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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Expectations about word stress modulate neural activity in speech-sensitive cortical areas.

A recent dual-stream model of language processing proposed that the postero-dorsal stream performs predictive sequential processing of linguistic information via hierarchically organized internal models. However, it remains unexplored whether the prosodic segmentation of linguistic information involves predictive processes. Here, we addressed this question by investigating the processing of word stress, a major component of speech segmentation, using probabilistic repetition suppression (RS) modulation as a marker of predictive processing. In an event-related acoustic fMRI RS paradigm, we presented pairs of pseudowords having the same (Rep) or different (Alt) stress patterns, in blocks with varying Rep and Alt trial probabilities. We found that the BOLD signal was significantly lower for Rep than for Alt trials, indicating RS in the posterior and middle superior temporal gyrus (STG) bilaterally, and in the anterior STG in the left hemisphere. Importantly, the magnitude of RS was modulated by repetition probability in the posterior and middle STG. These results reveal the predictive processing of word stress in the STG areas and raise the possibility that words stress processing is related to the dorsal "where" auditory stream.

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.

Efficacy of weight loss intervention can be predicted based on early alterations of fMRI food cue reactivity in the striatum.

Increased fMRI food cue reactivity in obesity, i.e. higher responses to high- vs. low-calorie food images, is a promising marker of the dysregulated brain reward system underlying enhanced susceptibility to obesogenic environmental cues. Recently, it has also been shown that weight loss interventions might affect fMRI food cue reactivity and that there is a close association between the alteration of cue reactivity and the outcome of the intervention. Here we tested whether fMRI food cue reactivity could be used as a marker of diet-induced early changes of neural processing in the striatum that are predictive of the outcome of the weight loss intervention. To this end we investigated the relationship between food cue reactivity in the striatum measured one month after the onset of the weight loss program and weight changes obtained at the end of the six-month intervention. We observed a significant correlation between BMI change measured after six months and early alterations of fMRI food cue reactivity in the striatum, including the bilateral putamen, right pallidum, and left caudate. Our findings provide evidence for diet-induced early alterations of fMRI food cue reactivity in the striatum that can predict the outcome of the weight loss intervention.

Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks.

Background: Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the proper training of deep neural networks requires thousands of exemplars. Collecting this amount of data is not feasible in typical neuroimaging experiments. A handy solution to this problem, which has largely fallen outside the scope of deep learning applications in neuroimaging, is to repurpose deep networks that have already been trained on large datasets by fine-tuning them to target datasets/tasks with fewer exemplars. Here, we investigated how this method, called transfer learning, can aid age category classification and regression based on brain functional connectivity patterns derived from resting-state functional magnetic resonance imaging. We trained a connectome-convolutional neural network on a larger public dataset and then examined how the knowledge learned can be used effectively to perform these tasks on smaller target datasets collected with a different type of scanner and/or imaging protocol and pre-processing pipeline. Results: Age classification on the target datasets benefitted from transfer learning. Significant improvement (∼9%-13% increase in accuracy) was observed when the convolutional layers' weights were initialized based on the values learned on the public dataset and then fine-tuned to the target datasets. Transfer learning also appeared promising in improving the otherwise poor prediction of chronological age. Conclusions: Transfer learning is a plausible solution to adapt convolutional neural networks to neuroimaging data with few exemplars and different data acquisition and pre-processing protocols.

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.

Face inversion reveals holistic processing of peripheral faces.

Face perception is accomplished by face-selective neural processes, involving holistic processing that enables highly efficient integration of facial features into a whole-face representation. It has been shown that in face-selective regions of the ventral temporal cortex (VTC), neural resources involved in holistic processing are primarily dedicated to the central portion of the visual field. These findings raise the intriguing possibility that holistic processing might be the privilege of centrally presented faces and could be strongly diminished in the case of peripheral faces. We addressed this question using the face inversion effect (FIE), a well-established marker of holistic face processing. The behavioral results revealed impaired identity discrimination performance for inverted peripheral faces scaled according to the V1 magnification factor, compared to upright presented faces. The size of peripheral FIE was comparable to that found for centrally displayed faces. Face inversion affected the early ERP responses to faces in two time intervals. The earliest FIE was most pronounced in the time window between 130 and 140 msec following stimulus presentation, for both centrally and peripherally displayed faces and in the latter case, it was present only over the contralateral hemisphere. The timing of the next component FIE corresponded closely with the temporal interval of the N170 ERP component and showed strong right hemisphere (RH) lateralization, both when faces were displayed in the left or right visual field (RVF). Furthermore, we also showed that centrally presented face masks impaired peripheral face identity discrimination performance, but did not reduce the magnitude of the FIE. These findings revealed robust behavioral and neural inversion effects for peripheral faces and thus suggest that faces are processed holistically throughout the visual field.

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.

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping.

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks.

The relationship between repetition suppression and face perception.

Repetition of identical face stimuli leads to fMRI response attenuation (fMRI adaptation, fMRIa) in the core face-selective occipito-temporal visual cortical network, involving the bilateral fusiform face area (FFA) and the occipital face area (OFA). However, the functional relevance of fMRIa observed in these regions is unclear as of today. Therefore, here we aimed at investigating the relationship between fMRIa and face perception ability by measuring in the same human participants both the repetition-induced reduction of fMRI responses and identity discrimination performance outside the scanner for upright and inverted face stimuli. In the correlation analysis, the behavioral and fMRI results for the inverted faces were used as covariates to control for the individual differences in overall object perception ability and basic visual feature adaptation processes, respectively. The results revealed a significant positive correlation between the participants' identity discrimination performance and the strength of fMRIa in the core face processing network, but not in the extrastriate body area (EBA). Furthermore, we found a strong correlation of the fMRIa between OFA and FFA and also between OFA and EBA, but not between FFA and EBA. These findings suggest that there is a face-selective component of the repetition-induced reduction of fMRI responses within the core face processing network, which reflects functionally relevant adaptation processes involved in face identity perception.