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Background: Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used. Objectives: This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis. Methods: We performed a PubMed search for "FreeSurfer AND Dementia" and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations). Results: In the studies identified, the main diseases and cohorts represented were Alzheimer's disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson's disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed. Conclusion: Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research. [ABSTRACT FROM AUTHOR]

Despite bilateral hippocampal damage dating to the perinatal or early childhood period and severely impaired episodic memory, patients with developmental amnesia continue to exhibit well‐developed semantic memory across the developmental trajectory. Detailed information on the extent and focality of brain damage in these patients is needed to hypothesize about the neural substrate that supports their remarkable capacity for encoding and retrieval of semantic memory. In particular, we need to assess whether the residual hippocampal tissue is involved in this preservation, or whether the surrounding cortical areas reorganize to rescue aspects of these critical cognitive memory processes after early injury. We used voxel‐based morphometry (VBM) analysis, automatic (FreeSurfer) and manual segmentation to characterize structural changes in the brain of an exceptionally large cohort of 23 patients with developmental amnesia in comparison with 32 control subjects. Both the VBM and the FreeSurfer analyses revealed severe structural alterations in the hippocampus and thalamus of patients with developmental amnesia. Milder damage was found in the amygdala, caudate, and parahippocampal gyrus. Manual segmentation demonstrated differences in the degree of atrophy of the hippocampal subregions in patients. The level of atrophy in CA‐DG subregions and subicular complex was more than 40%, while the atrophy of the uncus was moderate (−24%). Anatomo‐functional correlations were observed between the volumes of residual hippocampal subregions in patients and selective aspects of their cognitive performance, viz, intelligence, working memory, and verbal and visuospatial recall. Our findings suggest that in patients with developmental amnesia, cognitive processing is compromised as a function of the extent of atrophy in hippocampal subregions. More severe hippocampal damage may be more likely to promote structural and/or functional reorganization in areas connected to the hippocampus. In this hypothesis, different levels of hippocampal function may be rescued following this variable reorganization. Our findings document not only the extent, but also the limits of circuit reorganization occurring in the young brain after early bilateral hippocampal damage. [ABSTRACT FROM AUTHOR]

Background: Neuroimaging studies have suggested a pivotal role for the amygdala involvement in chronic low back pain (CLBP). However, the relationship between the amygdala subregions and CLBP has not yet been delineated. This study aimed to analyze whether the amygdala subregions were linked to the development of CLBP. Methods: A total of 45 patients with CLBP and 45 healthy controls (HCs) were included in this study. All subjects were asked to complete a threedimensional T1-weighted magnetic resonance imaging (3D-T1 MRI) scan. FreeSurfer 7.3.2 was applied to preprocess the structural MRI images and segment the amygdala into nine subregions. Afterwards, comparisons were made between the two groups in terms of the volumes of the amygdala subregions. Correlation analysis is utilized to examine the relationship between the amygdala subregion and the scale scores, as well as the pain duration in patients with CLBP. Additionally, logistic regression was used to explore the risk of the amygdala and its subregions for CLBP. Results: In comparison to HCs, patients with CLBP exhibited a significant enlargement of the left central nucleus (Ce) and left cortical nucleus (Co). Furthermore, the increased volume of the left Ce was associated with a higher risk of CLBP. Conclusion: Our study suggests that the left Ce and left Co may be involved in the pathophysiological processes of CLBP. Moreover, the volume of the left Ce may be a biomarker for detecting the risk of CLBP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

At present, current diagnostic criteria and systems neglect affective symptom expression in catatonia. This potentially serious omission could explain why putative contributions of limbic system structures, such as amygdala, hippocampus or hypothalamus, to catatonia in schizophrenia spectrum disorders (SSD) have been scarcely investigated so far. To determine whether topographical alterations of the amygdala, hippocampus and hypothalamus contribute to catatonia in SSD patients, we conducted structural magnetic resonance imaging (MRI) of SSD patients with (SSD-Cat, n = 30) and without (SSD-nonCat, n = 28) catatonia as defined by a Northoff Catatonia Rating Scale (NCRS) total score of ≥3 and =0, respectively, in comparison with healthy controls (n = 20). FreeSurfer v7.2 was used for automated segmentation of the amygdala and its 9 nuclei, hippocampus and its 21 subfields and hypothalamus and its associated 5 subunits. SSD-Cat had significantly smaller anterior inferior hypothalamus, cortical nucleus of amygdala, and hippocampal fimbria volumes when compared to SSD-nonCat. SSD-Cat had significantly smaller amygdala, hippocampus and hypothalamus whole and subunit volumes when compared to healthy controls. In SSD-Cat according to DSM-IV-TR (n = 44), we identified positive correlations between Brief Psychiatric Rating Scale (BPRS) item #2 (reflecting anxiety) and respective amygdala nuclei as well as negative correlation between NCRS behavioral score and hippocampus subiculum head. The lower volumes of respective limbic structures involved in affect regulation may point towards central affective pathomechanisms in catatonia. [ABSTRACT FROM AUTHOR]

• PANSS G#7 and TMT-B have a surrogate value for psychomotor slowing in SZ. • Primary motor cortex thinning contribute to psychomotor slowing in SZ. • Cortical rather than subcortical regions contribute to psychomotor slowing in SZ. Psychomotor slowing (PS) is characterized by slowed movements and lower activity levels. PS is frequently observed in schizophrenia (SZ) and distressing because it impairs performance of everyday tasks and social activities. Studying brain topography contributing to PS in SZ can help to understand the underlying neurobiological mechanisms as well as help to develop more effective treatments that specifically target affected brain areas. Here, we conducted structural magnetic resonance imaging (sMRI) of three independent cohorts of right-handed SZ patients (SZ#1: n = 72, SZ#2: n = 37, SZ#3: n = 25) and age, gender and education matched healthy controls (HC) (HC#1: n = 40, HC#2: n = 37, HC#3: n = 38). PS severity in the three SZ cohorts was determined using the Positive and Negative Syndrome Scale (PANSS) item #G7 (motor retardation) and Trail-Making-Test B (TMT-B). FreeSurfer v7.2 was used for automated parcellation and segmentation of cortical and subcortical regions. SZ#1 patients showed reduced cortical thickness in right precentral gyrus (M1; p = 0.04; Benjamini-Hochberg [BH] corr.). In SZ#1, cortical thinning in right M1 was associated with PANSS item #G7 (p = 0.04; BH corr.) and TMT-B performance (p = 0.002; BH corr.). In SZ#1, we found a significant correlation between PANSS item #G7 and TMT-B (p = 0.005, ρ =0.326). In conclusion, PANSS G#7 and TMT-B might have a surrogate value for predicting PS in SZ. Cortical thinning of M1 rather than alterations of subcortical structures may point towards cortical pathomechanism underlying PS in SZ. [ABSTRACT FROM AUTHOR]

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.

Background: Segmentation and morphometric measurement of brain tissue and regions from non-invasive magnetic resonance images have clinical and research applications. Several software tools and models have been developed by different research groups which are increasingly used for segmentation and morphometric measurements. Variability in results has been observed in the imaging data processed with different neuroimaging pipelines which have increased the focus on standardization. Purpose: The availability of several tools and models for brain morphometry poses challenges as an analysis done on the same set of data using different sets of tools and pipelines may result in different results and interpretations and there is a need for understanding the reliability and accuracy of such models. Methods: T1-weighted (T1-w) brain volumes from the publicly available OASIS3 dataset have been analysed using recent versions of FreeSurfer, FSL-FAST, CAT12, and ANTs pipelines. grey matter (GM), white matter (WM), and estimated total intracranial volume (eTIV) have been extracted and compared for inter-method variability and accuracy. Results: All four methods are consistent and strongly reproducible in their measurement across subjects however there is a significant degree of variability between these methods. Conclusion: CAT12 and FreeSurfer methods have the highest degree of agreement in tissue class segmentation and are most reproducible compared to others. [ABSTRACT FROM AUTHOR]

Objectives: Histological studies have shown alterations of thalamic nuclei in patients with Down syndrome (DS). The correlation of these changes on MRI (magnetic resonance imaging) is unclear. Therefore, this study investigates volumetric differences of thalamic nuclei in children with DS compared to controls. Methods: Patients were retrospectively identified between 01/2000 and 10/2021. Patient inclusion criteria were: (1) 0–18 years of age, (2) diagnosis of DS, and (3) availability of a brain MRI without parenchymal injury and a non-motion-degraded volumetric T1-weighted sequence. Whole thalamus and thalamic nuclei (n = 25) volumes were analyzed bilaterally relative to the total brain volume (TBV). Two-sided t-tests were used to evaluate differences between groups. Differences were considered significant if the adjusted p -value was <0.05 after correction for multiple hypothesis testing using the Holm-Bonferroni method. Results: 21 children with DS (11 females, 52.4%, mean age: 8.6 ± 4.3 years) and 63 age- and sex-matched controls (32 females, 50.8%, 8.6 ± 4.3 years) were studied using automated volumetric segmentation. Significantly smaller ratios were found for nine thalamic nuclei and the whole thalamus on the right and five thalamic nuclei on the left. TBV was significantly smaller in patients with DS (p < 0.001). No significant differences were found between the groups for age and sex. Conclusions: In this exploratory volumetric analysis of the thalamus and thalamic nuclei, we observed statistically significant volumetric changes in children with DS. Our findings confirm prior neuroimaging and histological studies and extend the range of involved thalamic nuclei in pediatric DS. [ABSTRACT FROM AUTHOR]

Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images.

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. [ABSTRACT FROM AUTHOR]

Introduction: Accurate segmentation of brain tissue in magnetic resonance imaging (MRI) is an important step in the analysis of brain images. There are automated methods used to segmentation the brain and minimize the disadvantages of manual segmentation, including time consuming and misinterpretations. These procedures usually involve a combination of skull removal, bias field correction, and segmentation. Therefore, segmented tissue quality assessment segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is required for the analysis of neuroimages. Material and Methods: This paper presents the performance evaluation of three automatic methods brain segmentation, fluid and white matter suppression [FSL, Freesurfer (FreeSurfer is an open source package for the analysis and visualization of structural, functional, and diffusion neuroimaging data from cross-sectional and longitudinal studies) and SPM12 (Statistical Parametric Mapping)]. Segmentation with SPM12 was performed on three tissue probability maps: i) threshold 0.5, ii) threshold 0.7 and iii) threshold 0.9. In order to compare and evaluate the automatic methods, the reference standard method, i.e., manual segmentation, was performed by three radiologists. Results: Comparison of GM, WM and CSF segmentation in MR images was performed using similarities between manual and automatic segmentation. The similarity between the segmented tissues was calculated using diagnostic criteria. Conclusion: Several studies have examined the classification of GM, WM, and CSF using software packages. In these studies, different results have been obtained depending on the type of method and images used and the type of segmented tissues. In this study, the evaluation of the segmentation of these packages with reference standard method is performed. The results can help users in selecting an appropriate segmentation tool for neuroimages analysis. [ABSTRACT FROM AUTHOR]

Amygdala and serotonergic system abnormalities have been documented in major depressive disorder (MDD). However, most studies have been conducted on recurrent MDD, and only a few have assessed their interaction. This study aimed to concurrently examine both the amygdala and serotonergic systems and their clinical relevance in first-episode, drug-naïve MDD. This study included 27 patients with first-episode, drug-naïve MDD and 27 age- and gender-matched healthy controls (HCs). The amygdala substructure volumes were performed with Freesurfer from a 1.5 T magnetic resonance image. Serotonin transporter (SERT) availability was detected by single-photon emission computed tomography with 123I-ADAM. The Benjamini-Hochberg method was applied to adjust for multiple comparisons. No significant difference was found in the amygdala substructure volume and SERT availability between the two groups, respectively. Within MDD patients, the right medial, cortical nucleus, and centromedial volumes were positively associated with caudate SERT availability, respectively. Moreover, the right lateral nucleus volume in the amygdala was positively correlated with depression severity. However, these significances did not survive correction for multiple testing. There were no significant abnormalities in the amygdala substructure volumes and SERT availability in patients with first-episode, drug-naïve MDD. We did not observe an association between amygdala substructure volume and serotonergic dysregulation and their correlations with depression severity in patients with MDD. A larger sample size is warranted to elucidate the actual correlation. • The MDD group vs. controls showed no significant differences in the amygdala substructure volumes and SERT availability. • Amygdala substructure volumes were not associated with SERT availability in patients with MDD. • Neither amygdala nucleus volume nor SERT availability was associated with depression severity in MDD patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is an important public health problem that has enormous consequences for patients, their families, the health care system, and the economy. MCI is highly likely to progress to dementia. Early premedical intervention for MCI could prevent or delay dementia's onset, which in turn would improve patients' quality of life. Thus, the goal of our research is to develop an MCI detection system using MRI-based measures. The dataset for the study consists of 62 healthy volunteers and 145 patients with MCI obtained from the «Memory Clinic» (Moscow, Russia). The morphometric data were extracted from T1-weighted structural MR images. We propose a machine learning method, which achieved an accuracy of 73%, and extract key features in brain structures that can distinguish the healthy control (HC) group from the MCI group. This study confirms the previously identified changes in the structural regions of the brain in MCI patients. Compared to the healthy control group, in the MCI group, volume increases in the Lateral Ventricle, Inferior Lateral Ventricle, and Cerebrospinal Fluid as well as volume reductions in the Amygdala, Hippocampus, and Accumbens have been found. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Data on structural brain changes after infection with SARS‐CoV‐2 is sparse. We postulate multiple sclerosis as a model to study the effects of SARS‐CoV‐2 on brain atrophy due to the unique availability of longitudinal imaging data in this patient group, enabling assessment of intraindividual brain atrophy rates. Methods: Global and regional cortical gray matter volumes were derived from structural MRIs using FreeSurfer. A linear model was fitted to the measures of the matching pre‐SARS‐CoV‐2 images with age as an explanatory variable. The residuals were used to determine whether the post‐SARS‐CoV‐2 volumes differed significantly from the baseline. Results: Fourteen RRMS patients with a total of 113 longitudinal magnetic resonance images were retrospectively analyzed. We found no acceleration of brain atrophy after infection with SARS‐CoV‐2 for global gray matter volume (p = 0.17). However, on the regional level, parahippocampal gyri showed a tendency toward volume reduction (p = 0.0076), suggesting accelerated atrophy during or after infection. Conclusions: Our results illustrate the opportunity of using longitudinal MRIs from existing MS registries to study brain changes associated with SARS‐CoV‐2 infections. We would like to address the global MS community with a call for action to use the available cohorts, reproduce the proposed analysis, and pool the results. [ABSTRACT FROM AUTHOR]

BackgroundMagnetic resonance imaging studies of cigarette smoking-related effects on human brain structure primarily focused on cortical volumes. Much less is known about the effects of smoking on cortical thickness. Smokers and Non-smokers were compared on regional cortical thickness. We predicted smokers would demonstrate greater age-related thinning localized to anterior frontal regions that serve as nodes for the executive, salience, and emotional regulation networks (ESER regions) and those demonstrating significant atrophy in early Alzheimer's Disease (AD regions).MethodsNon-smokers (n = 41) and smokers (n = 41), 22-70 years of age, completed a 4 T MRI study. Regional cortical thickness was quantitated via FreeSurfer. In smokers, associations between smoking severity, decision-making, impulsivity, and regional cortical thickness were examined.ResultsSmokers demonstrated cortical thinning in the medial and lateral OFC, insula, entorhinal, fusiform, middle temporal, and Composite AD regions. In Smokers, greater pack-years were associated with thinner lateral OFC, middle temporal, inferior parietal, fusiform, precuneus, and Composite AD regions. In Smokers, poorer decision-making/greater risk taking was related to thinner cortices in caudal ACC, rostral middle frontal and superior frontal gyri, and Composite ESER. Higher self-reported impulsivity was associated with thinner rostral and caudal ACC.ConclusionsThis study provides additional evidence that cigarette smoking is associated with thinner cortices in regions implicated in the development and maintenance of substance use disorders and in regions demonstrating significant atrophy in early AD. The novel structure-function relationships in Smokers further our understanding of the neurobiological substrates potentially underlying the neuropsychological abnormalities documented in smokers.

Background: Maternal smoking during pregnancy has been shown to associate with smaller frontal lobe and cerebellar volumes in brain magnetic resonance imaging (MRI) at term age in very preterm infants. The aim of this study was to examine the effect of maternal smoking during pregnancy on volumetric brain MRI findings at 13 years. We hypothesized that adverse effects of smoking during pregnancy on brain volumes are still seen during adolescence. Methods: Included adolescents were born very preterm (gestational age < 32 weeks and/or birth weight ≤ 1,500 g) between April 2004 and December 2006 at the Turku University Hospital, Finland. Information on maternal smoking status (yes or no) during pregnancy was collected from medical records and maternal questionnaires before discharge. Adolescents underwent volumetric brain MRI at 13 years of age. Image post-processing was performed with FreeSurfer. Regional volumes, cortical thickness, surface area, and curvature were computed from 33 cortical regions of interest (ROIs). Additionally, volumes were calculated for 18 subcortical regions, as well as for white matter, gray matter, and intracranial volume. We normalized quantified absolute volumes for head size by dividing volumes with corresponding intracranial volumes. false discovery rate (FDR) correction for multiple comparisons across regions was used. Results: A total of 9/44 (21%) adolescents had been exposed to maternal smoking during pregnancy. No statistically significant differences in absolute volumes were observed between the groups (p > 0.05). Regarding volumes proportional to intracranial volume, the adolescents in the exposed group exhibited smaller gray matter volumes in the inferotemporal (FDR corrected p = 0.022) and parahippocampal (p = 0.018) regions compared to the unexposed group. The surface area in the exposed group was also smaller in the parahippocampal (p = 0.046) and postcentral (p = 0.046) regions compared to the unexposed group. No statistically significant differences after correction for multiple comparisons were found for either curvature or cortical thickness between the groups. Conclusion: Maternal smoking exposure during pregnancy may have longterm effects on brain volumes up to 13 years in adolescents born very preterm. Our findings emphasize the importance of smoking-free pregnancy. [ABSTRACT FROM AUTHOR]

Abstract: Chronic low back pain (CLBP) is the leading cause of years lived with disability. Recently, it has been reported that CLBP is associated with alterations in the central nervous system. The present study aimed to investigate the association between CLBP and regional brain atrophy in an older Japanese population. A total of 1106 community-dwelling participants aged ≥65 years underwent brain magnetic resonance imaging scans and a health examination in 2017 to 2018. We used the FreeSurfer software for the analysis of brain magnetic resonance imaging. Chronic pain was defined as subjective pain for ≥3 months. Participants were divided into 3 groups according to the presence or absence of chronic pain and the body part that mainly suffered from pain: a "no chronic pain (NCP)" group (n = 541), "CLBP" group (n = 189), and "chronic pain in body parts other than the lower back (OCP)" group (n = 376). The brain volumes of the ventrolateral and dorsolateral prefrontal cortex, the posterior cingulate gyrus, and the amygdala were significantly lower in the CLBP group than in the NCP group after adjustment for sociodemographic, physical, and lifestyle factors and depressive symptoms. In addition, the left superior frontal gyrus was identified as a significant cluster by the Query, Design, Estimate, Contrast interface. There were no significant differences in the brain volumes of pain-related regions between the NCP and the OCP groups. The present study suggests that CLBP is associated with lower brain volumes of pain-related regions in a general older population of Japanese. [ABSTRACT FROM AUTHOR]

• High-resolution T2-weighted MR data provides a better basis for manual labeling. • T2-based ground truth labels improve hippocampus segmentation on T1-weighted scans. • Proposed hippocampus segmentation is based on stacked deep hourglass CNNs. • Our deep SHG-CNN outperformes FreeSurfer, ASHS, HippoDeep, and HippMapp3r. 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. [ABSTRACT FROM AUTHOR]

ObjectiveBrain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.MethodT1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.ResultsIn adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.ConclusionsThe parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

BackgroundOur understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.MethodsThis study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).ResultsMeta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (β std = -0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.ConclusionsUsing an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.

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. [ABSTRACT FROM AUTHOR]

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

Background: Several studies have reported that the pandemic of coronavirus disease 2019 (COVID-19) influenced cognitive function in the elderly. However, the effect of COVID-19-related fear on brain atrophy has not been evaluated. In this study, we evaluated the relation between brain atrophy and the effect of COVID-19-related fear by analysing changes in brain volume over time using magnetic resonance imaging (MRI). Methods: Participants were 25 Japanese patients with mild cognitive impairment (MCI) or subjective cognitive decline (SCD), who underwent 1.5-tesla MRI scan twice, once before and once after the pandemic outbreak of COVID-19, and the Fear of Coronavirus Disease 2019 Scale (FCV-19S) assessment during that period. We computed regional brain atrophy per day between the 1st and 2nd scan, and evaluated the relation between the FCV-19S scores and regional shrinkage. Results: There was significant positive correlation between the total FCV-19S score and volume reduction per day in the right posterior cingulate cortex. Regarding the subscales of FCV-19S, we found significant positive correlation between factor 2 of the FCV-19S and shrinkage of the right posterior cingulate cortex. Conclusions: There was positive correlation between the FCV-19S score and regional brain atrophy per day. Although it is already known that the psychological effects surrounding the COVID-19 pandemic cause cognitive function decline, our results further suggest that anxiety and fear related to COVID-19 cause regional brain atrophy. [ABSTRACT FROM AUTHOR]

BackgroundMagnetic resonance imaging studies of cigarette smoking-related effects on human brain structure have primarily employed voxel-based morphometry, and the most consistently reported finding was smaller volumes or lower density in anterior frontal regions and the insula. Much less is known about the effects of smoking on subcortical regions. We compared smokers and non-smokers on regional subcortical volumes, and predicted that smokers demonstrate greater age-related volume loss across subcortical regions than non-smokers.MethodsNon-smokers (n=43) and smokers (n=40), 22-70 years of age, completed a 4T MRI study. Bilateral total subcortical lobar white matter (WM) and subcortical nuclei volumes were quantitated via FreeSurfer. In smokers, associations between smoking severity measures and subcortical volumes were examined.ResultsSmokers demonstrated greater age-related volume loss than non-smokers in the bilateral subcortical lobar WM, thalamus, and cerebellar cortex, as well as in the corpus callosum and subdivisions. In smokers, higher pack-years were associated with smaller volumes of the bilateral amygdala, nucleus accumbens, total corpus callosum and subcortical WM.ConclusionsResults provide novel evidence that chronic smoking in adults is associated with accelerated age-related volume loss in subcortical WM and GM nuclei. Greater cigarette quantity/exposure was related to smaller volumes in regions that also showed greater age-related volume loss in smokers. Findings suggest smoking adversely affected the structural integrity of subcortical brain regions with increasing age and exposure. The greater age-related volume loss in smokers may have implications for cortical-subcortical structural and/or functional connectivity, and response to available smoking cessation interventions.

ObjectiveBased on the role of the superior temporal gyrus (STG) in auditory processing, language comprehension and self-monitoring, this study aimed to investigate the relationship between STG cortical thickness and positive symptom severity in schizophrenia.MethodThis prospective meta-analysis includes data from 1987 individuals with schizophrenia collected at seventeen centres around the world that contribute to the ENIGMA Schizophrenia Working Group. STG thickness measures were extracted from T1-weighted brain scans using FreeSurfer. The study performed a meta-analysis of effect sizes across sites generated by a model predicting left or right STG thickness with a positive symptom severity score (harmonized SAPS or PANSS-positive scores), while controlling for age, sex and site. Secondary models investigated relationships between antipsychotic medication, duration of illness, overall illness severity, handedness and STG thickness.ResultsPositive symptom severity was negatively related to STG thickness in both hemispheres (left: βstd = -0.052; P = 0.021; right: βstd = -0.073; P = 0.001) when statistically controlling for age, sex and site. This effect remained stable in models including duration of illness, antipsychotic medication or handedness.ConclusionOur findings further underline the important role of the STG in hallmark symptoms in schizophrenia. These findings can assist in advancing insight into symptom-relevant pathophysiological mechanisms in schizophrenia.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Pediatric obsessive-compulsive disorder (OCD) and clinically relevant obsessive-compulsive symptoms in the general population are associated with increased thalamic volume. It is unknown whether this enlargement is explained by specific thalamic subregions. The relation between obsessive-compulsive symptoms and volume of thalamic subregions was investigated in a population-based sample of children.Method: Obsessive-compulsive symptoms were measured in children (9-12 years of age) from the Generation R Study using the Short Obsessive-Compulsive Disorder Screener (SOCS). Thalamic nuclei volumes were extracted from structural 3T magnetic resonance imaging scans using the ThalamicNuclei pipeline and regrouped into anterior, ventral, intralaminar/medial, lateral, and pulvinar subregions. Volumes were compared between children with symptoms above clinical cutoff (probable OCD cases, SOCS ≥ 6, n = 156) and matched children without symptoms (n = 156). Linear regression models were fitted to investigate the association between continuous SOCS score and subregional volume in the whole sample (N = 2500).Results: Children with probable OCD had larger ventral nuclei compared with children without symptoms (d = 0.25, p = .025, false discovery rate adjusted p = .126). SOCS score showed a negative association with pulvinar volume when accounting for overall thalamic volume (β = -0.057, p = .009, false discovery rate adjusted p = .09). However, these associations did not survive multiple testing correction.Conclusion: The results suggest that individual nuclei groups contribute in varying degrees to overall thalamic volume in children with probable OCD, although this did not survive multiple comparisons correction. Understanding the role of thalamic nuclei and their associated circuits in pediatric OCD could lead toward treatment strategies targeting these circuits. [ABSTRACT FROM AUTHOR]

The volumetric quantification of brain structures is of great interest in pediatric populations because it allows the investigation of different factors influencing neurodevelopment. FreeSurfer and FSL both provide frequently used packages for automatic segmentation of brain structures. In this study, we examined the accuracy and consistency of those two automated protocols relative to manual segmentation, commonly considered as the "gold standard" technique, for estimating hippocampus and amygdala volumes in a sample of preadolescent children aged between 6 to 11 years. The volumes obtained with FreeSurfer and FSL-FIRST were evaluated and compared with manual segmentations with respect to volume difference, spatial agreement and between- and within-method correlations. Results highlighted a tendency for both automated techniques to overestimate hippocampus and amygdala volumes, in comparison to manual segmentation. This was more pronounced when using FreeSurfer than FSL-FIRST and, for both techniques, the overestimation was more marked for the amygdala than the hippocampus. Pearson correlations support moderate associations between manual tracing and FreeSurfer for hippocampus (right r=0.69, p

A wide spectrum of neurocognitive deficits characterises HIV infection in adults. HIV infection is additionally associated with morphological brain abnormalities affecting neural substrates that subserve neurocognitive function. Early life stress (ELS) also has a direct influence on brain morphology. However, the combined impact of ELS and HIV on brain structure and neurocognitive function has not been examined in an all-female sample with advanced HIV disease. The present study examined the effects of HIV and childhood trauma on brain morphometry and neurocognitive function. Structural data were acquired using a 3T Magnetom MRI scanner, and a battery of neurocognitive tests was administered to 124 women: HIV-positive with ELS (n = 32), HIV-positive without ELS (n = 30), HIV-negative with ELS (n = 31) and HIV-negative without ELS (n = 31). Results revealed significant group volumetric differences for right anterior cingulate cortex (ACC), bilateral hippocampi, corpus callosum, left and right caudate and left and right putamen. Mean regional volumes were lowest in HIV-positive women with ELS compared to all other groups. Although causality cannot be inferred, findings also suggest that alterations in the left frontal lobe, right ACC, left hippocampus, corpus callosum, left and right amygdala and left caudate may be associated with poorer neurocognitive performance in the domains of processing speed, attention/working memory, abstraction/executive functions, motor skills, learning and language/fluency with these effects more pronounced in women living with both HIV and childhood trauma. This study highlights the potential contributory role of childhood trauma to brain alterations and neurocognitive decline in HIV-infected individuals.

The Autism Phenome Project is the largest, single site, longitudinal magnetic resonance imaging (MRI) study of young children with autism spectrum disorder (ASD). Previous analyses from this cohort have shown that the children with autism have a total brain volume at time 1 (∼3 years of age) that is 6% larger than typically developing (TD) children. This finding is driven primarily by 15% of the boys with ASD that have disproportionate megalencephaly (ASD-DM) or brain size that is 1.5 standard deviations above what would be expected for the child's height. In the current study, cerebral cortical grey matter volume, thickness, and surface area were assayed from MRI scans of 112, 3-year-old boys with ASD and 50 age-matched TD boys. The boys with ASD-DM (n = 17) were analyzed separately from the boys with normal brain size (ASD-N, n = 95). Previous studies of cortical thickness and surface area for ASD children in this age range have come to diametrically different conclusions concerning the significance of cortical thickness vs. surface area. Current analyses indicate that cortical thickness was comparable across the ASD and TD groups. However, surface area was significantly greater in the ASD group compared to the TD group. This result was driven largely by the children with ASD-DM. Even in the ASD-DM group, not all cortical regions demonstrated increased surface area. These results provide strong evidence that the early cortical overgrowth associated with ASD is due primarily to increased surface area and not to increased cortical thickness.

BackgroundAcute-on-chronic liver failure (ACLF) is a form of liver disease with high short-term mortality. ACLF offers considerable potential to affect the cortical areas by significant tissue injury due to loss of neurons and other supporting cells. We measured changes in cortical thickness and metabolites profile in ACLF patients following treatment, and compared it with those of age matched healthy volunteers.MethodsFor the cortical thickness analysis we performed whole brain high resolution T1-weighted magnetic resonance imaging (MRI) on 15 ACLF and 10 healthy volunteers at 3T clinical MR scanner. Proton MR Spectroscopy ((1)H MRS) was also performed to measure level of altered metabolites. Out of 15 ACLF patients 10 survived and underwent follow-up study after clinical recovery at 3 weeks. FreeSurfer program was used to quantify cortical thickness and LC- Model software was used to quantify absolute metabolites concentrations. Neuropsychological (NP) test was performed to assess the cognitive performance in follow-up ACLF patients compared to controls.ResultsSignificantly reduced cortical thicknesses in multiple brain sites, and significantly decreased N-acetyl aspartate (NAA), myo-inositol (mI) and significantly increased glutamate/glutamine (glx) metabolites were observed in ACLF compared to those of controls at baseline study. Follow-up patients showed significant recovery in cortical thickness and Glx level, while NAA and mI were partially recovered compared to baseline study. When compared to controls, follow-up patients still showed reduced cortical thickness and altered metabolites level. Follow-up patients had abnormal neuropsychological (NP) scores compared to controls.ConclusionsNeuronal loss as suggested by the reduced NAA, decreased cellular density due to increased cerebral hyperammonemia as supported by the increased glx level, and increased proinflammatory cytokines and free radicals may account for the reduced cortical thickness in ACLF patients. Presence of reduced cortical thickness, altered metabolites and abnormal NP test scores in post recovery subjects as compared to those of controls is associated with incomplete clinical recovery. The current imaging protocol can be easily implemented in clinical settings to evaluate and monitor brain tissue changes in patients with ACLF during the course of treatment.

Volumetric assessments of the hippocampus and other brain structures during childhood provide useful indices of brain development and correlates of cognitive functioning in typically and atypically developing children. Automated methods such as FreeSurfer promise efficient and replicable segmentation, but may include errors which are avoided by trained manual tracers. A recently devised automated correction tool that uses a machine learning algorithm to remove systematic errors, the Automatic Segmentation Adapter Tool (ASAT), was capable of substantially improving the accuracy of FreeSurfer segmentations in an adult sample [Wang et al., 2011], but the utility of ASAT has not been examined in pediatric samples. In Study 1, the validity of FreeSurfer and ASAT corrected hippocampal segmentations were examined in 20 typically developing children and 20 children with autism spectrum disorder aged 2 and 3 years. We showed that while neither FreeSurfer nor ASAT accuracy differed by disorder or age, the accuracy of ASAT corrected segmentations were substantially better than FreeSurfer segmentations in every case, using as few as 10 training examples. In Study 2, we applied ASAT to 89 typically developing children aged 2 to 4 years to examine relations between hippocampal volume, age, sex, and expressive language. Girls had smaller hippocampi overall, and in left hippocampus this difference was larger in older than younger girls. Expressive language ability was greater in older children, and this difference was larger in those with larger hippocampi, bilaterally. Overall, this research shows that ASAT is highly reliable and useful to examinations relating behavior to hippocampal structure.

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. [ABSTRACT FROM AUTHOR]

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

The relative roles of brainstem, thalamus and striatum in parkinsonism in schizophrenia spectrum disorder (SSD) patients are largely unknown. To determine whether topographical alterations of the brainstem, thalamus and striatum contribute to parkinsonism in SSD patients, we conducted structural magnetic resonance imaging (MRI) of SSD patients with (SSD-P, n = 35) and without (SSD-nonP, n = 64) parkinsonism, as defined by a Simpson and Angus Scale (SAS) total score of ≥ 4 and < 4, respectively, in comparison with healthy controls (n = 20). FreeSurfer v6.0 was used for segmentation of four brainstem regions (medulla oblongata, pons, superior cerebellar peduncle and midbrain), caudate nucleus, putamen and thalamus. Patients with parkinsonism had significantly smaller medulla oblongata (p = 0.01, false discovery rate (FDR)-corrected) and putamen (p = 0.02, FDR-corrected) volumes when compared to patients without parkinsonism. Across the entire patient sample (n = 99), significant negative correlations were identified between (a) medulla oblongata volumes and both SAS total (p = 0.034) and glabella-salivation (p = 0.007) scores, and (b) thalamic volumes and both SAS total (p = 0.033) and glabella-salivation (p = 0.007) scores. These results indicate that brainstem and thalamic structures as well as basal ganglia-based motor circuits play a crucial role in the pathogenesis of parkinsonism in SSD. [ABSTRACT FROM AUTHOR]

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image‐acquisition protocols—negatively influence the reliability and reproducibility of image‐analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling‐subject (TS) dataset including 56 T1‐weighted MRI scans of 20 healthy participants in three different MRI procedures—20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively—was considered to compare the reproducibility of TS‐GLM, ComBat, and TS‐ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement‐bias indicator. The measurement‐bias reduction realized with different methods was evaluated by comparing test–retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test–retest datasets. The results revealed that TS‐GLM and TS‐ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS‐based harmonization outperforms ComBat for measurement‐bias reduction and is optimal for MRI data in well‐prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large‐scale study is an interesting future problem. [ABSTRACT FROM AUTHOR]

The accurate segmentation of in vivo magnetic resonance imaging (MRI) data is a crucial prerequisite for the reliable assessment of disease progression, patient stratification or the establishment of putative imaging biomarkers. This is especially important for the hippocampal formation, a brain area involved in memory formation and often affected by neurodegenerative or psychiatric diseases. FreeSurfer, a widely used automated segmentation software, offers hippocampal subfield delineation with multiple input options. While a single T1-weighted (T1) sequence is regularly used by most studies, it is also possible and advised to use a high-resolution T2-weighted (T2H) sequence or multispectral information. In this investigation it was determined whether there are differences in volume estimations depending on the input images and which combination of these deliver the most reliable results in each hippocampal subfield. 41 healthy participants (age = 25.2 years ± 4.2 SD) underwent two structural MRIs at three Tesla (time between scans: 23 days ± 11 SD) using three different structural MRI sequences, to test five different input configurations (T1, T2, T2H, T1 and T2, and T1 and T2H). We compared the different processing pipelines in a cross-sectional manner and assessed reliability using test-retest variability (%TRV) and the dice coefficient. Our analyses showed pronounced significant differences and large effect sizes between the processing pipelines in several subfields, such as the molecular layer (head), CA1 (head), hippocampal fissure, CA3 (head and body), fimbria and CA4 (head). The longitudinal analysis revealed that T1 and multispectral analysis (T1 and T2H) showed overall higher reliability across all subfields than T2H alone. However, the specific subfields had a substantial influence on the performance of segmentation results, regardless of the processing pipeline. Although T1 showed good test-retest metrics, results must be interpreted with caution, as a standard T1 sequence relies heavily on prior information of the atlas and does not take the actual fine structures of the hippocampus into account. For the most accurate segmentation, we advise the use of multispectral information byusing a combination of T1 and high-resolution T2-weighted sequences or a T2 high-resolution sequence alone. [ABSTRACT FROM AUTHOR]

Introduction: Genetically defined spinocerebellar ataxia (SCA) type 1 and 2 patients have differential clinical profile along with probable distinctive cortical and subcortical neurodegeneration. We compared the degree of brain atrophy in the two subtypes with their phenotypic and genotypic parameters.Methods: MRI was performed using a 3T scanner (Philips, Achieva) to obtain 3D T1-weighted scans of the whole brain and analyzed by FreeSurfer (version 5.3 and 6 dev.) software. Genetically proven SCA1 (n = 18) and SCA2 (n = 25) patients with age-matched healthy controls (n = 8) were recruited. Clinical severity was assessed by the International Cooperative Ataxia Rating Scale (ICARS). To know the differential pattern of atrophy, the groups were compared using ANOVA/Kruskal-Wallis test and followed by correlation analysis with multiple corrections. Further, machine learning-based classification of SCA subtypes was carried out.Result: We found (i) bilateral frontal, parietal, temporal, and occipital atrophy in SCA1 and SCA2 patients; (ii) reduced volume of cerebellum, regions of brain stem, basal ganglia along with the certain subcortical areas such as hippocampus, amygdala, thalamus, diencephalon, and corpus callosum in SCA1 and SCA2 subtypes; (iii) higher subcortical atrophy SCA2 than SCA1 (iv) correlation between brain atrophy and disease attributes; (v) differential predictive pattern of two SCA subtypes using machine learning approach.Conclusion: The present study suggests that SCA1 and SCA2 do not differ in cortical thinning while a characteristic pattern of subcortical atrophy SCA2 > SCA1 is observed along with correlation of brain atrophy and disease attributes. This may provide the diagnostic guidance of MRI to SCA subtypes and differential therapies. [ABSTRACT FROM AUTHOR]

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker. [ABSTRACT FROM AUTHOR]

A wide spectrum of neurocognitive deficits characterises HIV infection in adults. HIV infection is additionally associated with morphological brain abnormalities affecting neural substrates that subserve neurocognitive function. Early life stress (ELS) also has a direct influence on brain morphology. However, the combined impact of ELS and HIV on brain structure and neurocognitive function has not been examined in an all-female sample with advanced HIV disease. The present study examined the effects of HIV and childhood trauma on brain morphometry and neurocognitive function. Structural data were acquired using a 3T Magnetom MRI scanner, and a battery of neurocognitive tests was administered to 124 women: HIV-positive with ELS (n = 32), HIV-positive without ELS (n = 30), HIV-negative with ELS (n = 31) and HIV-negative without ELS (n = 31). Results revealed significant group volumetric differences for right anterior cingulate cortex (ACC), bilateral hippocampi, corpus callosum, left and right caudate and left and right putamen. Mean regional volumes were lowest in HIV-positive women with ELS compared to all other groups. Although causality cannot be inferred, findings also suggest that alterations in the left frontal lobe, right ACC, left hippocampus, corpus callosum, left and right amygdala and left caudate may be associated with poorer neurocognitive performance in the domains of processing speed, attention/working memory, abstraction/executive functions, motor skills, learning and language/fluency with these effects more pronounced in women living with both HIV and childhood trauma. This study highlights the potential contributory role of childhood trauma to brain alterations and neurocognitive decline in HIV-infected individuals.

Objective: Obsessive-compulsive (OC) symptoms are common in the general population, but it is unclear whether subclinical OC symptoms and obsessive-compulsive disorder (OCD) are part of a neuroanatomical continuum. The goal of this study was to investigate the relation between OC symptoms and subcortical and cortical morphology in a population-based sample of children.Method: The study included 2,551 participants, aged 9-12 years, from the population-based Generation R Study. OC symptoms were measured using the 7-item caregiver-rated Short Obsessive-Compulsive Disorder Screener (SOCS). Structural (3T) magnetic resonance imaging scans were processed using FreeSurfer to study the thalamus and other subcortical volumes, intracranial volume, vertexwise cortical thickness, and surface area. We used linear regression models to investigate the association between OC symptoms and brain morphology. Emulating case-control studies from the literature, we compared children scoring above the clinical cutoff of the SOCS (probable OCD cases, n = 164) with matched children without symptoms.Results: Children with probable OCD had larger thalami compared with the control group (d 0.16, p = .044). Vertexwise analysis showed a positive association between OC symptoms and thickness of the right inferior parietal cortex, which disappeared after adjusting for total behavioral problems. SOCS scores correlated negatively with intracranial volume (B = -2444, p = .038).Conclusion: Children with probable OCD showed thalamus alterations similar to those previously reported in unmedicated children with OCD. OC symptoms showed a stronger association with total intracranial volume than regional brain measures. Longitudinal studies are needed to further elucidate similarities and distinctions between neural correlates of subclinical and clinical OC symptoms. [ABSTRACT FROM AUTHOR]

This study used structural magnetic resonance imaging to examine whether specific anatomical features of Broca's and Wernicke's areas are related to language functions in typically developing older subjects with no specific language expertize. Data from 231 subjects from the Zurich LHAB‐study are used for this study. For these subjects, we obtained several psychometric measures from which we calculated performance measures reflecting specific psychological functions (language comprehension, verbal fluency, perceptual speed, visual memory, recognition of regularities, and logical thinking). From the MRI measurements, we calculated the cortical thickness and cortical surface of Broca's and Wernicke's areas. Applying multiple regression analyses, we identified a moderately strong relationship between language comprehension and the brain metrics from Broca's and Wernicke's areas and showed that approximately 10% of the variance in language comprehension performance is explained by the linear combination of all perisylvian brain metrics. The other psychological functions (verbal fluency, perceptual speed, visual memory, recognition of regularities, and logical thinking) are not related to these brain metrics. Subsequent detailed analyses revealed that the cortical thickness of Wernicke's area, in particular, contributed most to this structure‐function relationship. The better performance in the language comprehension tests was related to a thicker cortex in Wernicke's area. Thus, this study demonstrates a structure‐function relationship between the anatomical features of the perisylvian language areas and language comprehension, suggesting that particular anatomical features are associated with better language performance. [ABSTRACT FROM AUTHOR]

Adolescence is characterized by significant neuromaturation, including extensive cortical thinning, particularly in frontal regions. The goal of this study was to examine the behavioral correlates of neurostructural development in early adolescence. Participants were 185 healthy 12- to 14-year-olds (44% female) recruited from local schools. Participants completed a comprehensive neuropsychological test battery and magnetic resonance imaging session. Cortical surface reconstruction and thickness estimates were performed via FreeSurfer. Age and cortical thickness were negatively correlated in 10 brain regions, 7 of which were in frontal areas (β = −.15 to −.25, ps ≤ .05). Hierarchical linear regressions examined the influence of cortical thickness on working memory, attention, verbal learning and memory, visuospatial functioning, spatial planning and problem solving, and inhibition, controlling for age and intracranial volume. Thinner parietal cortices predicted better performances on tests of verbal learning and memory, visuospatial functioning, and spatial planning and problem solving (β = −.14 to −.24, ps ≤ .05). Age, spanning from 12 to 14 years, accounted for up to 6% of cortical thickness, suggesting substantial thinning during early adolescence, with males showing more accelerated thinning than females between ages 12 and 14. For both males and females, thinner parietal association cortices corresponded with better neurocognitive functioning above and beyond age alone.

Catechol-O-methyltransferase (COMT) Val158Met (rs4680) polymorphism plays a crucial role in regulating brain dopamine level. Converging evidence from Caucasian samples showed that, compared with rs4680 Val allele, the Met allele was linked to lower COMT activity, which in turn was linked to better cognitive performance such as working memory (WM) and to a larger hippocampus (a brain region important for WM). However, some behavioral studies have shown that the function of rs4680 appears to vary across different ethnic groups, with Chinese subjects showing an opposite pattern as that for Caucasians (i.e. the Val allele is linked to better cognitive functions related to WM in Chinese). Using a sample of healthy Han Chinese college students (ages from 19 to 21 years), this study investigated the association of COMT Val158Met genotype with behavioral data on a two-back WM task (n = 443, 189M/254F) and T1 MRI data (n = 320, 134M/186F). Results showed that, compared to the Met allele, the Val allele was associated with larger hippocampal volume (the right hippocampus: β = -0.118, t = -2.367, P = 0.019, and the left hippocampus: β = -0.099, t = -1.949, P = 0.052) and better WM performance (β = -0.110, t = -2.315, P = 0.021). These results add to the growing literature on differentiated effects of COMT rs4680 polymorphism on WM across populations and offer a brain structural mechanism for such population-specific genetic effects.

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results. [ABSTRACT FROM AUTHOR]