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Extensive research with musicians has shown that instrumental musical training can have a profound impact on how acoustic features are processed in the brain. However, less is known about the influence of singing training on neural activity during voice perception, particularly in response to salient acoustic features, such as the vocal vibrato in operatic singing. To address this gap, the present study employed functional magnetic resonance imaging (fMRI) to measure brain responses in trained opera singers and musically untrained controls listening to recordings of opera singers performing in two distinct styles: a full operatic voice with vibrato, and a straight voice without vibrato. Results indicated that for opera singers, perception of operatic voice led to differential fMRI activations in bilateral auditory cortical regions and the default mode network. In contrast, musically untrained controls exhibited differences only in bilateral auditory cortex. These results suggest that operatic singing training triggers experience-dependent neural changes in the brain that activate self-referential networks, possibly through embodiment of acoustic features associated with one's own singing style., (© 2024. The Author(s).)

Contextual features are integral to episodic memories; yet, we know little about context effects on pattern separation, a hippocampal function promoting orthogonalization of overlapping memory representations. Recent studies suggested that various extrahippocampal brain regions support pattern separation; however, the specific role of the parahippocampal cortex-a region involved in context representation-in pattern separation has not yet been studied. Here, we investigated the contribution of the parahippocampal cortex (specifically, the parahippocampal place area) to context reinstatement effects on mnemonic discrimination, using functional magnetic resonance imaging. During scanning, participants saw object images on unique context scenes, followed by a recognition task involving the repetitions of encoded objects or visually similar lures on either their original context or a lure context. Context reinstatement at retrieval improved item recognition but hindered mnemonic discrimination. Crucially, our region of interest analyses of the parahippocampal place area and an object-selective visual area, the lateral occipital cortex indicated that while during successful mnemonic decisions parahippocampal place area activity decreased for old contexts compared to lure contexts irrespective of object novelty, lateral occipital cortex activity differentiated between old and lure objects exclusively. These results imply that pattern separation of contextual and item-specific memory features may be differentially aided by scene and object-selective cortical areas., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: Declaration of competing interest The authors report no competing interest., (Copyright © 2024. Published by Elsevier Inc.)

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., (© 2024. The Author(s).)

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., (© 2024 Massachusetts Institute of Technology.)

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.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2022 The Author(s). Published by Elsevier India Pvt Ltd. All rights reserved.)

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., (© 2022. The Author(s).)

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., Competing Interests: Declaration of Competing Interest The authors report no competing interest., (Copyright © 2022. Published by Elsevier Inc.)

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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 β'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., (© 2021. The Author(s).)

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., (© 2021 Manga et al.; Published by Cold Spring Harbor Laboratory Press.)

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.

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.

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., Competing Interests: Declaration of competing interest The authors claim no conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2020 Vakli, Deák-Meszlényi, Auer and Vidnyánszky.)

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., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

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 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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

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.

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.

External periodic stimuli entrain brain oscillations and affect perception and attention. It has been shown that background music can change oculomotor behavior and facilitate detection of visual objects occurring on the musical beat. However, whether musical beats in different tempi modulate information sampling differently during natural viewing remains to be explored. Here we addressed this question by investigating how listening to naturalistic drum grooves in two different tempi affects eye movements of participants viewing natural scenes on a computer screen. We found that the beat frequency of the drum grooves modulated the rate of eye movements: fixation durations were increased at the lower beat frequency (1.7Hz) as compared to the higher beat frequency (2.4Hz) and no music conditions. Correspondingly, estimated visual sampling frequency decreased as fixation durations increased with lower beat frequency. These results imply that slow musical beats can retard sampling of visual information during natural viewing by increasing fixation durations., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Multi-echo fMRI data acquisition has been widely investigated and suggested to optimize sensitivity for detecting the BOLD signal. Several methods have also been proposed for the combination of data with different echo times. The aim of the present study was to investigate whether these advanced echo combination methods provide advantages over the simple averaging of echoes when state-of-the-art group-level random-effect analyses are performed. Both resting-state and task-based dual-echo fMRI data were collected from 27 healthy adult individuals (14 male, mean age = 25.75 years) using standard echo-planar acquisition methods at 3T. Both resting-state and task-based data were subjected to a standard image pre-processing pipeline. Subsequently the two echoes were combined as a weighted average, using four different strategies for calculating the weights: (1) simple arithmetic averaging, (2) BOLD sensitivity weighting, (3) temporal-signal-to-noise ratio weighting and (4) temporal BOLD sensitivity weighting. Our results clearly show that the simple averaging of data with the different echoes is sufficient. Advanced echo combination methods may provide advantages on a single-subject level but when considering random-effects group level statistics they provide no benefit regarding sensitivity (i.e., group-level t -values) compared to the simple echo-averaging approach. One possible reason for the lack of clear advantages may be that apart from increasing the average BOLD sensitivity at the single-subject level, the advanced weighted averaging methods also inflate the inter-subject variance. As the echo combination methods provide very similar results, the recommendation is to choose between them depending on the availability of time for collecting additional resting-state data or whether subject-level or group-level analyses are planned.

Reading is a unique human ability that plays a pivotal role in the development and functioning of our modern society. However, its neural basis remains poorly understood since previous research was focused on reading words with fixed gaze. Here we developed a methodological framework for single-trial analysis of fixation onset-related EEG activity (FOREA) that enabled us to investigate visual information processing during natural reading. To reveal the effect of reading skills on orthographic processing during natural reading, we measured how altering the configural properties of the written text by modifying inter-letter spacing affects FOREA. We found that orthographic processing is reflected in FOREA in three consecutive time windows (120-175 ms, 230-265 ms, 345-380 ms after fixation onset) and the magnitude of FOREA effects in the two later time intervals showed a close association with the participants' reading speed: FOREA effects were larger in fast than in slow readers. Furthermore, these expertise-driven configural effects were clearly dissociable from the FOREA signatures of visual perceptual processes engaged to handle the increased crowding (155-220 ms) as a result of decreasing letter spacing. Our findings revealed that with increased reading skills orthographic processing becomes more sensitive to the configural properties of the written text.

In recent years, several functional magnetic resonance imaging (fMRI) studies showed that correct stimulus predictions reduce the neural responses when compared to surprising events (Egner et al., 2010). Further, it has been shown that such fulfilled expectations enhance the magnitude of repetition suppression (RS, i.e. a decreased neuronal response after the repetition of a given stimulus) in face selective visual cortex as well (Summerfield et al., 2008). Current MEG and neuroimaging studies suggest that the underlying mechanisms of expectation effects are independent from these of RS (Grotheer and Kovács, 2015; Todorovic and Lange, 2012). However, it is not clear as of today how perceptual expectations modulate the neural responses: is the difference between correctly predicted and surprising stimuli due to a genuine response reduction for correctly predicted stimuli or is it due to an increased response for surprising stimuli? Therefore, here we used a modified version of the paradigm of Grotheer and Kovács (2015) to induce predictions independently from repetition probability by presenting pairs of faces (female, male or infant) that were either repeated or alternating. Orthogonally to this, predictions were manipulated by the gender of the first face within each pair so that it signaled high, low or equal probability of repetitions. An unpredicted, neutral condition with equal probabilities for alternating and repeated trials was used to identify the role of surprising and enhancing modulations. Similarly, to Grotheer and Kovács (2015), we found significant RS and significant expectation effect in the FFA. Importantly, we observed larger response for surprising events in comparison to the neutral and correctly predicted conditions for alternating trials. Altogether, these results emphasize the role of surprise in prediction effects., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2015 the authors 0270-6474/15/357165-09$15.00/0.)

Visual cortical alpha oscillations are involved in attentional gating of incoming visual information. It has been shown that spatial and feature-based attentional selection result in increased alpha oscillations over the cortical regions representing sensory input originating from the unattended visual field and task-irrelevant visual features, respectively. However, whether attentional gating in the case of object based selection is also associated with alpha oscillations has not been investigated before. Here we measured anticipatory electroencephalography (EEG) alpha oscillations while participants were cued to attend to foveal face or word stimuli, the processing of which is known to have right and left hemispheric lateralization, respectively. The results revealed that in the case of simultaneously displayed, overlapping face and word stimuli, attending to the words led to increased power of parieto-occipital alpha oscillations over the right hemisphere as compared to when faces were attended. This object category-specific modulation of the hemispheric lateralization of anticipatory alpha oscillations was maintained during sustained attentional selection of sequentially presented face and word stimuli. These results imply that in the case of object-based attentional selection-similarly to spatial and feature-based attention-gating of visual information processing might involve visual cortical alpha oscillations.

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., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: Amblyopia was first described as a deficit of central vision. However, it has long been debated whether this dysfunction is limited to the fovea or whether extrafoveal vision is also affected, as studies concerning the latter are equivocal. The purpose of the study was to resolve this issue., Methods: We investigated the amblyopic effect on event-related potentials (ERPs) with foveal and perifoveal stimuli, either matched in size based on cortical magnification or presented as large annular stimuli. In two separate experiments we measured ERPs on amblyopic patients and control subjects using face images. Latency and amplitude of averaged ERPs and their single-trial distributions were analyzed., Results: When the fovea was stimulated, latency and amplitude of the early averaged ERP components increased and were reduced, respectively, in the amblyopic compared with the fellow eye. Importantly, perifoveal stimulation also elicited similar amblyopic deficits, which were clearly significant in the case of using cortical magnification scaled stimuli. However, single-trial peak analysis revealed that foveal and perifoveal effects differed in nature: Peak amplitudes were reduced only in foveal stimulation, while latencies were delayed and jittered at both the fovea and perifovea. Event-related potentials obtained from fellow eyes were not significantly different from those of normal observers., Conclusions: Our findings revealed the existence of amblyopic deficits at the perifovea when the stimulated cortical area was matched in size to that of foveal stimulation. These deficits manifested themselves only in the temporal structure of the responses, unlike foveal deficits, which affected both component amplitude and latency.

Adding noise to a visual image makes object recognition more effortful and has a widespread effect on human electrophysiological responses. However, visual cortical processes directly involved in handling the stimulus noise have yet to be identified and dissociated from the modulation of the neural responses due to the deteriorated structural information and increased stimulus uncertainty in the case of noisy images. Here we show that the impairment of face gender categorization performance in the case of noisy images in amblyopic patients correlates with amblyopic deficits measured in the noise-induced modulation of the P1/P2 components of single-trial event-related potentials (ERP). On the other hand, the N170 ERP component is similarly affected by the presence of noise in the two eyes and its modulation does not predict the behavioral deficit. These results have revealed that the efficient processing of noisy images depends on the engagement of additional processing resources both at the early, feature-specific as well as later, object-level stages of visual cortical processing reflected in the P1 and P2 ERP components, respectively. Our findings also suggest that noise-induced modulation of the N170 component might reflect diminished face-selective neuronal responses to face images with deteriorated structural information.

Previously several functional magnetic resonance imaging (fMRI) studies point toward the role of perceptual expectations in determining adaptation or repetition suppression (RS) in humans. These studies showed that the probability of repetitions of faces within a block influences the magnitude of adaptation in face-related areas of the human brain (Summerfield et al., 2008). However, a current macaque single-cell/local field potential (LFP) recording study using objects as stimuli found no evidence for the modulation of the neural response by the repetition probability in the inferior temporal cortex (Kaliukhovich and Vogels, 2010). Here we examined whether stimulus repetition probability affects fMRI repetition suppression for nonface object stimuli in the human brain. Subjects were exposed to either two identical [repetition trials (RTs)] or two different [alternation trials (ATs)] object stimuli. Both types of trials were presented blocks consisting of either 75% [repetition blocks (RBs)] or 25% [alternation blocks (ABs)] of RTs. We found strong RS, i.e., a lower signal for RTs compared to ATs, in the object sensitive lateral occipital cortex as well as in the face-sensitive occipital and fusiform face areas. More importantly, however, there was no significant difference in the magnitude of RS between RBs and ABs in each of the areas. This is in agreement with the previous monkey single-unit/LFP findings and suggests that RS in the case of nonface visual objects is not modulated by the repetition probability in humans. Our results imply that perceptual expectation effects vary for different visual stimulus categories.

According to predictive coding models of sensory processing, stimulus expectations have a profound effect on sensory cortical responses. This was supported by experimental results, showing that fMRI repetition suppression (fMRI RS) for face stimuli is strongly modulated by the probability of stimulus repetitions throughout the visual cortical processing hierarchy. To test whether processing of voices is also affected by stimulus expectations, here we investigated the effect of repetition probability on fMRI RS in voice-selective cortical areas. Changing ('alt') and identical ('rep') voice stimulus pairs were presented to the listeners in blocks, with a varying probability of alt and rep trials across blocks. We found auditory fMRI RS in the nonprimary voice-selective cortical regions, including the bilateral posterior STS, the right anterior STG and the right IFC, as well as in the IPL. Importantly, fMRI RS effects in all of these areas were strongly modulated by the probability of stimulus repetition: auditory fMRI RS was reduced or not present in blocks with low repetition probability. Our results revealed that auditory fMRI RS in higher-level voice-selective cortical regions is modulated by repetition probabilities and thus suggest that in audition, similarly to the visual modality, processing of sensory information is shaped by stimulus expectation processes., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Behavioral research revealed that object vision is impaired in amblyopia. Nevertheless, neurophysiological research in humans has focused on the amblyopic effects at the earliest stage of visual cortical processing, leaving the question of later, object-specific neural processing deficits unexplored. By measuring event-related potentials (ERPs) to foveal face stimuli we characterized the amblyopic effects on the N170 component, reflecting higher-level structural face processing. Single trial analysis revealed that latencies of the ERP components increased and were more variable in the amblyopic eye compared to the fellow eye both in strabismic and anisometropic patent groups. Moreover, there was an additional delay of N170 relative to the early P1 component over the right hemisphere, which was absent in the fellow eye, suggesting a slower evolution of face specific cortical responses in amblyopia. On the other hand, distribution of single trial N170 peak amplitudes differed between the amblyopic and fellow eye only in the strabismic but not in the anisometropic patients. Furthermore, the amblyopic N170 latency increment but not the amplitude reduction correlated with the interocular differences in visual acuity and fixation stability. We found no difference in the anticipatory neural oscillations between stimulation of the amblyopic and the fellow eye implying that impairment of the neural processes underlying generation of stimulus-driven visual cortical responses might be the primary reason behind the observed amblyopic effects. These findings provide evidence that amblyopic disruption of early visual experience leads to deficits in the strength and timing of higher-level, face specific visual cortical responses, reflected in the N170 component., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Diffusion weighted magnetic resonance imaging is increasingly being used for neonatal and young pediatric subjects. Our purpose was to investigate a) whether cardiac triggering was needed to reduce variability of diffusion (tensor) imaging data, b) how pulsation artifacts affect the fitted diffusion tensor when triggering is not used and c) the feasibility of triggered data acquisition in neonates and young children. Data were collected from 11 infants and 7 adults. In seven infants and seven adults, diffusion encoding was applied solely along the z gradient direction with and without cardiac triggering. Non-parametric bootstrap statistical methods were applied to investigate the dependence of variance on triggering. One infant and all adults served as test-retest controls. From the remaining three infants diffusion tensor imaging data were acquired with and without triggering. Our findings that used the repeated measurements in a single diffusion-encoding direction indicated that without triggering the variability in the data was increased significantly both in infants and adults. When collecting diffusion tensor data in infants, this increased variability results in erroneous fractional anisotropy values and artifactual fiber direction estimates. Contrary to previous reports but supported by our findings involving adults, pulsation artifacts were present in a larger extent of the brain in the infant population. In conclusion, triggering is feasible in young subjects and is preferred when acquiring diffusion MRI data. In doing so, the amount of erroneous estimations due to image artifacts will be minimized, which in turn will lead to more specific and less ambiguous interpretations. Although fitting the pulse-monitoring device requires additional set-up time, the total imaging time is usually shorter than acquiring multiple data sets to compile a single, artifact-free set., (Copyright © 2012 Elsevier Inc. All rights reserved.)

It has been shown that the probability of face repetitions influences the magnitude of repetition-related response reductions (Summerfield et al., 2008), implying that perceptual expectations affect adaptation and repetition suppression processes in the human central nervous system. An unresolved question is whether probability effects are specific for the retinal position of the stimuli or affect stimulus processing globally, throughout the visual field. To address this question we tested whether face repetition probability affects fMRI adaptation (fMRIa) when the repeated stimuli are presented on the same retinal position, overlapping each other or when they are presented in opposite hemifields. Subjects were exposed to either two identical (repeated trial, RT) or two different (alternating trial, AT) face stimuli. Both types of trials were presented either in blocks consisting of 75% (repeated block, RB) or 25% (alternating block, AB) of RTs. We found that repetition probability influences fMRIa equally for overlapping and nonoverlapping arrangements: the signal reduction after RT was more pronounced in RB than in AB for both spatial arrangements of stimulus-pairs. This effect was present in bilateral fusiform and occipital face areas, as well as in the lateral occipital cortex. Our results support the role of stimulus repetition probability in determining fMRIa and shows that the effect is invariant to the retinal position of stimuli., (Copyright © 2012 Elsevier Inc. All rights reserved.)

It has been proposed that perceptual decision making involves a task-difficulty component, which detects perceptual uncertainty and guides allocation of attentional resources. It is thought to take place immediately after the early extraction of sensory information and is specifically reflected in a positive component of the event related potentials, peaking at ∼ 220 ms after stimulus onset. However, in the previous research, neural processes associated with the monitoring of overall task difficulty were confounded by those associated with the increased sensory processing demands as a result of adding noise to the stimuli. Here we dissociated the effect of phase noise on sensory processing and overall decision difficulty using a face gender categorization task. Task difficulty was manipulated either by adding noise to the stimuli or by adjusting the female/male characteristics of the face images. We found that it is the presence of noise and not the increased overall task difficulty that affects the electrophysiological responses in the first 300 ms following stimulus onset in humans. Furthermore, we also showed that processing of phase-randomized as compared to intact faces is associated with increased fMRI responses in the lateral occipital cortex. These results revealed that noise-induced modulation of the early electrophysiological responses reflects increased visual cortical processing demands and thus failed to provide support for a task-difficulty component taking place between the early sensory processing and the later sensory accumulation stages of perceptual decision making.