FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI

Scale-dependent brain age with higher-order statistics from structural magnetic resonance imaging

Inferring chronological age from magnetic resonance imaging (MRI) brain data has become a valuable tool for the early detection of neurodegenerative diseases. We present a method inspired by cosmological techniques for analyzing galaxy surveys, utilizing higher-order summary statistics with multivariate two- and three-point analyses in 3D Fourier space. This method identifies outliers while offering physiological interpretability, allowing the detection of scales where brain anatomy differs across age groups and providing insights into brain aging processes. Similarly to the evolution of cosmic structures, the brain structure also evolves naturally but displays contrasting behaviors at different scales. On larger scales, structure loss occurs with age, possibly due to ventricular expansion, while smaller scales show increased structure, likely related to decreased cortical thickness and gray/white matter volume. Using MRI data from the OASIS-3 database for the complete sample of 864 sessions (reduced sample: 827 sessions), our method predicts chronological age with a Mean Absolute Error (MAE) of 3.8 years (~3.6 years) for individuals aged ~40-100 (50-85), while providing information as a function of scale. A neural density posterior estimation shows that the 1- σ uncertainty for each individual varies between ~3 and 7 years, suggesting that, beyond sample variance, complex genetic or lifestyle-related factors may influence brain aging. Applying this method to an independent database, Cam-CAN, validates our analysis, yielding a MAE of ~3.4 for the age range from 18 to 88 years. This work demonstrates the utility of interdisciplinary research, bridging cosmological methods and neuroscience.

Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis

Introduction

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, brain age research in psychiatry has primarily examined mood and psychotic disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group.

Methods

3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1,134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effect models examined group differences in brain-PAD and moderation by age.

Results

No significant group difference in brain-PAD manifested (β diagnosis (SE)=0.37 years (0.43), p=0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (β diagnosis-by-age=-0.08 (0.03), pFDR=0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post-hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; β diagnosis=1.20 (0.60), p<0.05, mixed-effects d (95% confidence interval)=0.14 (0.00-0.28)), but not older participants (36-75 years; β diagnosis=0.07 (0.65), p=0.91).

Conclusions

Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post-hoc analyses showed a subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

Brain Volume Loss After Stereotactic Laser Interstitial Thermal Therapy in Patients With Temporal Lobe Epilepsy

BACKGROUND AND PURPOSE

Temporal lobe epilepsy is the most common form of focal epilepsy. MR-guided laser interstitial thermal therapy (LITT) of the amygdalohippocampal complex has become an established therapy option in case of drug resistance. Long-term anatomic network effects on the brain due to deafferentiation have not yet been evaluated.

METHODS

We analyzed brain volumes of 11 patients with temporal lobe epilepsy before and 1-year after hippocampal LITT with FastSurfer segmenting T1-weighted data. Additionally, we performed visual ratings and measurements.

RESULTS

A total of 11 patients with temporal lobe epilepsy (7 left-sided, 4 right-sided) were included (5 females); the mean age years (±standard deviation) at surgery was 41.5 (±18.4) years. The mean postoperative defect size was 1427 (±517) mm3. Volumetry as well as visual ratings found a progressive volume loss after left-sided surgery in the ipsilateral temporal lobe, the contralateral (right) part of the thalamus, and especially contralateral (right) fusiform cortex. These changes could not be detected for right-sided surgery.

CONCLUSION

A (partial) ablation of the left (dominant) hippocampus appears to exert long-term effects on the right thalamus and right-sided temporal cortices. However, we could not observe this effect in the reverse direction. Volumetric studies for larger cohorts should be conducted to investigate these findings.

Neuronal ceroid lipofuscinosis 11 (CLN11) presenting with early-onset cone-rod dystrophy and learning difficulties

Neuronal Ceroid Lipofuscinosis 11 (CLN11) is an ultra-rare subtype of adult-onset Neuronal Ceroid Lipofuscinosis. Its phenotype is variable and not fully known. A 21-year-old man was evaluated in our neurogenetic outpatient clinic for early onset complex phenotype, including learning difficulties, cerebellar ataxia, cone-rod dystrophy, epilepsy, and dystonia. The patient was submitted to neurological and neuropsychological assessment, neuro-ophthalmological tests, brain MRI, EEG and whole exome sequencing. A homozygous frameshift variant (NM_002087.4: c.768_769dup; p.Gln257Profs*27) was found. Distinct type descriptions, as in this case, increase the clinical spectrum of the disease.

FastSurfer-LIT: Lesion inpainting tool for whole-brain MRI segmentation with tumors, cavities, and abnormalities

Resection cavities, tumors, and other lesions can fundamentally alter brain structure and present as abnormalities in brain MRI. Specifically, quantifying subtle neuroanatomical changes in other, not directly affected regions of the brain is essential to assess the impact of tumors, surgery, chemo/radiotherapy, or drug treatments. However, only a limited number of solutions address this important task, while many standard analysis pipelines simply do not support abnormal brain images at all. In this paper, we present a method to perform sensitive neuroanatomical analysis of healthy brain regions in the presence of large lesions and cavities. Our approach called "FastSurfer Lesion Inpainting Tool" (FastSurfer-LIT) leverages the recently emerged Denoising Diffusion Probabilistic Models (DDPM) to fill lesion areas with healthy tissue that matches and extends the surrounding tissue. This enables subsequent processing with established MRI analysis methods such as the calculation of adjusted volume and surface measurements using FastSurfer or FreeSurfer. FastSurfer-LIT significantly outperforms previously proposed solutions on a large dataset of simulated brain tumors (N = 100) and synthetic multiple sclerosis lesions (N = 39) with improved Dice and Hausdorff measures, and also on a highly heterogeneous dataset with lesions and cavities in a manual assessment (N = 100). Finally, we demonstrate increased reliability to reproduce pre-operative cortical thickness estimates from corresponding post-operative temporo-mesial resection surgery MRIs. The method is publicly available at https://github.com/Deep-MI/LIT and will be integrated into the FastSurfer toolbox.

Hypothalamic volume is associated with age, sex and cognitive function across lifespan: a comparative analysis of two large population-based cohort studies

BACKGROUND

Emerging findings indicate that the hypothalamus, the body's principal homeostatic centre, plays a crucial role in modulating cognition, but comprehensive population-based studies are lacking.

METHODS

We used cross-sectional data from the Rhineland Study (N = 5812, 55.2 ± 13.6 years, 58% women) and the UK Biobank Imaging Study (UKB) (N = 45,076, 64.2 ± 7.7 years, 53% women), two large-scale population-based cohort studies. Volumes of hypothalamic structures were obtained from 3T structural magnetic resonance images through an automatic parcellation procedure (FastSurfer-HypVINN). The standardised cognitive domain scores were derived from extensive neuropsychological test batteries. We employed multivariable linear regression to assess associations of hypothalamic volumes with age, sex and cognitive performance.

FINDINGS

In older individuals, volumes of total, anterior and posterior hypothalamus, and mammillary bodies were smaller, while those of medial hypothalamus and tuberal region were larger. Larger medial hypothalamus volume was related to higher cortisol levels in older individuals, providing functional validation. Volumes of all hypothalamic structures were larger in men compared to women. In both sexes, larger volumes of total, anterior and posterior hypothalamus, and mammillary bodies were associated with better domain-specific cognitive performance, whereas larger volumes of medial hypothalamus and tuberal region were associated with worse domain-specific cognitive performance.

INTERPRETATION

We found strong age and sex effects on hypothalamic structures, as well as robust associations between these structures and domain-specific cognitive functions. Overall, these findings thus implicate specific hypothalamic subregions as potential therapeutic targets against age-associated cognitive decline.

FUNDING

Institutional funds, Federal Ministry of Education and Research of Germany, Alzheimer's Association.

Versatile MRI acquisition and processing protocol for population-based neuroimaging

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models

Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods. Recently, new techniques using synthetic images have emerged, reducing the need for manual annotation. Here we provide a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, which can be used to develop segmentation methods using synthetic data. The label maps are obtained with a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains. We also provide the pre-processed ex vivo scans, as this dataset can support future projects to include other structures after these are manually segmented.

Tractometry of Human Visual White Matter Pathways in Health and Disease

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.