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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]

Identifying disease-specific imaging features of idiopathic Normal Pressure Hydrocephalus (iNPH) is crucial to develop accurate diagnoses, although the abnormal brain anatomy of patients with iNPH creates challenges in neuroimaging analysis. We quantified cortical thickness and volume using FreeSurfer 7.3.2 in 19 patients with iNPH, 28 patients with Alzheimer's disease (AD), and 30 healthy controls (HC). We noted the frequent need for manual correction of the automated segmentation in iNPH and examined the effect of correction on the results. We identified statistically significant higher proportion of volume changes associated with manual edits in individuals with iNPH compared to both HC and patients with AD. Changes in cortical thickness and volume related to manual correction were also partly correlated with the severity of radiological features of iNPH. We highlight the challenges posed by the abnormal anatomy in iNPH when conducting neuroimaging analysis and emphasise the importance of quality checking and correction in this clinical population. [ABSTRACT FROM AUTHOR]

Abstract This study aims to analyse the volumetric changes in brain MRI after cochlear implantation (CI), focusing on the speech perception in postlingually deaf adults. We conducted a prospective cohort study with 16 patients who had bilateral hearing loss and received unilateral CI. Based on the surgical side, patients were categorized into left and right CI groups. Volumetric T1-weighted brain MRI were obtained before and one year after the surgery. To overcome the artifact caused by the internal device in post-CI scan, image reconstruction method was newly devised and applied using the contralateral hemisphere of the pre-CI MRI data, to run FreeSurfer. We conducted within-subject template estimation for unbiased longitudinal image analysis, based on the linear mixed effect models. When analyzing the contralateral cerebral hemisphere before and after CI, a substantial increase in superior frontal gyrus and superior temporal gyrus (STG) volumes was observed in the left CI group. A positive correlation was observed in the STG and post-CI word recognition score in both groups. As far as we know, this is the first study attempting longitudinal brain volumetry based on post-CI MRI scans. We demonstrate that better auditory performance after CI is associated with structural restoration in central auditory structures.

Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison. We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed. Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age. ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered. [ABSTRACT FROM AUTHOR]

Objectives: Fibromyalgia (FM) has been associated with decreased hippocampal volume; however, the atrophy patterns of hippocampal subregions have not yet been identified. We therefore aimed to evaluate the volumes of hippocampal subregions in FM patients with mild cognitive impairment (MCI), and to explore the relationship between different subregional alterations and cognitive function. Methods: The study included 35 FM patients (21 with MCI and 14 without MCI) and 35 healthy subjects. All subjects performed the Montreal Cognitive Assessment (MoCA) to assess cognitive function. FreeSurfer V.7.3.2 was used to calculate hippocampal subregion volumes. We then compared hippocampal subregion volumes between the groups, and analyzed the relationship between hippocampal subregion volume and cognitive function using a partial correlation analysis method. Results: Compared with the healthy subjects, FM patients with MCI had smaller hippocampal volumes in the left and right CA1 head, Molecular layer head, GCDG head, and CA4 head, and in the left Presubiculum head. Poorer executive function, naming ability, and attention were associated with left CA1 head and left Molecular layer head atrophy. By contrast, hippocampal subregion volumes in the FM patients without MCI were slightly larger than or similar to those in the healthy subjects, and were not significantly correlated with cognitive function. Conclusion: Smaller volumes of left CA1 head and left Molecular layer head were associated with poorer executive function, naming ability, and attention in FM patients with MCI. However, these results were not observed in the FM patients without MCI. These findings suggest that the hippocampal subregions of FM patients might present compensatory mechanisms before cognitive decline occurs. [ABSTRACT FROM AUTHOR]

The hippocampus is a crucial brain structure in etiological models of major depressive disorder (MDD). It remains unclear whether sex differences in the incidence and symptoms of MDD are related to differential illness-associated brain alterations, including alterations in the hippocampus. This study investigated divergent the effects of sex on hippocampal subfield alterations in drug-naive patients with MDD. High-resolution structural MR images were obtained from 144 drug-naive individuals with MDD early in their illness course and 135 age- and sex-matched healthy controls (HCs). Hippocampal subfields were segmented using FreeSurfer software and analyzed in terms of both histological subfields (CA1–4, dentate gyrus, etc.) and more integrative larger functional subregions (head, body and tail). We observed a significant overall reduction in hippocampal volume in MDD patients, with deficits more prominent deficits in the posterior hippocampus. Differences in anatomic alterations between male and female patients were observed in the CA1-head, presubiculum-body and fimbria in the left hemisphere. Exploratory analyses revealed different patterns of clinical and memory function correlations with histological subfields and functional subregions between male and female patients primarily in the hippocampal head and body. This cross-sectional study cannot clarify the causality of hippocampal alterations or their association with illness risk or onset. These findings represent the first reported sex-specific alterations in hippocampal histological subfields in patients with MDD early in the illness course prior to treatment. Sex-specific hippocampal alterations may contribute to diverse sex differences in the clinical presentation of MDD. • The MDD group exhibits a significant volume decrease in overall hippocampus, particularly in the body and tail, regardless of sex. • Sex-specific effects were detected only in the left hemisphere histological subfields including the CA1-head, presubiculum-body and fimbria. • Association between hippocampal substructures and clinical/memory ratings showed different patterns between sexes in exploratory analyses. [ABSTRACT FROM AUTHOR]

Background: Stimulant medication is commonly prescribed as treatment for attention-deficit/hyperactivity disorder (ADHD). While we previously found that short-term stimulant-treatment influences apparent cortical thickness development in an age-dependent manner, it remains unknown whether these effects persist throughout development into adulthood. Purpose: Investigate the long-term age-dependent effects of stimulant medication use on apparent cortical thickness development in adolescents and adults previously diagnosed with ADHD. Methods: This prospective study included the baseline and 4-year follow-up assessment of the "effects of Psychotropic drugs On the Developing brain-MPH" ("ePOD-MPH") project, conducted between June-1-2011 and December-28-2019. The analyses were pre-registered (https://doi.org/10.17605/OSF.IO/32BHF). T1-weighted MR scans were obtained from male adolescents and adults, and cortical thickness was estimated for predefined regions of interest (ROIs) using Freesurfer. We determined medication use and assessed symptoms of ADHD, anxiety, and depression at both time points. Linear mixed models were constructed to assess main effects and interactions of stimulant medication use, time, and age group on regional apparent cortical thickness. Results: A total of 32 male adolescents (aged mean ± SD, 11.2 ± 0.9 years at baseline) and 24 men (aged mean ± SD, 29.9 ± 5.0 years at baseline) were included that previously participated in the ePOD-MPH project. We found no evidence for long-term effects of stimulant medication use on ROI apparent cortical thickness. As expected, we did find age-by-time interaction effects in all ROIs (left prefrontal ROI: P=.002, right medial and posterior ROIs: P<.001), reflecting reductions in apparent cortical thickness in adolescents. Additionally, ADHD symptom severity (adolescents: P<.001, adults: P=.001) and anxiety symptoms (adolescents: P=0.03) were reduced, and more improvement of ADHD symptoms was associated with higher medication use in adults (P=0.001). Conclusion: We found no evidence for long-term effects of stimulant-treatment for ADHD on apparent cortical thickness development in adolescents and adults. The identified age-dependent differences in apparent cortical thickness development are consistent with existing literature on typical cortical development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of this study was to investigate the volumetric alterations of the amygdala subregions in FTD phenotypes and how they evolve with disease progression. Patients clinically diagnosed with behavioral variant FTD (bvFTD) (n = 20), semantic dementia (SD) (n = 20), primary nonfluent aphasia (PNFA) (n = 20), Alzheimer's disease (AD) (n = 20), and 20 matched healthy controls underwent whole brain structural MRI. The patient groups were followed up annually for up to 3.5 years. Amygdala nuclei were segmented using FreeSurfer, corrected by total intracranial volumes, and grouped into the basolateral, superficial, and centromedial subregions. Linear mixed effects models were applied to identify changes in amygdala subregional volumes over time. At baseline, bvFTD, SD, and AD displayed global amygdala volume reduction, whereas amygdala volume appeared to be preserved in PNFA. Asymmetrical amygdala atrophy (left > right) was most pronounced in SD. Longitudinally, SD and PNFA showed greater rates of annual decline in the right basolateral and superficial subregions compared to bvFTD and AD. The findings provide comprehensive insights into the differential impact of FTD pathology on amygdala subregions, revealing distinct atrophy patterns that evolve over disease progression. The characterization of amygdala subregional involvement in FTD and their potential role as biomarkers carry substantial clinical implications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BackgroundDementia 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.ObjectivesThis Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis.MethodsWe 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).ResultsIn 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.ConclusionOur 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.

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 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.

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]

Background: The Huntington's Disease Integrated Staging System (HD-ISS) defined disease onset using volumetric cut-offs for caudate and putamen derived from FreeSurfer 6 (FS6). The impact of the latest software update (FS7) on volumes remains unknown. The Huntington's Disease Young Adult Study (HD-YAS) is appropriately positioned to explore differences in FS bias when detecting early atrophy. Objective: Explore the relationships and differences between raw caudate and putamen volumes, calculated total intracranial volumes (cTICV), and adjusted caudate and putamen volumes, derived from FS6 and FS7, in HD-YAS. Methods: Images from 123 participants were segmented and quality controlled. Relationships and differences between volumes were explored using intraclass correlation (ICC) and Bland-Altman analysis. Results: Across the whole cohort, ICC for raw caudate and putamen was 0.99, cTICV 0.93, adjusted caudate 0.87, and adjusted putamen 0.86 (all p < 0.0005). Compared to FS6, FS7 calculated: i) larger raw caudate (+0.8%, p < 0.00005) and putamen (+1.9%, p < 0.00005), with greater difference for larger volumes; and ii) smaller cTICV (–5.1%, p < 0.00005), with greater difference for smaller volumes. The systematic and proportional difference in cTICV was greater than raw volumes. When raw volumes were adjusted for cTICV, these effects compounded (adjusted caudate +7.0%, p < 0.00005; adjusted putamen +8.2%, p < 0.00005), with greater difference for larger volumes. Conclusions: As new software is released, it is critical that biases are explored since differences have the potential to significantly alter the findings of HD trials. Until conversion factors are defined, the HD-ISS must be applied using FS6. This should be incorporated into the HD-ISS online calculator. [ABSTRACT FROM AUTHOR]

Thyroid hormones play a critical role in brain development, but paradoxically, patients with hyperthyroidism often exhibit cognitive decline and irritability. This study aims to explore the pattern of atrophy in hyperthyroid patients, changes in specific areas of the brain, including hypothalamic subfields and limbic structures, and their relationships with hormonal levels and psychometric tests. This prospective cross‐sectional study involves 19 newly diagnosed, untreated hyperthyroid patients, and 15 age and gender‐matched control subjects. The participants underwent psychometric and cognitive tests and volumetric MRI. The hypothalamic subfield (anterior‐inferior, anterior‐superior, superior‐tubular, inferior‐tubular, and posterior hypothalamus) and limbic structures (fornix, basal forebrain, nucleus accumbens, and septal nucleus) were segmented using voxel‐based morphometry, surface‐based morphometry, and deep learning algorithms. The groups were compared using the t‐test, and correlation analyses were performed between clinical parameters and volumetric measurements. The correlation between hormonal parameters and volumetric measurements in patient and control groups was assessed with the Meng test. Hyperthyroid patients displayed widespread grey matter loss and sulcal shallowing in the left hemisphere. However, no local gyrification index changes or cortical thickness variations were detected. The limbic structures and hypothalamic subunits did not show any volume discrepancies. Free thyroxine in the patient group negatively correlated with bilateral anterior‐inferior and right septal nucleus, but positively correlated with left anterior‐inferior in the control group. Thyroid stimulating hormone in the patient group showed a positive correlation with bilateral fornix volume, a correlation absent in the control group. Disease duration negatively correlated with right anterior‐inferior, right tubular inferior, and right septal nucleus. Changes in cognitive and psychometric test scores in the patient group correlated with the bilateral septal nucleus volume. Hyperthyroidism primarily leads to a reduction in grey matter volume and sulcal shallowing. Thyroid hormones have differing volumetric effects in limbic structures and hypothalamic subunits under physiological and hyperthyroid conditions. [ABSTRACT FROM AUTHOR]

Identifying disease-specific imaging features of idiopathic Normal Pressure Hydrocephalus (iNPH) is crucial to develop accurate diagnoses, although the abnormal brain anatomy of patients with iNPH creates challenges in neuroimaging analysis. We quantified cortical thickness and volume using FreeSurfer 7.3.2 in 19 patients with iNPH, 28 patients with Alzheimer's disease (AD), and 30 healthy controls (HC). We noted the frequent need for manual correction of the automated segmentation in iNPH and examined the effect of correction on the results. We identified statistically significant higher proportion of volume changes associated with manual edits in individuals with iNPH compared to both HC and patients with AD. Changes in cortical thickness and volume related to manual correction were also partly correlated with the severity of radiological features of iNPH. We highlight the challenges posed by the abnormal anatomy in iNPH when conducting neuroimaging analysis and emphasise the importance of quality checking and correction in this clinical population.

BackgroundStimulant medication is commonly prescribed as treatment for attention-deficit/hyperactivity disorder (ADHD). While we previously found that short-term stimulant-treatment influences apparent cortical thickness development in an age-dependent manner, it remains unknown whether these effects persist throughout development into adulthood.PurposeInvestigate the long-term age-dependent effects of stimulant medication use on apparent cortical thickness development in adolescents and adults previously diagnosed with ADHD.MethodsThis prospective study included the baseline and 4-year follow-up assessment of the “effects of Psychotropic drugs On the Developing brain-MPH” (“ePOD-MPH”) project, conducted between June-1-2011 and December-28-2019. The analyses were pre-registered (https://doi.org/10.17605/OSF.IO/32BHF). T1-weighted MR scans were obtained from male adolescents and adults, and cortical thickness was estimated for predefined regions of interest (ROIs) using Freesurfer. We determined medication use and assessed symptoms of ADHD, anxiety, and depression at both time points. Linear mixed models were constructed to assess main effects and interactions of stimulant medication use, time, and age group on regional apparent cortical thickness.ResultsA total of 32 male adolescents (aged mean ± SD, 11.2 ± 0.9 years at baseline) and 24 men (aged mean ± SD, 29.9 ± 5.0 years at baseline) were included that previously participated in the ePOD-MPH project. We found no evidence for long-term effects of stimulant medication use on ROI apparent cortical thickness. As expected, we did find age-by-time interaction effects in all ROIs (left prefrontal ROI: P=.002, right medial and posterior ROIs: P

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]

Objectives: Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods. Materials & Methods ISurface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups. Results Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods. Conclusion The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method. [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]

Abstract Background The primary goal of this study is to evaluate the precision, time effectiveness and reproducibility of open source automated magnetic resonance imaging (MRI) brain volumetric measurement platforms as compared to the semi-automated methods. The secondary goal of the study is to create age-stratified database of MRI brain volumetric measures of healthy Egyptian adults. Results Statistically significant variation were found between the absolute but not the relative volumetric measures (P-value

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.

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.

Alzheimer’s Disease (AD) is a progressive, neurodegenerative disorder where two-thirds of the affected population are women. Along with cognitive impairments, AD is associated with behavioral changes such as aggression towards caretakers. The limbic system consists of various brain structures that play a role in emotions and behavioral reactions. Some of the limbic system-related areas are the amygdala, hippocampus, thalamus, corpus collosum (CC), and white matter (WM). Cognitive changes with AD can be measured using the clinical dementia rating (CDR) scale. Physical changes in living patients require brain imaging tools, such as Magnetic Resonance Imaging (MRI). As there is currently sparse research present for these areas relating to the female brain, we used clinical data and FreeSurfer-processed imaging data from an open-access database, OASIS-3, to explore the associations between dementia severity and the volume of the limbic system-related brain components in women. A control group consisted of participants with no dementia and multiple brain scans while comparison groups consisted of participants with a single brain scan and 1) no dementia 2) mild dementia or 3) moderate/severe dementia. Hemisphere differences with increasing CDR were found for the thalamus as well as simple hemisphere differences for the hippocampus, thalamus, and WM. When using age-matched controls and normalized volume data, the amygdala, hippocampus, thalamus, and CC volumes for subjects with AD were different than those in the control group, with the amygdala and hippocampus also showing statistically significant volume loss with increasing dementia severity. As the areas included in this study are related to the limbic system, this provides insight into the physical changes occurring in the brain of women with increasing AD, who often show changes in emotions. This can be an area to longitudinally explore whether there are associated behavioral changes as physical changes at the individual level occur over time. Abbreviations: AD – Alzheimer’s Disease; CC – Corpus Collosum; CDR – Clinical Dementia Ratings; MRI – Magnetic Resonance Imaging; NFT– Neurofibrillary Tangles; WM – White Matter

Introduction: There is evidence showing that central nervous system TB (CNS-TB) causes meningitis, pachymeningitis, tuberculomas, and granulomas. However, the impact of pulmonary or spine TB on brain morphology and thickness is yet to be documented. TB is associated with increased levels of inflammatory biomarkers in specific brain regions. Objectives: The primary aim of this study was to compare cortical-brain volume and thickness between patients with pulmonary or spine TB and non-TB individuals and investigate the association between inflammatory biomarkers and brain volume or thickness among patients with pulmonary or spine TB. Methods: Participants ranging in age from 18 to 65 years (23 TB patients and 50 healthy controls), who were scanned using 1.5-T MRI at Jazan Hospital, were compared in terms of brain volumes and thicknesses. Brain volume and thickness were measured using FreeSurfer. Results: There were significant differences in the volumes of the bilateral and total amygdala and accumbens areas, right hippocampus and cerebellum, and CSF, and in the thickness of the right pericalcarine area between patients with pulmonary or spine TB and healthy controls. We also found significant associations between inflammatory biomarkers (CRP, WBC, and platelets) and brain volume but not thickness in patients with TB, p

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. [ABSTRACT FROM AUTHOR]

Patients with bipolar disorder (BD) who are presenting with cognitive impairment and associated structural brain abnormalities have generally a poorer clinical outcome. This study aims to map the early longitudinal trajectories in brain structure and cognition in patients with recently diagnosed BD. Fully or partially remitted patients with a recent diagnosis of BD and matched healthy controls (HC) underwent structural MRI and neuropsychological testing at baseline (BD n = 97; HC n = 66) and again following an average of 16 (range 6–27) months (BD n = 50; HC n = 38). We investigated the differential trajectories in BD vs. HC in cortical gray matter volume and thickness, total cerebral white matter, hippocampal and amygdala volumes, estimated brain age, and cognitive functioning using linear mixed models. Within patients, we further investigated whether brain structural abnormalities detected at baseline were associated with subsequent mood episodes. Compared to HC, patients showed a decline in total white matter volume over time and they had a larger amygdala volume, both at baseline and at follow-up time. Patients further showed lower cognitive performance at both times of investigation with no significant change over time. There were no differences between patients and HC in cortical gray matter volume or thickness, hippocampal volume, or brain-aging patterns. Cognitive impairment and amygdala enlargement may represent stable markers of BD early in the course of illness, whereas subtle white matter decline may result from illness progression. • Bipolar disorder was associated with decline in total white matter following diagnosis. • Patients showed enlarged amygdala compared to controls. • Patients also showed lower cognitive performance. • Patients had typical cortical gray matter volume and brain-aging patterns. [ABSTRACT FROM AUTHOR]

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]

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 [ABSTRACT FROM AUTHOR]

Background: The primary goal of this study is to evaluate the precision, time effectiveness and reproducibility of open source automated magnetic resonance imaging (MRI) brain volumetric measurement platforms as compared to the semi-automated methods. The secondary goal of the study is to create age-stratified database of MRI brain volumetric measures of healthy Egyptian adults. Results: Statistically significant variation were found between the absolute but not the relative volumetric measures (P-value < 0.001). Both the absolute and relative global volumetric measures were strongly correlated with age. Only the absolute volumes showed strong correlation with gender with male volumes being significantly higher than females (P-value < 0.0001), while the normative ratio correlation with gender varied with the utilized software. Conclusions: FMRIB's software library automated segmentation tool (FSL-FAST) was considered the most efficient program to run global volumetric analysis, unless concurrent advanced volumetric analysis is needed or gray matter volume is the main objective of the volumetric analysis, then FreeSurfer is considered the most efficient volumetric software. [ABSTRACT FROM AUTHOR]

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. Scoring by Nonlocal Image Patch Estimator, a non‐local patch‐based measure of anatomical similarity, was used to examine hippocampal change in NC, MCI, and AD using hippocampal grading. Hippocampal grading provides higher classification accuracy with lower error rates than using hippocampal volume. [ABSTRACT FROM AUTHOR]

Introduction: Present study was to investigate hs-CRP concentration, brain structural alterations, and cognitive function in the context of AD [Subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD]. Methods: We retrospectively included 313 patients (Mean age = 76.40 years, 59 SCD, 101 MCI, 153 AD) in a cross-sectional analysis and 91 patients (Mean age = 75.83 years, 12 SCD, 43 MCI, 36 AD) in a longitudinal analysis. Multivariable linear regression was conducted to investigate the relationship between hs-CRP concentration and brain structural alterations, and cognitive function, respectively. Results: Hs-CRP was positively associated with gray matter volume in the left fusiform (b = 0.16, pFDR = 0.023) and the left parahippocampal gyrus (β = 0.16, pFDR = 0.029). Post hoc analysis revealed that these associations were mainly driven by patients with MCI and AD. The interaction of diagnosis and CRP was significantly associated with annual cognitive changes (β = 0.43, p = 0.008). Among these patients with AD, lower baseline CRP was correlated with greater future cognitive decline (r = -0.41, p = 0.013). Conclusion: Our study suggests that increased hs-CRP level may exert protective effect on brain structure alterations and future cognitive changes among patients already with cognitive impairment. [ABSTRACT FROM AUTHOR]

Mesial temporal lobe epilepsy is the most common type of focal epilepsy, imposing a significant burden on the health care system worldwide. Approximately one-third of patients with this disease who do not adequately respond to pharmacotherapy are considered drug-resistant subjects. Despite having some clues of how such epileptic activity and resistance to therapy emerge, coming mainly from preclinical models, we still witness a scarcity of human data. To narrow this gap, in this study, we aimed to estimate the relationship between hippocampal and serum levels of brain-derived neurotrophic factor (BDNF), one of the main and most widely studied neurotrophins, and hippocampal subfield volumes in patients with drugresistant mesial temporal epilepsy undergoing neurosurgical treatment. We found that hippocampal (but not serum) BDNF levels were negatively correlated with the contralateral volumes of the CA1 and CA4 subfields, presubiculum, subiculum, dentate gyrus, and molecular layer of the hippocampus. Taken together, these findings are generally in accordance with existing data, arguing for a proepileptic nature of BDNF effects in the hippocampus and related brain structures. [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.

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

Abstract 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

BackgroundRecent neuroimaging studies have highlighted differences in cerebral maturation in individuals with autism spectrum disorder (ASD) in comparison to typical development. For instance, the contrast of the gray-white matter boundary is decreased in adults with ASD. To determine how gray-white matter boundary integrity relates to early ASD phenotypes, we used a regional structural MRI index of gray-white matter contrast (GWC) on a sample of toddlers with a hereditary high risk for ASD.Materials and methodsWe used a surface-based approach to compute vertex-wise GWC in a longitudinal cohort of toddlers at high-risk for ASD imaged twice between 12 and 24 months (n = 20). A full clinical assessment of ASD-related symptoms was performed in conjunction with imaging and again at 3 years of age for diagnostic outcome. Three outcome groups were defined (ASD, n = 9; typical development, n = 8; non-typical development, n = 3).ResultsASD diagnostic outcome at age 3 was associated with widespread increases in GWC between age 12 and 24 months. Many cortical regions were affected, including regions implicated in social processing and language acquisition. In parallel, we found that early onset of ASD symptoms (i.e., prior to 18-months) was specifically associated with slower GWC rates of change during the second year of life. These alterations were found in areas mainly belonging to the central executive network.LimitationsOur study is the first to measure maturational changes in GWC in toddlers who developed autism, but given the limited size of our sample results should be considered exploratory and warrant further replication in independent and larger samples.ConclusionThese preliminary results suggest that ASD is linked to early alterations of the gray-white matter boundary in widespread brain regions. Early onset of ASD diagnosis constitutes an independent clinical parameter associated with a specific corresponding neurobiological developmental trajectory. Altered neural migration and/or altered myelination processes potentially explain these findings.

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.

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.

Introduction: Brain structure and function changes are considered major brain damages in type 2 diabetes mellitus (T2DM), which likely has a close relationship with cognitive impairment. Many previous studies have shown by using brain structural and functional magnetic resonance imaging (MRI) methods that brain white and gray matter are damaged in T2DM, leading to cognitive impairment. Researches neglected patients of T2DM without cognitive dysfunction might also have brain changes. Methods: In this study, subjects with early stage T2DM with no cognitive dysfunction were enrolled to detect brain damages using the tract-based spatial statistics analysis (TBSS) method to demonstrate white matter (WM) micro changes and surface-basedmorphometry (SBM)method to assess cerebral cortex macro changes. Results: The whole-brain TBSS analysis revealed that there were no statistically significant changes in fractional anisotropy (FA) and mean diffusivity (MD), but the FA declined in some area of cerebral WM (p < 0.1). The SBM results showed no changes in cortical thickness (CT), cortical volume (CV), surface area (SA), and cortical sulcal curve (CSC) between these two groups, but pial local gyration index (LGI) was decreased in the precuneus (-log10, p = -3.327). Discussion: In conclusion, early stage T2DM patients without cognitive impairment had brain micro and macro structural damages, suggesting the potential use of MRI as an imaging marker to detect brain changes in early stage T2DM, which could not be observed and assessed clinically. [ABSTRACT FROM AUTHOR]

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]