a hallmark of normal neuronal aging. The data implicate both lipid peroxidation-dependent non-synaptic as well as apparently lipid peroxidation-independent synaptic mechanisms in the age-dependent decline in behavioural plasticity in this model system.

Visual cortical responses to the input from the amblyopic eye are suppressed during binocular viewing.

Amblyopia is a visual disorder caused by an anomalous early visual experience. It has been suggested that suppression of the visual input from the weaker eye might be a primary underlying mechanism of the amblyopic syndrome. However, it is still an unresolved question to what extent neural responses to the visual information coming from the amblyopic eye are suppressed during binocular viewing. To address this question we measured event-related potentials (ERP) to foveal face stimuli in amblyopic patients, both in monocular and binocular viewing conditions. The results revealed no difference in the amplitude and latency of early components of the ERP responses between the binocular and fellow eye stimulation. On the other hand, early ERP components were reduced and delayed in the case of monocular stimulation of the amblyopic eye as compared to the fellow eye stimulation or to binocular viewing. The magnitude of the amblyopic effect measured on the ERP amplitudes was comparable to that found on the fMRI responses in the fusiform face area using the same face stimuli and task conditions. Our findings showing that the amblyopic effects present on the early ERP components in the case of monocular stimulation are not manifested in the ERP responses during binocular viewing suggest that input from the amblyopic eye is completely suppressed already at the earliest stages of visual cortical processing when stimuli are viewed by both eyes.

Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation.

Due to their robustness and speed, recently developed deep learning-based methods have the potential to provide a faster and hence more scalable alternative to more conventional neuroimaging analysis pipelines in terms of whole-brain segmentation based on magnetic resonance (MR) images. These methods were also shown to have higher test-retest reliability, raising the possibility that they could also exhibit superior head motion tolerance. We investigated this by comparing the effect of head motion-induced artifacts in structural MR images on the consistency of segmentation performed by FreeSurfer and recently developed deep learning-based methods to a similar extent. We used state-of-the art neural network models (FastSurferCNN and Kwyk) and developed a new whole-brain segmentation pipeline (ReSeg) to examine whether reliability depends on choice of deep learning method. Structural MRI scans were collected from 110 participants under rest and active head motion and were evaluated for image quality by radiologists. Compared to FreeSurfer, deep learning-based methods provided more consistent segmentations across different levels of image quality, suggesting that they also have the advantage of providing more reliable whole-brain segmentations of MR images corrupted by motion-induced artifacts, and provide evidence for their practical applicability in the study of brain structural alterations in health and disease.

Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans.

Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.