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.

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.

Lateralization of orthographic processing in fixed-gaze and natural reading conditions.

Lateralized processing of orthographic information is a hallmark of proficient reading. However, how this finding obtained for fixed-gaze processing of orthographic stimuli translates to ecologically valid reading conditions remained to be clarified. To address this shortcoming, here we assessed the lateralization of early orthographic processing in fixed-gaze and natural reading conditions using concurrent eye-tracking and EEG data recorded from young adults without reading difficulties. Sensor-space analyses confirmed the well-known left-lateralized negative-going deflection of fixed-gaze EEG activity throughout the period of early orthographic processing. At the same time, fixation-related EEG activity exhibited left-lateralized followed by right-lateralized processing of text stimuli during natural reading. A strong positive relationship was found between the early leftward lateralization in fixed-gaze and natural reading conditions. Using source-space analyses, early left-lateralized brain activity was obtained in lateraloccipital and posterior ventral occipito-temporal cortices reflecting letter-level processing in both conditions. In addition, in the same time interval, left-lateralized source activity was found also in premotor and parietal brain regions during natural reading. While brain activity remained left-lateralized in later stages representing word-level processing in posterior and middle ventral temporal regions in the fixed-gaze condition, fixation-related source activity became stronger in the right hemisphere in medial and more anterior ventral temporal brain regions indicating higher-level processing of orthographic information. Although our results show a strong positive relationship between the lateralization of letter-level processing in the two reading modes and suggest lateralized brain activity as a general marker for processing of orthographic information, they also clearly indicate the need for reading research in ecologically valid conditions to identify the neural basis of visuospatial attentional, oculomotor and higher-level processes specific to natural reading.

Lateralization of early orthographic processing during natural reading is impaired in developmental dyslexia.

Skilled reading requires specialized visual cortical processing of orthographic information and its impairment has been proposed as a potential correlate of compromised reading in dyslexia. However, which stage of orthographic information processing during natural reading is disturbed in dyslexics remains unexplored. Here we addressed this question by simultaneously measuring the eye movements and EEG of dyslexic and control young adults during natural reading. Isolated meaningful sentences were presented at five inter-letter spacing levels spanning the range from minimal to extra-large spacing, and participants were instructed to read the text silently at their own pace. Control participants read faster, performed larger saccades and shorter fixations compared to dyslexics. While reading speed peaked around the default letter spacing, saccade amplitude increased and fixation duration decreased with the increase of letter spacing in both groups. Lateralization of occipito-temporal fixation-related EEG activity (FREA) was found in three consecutive time intervals corresponding to early orthographic processing in control readers. Importantly, the lateralization in the time range of the first negative left occipito-temporal FREA peak was specific for first fixations and exhibited an interaction effect between reading ability and letter spacing. The interaction originated in the significant decrease of FREA lateralization at extra-large compared to default letter spacing in control readers and the lack of lateralization in both letter spacing conditions in the case of dyslexics. These findings suggest that expertise-driven hemispheric functional specialization for early orthographic processing thought to be responsible for letter identity extraction during natural reading is compromised in dyslexia.

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.