Decoding the best automated segmentation tools for vascular white matter hyperintensities in the aging brain: a clinician's guide to precision and purpose.

White matter hyperintensities of vascular origin (WMH) are commonly found in individuals over 60 and increase in prevalence with age. The significance of WMH is well-documented, with strong associations with cognitive impairment, risk of stroke, mental health, and brain structure deterioration. Consequently, careful monitoring is crucial for the early identification and management of individuals at risk. Luckily, WMH are detectable and quantifiable on standard MRI through visual assessment scales, but it is time-consuming and has high rater variability. Addressing this issue, the main aim of our study is to decipher the utility of quantitative measures of WMH, assessed with automatic tools, in establishing risk profiles for cerebrovascular deterioration. For this purpose, first, we work to determine the most precise WMH segmentation open access tool compared to clinician manual segmentations (LST-LPA, LST-LGA, SAMSEG, and BIANCA), offering insights into methodology and usability to balance clinical precision with practical application. The results indicated that supervised algorithms (LST-LPA and BIANCA) were superior, particularly in detecting small WMH, and can improve their consistency when used in parallel with unsupervised tools (LST-LGA and SAMSEG). Additionally, to investigate the behavior and real clinical utility of these tools, we tested them in a real-world scenario (N = 300; age > 50 y.o. and MMSE > 26), proposing an imaging biomarker for moderate vascular damage. The results confirmed its capacity to effectively identify individuals at risk comparing the cognitive and brain structural profiles of cognitively healthy adults above and below the resulted threshold.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants.

INTRODUCTION: Electrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimer's disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults. METHODS: 73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each source's strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters' connectivity. RESULTS: Frontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity. DISCUSSION: These results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology.

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity.

Resting State Electrophysiological Profiles and Their Relationship with Cognitive Performance in Cognitively Unimpaired Older Adults: A Systematic Review.

Background: Aging is a complex and natural process. The physiological decline related to aging is accompanied by a slowdown in cognitive processes, which begins shortly after individuals reach maturity. These changes have been sometimes interpreted as a compensatory sign and others as a fingerprint of deterioration. Objective: In this context, our aim is to uncover the mechanisms that underlie and support normal cognitive functioning in the brain during the later stages of life. Methods: With this purpose, a systematic literature search was conducted using PubMed, Scopus, and Web of Science databases, which identified 781 potential articles. After applying inclusion and exclusion criteria, we selected 12 studies that examined the brain oscillations patterns in resting-state conditions associated with cognitive performance in cognitively unimpaired older adults. Results: Although cognitive healthy aging was characterized differently across studies, and various approaches to analyzing brain activity were employed, our review indicates a relationship between alpha peak frequency (APF) and improved performance in neuropsychological scores among cognitively unimpaired older adults. Conclusions: A higher APF is linked with a higher score in intelligence, executive function, and general cognitive performance, and could be considered an optimal, and easy-to-assess, electrophysiological marker of cognitive health in older adults.

Decoding the Best Automated Segmentation Tools for Vascular White Matter Hyperintensities in the Aging Brain: A Clinician's Guide to Precision and Purpose.

Cerebrovascular damage from small vessel disease (SVD) occurs in healthy and pathological aging. SVD markers, such as white matter hyperintensities (WMH), are commonly found in individuals over 60 and increase in prevalence with age. WMHs are detectable on standard MRI by adhering to the STRIVE criteria. Currently, visual assessment scales are used in clinical and research scenarios but is time-consuming and has rater variability, limiting its practicality. Addressing this issue, our study aimed to determine the most precise WMH segmentation software, offering insights into methodology and usability to balance clinical precision with practical application. This study employed a dataset comprising T1, FLAIR, and DWI images from 300 cognitively healthy older adults. WMHs in this cohort were evaluated using four automated neuroimaging tools: Lesion Prediction Algorithm (LPA) and Lesion Growth Algorithm (LGA) from Lesion Segmentation Tool (LST), Sequence Adaptive Multimodal Segmentation (SAMSEG), and Brain Intensity Abnormalities Classification Algorithm (BIANCA). Additionally, clinicians manually segmented WMHs in a subsample of 45 participants to establish a gold standard. The study assessed correlations with the Fazekas scale, algorithm performance, and the influence of WMH volume on reliability. Results indicated that supervised algorithms were superior, particularly in detecting small WMHs, and can improve their consistency when used in parallel with unsupervised tools. The research also proposed a biomarker for moderate vascular damage, derived from the top 95th percentile of WMH volume in healthy individuals aged 50 to 60. This biomarker effectively differentiated subgroups within the cohort, correlating with variations in brain structure and behavior.

The effects of white matter hyperintensities on MEG power spectra in cognitively healthy aging.

Objective: This study sought to identify magnetoencephalography (MEG) power spectra patterns associated with cerebrovascular damage (white matter hyperintensities - WMH) and their relationship with cognitive performance and brain structure integrity in aging individuals without cognitive impairment. Methods: We hypothesized a "slowness" pattern characterized by increased power in δ and θ bands and decreased power in the ß band associated with the severity of vascular damage. MEG signals were analyzed in cognitively healthy older adults to investigate these associations. Results: Contrary to expectations, we did not observe an increase in δ and θ power. However, we found a significant negative correlation between ß band power and WMH volume. This ß power reduction was linked to structural brain changes, such as larger lateral ventricles, reduced white matter volume, and decreased fractional anisotropy in critical white matter tracts, but not to cognitive performance. This suggests that ß band power reduction may serve as an early marker of vascular damage before the onset of cognitive symptoms. Conclusion: Our findings partially confirm our initial hypothesis by demonstrating a decrease in ß band power with increased vascular damage but not the anticipated increase in slow band power. The lack of correlation between the ßpow marker and cognitive performance suggests its potential utility in early identification of at-risk individuals for future cognitive impairment due to vascular origins. These results contribute to understanding the electrophysiological signatures of preclinical vascular damage and highlight the importance of MEG in detecting subtle brain changes associated with aging.

Measurement invariance across countries of the Test of Memory Strategies (TMS): A contribution to the cross-national validity study.

Previous literature showed a complex interpretation of recall tasks due to the complex relationship between Executive Functions (EF) and Long Term Memory (M). The Test of Memory Strategies (TMS) could be useful for assessing this issue, because it evaluates EF and M simultaneously. This study aims to explore the validity of the TMS structure, comparing the models proposed by Vaccaro et al. (2022) and evaluating the measurement invariance according to three countries (Italy, Spain, and Portugal) through Confirmatory Factor Analysis (CFA). Four hundred thirty-one healthy subjects (Age mean = 54.84, sd = 20.43; Education mean = 8.85, sd =4.05; M = 177, F = 259) were recruited in three countries (Italy, Spain, and Portugal). Measurement invariance across three country groups was evaluated through Structural Equation modeling. Also, convergent and divergent validity were examined through the correlation between TMS and classical neuropsychological tests. CFA outcomes suggested that the best model was the three-dimensional model, in which list 1 and list2 reflect EF, list 3 reflects a mixed factor of EF and M (EFM) and list4 and list5 reflect M. This result is in line with the theory that TMS decreases EF components progressively. TMS was metric invariant to the country, but scalar invariance was not tenable. Finally, the factor scores of TMS showed convergent validity with the classical neuropsychological tests. The overall results support cross-validation of TMS in the three countries considered.

A Multiscale Closed-Loop Neurotoxicity Model of Alzheimer's Disease Progression Explains Functional Connectivity Alterations.

The accumulation of amyloid-ß (Aß) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer's disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen-Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aß effects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aß and hp-tau depositions' location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aß and hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.

Longitudinal changes in the functional connectivity of individuals at risk of Alzheimer's disease.

First-degree relatives of Alzheimer's disease patients constitute a key population in the search for early markers. Our group identified functional connectivity differences between cognitively unimpaired individuals with and without a family history. In this unprecedented follow-up study, we examine whether family history is associated with a longitudinal increase in the functional connectivity of those regions. Moreover, this is the first work to correlate electrophysiological measures with plasma p-tau231 levels, a known pathology marker, to interpret the nature of the change. We evaluated 69 cognitively unimpaired individuals with a family history of Alzheimer's disease and 28 without, at two different time points, approximately 3 years apart, including resting state magnetoencephalography recordings and plasma p-tau231 determinations. Functional connectivity changes in both precunei and left anterior cingulate cortex in the high-alpha band were studied using non-parametric cluster-based permutation tests. Connectivity values were correlated with p-tau231 levels. Three clusters emerged in individuals with family history, exhibiting a longitudinal increase of connectivity. Notably, the clusters for both precunei bore a striking resemblance to those found in previous cross-sectional studies. The connectivity values at follow-up and the change in connectivity in the left precuneus cluster showed significant positive correlations with p-tau231. This study consolidates the use of electrophysiology, in combination with plasma biomarkers, to monitor healthy individuals at risk of Alzheimer's disease and emphasizes the value of combining noninvasive markers to understand the underlying mechanisms and track disease progression. This could facilitate the design of more effective intervention strategies and accurate progression assessment tools.

Sex differences in the progression to Alzheimer's disease: a combination of functional and structural markers.

Mild cognitive impairment (MCI) has been frequently interpreted as a transitional phase between healthy cognitive aging and dementia, particularly of the Alzheimer's disease (AD) type. Of note, few studies explored that transition from a multifactorial perspective, taking into consideration the effect of basic factors such as biological sex. In the present study 96 subjects with MCI (37 males and 59 females) were followed-up and divided into two subgroups according to their clinical outcome: "progressive" MCI (pMCI = 41), if they fulfilled the diagnostic criteria for AD at the end of follow-up; and "stable" MCI (sMCI = 55), if they remained with the initial diagnosis. Different markers were combined to characterize sex differences between groups, including magnetoencephalography recordings, cognitive performance, and brain volumes derived from magnetic resonance imaging. Results indicated that the pMCI group exhibited higher low-frequency activity, lower scores in neuropsychological tests and reduced brain volumes than the sMCI group, being these measures significantly correlated. When sex was considered, results revealed that this pattern was mainly due to the influence of the females' sample. Overall, females exhibited lower cognitive scores and reduced brain volumes. More interestingly, females in the pMCI group showed an increased theta activity that correlated with a more abrupt reduction of cognitive and volumetric scores as compared with females in the sMCI group and with males in the pMCI group. These findings suggest that females' brains might be more vulnerable to the effects of AD pathology, since regardless of age, they showed signs of more pronounced deterioration than males.

Understanding the Episodic Memory and Executive Functioning Axis Impairment in MCI Patients: A Multicenter Study in Comparison with CSF Biomarkers.

BACKGROUND: This study aimed to explore the association between a verbal learning task that evaluates the potential mutual dependency between memory and executive functions (i.e., the Test of Memory Strategies, TMS) and cerebrospinal fluid (CSF) Alzheimer's Disease (AD) biomarkers. METHODS: A sample of 47 mild cognitive impairment (MCI) participants from Poland and Spain were classified according to the Erlangen Score Diagnostic Algorithm (ESA) into CSF- (n = 16) and CSF+ (n = 31) groups. Correlation analyses between TMS word-list conditions and CSF biomarkers were conducted. Additionally, an analysis of covariance was performed to define the effect on ESA classification in the sample, using as a covariable the country of origin of the participants. RESULTS: Significant associations between the TMS-3 condition and Aß42, t-tau, and p-tau were observed for the whole sample. In addition, the CSF- participants obtained higher cognitive performance in TMS-3 compared to the CSF+ group. This outcome persisted if the groups were based on Aß42 scores, but not t-tau or p-tau values. CONCLUSIONS: These findings could indicate that poor performance on verbal learning tests may be affected by executive dysfunctions. Therefore, future intervention plans focused on training executive functions would be of interest to improve the ability of MCI patients to encode and organize information.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Crisis del lóbulo temporal registrada mediante magnetoencefalografía: caso clínico / Temporal lobe seizure recorded by magnetoencephalography: case report

La localización del inicio de las crisis es un factor importante para la evaluación prequirúrgica de la epilepsia. En este trabajo se describe la localización del inicio de una crisis registrada mediante magnetoencefalografía (MEG) en un niño de 12 años que presenta crisis parciales complejas farmacorresistentes. La RM muestra una lesión de 20mm de diámetro en el hipocampo izquierdo. EEG de superficie con ondas theta temporales izquierdas. Registro MEG interictal con punta-onda aislada posterior e inferior a la lesión de la RM. Registro MEG ictal con punta-onda (2 Hz). La localización de los dipolos indica el inicio de la crisis en la circunvolución temporal inferior en la misma localización que la actividad interictal MEG. Esta actividad ictal se propaga bilateralmente a áreas frontales. El registro corticográfico intraquirúrgico confirma los resultados de la localización interictal mediante MEG.