A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes

Meta-Analysis of Hippocampal Volume and Episodic Memory in Preterm and Term Born Individuals

Preterm birth (< 37 weeks gestation) has been associated with memory deficits, which has prompted investigation of possible alterations in hippocampal volume in this population. However, existing literature reports varying effects of premature birth on hippocampal volume. Specifically, it is unclear whether smaller hippocampal volume in preterm-born individuals is merely reflective of smaller total brain volume. Further, it is not clear if hippocampal volume is associated with episodic memory functioning in preterm-born individuals. Meta-analysis was used to investigate the effects of premature birth on hippocampal volume and episodic memory from early development to young adulthood (birth to 26). PubMed, PsychINFO, and Web of Science were searched for English peer-reviewed articles that included hippocampal volume of preterm and term-born individuals. Thirty articles met the inclusion criteria. Separate meta-analyses were used to evaluate standardized mean differences between preterm and term-born individuals in uncorrected and corrected hippocampal volume, as well as verbal and visual episodic memory. Both uncorrected and corrected hippocampal volume were smaller in preterm-born compared to term-born individuals. Although preterm-born individuals had lower episodic memory performance than term-born individuals, the limited number of studies only permitted a qualitative review of the association between episodic memory performance and hippocampal volume. Tested moderators included mean age, pre/post-surfactant era, birth weight, gestational age, demarcation method, magnet strength, and slice thickness. With this meta-analysis, we provide novel evidence of the effects of premature birth on hippocampal volume.

The effects of psychotherapy for anhedonia on subcortical brain volumes measured with ultra-high field MRI

BACKGROUND

Anhedonia is a transdiagnostic symptom often resistant to treatment. The identification of biomarkers sensitive to anhedonia treatment will aid in the evaluation of novel anhedonia interventions.

METHODS

This is an exploratory analysis of changes in subcortical brain volumes accompanying psychotherapy in a transdiagnostic anhedonic sample using ultra-high field (7-Tesla) MRI. Outpatients with clinically impairing anhedonia (n = 116) received Behavioral Activation Treatment for Anhedonia, a novel psychotherapy, or Mindfulness-Based Cognitive Therapy (ClinicalTrials.gov Identifiers NCT02874534 and NCT04036136). Subcortical brain volumes were estimated via the MultisegPipeline, and regions of interest were the amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus. Bivariate mixed effects models estimated pre-treatment relations between anhedonia severity and subcortical brain volumes, change over time in subcortical brain volumes, and associations between changes in subcortical brain volumes and changes in anhedonia symptoms.

RESULTS

As reported previously (Cernasov et al., 2023), both forms of psychotherapy resulted in equivalent and significant reductions in anhedonia symptoms. Pre-treatment anhedonia severity and subcortical brain volumes were not related. No changes in subcortical brain volumes were observed over the course of treatment. Additionally, no relations were observed between changes in subcortical brain volumes and changes in anhedonia severity over the course of treatment.

LIMITATIONS

This trial included a modest sample size and did not have a waitlist-control condition or a non-anhedonic comparison group.

CONCLUSIONS

In this exploratory analysis, psychotherapy for anhedonia was not accompanied by changes in subcortical brain volumes, suggesting that subcortical brain volumes may not be a candidate biomarker sensitive to response to psychotherapy.

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement

OBJECTIVE

This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).

MATERIALS AND METHODS

In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.

RESULTS

The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.

DISCUSSION

The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.

Comparison of Inter-Method Agreement and Reliability for Automatic Brain Volumetry Using Three Different Clinically Available Software Packages

Background and Objectives: No comparative study has evaluated the inter-method agreement and reliability between Heuron AD and other clinically available brain volumetric software packages. Hence, we aimed to investigate the inter-method agreement and reliability of three clinically available brain volumetric software packages: FreeSurfer (FS), NeuroQuant® (NQ), and Heuron AD (HAD). Materials and Methods: In this study, we retrospectively included 78 patients who underwent conventional three-dimensional (3D) T1-weighed imaging (T1WI) to evaluate their memory impairment, including 21 with normal objective cognitive function, 24 with mild cognitive impairment, and 33 with Alzheimer's disease (AD). All 3D T1WI scans were analyzed using three different volumetric software packages. Repeated-measures analysis of variance, intraclass correlation coefficient, effect size measurements, and Bland-Altman analysis were used to evaluate the inter-method agreement and reliability. Results: The measured volumes demonstrated substantial to almost perfect agreement for most brain regions bilaterally, except for the bilateral globi pallidi. However, the volumes measured using the three software packages showed significant mean differences for most brain regions, with consistent systematic biases and wide limits of agreement in the Bland-Altman analyses. The pallidum showed the largest effect size in the comparisons between NQ and FS (5.20-6.93) and between NQ and HAD (2.01-6.17), while the cortical gray matter showed the largest effect size in the comparisons between FS and HAD (0.79-1.91). These differences and variations between the software packages were also observed in the subset analyses of 45 patients without AD and 33 patients with AD. Conclusions: Despite their favorable reliability, the software-based brain volume measurements showed significant differences and systematic biases in most regions. Thus, these volumetric measurements should be interpreted based on the type of volumetric software used, particularly for smaller structures. Moreover, users should consider the replaceability-related limitations when using these packages in real-world practice.

The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease

BACKGROUND

Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD.

METHODS

This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index.

RESULTS

QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA.

CONCLUSIONS

QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.

Neuromelanin-sensitive magnetic resonance imaging in the study of mental disorder: A systematic review

Dopamine and norepinephrine are implicated in the pathophysiology of mental disorders, but non-invasive study of their neuronal function remains challenging. Recent research suggests that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) techniques may overcome this limitation by enabling the non-invasive imaging of the substantia nigra (SN)/ ventral tegmental area (VTA) dopaminergic and locus coeruleus (LC) noradrenergic systems. A review of 19 studies that met the criteria for NM-MRI application in mental disorders found that despite the use of heterogeneous sequence parameters and metrics, nearly all studies reported differences in contrast ratio (CNR) of LC or SN/VTA between patients with mental disorders and healthy controls. These findings suggest that NM-MRI is a valuable tool in psychiatry, but the differences in sequence parameters across studies hinder comparability, and a standardized analysis pipeline is needed to improve the reliability of results. Further research using standardized methods is needed to better understand the role of dopamine and norepinephrine in mental disorders.

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.

The effect of processing pipelines, input images and age on automatic cortical morphology estimates

BACKGROUND AND OBJECTIVE

Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age.

METHODS

For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision.

RESULTS

Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction.

CONCLUSIONS

Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.

Phylogenetic comparative analysis of the cerebello-cerebral system in 34 species highlights primate-general expansion of cerebellar crura I-II

The reciprocal connections between the cerebellum and the cerebrum have been suggested to simultaneously play a role in brain size increase and to support a broad array of brain functions in primates. The cerebello-cerebral system has undergone marked functionally relevant reorganization. In particular, the lateral cerebellar lobules crura I-II (the ansiform) have been suggested to be expanded in hominoids. Here, we manually segmented 63 cerebella (34 primate species; 9 infraorders) and 30 ansiforms (13 species; 8 infraorders) to understand how their volumes have evolved over the primate lineage. Together, our analyses support proportional cerebellar-cerebral scaling, whereas ansiforms have expanded faster than the cerebellum and cerebrum. We did not find different scaling between strepsirrhines and haplorhines, nor between apes and non-apes. In sum, our study shows primate-general structural reorganization of the ansiform, relative to the cerebello-cerebral system, which is relevant for specialized brain functions in an evolutionary context.

Early emotional caregiving environment and associations with memory performance and hippocampal volume in adolescents with prenatal drug exposure

Early adversities, including prenatal drug exposure (PDE) and a negative postnatal emotional caregiving environment, impact children's long-term development. The protracted developmental course of memory and its underlying neural systems offer a valuable framework for understanding the longitudinal associations of pre- and postnatal factors on children with PDE. This study longitudinally examines memory and hippocampal development in 69 parent-child dyads to investigate how the early caregiving emotional environment affects children with PDE's neural and cognitive systems. Measures of physical health, drug exposure, caregiver stress, depression, and distress were collected between 0 and 24 months At age 14 years, adolescents completed multiple measures of episodic memory, and at ages 14 and 18 years, adolescents underwent magnetic resonance imaging (MRI) scans. Latent constructs of episodic memory and the caregiving environment were created using Confirmatory Factor Analysis. Multiple regressions revealed a negative emotional caregiving environment during infancy was associated with poor memory performance and smaller left hippocampal volumes at 14 years. Better memory performance at 14 years predicted larger right hippocampal volume at 18 years. At 18 years, the association between the emotional caregiving environment and hippocampal volume was moderated by sex, such that a negative emotional caregiving environment was associated with larger left hippocampal volumes in males but not females. Findings suggest that the postnatal caregiving environment may modulate the effects of PDE across development, influencing neurocognitive development.

Differential alterations of amygdala nuclei volumes in acutely ill patients with anorexia nervosa and their associations with leptin levels

BACKGROUND

The amygdala is a subcortical limbic structure consisting of histologically and functionally distinct subregions. New automated structural magnetic resonance imaging (MRI) segmentation tools facilitate the in vivo study of individual amygdala nuclei in clinical populations such as patients with anorexia nervosa (AN) who show symptoms indicative of limbic dysregulation. This study is the first to investigate amygdala nuclei volumes in AN, their relationships with leptin, a key indicator of AN-related neuroendocrine alterations, and further clinical measures.

METHODS

T1-weighted MRI scans were subsegmented and multi-stage quality controlled using FreeSurfer. Left/right hemispheric amygdala nuclei volumes were cross-sectionally compared between females with AN (n = 168, 12-29 years) and age-matched healthy females (n = 168) applying general linear models. Associations with plasma leptin, body mass index (BMI), illness duration, and psychiatric symptoms were analyzed via robust linear regression.

RESULTS

Globally, most amygdala nuclei volumes in both hemispheres were reduced in AN v. healthy control participants. Importantly, four specific nuclei (accessory basal, cortical, medial nuclei, corticoamygdaloid transition in the rostral-medial amygdala) showed greater volumetric reduction even relative to reductions of whole amygdala and total subcortical gray matter volumes, whereas basal, lateral, and paralaminar nuclei were less reduced. All rostral-medially clustered nuclei were positively associated with leptin in AN independent of BMI. Amygdala nuclei volumes were not associated with illness duration or psychiatric symptom severity in AN.

CONCLUSIONS

In AN, amygdala nuclei are altered to different degrees. Severe volume loss in rostral-medially clustered nuclei, collectively involved in olfactory/food-related reward processing, may represent a structural correlate of AN-related symptoms. Hypoleptinemia might be linked to rostral-medial amygdala alterations.

Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank

Background

Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder.

Methods

Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects.

Results

Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (β range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [β range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: β = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (β = 0.2218, pFDR = 1.34 × 10-7), distress (β = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (β = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance.

Conclusions

Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.

Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment

Introduction

Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI).

Methods

Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity.

Results

Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI.

Discussion

Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD.

Highlights

Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.

Omega-3 Fatty Acids, Cognition, and Brain Volume in Older Adults

The elderly population is growing at increased rates and is expected to double in size by 2050 in the United States and worldwide. The consumption of healthy foods and enriched diets have been associated with improved cognition and brain health. The key nutrients common to many healthy foods and diets are the omega-3 polyunsaturated fatty acids (omega-3 FAs), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). We explored whether omega-3 FA levels are associated with brain volume and cognition. Forty healthy, cognitively normal, Seventh-day Adventist older adults (mean age 76.3 years at MRI scan, 22 females) completed neurocognitive testing, a blood draw, and structural neuroimaging from 2016 to 2018. EPA and an overall omega-3 index were associated with individual measures of delayed recall (RAVLT-DR) and processing speed (Stroop Color) as well as entorhinal cortex thickness. EPA, DHA, and the omega-3 index were significantly correlated with the total white matter volume. The entorhinal cortex, frontal pole, and total white matter were associated with higher scores on delayed memory recall. This exploratory study found that among healthy, cognitively older adults, increased levels of omega-3 FAs are associated with better memory, processing speed, and structural brain measures.

Presurgical microstructural coherence predicts cognitive change for bariatric surgery patients

OBJECTIVE

This observational study examined the relationship between presurgical white matter microstructural coherence and cognitive change after weight loss. It was hypothesized that higher baseline fractional anisotropy (FA) would predict greater baseline and change cognition.

METHODS

A sample of 24 adults (BMI ≥ 35 kg/m2 ) underwent neuropsychological assessment at baseline and 12 weeks after bariatric surgery. A magnetic resonance imaging brain scan was administered at baseline and processed through Tract-Based Spatial Statistics to compute FA in white matter tracts of interest. Composite scores for attention, learning, processing speed, executive function, verbal fluency, working memory, and overall cognition were calculated.

RESULTS

As expected, FA in some tracts of interest was significantly (p < 0.05) positively associated with change in cognition. Inverse relationships were observed between baseline FA and presurgical cognition, which may be explained by increased medial and radial diffusivity and preserved axonal diffusivity. Cognition generally improved after surgery; however, relative but clinically nonsignificant deterioration was observed on learning measures. Poorer baseline cognitive performance was associated with greater postsurgical cognitive improvement.

CONCLUSIONS

Presurgical microstructural coherence is associated with magnitude of cognitive change after weight loss. An observed reduction in learning suggests that bariatric surgery may lead to negative outcomes in some cognitive domains, at least temporarily.

Automated hippocampal segmentation algorithms evaluated in stroke patients

Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities-such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.

Serotonin transporter (5-HTT) gene network moderates the impact of prenatal maternal adversity on orbitofrontal cortical thickness in middle childhood

In utero, the developing brain is highly susceptible to the environment. For example, adverse maternal experiences during the prenatal period are associated with outcomes such as altered neurodevelopment and emotion dysregulation. Yet, the underlying biological mechanisms remain unclear. Here, we investigate whether the function of a network of genes co-expressed with the serotonin transporter in the amygdala moderates the impact of prenatal maternal adversity on the structure of the orbitofrontal cortex (OFC) in middle childhood and/or the degree of temperamental inhibition exhibited in toddlerhood. T1-weighted structural MRI scans were acquired from children aged 6-12 years. A cumulative maternal adversity score was used to conceptualize prenatal adversity and a co-expression based polygenic risk score (ePRS) was generated. Behavioural inhibition at 18 months was assessed using the Early Childhood Behaviour Questionnaire (ECBQ). Our results indicate that in the presence of a low functioning serotonin transporter gene network in the amygdala, higher levels of prenatal adversity are associated with greater right OFC thickness at 6-12 years old. The interaction also predicts temperamental inhibition at 18 months. Ultimately, we identified important biological processes and structural modifications that may underlie the link between early adversity and future deviations in cognitive, behavioural, and emotional development.

Development and validation of deep learning-based automatic brain segmentation for East Asians: A comparison with Freesurfer

Purpose

To develop and validate deep learning-based automatic brain segmentation for East Asians with comparison to data for healthy controls from Freesurfer based on a ground truth.

Methods

A total of 30 healthy participants were enrolled and underwent T1-weighted magnetic resonance imaging (MRI) using a 3-tesla MRI system. Our Neuro I software was developed based on a three-dimensional convolutional neural networks (CNNs)-based, deep-learning algorithm, which was trained using data for 776 healthy Koreans with normal cognition. Dice coefficient (D) was calculated for each brain segment and compared with control data by paired t-test. The inter-method reliability was assessed by intraclass correlation coefficient (ICC) and effect size. Pearson correlation analysis was applied to assess the relationship between D values for each method and participant ages.

Results

The D values obtained from Freesurfer (ver6.0) were significantly lower than those from Neuro I. The histogram of the Freesurfer results showed remarkable differences in the distribution of D values from Neuro I. Overall, D values obtained by Freesurfer and Neuro I showed positive correlations, but the slopes and intercepts were significantly different. It was showed the largest effect sizes ranged 1.07-3.22, and ICC also showed significantly poor to moderate correlations between the two methods (0.498 ≤ ICC ≤ 0.688). For Neuro I, D values resulted in reduced residuals when fitting data to a line of best fit, and indicated consistent values corresponding to each age, even in young and older adults.

Conclusion

Freesurfer and Neuro I were not equivalent when compared to a ground truth, where Neuro I exhibited higher performance. We suggest that Neuro I is a useful alternative for the assessment of the brain volume.

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation

Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-scale database consisting of 7039 brain T1-weighted MRI data samples. We evaluated the method by using experiments focused on three distinct topics, namely, the necessity of preprocessing steps, cross-institutional and longitudinal reproducibility, and volumetric accuracy. The optimized model, MX_RW-where "MX" is a mix of RW and nonuniform intensity normalization data and "RW" is raw data with basic preprocessing-did not require time-consuming preprocessing steps, such as nonuniform intensity normalization or image registration, for brain MRI before segmentation. Cross-institutional testing revealed that MX_RW (Dice similarity coefficient: 0.809, coefficient of variation: 4.6%, and Pearson's correlation coefficient: 0.979) exhibited a performance comparable with that of FreeSurfer (Dice similarity coefficient: 0.798, coefficient of variation: 5.6%, and Pearson's correlation coefficient: 0.973). The computation time per dataset of MX_RW was generally less than 5 s (even when tested without graphics processing units), which was notably faster than FreeSurfer. Thus, for time-restricted applications, MX_RW represents a competitive alternative to FreeSurfer.

Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.

Pentad: A reproducible cytoarchitectonic protocol and its application to parcellation of the human hippocampus

Introduction

The hippocampus is integral for learning and memory and is targeted by multiple diseases. Neuroimaging approaches frequently use hippocampal subfield volumes as a standard measure of neurodegeneration, thus making them an essential biomarker to study. Collectively, histologic parcellation studies contain various disagreements, discrepancies, and omissions. The present study aimed to advance the hippocampal subfield segmentation field by establishing the first histology based parcellation protocol, applied to n = 22 human hippocampal samples.

Methods

The protocol focuses on five cellular traits observed in the pyramidal layer of the human hippocampus. We coin this approach the pentad protocol. The traits were: chromophilia, neuron size, packing density, clustering, and collinearity. Subfields included were CA1, CA2, CA3, CA4, prosubiculum, subiculum, presubiculum, parasubiculum, as well as the medial (uncal) subfields Subu, CA1u, CA2u, CA3u, and CA4u. We also establish nine distinct anterior-posterior levels of the hippocampus in the coronal plane to document rostrocaudal differences.

Results

Applying the pentad protocol, we parcellated 13 subfields at nine levels in 22 samples. We found that CA1 had the smallest neurons, CA2 showed high neuronal clustering, and CA3 displayed the most collinear neurons of the CA fields. The border between presubiculum and subiculum was staircase shaped, and parasubiculum had larger neurons than presubiculum. We also demonstrate cytoarchitectural evidence that CA4 and prosubiculum exist as individual subfields.

Discussion

This protocol is comprehensive, regimented and supplies a high number of samples, hippocampal subfields, and anterior-posterior coronal levels. The pentad protocol utilizes the gold standard approach for the human hippocampus subfield parcellation.

Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.

A structural connectivity atlas of limbic brainstem nuclei

Background

Understanding the structural connectivity of key brainstem nuclei with limbic cortical regions is essential to the development of therapeutic neuromodulation for depression, chronic pain, addiction, anxiety and movement disorders. Several brainstem nuclei have been identified as the primary central nervous system (CNS) source of important monoaminergic ascending fibers including the noradrenergic locus coeruleus, serotonergic dorsal raphe nucleus, and dopaminergic ventral tegmental area. However, due to practical challenges to their study, there is limited data regarding their in vivo anatomic connectivity in humans.

Objective

To evaluate the structural connectivity of the following brainstem nuclei with limbic cortical areas: locus coeruleus, ventral tegmental area, periaqueductal grey, dorsal raphe nucleus, and nucleus tractus solitarius. Additionally, to develop a group average atlas of these limbic brainstem structures to facilitate future analyses.

Methods

Each nucleus was manually masked from 197 Human Connectome Project (HCP) structural MRI images using FSL software. Probabilistic tractography was performed using FSL's FMRIB Diffusion Toolbox. Connectivity with limbic cortical regions was calculated and compared between brainstem nuclei. Results were aggregated to produce a freely available MNI structural atlas of limbic brainstem structures.

Results

A general trend was observed for a high probability of connectivity to the amygdala, hippocampus and DLPFC with relatively lower connectivity to the orbitofrontal cortex, NAc, hippocampus and insula. The locus coeruleus and nucleus tractus solitarius demonstrated significantly greater connectivity to the DLPFC than amygdala while the periaqueductal grey, dorsal raphe nucleus, and ventral tegmental area did not demonstrate a significant difference between these two structures.

Conclusion

Monoaminergic and other modulatory nuclei in the brainstem project widely to cortical limbic regions. We describe the structural connectivity across the several key brainstem nuclei theorized to influence emotion, reward, and cognitive functions. An increased understanding of the anatomic basis of the brainstem's role in emotion and other reward-related processing will support targeted neuromodulatary therapies aimed at alleviating the symptoms of neuropsychiatric disorders.

Neuroanatomical Correlates of the Late Positive Potential in Youth with Pediatric Bipolar Disorder

BACKGROUND

The late positive potential (LPP) could be a marker of emotion dysregulation in youth with pediatric bipolar disorder (PBD). However, the neuroanatomical correlates of the LPP are still not clarified.

OBJECTIVE

To provide cortical and deep gray matter correlates of the LPP in youth, specifically, youth with PBD.

METHODS

Twenty-four 7 to 17 years-old children with PBD and 28 healthy controls (HC) underwent cortical thickness and deep gray matter volumes measurements through magnetic resonance imaging and LPP measurement elicited by passively viewing emotional faces through electroencephalography. T-tests compared group differences in LPP, cortical thickness, and deep gray matter volumes. Linear regressions tested the relationship between LPP amplitude and cortical thickness/deep gray matter volumes.

RESULTS

PBD had a more pronounced LPP amplitude for happy faces and a thinner cortex in prefrontal areas than HC. While considering both groups, a higher LPP amplitude was associated with a thicker cortex across occipital and frontal lobes, and with a smaller right globus pallidus volume. In addition, a higher LPP amplitude for happy faces was associated with smaller left caudate and left globus pallidus volumes across both groups. Finally, the LPP amplitude correlated negatively with right precentral gyrus thickness across youth with PBD, but positively across HC.

CONCLUSION

Neural correlates of LPP in youth included fronto-occipital areas that have been associated also with emotion processing and control. The opposite relationship between BPD and HC of LPP amplitude and right precentral gyrus thickness might explain the inefficacy of the emotional control system in PBD.

Auditory hallucinations, childhood sexual abuse, and limbic gray matter volume in a transdiagnostic sample of people with psychosis

Childhood sexual abuse (CSA) is a potentially unique risk factor for auditory hallucinations (AH), but few studies have examined the moderating effects of sex or the association of CSA with limbic gray matter volume (GMV) in transdiagnostic samples of people with psychotic disorders. Here we found that people with psychotic disorders reported higher levels of all surveyed maltreatment types (e.g., physical abuse) than healthy controls, but people with psychotic disorders with AH (n = 41) reported greater CSA compared to both those without AH (n = 37; t = -2.21, p = .03) and controls (n = 37; t = -3.90, p < .001). Among people with psychosis, elevated CSA was most pronounced among females with AH (sex × AH status: F = 4.91, p = .009), held controlling for diagnosis, medications, and other maltreatment (F = 3.88, p = .02), and correlated with the current severity of AH (r = .26, p = .03) but not other symptoms (p's > .16). Greater CSA among patients related to larger GMV of the left amygdala accounting for AH status, diagnosis, medications, and other maltreatment (t = 2.12, p = .04). Among people with psychosis, females with AH may represent a unique subgroup with greater CSA. Prospective high-risk studies integrating multiple measures of maltreatment and brain structure/function may help elucidate the mechanisms linking CSA with amygdala alterations and AH.

Effects of the COVID-19 Pandemic on Mental Health and Brain Maturation in Adolescents: Implications for Analyzing Longitudinal Data

Background

The COVID-19 pandemic has caused significant stress and disruption for young people, likely leading to alterations in their mental health and neurodevelopment. In this context, it is not clear whether youth who lived through the pandemic and its shutdowns are comparable psychobiologically to their age- and sex-matched peers assessed before the pandemic. This question is particularly important for researchers who are analyzing longitudinal data that span the pandemic.

Methods

In this study we compared carefully matched youth assessed before the pandemic (n=81) and after the pandemic-related shutdowns ended (n=82).

Results

We found that youth assessed after the pandemic shutdowns had more severe internalizing mental health problems, reduced cortical thickness, larger hippocampal and amygdala volume, and more advanced brain age.

Conclusions

Thus, not only does the COVID-19 pandemic appear to have led to poorer mental health and accelerated brain aging in adolescents, but it also poses significant challenges to researchers analyzing data from longitudinal studies of normative development that were interrupted by the pandemic.

Volumetric assessment and longitudinal changes of subcortical structures in formalinized Beagle brains

High field MRI is an advanced technique for diagnostic and research purposes on animal models, such as the Beagle dog. In this context, studies on neuroscience applications, e.g. aging and neuro-pathologies, are currently increasing. This led to a need for reference values, in terms of volumetric assessment, for the structures typically involved. Nowadays, several canine brain MRI atlases have been provided. However, no reports are available regarding the measurements’ reproducibility and little is known about the effect of formalin on MRI segmentation. Here, we assessed the segmentation variability of selected structures among operators (two operators segmented the same data) in a sample of 11 Beagle dogs. Then, we analyzed, for one Beagle dog, the longitudinal volumetric changes of these structures. We considered four conditions: in vivo, post mortem (after euthanasia), ex vivo (brain extracted and studied after 1 month in formalin, and after 12 months). The MRI data were collected with a 3 T scanner. Our findings suggest that the segmentation procedure was overall reproducible since only slight statistical differences were detected. In the post mortem/ ex vivo comparison, most structures showed a higher contrast, thereby leading to greater reproducibility between operators. We observed a net increase in the volume of the studied structures. This could be justified by the intrinsic relaxation time changes observed because of the formalin fixation. This led to an improvement in brain structure visualization and segmentation. To conclude, MRI-based segmentation seems to be a useful and accurate tool that allows longitudinal studies on formalin-fixed brains.

Robustness of radiomics to variations in segmentation methods in multimodal brain MRI

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.

Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions

OBJECTIVE

To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB.

MATERIALS AND METHODS

Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability.

RESULTS

Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions.

CONCLUSION

NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

Integrated 3d flow-based multi-atlas brain structure segmentation

MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method’s potential for general applicability in various brain structures and settings.

The hypertrophic amygdala shape associated with anxiety in patients with primary dysmenorrhea during pain-free phase: insight from surface-based shape analysis

Background

Primary dysmenorrhea (PDM) is highly associated with mood symptoms. However, the neuropathology of these comorbidities is unclear. In the present study, we aimed to investigate the structural changes in the amygdala of patients with PDM during the pain-free phase using a surface-based shape analysis.

Methods

Forty-three PDM patients and forty healthy controls were recruited in the study, and all participants underwent structural magnetic resonance imaging scans during their periovulatory phase. FMRIB’s Integrated Registration and Segmentation Tool (FIRST) was employed to assess the subcortical volumetric and surface alterations in patients with PDM. Moreover, correlation and mediation analyses were used to detect the clinical significance of the subcortical morphometry alteration.

Results

PDM patients showed hypertrophic alteration of the amygdala in the left superficial nuclei and right basolateral and superficial nuclei but not for the whole amygdala volume. The hypertrophic amygdala was associated with disease duration, pain severity and anxiety symptoms during the menstrual period. Furthermore, the hypertrophic left amygdala could mediate the association between disease duration and anxiety severity.

Conclusions

The results of the current study demonstrated that the localized amygdala shape hypertrophy was present in PDM patients even in the pain-free phase. In addition, the mediator role of the hypertrophic amygdala indicates the potential target of amygdala for anxiety treatment in PDM treatment in the pain-free phase.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11682-022-00664-3.

DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.

Subcortical and hippocampal brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation

Developing accurate subcortical volumetric quantification tools is crucial for neurodevelopmental studies, as they could reduce the need for challenging and time‐consuming manual segmentation. In this study, the accuracy of two automated segmentation tools, FSL‐FIRST (with three different boundary correction settings) and FreeSurfer, were compared against manual segmentation of the hippocampus and subcortical nuclei, including the amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5‐year‐olds.

Both FSL‐FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL‐FIRST's default pipeline were the most accurate, whereas FreeSurfer's results were weaker across the structures.

In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL‐FIRST's agreement could be considered satisfactory (Pearson correlation > 0.74, intraclass correlation coefficient (ICC) > 0.68 and Dice score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus, caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.

The relationship between brain structure and general psychopathology in preadolescents

BACKGROUND

An emerging body of literature has indicated that broad, transdiagnostic dimensions of psychopathology are associated with alterations in brain structure across the life span. The current study aimed to investigate the relationship between brain structure and broad dimensions of psychopathology in the critical preadolescent period when psychopathology is emerging.

METHODS

This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study^® (n = 11,875; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area.

RESULTS

Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, parental education, income, and maternal psychopathology. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There were no statistically significant relationships between psychopathology and cortical thickness in this sample of preadolescents.

CONCLUSIONS

The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. Future research may focus on whether the widespread and pervasive relationships between general psychopathology and brain structure reflect cognitive dysfunction that is a feature across a range of mental illnesses.

Effects of a multi-strain probiotic on hippocampal structure and function, cognition, and emotional well-being in healthy individuals: a double-blind randomised-controlled trial

BACKGROUND

Animal studies have shown beneficial effects of probiotic supplementation on the hippocampus (HC) and cognitive performance. Evidence in humans is scarce. It was hypothesised that probiotic supplementation is associated with enhanced hippocampal (HC) regional grey matter volume (rGMV), as well as HC functional connectivity (FC). Relatedly improvements in mnestic and navigational performance, or emotional well-being, were expected to be observed in healthy human volunteers.

METHODS

A randomised-controlled, double-blind trial (RCT) was conducted in N = 59 volunteers (age Mean = 27.1, s.d. = 6.7), applying a multi-strain probiotic (Vivomixx®) v. non-probiotic milk-powder placebo, each with 4.4 g/day, for 4 weeks. Volumetric data was extracted from 3T structural magnetic resonance images of total HC and -subfields. Voxel-based morphometry (VBM) and FreeSurfer-based analyses were performed. Potential neuroplastic change beyond HC was explored using whole-brain-VBM for white- and GMV. Seed-based FC was calculated based on HC. Cognitive tests included visual, map-based, object-location, and verbal memory, and spatial navigation. Mental health status (stress, anxiety, depression, and emotion-regulation) was assessed using self-reports.

RESULTS

There were no changes in HC-total, -subfield GMV, or FC, through probiotics. VBM revealed no changes at a whole-brain-level. There were no effects on cognitive performance or mental health. Evidence in favor of the null-hypothesis, using Bayesian statistics, was consistent.

CONCLUSIONS

The applied multi-strain probiotic did not elicit any effects concerning hippocampal structural plasticity, cognition, or mental well-being in young, healthy adults. For future studies, longer application/observation RCTs, perhaps in stressed, otherwise psychologically/ cognitively vulnerable, or ageing groups, with well-founded strain selection and investigation of mechanism, are advised.

Tonic pain alters functional connectivity of the descending pain modulatory network involving amygdala, periaqueductal gray, parabrachial nucleus and anterior cingulate cortex

INTRODUCTION

Resting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC accompanying tonic pain in pain-free persons are rare. A network we term the Descending Pain Modulatory Network (DPMN) is implicated in healthy and pathologic pain modulation. Here, we evaluate the effect of tonic pain on FC of specific nodes of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN).

METHODS

In 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. functional MRI measured resting BOLD signal during pain-free rest with a 32°C thermode and then tonic pain where participants experienced a previously warm temperature combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity during both states. We hypothesized tonic pain would diminish FC dyads within the DPMN.

RESULTS

Of all hypothesized FC dyads, only PAG and subgenual ACC was weakly altered during pain (F=3.34; p=0.074; pain-free>pain d=0.25). After pain induction sACC-PAG FC became positively correlated with pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During pain, this connection's FC was diminished (paired t=-3.17; p=0.0026). In whole-brain analyses, during pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During pain, FC between left AMYG and right inferior temporal gyrus negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC with right claustrum; right primary visual cortex and right temporo-occipitoparietal junction Conclusion: We demonstrate sACC-PAG tonic pain FC positively correlates with experienced pain and resting right PBN-PAG FC correlates with subsequent pain and is diminished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which correlate with subsequently experienced pain intensity. Our findings suggest specific connectivity patterns within the DPMN at rest are associated with subsequently experienced pain and modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation or biomarkers to guide interventions.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

OBJECTIVES

QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.

METHODS

Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.

RESULTS

The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.

CONCLUSIONS

QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

KEY POINTS

• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Diffusion Tensor Imaging Reveals Deep Brain Structure Changes in Early Parkinson's Disease Patients with Various Sleep Disorders

Parkinson’s disease (PD) is a progressive age-related movement disorder caused by dopaminergic neuron loss in the substantia nigra. Diffusion-based magnetic resonance imaging (MRI) studies—namely, diffusion tensor imaging (DTI)—have been performed in the context of PD, either with or without the involvement of sleep disorders (SDs), to deepen our understanding of cerebral microstructural alterations. Analyzing the clinical characteristics and neuroimaging features of SDs in early PD patients is beneficial for early diagnosis and timely invention. In our present study, we enrolled 36 early PD patients (31 patients with SDs and 5 patients without) and 22 healthy controls. Different types of SDs were assessed using the Rapid Eye Movement Sleep Behavior Disorder Questionnaire—Hong Kong, Epworth Sleepiness Scale, International Restless Legs Scale and PD Sleep Scale-2. Brain MRI examinations were carried out in all the participants, and a region-of-interest (ROI) analysis was used to determine the DTI-based fractional anisotropy (FA) values in the substantia nigra (SN), thalamus (Thal) and hypothalamus (HT). The results illustrate that SDs showed a higher prevalence in the early PD patients than in the healthy controls (86.11% vs. 27.27%). Early PD patients with nighttime problems (NPs) had longer courses of PD than those without (5.097 ± 2.925 vs. 2.200 ± 1.095; p < 0.05), and these patients with excessive daytime sleepiness (EDS) or restless legs syndrome (RLS) had more advanced Hoehn and Yahr stages (HY stage) than those without (1.522 ± 0.511 and 1.526 ± 0.513, respectively; both p < 0.05). Compared with the early PD patients without probable rapid eye movement sleep behavior disorder (pRBD), those with pRBD had longer courses, more advanced HY stages and worse motor and non-motor symptoms of PD (course(years), 3.385 ± 1.895 vs. 5.435 ± 3.160; HY stages, 1.462 ± 0.477 vs. 1.848 ± 0.553; UPDRS, 13.538 ± 7.333 vs. 21.783 ± 10.766; UPDRS, 6.538 ± 1.898 vs. 7.957 ± 2.345; all p < 0.05). In addition, the different number of SD types in early PD patients was significantly inversely associated with the severity of damage in the SN and HT. All of the early PD patients with various SDs had injuries in the SN, in whom the damage was more pronounced in patients with NP than those without. Moreover, early PD patients with NP, RLS or pRBD had worse degrees of HT damage than those without. The current study demonstrated the pathophysiological features and neuroimaging changes in early PD patients with various types of sleep disorders, which will help in early diagnosis and therapy.

Lateralizing Characteristics of Morphometric Changes to Hippocampus and Amygdala in Unilateral Temporal Lobe Epilepsy with Hippocampal Sclerosis

Background andObjective: In the present study, a detailed investigation of substructural volume change in the hippocampus (HC) and amygdala (AMG) was performed and the association with clinical features in patients with mesial temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) determined. Methods: The present study included 22 patients with left-sided TLE-HS (LTLE-HS) and 26 patients with right-sided TLE-HS (RTLE-HS). In addition, 28 healthy controls underwent high-resolution T2-weighted image (T2WI) and T1-weighted image (T1WI) MRI scanning. Subfield analysis of HC and AMG was performed using FreeSurfer version 6.0. Results: Patients with TLE-HS showed a decrease in the volume of substructures in both HC and AMG, and this change was observed on the contralateral side and the ipsilateral side with HS. The volume reduction pattern of substructures showed laterality-dependent characteristics. Patients with LTLE-HS had smaller volumes of the ipsilateral subiculum (SUB), contralateral SUB, and ipsilateral cortical nucleus of AMG than patients with RTLE-HS. Patients with RTLE-HS had reduced ipsilateral cornu ammonis (CA) 2/3 and contralateral cortico-amygdaloid transition area (CAT) volumes. The relationship between clinical variables and subregions was different based on the lateralization of the seizure focus. Focal to bilateral tonic-clonic seizures (FTBTCS) was associated with contralateral and ipsilateral side subregions only in LTLE-HS. The abdominal FAS was associated with the volume reduction of AMG subregions only in LTLE-HS, but the volume reduction was less than in patients without FAS. Conclusions: The results indicate that unilateral TLE-HS is a bilateral disease that shows different laterality-dependent characteristics based on the subfield analysis of HC and AMG. Subfield volumes of HC and AMG were associated with clinical variables, and the more damaged substructures depended on laterality in TLE-HS. These findings support the evidence that LTLE-HS and RTLE-HS are disparate epilepsy entities rather than simply identical syndromes harboring a mesial temporal lesion. In addition, the presence of FAS supports good localization value, and abdominal FAS has a high localization value, especially in patients with LTLE-HS.

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Purpose

Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.

Materials and Methods

We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).

Results

Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= −4.2, right = 4.2) than with FreeSurfer (left= −3.1, right = 3.7) and FSL (left= −2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.

Conclusion

Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.

Reliability of structural MRI measurements: The effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream

BACKGROUND

Large-scale longitudinal and multi-centre studies are used to explore neuroimaging markers of normal ageing, and neurodegenerative and mental health disorders. Longitudinal changes in brain structure are typically small, therefore the reliability of automated techniques is crucial. Determining the effects of different factors on reliability allows investigators to control those adversely affecting reliability, calculate statistical power, or even avoid particular brain measures with low reliability. This study examined the impact of several image acquisition and processing factors and documented the test-retest reliability of structural MRI measurements.

METHODS

In Phase I, 20 healthy adults (11 females; aged 20-30 years) were scanned on two occasions three weeks apart on the same scanner using the ADNI-3 protocol. On each occasion, individuals were scanned twice (repetition), after re-entering the scanner (reposition) and after tilting their head forward. At one year follow-up, nine returning individuals and 11 new volunteers were recruited for Phase II (11 females; aged 22-31 years). Scans were acquired on two different scanners using the ADNI-2 and ADNI-3 protocols. Structural images were processed using FreeSurfer (v5.3.0, 6.0.0 and 7.1.0) to provide subcortical and cortical volume, cortical surface area and thickness measurements. Intra-class correlation coefficients (ICC) were calculated to estimate test-retest reliability. We examined the effect of repetition, reposition, head tilt, time between scans, MRI sequence and scanner on reliability of structural brain measurements. Mean percentage differences were also calculated in supplementary analyses.

RESULTS

Using the FreeSurfer v7.1.0 longitudinal pipeline, we observed high reliability for subcortical and cortical volumes, and cortical surface areas at repetition, reposition, three weeks and one year (mean ICCs>0.97). Cortical thickness reliability was lower (mean ICCs>0.82). Head tilt had the greatest adverse impact on ICC estimates, for example reducing mean right cortical thickness to ICC=0.74. In contrast, changes in ADNI sequence or MRI scanner had a minimal effect. We observed an increase in reliability for updated FreeSurfer versions, with the longitudinal pipeline consistently having a higher reliability than the cross-sectional pipeline.

DISCUSSION

Longitudinal studies should monitor or control head tilt to maximise reliability. We provided the ICC estimates and mean percentage differences for all FreeSurfer brain regions, which may inform power analyses for clinical studies and have implications for the design of future longitudinal studies.

Neurite density imaging in amygdala nuclei reveals interindividual differences in neuroticism

Neuroticism is known to have significant health implications. While previous research revealed that interindividual differences in the amygdala function are associated with interindividual differences in neuroticism, the impact of the amygdala’s structure and especially microstructure on variations in neuroticism remains unclear. Here, we present the first study using NODDI to examine the association between the in vivo microstructural architecture of the amygdala and neuroticism at the level of neurites. We, therefore, acquired brain images from 221 healthy participants using advanced multi‐shell diffusion‐weighted imaging. Because the amygdala comprises several nuclei, we, moreover, used a high‐resolution T1 image to automatically segment the amygdala into eight different nuclei. Neuroticism and its facets have been assessed using the NEO‐PI‐R. Finally, we associated neuroticism and its facets with the volume and microstructure of the amygdala nuclei. Statistical analysis revealed that lower neurite density in the lateral amygdala nucleus (La) was significantly associated with higher scores in depression, one of the six neuroticism facets. The La is the sensory relay of the amygdala, filtering incoming information based on previous experiences. Reduced neurite density and related changes in the dendritic structure of the La could impair its filtering function. This again might cause harmless sensory information to be misevaluated as threatening and lead to the altered amygdala responsivity as reported in previous studies investigating the functional correlates of neuroticism and neuroticism‐related disorders like depression.

Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal

BACKGROUND

Functional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala).

NEW METHOD

We explored seed-based dual regression (SDR) from theoretical and practical points of view. SDR is a modified implementation of dual regression where the set of spatial regressors is replaced by a single binary spatial map of the seed region.

RESULTS

SDR allowed detection of FC with small brain regions.

COMPARISON WITH EXISTING METHOD

For both synthetic and natural fMRI data, detection of FC with SDR was identical to that obtained with SCA after removal of global signal from fMRI data with global signal regression (GSR). In the absence of GSR, detection of FC was significantly improved when using SDR compared with SCA.

CONCLUSION

The improved FC detection achieved with SDR was related to a partial filtering of the global signal that occurred during spatial regression, an integral part of dual regression. This filtering can sometimes lead to spurious negative correlations that result in a widespread negative bias in FC derived with any application of dual regression. We provide guidelines for how to identify and correct this potential problem.

Different FreeSurfer versions might generate different statistical outcomes in case-control comparison studies

PURPOSE

Neuroimaging pipelines have long been known to generate mildly differing results depending on various factors, including software version. While considered generally acceptable and within the margin of reasonable error, little is known about their effect in common research scenarios such as inter-group comparisons between healthy controls and various pathological conditions. The aim of the presented study was to explore the differences in the inferences and statistical significances in a model situation comparing volumetric parameters between healthy controls and type 1 diabetes patients using various FreeSurfer versions.

METHODS

T1- and T2-weighted structural scans of healthy controls and type 1 diabetes patients were processed with FreeSurfer 5.3, FreeSurfer 5.3 HCP, FreeSurfer 6.0 and FreeSurfer 7.1, followed by inter-group statistical comparison using outputs of individual FreeSurfer versions.

RESULTS

Worryingly, FreeSurfer 5.3 detected both cortical and subcortical volume differences out of the preselected regions of interest, but newer versions such as FreeSurfer 5.3 HCP and FreeSurfer 6.0 reported only subcortical differences of lower magnitude and FreeSurfer 7.1 failed to find any statistically significant inter-group differences.

CONCLUSION

Since group averages of individual FreeSurfer versions closely matched, in keeping with previous literature, the main origin of this disparity seemed to lie in substantially higher within-group variability in the model pathological condition. Ergo, until validation in common research scenarios as case-control comparison studies is included into the development process of new software suites, confirmatory analyses utilising a similar software based on analogous, but not fully equivalent principles, might be considered as supplement to careful quality control.

A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance imaging (MRI) scans collected for this study. We find a lack of linear correlation between the segmentation volume estimates obtained from FreeSurfer and FSL. On the other hand, FreeSurfer volume estimates tend to be larger thanFSL estimates of the areas putamen, thalamus, amygdala, caudate, pallidum, hippocampus, and accumbens. The characterization of the performance of brain segmentation algorithms in large datasets as the one presented here is a necessary step towards partially or fully automated end-to-end neuroimaging workflow both in clinical and research settings.

Metaheuristic based Optimization Methods for the Segmentation of Tuberculosis Sputum Smear Images

Tuberculosis (TB) is a worldwide health crisis and second primary infectious disease that causes death. An attempt has been made to detect the presence of bacilli in sputum smears. The smear images recorded under standard image acquisition protocol are segmented by metaheuristic-based methods. Morphological operators are embedded in Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) segmentation to retain concavity of rod-shaped bacilli. Results demonstrate that hybrid ACO segmentation is able to retain the shape of bacilli in TB images. Segmented images are validated with ground truth using overlap, distance and probability-based measures. Significant shape-based features such as area, perimeter, compactness, shape factor and tortuosity are extracted from the segmented images. It is observed that hybrid method preserves more edges, detects the presence of bacilli and facilitates direct segmentation with reduced number of redundant searches to generate edges. Thus this hybrid ACO-morphology segmentation technique aid in the diagnostic relevance of TB images.

Volumetric alterations in subregions of the amygdala in adults with major depressive disorder

BACKGROUND

Although major depressive disorder (MDD) has been associated with volumetric abnormalities in the amygdala, studies investigating the association between structural alterations of the amygdala and depression have yielded varying results. Since the amygdala comprises several subregions, it is difficult to detect subtle regional changes by measuring the total amygdala volume. This study aimed to examine the volume in each amygdala subregion in adults with and without a diagnosis of MDD.

METHODS

A total of 147 participants with a current history of major depression and 144 healthy participants ranging in age from 19 to 64 years underwent 3T magnetic resonance imaging scanning. Automatic segmentation of the nine nuclei of the amygdala was performed using FreeSurfer. One-way analysis of covariance, with individual volumes as dependent variables, and age, sex, and total intracranial volume as covariates, was performed to analyze volume differences.

RESULTS

Patients with MDD had significantly lower volumes of the entire amygdala and subregions, including the lateral nucleus and anterior amygdaloid area, than healthy volunteers (HCs). There were no significant associations between subregion volumes and antidepressant use, illness duration, or depression severity.

LIMITATIONS

Our cross-sectional design cannot provide a causal relationship between the volume change in the amygdala subregion and the risk of MDD.

CONCLUSION

Our findings suggest that specific amygdala subregions are more susceptible to volumetric alterations in patients with MDD than in HCs. These findings may advance our understanding of the neuroanatomic basis on MDD.

Charting the human amygdala development across childhood and adolescence: Manual and automatic segmentation

The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the 'gold standard', exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project (http://deepneuro.bnu.edu.cn/?p=163). Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate amygdala structure at school age, calling for next generation tools.

Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good (>0.75), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients’ z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.