Whole Brain and Cranial Size Adjustments in Volumetric Brain Analyses of Sex- and Age-Related Trends

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

BACKGROUND

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.

OBJECTIVE

We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.

METHODS

The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.

RESULTS

The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%).

CONCLUSIONS

The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J

PURPOSE

Aging is a major societal concern due to age-related functional losses. Synapses are crucial components of neural circuits, and synaptic density could be a sensitive biomarker to evaluate brain function. [11C]UCB-J is a positron emission tomography (PET) ligand targeting synaptic vesicle glycoprotein 2A (SV2A), which can be used to evaluate brain synaptic density in vivo.

METHODS

We evaluated age-related changes in gray matter synaptic density, volume, and blood flow using [11C]UCB-J PET and magnetic resonance imaging (MRI) in a wide age range of 80 cognitive normal subjects (21-83 years old). Partial volume correction was applied to the PET data.

RESULTS

Significant age-related decreases were found in 13, two, and nine brain regions for volume, synaptic density, and blood flow, respectively. The prefrontal cortex showed the largest volume decline (4.9% reduction per decade: RPD), while the synaptic density loss was largest in the caudate (3.6% RPD) and medial occipital cortex (3.4% RPD). The reductions in caudate are consistent with previous SV2A PET studies and likely reflect that caudate is the site of nerve terminals for multiple major tracts that undergo substantial age-related neurodegeneration. There was a non-significant negative relationship between volume and synaptic density reductions in 16 gray matter regions.

CONCLUSION

MRI and [11]C-UCB-J PET showed age-related decreases of gray matter volume, synaptic density, and blood flow; however, the regional patterns of the reductions in volume and SV2A binding were different. Those patterns suggest that MR-based measures of GM volume may not be directly representative of synaptic density.

Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Preclinical evidence suggests that inter-individual variation in the structure of the hypothalamus at birth is associated with variation in the intrauterine environment, with downstream implications for future disease susceptibility. However, scientific advancement in humans is limited by a lack of validated methods for the automatic segmentation of the newborn hypothalamus. N = 215 healthy full-term infants with paired T1-/T2-weighted MR images across four sites were considered for primary analyses (mean postmenstrual age = 44.3 ± 3.5 weeks, nmale /nfemale  = 110/106). The outputs of FreeSurfer's hypothalamic subunit segmentation tools designed for adults (segFS) were compared against those of a novel registration-based pipeline developed here (segATLAS) and against manually edited segmentations (segMAN) as reference. Comparisons were made using Dice Similarity Coefficients (DSCs) and through expected associations with postmenstrual age at scan. In addition, we aimed to demonstrate the validity of the segATLAS pipeline by testing for the stability of inter-individual variation in hypothalamic volume across the first year of life (n = 41 longitudinal datasets available). SegFS and segATLAS segmentations demonstrated a wide spread in agreement (mean DSC = 0.65 ± 0.14 SD; range = {0.03-0.80}). SegATLAS volumes were more highly correlated with postmenstrual age at scan than segFS volumes (n = 215 infants; RsegATLAS 2  = 65% vs. RsegFS 2  = 40%), and segATLAS volumes demonstrated a higher degree of agreement with segMAN reference segmentations at the whole hypothalamus (segATLAS DSC = 0.89 ± 0.06 SD; segFS DSC = 0.68 ± 0.14 SD) and subunit levels (segATLAS DSC = 0.80 ± 0.16 SD; segFS DSC = 0.40 ± 0.26 SD). In addition, segATLAS (but not segFS) volumes demonstrated stability from near birth to ~1 years age (n = 41; R2  = 25%; p < 10-3 ). These findings highlight segATLAS as a valid and publicly available (https://github.com/jerodras/neonate_hypothalamus_seg) pipeline for the segmentation of hypothalamic subunits using human newborn MRI up to 3 months of age collected at resolutions on the order of 1 mm isotropic. Because the hypothalamus is traditionally understudied due to a lack of high-quality segmentation tools during the early life period, and because the hypothalamus is of high biological relevance to human growth and development, this tool may stimulate developmental and clinical research by providing new insight into the unique role of the hypothalamus and its subunits in shaping trajectories of early life health and disease.

Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults

Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by  ∼  6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Incorporation of rapid association/dissociation processes in tissues into the monkey and human physiologically based pharmacokinetic models for manganese

In earlier physiologically based pharmacokinetic (PBPK) models for manganese (Mn), the kinetics of transport of Mn into and out of tissues were primarily driven by slow rates of association and dissociation of Mn with tissue binding sites. However, Mn is known to show rapidly reversible binding in tissues. An updated Mn model for primates, following similar work with rats, was developed that included rapid association/dissociation processes with tissue Mn-binding sites, accumulation of free Mn in tissues after saturation of these Mn-binding sites and rapid rates of entry into tissues. This alternative structure successfully described Mn kinetics in tissues in monkeys exposed to Mn via various routes including oral, inhalation, and intraperitoneal, subcutaneous, or intravenous injection and whole-body kinetics and tissue levels in humans. An important contribution of this effort is showing that the extension of the rate constants for binding and cellular uptake established in the monkey were also able to describe kinetic data from humans. With a consistent model structure for monkeys and humans, there is less need to rely on cadaver data and whole-body tracer studies alone to calibrate a human model. The increased biological relevance of the Mn model structure and parameters provides greater confidence in applying the Mn PBPK models to risk assessment. This model is also well-suited to explicitly incorporate emerging information on the role of transporters in tissue disposition, intestinal uptake, and hepatobiliary excretion of Mn.

Both brain size and biological sex contribute to variation in white matter microstructure in middle-aged healthy adults

Whether head size and/or biological sex influence proxies of white matter (WM) microstructure such as fractional anisotropy (FA) and mean diffusivity (MD) remains controversial. Diffusion tensor imaging (DTI) indices are also associated with age, but there are large discrepancies in the spatial distribution and timeline of age-related differences reported. The aim of this study was to evaluate the associations between intracranial volume (ICV), sex, and age and DTI indices from WM in a population-based study of healthy individuals (n = 812) aged 50-66 in the Nord-Trøndelag health survey. Semiautomated tractography and tract-based spatial statistics (TBSS) analyses were performed on the entire sample and in an ICV-matched sample of men and women. The tractography results showed a similar positive association between ICV and FA in all major WM tracts in men and women. Associations between ICV and MD, radial diffusivity and axial diffusivity were also found, but to a lesser extent than FA. The TBSS results showed that both men and women had areas of higher and lower FA when controlling for age, but after controlling for age and ICV only women had areas with higher FA. The ICV matched analysis also demonstrated that only women had areas of higher FA. Age was negatively associated with FA across the entire WM skeleton in the TBSS analysis, independent of both sex and ICV. Combined, these findings demonstrated that both ICV and sex contributed to variation in DTI indices and emphasized the importance of considering ICV as a covariate in DTI analysis.

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

Variation of cerebrospinal fluid in specific regions regulates focality in transcranial direct current stimulation

Background

Conventionally, transcranial direct current stimulation (tDCS) aims to focalize the current reaching the target region-of-interest (ROI). The focality can be quantified by the dose-target-determination-index (DTDI). Despite having a uniform tDCS setup, some individuals receive focal stimulation (high DTDI) while others show reduced focality (“non-focal”). The volume of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) underlying each ROI govern the tDCS current distribution inside the brain, thereby regulating focality.

Aim

To determine the regional volume parameters that differentiate the focal and non-focal groups.

Methods

T1-weighted images of the brain from 300 age-sex matched adults were divided into three equal groups- (a) Young (20 ≤ × &lt; 40 years), (b) Middle (40 ≤ × &lt; 60 years), and (c) Older (60 ≤ × &lt; 80 years). For each group, inter and intra-hemispheric montages with electrodes at (1) F3 and right supraorbital region (F3-RSO), and (2) CP5 and Cz (CP5-Cz) were simulated, targeting the left- Dorsolateral Prefrontal Cortex (DLPFC) and -Inferior Parietal Lobule (IPL), respectively. Both montages were simulated for two current doses (1 and 2 mA). For each individual head simulated for a tDCS configuration (montage and dose), the current density at each region-of-interest (ROI) and their DTDI were calculated. The individuals were categorized into two groups- (1) Focal (DTDI ≥ 0.75), and (2) Non-focal (DTDI &lt; 0.75). The regional volume of CSF, GM, and WM of all the ROIs was determined. For each tDCS configuration and ROI, three 3-way analysis of variance was performed considering- (i) GM, (ii) WM, and (iii) CSF as the dependent variable (DV). The age group, sex, and focality group were the between-subject factors. For a given ROI, if any of the 3 DV’s showed a significant main effect or interaction involving the focality group, then that ROI was classified as a “focal ROI.”

Results

Regional CSF was the principal determinant of focality. For interhemispheric F3-RSO montage, interaction effect (p &lt; 0.05) of age and focality was observed at Left Caudate Nucleus, with the focal group exhibiting higher CSF volume. The CSF volume of focal ROI correlated positively (r ∼ 0.16, p &lt; 0.05) with the current density at the target ROI (DLPFC). For intrahemispheric CP5-Cz montage, a significant (p &lt; 0.05) main effect was observed at the left pre- and post-central gyrus, with the focal group showing lower CSF volume. The CSF volume correlated negatively (r ∼ –0.16, p &lt; 0.05) with current density at left IPL. The results were consistent for both current doses.

Conclusion

The CSF channels the flow of tDCS current between electrodes with focal ROIs acting like reservoirs of current. The position of focal ROI in the channel determines the stimulation intensity at the target ROI. For focal stimulation in interhemispheric F3-RSO, the proximity of focal ROI reserves the current density at the target ROI (DLPFC). In contrast, for intrahemispheric montage (CP5-Cz), the far-end location of focal ROI reduces the current density at the target (IPL).

Episodic Memory, Hippocampal Volume, and Function for Classification of Mild Cognitive Impairment Patients Regarding Amyloid Pathology

Background:

Previous studies of hippocampal function and volume related to episodic memory deficits in patients with amnestic mild cognitive impairment (aMCI) have produced mixed results including increased or decreased activity and volume. However, most of them have not included biomarkers, such as amyloid-β (Aβ) deposition which is the hallmark for early identification of the Alzheimer’s disease continuum.

Objective:

We investigated the role of Aβ deposition, functional hippocampal activity and structural volume in aMCI patients and healthy elderly controls (HC) using a new functional MRI (fMRI) ecological episodic memory task.

Methods:

Forty-six older adults were included, among them Aβ PET PIB positive (PIB+) aMCI (N = 17), Aβ PET PIB negative (PIB–) aMCI (N = 15), and HC (N = 14). Hippocampal volume and function were analyzed using Freesurfer v6.0 and FSL for news headlines episodic memory fMRI task, and logistic regression for group classification in conjunction with episodic memory task and traditional neuropsychological tests.

Results:

The aMCI PIB+ and PIB–patients showed significantly worse performance in relation to HC in most traditional neuropsychological tests and within group difference only on story recall and the ecological episodic memory fMRI task delayed recall. The classification model reached a significant accuracy (78%) and the classification pattern characterizing the PIB+ included decreased left hippocampal function and volume, increased right hippocampal function and volume, and worse episodic memory performance differing from PIB–which showed increased left hippocampus volume.

Conclusion:

The main findings showed differential neural correlates, hippocampal volume and function during episodic memory in aMCI patients with the presence of Aβ deposition.

Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment

Purpose

Total intracranial volume (TIV) is often a nuisance covariate in MRI-based brain volumetry. This study compared two TIV adjustment methods with respect to their impact on z-scores in single subject analyses of regional brain volume estimates.

Methods

Brain parenchyma, hippocampus, thalamus, and TIV were segmented in a normal database comprising 5059 T1w images. Regional volume estimates were adjusted for TIV using the residual method or the proportion method. Age was taken into account by regression with both methods. TIV- and age-adjusted regional volumes were transformed to z-scores and then compared between the two adjustment methods. Their impact on the detection of thalamus atrophy was tested in 127 patients with multiple sclerosis.

Results

The residual method removed the association with TIV in all regions. The proportion method resulted in a switch of the direction without relevant change of the strength of the association. The reduction of physiological between-subject variability was larger with the residual method than with the proportion method. The difference between z-scores obtained with the residual method versus the proportion method was strongly correlated with TIV. It was larger than one z-score point in 5% of the subjects. The area under the ROC curve of the TIV- and age-adjusted thalamus volume for identification of multiple sclerosis patients was larger with the residual method than with the proportion method (0.84 versus 0.79).

Conclusion

The residual method should be preferred for TIV and age adjustments of T1w-MRI-based brain volume estimates in single subject analyses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-022-02961-6.

Episodic Memory Performance Modifies the Strength of the Age-Brain Structure Relationship

The bivariate relationships between brain structure, age, and episodic memory performance are well understood. Advancing age and poorer episodic memory performance are each associated with smaller brain volumes and lower cortical thickness measures, respectively. Advancing age is also known to be associated with poorer episodic memory task scores on average. However, the simultaneous interrelationship between all three factors—brain structure, age, and episodic memory—is not as well understood. We tested the hypothesis that the preservation of episodic memory function would modify the typical trajectory of age-related brain volume loss in regions known to support episodic memory function using linear mixed models in a large adult lifespan sample. We found that the model allowing for age and episodic memory scores to interact predicted the hippocampal volume better than simpler models. Furthermore, we found that a model including a fixed effect for age and episodic memory scores (but without the inclusion of the interaction term) predicted the cortical volumes marginally better than a simpler model in the prefrontal regions and significantly better in the posterior parietal regions. Finally, we observed that a model containing only a fixed effect for age (e.g., without the inclusion of memory scores) predicted the cortical thickness estimates and regional volume in a non-memory control region. Together, our findings provide support for the idea that the preservation of memory function in late life can buffer against typical patterns of age-related brain volume loss in regions known to support episodic memory.

The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Glymphatic system impairment in multiple sclerosis: relation with brain damage and disability

Recent evidences showed the existence of a central nervous system ‘waste clearance’ system, defined as glymphatic system. Glymphatic abnormalities have been described in several neurodegenerative conditions, including Alzheimer’s and Parkinson’s disease. Glymphatic function has not been thoroughly explored in multiple sclerosis, where neurodegenerative processes are intermingled with inflammatory processes.

We aimed to investigate glymphatic system function in multiple sclerosis and to evaluate its association with clinical disability, disease course, demyelination and neurodegeneration, quantified using different MRI techniques.

In this retrospective study, we enrolled 71 multiple sclerosis patients (49 relapsing-remitting and 22 progressive multiple sclerosis) and 32 age- and sex- matched healthy controls. All subjects underwent neurological and MRI assessment including high-resolution T1, T2 and double inversion recovery sequences, diffusion- and susceptibility weighted imaging. We calculated the diffusion along perivascular space index, a proxy for glymphatic function, cortical and deep gray matter volume, white and cortical gray matter lesion volume and normal appearing white matter microstructural damage.

Multiple sclerosis patients showed an overall lower diffusion along perivascular space index vs healthy controls (estimated mean difference: −0.09, P = 0.01). Both relapsing-remitting and progressive multiple sclerosis patients had lower diffusion along perivascular space index vs healthy controls (estimated mean difference: −0.06, P = 0.04 for relapsing-remitting and −0.19, P = 0.001 for progressive multiple sclerosis patients). Progressive multiple sclerosis patients showed lower diffusion along perivascular space index vs relapsing-remitting multiple sclerosis patients (estimated mean difference: −0.09, P = 0.03). In multiple sclerosis patients, lower diffusion along perivascular space index was associated with more severe clinical disability (r = −0.45, P = 0.001) and longer disease duration (r = −0.37, P = 0.002). Interestingly, we detected a negative association between diffusion along perivascular space index and disease duration in the first 4.13 years of the disease course (r = −0.38, P = 0.04) without any association thereafter (up to 34 years of disease duration). Lower diffusion along perivascular space index was associated with higher white (r = −0.36, P = 0.003) and cortical (r = −0.41, P = 0.001) lesion volume, more severe cortical (r = 0.30, P = 0.007) and deep (r = 0.42, P = 0.001) gray matter atrophy, reduced fractional anisotropy (r = 0.42, P = 0.001) and increased mean diffusivity (r = −0.45, P = 0.001) in the normal-appearing white matter.

Our results suggest that the glymphatic system is impaired in multiple sclerosis, especially in progressive stages. Impaired glymphatic function was associated with measures of both demyelination and neurodegeneration and reflects a more severe clinical disability. These findings suggest that glymphatic impairment may be a pathological mechanism underpinning multiple sclerosis. The dynamic interplay with other pathological substrates of the disease deserves further investigation.

Non-parametric MRI Brain Atlas for the Polish Population

Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence. Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed. Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller. Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.

Self-reported subjective cognitive decline is associated with global cognition in a community sample of Latinos/as/x living in the United States

INTRODUCTION

Although subjective cognitive decline (SCD) may be an early risk marker of Alzheimer's Disease (AD), research on SCD among Hispanics/Latinos/as/x (henceforth Latinos/as) living in the U.S. is lacking. We investigated if the cross-sectional relationship of self-reported SCD with objective cognition varies as a function of ethnic background (Latinos/as versus Non-Hispanic Whites [NHWs]). Secondary analyses conducted solely within the Latino/a group investigated if informant reported SCD is associated with objective cognition and whether self-reported SCD is related to markers of brain health in a sub-sample of Latinos/as with available MRI data.

METHODS

Eighty-three participants (≥60 years of age) without dementia (35 Latinos/as; 48 NHWs) completed the Mattis Dementia Rating Scale (MDRS) and the Subjective Cognitive Decline-Questionnaire (SCD-Q). Additionally, 22 Latino/a informants completed the informant-version of the SCD-Q. Hierarchical regression models investigated if ethnicity moderates the association of MDRS and SCD-Q scores after adjusting for demographics and depressive symptoms. Correlational analyses within the Latino/a group investigated self- and informant-reported associations of SCD-Q scores with objective cognition, and associations of self-reported SCD-Q scores with medial temporal lobe volume and thickness.

RESULTS

Latinos/as had lower education and MDRS scores than NHWs. Higher SCD-Q scores were associated with lower MDRS scores only in Latinos/as. Within the Latino/a group, self, but not informant reported SCD was related to objective cognition. Medium to large effect sizes were found whereby higher self-reported SCD was associated with lower entorhinal cortex thickness and left hippocampal volume in Latinos/as.

CONCLUSIONS

The association of SCD and concurrent objectively measured global cognition varied by ethnic background and was only significant in Latinos/as. Self-reported SCD may be an indicator of cognitive and brain health in Latinos/as without dementia, prompting clinicians to monitor cognition. Future studies should explore if SCD predicts objective cognitive decline in diverse groups of Latinos/as living in the U.S.

Altered gray matter development in pre-reading children with a family history of reading disorder

Reading disorders are common in children and can impact academic success, mental health, and career prospects. Reading is supported by network of interconnected left hemisphere brain regions, including temporo-parietal, occipito-temporal, and inferior-frontal circuits. Poor readers often show hypoactivation and reduced gray matter volumes in this reading network, with hyperactivation and increased volumes in the posterior right hemisphere. We assessed gray matter development longitudinally in pre-reading children aged 2-5 years using magnetic resonance imaging (MRI) (N = 32, 110 MRI scans; mean age: 4.40 ± 0.77 years), half of whom had a family history of reading disorder. The family history group showed slower proportional growth (relative to total brain volume) in the left supramarginal and inferior frontal gyri, and faster proportional growth in the right angular, right fusiform, and bilateral lingual gyri. This suggests delayed development of left hemisphere reading areas in children with a family history of dyslexia, along with faster growth in right homologues. This alternate development pattern may predispose the brain to later reading difficulties and may later manifest as the commonly noted compensatory mechanisms. The results of this study further shows our understanding of structural brain alterations that may form the neurological basis of reading difficulties.

Connectome analysis of male world-class gymnasts using probabilistic multishell, multitissue constrained spherical deconvolution tracking

In athletes, long‐term intensive training has been shown to increase unparalleled athletic ability and might induce brain plasticity. We evaluated the structural connectome of world‐class gymnasts (WCGs), as mapped by diffusion‐weighted magnetic resonance imaging probabilistic tractography and a multishell, multitissue constrained spherical deconvolution method to increase the precision of tractography at the tissue interfaces. The connectome was mapped in 10 Japanese male WCGs and in 10 age‐matched male controls. Network‐based statistic identified subnetworks with increased connectivity density in WCGs, involving the sensorimotor, default mode, attentional, visual, and limbic areas. It also revealed a significant association between the structural connectivity of some brain structures with functions closely related to the gymnastic skills and the D‐score, which is used as an index of the gymnasts' specific physical abilities for each apparatus. Furthermore, graph theory analysis demonstrated the characteristics of brain anatomical topology in the WCGs. They displayed significantly increased global connectivity strength with decreased characteristic path length at the global level and higher nodal strength and degree in the sensorimotor, default mode, attention, and limbic/subcortical areas at the local level as compared with controls. Together, these findings extend the current understanding of neural mechanisms that distinguish WCGs from controls and suggest brain anatomical network plasticity in WCGs resulting from long‐term intensive training. Future studies should assess the contribution of genetic or early‐life environmental factors in the brain network organization of WCGs. Furthermore, the indices of brain topology (i.e., connection density and graph theory indices) could become markers for the objective evaluation of gymnastic performance.

Neuroimaging of Sex/Gender Differences in Obesity: A Review of Structure, Function, and Neurotransmission

While the global prevalence of obesity has risen among both men and women over the past 40 years, obesity has consistently been more prevalent among women relative to men. Neuroimaging studies have highlighted several potential mechanisms underlying an individual’s propensity to become obese, including sex/gender differences. Obesity has been associated with structural, functional, and chemical alterations throughout the brain. Whereas changes in somatosensory regions appear to be associated with obesity in men, reward regions appear to have greater involvement in obesity among women than men. Sex/gender differences have also been observed in the neural response to taste among people with obesity. A more thorough understanding of these neural and behavioral differences will allow for more tailored interventions, including diet suggestions, for the prevention and treatment of obesity.