Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite.

Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.

Reduced gray matter volume of the hippocampal tail in melancholic depression: evidence from an MRI study.

BACKGROUND: Melancholic depression (MD) is one of the most prevalent and severe subtypes of major depressive disorder (MDD). Previous studies have revealed inconsistent results regarding alterations in grey matter volume (GMV) of the hippocampus and amygdala of MD patients, possibly due to overlooking the complexity of their internal structure. The hippocampus and amygdala consist of multiple and functionally distinct subregions, and these subregions may play different roles in MD. This study aims to investigate the volumetric alterations of each subregion of the hippocampus and amygdala in patients with MD and non-melancholic depression (NMD). METHODS: A total of 146 drug-naïve, first-episode MDD patients (72 with MD and 74 with NMD) and 81 gender-, age-, and education-matched healthy controls (HCs) were included in the study. All participants underwent magnetic resonance imaging (MRI) scans. The subregional segmentation of hippocampus and amygdala was performed using the FreeSurfer 6.0 software. The multivariate analysis of covariance (MANCOVA) was used to detect GMV differences of the hippocampal and amygdala subregions between three groups. Partial correlation analysis was conducted to explore the relationship between hippocampus or amygdala subfields and clinical characteristics in the MD group. Age, gender, years of education and intracranial volume (ICV) were included as covariates in both MANCOVA and partial correlation analyses. RESULTS: Patients with MD exhibited a significantly lower GMV of the right hippocampal tail compared to HCs, which was uncorrelated with clinical characteristics of MD. No significant differences were observed among the three groups in overall and subregional GMV of amygdala. CONCLUSIONS: Our findings suggest that specific hippocampal subregions in MD patients are more susceptible to volumetric alterations than the entire hippocampus. The reduced right hippocampal tail may underlie the unique neuropathology of MD. Future longitudinal studies are required to better investigate the associations between reduced right hippocampal tail and the onset and progression of MD.

The volume and structural covariance network of thalamic nuclei in patients with Wilson's disease: an investigation of the association with neurological impairment.

OBJECTIVE: This study aimed to examine the volumes of thalamic nuclei and the intrinsic thalamic network in patients with Wilson's disease (WDs), and to explore the correlation between these volumes and the severity of neurological symptoms. METHODS: A total of 61 WDs and 33 healthy controls (HCs) were included in the study. The volumes of 25 bilateral thalamic nuclei were measured using structural imaging analysis with Freesurfer, and the intrinsic thalamic network was evaluated through structural covariance network (SCN) analysis. RESULTS: The results indicated that multiple thalamic nuclei were smaller in WDs compared to HCs, including mediodorsal medial magnocellular (MDm), anterior ventral (AV), central median (CeM), centromedian (CM), lateral geniculate (LGN), limitans-suprageniculate (L-Sg), reuniens-medial ventral (MV), paracentral (Pc), parafascicular (Pf), paratenial (Pt), pulvinar anterior (PuA), pulvinar inferior (PuI), pulvinar medial (PuM), ventral anterior (VA), ventral anterior magnocellular (VAmc), ventral lateral anterior (VLa), ventral lateral posterior (VLp), ventromedial (VM), ventral posterolateral (VPL), and right middle dorsal intralaminar (MDI). The study also found a negative correlation between the UWDRS scores and the volume of the right MDm. The intrinsic thalamic network analysis showed abnormal topological properties in WDs, including increased mean local efficiency, modularity, normalized clustering coefficient, small-world index, and characteristic path length, and a corresponding decrease in mean node betweenness centrality. WDs with cerebral involvement had a lower modularity compared to HCs. CONCLUSIONS: The findings suggest that the majority of thalamic nuclei in WDs exhibit significant volume reduction, and the atrophy of the right MDm is closely related to the severity of neurological symptoms. The intrinsic thalamic network in WDs demonstrated abnormal topological properties, indicating a close relationship with neurological impairment.

Differential longitudinal changes of hippocampal subfields in patients with anorexia nervosa.

BACKGROUND: Anorexia nervosa (AN) is a mental disorder characterized by dietary restriction, fear of gaining weight, and distorted body image. Recent studies indicate that the hippocampus, crucial for learning and memory, may be affected in AN, yet subfield-specific effects remain unclear. We investigated hippocampal subfield alterations in acute AN, changes following weight restoration, and their associations with leptin levels. METHODS: T1-weighted magnetic resonance imaging scans were processed using FreeSurfer. We compared 22 left and right hemispheric hippocampal subfield volumes cross-sectionally and longitudinally in females with acute AN (n = 165 at baseline, n = 110 after partial weight restoration), healthy female controls (HCs; n = 271), and females after long-term recovery from AN (n = 79) using linear models. RESULTS: We found that most hippocampal subfield volumes were significantly reduced in patients with AN compared with HCs (~-3.9%). Certain areas such as the subiculum exhibited no significant reduction in the acute state of AN, while other areas, such as the hippocampal tail, showed strong decreases (~-9%). Following short-term weight recovery, most subfields increased in volume. Comparisons between participants after long-term weight-recovery and HC yielded no differences. The hippocampal tail volume was positively associated with leptin levels in AN independent of body mass index. CONCLUSIONS: Our study provides evidence of differential volumetric differences in hippocampal subfields between individuals with AN and HC and almost complete normalization after weight rehabilitation. These alterations are spatially inhomogeneous and more pronounced compared with other major mental disorders (e.g. major depressive disorder and schizophrenia). We provide novel insights linking hypoleptinemia to hippocampal subfield alterations hinting towards clinical relevance of leptin normalization in AN recovery.

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.

FreeSurfer 7 quality control: Key problem areas and importance of manual corrections.

We have studied the effects of manual quality control of brain Magnetic Resonance Imaging (MRI) images processed with Freesurfer. T1 images of first episode psychosis patients (N = 60) and healthy controls (N = 41) were inspected for gray matter boundary errors. The errors were fixed, and the effects of error correction on brain volume, thickness, and surface area were measured. It is commonplace to apply quality control to Freesurfer MRI recordings to ensure that the edges of gray and white matter are detected properly, as incorrect edge detection leads to changes in variables such as volume, cortical thickness, and cortical surface area. We find that while Freesurfer v7.1.1. does regularly make mistakes in identifying the edges of cortical gray matter, correcting these errors yields limited changes in the commonly measured variables listed above. We further find that the software makes fewer gray matter boundary errors when processing female brains. The results suggest that manually correcting gray matter boundary errors may not be worthwhile due to its small effect on the measurements, with potential exceptions for studies that focus on the areas that are more commonly affected by errors: the areas around the cerebellar tentorium, paracentral lobule, and the optic nerves, specifically the horizontal segment of the middle cerebral artery.

Hippocampal grading provides higher classification accuracy for those in the AD trajectory than hippocampal volume.

Much research has focused on neurodegeneration in aging and Alzheimer's disease (AD). We developed Scoring by Nonlocal Image Patch Estimator (SNIPE), a non-local patch-based measure of anatomical similarity and hippocampal segmentation to measure hippocampal change. While SNIPE shows enhanced predictive power over hippocampal volume, it is unknown whether SNIPE is more strongly associated with group differences between normal controls (NC), early MCI (eMCI), late (lMCI), and AD than hippocampal volume. Alzheimer's Disease Neuroimaging Initiative older adults were included in the first analyses (N = 1666, 513 NCs, 269 eMCI, 556 lMCI, and 328 AD). Sub-analyses investigated amyloid positive individuals (N = 834; 179 NC, 148 eMCI, 298 lMCI, and 209 AD) to determine accuracy in those on the AD trajectory. We compared SNIPE grading, SNIPE volume, and Freesurfer volume as features in seven different machine learning techniques classifying participants into their correct cohort using 10-fold cross-validation. The best model was then validated in the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). SNIPE grading provided the highest classification accuracy for all classifications in both the full and amyloid positive sample. When classifying NC:AD, SNIPE grading provided an 89% accuracy (full sample) and 87% (amyloid positive sample). Freesurfer volume provided much lower accuracies of 65% (full sample) and 46% (amyloid positive sample). In the AIBL validation cohort, SNIPE grading provided a 90% classification accuracy for NC:AD. These findings suggest SNIPE grading provides increased classification accuracy over both SNIPE and Freesurfer volume. SNIPE grading offers promise to accurately identify people with and without AD.

Similar cortical morphometry trajectories from 5 to 9 years in children with perinatal HIV who started treatment before age 2 years and uninfected controls.

BACKGROUND: Life-long early ART (started before age 2 years), often with periods of treatment interruption, is now the standard of care in pediatric HIV infection. Although cross-sectional studies have investigated HIV-related differences in cortical morphology in the setting of early ART and ART interruption, the long-term impact on cortical developmental trajectories is unclear. This study compares the longitudinal trajectories of cortical thickness and folding (gyrification) from age 5 to 9 years in a subset of children perinatally infected with HIV (CPHIV) from the Children with HIV Early antiRetroviral therapy (CHER) trial to age-matched children without HIV infection. METHODS: 75 CHER participants in follow-up care at FAMCRU (Family Centre for Research with Ubuntu), as well as 66 age-matched controls, received magnetic resonance imaging (MRI) on a 3 T Siemens Allegra at ages 5, 7 and/or 9 years. MR images were processed, and cortical surfaces reconstructed using the FreeSurfer longitudinal processing stream. Vertex-wise linear mixed effects (LME) analyses were performed across the whole brain to compare the means and linear rates of change of cortical thickness and gyrification from 5 to 9 years between CPHIV and controls, as well as to examine effects of ART interruption. RESULTS: Children without HIV demonstrated generalized cortical thinning from 5 to 9 years, with the rate of thinning varying by region, as well as regional age-related gyrification increases. Overall, the means and developmental trajectories of cortical thickness and gyrification were similar in CPHIV. However, at an uncorrected p < 0.005, 6 regions were identified where the cortex of CPHIV was thicker than in uninfected children, namely bilateral insula, left supramarginal, lateral orbitofrontal and superior temporal, and right medial superior frontal regions. Planned ART interruption did not affect development of cortical morphometry. CONCLUSIONS: Although our results suggest that normal development of cortical morphometry between the ages of 5 and 9 years is preserved in CPHIV who started ART early, these findings require further confirmation with longitudinal follow-up through the vulnerable adolescent period.

Reconsidering the olfactory and brain structures in Kallmann's syndrome: New findings in the analysis of volumetry.

OBJECTIVE: Kallmann's syndrome (KS) is characterized by hypogonadotropic hypogonadism and olfactory disorders. The complementary exams for evaluating of patients with hypogonadotrophic hypogonadism are important for the diagnosis and management of these patients. PATIENTS: We performed a well-established olfactory Sniffin' Stick test (SST) on 17 adult patients with KS and brain magnetic resonance imaging (MRI) to evaluate olfactory structures and further analysis by Freesurfer, a software for segmentation and volumetric evaluation of brain structures. We compared the Freesurfer results with 34 healthy patients matched for age and sex and performed correlations between the data studied. RESULTS: More than half of the patients with KS reported preserved smell but had olfactory disorders in the SST. In the MRI, 16 patients showed changes in the olfactory groove, the olfactory bulb-tract complex was altered in all of them and 52% had symmetrical structural changes. Interestingly, the pituitary gland was normal in only 29%. Regarding correlations, symmetrical changes in the olfactory structures were related to anosmia in 100%, while asymmetric changes induced anosmia in only 50% (p = .0294). In Freesurfer's assessment, patients with KS, compared to controls, had lower brainstem volume. In those with aplastic anterior olfactory sulcus, the brainstem volume was lower than in hypoplasia (p = .0333). CONCLUSIONS: Olfactory assessment and MRI proved to be important auxiliary tools for the diagnosis and management of patients with KS. New studies are needed to confirm the decrease in brainstem volume found by the Freesurfer software in patients with KS. Further studies are needed to confirm the decrease in brainstem volume found by the Freesurfer software in patients with KS.

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.

Negative symptoms in First-Episode Schizophrenia related to morphometric alterations in orbitofrontal and superior temporal cortex: the OPTiMiSE study.

BACKGROUND: Negative symptoms are one of the most incapacitating features of Schizophrenia but their pathophysiology remains unclear. They have been linked to alterations in grey matter in several brain regions, but findings have been inconsistent. This may reflect the investigation of relatively small patient samples, and the confounding effects of chronic illness and exposure to antipsychotic medication. We sought to address these issues by investigating concurrently grey matter volumes (GMV) and cortical thickness (CTh) in a large sample of antipsychotic-naïve or minimally treated patients with First-Episode Schizophrenia (FES). METHODS: T1-weighted structural MRI brain scans were acquired from 180 antipsychotic-naïve or minimally treated patients recruited as part of the OPTiMiSE study. The sample was stratified into subgroups with (N = 88) or without (N = 92) Prominent Negative Symptoms (PMN), based on PANSS ratings at presentation. Regional GMV and CTh in the two groups were compared using Voxel-Based Morphometry (VBM) and FreeSurfer (FS). Between-group differences were corrected for multiple comparisons via Family-Wise Error (FWE) and Monte Carlo z-field simulation respectively at p < 0.05 (2-tailed). RESULTS: The presence of PMN symptoms was associated with larger left inferior orbitofrontal volume (p = 0.03) and greater CTh in the left lateral orbitofrontal gyrus (p = 0.007), but reduced CTh in the left superior temporal gyrus (p = 0.009). CONCLUSIONS: The findings highlight the role of orbitofrontal and temporal cortices in the pathogenesis of negative symptoms of Schizophrenia. As they were evident in generally untreated FEP patients, the results are unlikely to be related to effects of previous treatment or illness chronicity.

A neurostructural biomarker of dissociative amnesia: a hippocampal study in dissociative identity disorder.

BACKGROUND: Little is known about the neural correlates of dissociative amnesia, a transdiagnostic symptom mostly present in the dissociative disorders and core characteristic of dissociative identity disorder (DID). Given the vital role of the hippocampus in memory, a prime candidate for investigation is whether total and/or subfield hippocampal volume can serve as biological markers of dissociative amnesia. METHODS: A total of 75 women, 32 with DID and 43 matched healthy controls (HC), underwent structural magnetic resonance imaging (MRI). Using Freesurfer (version 6.0), volumes were extracted for bilateral global hippocampus, cornu ammonis (CA) 1-4, the granule cell molecular layer of the dentate gyrus (GC-ML-DG), fimbria, hippocampal-amygdaloid transition area (HATA), parasubiculum, presubiculum and subiculum. Analyses of covariance showed volumetric differences between DID and HC. Partial correlations exhibited relationships between the three factors of the dissociative experience scale scores (dissociative amnesia, absorption, depersonalisation/derealisation) and traumatisation measures with hippocampal global and subfield volumes. RESULTS: Hippocampal volumes were found to be smaller in DID as compared with HC in bilateral global hippocampus and bilateral CA1, right CA4, right GC-ML-DG, and left presubiculum. Dissociative amnesia was the only dissociative symptom that correlated uniquely and significantly with reduced bilateral hippocampal CA1 subfield volumes. Regarding traumatisation, only emotional neglect correlated negatively with bilateral global hippocampus, bilateral CA1, CA4 and GC-ML-DG, and right CA3. CONCLUSION: We propose decreased CA1 volume as a biomarker for dissociative amnesia. We also propose that traumatisation, specifically emotional neglect, is interlinked with dissociative amnesia in having a detrimental effect on hippocampal volume.

An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma.

BACKGROUND: The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear. PURPOSE: To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG). STUDY TYPE: Retrospective. SUBJECTS: Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53). FIELD STRENGTH/SEQUENCE: 3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence. ASSESSMENT: In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software. STATISTICAL TESTS: General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant. RESULTS: For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively). DATA CONCLUSION: Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Atypical development of Broca's area in a large family with inherited stuttering.

Developmental stuttering is a condition of speech dysfluency, characterized by pauses, blocks, prolongations and sound or syllable repetitions. It affects around 1% of the population, with potential detrimental effects on mental health and long-term employment. Accumulating evidence points to a genetic aetiology, yet gene-brain associations remain poorly understood due to a lack of MRI studies in affected families. Here we report the first neuroimaging study of developmental stuttering in a family with autosomal dominant inheritance of persistent stuttering. We studied a four-generation family, 16 family members were included in genotyping analysis. T1-weighted and diffusion-weighted MRI scans were conducted on seven family members (six male; aged 9-63 years) with two age and sex matched controls without stuttering (n = 14). Using Freesurfer, we analysed cortical morphology (cortical thickness, surface area and local gyrification index) and basal ganglia volumes. White matter integrity in key speech and language tracts (i.e. frontal aslant tract and arcuate fasciculus) was also analysed using MRtrix and probabilistic tractography. We identified a significant age by group interaction effect for cortical thickness in the left hemisphere pars opercularis (Broca's area). In affected family members this region failed to follow the typical trajectory of age-related thinning observed in controls. Surface area analysis revealed the middle frontal gyrus region was reduced bilaterally in the family (all cortical morphometry significance levels set at a vertex-wise threshold of P < 0.01, corrected for multiple comparisons). Both the left and right globus pallidus were larger in the family than in the control group (left P = 0.017; right P = 0.037), and a larger right globus pallidus was associated with more severe stuttering (rho = 0.86, P = 0.01). No white matter differences were identified. Genotyping identified novel loci on chromosomes 1 and 4 that map with the stuttering phenotype. Our findings denote disruption within the cortico-basal ganglia-thalamo-cortical network. The lack of typical development of these structures reflects the anatomical basis of the abnormal inhibitory control network between Broca's area and the striatum underpinning stuttering in these individuals. This is the first evidence of a neural phenotype in a family with an autosomal dominantly inherited stuttering.

Volumetric changes in hypothalamic subunits in patients with relapsing remitting multiple sclerosis.

PURPOSE: Studies on hypothalamic changes in patients with relapsing remitting multiple sclerosis (RRMS) are very scarce, despite the fact that the relationship with the hypothalamus is frequently reported. The aim of the study was to determine the volume of the hypothalamic subunits and the total hypothalamus and its relationship with the total demyelinating lesion volume (TLV) and expanded disability status scale (EDSS) in RRMS patients. METHODS: In this cross-sectional study, anterior-superior, superior tubular, posterior hypothalamus, anterior-inferior, inferior tubular subunits of hypothalamus, and total hypothalamus volume were calculated, with fully automatic analysis methods using volumetric T1 images of 65 relapsed RRMS patients and 68 healthy controls (HC). Volume changes in the hypothalamus and its subunits in RRMS patients were examined using multivariate analysis of covariance (MANCOVA). The relationship of these volumes with EDSS and TLV was investigated by partial correlation analysis. RESULTS: There is volume reduction in total hypothalamus (F = 13.87, p < 0.001), anterior-superior (F = 19.2, p < 0.001), superior tubular (F = 10.1, p = 0.002) subunits, and posterior hypothalamus (F = 19.2, p < 0.001) volume in RRMS patients. EDSS correlates negatively with anterior-superior (p = 0.017, r = - 0.333), superior tubular subunits (p = 0.023, r = - 0.439), posterior hypothalamus (p < 0.001, r = - 0.511), and whole hypothalamus volume (p = 0.001, r = - 0.439). TLV correlates negatively with anterior superior (p < 0.001, r = - 0.565), anterior inferior (p = 0.002, r = - 0.431), superior tubular subunits (p = 0.002, r = - 0.432), posterior hypothalamus (p < 0.001, r = - 0.703), and whole hypothalamus (p < 0.001, r = - 0.627) volumes. CONCLUSION: This study demonstrates a reduction in total hypothalamus volume, anterior-superior, superior tubular, and posterior hypothalamus in patients with RRMS. Anterior-superior and superior tubular subunit, posterior hypothalamus, and total hypothalamus volume were negatively correlated with TLV and EDSS scores.

3D color multimodality fusion imaging as an augmented reality educational and surgical planning tool for extracerebral tumors.

Extracerebral tumors often occur on the surface of the brain or at the skull base. It is important to identify the peritumoral sulci, gyri, and nerve fibers. Preoperative visualization of three-dimensional (3D) multimodal fusion imaging (MFI) is crucial for surgery. However, the traditional 3D-MFI brain models are homochromatic and do not allow easy identification of anatomical functional areas. In this study, 33 patients with extracerebral tumors without peritumoral edema were retrospectively recruited. They underwent 3D T1-weighted MRI, diffusion tensor imaging (DTI), and CT angiography (CTA) sequence scans. 3DSlicer, Freesurfer, and BrainSuite were used to explore 3D-color-MFI and preoperative planning. To determine the effectiveness of 3D-color-MFI as an augmented reality (AR) teaching tool for neurosurgeons and as a patient education and communication tool, questionnaires were administered to 15 neurosurgery residents and all patients, respectively. For neurosurgical residents, 3D-color-MFI provided a better understanding of surgical anatomy and more efficient techniques for removing extracerebral tumors than traditional 3D-MFI (P < 0.001). For patients, the use of 3D-color MFI can significantly improve their understanding of the surgical approach and risks (P < 0.005). 3D-color-MFI is a promising AR tool for extracerebral tumors and is more useful for learning surgical anatomy, developing surgical strategies, and improving communication with patients.

A whole-brain 3D myeloarchitectonic atlas: Mapping the Vogt-Vogt legacy to the cortical surface.

Building precise and detailed parcellations of anatomically and functionally distinct brain areas has been a major focus in Neuroscience. Pioneer anatomists parcellated the cortical manifold based on extensive histological studies of post-mortem brain, harnessing local variations in cortical cyto- and myeloarchitecture to define areal boundaries. Compared to the cytoarchitectonic field, where multiple neuroimaging studies have recently translated this old legacy data into useful analytical resources, myeloarchitectonics, which parcellate the cortex based on the organization of myelinated fibers, has received less attention. Here, we present the neocortical surface-based myeloarchitectonic atlas based on the histology-derived maps of the Vogt-Vogt school and its 2D translation by Nieuwenhuys. In addition to a myeloarchitectonic parcellation, our package includes intracortical laminar profiles of myelin content based on Vogt-Vogt-Hopf original publications. Histology-derived myelin density mapped on our atlas demonstrated a close overlap with in vivo quantitative MRI markers for myelin and relates to cytoarchitectural features. Complementing the existing battery of approaches for digital cartography, the whole-brain myeloarchitectonic atlas offers an opportunity to validate imaging surrogate markers of myelin in both health and disease.

Identification of individual subjects based on neuroanatomical measures obtained 7 years earlier.

We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule-based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if-then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four-feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain-fingerprinting techniques.

FreeSurfer-based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts.

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.