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.

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.

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 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.

Fixation instability, astigmatism, and lack of stereopsis as factors impeding recovery of binocular balance in amblyopia following binocular therapy.

Dichoptic therapy is a promising method for improving vision in pediatric and adult patients with amblyopia. However, a systematic understanding about changes in specific visual functions and substantial variation of effect among patients is lacking. Utilizing a novel stereoscopic augmented-reality based training program, 24 pediatric and 18 adult patients were trained for 20 h along a three-month time course with a one-month post-training follow-up for pediatric patients. Changes in stereopsis, distance and near visual acuity, and contrast sensitivity for amblyopic and fellow eyes were measured, and interocular differences were analyzed. To reveal what contributes to successful dichoptic therapy, ANCOVA models were used to analyze progress, considering clinical baseline parameters as covariates that are potential requirements for amblyopic recovery. Significant and lasting improvements have been achieved in stereoacuity, interocular near visual acuity, and interocular contrast sensitivity. Importantly, astigmatism, fixation instability, and lack of stereopsis were major limiting factors for visual acuity, stereoacuity, and contrast sensitivity recovery, respectively. The results demonstrate the feasibility of treatment-efficacy prediction in certain aspects of dichoptic amblyopia therapy. Furthermore, our findings may aid in developing personalized therapeutic protocols, capable of considering individual clinical status, to help clinicians in tailoring therapy to patient profiles for better outcome.

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.

Investigation of the relationship between visual feature binding in short- and long-term memory in healthy aging.

Binding visual features into coherent object representations is essential both in short- and long-term memory. However, the relationship between feature binding processes at different memory delays remains unexplored. Here, we addressed this question by using the Mnemonic Similarity Task and a delayed-estimation working memory task on a large sample of older adults. The results revealed that higher propensity to misbind object features in working memory is associated with lower lure discrimination performance in the mnemonic similarity task, suggesting that shared feature binding processes underlie the formation of coherent short- and long-term visual object memory representations.

Dichoptic Spatial Contrast Sensitivity Reflects Binocular Balance in Normal and Stereoanomalous Subjects.

Purpose: To study binocular balance by comparing dichoptic and standard monocular contrast sensitivity function (CSF) in stereonormal and stereoanomalous/stereoblind amblyopic subjects. Methods: Sixteen amblyopes and 17 controls participated. Using the capability of the passive three-dimensional display, we measured their CSF both monocularly and dichoptically at spatial frequencies 0.5, 1, 2, 4, and 8 cpds using achromatic Gabor patches on a luminance noise background. During monocular stimulation, the untested eye was covered, while for the dichoptic stimulation the untested eye viewed background noise. Dichoptic CSF of both eyes was acquired within one block. Results: In patients with central fixation, dichoptic viewing had a large negative impact on the CSF of the amblyopic eye, although it hardly affected that of the dominant eye. In contrast, dichoptic viewing had a small but significant effect on both eyes for controls. In addition, all participants lay along a continuum in terms of how much their two eyes were affected by dichoptic stimulation: by using two predefined contrast sensitivity ratios, namely, amblyopic sensitivity decrement and dichoptic sensitivity decrement, not only did we find a significant correlation between these variables among all participants, but also the two groups were identified with minimum error using a cluster analysis. Conclusions: Dichoptic CSF may be considered to measure visual performance in patients with altered binocular vision, because it better reflects the visual capacity of the amblyopic eye than the standard monocular examinations. It may also be a more reliable parameter to assess the efficacy of modern approaches to treat amblyopia.

The influence of anticipated monetary incentives on visual working memory performance in healthy younger and older adults.

Motivation exerts substantial control over cognitive functions, including working memory. Although it is well known that both motivational control and working memory processes undergo a progressive decline with ageing, whether and to what extent their interaction is altered in old age remain unexplored. Here we aimed at uncovering the effect of reward anticipation on visual working memory performance in a large cohort of younger and older adults using a delayed-estimation task. We applied a three-component probabilistic model to dissociate the reward effects on three possible sources of error corrupting working memory performance: variability in recall, misbinding of object features and random guessing. The results showed that monetary incentives have a significant beneficial effect on overall working memory recall precision only in the group of younger adults. However, our model-based analysis resulted in significant reward effects on all three working memory component processes, which did not differ between the age groups, suggesting that model-based analysis is more sensitive to small reward-induced modulations in the case of older participants. These findings revealed that monetary incentives have a global boosting effect on working memory performance, which is deteriorated to some extent but still present in healthy older adults.

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.

Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network.

In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural brain imaging and DL can be used for predicting a physical trait that is of significant clinical relevance-the body mass index (BMI) of the individual. We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about age and sex. Localization maps computed for the CNN highlighted several brain structures that strongly contributed to BMI prediction, including the caudate nucleus and the amygdala. Comparison to the results obtained via a standard automatic brain segmentation method revealed that the CNN-based visualization approach yielded complementary evidence regarding the relationship between brain structure and BMI. Taken together, our results imply that predicting BMI from structural brain scans using DL represents a promising approach to investigate the relationship between brain morphological variability and individual differences in body weight and provide a new scope for future investigations regarding the potential clinical utility of brain-predicted BMI.

Full utilization of conjugate symmetry: combining virtual conjugate coil reconstruction with partial Fourier imaging for g-factor reduction in accelerated MRI.

PURPOSE: In this study we propose a method to combine the parallel virtual conjugate coil (VCC) reconstruction with partial Fourier (PF) acquisition to improve reconstruction conditioning and reduce noise amplification in accelerated MRI where PF is used. METHODS: Accelerated measurements are reconstructed in k-space by GRAPPA, with a VCC reconstruction kernel trained and applied in the central, symmetrically sampled part of k-space, while standard reconstruction is performed on the asymmetrically sampled periphery. The two reconstructed regions are merged to form a full reconstructed dataset, followed by PF reconstruction. The method is tested in vivo using T1-weighted spin-echo and T2*-weighted gradient-echo echo planar imaging (EPI) sequences, using both in-plane and simultaneous multislice (SMS) acceleration, at 1.5T and 3T field strengths. Noise amplification is estimated with theoretical calculations and pseudo-multiple-replica computations, for different PF factors, using zero-filling, homodyne, and projection onto convex sets (POCS) PF reconstruction. RESULTS: Depending on the PF algorithm and the inherent benefit of VCC reconstruction without PF, approximately 35% to 80%, 15% to 60%, and 5% to 30% of that intrinsic SNR gain can be retained for PF factors 7/8, 6/8, and 5/8, respectively, by including the VCC signals in the reconstruction. Compared with VCC-reconstructed acquisitions of higher acceleration, without PF, but having the same net acceleration, the combined method can provide a higher SNR if the inherent benefit of VCC is low or moderate. CONCLUSION: The proposed technique enables the partial application of VCC reconstruction to measurements with PF using either in-plane or SMS acceleration, and therefore can reduce the noise amplification of such acquisitions.

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.