Cortical Thickness Estimation: A Comparison of FreeSurfer and Three Voxel-Based Methods in a Test-Retest Analysis and a Clinical Application

Post mortem cadaveric and imaging mapping analysis of the influence of cochlear implants on cMRI assessment regarding implant positioning and artifact formation

OBJECTIVES

In times of an aging society and considering the escalating health economic costs, the indications for imaging, particularly magnetic resonance imaging (MRI), must be carefully considered and strictly adhered to. This cadaver study aims to examine the influence of cochlear implant (CI) on the assessment of intracranial structures, artifact formation, and size in cranial MRI (cMRI). Furthermore, it seeks to evaluate the potential limitations in the interpretability and diagnostic value of cMRI in CI patients. Additionally, the study investigates the imaging of the brain stem and the internal ear canal and the feasibility of excluding cholesteatomas in cMRI for CI patients.

MATERIALS AND METHODS

Two cadaveric specimens were implanted with cochlear implants at varying angular positions (90°, 120°, and 135°), both unilaterally and bilaterally, with and without magnet in situ. MRI acquisition consisted of sequences commonly used in brain MRI scans (T1-MP-RAGE, T2-TSE, T1-TIRM, DWI, CISS). Subsequently, the obtained MRI images were manually juxtaposed with a reference brain from the Computational Anatomy Toolbox CAT12. The size and formation of artifacts were scrutinized to ascertain the assessability of 22 predefined intracranial structures. Furthermore, the internal auditory canal, middle ear and mastoid were evaluated.

RESULTS

The cadaveric head mapping facilitated the analysis of all 22 predefined intracranial structures. Artifacts were assessed in terms of their minimum and maximum impact on image comparability. Image quality and assessability were stratified into four categories (0-25%, 25-50%, 50-75%, and 75-100% of assessability restriction). The visualization of the central, temporal, parietal, and frontal lobes was contingent upon CI positioning and the choice of imaging sequence. Diffusion-weighted cMRI proved inadequate for monitoring cholesteatoma recurrence in ipsilateral CI patients, regardless of magnet presence. The ipsilateral internal auditory canal was inadequately visualized in both magnet-present and magnet-absent conditions. We divided our results into four categories. Category 3 (orange) indicates considerable limitations, while category 4 (red) indicates no interpretability, as the image is entirely obscured by artifacts.

CONCLUSION

This study provides detailed predictive power for the assessability and therefore the relevance of performing cMRIs in CI patients. We advocate consulting the relevant CI center if artifact overlay exceeds 50% (categories 3 and 4), to evaluate magnet explantation and reassess the necessity of cMRI. When suspecting cholesteatoma or cholesteatoma recurrences in patients with ipsilateral cochlear implants, diagnostic investigation should preferably be pursued surgically, as the necessary MRI sequences are prone to artifact interference, even in the absence of a magnet. The ipsilateral internal auditory canal remains inadequately evaluable with a magnet in situ, while without the magnet, only rudimentary assessments can be made across most sequences.

Mapping morphological cortical networks with joint probability distributions from multiple morphological features

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

Realistic morphology-preserving generative modelling of the brain

Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem. But are current generative models synthesizing morphologically correct samples? In this work we present a three-dimensional generative model of the human brain that is trained at the necessary scale to generate diverse, realistic-looking, high-resolution and morphologically preserving samples and conditioned on patient characteristics (for example, age and pathology). We show that the synthetic samples generated by the model preserve biological and disease phenotypes and are realistic enough to permit use downstream in well-established image analysis tools. While the proposed model has broad future applicability, such as anomaly detection and learning under limited data, its generative capabilities can be used to directly mitigate data scarcity, limited data availability and algorithmic fairness.

Morphological changes of cerebral gray matter in spinocerebellar ataxia type 3 using fractal dimension analysis

Spinocerebellar ataxia type 3 (SCA3), or Machado-Joseph disease, presents as a cerebellar cognitive affective syndrome (CCAS) and represents the predominant SCA genotype in Taiwan. Beyond cerebellar involvement, SCA3 patients exhibit cerebral atrophy. While prior neurodegenerative disease studies relied on voxel-based morphometry (VBM) for brain atrophy assessment, its qualitative nature limits individual and region-specific evaluations. To address this, we employed fractal dimension (FD) analysis to quantify cortical complexity changes in SCA3 patients. We examined 50 SCA3 patients and 50 age- and sex-matched healthy controls (HC), dividing MRI cerebral gray matter (GM) into 68 auto-anatomical subregions. Using three-dimensional FD analysis, we identified GM atrophy manifestations in SCA3 patients. Results revealed lateral atrophy symptoms in the left frontal, parietal, and occipital lobes, and fewer symptoms in the right hemisphere's parietal and occipital lobes. Focal areas of atrophy included regions previously identified in SCA3 studies, alongside additional regions with decreased FD values. Bilateral postcentral gyrus and inferior parietal gyrus exhibited pronounced atrophy, correlating with Scale for the Assessment and Rating of Ataxia (SARA) scores and disease duration. Notably, the most notable focal areas were the bilateral postcentral gyrus and the left superior temporal gyrus, serving as imaging biomarkers for SCA3. Our study enhances understanding of regional brain atrophy in SCA3, corroborating known clinical features while offering new insights into disease progression.

Identifying tripartite relationship among cortical thickness, neuroticism, and mood and anxiety disorders

The number of young adults seeking help for emotional distress, subsyndromal-syndromal mood/anxiety symptoms, including those associated with neuroticism, is rising and can be an early manifestation of mood/anxiety disorders. Identification of gray matter (GM) thickness alterations and their relationship with neuroticism and mood/anxiety symptoms can aid in earlier diagnosis and prevention of risk for future mood and anxiety disorders. In a transdiagnostic sample of young adults (n = 252;177 females; age 21.7 ± 2), Hypothesis (H) 1:regularized regression followed by multiple regression examined relationships among GM cortical thickness and clinician-rated depression, anxiety, and mania/hypomania; H2:the neuroticism factor and its subfactors as measured by NEO Personality Inventory (NEO-PI-R) were tested as mediators. Analyses revealed positive relationships between left parsopercularis thickness and depression (B = 4.87, p = 0.002), anxiety (B = 4.68, p = 0.002), mania/hypomania (B = 6.08, p ≤ 0.001); negative relationships between left inferior temporal gyrus (ITG) thickness and depression (B = - 5.64, p ≤ 0.001), anxiety (B = - 6.77, p ≤ 0.001), mania/hypomania (B = - 6.47, p ≤ 0.001); and positive relationships between left isthmus cingulate thickness (B = 2.84, p = 0.011), and anxiety. NEO anger/hostility mediated the relationship between left ITG thickness and mania/hypomania; NEO vulnerability mediated the relationship between left ITG thickness and depression. Examining the interrelationships among cortical thickness, neuroticism and mood and anxiety symptoms enriches the potential for identifying markers conferring risk for mood and anxiety disorders and can provide targets for personalized intervention strategies for these disorders.

CAT: a computational anatomy toolbox for the analysis of structural MRI data

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Cortical signature of depressive symptoms in frontotemporal dementia: A surface-based analysis

BACKGROUND AND OBJECTIVES

Depressive symptoms are frequently reported in patients affected by frontotemporal dementia (FTD). At structural MRI, cortical features of depressed FTD patients have been poorly described. Our objective was to investigate correlations between cortical measures and depression severity in FTD patients.

METHODS

Data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) database. We included 98 controls and 92 FTD patients, n = 38 behavioral variant FTD (bvFTD), n = 26 non-fluent variant Primary Progressive Aphasia (nfvPPA), and n = 28 semantic variant Primary Progressive Aphasia (svPPA). Patients underwent clinical and cognitive evaluations, as well as a 3D T1-weighted MRI on a 3 Tesla scanner (Siemens, Trio Tim system). Depression was evaluated by means of Geriatric Depression Scale (GDS). Surface-based analysis was performed on T1-weighted images to evaluate cortical thickness, a measure of gray matter integrity, and local gyrification index (lGI), a quantitative metric of cortical folding.

RESULTS

Patients affected by svPPA were more depressed than controls at NPI and depression severity at GDS was higher in svPPA and bvFTD. Severity of depression correlated with a decrease in lGI in left precentral and superior frontal gyrus, supramarginal and postcentral gyrus and right precentral, supramarginal, superior parietal and superior frontal gyri. Furthermore, depression severity correlated positively with cortical thickness in the left medial orbitofrontal cortex.

DISCUSSION

We found that lGI was associated with depressive symptoms over brain regions involved in the pathophysiology of major depressive disorder. This finding provides novel insights into the mechanisms underlying psychiatric symptoms in FTD.

Systematic cortical thickness and curvature patterns in primates

Humans are known to have significant and consistent differences in thickness throughout the cortex, with thick outer gyral folds and thin inner sulcal folds. Our previous work has suggested a mechanical basis for this thickness pattern, with the forces generated during cortical folding leading to thick gyri and thin sulci, and shown that cortical thickness varies along a gyral-sulcal spectrum in humans. While other primate species are expected to exhibit similar patterns of cortical thickness, it is currently unknown how these patterns scale across different sizes, forms, and foldedness. Among primates, brains vary enormously from roughly the size of a grape to the size of a grapefruit, and from nearly smooth to dramatically folded; of these, human brains are the largest and most folded. These variations in size and form make comparative neuroanatomy a rich resource for investigating common trends that transcend differences between species. In this study, we examine 12 primate species in order to cover a wide range of sizes and forms, and investigate the scaling of their cortical thickness relative to the surface geometry. The 12 species were selected due to the public availability of either reconstructed surfaces and/or population templates. After obtaining or reconstructing 3D surfaces from publicly available neuroimaging data, we used our surface-based computational pipeline (https://github.com/mholla/curveball) to analyze patterns of cortical thickness and folding with respect to size (total surface area), geometry (i.e. curvature, shape, and sulcal depth), and foldedness (gyrification). In all 12 species, we found consistent cortical thickness variations along a gyral-sulcal spectrum, with convex shapes thicker than concave shapes and saddle shapes in between. Furthermore, we saw an increasing thickness difference between gyri and sulci as brain size increases. Our results suggest a systematic folding mechanism relating local cortical thickness to geometry. Finally, all of our reconstructed surfaces and morphometry data are available for future research in comparative neuroanatomy.

Neurocognitive basis of model-based decision making and its metacontrol in childhood

Human behavior is supported by both goal-directed (model-based) and habitual (model-free) decision-making, each differing in its flexibility, accuracy, and computational cost. The arbitration between habitual and goal-directed systems is thought to be regulated by a process known as metacontrol. However, how these systems emerge and develop remains poorly understood. Recently, we found that while children between 5 and 11 years displayed robust signatures of model-based decision-making, which increased during this developmental period, there were substantial individual differences in the display of metacontrol. Here, we inspect the neurocognitive basis of model-based decision-making and metacontrol in childhood and focus this investigation on executive functions, fluid reasoning, and brain structure. A total of 69 participants between the ages of 6-13 completed a two-step decision-making task and an extensive behavioral test battery. A subset of 44 participants also completed a structural magnetic resonance imaging scan. We find that individual differences in metacontrol are specifically associated with performance on an inhibition task and individual differences in thickness of dorsolateral prefrontal, temporal, and superior-parietal cortices. These brain regions likely reflect the involvement of cognitive processes crucial to metacontrol, such as cognitive control and contextual processing.

Predictive modeling of optimism bias using gray matter cortical thickness

People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual's (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R² = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex-areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being.

Brain magnetic resonance imaging surface-based analysis and cortical thickness measurement in relapsing remission multiple sclerosis

Background

Damage occurs in the brain tissue in MS which appears normal on standard conventional imaging (normal appearing brain tissue). This slow, evolving damage can be monitored by nonconventional advanced MR imaging techniques. New techniques for the measurement of cortical thickness have been validated against histological analysis and manual measurements. The aim of our study was to study the role of MRI surface-based analysis and cortical thickness measurement in the evaluation of patients with Relapsing Remitting Multiple Sclerosis and to detect if there is localized rather than generalized cortical atrophy in Multiple Sclerosis patients and correlating these findings with clinical data.

Results

30 patients and 30 healthy control were included in this study and they were subjected to cortical thickness analysis using MRI. The patients in our study showed decreased thickness of the precentral, paracentral, postcentral, posterior cingulate cortices and mean cortical thickness in both hemispheres when compared with the normal control group. Statistical analysis was significant (P value < 0.05) for the precentral, paracentral, postcentral, posterior cingulate cortices and mean cortical thickness in both hemispheres. On the other hand, statistical analysis was not significant (P value > 0.05) for other cortices. There was a significant negative correlation between the precentral, paracentral, postcentral, posterior cingulate cortices and mean cortical thickness in both hemispheres and EDSS scores with correlation coefficients ranging from − 0.9878 to − 0.7977.

Conclusions

MRI and post-processing segmentation analysis for cortical thickness is non-invasive imaging techniques that can increase the level of diagnostic confidence in diagnosis of MS patients and should be included as routine modality when evaluating patients with MS.

Comparison of FreeSurfer and CAT12 Software in Parcel-Based Cortical Thickness Calculations

Several approaches have emerged to measure the cortical thickness (CT), which can be broadly divided into surface-based and voxel-based algorithms. We aimed to compare parcel-based CT estimation of the widely used FreeSurfer (FS) software and CAT12 software, which is a widely used voxel-based approach, and evaluate the test-retest (TRT) reliability of both methods. MRI images of 417 healthy individuals were analyzed. TRT reliability was performed on 60 participants. The mean CT of the parcels of the Desikan-Killiany atlas were calculated both in FS and CAT12. Linear mixed model was performed to compare the two methods and the TRT reliability, and paired-sample t-test for post-hoc analyses. Linear regression analyses were utilized to examine the regressions between the two methods and between different sessions with each method. CT values calculated using the two methods were significantly correlated (R²_adj = 0.67). The significant interaction effect between the method and the parcels were due to larger CT values of FS in 32 of 68 parcels, whereas CT values of CAT12 were higher in 31 of 68 parcels. The TRT reliabilities of both approaches were excellent (FS R²_adj = 0.95, CAT12 R²_adj = 0.93). We conclude that both techniques can provide equally valid results for groups comparisons or follow-up studies as long as they are not mixed with each other.

Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains

Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow-up. Outliers have usually been detected in a supervised or semi-supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two-level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain ventricles, white matter, cortical thickness, and functional connectivity-based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good-to-excellent test-retest reliability, with the exception of resting-state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual-level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non-artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5% of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort.

Cortical and Subcortical Structural Abnormalities in Patients With Idiopathic Cervical and Generalized Dystonia

OBJECTIVES

In this study, we sought to investigate structural imaging alterations of patients with idiopathic dystonia at the cortical and subcortical levels. The common and specific changes in two subtypes of dystonia, cervical dystonia (CD) and generalized dystonia (GD), were intended to be explored. Additionally, we sought to identify the morphometric measurements which might be related to patients' clinical characteristics, thus providing more clues of specific brain regions involved in the mechanism of idiopathic dystonia.

METHODS

3D T1-weighted MRI scans were acquired from 56 patients with idiopathic dystonia and 30 healthy controls (HC). Patients were classified as CD or GD, according to the distinct symptom distributions. Cortical thickness (CT) of 30 CD and 26 GD were estimated and compared to HCs using Computational Anatomy Toolbox (CAT12), while volumes of subcortical structures and their shape alterations (29 CD, 25 GD, and 27 HCs) were analyzed via FSL software. Further, we applied correlation analyses between the above imaging measurements with significant differences and patients' clinical characteristics.

RESULTS

The results of comparisons between the two patient groups and HCs were highly consistent, demonstrating increased CT of bilateral postcentral, superiorparietal, superiorfrontal/rostralmiddlefrontal, occipital gyrus, etc., and decreased CT of bilateral cingulate, insula, entorhinal, and fusiform gyrus (PFWE < 0.005 at the cluster level). In CD, trends of negative correlations were found between disease severity and CT alterations mostly located in pre/postcentral, rostralmiddlefrontal, superiorparietal, and supramarginal regions. Besides, volumes of bilateral putamen, caudate, and thalamus were significantly reduced in both patient groups, while pallidum volume reduction was also presented in GD compared to HCs. Caudate volume reduction had a trend of correlation to increasing disease severity in GD. Last, shape analysis directly demonstrated regional surface alterations in bilateral thalamus and caudate, where the atrophy located in the head of caudate had a trend of correlation to earlier ages of onset in GD.

CONCLUSIONS

Our study demonstrates wide-spread morphometric changes of CT, subcortical volumes, and shapes in idiopathic dystonia. CD and GD presented similar patterns of morphometric abnormalities, indicating shared underlying mechanisms in two different disease forms. Especially, the clinical associations of CT of multiple brain regions with disease severity, and altered volume/shape of caudate with disease severity/age of onset separately in CD and GD might serve as potential biomarkers for further disease exploration.

Subcortical Volume Changes in Early Menopausal Women and Correlation With Neuropsychological Tests

Background: The aging process and declining estradiol levels are two important factors that cause structural brain alterations. Many prior studies have investigated these two elements and revealed controversial results in menopausal women. Here, a cross-sectional study was designed to individually evaluate estradiol-related structural changes in the brain. Methods: A total of 45 early menopausal women and 54 age-matched premenopausal controls were enrolled and subjected to magnetic resonance imaging (MRI) scans, blood biochemistry tests, and neuropsychological tests. MRI structural images were analyzed using FreeSurfer to detect changes in subcortical and cortical volumes as well as cortical thickness. Finally, structural brain data as well as clinical and neuropsychological data were used for Pearson's correlation analyses to individually determine estradiol-related structural and functional changes in the brains of early menopausal women. Results: Compared with the premenopausal controls, the early menopausal women showed significant subcortical volumetric loss in the left amygdala and right amygdala, higher serum follicle-stimulating hormone (FSH) levels, more recognizable climacteric and depressive symptoms, decreased quality of sleep, and decreased working memory and executive functions. Simultaneously, FSH levels were related to lower working memory accuracy and longer working memory reaction time. Decreased subcortical volume in the bilateral amygdala was also related to lower working memory accuracy and longer executive reaction time in early menopausal women. Conclusion: The data suggest that estradiol deficiency in early menopausal women can lead to subcortical volume and functional brain changes, which may contribute to further understanding the neurobiological role of declined estradiol levels in early menopausal women.