Sam M. Doesburg

The brain age gap (BAG), the divergence of an individual’s neurobiologically predicted brain age from their chronological age, is a key indicator of brain health. While BAG can be derived from diverse brain metrics, its interpretation often polarizes between early-life trait influences and current state-dependent factors like cognitive decline. Here, we propose an integrative framework that moves beyond single summary statistics by considering the full distribution of brain metrics across regions or time. We distinguish between a neural system’s “baseline” (typical values, e.g., mean) and its “capacity” (extreme values, e.g., maximum) within these distributions. To test this, we analyzed resting-state magnetoencephalography (MEG) from the Cam-CAN adult cohort, focusing on cross-frequency coupling (CFC) within functional MRI-defined networks. We derived network-specific CFC baseline (mean) and capacity (maximum) measures. Separate brain age prediction models were trained for each measure. The resulting BAGs (baseline-BAG and capacity-BAG) for each network were then correlated with cognitive performance on a picture priming task. Both baseline-BAG and capacity-BAG profiles showed associations with cognitive scores, with younger predicted brain age correlating with better performance. However, capacity-BAG exhibited more conclusive relationships, suggesting that metrics reflecting a neural system’s peak operational ability (capacity) may better capture an individual’s current cognitive state. These findings indicate that brain age models emphasizing neural capacity, rather than just baseline activity, could offer a more sensitive lens for understanding the state-dependent aspects of brain ageing.

Background and Objectives: The benefit of carotid artery stenting (CAS) for stroke prevention has been established, but less is known about CAS’s effect on cognition. Here, we investigate (1) changes in the blood flow in both treated and non-treated carotid arteries, (2) associations between the severity of artery occlusion and CAS-induced flow change, and (3) whether the flow changes relate to cognitive improvement. Materials and Methods: We used quantitative flow magnetic resonance imaging to assess blood flow and computerized neurocognitive assessment to evaluate cognitive performance. Fourteen patients identified for CAS as part of their standard care participated in this study; ten completed the CAS procedure and the pre- and post-CAS assessments (age = 77.0 ± 5.6; 70% males). Results: An increased ipsilateral flow following CAS was seen in 70% of the participants, while 50% also showed an increase in the total flow. The participants with ≥90% stenosis showed the greatest flow changes, such that the post-CAS flow was 60% higher relative to pre-CAS (p < 0.050). Cognitive responses to the flow increase were variable: attention showed a positive association; in comparison, higher cognitive flexibility and memory were only seen when treated stenosis was below 80%. Conclusions: Our preliminary findings highlight the impact of CAS and the complex relationship between blood flow and cognitive changes post-CAS, warranting larger-scale studies with extended follow-up periods.

Background/Objectives: Children with Autism Spectrum Disorder (ASD) face challenges in social communication due to difficulties in considering context, processing information, and interpreting social cues. This study aims to explore the neural processes related to pragmatic language communication in children with ASD and address the research question of how functional brain connectivity operates during complex pragmatic language tasks. Methods: We examined differences in brain functional connectivity between children with ASD and typically developing peers while they engaged in video recordings of spoken language tasks. We focused on two types of speech acts: semantic and pragmatic. Results: Our results showed differences between groups during the pragmatic and semantic language processing, indicating more idiosyncratic connectivity in children with ASD in the Left Somatomotor and Left Limbic networks, suggesting that these networks play a role in task-dependent functional connectivity. Additionally, these functional differences were mainly localized to the left hemisphere.

Electroencephalography (EEG) is a non-invasive tool widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into pseudostationary waveforms would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans ( n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the time-frequency decompositions such as the MIF can enhance CNN models’ performance in this task.

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain–behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.

Routine clinical EEG is a standard test used for the neurological evaluation of patients. A trained specialist interprets EEG recordings and classifies them into clinical categories. Given time demands and high inter-reader variability, there is an opportunity to facilitate the evaluation process by providing decision support tools that can classify EEG recordings automatically. Classifying clinical EEG is associated with several challenges: classification models are expected to be interpretable; EEGs vary in duration and EEGs are recorded by multiple technicians operating various devices. Our study aimed to test and validate a framework for EEG classification which satisfies these requirements by transforming EEG into unstructured text. We considered a highly heterogeneous and extensive sample of routine clinical EEGs (n = 5785), with a wide range of participants aged between 15 and 99 years. EEG scans were recorded at a public hospital, according to 10/20 electrode positioning with 20 electrodes. The proposed framework was based on symbolizing EEG signals and adapting a previously proposed method from natural language processing (NLP) to break symbols into words. Specifically, we symbolized the multichannel EEG time series and applied a byte-pair encoding (BPE) algorithm to extract a dictionary of the most frequent patterns (tokens) reflecting the variability of EEG waveforms. To demonstrate the performance of our framework, we used newly-reconstructed EEG features to predict patients' biological age with a Random Forest regression model. This age prediction model achieved a mean absolute error of 15.7 years. We also correlated tokens' occurrence frequencies with age. The highest correlations between the frequencies of tokens and age were observed at frontal and occipital EEG channels. Our findings demonstrated the feasibility of applying an NLP-based approach to classifying routine clinical EEG. Notably, the proposed algorithm could be instrumental in classifying clinical EEG with minimal preprocessing and identifying clinically-relevant short events, such as epileptic spikes.

Neuroimaging studies have reported numerous region-specific atypicalities in the brains of individuals with Autism Spectrum Disorder (ASD), including alterations in cortical thickness (CT). However, there are many inconsistent findings, and this is probably due to atypical CT developmental trajectories in ASD. To this end, we investigated group differences in terms of shapes of developmental trajectories of CT between ASD and typically developing (TD) populations. Using the Autism Brain Imaging Data Exchange (ABIDE) repository (releases I and II combined), we investigated atypical shapes of developmental trajectories in ASD using a linear, quadratic and cubic models at various scales of spatial coarseness, and their association with symptomatology using the Autism Diagnostic Observation Schedule (ADOS) scores. These parameters were also used to predict ASD and TD CT development. While no overall group differences in CT was observed across the entire age range, ASD and TD populations were different in terms of age-related changes. Developmental trajectories of CT in ASD were mostly characterized by decreased cortical thinning during early adolescence and increased thinning at later stages, involving mostly frontal and parietal areas. Such changes were associated with ADOS scores. The curvature of the trajectories estimated from the quadratic model was the most accurate and sensitive measure for detecting ASD. Our findings suggest that under the context of longitudinal changes in brain morphology, robust detection of ASD would require three time points to estimate the curvature of age-related changes.