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Background: The aim of this study was to investigate the dynamics of annual whole brain volume loss (BVL/year) and annual thalamic volume loss (ThalaVL/year) in patients with relapsing-remitting multiple sclerosis (PwRRMS) during the course of the disease., Methods: A longitudinal database of magnetic resonance imaging (MRI) scans of 195 healthy individuals (age range, 22.8-63.7 years) and longitudinal MRI data of 256 PwRRMS (age range, 20.1-60.8 years) were analyzed and compared. BVL/year and ThalaVL/year were computed for healthy individuals as well as for all patients with MS using a Jacobian integration approach. A linear regression was used to compute the relationship between age and BVL/year and ThalaVL/year for healthy individuals. The linear regression was then used to decompose the BVL/year and ThalaVL/year into a multiple sclerosis (MS)-related and an age-related component for each PwRRMS. PwRRMS were dichotomized into early-phase RRMS (disease duration ≤ 6 years) and later-phase RRMS (disease duration > 6 years), and a t-test was performed to test for differences between these groups., Results: The 135 early-phase patients (disease duration, ≤ 6 years) had statistically significantly higher MS-related BVL/year than the later-phase patients (n = 121) (- 0.21% vs. - 0.06%, p = 0.007). For MS-related ThalaVL/year, the difference between the groups was even more pronounced (- 0.39% vs. - 0.00%, p < 0.0001)., Conclusions: Our results indicate that in PwRRMS, the MS-related components of BVL/year and ThalaVL/year are accelerated in early phases and slowdown in later phases of the disease. This might explain why early intervention often leads to improved outcomes in patients with MS., Competing Interests: Declarations. Ethics approval/informed consent: The datasets comprised anonymized data from retrospective studies that had been approved by local ethics committees (ethics committee of the board of physicians in Hamburg, Germany; ethics committee of the Canton Zurich, Switzerland ethics committee of the University Hospital Carl Gustav Carus, Dresden, Germany; and ethics committee of the Carl Jena University Hospital, Jena, Germany). All patients had given written informed consent. Competing interests: RO, JK, and LS: Employees of jung diagnostics GmbH, a company that received funding from Bristol Myers Squibb to conduct this research. MS: Has served on advisory boards for and received funding for travel or speaker honoraria from Actelion-Janssen, Almirall, Bayer, Biogen, Bristol Myers Squibb, Celgene, Merck, Novartis, Roche, Sanofi-Genzyme, and Teva; received research support from Bayer and Novartis. DS, SB, and MB: Employees and/or shareholders of Bristol Myers Squibb. CGo: Employee of Conradia Hamburg MVZ GmbH and has nothing to disclose. CGa: Received research support from Novartis. SS: Reports compensation for consulting, serving on scientific advisory boards, speaking, or other activities from Biogen, Celgene, Merck, Sanofi, and Teva; currently an employee of Roche, Basel. HHK: Has received travel grants, speaker honoraria, financial research support, and consultancy fees from Bayer, Biogen Idec, Novartis, Siemens, and Teva; served on advisory boards for Biogen, Ixico, and Novartis; received research grants from Novartis. TZ: Has received advisory board fees from Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, Sanofi, and Teva; received speaker fees from Alexion, Almirall, Biogen, and Bristol Myers Squibb., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Anorexia nervosa is a psychiatric disorder characterised by undernutrition, significantly low body weight and large, although possibly transient, reductions in brain structure. Advanced brain ageing tracks accelerated age-related changes in brain morphology that have been linked to psychopathology and adverse clinical outcomes., Aim: The aim of the current case-control study was to characterise cross-sectional and longitudinal patterns of advanced brain age in acute anorexia nervosa and during the recovery process., Method: Measures of grey- and white-matter-based brain age were obtained from T1-weighted magnetic resonance imaging scans of 129 acutely underweight female anorexia nervosa patients (of which 95 were assessed both at baseline and after approximately 3 months of nutritional therapy), 39 recovered patients and 167 healthy female controls, aged 12-23 years. The difference between chronological age and grey- or white-matter-based brain age was calculated to indicate brain-predicted age difference (BrainAGE GM and BrainAGE WM )., Results: Acute anorexia nervosa patients at baseline, but not recovered patients, showed a higher BrainAGE GM of 1.79 years (95% CI [1.45, 2.13]) compared to healthy controls. However, the difference was largely reduced for BrainAGE WM . After partial weight restoration, BrainAGE GM decreased substantially (beta = -1.69; CI [-1.93, -1.46]). BrainAGEs were unrelated to symptom severity or depression, but larger weight gain predicted larger normalisation of BrainAGE GM in the longitudinal patient sample (beta = -0.65; CI [-0.75, -0.54])., Conclusions: Our findings suggest that in patients with anorexia nervosa, undernutrition is an important predictor of advanced grey-matter-based brain age, which itself might be transient in nature and largely undetectable after weight recovery.

Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings., (© 2024. Springer Nature America, Inc.)

Prenatal adversity affects cognitive and brain aging. Both lipid and leptin concentrations may be involved. We investigated if prenatal undernutrition is associated with a specific blood lipid profile and/or leptin concentrations, and if these relate to cognitive function and brain aging. 801 plasma samples of members of the Dutch famine birth cohort were assessed for lipidomics and leptin at age 58. Cognitive performance was measured with a Stroop task at 58, and MRI-based BrainAGE was derived in a subsample at 68. Out of 259 lipid signals, a signature of five identified individuals who were undernourished prenatally. These five lipids were not associated with cognitive performance, but three were predictive of BrainAGE. Leptin was not associated with prenatal famine exposure, Stroop performance, or BrainAGE. In conclusion, prenatal undernutrition was associated with an altered lipid profile predictive of BrainAGE 10 years later, demonstrating the potential of lipid profiles as early biomarkers for accelerated brain aging., (© 2024. The Author(s).)

Congenital adrenal hyperplasia has been reported to manifest with white matter aberrations. However, many previous studies included only small samples, restricted their analyses to females, lacked a control group and/or did not correct for brain size. Here, we examined the largest sample to date, comprising 53 male and female participants with congenital adrenal hyperplasia, who were matched with 53 male and female controls in terms of sex, age, education, and verbal intelligence. The four groups were compared with respect to their total white matter as well as white matter hyperintensities while applying brain size corrections. For both measures, total white matter and white matter hyperintensities, there were no significant sex differences or group-by-sex interactions. However, individuals with congenital adrenal hyperplasia had significantly smaller total white matter volumes compared to controls. Our findings align with previous reports of white matter variations in congenital adrenal hyperplasia. The absence of a group-by-sex interaction suggests that white matter variations in congenital adrenal hyperplasia may not be attributable to prenatal androgens. Instead, they may be a result of the condition itself and/or its treatment with glucocorticoids. The latter aspect warrants follow-up, particularly given that glucocorticoids are employed not only in congenital adrenal hyperplasia but also in other medical conditions., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Congenital Adrenal Hyperplasia (CAH) has been reported to involve structural alterations in some brain regions. However, it remains to be established whether there is also an impact on the size of the brain as a whole. Here, we compiled the largest CAH sample to date (n = 53), matched pair-wise to a control group (n = 53) on sex, age, and verbal intelligence. Using T1-weighted brain scans, we calculated intracranial volume (ICV) as well as total brain volume (TBV), which are both common estimates for brain size. The statistical analysis was performed using a general linear model assessing the effects of CAH (CAH vs. controls), sex (women vs. men), and any CAH-by-sex interaction. The outcomes were comparable for ICV and TBV, i.e., there was no significant main effect of CAH and no significant CAH-by-sex interaction. However, there was a significant main effect of sex, with larger ICVs and TBVs in men than in women. Our findings contribute to an understudied field of research exploring brain anatomy in CAH. In contrast to some existing studies suggesting a smaller brain size in CAH, we did not observe such an effect. In other words, ICV and TBV in women and men with CAH did not differ significantly from those in controls. Notwithstanding, we observed the well-known sex difference in brain size (12.69% for ICV and 12.50% for TBV), with larger volumes in men than in women, which is in agreement with the existing literature., Competing Interests: Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

The interrelation between acute ischemic stroke, persistent disability, and uncertain prognosis underscores the need for improved methods to predict clinical outcomes. Traditional approaches have largely focused on analysis of clinical metrics, lesion characteristics, and network connectivity, using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). However, these methods are not routinely used in acute stroke diagnostics. This study introduces an innovative approach that not only considers the lesion size in relation to the National Institutes of Health Stroke Scale (NIHSS score), but also evaluates the impact of disrupted fibers and their connections to cortical regions by introducing a disconnection value. By identifying fibers traversing the lesion and estimating their number within predefined regions of interest (ROIs) using a normative connectome atlas, our method bypasses the need for individual DTI scans. In our analysis of MRI data (T1 and T2) from 51 patients with acute or subacute subcortical stroke presenting with motor or sensory deficits, we used simple linear regression to assess the explanatory power of lesion size and disconnection value on NIHSS score. Subsequent hierarchical multiple linear regression analysis determined the incremental value of disconnection metrics over lesion size alone in relation to NIHSS score. Our results showed that models incorporating the disconnection value accounted for more variance than those based solely on lesion size (lesion size explained 44% variance, disconnection value 60%). Furthermore, hierarchical regression revealed a significant improvement ( p < 0.001) in model fit when adding the disconnection value, confirming its critical role in stroke assessment. Our approach, which integrates a normative connectome to quantify disconnections to cortical regions, provides a significant improvement in assessing the current state of stroke impact compared to traditional measures that focus on lesion size. This is achieved by taking into account the lesion's location and the connectivity of the affected white matter tracts, providing a more comprehensive assessment of stroke severity as reflected in the NIHSS score. Future research should extend the validation of this approach to larger and more diverse populations, with a focus on refining its applicability to clinical assessment and long-term outcome prediction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Weigel, Klingner, Brodoehl, Wagner, Schwab, Güllmar, Mayer, Güttler, Teichgräber and Gaser.)

Objectives: This study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation., Methods: A pooled NC of 3945 subjects (NC pool ) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer's disease (AD) patients (55-82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NC pool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC., Results: The maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran's Q, p = 0.19)., Conclusion: At least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study's context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used., Clinical Relevance Statement: The influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation., Key Points: • Software-based brain atrophy estimation often relies on normal cohorts for comparisons. • At least 15 subjects must be included in an age- and sex-specific normal cohort. • Using different normal cohorts does not influence regional atrophy estimation., (© 2024. The Author(s).)

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors., (© 2024. The Author(s).)

Previous studies have reported sex differences in cortical gyrification. Since most cortical folding is principally defined in utero, sex chromosomes as well as gonadal hormones are likely to influence sex-specific aspects of local gyrification. Classic congenital adrenal hyperplasia (CAH) causes high levels of androgens during gestation in females, whereas levels in males are largely within the typical male range. Therefore, CAH provides an opportunity to study the possible effects of prenatal androgens on cortical gyrification. Here, we examined the vertex-wise absolute mean curvature-a common estimate for cortical gyrification-in individuals with CAH (33 women and 20 men) and pair-wise matched controls (33 women and 20 men). There was no significant main effect of CAH and no significant CAH-by-sex interaction. However, there was a significant main effect of sex in five cortical regions, where gyrification was increased in women compared to men. These regions were located on the lateral surface of the brain, specifically left middle frontal (rostral and caudal), right inferior frontal, left inferior parietal, and right occipital. There was no cortical region where gyrification was increased in men compared to women. Our findings do not only confirm prior reports of increased cortical gyrification in female brains but also suggest that cortical gyrification is not significantly affected by prenatal androgen exposure. Instead, cortical gyrification might be determined by sex chromosomes either directly or indirectly-the latter potentially by affecting the underlying architecture of the cortex or the size of the intracranial cavity, which is smaller in women., (© 2024 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Borderline personality disorder (BPD) is characterised by structural and functional brain alterations. Yet, there is little data on functional connectivity (FC) across different levels of brain networks and parameters. In this study, we applied a multi-level approach to analyse abnormal functional connectivity. We analysed resting-state functional magnetic resonance imaging (fMRI) data sets of 69 subjects: 17 female BPD patients and 51 age-matched psychiatrically healthy female controls. fMRI was analysed using CONN toolbox including: a) seed-based FC analysis of amygdala connectivity, b) independent component analysis (ICA) based network analysis of intra- and inter-network FC of selected resting-state networks (DMN, SN, FPN), as well as c) graph-theory based measures of network-level characteristics. We show group-level seed FC differences with higher amygdala to contralateral (superior) occipital cortex connectivity in BPD, which correlated with schema-therapy derived measures of symptoms/traits across the entire cohort. While there was no significant group effect on DMN, SN, or FPN intra-network or inter-network FC, we show a significant group difference for local efficiency and cluster coefficient for a DMN-linked cerebellum cluster. Our findings demonstrate BPD-linked changes in FC across multiple levels of observation, which supports a multi-level analysis for future studies to consider different aspects of functional connectome alterations., Competing Interests: Declaration of competing interest The authors have no competing interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy in advanced Parkinson's disease (PD). Motor and non-motor outcomes, however, show considerable inter-individual variability. Preoperative morphometry-based metrics have recently received increasing attention to explain treatment effects. As evidence for the prediction of non-motor outcomes is limited, we sought to investigate the association between metrics of voxel-based morphometry and short-term non-motor outcomes following STN-DBS in this prospective open-label study. Forty-nine PD patients underwent structural MRI and a comprehensive clinical assessment at preoperative baseline and 6-month follow-up. Voxel-based morphometry was used to assess associations between cerebral volume and non-motor outcomes corrected for multiple comparisons using a permutation-based approach. We replicated existing results associating volume loss of the superior frontal cortex with subpar motor outcomes. Overall non-motor burden, however, was not significantly associated with morphometric features, limiting its use as a marker to inform patient selection and holistic preoperative counselling., (© 2024. The Author(s).)

Subanesthetic doses of ketamine induce an antidepressant effect within hours in individuals with treatment-resistant depression while it furthermore induces immediate but transient psychotomimetic effects. Among these psychotomimetic effects, an altered sense of self has specifically been associated with the antidepressant response to ketamine as well as psychedelics. However, there is plenty of variation in the extent of the drug-induced altered sense of self experience that might be explained by differences in basal morphological characteristics, such as cortical thickness. Regions that have been previously associated with a psychedelics-induced sense of self and with ketamine's mechanism of action, are the posterior cingulate cortex (PCC) and the pregenual anterior cingulate cortex (pgACC). In this randomized, placebo-controlled, double-blind cross-over magnetic resonance imaging study, thirty-five healthy male participants (mean age ± standard deviation (SD) = 25.1 ± 4.2 years) were scanned at 7 T. We investigated whether the cortical thickness of two DMN regions, the PCC and the pgACC, are associated with disembodiment and experience of unity scores, which were used to index the ketamine-induced altered sense of self. We observed a negative correlation between the PCC cortical thickness and the disembodiment scores (R = -0.54, p < 0.001). In contrast, no significant association was found between the pgACC cortical thickness and the ketamine-induced altered sense of self. In the context of the existing literature, our findings highlight the importance of the PCC as a structure involved in the mechanism of ketamine-induced altered sense of self that seems to be shared with different antidepressant agents with psychotomimetic effects operating on different classes of transmitter systems., Competing Interests: Declaration of competing interest MW is a member of the following advisory boards and has given presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. All other authors report no biomedical financial interests or other potential conflicts of interest. We have made a preprint of our work available on the PsyArXiv preprint server for the psychological sciences. Furthermore, results independent of the present analysis but retrieved from the same data set have been published in Danyeli et al. (2023) (https://doi.org/10.1038/s41398-023-02346-0)., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

As the heterogeneity of symptoms is increasingly recognized among long-COVID patients, it appears highly relevant to study potential pathophysiological differences along the different subtypes. Preliminary evidence suggests distinct alterations in brain structure and systemic inflammatory patterns in specific groups of long-COVID patients. To this end, we analyzed differences in cortical thickness and peripheral immune signature between clinical subgroups based on 3 T-MRI scans and signature inflammatory markers in n = 120 participants comprising healthy never-infected controls (n = 30), healthy COVID-19 survivors (n = 29), and subgroups of long-COVID patients with (n = 26) and without (n = 35) cognitive impairment according to screening with Montreal Cognitive Assessment. Whole-brain comparison of cortical thickness between the 4 groups was conducted by surface-based morphometry. We identified distinct cortical areas showing a progressive increase in cortical thickness across different groups, starting from healthy individuals who had never been infected with COVID-19, followed by healthy COVID-19 survivors, long-COVID patients without cognitive deficits (MoCA ≥ 26), and finally, long-COVID patients exhibiting significant cognitive deficits (MoCA < 26). These findings highlight the continuum of cortical thickness alterations associated with COVID-19, with more pronounced changes observed in individuals experiencing cognitive impairment (p < 0.05, FWE-corrected). Affected cortical regions covered prefrontal and temporal gyri, insula, posterior cingulate, parahippocampal gyrus, and parietal areas. Additionally, we discovered a distinct immunophenotype, with elevated levels of IL-10, IFNγ, and sTREM2 in long-COVID patients, especially in the group suffering from cognitive impairment. We demonstrate lingering cortical and immunological alterations in healthy and impaired subgroups of COVID-19 survivors. This implies a complex underlying pathomechanism in long-COVID and emphasizes the necessity to investigate the whole spectrum of post-COVID biology to determine targeted treatment strategies targeting specific sub-groups., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community., (© The Author(s) 2024. Published by Oxford University Press on behalf of GigaScience.)